From 5ea0766cece1e7d4cd4efcc3ea327fec36916b1e Mon Sep 17 00:00:00 2001 From: Christian Monch Date: Mon, 30 Jan 2023 23:09:33 +0100 Subject: [PATCH 01/28] add a metalad description This is a first draft of metalad documentation. It covers meta-add and meta-dump and the transport of metadata between repositories. --- docs/beyond_basics/101-181-metalad.rst | 132 ++++++++++++++++++++++++- 1 file changed, 130 insertions(+), 2 deletions(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index e4c395cbb..f4d9a05c4 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -8,6 +8,134 @@ A modernized approach, however, is developed in the `datalad-metalad extension < .. figure:: ../artwork/src/metadata.svg -.. importantnote:: Handbook content on datalad-metalad will come soon - ... Stay tuned ... \ No newline at end of file +What is MetaLad? +................ + +MetaLad is an extension to DataLad that allows you to associate metadata with a dataset, a subdataset, or a file. "What is 'metadata' then?" you might ask. Very simply put: metadata is data about data. In principle any kind of data could be metadata. What makes it metadata is the fact that is associated with some "primary" data and usually describes the primary data in some way. For example: in a library, the catalog contains metadata about the items in the library, including, for example, their location. Another example for metadata is the creation time of a file on a file system. The creation time is metadata about the file. The file itself is the primary data. Another example for metadata that is often associated with a file would be the id of the creator. File systems have mechanisms to associate metadata with files. DataLad has also a mechanism to associate metadata with datalad datasets and their items. This mechanism is MetaLad. + +Metadata can be extracted automatically from primary data, it can be created manually and it can be transported separately from primary data. You can dump all metadata and, for example, store it in a file, or search through it with a tool of your choice. + +The basic philosophy of metalad is: you provide arbitrary metadata, give it a name, and associate it with a specific version of a file or dataset. Metalad will store this association, allow you to transport metadata, and allow you to query the metadata. + +Installation of metalad: + +Metalad is installed by using `pip install datalad-metalad`. As with datalad, you might want to do that in a virtual environment. + + +Primary Data and Metadata +......................... + +Which metadata can be associated: + +MetaLad can associate metadata with dataset items, i.e. with a dataset, subdatasets, and files. Each item can have an arbitrary number of metadata elements assigned to them. The metadata elements are distinguished and identified by a name that identifies their type. The metadata content is defined to be a JSON-object. This object can have an arbitrary structure. This allows you to create your own metadata format and to represent all existing metadata formats. Metalad is able to associate paths in datasets with an arbitrary number of metadata in different formats. Metalad recognizes whether the path points to a dataset of + +Metadata Example +---------------- + +What does metadata do for you? Generally metadata provides additional information about primary data. This allows to identify primary data that has certain properties which may be in contained within the primary data -and probably automatically extracted from there- or may be assigned to primary data based on an external policy. + +Here are just a few examples for metadata from the virtually unlimited metadata space: + +1. Full text for scanned book pages (automatically extracted by OCR or manually created) +2. Usage information, e.g. license, in-group, in-organization, global +3. Context information, e.g. publications based on data +4. Structural data (dataset containment, modification date, hash) this can provide basic + structural information even without access to the primary data +5. Special search indices, e.g. graph-based search indices, search index for chess-games.... +6. Anonymized information extracted from medical documents. +7. Information about software, e.g. security assessments, citation.cff + +As long as the metadata-format has unique names, they can all be used simultaneously. + +Whether metadata is associated with the dataset or individual files is dependent on the nature of the metadata and the envisioned use. Metadata that describe dataset level properties should be associated with the dataset, e.g. dataset owner, dataset authors, dataset licenses, names of contained files, etc. Metadata that describes file level properties should be associated with the file, e.g. file-checksum. Although it seems unnecessary to record the names of contained files or file properties in metadata, it can be very useful, if a user receives only metadata and not primary data (see chapter XXX). + + +Let's look at a more concrete example. We have a datalad dataset that contains a single PNG-file called "picture.png" + +dataset_0 + picture_1.png + +Let's assume there is metadata stemming from an advance AI called "Picture2Words" that is able to describe the content of images. In this case the AI describes the image as:: + + A lake with waterlilies in a front of snow covered mountains + +We would like to add this description as metadata to the file ``./picture_1.png``. As mentioned above we have to select a name to identify the metadata format. We chose: ``Picture2Words``. Now we can use the command ``meta-add`` to add metadata. ``meta-add`` adds metadata into the git repository of the dataset (or any git-repository that you chose). It reads individual metadata entries from the command line or from standard input. The entries are JSON objects that identify the type of metadata (``file`` or ``dataset``), the file or dataset with which metadata should be associated, the identifier of the dataset, the version of the dataset to which metadata is associated, the name of the metadata format, and the metadata itself. There are a few more properties in the JSON object, describing the author of the metadata, the name of the metadata extractor, its version, and its parameterization. + For our example we could just handcraft the following record:: + + { + "type": "file", + "dataset_id": "52142b84-dc76-11ea-98c5-7cdd908c7490", + "dataset_version": "244a8ad43b00622989ae7f0d2b59c80697dadb80", + "path": "picture_1.png", + "extractor_name": "Picture2Words", + "extractor_version": "0.1", + "extraction_parameter": {}, + "extraction_time": 1675113291.1464975, + "agent_name": "Datalad User", + "agent_email": "datalad.user@example.com", + "extracted_metadata": { + "description": "A lake with waterlilies in a front of snow covered mountains" + } + } + +and, for convenience, save it as ``metadata-image_1.json``. Then we can use the command ``meta-add`` to add the metadata to the current dataset:: + + > datalad meta-add -d dataset_0 - < metadata-image_1.json + +This command will store metadata in the git repository of ``dataset_0``. You might have to adjust the dataset_id to the real id of the dataset, found via the command ``datalad configuration get datalad.dataset.id``, or you provide the switch ``-i`` to ``meta-add``, which tells it to just warn about id mismatches. + +Generally metadata can either be provided manually, by running ``extractors`` (datalad-metalad plugins that extract certain metadata from primary data), or by any other means that create correct metadata records. For example, you could copy the complete metadata from ``dataset_0`` to ``dataset_1``, by issuing the command:: + + > datalad meta-dump -d dataset_0 -r | datalad meta-add -d dataset_1 --json-lines - + + + + + + + +After the metadata has been added, you can view it via the command ``meta-dump``. The simplest form is ``meta-dump -r``, which will show all metadata that is stored in the git-repository of the dataset in the current directory. You can give a dataset-file-path-pattern to ``meta-dump``, much like an argument to ``ls``, that identifies dataset-ids and versions and a file within the dataset. The two parts are separated by ``:``. So:: + + > datalad meta-dump -d dataset_0 .:picture_1.png + +would just dump all metadata for ``picture_1.png``. + + +Distributing and Getting Metadata +--------------------------------- + +Instead of creating and adding metadata yourself, you can download metadata from remote git repositories, i.e. from remote datalad datasets. Also, if you have created your metadata and added it to a git repository (of a datalad dataset), you can export it into other git-repositories. Note that this export will only export the metadata itself, not the primary data. + +Download Metadata from a Git-Repository +--------------------------------------- + +First create a git repository that should hold the downloaded metadata:: + + > git init metadata-repo + > cd metadata-repo + +Now fetch metadata from the demo repository on github, i.e. from ``https://github.com/christian-monch/metadata-test.git`` demo repository:: + + > git add remote metadata-source "https://github.com/christian-monch/metadata-test.git" + > git fetch metadata-source "refs/datalad/*:refs/datalad/*" + +The metadata is now locally available in the git repository ``metadata-repo``. You can verify this by issuing the command ``datalad meta-dump -r``, which will list all metadata in the repository. + + +Publish Metadata to a Git-Repository +------------------------------------ + +You can also push your metadata to a remote Git-repository (if you have write-authorization). Let's assume you are in the directory that contains the git repository with your metadata. First you have to add the metadata destination as a remote:: + + > git add remote metadata-destination "" + +Then you can push the metadata to the remote repository:: + + > git push metadata-source "refs/datalad/*:refs/datalad/*" + +You will notice that no primary data is stored in the repository ``metadata-destination``. That allows you to publish metadata without publishing the primary data at the same time. + + + + NB! We are working on remote querying capabilities for metadata. That means, you can query for individual metadata records on a remote repository. If the metadata is large, it is a good idea to remotely query it, because metalad tries to minimize the number of objects that are downloaded while still allowing you to "drill down" into a dataset and file-hierarchy. This will be described here soon. From 0a8801ad9f71d4c7fd56211c42f6943c28b058fa Mon Sep 17 00:00:00 2001 From: Christian Monch Date: Wed, 1 Feb 2023 18:02:13 +0100 Subject: [PATCH 02/28] add description of remote-dump --- docs/beyond_basics/101-181-metalad.rst | 27 +++++++++++++------------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index f4d9a05c4..a66574a33 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -89,12 +89,6 @@ Generally metadata can either be provided manually, by running ``extractors`` (d > datalad meta-dump -d dataset_0 -r | datalad meta-add -d dataset_1 --json-lines - - - - - - - After the metadata has been added, you can view it via the command ``meta-dump``. The simplest form is ``meta-dump -r``, which will show all metadata that is stored in the git-repository of the dataset in the current directory. You can give a dataset-file-path-pattern to ``meta-dump``, much like an argument to ``ls``, that identifies dataset-ids and versions and a file within the dataset. The two parts are separated by ``:``. So:: > datalad meta-dump -d dataset_0 .:picture_1.png @@ -117,8 +111,7 @@ First create a git repository that should hold the downloaded metadata:: Now fetch metadata from the demo repository on github, i.e. from ``https://github.com/christian-monch/metadata-test.git`` demo repository:: - > git add remote metadata-source "https://github.com/christian-monch/metadata-test.git" - > git fetch metadata-source "refs/datalad/*:refs/datalad/*" + > git fetch "https://github.com/christian-monch/metadata-test.git" "refs/datalad/*:refs/datalad/*" The metadata is now locally available in the git repository ``metadata-repo``. You can verify this by issuing the command ``datalad meta-dump -r``, which will list all metadata in the repository. @@ -126,16 +119,22 @@ The metadata is now locally available in the git repository ``metadata-repo``. Y Publish Metadata to a Git-Repository ------------------------------------ -You can also push your metadata to a remote Git-repository (if you have write-authorization). Let's assume you are in the directory that contains the git repository with your metadata. First you have to add the metadata destination as a remote:: +You can also push your metadata to a remote Git-repository (if you have write-authorization). Let's assume you are in the directory that contains the git repository with your metadata, then you can push your metadata to a remote git repository ````:: - > git add remote metadata-destination "" + > git push "" "refs/datalad/*:refs/datalad/*" -Then you can push the metadata to the remote repository:: +You will notice that no primary data is stored in the repository ``metadata-destination``. That allows you to publish metadata without publishing the primary data at the same time. - > git push metadata-source "refs/datalad/*:refs/datalad/*" -You will notice that no primary data is stored in the repository ``metadata-destination``. That allows you to publish metadata without publishing the primary data at the same time. +Querying metadata remotely: +--------------------------- + +You do not have to download metadata to dump it. It is also possible to specify a git-repository, and let metalad only read the metadata that it requires to fulfill your request. For example:: + + > datalad meta-dump -d https://github.com/christian-monch/metadata-test.git ./study-100 +Would only download enough data to dump all metadata in the specified dataset tree-path. If you want to see all metadata in the git repository you could issue the following command:: + > datalad meta-dump -d https://github.com/christian-monch/metadata-test.git -r - NB! We are working on remote querying capabilities for metadata. That means, you can query for individual metadata records on a remote repository. If the metadata is large, it is a good idea to remotely query it, because metalad tries to minimize the number of objects that are downloaded while still allowing you to "drill down" into a dataset and file-hierarchy. This will be described here soon. +This will take a lot longer than the previous command because datalad has to fetch more item from the remote repository. If you use the remote meta-dump option properly, you can quickly examine small subsets of very large metadata repositories. From ad0a3fbbcd0facc64607711f81c926cc3bc6f40c Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Thu, 2 Feb 2023 14:36:51 +0100 Subject: [PATCH 03/28] Tweak MetaLad intro: Fewer subheadings, typos, and language edits --- docs/beyond_basics/101-181-metalad.rst | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index a66574a33..78fe7ec7c 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -4,23 +4,31 @@ Metadata-Management with MetaLad -------------------------------- For many years, :term:`metadata` related functionality was included in the DataLad core package. -A modernized approach, however, is developed in the `datalad-metalad extension `_. +A modernized approach, however, is now developed in the `datalad-metalad extension `_. .. figure:: ../artwork/src/metadata.svg +MetaLad is an extension to DataLad that allows you to associate :term:`metadata` with a dataset, a subdataset, or a file. "What is 'metadata' then?" you might ask. Very simply put: metadata is data about data. +In principle any kind of data could be metadata. What makes it metadata is the fact that it is associated with some "primary" data and usually describes the primary data in some way. +Consider the following examples: In a library, the catalog contains metadata about the items in the library, such as their *location*; On a file system, the *creation time* of a file as well as the ID of its creator is stored. +In these examples, the location, creation time, or creator ID is metadata, while the book in the library or the file on the file system are primary data. +Both the file system and the library have mechanisms to associate metadata with primary data. +And DataLad, too, has a mechanism to associate metadata with DataLad datasets and their items. This mechanism is MetaLad. -What is MetaLad? -................ +Metadata can be extracted automatically from primary data, it can be created manually, and it can be transported separately from primary data. You can dump all metadata and, for example, store it in a file, or search through it with a tool of your choice. -MetaLad is an extension to DataLad that allows you to associate metadata with a dataset, a subdataset, or a file. "What is 'metadata' then?" you might ask. Very simply put: metadata is data about data. In principle any kind of data could be metadata. What makes it metadata is the fact that is associated with some "primary" data and usually describes the primary data in some way. For example: in a library, the catalog contains metadata about the items in the library, including, for example, their location. Another example for metadata is the creation time of a file on a file system. The creation time is metadata about the file. The file itself is the primary data. Another example for metadata that is often associated with a file would be the id of the creator. File systems have mechanisms to associate metadata with files. DataLad has also a mechanism to associate metadata with datalad datasets and their items. This mechanism is MetaLad. +The basic philosophy of MetaLad is: A user provides arbitrary metadata, gives it a name, and associates it with a specific version of a file or dataset. MetaLad will store this association, enable metadata transport, and allow metadata queries. -Metadata can be extracted automatically from primary data, it can be created manually and it can be transported separately from primary data. You can dump all metadata and, for example, store it in a file, or search through it with a tool of your choice. +Installation +"""""""""""" -The basic philosophy of metalad is: you provide arbitrary metadata, give it a name, and associate it with a specific version of a file or dataset. Metalad will store this association, allow you to transport metadata, and allow you to query the metadata. +MetaLad is a stand-alone Python package, and can be installed using -Installation of metalad: +.. code-block:: bash -Metalad is installed by using `pip install datalad-metalad`. As with datalad, you might want to do that in a virtual environment. + pip install datalad-metalad + +As with DataLad and other Python packages, you might want to do the installation in a :term:`virtual environment`. Primary Data and Metadata From 5133434aabe1a1100e9363987d4ac5562ad660b7 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Thu, 2 Feb 2023 20:03:02 +0100 Subject: [PATCH 04/28] More rewriting of the Metalad section, slight restructuring --- docs/beyond_basics/101-181-metalad.rst | 172 +++++++++++++++---------- 1 file changed, 106 insertions(+), 66 deletions(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index 78fe7ec7c..bccf5a550 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -8,68 +8,77 @@ A modernized approach, however, is now developed in the `datalad-metalad extensi .. figure:: ../artwork/src/metadata.svg -MetaLad is an extension to DataLad that allows you to associate :term:`metadata` with a dataset, a subdataset, or a file. "What is 'metadata' then?" you might ask. Very simply put: metadata is data about data. -In principle any kind of data could be metadata. What makes it metadata is the fact that it is associated with some "primary" data and usually describes the primary data in some way. -Consider the following examples: In a library, the catalog contains metadata about the items in the library, such as their *location*; On a file system, the *creation time* of a file as well as the ID of its creator is stored. -In these examples, the location, creation time, or creator ID is metadata, while the book in the library or the file on the file system are primary data. -Both the file system and the library have mechanisms to associate metadata with primary data. -And DataLad, too, has a mechanism to associate metadata with DataLad datasets and their items. This mechanism is MetaLad. +MetaLad is a :term:`DataLad extension` that allows you to -Metadata can be extracted automatically from primary data, it can be created manually, and it can be transported separately from primary data. You can dump all metadata and, for example, store it in a file, or search through it with a tool of your choice. +* associate :term:`metadata` with a dataset, a subdataset, or a file, +* extract metadata automatically from primary data or handle manually supplied metadata, +* transport metadata separately from primary data, +* dump metadata and, for example, store it in a file, or search through it with a tool of your choice. The basic philosophy of MetaLad is: A user provides arbitrary metadata, gives it a name, and associates it with a specific version of a file or dataset. MetaLad will store this association, enable metadata transport, and allow metadata queries. -Installation -"""""""""""" - -MetaLad is a stand-alone Python package, and can be installed using - -.. code-block:: bash - - pip install datalad-metalad - -As with DataLad and other Python packages, you might want to do the installation in a :term:`virtual environment`. - - -Primary Data and Metadata -......................... - -Which metadata can be associated: - -MetaLad can associate metadata with dataset items, i.e. with a dataset, subdatasets, and files. Each item can have an arbitrary number of metadata elements assigned to them. The metadata elements are distinguished and identified by a name that identifies their type. The metadata content is defined to be a JSON-object. This object can have an arbitrary structure. This allows you to create your own metadata format and to represent all existing metadata formats. Metalad is able to associate paths in datasets with an arbitrary number of metadata in different formats. Metalad recognizes whether the path points to a dataset of +Primary Data versus Metadata +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +You now might ask: "What is 'metadata' then?" +Very simply put: Metadata is data about data. +In principle, any kind of data could be metadata. What makes it metadata is the fact that it is associated with some "primary" data, and usually describes the primary data in some way. +Consider two simple examples from the physical and the digital world: A library catalog contains metadata about the library's books, such as their *location*; and a file system stores the *creation time* of a file as well as the *user ID* of its creator. +The location, creation time, or creator ID is metadata, while the book in the library or the file on the file system are the primary data the metadata is associated with. + +And what does metadata do for you? +Generally, metadata provides additional information about primary data. +This allows to identify primary data with certain properties. +These properties could either be contained within the primary data and (automatically) extracted from it, such as digital photographs captured in a specific time frame, or assigned to primary data based on an external policy (such as the directory "Hiking in the alps 2019" on your phone). +Importantly, primary data can have virtually unlimited different metadata associated with it, depending on what is relevant in a given context. +Consider a publication in a medical field, and a few examples for metadata about it from the virtually unlimited metadata space: + +1. The full text for the scanned PDF (manually created, or automatically extracted by `optical character recognition `_) +2. Citation information, such as the geographic origin of citing papers or type of media outlet reporting about it +3. Context information, e.g. publications based on similar data +4. Structural data about the underlying medical acquisitions, (such as dataset containment, modification date, or hash) which can provide basic + structural information even without access to the primary data +5. Special search indices, e.g. graph-based search indices, medical abbreviations +6. Anonymized information extracted from medical documents. +7. Information about the used software, e.g. security assessments, citation.cff -Metadata Example ----------------- +Often, specific metadata is known as metadata of a given format or an ontology, and different metadata formats are useful in different contexts. -What does metadata do for you? Generally metadata provides additional information about primary data. This allows to identify primary data that has certain properties which may be in contained within the primary data -and probably automatically extracted from there- or may be assigned to primary data based on an external policy. +Adding metadata with MetaLad +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Here are just a few examples for metadata from the virtually unlimited metadata space: +Whether metadata is associated with the dataset or individual files inside of it is dependent on the nature of the metadata and the envisioned use. +Metadata that describe *dataset level* properties should be associated with the dataset, e.g. dataset owner, dataset authors, dataset licenses, or names of contained files. +Metadata that describes *file level* properties should be associated with the file, e.g. file :term:`checksum`\s. +Although it seems unnecessary to record the names of contained files or file properties in metadata, it can be very useful if a user receives only metadata and not primary data, for example in case of highly sensitive data where actual file content cannot be shared. -1. Full text for scanned book pages (automatically extracted by OCR or manually created) -2. Usage information, e.g. license, in-group, in-organization, global -3. Context information, e.g. publications based on data -4. Structural data (dataset containment, modification date, hash) this can provide basic - structural information even without access to the primary data -5. Special search indices, e.g. graph-based search indices, search index for chess-games.... -6. Anonymized information extracted from medical documents. -7. Information about software, e.g. security assessments, citation.cff +Let's look at a concrete example. +We have a DataLad dataset ``dataset_0` that contains a single PNG-file called ``picture_1.png``:: -As long as the metadata-format has unique names, they can all be used simultaneously. + $ tree dataset_0 + dataset_0 + └── picture_1.png + 0 directories, 1 file -Whether metadata is associated with the dataset or individual files is dependent on the nature of the metadata and the envisioned use. Metadata that describe dataset level properties should be associated with the dataset, e.g. dataset owner, dataset authors, dataset licenses, names of contained files, etc. Metadata that describes file level properties should be associated with the file, e.g. file-checksum. Although it seems unnecessary to record the names of contained files or file properties in metadata, it can be very useful, if a user receives only metadata and not primary data (see chapter XXX). +Let's assume there is metadata stemming from an advance AI called ``Picture2Words`` that is able to describe the content of images. +In this case the AI describes the image as:: -Let's look at a more concrete example. We have a datalad dataset that contains a single PNG-file called "picture.png" + A lake with waterlilies in front of snow covered mountains -dataset_0 - picture_1.png -Let's assume there is metadata stemming from an advance AI called "Picture2Words" that is able to describe the content of images. In this case the AI describes the image as:: +We would like to add this description as metadata to the file ``./picture_1.png``. +One distinct feature of MetaLad is that it can simultaneously use various metadata formats. +The only requirement is that each different metadata format is uniquely named. +Therefore, we have to select a name to identify the metadata format. +We chose: ``Picture2Words``. +Now we can use the :command:`meta-add` command to add metadata. +``meta-add`` adds metadata into the dataset (or any :term:`Git` repository that you chose). - A lake with waterlilies in a front of snow covered mountains - -We would like to add this description as metadata to the file ``./picture_1.png``. As mentioned above we have to select a name to identify the metadata format. We chose: ``Picture2Words``. Now we can use the command ``meta-add`` to add metadata. ``meta-add`` adds metadata into the git repository of the dataset (or any git-repository that you chose). It reads individual metadata entries from the command line or from standard input. The entries are JSON objects that identify the type of metadata (``file`` or ``dataset``), the file or dataset with which metadata should be associated, the identifier of the dataset, the version of the dataset to which metadata is associated, the name of the metadata format, and the metadata itself. There are a few more properties in the JSON object, describing the author of the metadata, the name of the metadata extractor, its version, and its parameterization. - For our example we could just handcraft the following record:: +In order to include metadata in the dataset, ``meta-add`` requires a metadata entry to be in a certain format, in particular, a JSON object [#f1]_. +Such objects can then be read from the command line or from standard input (:term:`stdin`). +Each metadata JSON object must contain information about the level the metadata applies to (``file`` or ``dataset``), the file or dataset with which metadata should be associated as a path, the :term:`dataset ID` and version, the name of the metadata format, and the metadata itself. +The examplary, hand-crafted record below even contains a few additional properties, describing the author of the metadata, the name of the metadata extractor, its version, and its parameterization:: { "type": "file", @@ -87,45 +96,59 @@ We would like to add this description as metadata to the file ``./picture_1.png` } } -and, for convenience, save it as ``metadata-image_1.json``. Then we can use the command ``meta-add`` to add the metadata to the current dataset:: +For convenience, we can create such a JSON object in an editor, and save it as ``metadata-image_1.json``. +Afterwards, we can redirect the content of the file into the :command:`meta-add` command in the command line. +The call below would add the metadata to the current dataset, ``dataset_0``:: + + $ datalad meta-add -d dataset_0 - < metadata-image_1.json - > datalad meta-add -d dataset_0 - < metadata-image_1.json +If you want to try it out yourself, make sure to adjust the ``dataset_id`` to the ID of your own dataset, found via the command ``datalad configuration get datalad.dataset.id``. +Alternatively, provide the switch ``-i`` to ``meta-add``, which tells it to just warn about ID mismatches instead of erroring out. -This command will store metadata in the git repository of ``dataset_0``. You might have to adjust the dataset_id to the real id of the dataset, found via the command ``datalad configuration get datalad.dataset.id``, or you provide the switch ``-i`` to ``meta-add``, which tells it to just warn about id mismatches. +Generally, metadata can either be provided -Generally metadata can either be provided manually, by running ``extractors`` (datalad-metalad plugins that extract certain metadata from primary data), or by any other means that create correct metadata records. For example, you could copy the complete metadata from ``dataset_0`` to ``dataset_1``, by issuing the command:: +* manually, +* by running :term:`extractor`\s (``datalad-metalad`` plugins that extract certain metadata from primary data), +* or by any other means that create correct metadata records. For example, you could copy the complete metadata from ``dataset_0`` to ``dataset_1``, by dumping it from one dataset into another:: - > datalad meta-dump -d dataset_0 -r | datalad meta-add -d dataset_1 --json-lines - + $ datalad meta-dump -d dataset_0 -r | \ + datalad meta-add -d dataset_1 --json-lines - -After the metadata has been added, you can view it via the command ``meta-dump``. The simplest form is ``meta-dump -r``, which will show all metadata that is stored in the git-repository of the dataset in the current directory. You can give a dataset-file-path-pattern to ``meta-dump``, much like an argument to ``ls``, that identifies dataset-ids and versions and a file within the dataset. The two parts are separated by ``:``. So:: +After the metadata has been added, you can view it via the command :command:`meta-dump`. +The simplest form of this command is ``meta-dump -r``, which will show all metadata that is stored in dataset in the current directory. +To get more specific metadata records, you can give a dataset-file-path-pattern to ``meta-dump``, much like an argument to ``ls``, that identifies :term:`dataset ID`, version and a file within the dataset. +The two parts are separated by ``:``. So:: - > datalad meta-dump -d dataset_0 .:picture_1.png + $ datalad meta-dump -d dataset_0 .:picture_1.png would just dump all metadata for ``picture_1.png``. Distributing and Getting Metadata ---------------------------------- +""""""""""""""""""""""""""""""""" -Instead of creating and adding metadata yourself, you can download metadata from remote git repositories, i.e. from remote datalad datasets. Also, if you have created your metadata and added it to a git repository (of a datalad dataset), you can export it into other git-repositories. Note that this export will only export the metadata itself, not the primary data. +Instead of creating and adding metadata yourself, you can download metadata from remote DataLad datasets. +Likewise, metadata added into one dataset can be exported into other dataset. +Such an export will only export the metadata itself, not the primary data. Download Metadata from a Git-Repository ---------------------------------------- +""""""""""""""""""""""""""""""""""""""" First create a git repository that should hold the downloaded metadata:: - > git init metadata-repo - > cd metadata-repo + $ git init metadata-repo + $ cd metadata-repo -Now fetch metadata from the demo repository on github, i.e. from ``https://github.com/christian-monch/metadata-test.git`` demo repository:: +Now fetch metadata from the demo repository on :term:`GitHub`, i.e. from ``https://github.com/christian-monch/metadata-test.git`` demo repository:: - > git fetch "https://github.com/christian-monch/metadata-test.git" "refs/datalad/*:refs/datalad/*" + $ git fetch "https://github.com/christian-monch/metadata-test.git" "refs/datalad/*:refs/datalad/*" -The metadata is now locally available in the git repository ``metadata-repo``. You can verify this by issuing the command ``datalad meta-dump -r``, which will list all metadata in the repository. +The metadata is now locally available in the Git repository ``metadata-repo``. +You can verify this by issuing the command ``datalad meta-dump -r``, which will list all metadata in the repository. -Publish Metadata to a Git-Repository ------------------------------------- +Publish metadata to a Git-Repository +"""""""""""""""""""""""""""""""""""" You can also push your metadata to a remote Git-repository (if you have write-authorization). Let's assume you are in the directory that contains the git repository with your metadata, then you can push your metadata to a remote git repository ````:: @@ -134,8 +157,8 @@ You can also push your metadata to a remote Git-repository (if you have write-au You will notice that no primary data is stored in the repository ``metadata-destination``. That allows you to publish metadata without publishing the primary data at the same time. -Querying metadata remotely: ---------------------------- +Querying metadata remotely +"""""""""""""""""""""""""" You do not have to download metadata to dump it. It is also possible to specify a git-repository, and let metalad only read the metadata that it requires to fulfill your request. For example:: @@ -146,3 +169,20 @@ Would only download enough data to dump all metadata in the specified dataset tr > datalad meta-dump -d https://github.com/christian-monch/metadata-test.git -r This will take a lot longer than the previous command because datalad has to fetch more item from the remote repository. If you use the remote meta-dump option properly, you can quickly examine small subsets of very large metadata repositories. + + +Installation +^^^^^^^^^^^^ + +MetaLad is a stand-alone Python package, and can be installed using + +.. code-block:: bash + + pip install datalad-metalad + +As with DataLad and other Python packages, you might want to do the installation in a :term:`virtual environment`. + + +.. rubric:: Footnotes + +.. [#f1] `JSON `_ is a language-independent, open and lightweight data interchange format. Data is represented as human readable text, organized in key-value pairs (e.g., 'name': 'Bob') or arrays, and thus easily readable by both humans and machines. A *JSON object* is a collection of key-value pairs. Its enclosed in curly brackets, and individual pairs in the object are separated by commas. \ No newline at end of file From 693e81878912a905f7af300c58eb26663ed06f0e Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Fri, 3 Feb 2023 11:27:57 +0100 Subject: [PATCH 05/28] Further restructuring, information on required fields and the concept of an extractor --- docs/beyond_basics/101-181-metalad.rst | 87 +++++++++++++++++--------- 1 file changed, 58 insertions(+), 29 deletions(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index bccf5a550..c2f998060 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -10,17 +10,19 @@ A modernized approach, however, is now developed in the `datalad-metalad extensi MetaLad is a :term:`DataLad extension` that allows you to -* associate :term:`metadata` with a dataset, a subdataset, or a file, +* associate :term:`metadata` in any format with a dataset, a subdataset, or a file, * extract metadata automatically from primary data or handle manually supplied metadata, * transport metadata separately from primary data, * dump metadata and, for example, store it in a file, or search through it with a tool of your choice. -The basic philosophy of MetaLad is: A user provides arbitrary metadata, gives it a name, and associates it with a specific version of a file or dataset. MetaLad will store this association, enable metadata transport, and allow metadata queries. +The basic usage model of MetaLad is: A user provides arbitrary metadata, gives it a name, and associates it with a specific version of a file or dataset. +MetaLad will store this association, enable metadata transport, and allow metadata queries. +The following section illustrates relevant concepts, commands, and workflows. Primary Data versus Metadata ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -You now might ask: "What is 'metadata' then?" +You might ask upfront: "What is 'metadata'?" Very simply put: Metadata is data about data. In principle, any kind of data could be metadata. What makes it metadata is the fact that it is associated with some "primary" data, and usually describes the primary data in some way. Consider two simple examples from the physical and the digital world: A library catalog contains metadata about the library's books, such as their *location*; and a file system stores the *creation time* of a file as well as the *user ID* of its creator. @@ -30,30 +32,45 @@ And what does metadata do for you? Generally, metadata provides additional information about primary data. This allows to identify primary data with certain properties. These properties could either be contained within the primary data and (automatically) extracted from it, such as digital photographs captured in a specific time frame, or assigned to primary data based on an external policy (such as the directory "Hiking in the alps 2019" on your phone). + Importantly, primary data can have virtually unlimited different metadata associated with it, depending on what is relevant in a given context. Consider a publication in a medical field, and a few examples for metadata about it from the virtually unlimited metadata space: 1. The full text for the scanned PDF (manually created, or automatically extracted by `optical character recognition `_) 2. Citation information, such as the geographic origin of citing papers or type of media outlet reporting about it 3. Context information, e.g. publications based on similar data -4. Structural data about the underlying medical acquisitions, (such as dataset containment, modification date, or hash) which can provide basic +4. Structural data about the underlying medical acquisitions (such as dataset containment, modification date, or hash), which can provide basic structural information even without access to the primary data 5. Special search indices, e.g. graph-based search indices, medical abbreviations 6. Anonymized information extracted from medical documents. 7. Information about the used software, e.g. security assessments, citation.cff -Often, specific metadata is known as metadata of a given format or an ontology, and different metadata formats are useful in different contexts. +The "extractor" concept +^^^^^^^^^^^^^^^^^^^^^^^ -Adding metadata with MetaLad -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +In the context of MetaLad, each one of those metadata examples above would be called a *schema*, and a process or tool deriving or generating a given schema would be called an *extractor*. + +Different metadata schemas are useful in different contexts: +In the example above, citation metadata might come in handy when evaluating the impact of the scientific finding, whereas the publications full text and special search indices could be used for automated meta-analyses. +To allow a variety of metadata use cases, MetaLad can use various metadata schemas simultaneously - If you want to, all schemas from the example above and many more could be created and managed in the same dataset in parallel. + +To handle different schemas in parallel, MetaLad represents them based on unique identifiers of the extraction process that generated them. +The automatically scanned full text might be identified with an extractor name ``OCR``, and the citation data could be called `altmetric `_. +But while the term "extractor" has a technical feel to it, an "extractor" can also be the manual process of annotating arbitrary information about a file - the medical annotations could be called ``first-year-med-student-tracing-brain-regions-by-hand`` following a manual annotation process. + +In addition to identifying schemas via extractor names, MetaLad and other :term:`DataLad extension`\s ship with specialized extractor tools to extract metadata of a certain schema. +But before we take a look into fully automated extraction, we'll illustrate the metadata concept of MetaLad with an extractor with manual elements. + +Adding metadata with meta-add +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In the context of DataLad datasets, metadata can either be associated with entire datasets or individual files inside of it. +Whether a piece of information is *dataset level* or *file level* metadata is dependent on the nature of the metadata and the envisioned use. +Metadata that describe dataset level properties could be dataset owner, dataset authors, dataset licenses, or names of contained files [#f1]_, whereas metadata that describe file level properties could be file :term:`checksum`\s or file-specific information like the time-stamp of a photograph. -Whether metadata is associated with the dataset or individual files inside of it is dependent on the nature of the metadata and the envisioned use. -Metadata that describe *dataset level* properties should be associated with the dataset, e.g. dataset owner, dataset authors, dataset licenses, or names of contained files. -Metadata that describes *file level* properties should be associated with the file, e.g. file :term:`checksum`\s. -Although it seems unnecessary to record the names of contained files or file properties in metadata, it can be very useful if a user receives only metadata and not primary data, for example in case of highly sensitive data where actual file content cannot be shared. Let's look at a concrete example. -We have a DataLad dataset ``dataset_0` that contains a single PNG-file called ``picture_1.png``:: +We have a DataLad dataset ``dataset_0`` that contains a single PNG-file called ``picture_1.png``:: $ tree dataset_0 dataset_0 @@ -61,24 +78,23 @@ We have a DataLad dataset ``dataset_0` that contains a single PNG-file called `` 0 directories, 1 file -Let's assume there is metadata stemming from an advance AI called ``Picture2Words`` that is able to describe the content of images. -In this case the AI describes the image as:: +Let's assume there is metadata stemming from an advanced AI called ``Picture2Words`` that is able to describe the content of images - in other words, this AI would be able to extract certain metadata from the file. +In this case the AI describes the image as - A lake with waterlilies in front of snow covered mountains +.. code-block:: bash + + "A lake with waterlilies in front of snow covered mountains" +and we would like to add this description as metadata to the file ``./picture_1.png``. +In order to store more than just one metadata schema, each added metadata entry needs to get a unique name to identify the schema and refer to it within a dataset. +Often, these names follow the extractor that generated it, but they can also be arbitrarily chosen. +In our example, we decide to identify the metadata schema as ``"Picture2Words"``. -We would like to add this description as metadata to the file ``./picture_1.png``. -One distinct feature of MetaLad is that it can simultaneously use various metadata formats. -The only requirement is that each different metadata format is uniquely named. -Therefore, we have to select a name to identify the metadata format. -We chose: ``Picture2Words``. -Now we can use the :command:`meta-add` command to add metadata. -``meta-add`` adds metadata into the dataset (or any :term:`Git` repository that you chose). -In order to include metadata in the dataset, ``meta-add`` requires a metadata entry to be in a certain format, in particular, a JSON object [#f1]_. -Such objects can then be read from the command line or from standard input (:term:`stdin`). -Each metadata JSON object must contain information about the level the metadata applies to (``file`` or ``dataset``), the file or dataset with which metadata should be associated as a path, the :term:`dataset ID` and version, the name of the metadata format, and the metadata itself. -The examplary, hand-crafted record below even contains a few additional properties, describing the author of the metadata, the name of the metadata extractor, its version, and its parameterization:: +In order to include metadata in a dataset, users need to provide a metadata entry to the :command:`meta-add` command. +This metadata entry has two major requirements: It needs to be supplied in a certain format, in particular, a JSON object [#f2]_, and it needs to include a set of required information in defined fields. +A valid metadata entry can then be read into ``meta-add`` either from the command line or from standard input (:term:`stdin`). +Let's take a look at the JSON object we could generate as a metadata entry for ``picture_1.png`` and identify required fields:: { "type": "file", @@ -96,13 +112,22 @@ The examplary, hand-crafted record below even contains a few additional properti } } -For convenience, we can create such a JSON object in an editor, and save it as ``metadata-image_1.json``. +When adding file-level metadata to a dataset that contains the file, the metadata JSON object must contain: + +* information about the level the metadata applies to (``type``, with ``file`` instead of ``dataset`` as a value), +* the file metadata belongs to with a ``path``, +* the :term:`dataset ID` (``dataset_id``) and version (``dataset_version``), +* an joint identifier for the metadata extractor and schema ``extractor_name``, as well as details about the metadata extractor like its version (``extractor_version``), its parameterization (``extraction_parameter``), and the date and time of extraction (``extraction_time``) in the form of a time stamp [#f3], +* information about the agent supplying the metadata (``agent_name`` and ``agent_email``), +* and finally the metadata itself (``extracted_metadata``). + +While certain extractors can generate metadata entries automatically, or one could write scripts wrapping extracting tools to generate them, we can also create such a JSON object manually in an editor, and save it as ``metadata-image_1.json``. Afterwards, we can redirect the content of the file into the :command:`meta-add` command in the command line. The call below would add the metadata to the current dataset, ``dataset_0``:: $ datalad meta-add -d dataset_0 - < metadata-image_1.json -If you want to try it out yourself, make sure to adjust the ``dataset_id`` to the ID of your own dataset, found via the command ``datalad configuration get datalad.dataset.id``. +If you want to run this command yourself, make sure to adjust the ``dataset_id`` to the ID of your own dataset, found via the command ``datalad configuration get datalad.dataset.id``. Alternatively, provide the switch ``-i`` to ``meta-add``, which tells it to just warn about ID mismatches instead of erroring out. Generally, metadata can either be provided @@ -185,4 +210,8 @@ As with DataLad and other Python packages, you might want to do the installation .. rubric:: Footnotes -.. [#f1] `JSON `_ is a language-independent, open and lightweight data interchange format. Data is represented as human readable text, organized in key-value pairs (e.g., 'name': 'Bob') or arrays, and thus easily readable by both humans and machines. A *JSON object* is a collection of key-value pairs. Its enclosed in curly brackets, and individual pairs in the object are separated by commas. \ No newline at end of file +.. [#f1] It may seem like an unnecessary duplicated effort to record the names of contained files or certain file properties as metadata in a dataset already containing these files. However, metadata can be very useful whenever the primary data can't be shared, for example due to its large size or sensitive nature, allowing consumers to, for example, derive anonymized information, aggregate data with search queries, or develop code and submit it to the data holders to be ran on their behalf. + +.. [#f2] `JSON `_ is a language-independent, open and lightweight data interchange format. Data is represented as human readable text, organized in key-value pairs (e.g., 'name': 'Bob') or arrays, and thus easily readable by both humans and machines. A *JSON object* is a collection of key-value pairs. Its enclosed in curly brackets, and individual pairs in the object are separated by commas. + +.. [#f3] what is a time stamp \ No newline at end of file From 355e8b9e2fab784de89d0397da565bebab418e4f Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Fri, 3 Feb 2023 16:25:10 +0100 Subject: [PATCH 06/28] GLOSS: add ref to glossary --- docs/glossary.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/glossary.rst b/docs/glossary.rst index 961f46051..6875d106d 100644 --- a/docs/glossary.rst +++ b/docs/glossary.rst @@ -291,6 +291,9 @@ Glossary pull request Also known as :term:`merge request`. Contributions to Git repositories/DataLad datasets can be proposed to be :term:`merge`\d into the dataset by "requesting a pull/update" from the dataset maintainer to obtain a proposed change from a dataset clone or sibling. It is implemented as a feature in repository hosting sites such as :term:`GitHub`, :term:`Gin`, or :term:`GitLab`. + ref + Git concept. A "Git Reference", typically shortened to "ref", is a text file containing a :term:`commit` :term:`shasum` as a human-readable reference to a specific version of your dataset or Git repository. Thanks to refs, Git users do not need to memorize or type shasums when switching between dataset states, and can use simple names instead: For example, a :term:`branch` such as ``main`` is a ref, and a :term:`tag` is one, too. In both cases, those refs are text files that contain the shasum of the commit at the tip of a branch, or the shasum of the commit you added the tag to. Refs are organized in the directory ``.git/refs`` and Git commands and configurations can use refs to perform updating operations or determine their behavior. More details can be found at `at git-scm.com `_ + relative path A path related to the present working directory. Relative paths never start with ``/``. Example: ``../Pictures/xkcd-webcomics/530.png``. See also :term:`absolute path`. From c9863baade240c16256b6cf029a348dcff181a9b Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Fri, 3 Feb 2023 16:25:35 +0100 Subject: [PATCH 07/28] Another round of restructuring and rewriting, include next TODOs --- docs/beyond_basics/101-181-metalad.rst | 180 +++++++++++++++++-------- 1 file changed, 121 insertions(+), 59 deletions(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index c2f998060..9c5fa5d87 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -15,8 +15,6 @@ MetaLad is a :term:`DataLad extension` that allows you to * transport metadata separately from primary data, * dump metadata and, for example, store it in a file, or search through it with a tool of your choice. -The basic usage model of MetaLad is: A user provides arbitrary metadata, gives it a name, and associates it with a specific version of a file or dataset. -MetaLad will store this association, enable metadata transport, and allow metadata queries. The following section illustrates relevant concepts, commands, and workflows. Primary Data versus Metadata @@ -31,7 +29,7 @@ The location, creation time, or creator ID is metadata, while the book in the li And what does metadata do for you? Generally, metadata provides additional information about primary data. This allows to identify primary data with certain properties. -These properties could either be contained within the primary data and (automatically) extracted from it, such as digital photographs captured in a specific time frame, or assigned to primary data based on an external policy (such as the directory "Hiking in the alps 2019" on your phone). +These properties could either be contained within the primary data and (automatically) extracted from it, such as digital photographs captured in a specific time frame at a specific GPS location, or assigned to primary data based on an external policy, such as the directory "Hiking in the alps 2019" on your phone. Importantly, primary data can have virtually unlimited different metadata associated with it, depending on what is relevant in a given context. Consider a publication in a medical field, and a few examples for metadata about it from the virtually unlimited metadata space: @@ -45,21 +43,22 @@ Consider a publication in a medical field, and a few examples for metadata about 6. Anonymized information extracted from medical documents. 7. Information about the used software, e.g. security assessments, citation.cff -The "extractor" concept -^^^^^^^^^^^^^^^^^^^^^^^ +MetaLad's *extractor* concept +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ In the context of MetaLad, each one of those metadata examples above would be called a *schema*, and a process or tool deriving or generating a given schema would be called an *extractor*. Different metadata schemas are useful in different contexts: In the example above, citation metadata might come in handy when evaluating the impact of the scientific finding, whereas the publications full text and special search indices could be used for automated meta-analyses. -To allow a variety of metadata use cases, MetaLad can use various metadata schemas simultaneously - If you want to, all schemas from the example above and many more could be created and managed in the same dataset in parallel. +To allow a variety of metadata use cases, MetaLad can use various metadata schemas simultaneously - if you want to, all schemas from the example above and many more could be created and managed in the same dataset in parallel. To handle different schemas in parallel, MetaLad represents them based on unique identifiers of the extraction process that generated them. -The automatically scanned full text might be identified with an extractor name ``OCR``, and the citation data could be called `altmetric `_. -But while the term "extractor" has a technical feel to it, an "extractor" can also be the manual process of annotating arbitrary information about a file - the medical annotations could be called ``first-year-med-student-tracing-brain-regions-by-hand`` following a manual annotation process. +The automatically scanned full text might be identified with an extractor name ``OCR``, and the citation data could be called "`altmetric `_". +But while the term "extractor" has a technical feel to it, an "extractor" can also be the manual process of annotating arbitrary information about a file - nothing prevents metadata from medical annotations to be called ``Sam-tracing-brain-regions-by-hand``. In addition to identifying schemas via extractor names, MetaLad and other :term:`DataLad extension`\s ship with specialized extractor tools to extract metadata of a certain schema. -But before we take a look into fully automated extraction, we'll illustrate the metadata concept of MetaLad with an extractor with manual elements. +Likewise, anyone can build their own extractor to generate schemas of their choice. +But before we take a closer look into that, let's illustrate the metadata concepts and commands of MetaLad with a toy example. Adding metadata with meta-add ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -68,14 +67,17 @@ In the context of DataLad datasets, metadata can either be associated with entir Whether a piece of information is *dataset level* or *file level* metadata is dependent on the nature of the metadata and the envisioned use. Metadata that describe dataset level properties could be dataset owner, dataset authors, dataset licenses, or names of contained files [#f1]_, whereas metadata that describe file level properties could be file :term:`checksum`\s or file-specific information like the time-stamp of a photograph. +.. gitusernote:: Metadata is stored in Git + + When MetaLad adds metadata to your datasets, it will store the metadata in :term:`Git` only. Thus, even a plain Git repository is sufficient to work with ``datalad-metalad``. However, the metadata is stored in an unusual and somewhat hidden place, inside of the `Git object store `_. If you're interested in the technical details, you can find a :ref:`Findoutmore ` a bit further down in this section. + Let's look at a concrete example. -We have a DataLad dataset ``dataset_0`` that contains a single PNG-file called ``picture_1.png``:: +We have a DataLad dataset ``cozy-screensavers`` that contains a single PNG-file called ``zen.png``:: - $ tree dataset_0 - dataset_0 - └── picture_1.png - 0 directories, 1 file + $ tree + cozy-screensavers + └── zen.png Let's assume there is metadata stemming from an advanced AI called ``Picture2Words`` that is able to describe the content of images - in other words, this AI would be able to extract certain metadata from the file. @@ -85,28 +87,24 @@ In this case the AI describes the image as "A lake with waterlilies in front of snow covered mountains" -and we would like to add this description as metadata to the file ``./picture_1.png``. -In order to store more than just one metadata schema, each added metadata entry needs to get a unique name to identify the schema and refer to it within a dataset. -Often, these names follow the extractor that generated it, but they can also be arbitrarily chosen. -In our example, we decide to identify the metadata schema as ``"Picture2Words"``. +We would like to add this description as metadata to the file ``./zen.png``, and will identify it with a name corresponding to its extractor, ``"Picture2Words"``. In order to include metadata in a dataset, users need to provide a metadata entry to the :command:`meta-add` command. -This metadata entry has two major requirements: It needs to be supplied in a certain format, in particular, a JSON object [#f2]_, and it needs to include a set of required information in defined fields. -A valid metadata entry can then be read into ``meta-add`` either from the command line or from standard input (:term:`stdin`). -Let's take a look at the JSON object we could generate as a metadata entry for ``picture_1.png`` and identify required fields:: +This metadata entry has two major requirements: It needs to be supplied in a certain format, in particular, as a JSON object [#f2]_, and it needs to include a set of required information in defined fields. +Let's take a look at the JSON object we could generate as a metadata entry for ``zen.png`` and identify required fields:: { "type": "file", + "path": "zen.png", "dataset_id": "52142b84-dc76-11ea-98c5-7cdd908c7490", "dataset_version": "244a8ad43b00622989ae7f0d2b59c80697dadb80", - "path": "picture_1.png", "extractor_name": "Picture2Words", "extractor_version": "0.1", "extraction_parameter": {}, "extraction_time": 1675113291.1464975, - "agent_name": "Datalad User", - "agent_email": "datalad.user@example.com", + "agent_name": "Overworked CTO", + "agent_email": "closetoburnout@randomtechconsultancy.com", "extracted_metadata": { "description": "A lake with waterlilies in a front of snow covered mountains" } @@ -115,58 +113,91 @@ Let's take a look at the JSON object we could generate as a metadata entry for ` When adding file-level metadata to a dataset that contains the file, the metadata JSON object must contain: * information about the level the metadata applies to (``type``, with ``file`` instead of ``dataset`` as a value), -* the file metadata belongs to with a ``path``, +* the file the metadata belongs to with a ``path``, * the :term:`dataset ID` (``dataset_id``) and version (``dataset_version``), -* an joint identifier for the metadata extractor and schema ``extractor_name``, as well as details about the metadata extractor like its version (``extractor_version``), its parameterization (``extraction_parameter``), and the date and time of extraction (``extraction_time``) in the form of a time stamp [#f3], +* an joint identifier for the metadata extractor and schema ``extractor_name`` (i.e., ``Picture2Words``, as well as details about the metadata extractor like its version (``extractor_version``), its parameterization (``extraction_parameter``), and the date and time of extraction (``extraction_time``) in the form of a Unix time stamp [#f3]_, * information about the agent supplying the metadata (``agent_name`` and ``agent_email``), * and finally the metadata itself (``extracted_metadata``). -While certain extractors can generate metadata entries automatically, or one could write scripts wrapping extracting tools to generate them, we can also create such a JSON object manually in an editor, and save it as ``metadata-image_1.json``. -Afterwards, we can redirect the content of the file into the :command:`meta-add` command in the command line. -The call below would add the metadata to the current dataset, ``dataset_0``:: - - $ datalad meta-add -d dataset_0 - < metadata-image_1.json +While certain extractors can generate metadata entries automatically, or one could write scripts wrapping extracting tools to generate them, we can also create such a JSON object manually, for example in an editor. +A valid metadata entry can then be read into ``meta-add`` either from the command line or from standard input (:term:`stdin`). +For example, we can save the metadata entry above as ``metadata-zen.json`` and then redirect the content of the file into the :command:`meta-add` command in the command line. +The following call would add the metadata entry to the current dataset, ``cozy-screensavers``:: -If you want to run this command yourself, make sure to adjust the ``dataset_id`` to the ID of your own dataset, found via the command ``datalad configuration get datalad.dataset.id``. -Alternatively, provide the switch ``-i`` to ``meta-add``, which tells it to just warn about ID mismatches instead of erroring out. + $ datalad meta-add -d cozy-screensavers - < metadata-zen.json -Generally, metadata can either be provided +.. find-out-more:: meta-add validity checks -* manually, -* by running :term:`extractor`\s (``datalad-metalad`` plugins that extract certain metadata from primary data), -* or by any other means that create correct metadata records. For example, you could copy the complete metadata from ``dataset_0`` to ``dataset_1``, by dumping it from one dataset into another:: - - $ datalad meta-dump -d dataset_0 -r | \ - datalad meta-add -d dataset_1 --json-lines - + When adding metadata for the first time, its not uncommon to run into errors. + Its quite easy, for example, to miss a comma or quotation mark when creating a JSON object by hand. + But there are also some internal checks that might be surprising. + If you want to add the metadata above to your own dataset, you should make sure to adjust the ``dataset_id`` to the ID of your own dataset, found via the command ``datalad configuration get datalad.dataset.id`` - otherwise you'll see an error [#f4]_. + And in case you'd supply the ``extraction_time`` as "this morning at 8AM" instead of a time stamp, the command will be unhappy as well. + In case an error occurs, make sure to read the error message, and turn the the commands' ``--help`` for insights about requirements you might have missed. After the metadata has been added, you can view it via the command :command:`meta-dump`. The simplest form of this command is ``meta-dump -r``, which will show all metadata that is stored in dataset in the current directory. To get more specific metadata records, you can give a dataset-file-path-pattern to ``meta-dump``, much like an argument to ``ls``, that identifies :term:`dataset ID`, version and a file within the dataset. -The two parts are separated by ``:``. So:: +The two parts are separated by ``:``. The following line would just dump all metadata for ``zen.png``. + +.. code-block:: bash + + $ datalad meta-dump -d cozy-screensavers .:zen.png + TODO: how would the output of this look like? + +.. find-out-more:: More complex metadata-dumps + + TODO: add complex Dataset-file-path-pattern examples, e.g., with UUIDs, versions, etc + +Using existing extractors to add metadata +""""""""""""""""""""""""""""""""""""""""" + +TODO: This needs and example - $ datalad meta-dump -d dataset_0 .:picture_1.png -would just dump all metadata for ``picture_1.png``. +Creating your own extractor +""""""""""""""""""""""""""" + +TODO: This also needs an example Distributing and Getting Metadata -""""""""""""""""""""""""""""""""" +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Once metadata has been added to a dataset, it can be distributed and retrieved. +Instead of creating and adding metadata yourself, you could download fitting pre-existing metadata. +Similarly, instead of repeating a ``meta-add`` process for one and the same files across hierarchies of datasets, metadata added into one dataset can be exported into other datasets. +Regardless of whether it is a distribution or a retrieval process, though, an export with MetaLad will only concern the *metadata*, and never the primary data. + +Download Metadata from a remote repository +"""""""""""""""""""""""""""""""""""""""""" + +Let's start by creating a place where someone else's metadata could live. + +.. code-block:: bash -Instead of creating and adding metadata yourself, you can download metadata from remote DataLad datasets. -Likewise, metadata added into one dataset can be exported into other dataset. -Such an export will only export the metadata itself, not the primary data. + $ datalad create metadata-assimilation + $ cd metadata-assimilation -Download Metadata from a Git-Repository -""""""""""""""""""""""""""""""""""""""" +Because MetaLad stores metadata in :term:`Git`'s object store, we use Git to directly fetch metadata from a remote repository, such as this demo on :term:`GitHub`: ``https://github.com/christian-monch/metadata-test.git``. +Because metadata added by MetaLad is not transported automatically but needs to be specifically requested, the command to retrieve it looks unfamiliar to non-Git-users: It identifies the precise location of :term:`ref` that contains the metadata. -First create a git repository that should hold the downloaded metadata:: +.. find-out-more:: Exactly where is metadata stored? + :name: fom-metadataobjecttree - $ git init metadata-repo - $ cd metadata-repo + MetaLad employs an internal metadata model that makes the following properties possible: -Now fetch metadata from the demo repository on :term:`GitHub`, i.e. from ``https://github.com/christian-monch/metadata-test.git`` demo repository:: + * Metadata has a version encoded, but isn't itself version controlled + * Metadata should not be transported if not explicitly requested + * It should be possible to only retrieve parts of the overall metadata tree, e.g. certain sub-nodes - $ git fetch "https://github.com/christian-monch/metadata-test.git" "refs/datalad/*:refs/datalad/*" + TODO this needs much more + +.. code-block:: bash + + $ git fetch \ + "https://github.com/christian-monch/metadata-test.git" \ + "refs/datalad/*:refs/datalad/*" The metadata is now locally available in the Git repository ``metadata-repo``. You can verify this by issuing the command ``datalad meta-dump -r``, which will list all metadata in the repository. @@ -175,23 +206,30 @@ You can verify this by issuing the command ``datalad meta-dump -r``, which will Publish metadata to a Git-Repository """""""""""""""""""""""""""""""""""" -You can also push your metadata to a remote Git-repository (if you have write-authorization). Let's assume you are in the directory that contains the git repository with your metadata, then you can push your metadata to a remote git repository ````:: +You can also push your metadata to a remote :term:`sibling` (if you have write :term:`permissions`). +This, too, uses a Git command to push only specific :term:`ref`\s. +Let's assume you are in the directory that contains the git repository with your metadata, then you can push your metadata to a remote git repository ````:: - > git push "" "refs/datalad/*:refs/datalad/*" + $ git push "" "refs/datalad/*:refs/datalad/*" You will notice that no primary data is stored in the repository ``metadata-destination``. That allows you to publish metadata without publishing the primary data at the same time. +Querying metadata +^^^^^^^^^^^^^^^^^ + +TODO: something more about meta-dump and concrete usage example with, e.g., ``jq`` + Querying metadata remotely """""""""""""""""""""""""" You do not have to download metadata to dump it. It is also possible to specify a git-repository, and let metalad only read the metadata that it requires to fulfill your request. For example:: - > datalad meta-dump -d https://github.com/christian-monch/metadata-test.git ./study-100 + $ datalad meta-dump -d https://github.com/christian-monch/metadata-test.git ./study-100 Would only download enough data to dump all metadata in the specified dataset tree-path. If you want to see all metadata in the git repository you could issue the following command:: - > datalad meta-dump -d https://github.com/christian-monch/metadata-test.git -r + $ datalad meta-dump -d https://github.com/christian-monch/metadata-test.git -r This will take a lot longer than the previous command because datalad has to fetch more item from the remote repository. If you use the remote meta-dump option properly, you can quickly examine small subsets of very large metadata repositories. @@ -208,10 +246,34 @@ MetaLad is a stand-alone Python package, and can be installed using As with DataLad and other Python packages, you might want to do the installation in a :term:`virtual environment`. + + + + + +Generally, metadata can either be provided + +* manually, +* by running :term:`extractor`\s (``datalad-metalad`` plugins that extract certain metadata from primary data), +* or by any other means that create correct metadata records. For example, you could copy the complete metadata from ``dataset_0`` to ``dataset_1``, by dumping it from one dataset into another:: + + $ datalad meta-dump -d dataset_0 -r | \ + datalad meta-add -d dataset_1 --json-lines - + + + + + .. rubric:: Footnotes .. [#f1] It may seem like an unnecessary duplicated effort to record the names of contained files or certain file properties as metadata in a dataset already containing these files. However, metadata can be very useful whenever the primary data can't be shared, for example due to its large size or sensitive nature, allowing consumers to, for example, derive anonymized information, aggregate data with search queries, or develop code and submit it to the data holders to be ran on their behalf. .. [#f2] `JSON `_ is a language-independent, open and lightweight data interchange format. Data is represented as human readable text, organized in key-value pairs (e.g., 'name': 'Bob') or arrays, and thus easily readable by both humans and machines. A *JSON object* is a collection of key-value pairs. Its enclosed in curly brackets, and individual pairs in the object are separated by commas. -.. [#f3] what is a time stamp \ No newline at end of file +.. [#f3] A Unix timestamp is widely used in computing and measures time as the number of seconds passed since January 1st, 1970. The timestamp in the example metadata entry (``1675113291.1464975``) translates to January 30th, 2023, 22:14:51.146497 with the code snippet below. Lots of software tools have the ability to generate timestamps for you, for example Python's `time `_ module or the command ``date +%s`` in a command line on Unix systems. + + >>> from datetime import datetime + >>> datetime.fromtimestamp(1675113291.1464975) + datetime.datetime(2023, 1, 30, 22, 14, 51, 146497) + +.. [#f4] Alternatively, provide the switch ``-i`` to ``meta-add``, which tells it to just warn about ID mismatches instead of erroring out. \ No newline at end of file From 8be9e3ee13d2ad280bb1332522a9f1ffcfee522f Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Thu, 9 Feb 2023 15:31:51 +0100 Subject: [PATCH 08/28] better wording Co-authored-by: Stephan Heunis --- docs/beyond_basics/101-181-metalad.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index 9c5fa5d87..95fd58813 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -53,7 +53,7 @@ In the example above, citation metadata might come in handy when evaluating the To allow a variety of metadata use cases, MetaLad can use various metadata schemas simultaneously - if you want to, all schemas from the example above and many more could be created and managed in the same dataset in parallel. To handle different schemas in parallel, MetaLad represents them based on unique identifiers of the extraction process that generated them. -The automatically scanned full text might be identified with an extractor name ``OCR``, and the citation data could be called "`altmetric `_". +For example, the automatically scanned full text might be identified with an extractor name ``OCR``, and that of the citation data could be called "`altmetric `_". But while the term "extractor" has a technical feel to it, an "extractor" can also be the manual process of annotating arbitrary information about a file - nothing prevents metadata from medical annotations to be called ``Sam-tracing-brain-regions-by-hand``. In addition to identifying schemas via extractor names, MetaLad and other :term:`DataLad extension`\s ship with specialized extractor tools to extract metadata of a certain schema. From 31f940b7375ea393963cae22dbce4c639cbb8d4a Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Thu, 9 Feb 2023 15:32:25 +0100 Subject: [PATCH 09/28] typo Co-authored-by: Stephan Heunis --- docs/beyond_basics/101-181-metalad.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index 95fd58813..765da6839 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -136,7 +136,7 @@ The following call would add the metadata entry to the current dataset, ``cozy-s In case an error occurs, make sure to read the error message, and turn the the commands' ``--help`` for insights about requirements you might have missed. After the metadata has been added, you can view it via the command :command:`meta-dump`. -The simplest form of this command is ``meta-dump -r``, which will show all metadata that is stored in dataset in the current directory. +The simplest form of this command is ``meta-dump -r``, which will show all metadata that is stored in the dataset in the current directory. To get more specific metadata records, you can give a dataset-file-path-pattern to ``meta-dump``, much like an argument to ``ls``, that identifies :term:`dataset ID`, version and a file within the dataset. The two parts are separated by ``:``. The following line would just dump all metadata for ``zen.png``. From 8332b283dbba6dcf33593ac423ae495259ace18f Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Thu, 9 Feb 2023 15:32:55 +0100 Subject: [PATCH 10/28] typo Co-authored-by: Stephan Heunis --- docs/beyond_basics/101-181-metalad.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index 765da6839..205f40668 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -180,7 +180,7 @@ Let's start by creating a place where someone else's metadata could live. $ cd metadata-assimilation Because MetaLad stores metadata in :term:`Git`'s object store, we use Git to directly fetch metadata from a remote repository, such as this demo on :term:`GitHub`: ``https://github.com/christian-monch/metadata-test.git``. -Because metadata added by MetaLad is not transported automatically but needs to be specifically requested, the command to retrieve it looks unfamiliar to non-Git-users: It identifies the precise location of :term:`ref` that contains the metadata. +Because metadata added by MetaLad is not transported automatically but needs to be specifically requested, the command to retrieve it looks unfamiliar to non-Git-users: It identifies the precise location of the :term:`ref` that contains the metadata. .. find-out-more:: Exactly where is metadata stored? :name: fom-metadataobjecttree From 96009783a8b47a498803070c3038b6344031f702 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 07:43:20 +0100 Subject: [PATCH 11/28] link to code instead of docs Co-authored-by: Stephan Heunis --- docs/beyond_basics/101-181-metalad.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index 205f40668..d766c5e73 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -4,7 +4,7 @@ Metadata-Management with MetaLad -------------------------------- For many years, :term:`metadata` related functionality was included in the DataLad core package. -A modernized approach, however, is now developed in the `datalad-metalad extension `_. +A modernized approach, however, is now developed in the `datalad-metalad extension `_. .. figure:: ../artwork/src/metadata.svg From c9574d31142cfa01e016c09ca4d23d8f173a1d96 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 08:34:29 +0100 Subject: [PATCH 12/28] add a link for citation.cff --- docs/beyond_basics/101-181-metalad.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index d766c5e73..e382346a8 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -41,7 +41,7 @@ Consider a publication in a medical field, and a few examples for metadata about structural information even without access to the primary data 5. Special search indices, e.g. graph-based search indices, medical abbreviations 6. Anonymized information extracted from medical documents. -7. Information about the used software, e.g. security assessments, citation.cff +7. Information about the used software, e.g. security assessments, `citation.cff `_ MetaLad's *extractor* concept ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ From 9b355311df269f8c2dcaedea30c20478dc83a7b7 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 08:35:23 +0100 Subject: [PATCH 13/28] Actually execute the image metadata example --- docs/beyond_basics/101-181-metalad.rst | 78 +++++++++++++++++++++----- 1 file changed, 65 insertions(+), 13 deletions(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index e382346a8..c78fddd60 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -73,12 +73,26 @@ Metadata that describe dataset level properties could be dataset owner, dataset Let's look at a concrete example. -We have a DataLad dataset ``cozy-screensavers`` that contains a single PNG-file called ``zen.png``:: +We have a DataLad dataset ``cozy-screensavers`` that contains a single PNG-file called ``zen.png``. + +.. runrecord:: _examples/DL-101-181-101 + :language: console + :workdir: beyond_basics/meta + + $ datalad clone https://github.com/datalad-handbook/cozy-screensavers.git + $ cd cozy-screensavers + +.. runrecord:: _examples/DL-101-181-102 + :language: console + :workdir: beyond_basics/meta/cozy-screensavers $ tree - cozy-screensavers - └── zen.png +.. runrecord:: _examples/DL-101-181-103 + :language: console + :workdir: beyond_basics/meta/cozy-screensavers + + $ datalad get zen.jpg Let's assume there is metadata stemming from an advanced AI called ``Picture2Words`` that is able to describe the content of images - in other words, this AI would be able to extract certain metadata from the file. In this case the AI describes the image as @@ -97,8 +111,8 @@ Let's take a look at the JSON object we could generate as a metadata entry for ` { "type": "file", "path": "zen.png", - "dataset_id": "52142b84-dc76-11ea-98c5-7cdd908c7490", - "dataset_version": "244a8ad43b00622989ae7f0d2b59c80697dadb80", + "dataset_id": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7", + "dataset_version": "19f2d98d758116d099d467260a5a71082b2c6a29", "extractor_name": "Picture2Words", "extractor_version": "0.1", "extraction_parameter": {}, @@ -121,17 +135,46 @@ When adding file-level metadata to a dataset that contains the file, the metadat While certain extractors can generate metadata entries automatically, or one could write scripts wrapping extracting tools to generate them, we can also create such a JSON object manually, for example in an editor. A valid metadata entry can then be read into ``meta-add`` either from the command line or from standard input (:term:`stdin`). -For example, we can save the metadata entry above as ``metadata-zen.json`` and then redirect the content of the file into the :command:`meta-add` command in the command line. -The following call would add the metadata entry to the current dataset, ``cozy-screensavers``:: - - $ datalad meta-add -d cozy-screensavers - < metadata-zen.json +For example, we can save the metadata entry above as ``metadata-zen.json``: + +.. runrecord:: _examples/DL-101-181-104 + :language: console + :workdir: beyond_basics/meta/cozy-screensavers + + $ cat << EOT > metadata-zen.json + { + "type": "file", + "path": "zen.png", + "dataset_id": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7", + "dataset_version": "19f2d98d758116d099d467260a5a71082b2c6a29", + "extractor_name": "Picture2Words", + "extractor_version": "0.1", + "extraction_parameter": {}, + "extraction_time": 1675113291.1464975, + "agent_name": "Overworked CTO", + "agent_email": "closetoburnout@randomtechconsultancy.com", + "extracted_metadata": { + "description": "A lake with waterlilies in a front of snow covered mountains" + } + } + EOT + +Then, we redirect the content of the file into the :command:`meta-add` command in the command line. +The following call would add the metadata entry to the current dataset, ``cozy-screensavers``: + + +.. runrecord:: _examples/DL-101-181-105 + :language: console + :workdir: beyond_basics/meta/cozy-screensavers + + $ datalad meta-add -d . - < metadata-zen.json .. find-out-more:: meta-add validity checks When adding metadata for the first time, its not uncommon to run into errors. Its quite easy, for example, to miss a comma or quotation mark when creating a JSON object by hand. But there are also some internal checks that might be surprising. - If you want to add the metadata above to your own dataset, you should make sure to adjust the ``dataset_id`` to the ID of your own dataset, found via the command ``datalad configuration get datalad.dataset.id`` - otherwise you'll see an error [#f4]_. + If you want to add the metadata above to your own dataset, you should make sure to adjust the ``dataset_id`` to the ID of your own dataset, found via the command ``datalad configuration get datalad.dataset.id`` - otherwise you'll see an error [#f4]_, and likewise the ``dataset_version``. And in case you'd supply the ``extraction_time`` as "this morning at 8AM" instead of a time stamp, the command will be unhappy as well. In case an error occurs, make sure to read the error message, and turn the the commands' ``--help`` for insights about requirements you might have missed. @@ -140,10 +183,19 @@ The simplest form of this command is ``meta-dump -r``, which will show all metad To get more specific metadata records, you can give a dataset-file-path-pattern to ``meta-dump``, much like an argument to ``ls``, that identifies :term:`dataset ID`, version and a file within the dataset. The two parts are separated by ``:``. The following line would just dump all metadata for ``zen.png``. -.. code-block:: bash +.. runrecord:: _examples/DL-101-181-106 + :language: console + :workdir: beyond_basics/meta/cozy-screensavers + + $ datalad meta-dump -d . .:zen.png + +This could also be printed a bit more readable: + +.. runrecord:: _examples/DL-101-181-107 + :language: console + :workdir: beyond_basics/meta/cozy-screensavers - $ datalad meta-dump -d cozy-screensavers .:zen.png - TODO: how would the output of this look like? + $ datalad -f json_pp meta-dump -d . .:zen.png .. find-out-more:: More complex metadata-dumps From 6b58b5101b4cfecbc01867ef34606b1db411fed5 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 08:39:00 +0100 Subject: [PATCH 14/28] Add link to user guide to writing own extensions --- docs/beyond_basics/101-181-metalad.rst | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index c78fddd60..d258ed08a 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -57,7 +57,7 @@ For example, the automatically scanned full text might be identified with an ext But while the term "extractor" has a technical feel to it, an "extractor" can also be the manual process of annotating arbitrary information about a file - nothing prevents metadata from medical annotations to be called ``Sam-tracing-brain-regions-by-hand``. In addition to identifying schemas via extractor names, MetaLad and other :term:`DataLad extension`\s ship with specialized extractor tools to extract metadata of a certain schema. -Likewise, anyone can build their own extractor to generate schemas of their choice. +Likewise, `anyone can build their own extractor to generate schemas of their choice `_. But before we take a closer look into that, let's illustrate the metadata concepts and commands of MetaLad with a toy example. Adding metadata with meta-add @@ -210,8 +210,7 @@ TODO: This needs and example Creating your own extractor """"""""""""""""""""""""""" -TODO: This also needs an example - +The MetaLad docs have a dedicated user guide that walks you through the process of creating your own extractor. Have a look at `docs.datalad.org/projects/metalad/user_guide/writing-extractors.html `_. Distributing and Getting Metadata ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ From fb482645c702772b593a5becb88099ed3171b294 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 08:48:31 +0100 Subject: [PATCH 15/28] Add Stephan's proposed 'Using Metadata' paragraph --- docs/beyond_basics/101-181-metalad.rst | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index d258ed08a..8b6ba9615 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -284,6 +284,17 @@ Would only download enough data to dump all metadata in the specified dataset tr This will take a lot longer than the previous command because datalad has to fetch more item from the remote repository. If you use the remote meta-dump option properly, you can quickly examine small subsets of very large metadata repositories. +Using metadata +^^^^^^^^^^^^^^ + +Now that we know all about metadata and how it is handled by MetaLad, here's a final note on its utility: +Metadata, especially when it originates from different sources and gets harmonized to a single schema, provides the powerful opportunity to aid data discoverability. +An example of a good use case for metadata is therefore a search or browsing interface, or data bases, such as data portals and graph query databases. +MetaLad-extracted metadata can be used in workflows to generate such interfaces, and a concrete example is the :ref:`DataLad Catalog `, which the next section will introduce. +So to aid with the discoverability of data, one could add metadata to DataLad datasets, extract metadata with MetaLad and multiple extractors, translate extracted metadata to the catalog schema, submit it to ``datalad-catalog`` in order to generate catalog entries, which can all be browsed in a user friendly web-based interface. +Intrigued? Read on to the next section for more information. + + Installation ^^^^^^^^^^^^ From d8c8f46f9fda0ad135b1a7cdd3abb4f5a921f7c7 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 09:04:10 +0100 Subject: [PATCH 16/28] Make other exampels runrecords as well --- docs/beyond_basics/101-181-metalad.rst | 45 ++++++++++++++++++++------ 1 file changed, 35 insertions(+), 10 deletions(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index 8b6ba9615..e6a50b4cb 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -225,10 +225,12 @@ Download Metadata from a remote repository Let's start by creating a place where someone else's metadata could live. -.. code-block:: bash +.. runrecord:: _examples/DL-101-181-110 + :language: console + :workdir: beyond_basics/meta - $ datalad create metadata-assimilation - $ cd metadata-assimilation + $ datalad create metadata-assimilation + $ cd metadata-assimilation Because MetaLad stores metadata in :term:`Git`'s object store, we use Git to directly fetch metadata from a remote repository, such as this demo on :term:`GitHub`: ``https://github.com/christian-monch/metadata-test.git``. Because metadata added by MetaLad is not transported automatically but needs to be specifically requested, the command to retrieve it looks unfamiliar to non-Git-users: It identifies the precise location of the :term:`ref` that contains the metadata. @@ -244,15 +246,23 @@ Because metadata added by MetaLad is not transported automatically but needs to TODO this needs much more -.. code-block:: bash +.. runrecord:: _examples/DL-101-181-111 + :language: console + :workdir: beyond_basics/meta/metadata-assimilation - $ git fetch \ + $ git fetch \ "https://github.com/christian-monch/metadata-test.git" \ "refs/datalad/*:refs/datalad/*" The metadata is now locally available in the Git repository ``metadata-repo``. You can verify this by issuing the command ``datalad meta-dump -r``, which will list all metadata in the repository. +Can you guess what type of metadata it contains [#f5]_ ? + +.. runrecord:: _examples/DL-101-181-112 + :language: console + :workdir: beyond_basics/meta/metadata-assimilation + $ datalad meta-dump -r Publish metadata to a Git-Repository """""""""""""""""""""""""""""""""""" @@ -274,13 +284,25 @@ TODO: something more about meta-dump and concrete usage example with, e.g., ``jq Querying metadata remotely """""""""""""""""""""""""" -You do not have to download metadata to dump it. It is also possible to specify a git-repository, and let metalad only read the metadata that it requires to fulfill your request. For example:: +You do not have to download metadata to dump it. It is also possible to specify a git-repository, and let metalad only read the metadata that it requires to fulfill your request. For example, in order to only retrieve metadata from a metadata entry that has the ``dataset_path`` value of ``study-100``, you can simply run: - $ datalad meta-dump -d https://github.com/christian-monch/metadata-test.git ./study-100 +.. runrecord:: _examples/DL-101-181-115 + :language: console + :workdir: beyond_basics/meta + + $ datalad meta-dump \ + -d https://github.com/christian-monch/metadata-test.git \ + ./study-100 -Would only download enough data to dump all metadata in the specified dataset tree-path. If you want to see all metadata in the git repository you could issue the following command:: +As the output shows, this command only downloaded enough data from the remote repository to dump all metadata in the specified dataset tree-path. +If you want to query all metadata remotely from the repository you could issue the following command: - $ datalad meta-dump -d https://github.com/christian-monch/metadata-test.git -r +.. runrecord:: _examples/DL-101-181-116 + :language: console + :workdir: beyond_basics/meta + + $ datalad meta-dump \ + -d https://github.com/christian-monch/metadata-test.git -r This will take a lot longer than the previous command because datalad has to fetch more item from the remote repository. If you use the remote meta-dump option properly, you can quickly examine small subsets of very large metadata repositories. @@ -319,6 +341,7 @@ Generally, metadata can either be provided * by running :term:`extractor`\s (``datalad-metalad`` plugins that extract certain metadata from primary data), * or by any other means that create correct metadata records. For example, you could copy the complete metadata from ``dataset_0`` to ``dataset_1``, by dumping it from one dataset into another:: + $ datalad meta-dump -d dataset_0 -r | \ datalad meta-add -d dataset_1 --json-lines - @@ -338,4 +361,6 @@ Generally, metadata can either be provided >>> datetime.fromtimestamp(1675113291.1464975) datetime.datetime(2023, 1, 30, 22, 14, 51, 146497) -.. [#f4] Alternatively, provide the switch ``-i`` to ``meta-add``, which tells it to just warn about ID mismatches instead of erroring out. \ No newline at end of file +.. [#f4] Alternatively, provide the switch ``-i`` to ``meta-add``, which tells it to just warn about ID mismatches instead of erroring out. + +.. [#f5] The answer is minimal information about archived scientific projects of a research institute. While some personal information has been obfuscated, you can still figure out which information is associated with each entry, such as the project name, its authors, or associated publications. \ No newline at end of file From 49d244b06e8afda9c2634c2916640221881aaa66 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 09:04:28 +0100 Subject: [PATCH 17/28] add code-snippets for metalad section --- docs/beyond_basics/_examples/DL-101-181-101 | 13 ++++++++++ docs/beyond_basics/_examples/DL-101-181-102 | 5 ++++ docs/beyond_basics/_examples/DL-101-181-103 | 2 ++ docs/beyond_basics/_examples/DL-101-181-104 | 17 +++++++++++++ docs/beyond_basics/_examples/DL-101-181-105 | 2 ++ docs/beyond_basics/_examples/DL-101-181-106 | 2 ++ docs/beyond_basics/_examples/DL-101-181-107 | 24 ++++++++++++++++++ docs/beyond_basics/_examples/DL-101-181-110 | 3 +++ docs/beyond_basics/_examples/DL-101-181-111 | 7 ++++++ docs/beyond_basics/_examples/DL-101-181-112 | 27 +++++++++++++++++++++ docs/beyond_basics/_examples/DL-101-181-115 | 2 ++ docs/beyond_basics/_examples/DL-101-181-116 | 27 +++++++++++++++++++++ 12 files changed, 131 insertions(+) create mode 100644 docs/beyond_basics/_examples/DL-101-181-101 create mode 100644 docs/beyond_basics/_examples/DL-101-181-102 create mode 100644 docs/beyond_basics/_examples/DL-101-181-103 create mode 100644 docs/beyond_basics/_examples/DL-101-181-104 create mode 100644 docs/beyond_basics/_examples/DL-101-181-105 create mode 100644 docs/beyond_basics/_examples/DL-101-181-106 create mode 100644 docs/beyond_basics/_examples/DL-101-181-107 create mode 100644 docs/beyond_basics/_examples/DL-101-181-110 create mode 100644 docs/beyond_basics/_examples/DL-101-181-111 create mode 100644 docs/beyond_basics/_examples/DL-101-181-112 create mode 100644 docs/beyond_basics/_examples/DL-101-181-115 create mode 100644 docs/beyond_basics/_examples/DL-101-181-116 diff --git a/docs/beyond_basics/_examples/DL-101-181-101 b/docs/beyond_basics/_examples/DL-101-181-101 new file mode 100644 index 000000000..bd0f7967c --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-101 @@ -0,0 +1,13 @@ +$ datalad clone https://github.com/datalad-handbook/cozy-screensavers.git +$ cd cozy-screensavers +[INFO] Attempting a clone into /home/me/beyond_basics/meta/cozy-screensavers +[INFO] Attempting to clone from https://github.com/datalad-handbook/cozy-screensavers.git to /home/me/beyond_basics/meta/cozy-screensavers +[INFO] Start enumerating objects +[INFO] Start counting objects +[INFO] Start compressing objects +[INFO] Start receiving objects +[INFO] Start resolving deltas +[INFO] Completed clone attempts for Dataset(/home/me/beyond_basics/meta/cozy-screensavers) +[INFO] Remote origin not usable by git-annex; setting annex-ignore +[INFO] https://github.com/datalad-handbook/cozy-screensavers.git/config download failed: Not Found +install(ok): /home/me/beyond_basics/meta/cozy-screensavers (dataset) diff --git a/docs/beyond_basics/_examples/DL-101-181-102 b/docs/beyond_basics/_examples/DL-101-181-102 new file mode 100644 index 000000000..ec0a2a2b7 --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-102 @@ -0,0 +1,5 @@ +$ tree +. +└── zen.jpg -> .git/annex/objects/PV/X5/MD5E-s5217743--ad8f6e7832f1752fe480051bcd06c719.jpg/MD5E-s5217743--ad8f6e7832f1752fe480051bcd06c719.jpg + +0 directories, 1 file diff --git a/docs/beyond_basics/_examples/DL-101-181-103 b/docs/beyond_basics/_examples/DL-101-181-103 new file mode 100644 index 000000000..04c5c085f --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-103 @@ -0,0 +1,2 @@ +$ datalad get zen.jpg +get(ok): zen.jpg (file) [from web...] diff --git a/docs/beyond_basics/_examples/DL-101-181-104 b/docs/beyond_basics/_examples/DL-101-181-104 new file mode 100644 index 000000000..a1830884f --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-104 @@ -0,0 +1,17 @@ +cat << EOT > metadata-zen.json +{ + "type": "file", + "path": "zen.png", + "dataset_id": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7", + "dataset_version": "19f2d98d758116d099d467260a5a71082b2c6a29", + "extractor_name": "Picture2Words", + "extractor_version": "0.1", + "extraction_parameter": {}, + "extraction_time": 1675113291.1464975, + "agent_name": "Overworked CTO", + "agent_email": "closetoburnout@randomtechconsultancy.com", + "extracted_metadata": { + "description": "A lake with waterlilies in a front of snow covered mountains" + } +} +EOT diff --git a/docs/beyond_basics/_examples/DL-101-181-105 b/docs/beyond_basics/_examples/DL-101-181-105 new file mode 100644 index 000000000..8bf100427 --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-105 @@ -0,0 +1,2 @@ +$ datalad meta-add -d . - < metadata-zen.json +meta_add(ok): /home/me/beyond_basics/meta/cozy-screensavers/zen.png (file) [added file metadata to /home/me/beyond_basics/meta/cozy-screensavers] diff --git a/docs/beyond_basics/_examples/DL-101-181-106 b/docs/beyond_basics/_examples/DL-101-181-106 new file mode 100644 index 000000000..aaa6aae25 --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-106 @@ -0,0 +1,2 @@ +$ datalad meta-dump -d . .:zen.png +{"type": "file", "path": "zen.png", "dataset_id": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7", "dataset_version": "19f2d98d758116d099d467260a5a71082b2c6a29", "extraction_time": 1675113291.1464975, "agent_name": "Overworked CTO", "agent_email": "closetoburnout@randomtechconsultancy.com", "extractor_name": "Picture2Words", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"description": "A lake with waterlilies in a front of snow covered mountains"}} diff --git a/docs/beyond_basics/_examples/DL-101-181-107 b/docs/beyond_basics/_examples/DL-101-181-107 new file mode 100644 index 000000000..37dcbbd93 --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-107 @@ -0,0 +1,24 @@ +$ datalad -f json_pp meta-dump -d . .:zen.png +{ + "action": "meta_dump", + "backend": "git", + "metadata": { + "agent_email": "closetoburnout@randomtechconsultancy.com", + "agent_name": "Overworked CTO", + "dataset_id": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7", + "dataset_version": "19f2d98d758116d099d467260a5a71082b2c6a29", + "extracted_metadata": { + "description": "A lake with waterlilies in a front of snow covered mountains" + }, + "extraction_parameter": {}, + "extraction_time": 1675113291.1464975, + "extractor_name": "Picture2Words", + "extractor_version": "0.1", + "path": "zen.png", + "type": "file" + }, + "metadata_source": ".", + "path": "/home/me/beyond_basics/meta/cozy-screensavers/zen.png", + "status": "ok", + "type": "dataset" +} diff --git a/docs/beyond_basics/_examples/DL-101-181-110 b/docs/beyond_basics/_examples/DL-101-181-110 new file mode 100644 index 000000000..448101328 --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-110 @@ -0,0 +1,3 @@ +$ datalad create metadata-assimilation +$ cd metadata-assimilation +create(ok): /home/me/beyond_basics/meta/metadata-assimilation (dataset) diff --git a/docs/beyond_basics/_examples/DL-101-181-111 b/docs/beyond_basics/_examples/DL-101-181-111 new file mode 100644 index 000000000..536a4a164 --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-111 @@ -0,0 +1,7 @@ +$ git fetch \ + "https://github.com/christian-monch/metadata-test.git" \ + "refs/datalad/*:refs/datalad/*" +From https://github.com/christian-monch/metadata-test + * [new ref] refs/datalad/dataset-tree-version-list -> refs/datalad/dataset-tree-version-list + * [new ref] refs/datalad/dataset-uuid-set -> refs/datalad/dataset-uuid-set + * [new ref] refs/datalad/object-references-2.0 -> refs/datalad/object-references-2.0 diff --git a/docs/beyond_basics/_examples/DL-101-181-112 b/docs/beyond_basics/_examples/DL-101-181-112 new file mode 100644 index 000000000..dd50fa3ea --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-112 @@ -0,0 +1,27 @@ +$ datalad meta-dump -r +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-99", "dataset_id": "52b2f098-dc76-11ea-a6da-7cdd908c7490", "dataset_version": "1b0d92f68a38129b950733e0a653595806014bf8", "extraction_time": 1675090458.2812586, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller14_EEGSZ", "keywords": ["Schizophrenia"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52b2f098-dc76-11ea-a6da-7cdd908c7490", "@type": "Dataset", "version": "1b0d92f68a38129b950733e0a653595806014bf8", "name": "Dataset_omodpirgos_0", "url": "https://example.com/users/omodpirgos/dataset_0", "author": [{"@id": "e.omodpirgos@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "in congruent Schizophrenia face emotional by of affective deficits", "datePublished": 2102, "sameAs": "10.100/99.abc-1", "pagination": "525-553", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.mal@example.com"}, {"@id": "o.gos@example.com"}, {"@id": "e.sane@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Soc Cogn Affect Neurosci", "@type": "PublicationEvent", "name": "Soc Cogn Affect Neurosci"}, "isPartOf": {"@id": "#issue(4)", "@type": "PublicationIssue", "issue_number": 4, "isPartOf": {"@id": "#volume(9)", "@type": "PublicationVolume", "volumeNumber": 9}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#a.gose@example.com", "@type": "Person", "email": "a.gose@example.com", "name": "Asulmolsana Gose", "givenName": "Asulmolsana", "familyName": "Gose"}, {"@id": "https://schema.datalad.org/person#g.agor@example.com", "@type": "Person", "email": "g.agor@example.com", "name": "Gossulsela Agor", "givenName": "Gossulsela", "familyName": "Agor"}, {"@id": "https://schema.datalad.org/person#e.ekos@example.com", "@type": "Person", "email": "e.ekos@example.com", "name": "Esulsana Ekos", "givenName": "Esulsana", "familyName": "Ekos"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}]}]}} +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-98", "dataset_id": "535edf84-dc76-11ea-979c-7cdd908c7490", "dataset_version": "96883aefec188338b3b3e8790fcad734819615a8", "extraction_time": 1675090458.2790313, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller15_cognflexibility", "keywords": ["Interindividual differences"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#535edf84-dc76-11ea-979c-7cdd908c7490", "@type": "Dataset", "version": "96883aefec188338b3b3e8790fcad734819615a8", "name": "Dataset_omodpirgos_3", "url": "https://example.com/users/omodpirgos/dataset_3", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "Interindividual of volume, differences impulsivity functional flexibility: influence matter and connectivity in", "datePublished": 2105, "sameAs": "10.100/99.abc-4", "pagination": "151-169", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}, {"@id": "e.malu@example.com"}, {"@id": "p.molludsulo@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Brain Struct Func", "@type": "PublicationEvent", "name": "Brain Struct Func"}, "isPartOf": {"@id": "#issue(4)", "@type": "PublicationIssue", "issue_number": 4, "isPartOf": {"@id": "#volume(220)", "@type": "PublicationVolume", "volumeNumber": 220}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#a.ugorsan@example.com", "@type": "Person", "email": "a.ugorsan@example.com", "name": "Dr. Apirsan Ugorsan", "givenName": "Apirsan", "familyName": "Ugorsan", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#m.pal@example.com", "@type": "Person", "email": "m.pal@example.com", "name": "Dr. phil. 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Sansul Omelgorladi", "givenName": "Sansul", "familyName": "Omelgorladi", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#i.mora@example.com", "@type": "Person", "email": "i.mora@example.com", "name": "Imolpir Mora", "givenName": "Imolpir", "familyName": "Mora"}, {"@id": "https://schema.datalad.org/person#m.imolsel@example.com", "@type": "Person", "email": "m.imolsel@example.com", "name": "Melmodmad Imolsel", "givenName": "Melmodmad", "familyName": "Imolsel"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#g.pirpirludu@example.com", "@type": "Person", "email": "g.pirpirludu@example.com", "name": "Goso Pirpirludu", "givenName": "Goso", "familyName": "Pirpirludu"}]}]}} diff --git a/docs/beyond_basics/_examples/DL-101-181-115 b/docs/beyond_basics/_examples/DL-101-181-115 new file mode 100644 index 000000000..1e7d65ccb --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-115 @@ -0,0 +1,2 @@ +$ datalad meta-dump -d https://github.com/christian-monch/metadata-test.git ./study-100 +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-100", "dataset_id": "52142b84-dc76-11ea-98c5-7cdd908c7490", "dataset_version": "244a8ad43b00622989ae7f0d2b59c80697dadb80", "extraction_time": 1675090457.6464868, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Camilleri18_eMDN", "keywords": ["neural correlates"], "accountablePerson": "g.pirpirludu@example.com", "contributor": [{"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52142b84-dc76-11ea-98c5-7cdd908c7490", "@type": "Dataset", "version": "244a8ad43b00622989ae7f0d2b59c80697dadb80", "name": "Dataset_omorpallud_69", "url": "https://example.com/users/omorpallud/dataset_69", "author": [{"@id": "o.omorpallud@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "multiple-demand Definition extended network and", "datePublished": 2171, "sameAs": "10.100/99.abc-70", "pagination": "1549-1564", "author": [{"@id": "o.omorpallud@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "m.meli@example.com"}, {"@id": "m.asan@example.com"}, {"@id": "m.kolkospale@example.com"}, {"@id": "m.emodgos@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Neuroimage", "@type": "PublicationEvent", "name": "Neuroimage"}, "isPartOf": {"@id": "#volume(165)", "@type": "PublicationVolume", "volumeNumber": 165}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#k.umalpalo@example.com", "@type": "Person", "email": "k.umalpalo@example.com", "name": "Kosselo Umalpalo", "givenName": "Kosselo", "familyName": "Umalpalo"}, {"@id": "https://schema.datalad.org/person#s.omelgorladi@example.com", "@type": "Person", "email": "s.omelgorladi@example.com", "name": "Dr. phil. Sansul Omelgorladi", "givenName": "Sansul", "familyName": "Omelgorladi", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#i.mora@example.com", "@type": "Person", "email": "i.mora@example.com", "name": "Imolpir Mora", "givenName": "Imolpir", "familyName": "Mora"}, {"@id": "https://schema.datalad.org/person#m.imolsel@example.com", "@type": "Person", "email": "m.imolsel@example.com", "name": "Melmodmad Imolsel", "givenName": "Melmodmad", "familyName": "Imolsel"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#g.pirpirludu@example.com", "@type": "Person", "email": "g.pirpirludu@example.com", "name": "Goso Pirpirludu", "givenName": "Goso", "familyName": "Pirpirludu"}]}]}} diff --git a/docs/beyond_basics/_examples/DL-101-181-116 b/docs/beyond_basics/_examples/DL-101-181-116 new file mode 100644 index 000000000..c440abdf1 --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-116 @@ -0,0 +1,27 @@ +$ datalad meta-dump -d https://github.com/christian-monch/metadata-test.git -r +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-99", "dataset_id": "52b2f098-dc76-11ea-a6da-7cdd908c7490", "dataset_version": "1b0d92f68a38129b950733e0a653595806014bf8", "extraction_time": 1675090458.2812586, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller14_EEGSZ", "keywords": ["Schizophrenia"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52b2f098-dc76-11ea-a6da-7cdd908c7490", "@type": "Dataset", "version": "1b0d92f68a38129b950733e0a653595806014bf8", "name": "Dataset_omodpirgos_0", "url": "https://example.com/users/omodpirgos/dataset_0", "author": [{"@id": "e.omodpirgos@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "in congruent Schizophrenia face emotional by of affective deficits", "datePublished": 2102, "sameAs": "10.100/99.abc-1", "pagination": "525-553", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.mal@example.com"}, {"@id": "o.gos@example.com"}, {"@id": "e.sane@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Soc Cogn Affect Neurosci", "@type": "PublicationEvent", "name": "Soc Cogn Affect Neurosci"}, "isPartOf": {"@id": "#issue(4)", "@type": "PublicationIssue", "issue_number": 4, "isPartOf": {"@id": "#volume(9)", "@type": "PublicationVolume", "volumeNumber": 9}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#a.gose@example.com", "@type": "Person", "email": "a.gose@example.com", "name": "Asulmolsana Gose", "givenName": "Asulmolsana", "familyName": "Gose"}, {"@id": "https://schema.datalad.org/person#g.agor@example.com", "@type": "Person", "email": "g.agor@example.com", "name": "Gossulsela Agor", "givenName": "Gossulsela", "familyName": "Agor"}, {"@id": "https://schema.datalad.org/person#e.ekos@example.com", "@type": "Person", "email": "e.ekos@example.com", "name": "Esulsana Ekos", "givenName": "Esulsana", "familyName": "Ekos"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}]}]}} +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-98", "dataset_id": "535edf84-dc76-11ea-979c-7cdd908c7490", "dataset_version": "96883aefec188338b3b3e8790fcad734819615a8", "extraction_time": 1675090458.2790313, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller15_cognflexibility", "keywords": ["Interindividual differences"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#535edf84-dc76-11ea-979c-7cdd908c7490", "@type": "Dataset", "version": "96883aefec188338b3b3e8790fcad734819615a8", "name": "Dataset_omodpirgos_3", "url": "https://example.com/users/omodpirgos/dataset_3", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "Interindividual of volume, differences impulsivity functional flexibility: influence matter and connectivity in", "datePublished": 2105, "sameAs": "10.100/99.abc-4", "pagination": "151-169", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}, {"@id": "e.malu@example.com"}, {"@id": "p.molludsulo@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Brain Struct Func", "@type": "PublicationEvent", "name": "Brain Struct Func"}, "isPartOf": {"@id": "#issue(4)", "@type": "PublicationIssue", "issue_number": 4, "isPartOf": {"@id": "#volume(220)", "@type": "PublicationVolume", "volumeNumber": 220}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#a.ugorsan@example.com", "@type": "Person", "email": "a.ugorsan@example.com", "name": "Dr. Apirsan Ugorsan", "givenName": "Apirsan", "familyName": "Ugorsan", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#m.pal@example.com", "@type": "Person", "email": "m.pal@example.com", "name": "Dr. phil. Madludlade Pal", "givenName": "Madludlade", "familyName": "Pal", "honorificSuffix": "Dr. phil."}]}]}} +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-97", "dataset_id": "542308c8-dc76-11ea-940a-7cdd908c7490", "dataset_version": "4bcda921e409b4806e991e2bddfa712c77090935", "extraction_time": 1675090458.2600777, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller18_FaceALE", "keywords": ["meta-analysis"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#542308c8-dc76-11ea-940a-7cdd908c7490", "@type": "Dataset", "version": "4bcda921e409b4806e991e2bddfa712c77090935", "name": "Dataset_omodpirgos_6", "url": "https://example.com/users/omodpirgos/dataset_6", "author": [{"@id": "e.omodpirgos@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "the An Influence task processing: of neural and meta-analysis of face network ALE", "datePublished": 2108, "sameAs": "10.100/99.abc-7", "pagination": "1634-1655", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "m.amol@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Cortex", "@type": "PublicationEvent", "name": "Cortex"}, "isPartOf": {"@id": "#volume(103)", "@type": "PublicationVolume", "volumeNumber": 103}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#o.ladlud@example.com", "@type": "Person", "email": "o.ladlud@example.com", "name": "Dr. Omorkolsan Ladlud", "givenName": "Omorkolsan", "familyName": "Ladlud", "honorificSuffix": "Dr."}]}]}} +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-96", "dataset_id": "54ded198-dc76-11ea-9eff-7cdd908c7490", "dataset_version": "c9f7737806d40c958f5f498fd1a060c0f2ddd5ff", "extraction_time": 1675090458.262438, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Nostro18_Prediction", "keywords": ["study"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#54ded198-dc76-11ea-9eff-7cdd908c7490", "@type": "Dataset", "version": "c9f7737806d40c958f5f498fd1a060c0f2ddd5ff", "name": "Dataset_omodpirgos_9", "url": "https://example.com/users/omodpirgos/dataset_9", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "network-based connectivity 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{"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "OHBM_2019", "keywords": ["OHBM 2019"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#506ce096-dc76-11ea-95f0-7cdd908c7490", "@type": "Dataset", "version": "15f405a8e6b28ff641e8b3df0326aa06392341a1", "name": "Dataset_omodpirgos_65", "url": "https://example.com/users/omodpirgos/dataset_65", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}], "description": ""}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": 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Epuli Mori", "givenName": "Epuli", "familyName": "Mori", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#a.osel@example.com", "@type": "Person", "email": "a.osel@example.com", "name": "Dr. phil. Amellade Osel", "givenName": "Amellade", "familyName": "Osel", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}]}]}} +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-100", "dataset_id": "52142b84-dc76-11ea-98c5-7cdd908c7490", "dataset_version": "244a8ad43b00622989ae7f0d2b59c80697dadb80", "extraction_time": 1675090457.6464868, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Camilleri18_eMDN", "keywords": ["neural correlates"], "accountablePerson": "g.pirpirludu@example.com", "contributor": [{"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52142b84-dc76-11ea-98c5-7cdd908c7490", "@type": "Dataset", "version": "244a8ad43b00622989ae7f0d2b59c80697dadb80", "name": "Dataset_omorpallud_69", "url": "https://example.com/users/omorpallud/dataset_69", "author": [{"@id": "o.omorpallud@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "multiple-demand Definition extended network and", "datePublished": 2171, "sameAs": "10.100/99.abc-70", "pagination": "1549-1564", "author": [{"@id": "o.omorpallud@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "m.meli@example.com"}, {"@id": "m.asan@example.com"}, {"@id": "m.kolkospale@example.com"}, {"@id": "m.emodgos@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Neuroimage", "@type": "PublicationEvent", "name": "Neuroimage"}, "isPartOf": {"@id": "#volume(165)", "@type": "PublicationVolume", "volumeNumber": 165}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#k.umalpalo@example.com", "@type": "Person", "email": "k.umalpalo@example.com", "name": "Kosselo Umalpalo", "givenName": "Kosselo", "familyName": "Umalpalo"}, {"@id": "https://schema.datalad.org/person#s.omelgorladi@example.com", "@type": "Person", "email": "s.omelgorladi@example.com", "name": "Dr. phil. Sansul Omelgorladi", "givenName": "Sansul", "familyName": "Omelgorladi", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#i.mora@example.com", "@type": "Person", "email": "i.mora@example.com", "name": "Imolpir Mora", "givenName": "Imolpir", "familyName": "Mora"}, {"@id": "https://schema.datalad.org/person#m.imolsel@example.com", "@type": "Person", "email": "m.imolsel@example.com", "name": "Melmodmad Imolsel", "givenName": "Melmodmad", "familyName": "Imolsel"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#g.pirpirludu@example.com", "@type": "Person", "email": "g.pirpirludu@example.com", "name": "Goso Pirpirludu", "givenName": "Goso", "familyName": "Pirpirludu"}]}]}} From 9124210e6c92bd10e2efe8a5c6ef2ca6c51ae361 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 15:16:48 +0100 Subject: [PATCH 18/28] Add a findoutmore on the metadata model --- docs/beyond_basics/101-181-metalad.rst | 79 +++++++++++++++++---- docs/beyond_basics/_examples/DL-101-181-108 | 17 +++++ docs/beyond_basics/_examples/DL-101-181-113 | 14 ++++ docs/beyond_basics/_examples/DL-101-181-114 | 2 + docs/beyond_basics/_examples/DL-101-181-115 | 19 ++++- docs/beyond_basics/_examples/DL-101-181-116 | 55 +++++++------- docs/beyond_basics/_examples/DL-101-181-120 | 4 ++ docs/beyond_basics/_examples/DL-101-181-121 | 28 ++++++++ 8 files changed, 177 insertions(+), 41 deletions(-) create mode 100644 docs/beyond_basics/_examples/DL-101-181-108 create mode 100644 docs/beyond_basics/_examples/DL-101-181-113 create mode 100644 docs/beyond_basics/_examples/DL-101-181-114 create mode 100644 docs/beyond_basics/_examples/DL-101-181-120 create mode 100644 docs/beyond_basics/_examples/DL-101-181-121 diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index e6a50b4cb..ca79d4ad3 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -235,7 +235,16 @@ Let's start by creating a place where someone else's metadata could live. Because MetaLad stores metadata in :term:`Git`'s object store, we use Git to directly fetch metadata from a remote repository, such as this demo on :term:`GitHub`: ``https://github.com/christian-monch/metadata-test.git``. Because metadata added by MetaLad is not transported automatically but needs to be specifically requested, the command to retrieve it looks unfamiliar to non-Git-users: It identifies the precise location of the :term:`ref` that contains the metadata. -.. find-out-more:: Exactly where is metadata stored? +.. runrecord:: _examples/DL-101-181-111 + :language: console + :workdir: beyond_basics/meta/metadata-assimilation + + $ git fetch \ + "https://github.com/christian-monch/metadata-test.git" \ + "refs/datalad/*:refs/datalad/*" + + +.. find-out-more:: Exactly where is metadata stored, and why? :name: fom-metadataobjecttree MetaLad employs an internal metadata model that makes the following properties possible: @@ -244,19 +253,49 @@ Because metadata added by MetaLad is not transported automatically but needs to * Metadata should not be transported if not explicitly requested * It should be possible to only retrieve parts of the overall metadata tree, e.g. certain sub-nodes - TODO this needs much more + To fulfill this, metadata is stored in Git's internal object store as a `blob `_, and Git :term:`ref`\'s are used to point to these blobs. + To not automatically transport them, they are organized in a directory that isn't fetched or pushed by default, but can be transported by explicitly fetching or pushing it: ``.git/refs/datalad`` [#f5]_. -.. runrecord:: _examples/DL-101-181-111 - :language: console - :workdir: beyond_basics/meta/metadata-assimilation + After fetching these refs, they can be found in the ``metadata-assimilation`` dataset: - $ git fetch \ - "https://github.com/christian-monch/metadata-test.git" \ - "refs/datalad/*:refs/datalad/*" + .. runrecord:: _examples/DL-101-181-113 + :language: console + :workdir: beyond_basics/meta/metadata-assimilation + + $ tree .git/refs + + Just like other Git :term:`ref`\s, these refs are files that identify Git objects or trees. + By utilizing Git's internal plumbing commands, we can follow them: + + .. runrecord:: _examples/DL-101-181-114 + :language: console + :workdir: beyond_basics/meta/metadata-assimilation + + $ cat .git/refs/datalad/dataset-tree-version-list + + .. runrecord:: _examples/DL-101-181-115 + :language: console + :workdir: beyond_basics/meta/metadata-assimilation + :realcommand: echo "$ git show $(cat .git/refs/datalad/dataset-tree-version-list) | jq" && git show $(cat .git/refs/datalad/dataset-tree-version-list) | jq + + .. runrecord:: _examples/DL-101-181-116 + :language: console + :workdir: beyond_basics/meta/metadata-assimilation + :realcommand: echo "$ git ls-tree $(git show $(cat .git/refs/datalad/dataset-tree-version-list) | jq | grep "location" | awk '{gsub(/"/, "", $2); print $2}') " && git ls-tree $(git show $(cat .git/refs/datalad/dataset-tree-version-list) | jq | grep "location" | awk '{gsub(/"/, "", $2); print $2}') + + The identifier ``study-100`` in a line such as ``040000 tree d1ad9bfa56f5aa25a1d28caf13db719b9e710d28 study-100`` is the ``dataset_path`` value of a given metadata entry. + Commands such as ``meta-dump`` can use them to, e.g., only report on metadata for certain datasets, following the pattern + + .. code-block:: + + [DATASET_PATH] ["@" VERSION-DIGITS] [":" [LOCAL_PATH]] + + e.g., ``./study-100``. + While this is no workflow a user would have to do, this exploration might have nevertheless gotten you some insights into the inner workings of the commands and MetaLad's internal storage model. The metadata is now locally available in the Git repository ``metadata-repo``. You can verify this by issuing the command ``datalad meta-dump -r``, which will list all metadata in the repository. -Can you guess what type of metadata it contains [#f5]_ ? +Can you guess what type of metadata it contains [#f6]_ ? .. runrecord:: _examples/DL-101-181-112 :language: console @@ -286,7 +325,7 @@ Querying metadata remotely You do not have to download metadata to dump it. It is also possible to specify a git-repository, and let metalad only read the metadata that it requires to fulfill your request. For example, in order to only retrieve metadata from a metadata entry that has the ``dataset_path`` value of ``study-100``, you can simply run: -.. runrecord:: _examples/DL-101-181-115 +.. runrecord:: _examples/DL-101-181-120 :language: console :workdir: beyond_basics/meta @@ -297,7 +336,7 @@ You do not have to download metadata to dump it. It is also possible to specify As the output shows, this command only downloaded enough data from the remote repository to dump all metadata in the specified dataset tree-path. If you want to query all metadata remotely from the repository you could issue the following command: -.. runrecord:: _examples/DL-101-181-116 +.. runrecord:: _examples/DL-101-181-121 :language: console :workdir: beyond_basics/meta @@ -363,4 +402,20 @@ Generally, metadata can either be provided .. [#f4] Alternatively, provide the switch ``-i`` to ``meta-add``, which tells it to just warn about ID mismatches instead of erroring out. -.. [#f5] The answer is minimal information about archived scientific projects of a research institute. While some personal information has been obfuscated, you can still figure out which information is associated with each entry, such as the project name, its authors, or associated publications. \ No newline at end of file +.. [#f5] Other directories underneath ``.git/refs`` are automatically transported, such as ``.git/refs/heads`` or ``.git/refs/remotes`` - this is configured for each remote with a repositories ``.git/config`` file + + .. code-block:: bash + + $ cat .git/config + [core] + repositoryformatversion = 0 + filemode = true + bare = false + logallrefupdates = true + editor = vim + [remote "origin"] + url = git@github.com:my-user/my-dataset.git + fetch = +refs/heads/*:refs/remotes/origin/* + + +.. [#f6] The answer is minimal information about archived scientific projects of a research institute. While some personal information has been obfuscated, you can still figure out which information is associated with each entry, such as the project name, its authors, or associated publications. \ No newline at end of file diff --git a/docs/beyond_basics/_examples/DL-101-181-108 b/docs/beyond_basics/_examples/DL-101-181-108 new file mode 100644 index 000000000..2adb370a3 --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-108 @@ -0,0 +1,17 @@ +$ tree .git/refs +.git/refs +├── annex +│   └── last-index +├── datalad +│   ├── dataset-tree-version-list +│   ├── dataset-uuid-set +│   └── object-references-2.0 +├── heads +│   ├── git-annex +│   └── main +├── remotes +│   └── origin +│   └── HEAD +└── tags + +6 directories, 7 files diff --git a/docs/beyond_basics/_examples/DL-101-181-113 b/docs/beyond_basics/_examples/DL-101-181-113 new file mode 100644 index 000000000..cdfcc2cdc --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-113 @@ -0,0 +1,14 @@ +$ tree .git/refs +.git/refs +├── annex +│   └── last-index +├── datalad +│   ├── dataset-tree-version-list +│   ├── dataset-uuid-set +│   └── object-references-2.0 +├── heads +│   ├── git-annex +│   └── master +└── tags + +4 directories, 6 files diff --git a/docs/beyond_basics/_examples/DL-101-181-114 b/docs/beyond_basics/_examples/DL-101-181-114 new file mode 100644 index 000000000..f8af68aa0 --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-114 @@ -0,0 +1,2 @@ +$ cat .git/refs/datalad/dataset-tree-version-list +92dcce5f8634ec4f872addefad0cd7a2e4372a24 diff --git a/docs/beyond_basics/_examples/DL-101-181-115 b/docs/beyond_basics/_examples/DL-101-181-115 index 1e7d65ccb..53db511f0 100644 --- a/docs/beyond_basics/_examples/DL-101-181-115 +++ b/docs/beyond_basics/_examples/DL-101-181-115 @@ -1,2 +1,17 @@ -$ datalad meta-dump -d https://github.com/christian-monch/metadata-test.git ./study-100 -{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-100", "dataset_id": "52142b84-dc76-11ea-98c5-7cdd908c7490", "dataset_version": "244a8ad43b00622989ae7f0d2b59c80697dadb80", "extraction_time": 1675090457.6464868, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Camilleri18_eMDN", "keywords": ["neural correlates"], "accountablePerson": "g.pirpirludu@example.com", "contributor": [{"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52142b84-dc76-11ea-98c5-7cdd908c7490", "@type": "Dataset", "version": "244a8ad43b00622989ae7f0d2b59c80697dadb80", "name": "Dataset_omorpallud_69", "url": "https://example.com/users/omorpallud/dataset_69", "author": [{"@id": "o.omorpallud@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "multiple-demand Definition extended network and", "datePublished": 2171, "sameAs": "10.100/99.abc-70", "pagination": "1549-1564", "author": [{"@id": "o.omorpallud@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "m.meli@example.com"}, {"@id": "m.asan@example.com"}, {"@id": "m.kolkospale@example.com"}, {"@id": "m.emodgos@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Neuroimage", "@type": "PublicationEvent", "name": "Neuroimage"}, "isPartOf": {"@id": "#volume(165)", "@type": "PublicationVolume", "volumeNumber": 165}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#k.umalpalo@example.com", "@type": "Person", "email": "k.umalpalo@example.com", "name": "Kosselo Umalpalo", "givenName": "Kosselo", "familyName": "Umalpalo"}, {"@id": "https://schema.datalad.org/person#s.omelgorladi@example.com", "@type": "Person", "email": "s.omelgorladi@example.com", "name": "Dr. phil. Sansul Omelgorladi", "givenName": "Sansul", "familyName": "Omelgorladi", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#i.mora@example.com", "@type": "Person", "email": "i.mora@example.com", "name": "Imolpir Mora", "givenName": "Imolpir", "familyName": "Mora"}, {"@id": "https://schema.datalad.org/person#m.imolsel@example.com", "@type": "Person", "email": "m.imolsel@example.com", "name": "Melmodmad Imolsel", "givenName": "Melmodmad", "familyName": "Imolsel"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#g.pirpirludu@example.com", "@type": "Person", "email": "g.pirpirludu@example.com", "name": "Goso Pirpirludu", "givenName": "Goso", "familyName": "Pirpirludu"}]}]}} + +$ git show 92dcce5f8634ec4f872addefad0cd7a2e4372a24 | jq +[ + { + "primary_data_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", + "time_stamp": "1675090457.6913724", + "path": "", + "dataset_tree": { + "@": { + "type": "Reference", + "version": "2.0" + }, + "class_name": "MTreeNode", + "location": "65c896c86f0e920439bca908eb11c98c9a706606" + } + } +] diff --git a/docs/beyond_basics/_examples/DL-101-181-116 b/docs/beyond_basics/_examples/DL-101-181-116 index c440abdf1..ac27eb9f7 100644 --- a/docs/beyond_basics/_examples/DL-101-181-116 +++ b/docs/beyond_basics/_examples/DL-101-181-116 @@ -1,27 +1,28 @@ -$ datalad meta-dump -d https://github.com/christian-monch/metadata-test.git -r -{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-99", "dataset_id": "52b2f098-dc76-11ea-a6da-7cdd908c7490", "dataset_version": "1b0d92f68a38129b950733e0a653595806014bf8", "extraction_time": 1675090458.2812586, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller14_EEGSZ", "keywords": ["Schizophrenia"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52b2f098-dc76-11ea-a6da-7cdd908c7490", "@type": "Dataset", "version": "1b0d92f68a38129b950733e0a653595806014bf8", "name": "Dataset_omodpirgos_0", "url": "https://example.com/users/omodpirgos/dataset_0", "author": [{"@id": "e.omodpirgos@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "in congruent Schizophrenia face emotional by of affective deficits", "datePublished": 2102, "sameAs": "10.100/99.abc-1", "pagination": "525-553", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.mal@example.com"}, {"@id": "o.gos@example.com"}, {"@id": "e.sane@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Soc Cogn Affect Neurosci", "@type": "PublicationEvent", "name": "Soc Cogn Affect Neurosci"}, "isPartOf": {"@id": "#issue(4)", "@type": "PublicationIssue", "issue_number": 4, "isPartOf": {"@id": "#volume(9)", "@type": "PublicationVolume", "volumeNumber": 9}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#a.gose@example.com", "@type": "Person", "email": "a.gose@example.com", "name": "Asulmolsana Gose", "givenName": "Asulmolsana", "familyName": "Gose"}, {"@id": "https://schema.datalad.org/person#g.agor@example.com", "@type": "Person", "email": "g.agor@example.com", "name": "Gossulsela Agor", "givenName": "Gossulsela", "familyName": "Agor"}, {"@id": "https://schema.datalad.org/person#e.ekos@example.com", "@type": "Person", "email": "e.ekos@example.com", "name": "Esulsana Ekos", "givenName": "Esulsana", "familyName": "Ekos"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}]}]}} -{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-98", "dataset_id": "535edf84-dc76-11ea-979c-7cdd908c7490", "dataset_version": "96883aefec188338b3b3e8790fcad734819615a8", "extraction_time": 1675090458.2790313, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller15_cognflexibility", "keywords": ["Interindividual differences"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#535edf84-dc76-11ea-979c-7cdd908c7490", "@type": "Dataset", "version": "96883aefec188338b3b3e8790fcad734819615a8", "name": "Dataset_omodpirgos_3", "url": "https://example.com/users/omodpirgos/dataset_3", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "Interindividual of volume, differences impulsivity functional flexibility: influence matter and connectivity in", "datePublished": 2105, "sameAs": "10.100/99.abc-4", "pagination": "151-169", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}, {"@id": "e.malu@example.com"}, {"@id": "p.molludsulo@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Brain Struct Func", "@type": "PublicationEvent", "name": "Brain Struct Func"}, "isPartOf": {"@id": "#issue(4)", "@type": "PublicationIssue", "issue_number": 4, "isPartOf": {"@id": "#volume(220)", "@type": "PublicationVolume", "volumeNumber": 220}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#a.ugorsan@example.com", "@type": "Person", "email": "a.ugorsan@example.com", "name": "Dr. Apirsan Ugorsan", "givenName": "Apirsan", "familyName": "Ugorsan", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#m.pal@example.com", "@type": "Person", "email": "m.pal@example.com", "name": "Dr. phil. Madludlade Pal", "givenName": "Madludlade", "familyName": "Pal", "honorificSuffix": "Dr. phil."}]}]}} -{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-97", "dataset_id": "542308c8-dc76-11ea-940a-7cdd908c7490", "dataset_version": "4bcda921e409b4806e991e2bddfa712c77090935", "extraction_time": 1675090458.2600777, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller18_FaceALE", "keywords": ["meta-analysis"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#542308c8-dc76-11ea-940a-7cdd908c7490", "@type": "Dataset", "version": "4bcda921e409b4806e991e2bddfa712c77090935", "name": "Dataset_omodpirgos_6", "url": "https://example.com/users/omodpirgos/dataset_6", "author": [{"@id": "e.omodpirgos@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "the An Influence task processing: of neural and meta-analysis of face network ALE", "datePublished": 2108, "sameAs": "10.100/99.abc-7", "pagination": "1634-1655", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "m.amol@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Cortex", "@type": "PublicationEvent", "name": "Cortex"}, "isPartOf": {"@id": "#volume(103)", "@type": "PublicationVolume", "volumeNumber": 103}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#o.ladlud@example.com", "@type": "Person", "email": "o.ladlud@example.com", "name": "Dr. Omorkolsan Ladlud", "givenName": "Omorkolsan", "familyName": "Ladlud", "honorificSuffix": "Dr."}]}]}} -{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-96", "dataset_id": "54ded198-dc76-11ea-9eff-7cdd908c7490", "dataset_version": "c9f7737806d40c958f5f498fd1a060c0f2ddd5ff", "extraction_time": 1675090458.262438, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Nostro18_Prediction", "keywords": ["study"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#54ded198-dc76-11ea-9eff-7cdd908c7490", "@type": "Dataset", "version": "c9f7737806d40c958f5f498fd1a060c0f2ddd5ff", "name": "Dataset_omodpirgos_9", "url": "https://example.com/users/omodpirgos/dataset_9", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "network-based connectivity 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study-84 +040000 tree 0e6fa14639d0a0717b1971341d28ea1ae790bf5a study-85 +040000 tree 6308ff19e4abbe25d1e14fe76cddbd413d9521b2 study-90 +040000 tree 8cb5725b38463777d9e72ccdda9614884e2f4e79 study-91 +040000 tree bd485bc9d532d33f6a2c88d0e63cc21fb7753ad8 study-92 +040000 tree 32b389b5143669a0cb07fad657fad21408eecea5 study-93 +040000 tree c0ddb3e6cf209554f50ef41c374546fcbfb2cdca study-95 +040000 tree cc1dc24ef2282d7fa8d4b902d579e7275c484e6f study-96 +040000 tree 33cf358d5ea8b17f6d80f201cc94e49866d4797e study-97 +040000 tree 125fbd2318d75f9b431988833c78481650adecbf study-98 +040000 tree f61a7ddb02d162321d196239fd2d5e03af528230 study-99 diff --git a/docs/beyond_basics/_examples/DL-101-181-120 b/docs/beyond_basics/_examples/DL-101-181-120 new file mode 100644 index 000000000..e58c02e55 --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-120 @@ -0,0 +1,4 @@ +$ datalad meta-dump \ + -d https://github.com/christian-monch/metadata-test.git \ + ./study-100 +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-100", "dataset_id": "52142b84-dc76-11ea-98c5-7cdd908c7490", "dataset_version": "244a8ad43b00622989ae7f0d2b59c80697dadb80", "extraction_time": 1675090457.6464868, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Camilleri18_eMDN", "keywords": ["neural correlates"], "accountablePerson": "g.pirpirludu@example.com", "contributor": [{"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52142b84-dc76-11ea-98c5-7cdd908c7490", "@type": "Dataset", "version": "244a8ad43b00622989ae7f0d2b59c80697dadb80", "name": "Dataset_omorpallud_69", "url": "https://example.com/users/omorpallud/dataset_69", "author": [{"@id": "o.omorpallud@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "multiple-demand Definition extended network and", "datePublished": 2171, "sameAs": "10.100/99.abc-70", "pagination": "1549-1564", "author": [{"@id": "o.omorpallud@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "m.meli@example.com"}, {"@id": "m.asan@example.com"}, {"@id": "m.kolkospale@example.com"}, {"@id": "m.emodgos@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Neuroimage", "@type": "PublicationEvent", "name": "Neuroimage"}, "isPartOf": {"@id": "#volume(165)", "@type": "PublicationVolume", "volumeNumber": 165}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#k.umalpalo@example.com", "@type": "Person", "email": "k.umalpalo@example.com", "name": "Kosselo Umalpalo", "givenName": "Kosselo", "familyName": "Umalpalo"}, {"@id": "https://schema.datalad.org/person#s.omelgorladi@example.com", "@type": "Person", "email": "s.omelgorladi@example.com", "name": "Dr. phil. Sansul Omelgorladi", "givenName": "Sansul", "familyName": "Omelgorladi", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#i.mora@example.com", "@type": "Person", "email": "i.mora@example.com", "name": "Imolpir Mora", "givenName": "Imolpir", "familyName": "Mora"}, {"@id": "https://schema.datalad.org/person#m.imolsel@example.com", "@type": "Person", "email": "m.imolsel@example.com", "name": "Melmodmad Imolsel", "givenName": "Melmodmad", "familyName": "Imolsel"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#g.pirpirludu@example.com", "@type": "Person", "email": "g.pirpirludu@example.com", "name": "Goso Pirpirludu", "givenName": "Goso", "familyName": "Pirpirludu"}]}]}} diff --git a/docs/beyond_basics/_examples/DL-101-181-121 b/docs/beyond_basics/_examples/DL-101-181-121 new file mode 100644 index 000000000..a6b03cac5 --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-121 @@ -0,0 +1,28 @@ +$ datalad meta-dump \ + -d https://github.com/christian-monch/metadata-test.git -r +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-99", "dataset_id": "52b2f098-dc76-11ea-a6da-7cdd908c7490", "dataset_version": "1b0d92f68a38129b950733e0a653595806014bf8", "extraction_time": 1675090458.2812586, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller14_EEGSZ", "keywords": ["Schizophrenia"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52b2f098-dc76-11ea-a6da-7cdd908c7490", "@type": "Dataset", "version": "1b0d92f68a38129b950733e0a653595806014bf8", "name": "Dataset_omodpirgos_0", "url": "https://example.com/users/omodpirgos/dataset_0", "author": [{"@id": "e.omodpirgos@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "in congruent Schizophrenia face emotional by of affective deficits", "datePublished": 2102, "sameAs": "10.100/99.abc-1", "pagination": "525-553", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.mal@example.com"}, {"@id": "o.gos@example.com"}, {"@id": "e.sane@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Soc Cogn Affect Neurosci", "@type": "PublicationEvent", "name": "Soc Cogn Affect Neurosci"}, "isPartOf": {"@id": "#issue(4)", "@type": "PublicationIssue", "issue_number": 4, "isPartOf": {"@id": "#volume(9)", "@type": "PublicationVolume", "volumeNumber": 9}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#a.gose@example.com", "@type": "Person", "email": "a.gose@example.com", "name": "Asulmolsana Gose", "givenName": "Asulmolsana", "familyName": "Gose"}, {"@id": "https://schema.datalad.org/person#g.agor@example.com", "@type": "Person", "email": "g.agor@example.com", "name": "Gossulsela Agor", "givenName": "Gossulsela", "familyName": "Agor"}, {"@id": "https://schema.datalad.org/person#e.ekos@example.com", "@type": "Person", "email": "e.ekos@example.com", "name": "Esulsana Ekos", "givenName": "Esulsana", "familyName": "Ekos"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}]}]}} +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-98", "dataset_id": "535edf84-dc76-11ea-979c-7cdd908c7490", "dataset_version": "96883aefec188338b3b3e8790fcad734819615a8", "extraction_time": 1675090458.2790313, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller15_cognflexibility", "keywords": ["Interindividual differences"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#535edf84-dc76-11ea-979c-7cdd908c7490", "@type": "Dataset", "version": "96883aefec188338b3b3e8790fcad734819615a8", "name": "Dataset_omodpirgos_3", "url": "https://example.com/users/omodpirgos/dataset_3", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "Interindividual of volume, differences impulsivity functional flexibility: influence matter and connectivity in", "datePublished": 2105, "sameAs": "10.100/99.abc-4", "pagination": "151-169", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}, {"@id": "e.malu@example.com"}, {"@id": "p.molludsulo@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Brain Struct Func", "@type": "PublicationEvent", "name": "Brain Struct Func"}, "isPartOf": {"@id": "#issue(4)", "@type": "PublicationIssue", "issue_number": 4, "isPartOf": {"@id": "#volume(220)", "@type": "PublicationVolume", "volumeNumber": 220}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#a.ugorsan@example.com", "@type": "Person", "email": "a.ugorsan@example.com", "name": "Dr. Apirsan Ugorsan", "givenName": "Apirsan", "familyName": "Ugorsan", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#m.pal@example.com", "@type": "Person", "email": "m.pal@example.com", "name": "Dr. phil. Madludlade Pal", "givenName": "Madludlade", "familyName": "Pal", "honorificSuffix": "Dr. phil."}]}]}} +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-97", "dataset_id": "542308c8-dc76-11ea-940a-7cdd908c7490", "dataset_version": "4bcda921e409b4806e991e2bddfa712c77090935", "extraction_time": 1675090458.2600777, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller18_FaceALE", "keywords": ["meta-analysis"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#542308c8-dc76-11ea-940a-7cdd908c7490", "@type": "Dataset", "version": "4bcda921e409b4806e991e2bddfa712c77090935", "name": "Dataset_omodpirgos_6", "url": "https://example.com/users/omodpirgos/dataset_6", "author": [{"@id": "e.omodpirgos@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "the An Influence task processing: of neural and meta-analysis of face network ALE", "datePublished": 2108, "sameAs": "10.100/99.abc-7", "pagination": "1634-1655", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "m.amol@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Cortex", "@type": "PublicationEvent", "name": "Cortex"}, "isPartOf": {"@id": "#volume(103)", "@type": "PublicationVolume", "volumeNumber": 103}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#o.ladlud@example.com", "@type": "Person", "email": "o.ladlud@example.com", "name": "Dr. Omorkolsan Ladlud", "givenName": "Omorkolsan", "familyName": "Ladlud", "honorificSuffix": "Dr."}]}]}} +{"type": "dataset", "root_dataset_id": "", "root_dataset_version": "b3f02438b210c2bfbd4f0da79f2791c599c8856e", "dataset_path": "study-96", "dataset_id": "54ded198-dc76-11ea-9eff-7cdd908c7490", "dataset_version": "c9f7737806d40c958f5f498fd1a060c0f2ddd5ff", "extraction_time": 1675090458.262438, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Nostro18_Prediction", "keywords": ["study"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#54ded198-dc76-11ea-9eff-7cdd908c7490", "@type": "Dataset", "version": "c9f7737806d40c958f5f498fd1a060c0f2ddd5ff", "name": "Dataset_omodpirgos_9", "url": "https://example.com/users/omodpirgos/dataset_9", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}], "description": ""}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "network-based connectivity functional personality", "datePublished": 2111, "sameAs": "10.100/99.abc-10", "pagination": "1374-1394", "author": [{"@id": "a.ilud@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "a.emadsanpali@example.com"}, {"@id": "p.rigmorluda@example.com"}, {"@id": "m.kolkospale@example.com"}, {"@id": "i.upirmale@example.com"}, {"@id": "m.opalsulkosu@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Brain Struct Funct", "@type": "PublicationEvent", "name": "Brain Struct Funct"}, "isPartOf": {"@id": "#issue(6)", "@type": "PublicationIssue", "issue_number": 6, "isPartOf": {"@id": "#volume(223)", "@type": "PublicationVolume", "volumeNumber": 223}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": 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Sansul Omelgorladi", "givenName": "Sansul", "familyName": "Omelgorladi", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#i.mora@example.com", "@type": "Person", "email": "i.mora@example.com", "name": "Imolpir Mora", "givenName": "Imolpir", "familyName": "Mora"}, {"@id": "https://schema.datalad.org/person#m.imolsel@example.com", "@type": "Person", "email": "m.imolsel@example.com", "name": "Melmodmad Imolsel", "givenName": "Melmodmad", "familyName": "Imolsel"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#g.pirpirludu@example.com", "@type": "Person", "email": "g.pirpirludu@example.com", "name": "Goso Pirpirludu", "givenName": "Goso", "familyName": "Pirpirludu"}]}]}} From 507988a81bf18a545a1b109e8d06635d25153040 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 16:12:30 +0100 Subject: [PATCH 19/28] add meta-extract example --- docs/beyond_basics/101-181-metalad.rst | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index ca79d4ad3..54a9f75fe 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -204,8 +204,18 @@ This could also be printed a bit more readable: Using existing extractors to add metadata """"""""""""""""""""""""""""""""""""""""" -TODO: This needs and example +If writing JSON objects by hand sounds cumbersome, it indeed is. +To automate metadata extraction or generation, MetaLad can use extractors to do the job. +A few built-in extractors are already shipped with it, for example ``annex`` (reporting on information :term:`git-annex` provides about datasets or files), or ``studyminimeta`` (a `metadata schema for archived studies `_). +Once an extractor of choice is found, the :command:`datalad meta-extract` command can do its job: +.. runrecord:: _examples/DL-101-181-108 + :language: console + :workdir: beyond_basics/meta/cozy-screensavers + + $ datalad meta-extract -d . metalad_core | jq + +The extracted metadata can then either be saved into a file as before, or directly :term:`pipe`'d into :command:`meta-add`. Creating your own extractor """"""""""""""""""""""""""" From 9c2c56d546cb2c9062e7c4f67aa928d173449018 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 16:12:49 +0100 Subject: [PATCH 20/28] add small note explaining -r a bit more --- docs/beyond_basics/101-181-metalad.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index 54a9f75fe..51f2f2aa4 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -304,7 +304,7 @@ Because metadata added by MetaLad is not transported automatically but needs to While this is no workflow a user would have to do, this exploration might have nevertheless gotten you some insights into the inner workings of the commands and MetaLad's internal storage model. The metadata is now locally available in the Git repository ``metadata-repo``. -You can verify this by issuing the command ``datalad meta-dump -r``, which will list all metadata in the repository. +You can verify this by issuing the command ``datalad meta-dump -r``, which will list all metadata from all ``dataset_path``\s in the repository. Can you guess what type of metadata it contains [#f6]_ ? .. runrecord:: _examples/DL-101-181-112 From 1bfc6af6ddf3c870b3bd1b7cf2500e9e688238b9 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 16:13:13 +0100 Subject: [PATCH 21/28] add preliminary jq-based query example --- docs/beyond_basics/101-181-metalad.rst | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index 51f2f2aa4..d91a83d20 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -328,7 +328,19 @@ You will notice that no primary data is stored in the repository ``metadata-dest Querying metadata ^^^^^^^^^^^^^^^^^ -TODO: something more about meta-dump and concrete usage example with, e.g., ``jq`` +As the metadata is in a highly structured form, and could correspond to agreed-upon or established schemas, queries through such metadata can use flexible tooling and don't need to rely on DataLad. +One popular choice for working with JSON data, for example, is the JSON command line processor `jq `_. +In conjunction with Unix :term:`pipe`\s, one can assemble powerful queries in a single line. +The (cropped) query below, for example, lists all unique family names of the authors in the institute's scientific project metadata in ``metadata-assimilation``: + +.. runrecord:: _examples/DL-101-181-119 + :language: console + :workdir: beyond_basics/meta/metadata-assimilation + :lines: 1, 5-20 + + $ datalad meta-dump -r | jq '.extracted_metadata["@graph"][3]["@list"][].familyName' | sort | uniq + + Querying metadata remotely """""""""""""""""""""""""" From f63bd9fb30d26cc3114d5d23760d570a61f570be Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 16:13:27 +0100 Subject: [PATCH 22/28] Update code snippet --- docs/beyond_basics/_examples/DL-101-181-108 | 66 +++++++++++++++------ 1 file changed, 49 insertions(+), 17 deletions(-) diff --git a/docs/beyond_basics/_examples/DL-101-181-108 b/docs/beyond_basics/_examples/DL-101-181-108 index 2adb370a3..7a2fe06fe 100644 --- a/docs/beyond_basics/_examples/DL-101-181-108 +++ b/docs/beyond_basics/_examples/DL-101-181-108 @@ -1,17 +1,49 @@ -$ tree .git/refs -.git/refs -├── annex -│   └── last-index -├── datalad -│   ├── dataset-tree-version-list -│   ├── dataset-uuid-set -│   └── object-references-2.0 -├── heads -│   ├── git-annex -│   └── main -├── remotes -│   └── origin -│   └── HEAD -└── tags - -6 directories, 7 files +$ datalad meta-extract -d . metalad_core | jq +[INFO] Start core metadata extraction from Dataset(/home/me/beyond_basics/meta/cozy-screensavers) +[INFO] Extracted core metadata from /home/me/beyond_basics/meta/cozy-screensavers +[INFO] Finished core metadata extraction from Dataset(/home/me/beyond_basics/meta/cozy-screensavers) +{ + "type": "dataset", + "dataset_id": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7", + "dataset_version": "19f2d98d758116d099d467260a5a71082b2c6a29", + "extractor_name": "metalad_core", + "extractor_version": "1", + "extraction_parameter": {}, + "extraction_time": 1676301065.0952392, + "agent_name": "Elena Piscopia", + "agent_email": "elena@example.net", + "extracted_metadata": { + "@context": { + "@vocab": "http://schema.org/", + "datalad": "http://dx.datalad.org/" + }, + "@graph": [ + { + "@id": "d3765bf6e3a68497b42584fa0774695e", + "@type": "agent", + "name": "Adina Wagner", + "email": "adina.wagner@t-online.de" + }, + { + "@id": "19f2d98d758116d099d467260a5a71082b2c6a29", + "identifier": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7", + "@type": "Dataset", + "version": "0-2-g19f2d98", + "dateCreated": "2023-02-13T07:48:15+01:00", + "dateModified": "2023-02-13T07:55:38+01:00", + "hasContributor": { + "@id": "d3765bf6e3a68497b42584fa0774695e" + }, + "distribution": [ + { + "@id": "b8938507-77c0-4479-84a6-cbeb41214ca6" + }, + { + "name": "origin", + "url": "https://github.com/datalad-handbook/cozy-screensavers.git" + } + ] + } + ] + } +} From 53141d523817b58e7a91fa21dc33d4e44b6ee52b Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 16:13:41 +0100 Subject: [PATCH 23/28] GLOSS: add pipe --- docs/glossary.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/glossary.rst b/docs/glossary.rst index 6875d106d..c3a98407b 100644 --- a/docs/glossary.rst +++ b/docs/glossary.rst @@ -272,6 +272,9 @@ Glossary searches the Python package index `PyPi `_ for a package and installs it while resolving any potential dependencies. + pipe + Unix concept: A mechanism for providing the output of one command (:term:`stdout`) as the input of a next command (:term:`stdin`) in a Unix terminal. The standard syntax are multiple commands, separated by vertical bars (the "pipes", "|"). Read more `on Wikipedia `_. + provenance A record that describes entities and processes that were involved in producing or influencing a digital resource. It provides a critical foundation for assessing authenticity, enables trust, From 86b5a9f8021519838928663814def571e7da3583 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 16:19:33 +0100 Subject: [PATCH 24/28] move section on dumping metadata across datasets --- docs/beyond_basics/101-181-metalad.rst | 27 +++++++++----------------- 1 file changed, 9 insertions(+), 18 deletions(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index d91a83d20..6fa818487 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -222,6 +222,7 @@ Creating your own extractor The MetaLad docs have a dedicated user guide that walks you through the process of creating your own extractor. Have a look at `docs.datalad.org/projects/metalad/user_guide/writing-extractors.html `_. + Distributing and Getting Metadata ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -313,6 +314,14 @@ Can you guess what type of metadata it contains [#f6]_ ? $ datalad meta-dump -r +A final note is that :command:`meta-dump` can also be a source of metadata for :command:`meta-add`. +While metadata can indeed be provided manually, or by running :term:`extractor`\s as outlined so far, it can also be provided by any other means that create correct metadata records, and :command:`meta-dump` is one of them. +For example, you could copy the complete metadata from ``dataset_0`` to ``dataset_1``, by dumping it from one dataset into another:: + + $ datalad meta-dump -d dataset_0 -r | \ + datalad meta-add -d dataset_1 --json-lines - + + Publish metadata to a Git-Repository """""""""""""""""""""""""""""""""""" @@ -392,24 +401,6 @@ As with DataLad and other Python packages, you might want to do the installation - - - - -Generally, metadata can either be provided - -* manually, -* by running :term:`extractor`\s (``datalad-metalad`` plugins that extract certain metadata from primary data), -* or by any other means that create correct metadata records. For example, you could copy the complete metadata from ``dataset_0`` to ``dataset_1``, by dumping it from one dataset into another:: - - - $ datalad meta-dump -d dataset_0 -r | \ - datalad meta-add -d dataset_1 --json-lines - - - - - - .. rubric:: Footnotes .. [#f1] It may seem like an unnecessary duplicated effort to record the names of contained files or certain file properties as metadata in a dataset already containing these files. However, metadata can be very useful whenever the primary data can't be shared, for example due to its large size or sensitive nature, allowing consumers to, for example, derive anonymized information, aggregate data with search queries, or develop code and submit it to the data holders to be ran on their behalf. From f5131c38a0e606422752252c9d5f21b777af444b Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Mon, 13 Feb 2023 16:25:37 +0100 Subject: [PATCH 25/28] add left-over code snippet --- docs/beyond_basics/_examples/DL-101-181-119 | 68 +++++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 docs/beyond_basics/_examples/DL-101-181-119 diff --git a/docs/beyond_basics/_examples/DL-101-181-119 b/docs/beyond_basics/_examples/DL-101-181-119 new file mode 100644 index 000000000..bb0ba6070 --- /dev/null +++ b/docs/beyond_basics/_examples/DL-101-181-119 @@ -0,0 +1,68 @@ +$ datalad meta-dump -r | jq '.extracted_metadata["@graph"][3]["@list"][].familyName' | sort | uniq +jq: error (at :16): Cannot iterate over null (null) +jq: error (at :22): Cannot iterate over null (null) +jq: error (at :23): Cannot iterate over null (null) +jq: error (at :24): Cannot iterate over null (null) +"Agor" +"Akolkol" +"Akolmormal" +"Amodkosgoru" +"Apali" +"Apalsul" +"Apir" +"Aselmoro" +"Ekos" +"Ekoslud" +"Emalkosseli" +"Emalselkosu" +"Esanmodmadu" +"Esel" +"Eselmodi" +"Gos" +"Gosa" +"Gose" +"Igorsele" +"Ilud" +"Imadrig" +"Imolpala" +"Imolsel" +"Irigludkolo" +"Lad" +"Ladi" +"Ladlud" +"Ladmol" +"Lado" +"Modmadkol" +"Modmod" +"Modpiru" +"Modsul" +"Mora" +"Mori" +"Morkosu" +"Omadsulgor" +"Omelgorladi" +"Omol" +"Osel" +"Pal" +"Pir" +"Pirpirludu" +"Pirsul" +"Pulmelkol" +"Pulpula" +"Rigi" +"Rigmorsan" +"Sanmolmor" +"Selkolu" +"Selsulmor" +"Sul" +"Sulmod" +"Ugore" +"Ugorsan" +"Ugos" +"Uludpirkoli" +"Umalpalo" +"Umelmola" +"Upalo" +"Upalpal" +"Upirmor" +"Uselmale" From c10cea297717b3ed69735e9c33e898dcc562dda7 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Tue, 14 Feb 2023 14:50:53 +0100 Subject: [PATCH 26/28] add public link to studyminimeta schema --- docs/beyond_basics/101-181-metalad.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index 6fa818487..25a557555 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -206,7 +206,7 @@ Using existing extractors to add metadata If writing JSON objects by hand sounds cumbersome, it indeed is. To automate metadata extraction or generation, MetaLad can use extractors to do the job. -A few built-in extractors are already shipped with it, for example ``annex`` (reporting on information :term:`git-annex` provides about datasets or files), or ``studyminimeta`` (a `metadata schema for archived studies `_). +A few built-in extractors are already shipped with it, for example ``annex`` (reporting on information :term:`git-annex` provides about datasets or files), or ``studyminimeta`` (a `metadata schema for archived studies `_). Once an extractor of choice is found, the :command:`datalad meta-extract` command can do its job: .. runrecord:: _examples/DL-101-181-108 From a85e1b515a81c1b60a8df1c95b571830e11a5a61 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Tue, 14 Feb 2023 14:51:05 +0100 Subject: [PATCH 27/28] Hide jq warning in output --- docs/beyond_basics/101-181-metalad.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/beyond_basics/101-181-metalad.rst b/docs/beyond_basics/101-181-metalad.rst index 25a557555..3f1cf3f44 100644 --- a/docs/beyond_basics/101-181-metalad.rst +++ b/docs/beyond_basics/101-181-metalad.rst @@ -345,7 +345,7 @@ The (cropped) query below, for example, lists all unique family names of the aut .. runrecord:: _examples/DL-101-181-119 :language: console :workdir: beyond_basics/meta/metadata-assimilation - :lines: 1, 5-20 + :lines: 1, 6-20 $ datalad meta-dump -r | jq '.extracted_metadata["@graph"][3]["@list"][].familyName' | sort | uniq From d98c5f00c77cbd553c707f02f8dc58d8d3dfe703 Mon Sep 17 00:00:00 2001 From: Adina Wagner Date: Tue, 14 Feb 2023 14:55:43 +0100 Subject: [PATCH 28/28] Add uidmap to appveyor build environment --- .appveyor.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.appveyor.yml b/.appveyor.yml index 719527396..80a1fabe5 100644 --- a/.appveyor.yml +++ b/.appveyor.yml @@ -26,7 +26,7 @@ shallow_clone: false environment: DTS: datalad_next APPVEYOR_BUILD_WORKER_IMAGE: Ubuntu2004 - INSTALL_SYSPKGS: python3-virtualenv graphicsmagick-imagemagick-compat moreutils jq + INSTALL_SYSPKGS: python3-virtualenv graphicsmagick-imagemagick-compat moreutils jq uidmap INSTALL_GITANNEX: git-annex -m snapshot