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WIP: adds superdataset metadata in studyminimeta.yaml #61
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#<!-- METADATA START --> # DO NOT DELETE THIS LINE | ||
# All example text is surrounded with <ex> and </ex>. Please replace the example | ||
# text including the <ex> and </ex> with your data. Delete all non-applicable | ||
# lines and sections. | ||
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# Attention: indentation is important and must be preserved! | ||
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# information on the study the to-be-archived dataset was created for | ||
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# mandatory information on to-be-archived dataset | ||
dataset: | ||
# short name or label | ||
name: StudyForrest | ||
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# current location of the dataset | ||
location: https://github.com/psychoinformatics-de/studyforrest-data | ||
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# summary of purpose and content of the dataset | ||
description: | ||
"The StudyForrest project centers around the use of the movie Forrest Gump, which provides complex sensory input | ||
that is both reproducible and is also richly laden with real-life-like content and contexts. Since its initial release, | ||
the StudyForrest dataset has grown and been extended substantially, and now encompasses many hours of fMRI scans, | ||
structural brain scans, eye-tracking data, and extensive annotations of the movie. | ||
It is a one-of-a-kind resource for studying high-level cognition in the human brain under complex, | ||
natural stimulation. The versatility of the provided data (some individuals have nearly ten hours of fMRI data) | ||
enables studies far beyond this main focus. This covers a vast range from studies of low-level signal properties | ||
and brain structure, to sensory integration and attentional processes, to computational modeling of representational | ||
spaces and brain area interactions." | ||
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# optional list of employed standard and format | ||
# for automatic generation of rich metadata records | ||
# (see handbook for full list) | ||
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# optional list of keyword/phrase | ||
keyword: | ||
- human | ||
- fMRI | ||
- task | ||
- 7T | ||
- 3T | ||
- audio | ||
- visual | ||
- music | ||
- retinotopy | ||
- angiography | ||
- T1,T2 | ||
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# minimum of one dataset author required | ||
author: | ||
- [email protected] | ||
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# optional list of entities that funded the creation of | ||
# the dataset. This might also be a single value, i.e.: | ||
#`` funding: NIH´´. | ||
funding: | ||
- BMBF, 01GQ1411 | ||
- NSF, 1429999 | ||
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# one or more publications on or using the dataset, optional | ||
publication: | ||
- title: "The effect of acquisition resolution on orientation decoding from V1: comparison of 3T and 7T" | ||
author: | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
# when published | ||
year: 2018 | ||
# all other properties are optional | ||
# highly recommended to provide a DOI | ||
doi: https://doi.org/10.1101/305417 | ||
# journal name or publication venue label | ||
publication: bioRxiv | ||
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- title: "Spatial band-pass filtering aids decoding musical genres from auditory cortex 7T fMRI" | ||
author: | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
# when published | ||
year: 2018 | ||
# all other properties are optional | ||
# highly recommended to provide a DOI | ||
doi: https://doi.org/10.12688/f1000research.13689.2 | ||
# journal name or publication venue label | ||
publication: F1000Research | ||
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- title: "The effect of acquisition resolution on orientation decoding from V1 BOLD fMRI at 7 Tesla" | ||
author: | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
# when published | ||
year: 2017 | ||
# all other properties are optional | ||
# highly recommended to provide a DOI | ||
doi: https://doi.org/10.1016/j.neuroimage.2016.12.040 | ||
# journal name or publication venue label | ||
publication: NeuroImage | ||
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- title: "Ultra high-field multi-resolution fMRI data for orientation decoding in visual cortex" | ||
author: | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
- [email protected] | ||
# when published | ||
year: 2017 | ||
# all other properties are optional | ||
# highly recommended to provide a DOI | ||
doi: https://doi.org/10.1016/j.dib.2017.05.014 | ||
# journal name or publication venue label | ||
publication: Data in Brief | ||
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# person information, one record for every email-key used above | ||
person: | ||
[email protected]: | ||
given_name: Ayan | ||
last_name: Sengupta | ||
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[email protected]: | ||
given_name: Oliver | ||
last_name: Speck | ||
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[email protected]: | ||
given_name: Renat | ||
last_name: Yakupov | ||
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[email protected]: | ||
given_name: Martin | ||
last_name: Kanowski | ||
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[email protected]: | ||
given_name: Claus | ||
last_name: Tempelmann | ||
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[email protected]: | ||
given_name: Stefan | ||
last_name: Pollmann | ||
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[email protected]: | ||
given_name: Michael | ||
last_name: Hanke | ||
orcid-id: 0000-0001-6398-6370 | ||
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#<!-- METADATA END --> # DO NOT DELETE THIS LINE |
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Since it would be a great example to follow, I decided to ask a clarification question: why did you side for the meaningless a, b, c, d which might generally result in a confusion and misattribution (mixing up one person ID over another) in favor over some
or even just
or alike?