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# Introduction
---
## > `whoami`
::::: {.tiny}
:::: {.columns}
::: {.column width="80%"}
Quirin Würschinger
[[email protected]](mailto:[email protected])
Wissenschaftlicher Mitarbeiter and PostDoc in (computational) linguistics
LMU Munich
:::
::: {.column width="20%"}
{height=150 fig-align="right"}
:::
::::
:::: {.columns}
::: {.column width="50%"}
**Current work**
- **research**
- lexical innovation on the web and in social networks
- variation and change in language use and social polarization in social networks
- using Large Language Models (LLMs) like ChatGPT for research in linguistics and social science.
- **teaching**: corpus linguistics and research methodology
:::
::: {.column width="50%"}
**Promoting Open Science in (computational) linguistics at LMU**
- teaching and applying reproducible corpuslinguistic methods
- creating and sharing corpora among researchers and students
:::
::::
:::::
---
## Workshop materials
GitHub repository
: <https://github.com/wuqui/opensciws>
slides
: <https://wuqui.github.io/opensciws/opensciws_slides.html>
website version
: <https://wuqui.github.io/opensciws/opensciws_website.html>
---
## `Open` Open Science workshop
Focus on …
- ask questions
- discuss
- apply and practice
- collaborate
---
## Time table
::: {.tiny}
| Topic | Start | End |
|------------------------------|-------|-------|
| intro | 09:00 | |
| Open Science principles | | 10:30 |
| ~ | | |
| version control | 10:50 | |
| project structure | | 12:00 |
| ~ | | |
| code | 13:15 | |
| data and methods | | 14:40 |
| ~ | | |
| publishing | 15:00 | |
| authoring | | 15:25 |
| recap, open issues, feedback | 16:00 | 16:30 |
:::
---
## Addressing different backgrounds and goals
---
### Backgrounds and interests
CAIS: Forschung zu Digitalisierung und Digitale Gesellschaft
:::: {.columns .smallest}
::: {.column width="50%"}
**research fiels**
- education and pedagogy
- political science
- sociology
- communications studies
- …
:::
::: {.column width="50%"}
**data and methods**
- qualitative interviews
- text analysis
- quantitative surveys
- experimental designs
- social media studies
- …
:::
::::
---
### Survey: main interests
::: {.smallest}
- reproducible workflows
- managing files and folders
- programming with Python and R
- plain text authoring
- data and methods
- text analysis
- social media analysis
- questionnaires
- publishing
- sharing data and code
- authoring papers
:::
---
### Who are you?
Please briefly introduce yourself …
::::: {.smaller}
:::: {.columns}
::: {.column width="60%"}
1. name
1. place and position
1. your research interest in about 3 sentences for someone outside your field
:::
::: {.column width="40%"}
](att/dance-your-phd.png)
:::
::::
:::::
---
# Open Science principles
---
## What is Open Science?
---
## Why should we do Open Science?
](att/most-science-is-wrong.png)
::: {.notes}
- dataset/sample size
- effect sizes
- selection/number of relationships
- flexibility in design
- financial interests
- hype around topic/field
:::
---
What are the reasons why science can go wrong?
---
](att/factors-irreproducible-research.png)
---
](att/p-hacking.png)
---
](att/scientists-mostly-hackers.png)
---
](att/skills-to-pay-the-bills.png)
---
## Principles of Open Science
](att/open-science-principles.png)
::: {.notes}
09:20
:::
---
## Open Science lifecycle
](att/open-science-lifecycle.png)
---
## Roles in Open Science
::: {.smallest}
Funders
: make open science part of the selection process, and conditions for grantees conducting research.
Publishers
: make open science part of the review process, and conditions for articles published in their journals.
Institutions
: make open science part of academic training, and part of the selection process for research positions and evaluation for advancement and promotion.
Societies
: make open science part of their awards, events, and scholarly norms.
Researchers
: enact open science in their work and advocate for broader adoption in their communities.
([Center for Open Science](https://www.cos.io/open-science))
:::
---
## Who profits from Open Science?
](att/public-money.png)
---
## What is Open Science to you?
What do you find interesting, important, or attractive about Open Science?
<https://tinyurl.com/opnsci>
::: {.notes}
09:50
:::
---
## Learning outcomes
---
::: {.content-visible when-format="revealjs"}
# Implementing an open and reproducible workflow
1. version control
1. project structure
1. code
1. data and methods
1. authoring
1. publishing
---
:::
::: {.content-visible when-format="revealjs"}
# Break
:::
---
# Version control
---
## Why use version control?
:::: {.columns}
::: {.column width=40%}
:::
::: {.column width=60%}
:::
::::
::: {.smallest}
[source](https://docs.google.com/presentation/d/1Ae85nKcTAkr-omlTZopN04yV-lPBZJjX9kPMVUlBRVo/edit#slide=id.g23ae9873052_7_56)
:::
---
](att/version-control-multiple-users.png)
---
](att/version-control-benefits.png)
---
## git and GitHub/GitLab
:::: {.columns .r-fit-text}
::: {.column width="50%"}
git
: software on your machine
{height=250}
```sh
git add src/tests.py
git commit -m 'add tests'
git push
```
:::
::: {.column width="50%"}
GitHub and GitLab
: services on a remote server
:::: {.columns}
::: {.column width="50%"}
{height=250}
:::
::: {.column width="50%"}
{height=250}
:::
::::
:::
::::
---
## Collaborating using GitHub
](att/github-collaboration.png)
---
## git commands
](att/git-commands.png)
---
## GitHub workflow
](att/github-workflow.png)
---
### Example
](att/github-example-commit.png)
---
## How to set up a GitHub repository
---
### set up `git`
::: {.smallest}
**Installing** `git`: see [tutorial](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
**Using** `git`:
- from the command line
- using a standalone GUI^[Graphical User Interface] tool; e.g.:
- [GitKraken](https://www.gitkraken.com/)
- [GitHub Desktop](https://desktop.github.com/)
- from within your editor/IDE^[Integrated Development Environment]; e.g.:
- [RStudio](https://posit.co/download/rstudio-desktop/)
- [VSCode](https://code.visualstudio.com/)
:::
---
### set up `GitHub`
[tutorial](https://docs.github.com/en/get-started/quickstart/set-up-git)
- setting up git user information (name, passwort)
- setting up GitHub authentication
- setting and storing authentication (‘token’)
---
### create a repository on GitHub
:::: {.columns .smallest}
::: {.column width="50%"}
1. (create [GitHub](https://github.com/) account)
2. click on `New` (<https://github.com/new>)
3. specify repo name ^[safe: lowercase alphabet characters]
4. specify description
5. specify visibility: private or public
6. select `Add a README file`
6. specify licence ^[good choice for many purposes: MIT license]
:::
::: {.column width="50%"}
<!--  -->
:::
::::
---
### `clone` repositories
go to the folder where you want your project to live
```sh
git clone https://github.com/wuqui/opensciws.git
```
---
### `add`ing, `commit`ing, and `push`ing changes
](att/git-commands.png)
```sh
git add src/tests.py
git commit -m 'add tests'
git push
```
---
# Project structure
---
:::: {.columns}
::: {.column width="50%"}
:::
::: {.column width="50%"}
:::: {.content-visible unless-format="pdf"}
](att/genius-trump.gif)
::::
Let’s not pretend we’re all geniuses …
:::
::::
---
## File names
::: {.smallest}
File names should be:
- machine-readable
- human-readable
- consistent
- avoid non-ASCII characters (e.g. umlauts like ü, ä)
- optional: play well with default ordering (e.g. include timestamps)
:::
---
## File structure
```
.
├── analysis <- all things data analysis
│ └── src <- functions and other source files
├── comm
│ ├── internal-comm <- internal communication such as meeting notes
│ └── journal-comm <- communication with the journal, e.g. peer review
├── data
│ ├── data_clean <- clean version of the data
│ └── data_raw <- raw data (don't touch)
├── dissemination
│ ├── manuscripts
│ ├── posters
│ └── presentations
├── documentation <- documentation, e.g. data management plan
└── misc <- miscellaneous files that don't fit elsewhere
```
General principle
: keep the ‘inputs’ of your project/code (e.g. data) separate from its outputs (e.g. plots).
---
## Practice: project management
You have until 11:50 h to work on either …
1. developing a project structure for your needs from scratch
1. refactoring/cleaning an existing project^[make a backup first]
Optionally: set up version control via git/GitHub for this project.
---
::: {.content-visible when-format="revealjs"}
# Break
:::
---
# Code
---
## Reproducibility
---
### Reproducibility (crisis)
](att/reproducibility-numbers.png)
---
### Reproducibility et al.
](att/reproducibility-matrix.png)
::: {.notes}
interesting for student projects
:::
---
### The quality of tools
](att/excel-renaming.png)
---
## Testing code
### Why we should test code
](att/mars-mission.png)
---
::: {.note}
not all our projects have that high stakes
:::
### Professional testing
](att/testing-stan-lib.png)
::: {.note}
same is true of industry
:::
---
### Types of tests
::: {.tiny}
:::: {.columns}
::::: {.column width="50%"}
](att/types-of-tests.png){height=300}
**Testing frameworks**
- Python: `pytest`
- R: `testthat`
:::::
::::: {.column width="50%"}
**Analogy**
- during the process of manufacturing a ballpoint pen, the cap, the body, the tail, the ink cartridge and the ballpoint are produced separately and **unit tested** separately.
- When two or more units are ready, they are assembled and **integration testing** is performed, for example a test to check the cap fits on the body.
- When the complete pen is integrated, **system testing** is performed to check it can be used to write like any pen should.
- **Acceptance testing** could be a check to ensure the pen is the colour the customer ordered.
[source](https://the-turing-way.netlify.app/reproducible-research/testing/testing-overview)
:::::
::::
:::
---
### Testing example
using `pytest` for Python
---
## Documenting code
### Literate programming
::::: {.tiny}
:::: {.columns}
::: {.column width="70%"}
- ‘Literate programming is a methodology that combines a programming language with a **documentation language**,
- thereby making programs **more robust**, **more portable**, **more easily maintained**,
- and arguably **more fun** to write than programs that are written only in a high-level language.
- The main idea is to treat a program as a piece of literature, **addressed to human beings** rather than to a computer.
- The program is also viewed as a **hypertext document**, rather like the World Wide Web. (Indeed, I used the word WEB for this purpose long before CERN grabbed it!)’
[Donald Knuth](https://www-cs-faculty.stanford.edu/~knuth/lp.html)
:::
::: {.column width="30%"}
](att/knuth-literate-programming.png)
:::
::::
:::::
::: {.notes}
psychological benefit: conversation
- helps reasoning
- more fun (human)
- → ChatBots
:::
---
### Notebooks
::::: {.smallest}
:::: {.columns}
::: {.column width="50%"}
Python
](att/jupyter-notebook.png)
:::
::: {.column width="50%"}
R
](att/r-literate-programming-quarto.png)
:::
::::
→ both work with [Quarto](https://quarto.org/)
:::::
::: {.notes}
- who uses notebooks?
- which ones?
- good for novices & experts
:::
---
### Example using `nbdev` for Python
::: {.smallest}
Programming a deck of cards: <https://github.com/fastai/nbdev_cards/>
:::
---
### Literate testing with `nbdev`
::: {.notes}
compare with `pytest`: much easier/natural
:::
---
### Additional benefits of `nbdev`
- simple, integrated testing
- [continuous integration](https://the-turing-way.netlify.app/reproducible-research/ci.html)
- dependency management
- publishing code for PyPI and conda
- publishing documentation via Quarto
::: {.notes}
- good for novices & experts
- covers about 80% of programming setup for free
- more about Quarto later
:::
---
### R: Quarto and RMarkdown
::: {.notes}
benefit: visual editor
:::
---
## Licensing
::: {.smallest}
:::: {.columns}
::: {.column}
**Code**
often appropriate: [MIT license](https://choosealicense.com/licenses/mit/)
:::
::: {.column}
**Other materials**
:::
::::
:::
---
# Data and methods
---
## Diversity in data and methods
CAIS: Forschung zu Digitalisierung und Digitale Gesellschaft
:::: {.columns .smallest}
::: {.column width="50%"}
**research fiels**
- education and pedagogy
- political science
- sociology
- communications studies
- …
:::
::: {.column width="50%"}
**data and methods**
- qualitative interviews
- text analysis
- quantitative surveys
- experimental designs
- social media studies
- …
:::
::::
---
](att/one-size-fits-all.png)
---
## Reasons to share your data
::: {.tiny}
:::: {.columns}
::: {.column width="45%"}
](att/turing-share-data.png)
:::
::: {.column width="55%"}
- To allow the possibility to fully **reproduce** a scientific study.
- To prevent duplicate efforts and **speed up scientific progress**. Large amounts of research funds and careers of researchers can be wasted by only sharing a small part of research in the form of publications.
- To facilitate **collaboration** and increase the impact and quality of scientific research.
- To make results of research openly available as a **public good**, since research is often publicly funded.
:::
::::
:::
---
## FAIR data
](att/fair-data.png)
---
## How to share your data
::: {.tiny}
[Turing Way tutorial](https://the-turing-way.netlify.app/reproducible-research/rdm/rdm-sharing.html)
- Step 1: Select **what data** you want to share; eg.:
- ethical concerns
- commercial concerns
- Step 2: Choose a **data repository** or other sharing platform
- overview: [re3data](https://www.re3data.org/), [NIH](https://sharing.nih.gov/data-management-and-sharing-policy/sharing-scientific-data/generalist-repositories), [FAIRsharing](https://fairsharing.org/databases)
- examples: [Zenodo](https://zenodo.org/), [Dryad](https://datadryad.org/)
- Step 3: Choose a **licence** and link to your paper and code; e.g.:
- [Creative Commons](https://creativecommons.org/choose/)
- [Open Data Commons](https://opendatacommons.org/licenses/by/index.html)
- Step 4: **Upload** your data and documentation
- good file organisation
- appropriate file formats (e.g. `csv` > `xlsx`)
:::
::: {.notes}
- Excel issues
- formatting
- thousands separators: `,` vs `.`
- date conversion
- formulas
:::
---
## Sharing social media data
](att/akdeniz-paper-screenshot.png)
---
**Obstacles to data-sharing**
::: {.smallest}
- Reason 1: Preparing data for sharing is **resource-intensive**
- Reason 2: Not enough **credit** for data sharing
- Reason 3: Lack of **confidence** and **knowledge**
- Reason 4: Data protection **laws**
- Reason 5: Platform **terms of service**
- Reason 6: **Copyright**
- Reason 7: Informed **consent**
- Reason 8: **Ethical** challenges
- Reason 9: Lack of common **standards**
[source](https://www.frontiersin.org/articles/10.3389/fdata.2022.971974/full)
:::
::: {.notes}
- additional reason: sharing not **considered** (necessary)
- **ethical** and **privacy**: paper on incest on Twitter
:::
---
## The case of Twitter
::: {.smallest}
- stage 1: access costly & legal grey area for scraping
- stage 2: Research API 🎉
- stage 3: Elon Musik → X → …
:::
::: {.fragment}
](att/twitter-api-closed-musk.png)
:::
---
## Group work
get active: see <https://tinyurl.com/opnsci>
---
::: {.content-visible when-format="revealjs"}
# Break
:::
---
# Publishing
---
## Open access
[The Turing Way tutorial on open access](https://the-turing-way.netlify.app/reproducible-research/open/open-access.html)
](att/turing-open-access-advantages.png)
---
**Routes to open access publishing**
](att/turing-open-access-routes.png)
---
## Preregistration
### What is preregistration?
::: {.smallest}
When you preregister your research, you're simply specifying your **research plan** **in advance** of your study and submitting it to a registry.
Preregistration separates **hypothesis-generating** (exploratory) from **hypothesis-testing** (confirmatory) research.
- Both are important.
- But the same data cannot be used to generate and test a hypothesis, which can happen unintentionally and reduce the credibility of your results.
- Addressing this problem through planning improves the quality and transparency of your research.
- This helps you clearly report your study and helps others who may wish to build on it.
[Open Science Center tutorial](https://www.cos.io/initiatives/prereg)
:::
---
### The preregistration process
](att/pre-registration.png)
---
### Avoiding pitfalls through preregistration
](att/pre-registration-cycle.png)
::: {.tiny}
*HARKing*: hypothesizing after results are known
:::
---
## Outlets
::: {.smallest}
ArXiV
: preprints
Zenodo
: all kinds including data, code, preprints, etc.
GitHub and GitLab
: code, software
Open Science Framework
: all kinds including data, code, preprints, preregistration, etc.
Software Heritage
: archival of code (long-term)
Papers with Code
: code and data for and with papers, mostly Machine Learning
…
:
:::
---
# Authoring
---
## Plain text authoring
](att/tex-md-docs-learning-curve.png)
---
## Markdown syntax
[Quarto: Markdown basics](https://quarto.org/docs/authoring/markdown-basics.html)
---
## Benefits of Markdown
- Markdown provides **semantic meaning** for content in a relatively simple way
- You can write rich formatted content extremely **quickly** (compared to writing directly in HTML tags)
- You can **read** Markdown easily in plain text before rendered by HTML
- It doesn’t interrupt your **workflow** with the need to click buttons