debruine

@debruine@tech.lgbt

Psych prof in Scotland
❤️🧡💛💚💙💜 (she/they)
#rstats #PsyTeachR #PsySciAcc #OpenResearch #CodingClub #ManyFaces

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debruine, to random

OMG I just spent an hour debugging an website script that has worked fine for years, but suddenly produced mysterious pandoc errors referencing a line of YAML that didn't exist.

It turns out that if there is a line inside a table that consists of "---", pandoc assumes you're starting a new YAML header :/

(the table contents are read from an external database of talks for our Methods & MetaScience seminars, and I guess nobody had --- in an abstract until recently)

hadleywickham, to random
@hadleywickham@fosstodon.org avatar

Posit recently did layoffs across the company. This was a really tough and sad day for everyone involved, but there are now a bunch of awesome folks who are available for you to hire including https://fosstodon.org/@andyteucher, https://fosstodon.org/@malcolmbarrett@mstdn.jp, https://fosstodon.org/@dataandme@mstdn.social, https://fosstodon.org/@nischal, https://fosstodon.org/@romainfrancois@mastodon.social

debruine,

@thomas_mock @_kit @hadleywickham

I will now forever think of it as the “enshittification of Twitter” 😂

debruine, to random

I’m at the University of Sheffield today for the Perspectives on Teaching Reproducibility symposium at the Teaching Reproducible Research and Open Science conference.

https://www.sheffield.ac.uk/smi/events/teaching-reproducible-research-and-open-science-conference

I’ll add links and notes about the day in this thread

debruine,

First up, Jennifer Buckley on “Opportunities and challenges for teaching reproducibility in the context of UK Higher Education in the Social Sciences – insights from a consultation with teaching staff”

If you aren’t familiar with the UK Data Service, it’s a fantastic resource for managing social science data for research and teaching.

debruine,

The survey involved 109 lecturers in social science most of whom teach quantitative methods and 16 follow-up interviews. Most agree that teaching reproducibility is important and that demonstrations and examples would be useful.

Most still use SPSS (seems to be more polisci than psych in the dataset)

debruine,

Almost half of the lecturers surveyed prepare data to make it more usable for students. They often find there is no time to teach data preparation (one of the most important skills we emphasise in the curriculum)

debruine,

Link to the UK Data Service report:

https://ukdataservice.ac.uk/app/uploads/heconsultationreport2021-11-26.pdf

debruine,

Next up, Jon Reades on Building Foundations: Pythonic (Geo)Data Science from the Ground Up

https://jreades.github.io/talks/reproducible/#/building-foundations-reproducible-geographic-data-science

debruine,

I love this list of benefits of reproducible workflows:

  • Abstraction
  • Employability
  • Learning by seeing
  • Learning by breaking
  • Workload management
debruine,

The Docker method of making sure all students have the same packages and resources looks fruitful. I’d be curious to see how easy it is to deploy docker on students’ diverse machines.

I’m also impressed with teaching git/GitHub; I think version control is so important, but teaching it can be tricky and derail the class with esoteric problems.

And yay for more resources!

debruine,

Reades makes the excellent point that REF2028 has just been announced and it’s clear that they want to promote wider thinking on research environment — now called people and culture — and the contribution of more diverse outputs, which should include open teaching materials.

https://www.ukri.org/news/early-decisions-made-for-ref-2028/

debruine,

Next up, Marina Bazhydai on "The good, the bad and the ugly: Teaching first year psychology undergraduates about research integrity and open science" (with Emma Mills, Richard Philpot, Mike Vernon, & @dermotlynott from Lancaster University)

The UG methods course focuses on broad questions of how to do science, in addition to the stats. They are supported by the PROSPR network https://www.lancaster.ac.uk/psychology/research/open-science/

debruine,

It's a really interesting idea to teach undergrads how to use tools like StatCheck and GRIM to detect research errors (or fraud) and the engage with the repliCATS project. Also, this demo is fab!

https://fivethirtyeight.com/features/science-isnt-broken/

debruine,

The first keynote is by Norm Medeiros and Richard Ball from Project Tier – The New (Aspirational) Normal: Saturating Quantitative Methods Instruction with Reproducibility

https://www.projecttier.org/

This talk focusses on integrating computational reproducibility across all curricula as a precondition for other dimensions of research tansparency.

debruine,

Documentation is the key to Reproducibility

Essential elements:

  • Original data
  • Code

Additional elements:

  • Output of computational results
  • Additional information on data sources
  • A read-me file

((I’d argue a README is essential!))

debruine,

Very cool that the American Economic Association has a dedicated data editor and great online resources!

https://aeadataeditor.github.io/aea-de-guidance/

debruine,

I like the "reproducibity trifecta":

  1. Fixed folder structure
  2. Explicit management of the working directory
  3. Use of relative directory paths in scripts

And the "key dimensions of reproducibility":

  1. Soup-to-nuts reproducibility
  2. (Almost) automated reproducibility
  3. Portability
debruine,

Higher order educational goals served by teaching reproducibility

• Instructors can understand what students produce.
• Students can understand what they produce.
• Students can believe in what they produce.
• Dramatic enhancement of instructor's ability to advise and evaluate student projects (especially with use of a file sharing platform).
• Reinforces core lessons about intellectual integrity that are central to undergraduate education.

debruine,

Project TIER has been focussing their workshops on individual researchers/instructors, and will be explanding thier focus on making more department-wide changes, in collaboration with the UKRN (thanks for the lovely shout-out to Glasgow as a pioneer in this!)

debruine,

Librarians are key for facilitating (seriously, go make friends with your uni librarians!)

Data librarians can:

  • provide assistance with documentation and metadata
  • advise on file naming conventions and format consistency
  • recommend strategies for organising and backing up files

(It's very cool that they do basic code review to make sure data prep code runs on another computer)

debruine,

Terrific point from Norm Medeiros: reproducibility is difficult to retrofit; you need to integrate reproducibility practices at every point in the lifespan of a project.

debruine,

Now on, Carlos Utrilla Guerrero (https://carlosug.github.io) from TU Delft Library on "What can an open science educator do on teaching and building digital competences in reproducibility? Our lessons learned implementing the Research Data and Software management training"

https://www.tudelft.nl/en/library/research-data-management/r/training-events/training-for-researchers

debruine,

TU Delft Library vision for Research Data and Software Management training as part of the education and skills development of students and researchers.

https://zenodo.org/record/3516874

debruine,

The data flow map exercise from this course looks really interesting! It's adapted from https://dataflowtoolkit.dk/

  • Create a comprehensive list of datasets (incl. code) used in the project
  • Annotate with the actions required for each dataset (e.g. collect, reuse, annotate, anonymise, etc)
  • Flag datasets with special characteristics (e.g. personal data, commercial data)
debruine,

The TU Delft course for PhD candidates, Research Data Management 101 (RDM 101), is openly available as a self-learning course. It has 5 modules:

1: The importance of RDM
2: Essentials for Research Data
3: FAIR data principles and their main elements
4: Realizing FAIR data
5: How to plan for RDM

https://tu-delft-library.github.io/rdm101-book/intro.html

debruine,

Next, Julia Kasmire @JKasmireComplex from the UK Data Service on Teaching reproducibility to social scientists. This talk will describe a 5-week bootcamp course from the National Centre for Research Methods that covered:

1 – Intro, generals and specifics of reproducibility
2 – Collaboration, communication and tools thereof
3 – Documenting mind, workflow, processes
4 – Data basics and advanced topics
5 – Publication and AOB

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