# FAIRPoints Launch event!
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**Date**: January 28 2022
**Event Summary:** [FAIRPoints_Point_1](https://www.fairpoints.org/fairpoints_resources/)
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## Code of Conduct reminder
* Be respectful, honest, inclusive, accommodating, appreciative, and open to learning from everyone else.
* Do not attack, demean, disrupt, harass, or threaten others or encourage such behavior.
* Be patient, allow others to speak, and use the zoom reactions & chat if you would like to voice something.
* See also our [participation guidelines](https://www.fairpoints.org/participation_guides/).

# Rollcall:
🗣 Name / 🐸 pronouns/ 📣 Social media handle

# Q&A:
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❓ *Please add any questions you might have during the course of the session here:*

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### Notes:

- Housekeeping / Code of Conduct
- How did FAIR Points start - Carpentries experience
- How to FAIR at discipline, topic, etc levels
- Elements of FAIR Points - inclusive, keynotes, machine readable, top things/simple rules - community driven
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FAIR Workflows Community : https://workflows.community/

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# Breakout rooms
## Room 1

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Host: Nabil
**FAIR-Jargon**; in your opinion, which terms, concepts and/or phrases commonly used in FAIR discussions that need more definition?
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### Notes:

- "Accessible" (think this gets confused with 'open') Also means "electronic accessibility" for the differently abled community, e.g. those with sight, hearing impairments.
- Agreed. Open access is preferable, but technically not required for "accessible". There are also degress of openness.
- "Interoperable" - not one of the most common words in English + difficult concept which doesn't really help.
- fuzzy distinction between *interoperable* and *reusable* in common usage? +
- "the machine knows what you mean"
- Using precise and easy to understand language so that it's easy to translate into other languages.

- Maybe important to add tha it's not just about the jargon but also about the fact that the concepts are not well-defined even the word is clear (e.g., 'persistent' - I know what it means in general but *how* persistent does something need to be in order to be persistent enough?). Perhaps it's a different discussion though.

- Author confusion around sharing FAIR data, software, etc - there can be functionality to share along with a preprint - trying to work through the confusion, questions about duplication - that authors can hopefully share via a repository w/ persistent identifier

- Interpretation, opinion about metadata - researchers can draw the line of what metadata is needed - data about data meaningless phrase for researchers - not easy to find agreement on where you draw the line

- Training about FAIR - not at the last stage, publication - integrate early in workflows - researchers actually need metadata to capture/use for analysis
** Metadata **
- Data about Data?
- Example of metadata

- Hear more about community standards, have yet to hear a definition of community standard - FAIR principles says to use community standards, what does that mean

## Room 2
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Host: Katie
**FAIR-Easier said than done?** In your opinion, what challenges are encountered or anticipated along the implementation trajectory of FAIR?
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### Notes:
Educating researchers on mandatory FAIR training in the beginning to incorporate it into the research cycle.
Privacy issue: when researchers work on endagered species

Convincing the researchers don’t see the point hard why from start need to record it somewhere, see it all after the fact (now I have to publish)

People want to put in data, get citation, and go away; don’t think about user community that is accessing/finding/using data

Massive difference between academics (forced to make open), software devs are part of open source community so are more proactive and understand machine accessibility; academics are shifting towards FAIR and Open Science, but only in components (fail to understand large part of FAIRness is machine accessibility; onus on us is to provide tools and they have to fill in forms)

Can we improve that and make the benefits of FAIR visible to academic researchers (who may not understand machine-readable aspects of FAIR); what they think takes a lot of time they accept when they understand why; need to explain, if they just see surface, they want to know why to do this

Want to keep data closed so no one can scoop data: ways of blurring coordinates, issues of landowners (identification of land), repo providers can deal with that in terms of blurring certain fields; repo owners/managers should perhaps make this more obvious to researchers

So much is involved in metadata, expect a lot of metadata, adds a level of curation, getting researchers to understand and apply own metadata, don’t realize how labor intensive and onerous it is to do it correctly, giving study description and methods, trying to make easier and make metadata extraction and application more easy

## Room 3
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Host: Jo
**FAIR is a set of guiding principles**, do you have examples for practical implementation choices that are being made within your community of practice?
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### Notes:

Introductions
Jo - Experience teaching students, when teaching FAIR, implementation seems ALWAYS to elephant in the room nobody's daring to approach
Mary - Support at institution for researchers, interesting in https://www.nsf.gov/pubs/2022/nsf22553/nsf22553.htm
Katarina - Data stewardship, FAIR important part of work, resesarch projects - developing w/ community
Juliane - Data liason, shepherd grant related data - Alzheimers/Mental Disease - Sage has FAIR Workflows Group, manuscripts w/ grants that use external datasets, how to package, dataset level DOIs
Biru - Senior data officer, RDM/FAIR on campus, resources - FAIRsFAIR - “How to be FAIR with your data: A teaching and training handbook for higher education institutions” (https://doi.org/10.5281/zenodo.5665492).

Mary: In answer to Chris' question, this article is a touchstone for me: https://crl.acrl.org/index.php/crl/article/view/23610/30923

Chris: https://calendar.library.ucla.edu/event/8189683

Christine: researchers don't seem to care they just want to know how to do FAIR DM

Donny: FAIRification efforts in the materials science community:
* a common API for serving data: https://www.optimade.org/
* data repositories: <https://nomad-lab.eu/>, <https://archive.materialscloud.org/>, <https://mpcontribs.org/>

Donny: Domain-specific implementation profiles and examples:
* https://www.go-fair.org/how-to-go-fair/fair-implementation-profile/
* <https://www.go-fair.org/implementation-networks/overview/>

Donny: FAIR not necessarily open:
* https://citrine.io/why-data-fairness-is-important-in-the-corporate-world/ (chemical and materials informatics company)

Stephan: discrepancy between policy/principles and practise. Time efficiency important for motivating scientists to take interest.

Ludmilla: tooting my own horn here a bit, but my my project in the area started having a way to facilitate the implementation of open science principles by scientists with limited expertise and or time: https://github.com/FellowsFreiesWissen/computational_notebooks

Here's the slide from my talk that I referenced "How to FAIR". This is how I try to convince researchers they can take steps to be FAIR.

Arnold: need to bring in professionals ans data stewards to facilitate fair RDM because too much work and knowledge required (not easy or even doable for researchers)

Donny: importance of discipline-specific repositories -> people look there -> Findable.

**Directory of research data repositories: https://www.re3data.org/
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