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# System prepended metadata

title: 'FOSS Spring-2021 Week 4, Section: Wednesday'

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# FOSS Spring-2021 Week 4, Section: Wednesday

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## Topic: 

**FOSS Materials/Useful links**


- Instant Feedback (please complete before you leave class):
    - [https://cyver.se/foss-feedback](https://cyver.se/foss-feedback)
- Course Homepage 
    - [https://learning.cyverse.org/projects/foss/en/latest/index.html](https://learning.cyverse.org/projects/foss/en/latest/index.html)
- Course Schedule 
    - [https://learning.cyverse.org/projects/foss/en/latest/getting_started/schedule.html](https://learning.cyverse.org/projects/foss/en/latest/getting_started/schedule.html)

- [Review slides](https://de.cyverse.org/dl/d/7BA4D9B2-ABB2-4D00-9B09-EF80C565AF57/foss_week4_review.pdf)
- [CyVerse metadata](https://learning.cyverse.org/projects/data_store_guide/en/latest/step3.html?highlight=metadata)
- [Software Carpentry Unix Lessons](http://swcarpentry.github.io/shell-novice/)
- [GitHub](https://github.com/)
- [Intro to Markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)

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### Discussion and notes

**General notes**

*Self Assessment Questions*

1. Thinking about the data you work with, can you identify any areas where it would be good to apply FAIR principles?

2. Do you see a path using the tools we have used so far to get you closer to being FAIR? 



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**Breakout notes**

Team A 
1. Thinking about the data you work with, can you identify any areas where it would be good to apply FAIR principles?

-Findable: common file naming structure; location of file; metadata; depositing sequence data on public repositories;
-Accessible: common storage location; shared drive; public repository;
-Interoperable: metadata; common use of language/code
-Reusable: public repository

2. Do you see a path using the tools we have used so far to get you closer to being FAIR? 

data storage, public databases, share code on GitHub, operationalize your data management plan, teaching made easier via access (e.g., cyverse platform for programs instead of downloading everything) 

Team B
1. Thinking about the data you work with, can you identify any areas where it would be good to apply FAIR principles?
 - data accessbility for public GIS data is good, but its reproductivity is depending on data types and user skills
 - creating metadata

2. Do you see a path using the tools we have used so far to get you closer to being FAIR? 
- Ppl have start sharing their codes on GitHub, paper with codes, arXiv, etc. 
- Cyverse Data Store can help us share data with other researchers.
- Data Stewardship Wizard, DMP tool, etc.
- Uploading and sharing Data on CyVerse
- Creating metadata files using available templates on CyVerse


Team C
1. Thinking about the data you work with, can you identify any areas where it would be good to apply FAIR principles?
- DNA sequence data (depositing sequences in public databases and linking publications on such data to deposited sequences)
- storing and sharing of big geospatial data, increases accesibility and reusability 
- analysis of data is not widely shared making it hard to be interoperable and reusable

2. Do you see a path using the tools we have used so far to get you closer to being FAIR? 
Thinking of last week's lesson (CyVerse):
- uploading and storing data
- better organization of data 
- a better version of a google drive
- jupyter notebooks could help with keeping track of analysis 


Team D: 
Findable: We both want to make our code (regression and STATA) findable for others to use. 

Accessible: Putting code in open source formats (GitHub or Data Store)

Interoperable: Common code and language. 

Reusable: Both want our code, results, work to be resuable for other statisticians/policy makers. 

 2. Making a GitHub would be great for pubshing work on a GitHub page or finding other peoples work. Same with the data store. This is a good resource to share some forms of data. Also make it easier to share work with other collaborators. 

Team E:
Thinking about the data you work with, can you identify any areas where it would be good to apply FAIR principles?
    -making data publically available e.g. on NCBI for sequence data
    -making code avialable - using public repositories in other domains

Do you see a path using the tools we have used so far to get you closer to being FAIR?
    -DSW useful for planning how to make projects FAIR and getting all members of working group on the same page - Also DMP Tool 
    
    
Team Fantastic

1. Thinking about the data you work with, can you identify any areas where it would be good to apply FAIR principles?

    - Crop Variety testing data, working on making it findable and accessible, need license, should be interoperable between programs
    - genomic data- deposited at NCBI and other databases
    - use GitHub for code sharing
    - share data so other researchers can make use of it
    - probably always a good thing

2. Do you see a path using the tools we have used so far to get you closer to being FAIR? 

    - Dryad, but it costs money. Is this a viable long term solution? 
    - yes, Cyverse tools may prove useful 
    - what do we do with data that lacks a recognized standard? 

Team Great googly moogly
1. Thinking about the data you work with, can you identify any areas where it would be good to apply FAIR principles?
    - All of the elements for FAIR are essential to research and cooperation. 
    - Within data cleaning, FAIR is extremely important to ensure data integrity. 
    - Being able to catalog work that has been done and ensuring that data is being used correctly. 

2. Do you see a path using the tools we have used so far to get you closer to being FAIR?
    - Checking links in publications & prepublications for actual data 
    - Using CyVerse, Docker and Github (more)


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### Homework Reminders

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