FOSS Fall 2021 - Week 4, Thursday

Topic: Data Management Part 2

FOSS Materials/Useful Links

• Instant Feedback (please complete before you leave class):

  • https://cyver.se/foss-feedback

• Course Homepage

  • https://learning.cyverse.org/projects/foss/en/latest/index.html

• Course Schedule

  • https://learning.cyverse.org/projects/foss/en/latest/schedule.html

• Today's content

  • https://learning.cyverse.org/projects/foss/en/latest/03_managing_data.html#fair-data

Links from today

• [SRA Submission] (https://learning.cyverse.org/projects/sra_submission_quickstart/en/latest/index.html) (tutorial in CyVerse)
• More info on FAIR: https://fairsharing.org/
• FAIR assesment tool: https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/
• Data Validation: https://datacarpentry.org/spreadsheet-ecology-lesson/

Licenses

Various license choosers:

• Creative Commons license chooser (good for data)
• https://choosealicense.com/
• https://ufal.github.io/public-license-selector/

More info on licenses:

• https://www.gnu.org/licenses/license-list#SoftwareLicenses
• https://opensource.org/licenses

Discussion and notes

General notes

• FAIR is important to you whether you know about it or not
• FAIR assessment tool: https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/
• Data Management plan tools:
  • www.dmptool.org
  • https://ds-wizard.org/

Questions

Breakout Discussion questions & notes

Q: Which elements of FAIR do you struggle with? What particular challenges have you had with making your dataset FAIR?

Room 1

• Understanding origin of images.
• Finding data that built a map.
• Struggling to make datasets visible and reuseable, esp. to be in line with funder's requirements.

Room 2

• struggled with some of the questions because still learning about FAIR.
• Interoperable - haven't really linked my metadata to other data/metadata. Haven't linked the standardised vocab with global identifiers.
• my lab's data is not publically accessible but we don't work with data that would make sense to share because we work with animals, so i think our data is supposed to stay private to be within IACUC protocols, our data is accessible to only us on the college of pharmacy server and our lab computers

Room 3

• Data saved in excel files
• Sequencing data and associated metadata
  • When published they received identifiers from NCBI or GenBank
  • Metadata are published with a doi
• Making data sets identifiable is difficult for protected health information
  • clinical data HIPAA protection is explained by HHS.gov
• accessibility is also a challenge
• interoperability is very tricky
• Reuse?
  • Reproducibility is important and bad analysis is still a problem in scientific publications
  • Data reuse is also important

Room 4

• When the data comes from NSF or NASA or NCBI the data is shared with FAIR priniciples.
• Some of the socioeconomic data from data firms or government agencies may not be publicly accessible– firms may charge researchers outside of affiliated institutions a fee (e.g. oil drilling and firm-level data), and government agencies may require additional screening (e.g. certain census data). Firm-level economic data such as cash flows are often confidential.
• Local data (example where are community gardens?) that aren't tied to some sort of economic value (or oversight insitutions) and often come from communities that don't have the financial ability to create data management infrastructure may be less likely to meet the FAIR principles

Room 5

• Data not uploaded to any repositories (not Findable at the moment)
  • If using publicly available datasets the data can be considered somewaht FAIR

Question 2: Have you prepared a DMP? What are important elements you'd include in a DMP?

Room 1:

• Yes, very little university support for lining up the required components.
• No, not yet.
• In process, using a template makes it easier.

Room 2

• No, would be helpful to make one
• Others have no prior experience
• DMPTool through UC - not for a grant, wanted to document how data was collected, managed, and shared.

Room 3:

• No prior experience

Room 4

• data flow, standards about naming and organizing files

Room 5

Homework Reminders

Homework link will be sent out by Friday!