This document summarizes a panel discussion on researchers' data documentation practices. It finds that metadata is often not sufficient because data itself is messy and domain-specific. Researchers in different disciplines like chemistry, ecology, computer science, microbiology, and earth sciences approach documentation differently based on norms and needs within their fields. For example, chemists use numbering systems and documentation protocols while microbiologists emphasize keeping detailed lab notebooks. The document concludes more interviews are needed to better understand disciplinary practices and tailor library services accordingly.

1. Beyond Metadata:
Supporting non-standardized documentation
to facilitate data reuse
RDAP 2015
April 23, 2015

2. Panel Outline
Researchers on their data documentation
practices, Sara Mannheimer, @sara_mannh
Leveraging the ReadMe to support disciplinary
documentation needs, Lizzy Rolando
Providing access to restricted data in our
institutions, Sarah Pickle, @sarahepickle
Teaching documentation for researchers,
Kristin Briney, @KristinBriney

3. “This is just for me”
Researchers on their
data documentation practices
Sara Mannheimer
Montana State University Library
@sara_mannh
#RDAP15

4. Outline
Contextualizing data
Researchers on data documentation
Lessons learned + next steps

5. Why isn’t metadata sufficient?

6. Metadata is Structured

8. Data is Messy
http://edgerton-digital-collections.org/notebooks/22

10. Metadata is often domain-specific

11. Contextualizing Data
Associated publications
Lab notebooks
Readme files
Data dictionaries
Codebooks

12. Data by Discipline
Chemistry
Ecology/Climate Modeling
Computer Science
Microbiology
Earth Sciences/Geography

13. Chemistry
Training manual with data documentation protocols
Procedures document explaining experiment design
and data formatting
Numbering system to reference reactions

14. Chemistry
“The supplemental information describes a lot of
what would be found in a ReadMe”

15. Chemistry
“This is just for me”

16. Ecology/Climate Modeling

17. Ecology/Climate Modeling
“I think computer scientists would probably be
trained in these things, but we’re not computer
scientists—I mean, we are now!”

18. Computer Science
Notes, code, and project files are archived in Github.
Collected data is stored in a MySQL database, but
“we don’t generate any real documentation of
what's in our databases, outside of some initial
design schemas.”

19. Microbiology
On lab notebooks: “Effectively keeping a
diary of what you’re doing every day”
“Calculations work better on paper”
For analysis and processing, Excel or discipline-
specific software is used

20. Microbiology
“Write it so that you can figure it out, but with
the idea is that someone else should be able to
figure it out too”

21. Microbiology
http://www.rcsb.org/pdb/home/home.do#Category-deposit

22. Earth Sciences/Geography
Former projects have had more formatting, but
more recently free form
Templates document: time of interview,
observations of person, property, house

23. Earth Sciences/Geography
“I might try to anonymize, but it can get complex
to de-identify the data”
“I might make survey instrument available, but it’
s not a priority”

24. Lessons Learned + Next Steps
Discipline-specific approaches
Discipline-specific best practices
Different perspectives on the value of reuse
Different levels of need for library data services

25. Lessons Learned + Next Steps
Conduct more researcher interviews
Tailor outreach to most needy disciplines
Develop discipline-specific services

26. Thanks!
sara.mannheimer@montana.edu
@sara_mannh