This document outlines essential rules for effective data management in research, emphasizing the importance of loving and sharing data, using permanent identifiers, and ensuring data is accessible and reproducible. Researchers are encouraged to document workflows, link data to publications, publish their code, and credit contributors appropriately. Additionally, it advocates for the use of data repositories and rewarding peers who share data responsibly.

1. Building Your Data Mgmt Toolbox
Ann Lally, Digital Initiatives
Stephanie Wright, Data Services
UW Libraries

2. Rule 1. Love your data, and help others
love it too.
• Document & publish your data
• Encourage others to do so too.
– Your colleagues
– Your collaborators
– Your students
– Authors of papers you review

3. Rule 2. Share your data online, with a
permanent identifier.
• DOIs & ARKs
• Handl & CrossRef
EZID http://guides.lib.washington.edu/dmg
ResearchWorks Archive
http://researchworks.lib.washington.edu/rw-
archive.html

4. Rule 3. Conduct science with a
particular level of reuse in mind.
• Consider level of reuse
– Accessible, Verifiable, Reproducible
• Adopt standards
– Formats
– Metadata
DCC Disciplinary Metadata
http://www.dcc.ac.uk/resources/metadata-
standards

5. Rule 4. Publish workflow as context.
• Sketch of data flow across software
• Track queries and operations on existing data
Taverna http://www.taverna.org.uk/
Kepler https://kepler-project.org/
VisTrails http://www.vistrails.org/

6. Rule 5. Link your data to your
publications as often as possible.
• Share early, even before publication
• Embed links to your data (and code) with
persistent identifiers
ORCID http://orcid.org/
EZID

7. Rule 6. Publish your code (even the
small bits).
• Even if buggy
RunMyCode http://www.runmycode.org/
GitHub https://github.com/

8. Rule 7. Say how you want to get credit.
• Simply state expectations for
acknowledgement
• Licenses
Creative Commons http://creativecommons.org/
Open Data Commons
http://opendatacommons.org/

9. Rule 8. Foster and use data
repositories.
• Institutional, Journal, Domain, General
ResearchWorks Archive
http://researchworks.lib.washington.edu/rw-
archive.html
Databib http://databib.org/
re3data http://www.re3data.org/
Figshare http://figshare.com/

10. Rule 9. Reward colleagues who share
their data properly.
• Provide feedback on their data sets
• Encourage those following best practices
• Give credit to those whose data you use
Data Management Guide
http://guides.lib.washington.edu/dmg

11. Rule 10: Be a booster for data
science.
• Become informed about data mgmt best
practices
– Encourage your students to do so
– UW Libraries workshops on data mgmt
Data blog http://data.blogspot.com/
Data Management Guide

12. Rule 11: Practice Data
Management, Not Data Forensics
• Develop a data management plan at the
beginning of your research project
Data Management Guide
DMPTool https://dmp.cdlib.org/

13. www.authorea.com/users/3/articles/3410/
(Accessed Jan 15, 2013)
ResearchWorks
http://researchworks.lib.washington.edu/
10 Simple Rules for the Care and Feeding
of Scientific Data (Goodman et al)