building your data management toolbox

Building Your Data Mgmt Toolbox

Ann Lally, Digital InitiativesStephanie Wright, Data Services

UW Libraries

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

Rule 2. Share your data online, with a permanent identifier.

• DOIs & ARKs• Handl & CrossRef

EZID http://guides.lib.washington.edu/dmgResearchWorks Archive http://researchworks.lib.washington.edu/rw-archive.html

Rule 3. Conduct science with a particular level of reuse in mind.

• Consider level of reuse– Accessible, Verifiable, Reproducible

• Adopt standards– Formats– MetadataDCC Disciplinary Metadata http://www.dcc.ac.uk/resources/metadata-standards

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/

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 identifiersORCID http://orcid.org/EZID

Rule 6. Publish your code (even the small bits).

• Even if buggyRunMyCode http://www.runmycode.org/GitHub https://github.com/

Rule 7. Say how you want to get credit.

• Simply state expectations for acknowledgement

• LicensesCreative Commons http://creativecommons.org/Open Data Commons http://opendatacommons.org/

Rule 8. Foster and use data repositories.

• Institutional, Journal, Domain, GeneralResearchWorks Archive http://researchworks.lib.washington.edu/rw-archive.htmlDatabib http://databib.org/re3data http://www.re3data.org/Figshare http://figshare.com/

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

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 mgmtData blog http://data.blogspot.com/Data Management Guide

Rule 11: Practice Data Management, Not Data Forensics

• Develop a data management plan at the beginning of your research projectData Management Guide DMPTool https://dmp.cdlib.org/

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)