Research Data Lifecycle Management Workshop Report

1. Research Data Lifecycle Management Workshop Report Curt Hillegas 9/8/2011

2. The workshop • NSF funded • Joint initiative between CASC and EDUCAUSE ACTI CCI Working Group • 70 – 75 attendees • 1.5 days • 4 speakers • 7 break-out sessions • 2 panels

3. Secure Research Data • Create national working group to guide compliance to federal standards for research computing data • Catalog solutions for remote access to restricted data • Find data solutions for Clinical and Translational Science Awards

4. Policy • Create a catalog of issues (and approaches to solutions) with data ownership and responsibility • Workshop for campus leaders (VPR and Provost) • Workshop for community/discipline leaders • Develop a discipline-blind framework for data policies and standards • Researchers, librarians and IT professionals approach Provost and VPR together

5. Assessment and Selection of Research Data • Develop a framework for creating and implementing workflows that allow researchers to be a partner in the process • Educate key audiences about the need for curatorial practice and key concepts • Researchers • Graduate students • Encourage policy makers to rethink roles in key units of the institution

6. Funding and Operation • Repository builders must collaborate with others from the start • Make data movable – able to move from one caretaker to another • Funding will change throughout the lifecycle of the data • Prepare repositories for handing off data • Perform a study of existing models and create a report

7. Partnering Researchers, IT Staff, Librarians and Archivists • Communication of what’s out there • Institute more training for grad students • Substantial workshop report • Hold a workshop to define best institutional practices in communicating between researchers and librarians • Survey our campuses on data management practices

8. Standards for Provenance, Metadata and Discoverability • Common framework for data - some emerging, like Metadata Encoding and Transmission Standard (METS) • Role of ontologies – domains recognizing standardized terminologies • Instrumented data – if numeric data is off, then data is useless • Metadata needs to be captured at point of data creation • Need standards of provenance – what’s the purpose of creating this data? Relationships between datasets are critical

9. Partnering Funding Agencies, Research Institutions and Communities, and Industrial and Corporate Partnerships • Joint study of the feasibility of the “digital sheepskin” • Conduct an aggregated study of TCO models using trusted party (academia) for storage for perpetuity or for ten years. • Identify the missing pieces of the research data software stack, and encourage collaborations between academia and industry. • A study on criteria for throwing data away, by discipline. • Continue to emphasize that data volume is growing much faster than our ability to move data around. Think about where we need to site data. • What are the possible models for joint activity with industrial partners?

10. Summary • Researchers, Librarians/Archivists, IT Professionals, Funding Agencies, and Vendors must work together • Create frameworks of best practices that allow for discipline specific implementation • Involve Provosts and Chief Research Officers • Start educating early in researchers’ careers