RDMG Service Overview

MSU LibrariesResearch Data Management Guidance

Research Data Management Guidance

Aaron [email protected]


Volunstrordinaries!

Aaron Collie

Hailey Mooney

Devin Higgins

Brandon Locke

Ranti Junus Thomas Padilla

Judy Matthews

Tina Qin


We teach people about RDM

Librarianship

Training

Assessment

Consultation

High Touch

6-12 new clients per semester

100% satisfied / 100% would use again

71% of new clients are referrals

60% requested additional services

15% through NFO, 14% through website


Here is your crash course

• Who: You, as the designated steward• What: “the data”• When: Minimum 3 years after

publ./degree• Where: Managed networked storage• Why: Legal, Ethical, Scholarly• How: With fidelity and

documentation sufficient to reproduce the research


Want to hear more?

Faculty Seminars in Research & Instructional TechnologyResearch Data Management

2:00 pmMay 12, 2015112 Bessey Hall


But why are we really here?

• Impetus: NSF has mandated that all grant applications submitted after January 18th, 2011 must include a supplemental “Data Management Plan”

• Effect: The original NSF mandate has had a domino effect, and many funders now require or state guidelines for data management of grant funded research

• Response: Data management has not traditionally received a full treatment in (many) graduate and doctoral curricula; intervention is necessary


More Funder Policies

NASA “promotes the full and open sharing of all data”

“requires that data…be submitted to and archived by designated national data centers.”

“expects the timely release and sharing of final research data"

"IMLS encourages sharing of research data."

“…should describe how the project team will manage and disseminate data generated by the project”


Policies for re-use, re-distribution, and creation of derivatives

Plans for archiving data, samples, and other research outcomes, maintaining access

Types of data, samples, physical collections, software generated

• Standards for data and metadata format and content

• Access and sharing policies, with stipulations for privacy, confidentiality, security, intellectual property, or other rights or requirements

NSF Data Management Plan


• NSF will not evaluate any proposal missing a DMP

• PI may state that project will not generate data

• DMP is reviewed as part of intellectual merit or broader impacts of application, or both

• Costs to implement DMP may be included in proposal’s budget

• May be up to two pages long

NSF Data Management Plan


• Investigators seeking $500,000 or more in direct costs in any year should include a description of how final research data will be shared, or explain why data sharing is not possible.

• The precise content of the data-sharing plan will vary, depending on the data being collected and how the investigator is planning to share the data.

• More stringent data management and sharing requirements may be required in specific NIH Funding Opportunity Announcements. Principal Investigators must discuss how these requirements will be met in their Data Sharing Plans.

NIH Data Management Plan


Roles and responsibilities Expected Data Period of data retention• Data formats and dissemination• Data storage and preservation of access

NEH-ODH Data Management Plan


Positive reinforcement….

• National Science Foundation Data Management Plan mandate (January 18, 2011)

• Presidential Memorandum on Managing Government Records (August 24, 2012)–Managing Government Records

Directive: All permanent electronic records in Federal agencies will be managed electronically to the fullest extent possible for eventual transfer and accessioning by NARA in an electronic format.


Positive reinforcement… (cont.)

• White House policy memo (February 22, 2013)– Increasing Access to the Results of Federally Funded

Scientific Research: Federal agencies with more than $100M in R&D expenditures must develop plans to make the published results of federally funded research freely available to the public within one year of publication.

• OSTP policy memo (March 20, 2014)– Improving the Management of and Access to Scientific

Collections: directs each Federal agency that owns, maintains, or otherwise financially supports permanent scientific collections to develop a draft scientific-collections management and access policy within six months.


Teeth!

• AHRQ = “[unclassified digital data] should be stored and publicly accessible to search, retrieve, and analyze. For sharing of data in digital format, all AHRQ-funded researchers will be required to include a data management plan for sharing final research data in digital format, or state why data sharing is not possible.

• NASA = This plan extends NASA’s culture of open data access to all NASA-funded research.”

• USDA = Phased approach beginning with DMP


Local Response: Policy

– University Research Council Best PracticesResearch Data: Management, Control,

and Access• To assure that research data are appropriately

recorded, archived for a reasonable period of time, and available for review under the appropriate circumstances.– Ownership = MSU– “Stewardship” = You– Period of Retention = 3 years– Transfer of Responsibility = Written Request


Broader Response: Changing Data Landscapes

• Data Management Competencies– Standards & Best Practices– Discipline Specific Discourse

• Data sharing and open data– Data sets as publications– Data journals– Citations for data (e.g., used in secondary

analysis)– Data as supplementary materials to traditional

articles– Data repositories and archives


Curation responsibilities (Carlson, The Chronicle, 2006)

“Data from Big Science is … easier to handle, understand and archive.

Small Science is horribly heterogeneous and far more vast. In time Small Science will generate 2-3 times more data than Big Science.”

big science data

small science data

institution?

domain?

MacColl, John (2010). The Role of libraries in data curation. RLG Partnership Annual Meeting, Chicago. June 2010


Data Sharing Impacts

• Reinforces open scientific inquiry

• Encourages diversity of analysis and opinion

• Promotes new research, testing of new or alternative hypotheses and methods of analysis

• Supports studies on data collection methods and measurement

Cc http://www.flickr.com/photos/pinchof_10/


Data Sharing Impacts

• Facilitates education of new researchers

• Enables exploration of topics not envisioned by initial investigators

• Permits creation of new datasets by combining data from multiple sources


Questions?

• Aaron Collie• [email protected]

Education

RDMG Service Overview