Institutional Perspective on Credit Systems for Research Data MacKenzie Smith Research Director, MIT Libraries

Why Credit Matters  Academic research institutions depend on a reliable record of scholarly accomplishment for key decisions about hiring, promotion and tenure.  Publication credit (citation) mechanisms evolved over decades for books, peer-reviewed publications, and sometimes grey literature (theses, technical reports and working papers, conference proceedings, etc.)  Services emerged to make the record assessment easier (Impact Factor, Academic Analytics, etc.) Simple impact metrics are expected. 2Data Citation Practices and Standards ©MacKenzie Smith8/22/2011

Why Credit Matters Newer modes of scholarship and scholarly communication not part of this evaluative process  Preprint repositories like arXiv or SSRN  Blogs, websites, other social media  Digital Libraries like Perseus, Alexandria  Software tools, e.g. for processing, analysis, visualization  “Reference” or “Community” research datasets and databases even when widely used by a members of a discipline 3Data Citation Practices and Standards ©MacKenzie Smith8/22/2011

Why Credit Matters  Institutional Reputation Management  e.g. world rankings  Academic Business Intelligence  e.g. for industrial liaison programs, technology licensing  Important for recruitment, retention (faculty and students), PR and fundraising 4Data Citation Practices and Standards ©MacKenzie Smith8/22/2011

Publishing Process  Traditional publication process did not involve institutions, except as buyers Author  Society/Publisher/Press  Library  Data sharing process often involves institutional infrastructure, varies by discipline  Not so much in HEP or genomics  Somewhat in social sciences  Lots in fields without well-established, shared disciplinary infrastructure, e.g. neuroscience, oceanography 5Data Citation Practices and Standards ©MacKenzie Smith8/22/2011

Institutional Responsibilities  Compliance with funding agency policies  Grant contracts are with institutions, not PIs  Infrastructure provision  Network, compute, storage, software (e.g. matlab), Web servers…  Long-term stewardship of the scholarly record  access and preservation 6Data Citation Practices and Standards ©MacKenzie Smith8/22/2011

Intellectual Property To the extent that IP exists in data, or that it has commercial potential, who “owns” that IP and can dictate citation or attribution requirements is unclear…  Researchers assume they do (unlike publications requiring a CTA)  Funders don’t, but do have policies about this “b. Investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants. Grantees are expected to encourage and facilitate such sharing.” (NSF Award and Administration Guide, Jan 2011) Note that “Grantees” here mean universities 7Data Citation Practices and Standards ©MacKenzie Smith8/22/2011

University Copyright Policy “in the case of scholarly and academic works produced by academic and research faculty, the University cedes copyright ownership to the author(s), except where significant University resources (including sponsor-provided resources) were used in creation of the work.” Normally used, e.g., for software platforms developed with university infrastructure. Now being applied to data. 8Data Citation Practices and Standards ©MacKenzie Smith8/22/2011

University Patent Policy “Any person who may be engaged in University research shall be required to execute a patent agreement with the University in which the rights and obligations of both parties are defined.” When data has commercial potential (and is sometimes does) then it gets really interesting… 9Data Citation Practices and Standards ©MacKenzie Smith8/22/2011

Requirements for Data Citation  Persistent or discoverable location  Works even if the data moves or there are multiple copies  Verifiable content  Authenticity (“I’m looking at what was cited, unchanged”)  Requires discovery and provenance metadata  Standardized  Data identifiers: DataCite, DOIs  People identifers: ORCID registry  Institutional identifiers: OCLC? NISO I2?  Financial viability  Identifiers cost money to assign, maintain  Metadata is expensive to produce 10Data Citation Practices and Standards ©MacKenzie Smith8/22/2011