Baby Steps to Data Publication

1. Baby Steps to Data Publication 19 January 2011 John Kunze, Patricia Cruse, and Rachael Hu University of California Curation Center California Digital Library

2. Need to save data + processing Algorithms + Data Structures = Programs

3. California Digital Library (CDL) Organization Five programs, including UC Curation Center Digital repositories via Merritt Persistent id management (DOIs, ARKs, et al) via EZID Web Archiving Service (WAS) Publishing services eScholarship online journals, with peer review Electronic texts Search and display tools (XTF) • Serving the 10 UC campuses • 226,000 students • 134,000 faculty and staff • Working collaboratively • libraries • data centers • museums, archives • faculty and researchers

4. Complementary work at CDL • DataCite and citation standards • NSF DataONE data network • Open-source Excel add-in project • Merritt: general-purpose micro-services-based data repository • EZID: scheme-agnostic & de-coupled creation, resolution, and management of persistent ids

5. A vision for a “data paper” Idea: wrap the unfamiliar in a familiar façade • Funded by the Gordon and Betty Moore Foundation • A “data paper” minimally consists of a cover sheet and a set of links to archived artifacts • Cover sheet contains familiar elements such as title, date,authors, abstract, and persistent identifier (DOI, ARK, etc.) • Just enough to permit basic exposure to and discovery of datasets by internet search engines, which in turn permits • Building a basic data citation • Indexing by services such as Google Scholar • Instilling confidence in the identifier’s stability

6. Next could be the “data journal” There’s room for “data paper” format to grow in functionality • Incorporation of general-purpose and discipline-specific elements to enrich discovery, re-use, and archiving • Potential parallel emergence of a new kind of “data journal” • Like regular journals, data journals would spring up around disciplines and sub-disciplines as needed • Expect some of them to be peer-reviewed • Envisioned as “overlay” journal analogous to data paper • A variety of data paper sources • Table of contents, editorial policies, submission guidelines, etc.

7. Return incremental value for incremental effort

8. Envisioned outcomes • Data authors will be motivated to deposit for author credit • Data products routinely re-used, annotated, and corrected • Data products, including those resulting from synthesis efforts, will enter the scientific record instead of being lost • Data journals will spring up around disciplines, even if their “papers” are scattered in distributed repositories • Peer review will be optimized by authors’ ability to indicate which information is essential and which information is not • Relevant but non-essential information will be available for the interested reader but will not interfere with peer review

9. Summary The “data paper” as a recognizable, standardized form for previously unpublished artifacts ... • Makes it easier to approach, evaluate, and automatically index for basic discovery • Can be repurposed to create familiar-looking citations suitable for CVs and other publications • Unique persistent identifiers for data papers and artifacts help automate tracking impact and re-use

10. Questions? jak@ucop.edu University of California Curation Center http://www.cdlib.org/uc3