This document discusses challenges and opportunities around research data sharing. It notes that the amount of digital data is growing exponentially and outlines different types of research data. Key points addressed include having standards for metadata and identifiers, issues around data preservation across different formats, and making data interoperable while respecting intellectual property rights. The role of libraries, publishers and researchers in enabling effective data curation and sharing is examined. New approaches are emerging like data papers and integrated articles that combine text, data and tools. Widespread data sharing mandates from funders are driving the need for solutions to these issues.

1. MacKenzie Smith
Associate Director for Technology, MIT Libraries
Science Commons Research Fellow, Creative Commons
CrossRef Annual Meeting ©2010, MIT

2. 2

3. 3

4. The world’s first hard drive (5Mb)
IBM Almaden Research Center, 1952-1954
That Was Then
4
CrossRef Annual Meeting ©2010, MIT

5. Current capacity hard drive (>2Tb)
Google Data Center, 2010
This is Now
5
CrossRef Annual Meeting ©2010, MIT

6. How Much Information?
“IDC research shows that the digital universe —information that is
either created, captured, or replicated in digital form — was 281
exabytes in 2007. In 2011, the amount of digital information
produced in the year should equal nearly 1,800 exabytes, or 10
times that produced in 2006. The compound annual growth rate
between now and 2011 is expected to be almost 60%”
The Diverse and Exploding Digital Universe, 2008 IDC White Paper
6
CrossRef Annual Meeting ©2010, MIT

7. How Much Information?
7
Sequence Submissions to DNA DataBank of Japan 1993-2005
CrossRef Annual Meeting ©2010, MIT

8. What Is Research Data?
Observational e.g. sensor, telemetry, survey, sample data
Experimental e.g. genetic sequences, chromatograms
Simulation e.g. climate, economic, 3-D models
Media e.g. images, audio, video
Derived/compiled e.g. text/data mining, compiled databases
Often expensive or impossible to reproduce
8
CrossRef Annual Meeting ©2010, MIT

9. What Is Research Data?
Text e.g. flat text files, Word, PDF
Numerical e.g. SPSS, STATA, Excel, MySQL
Media e.g. jpeg, tiff, dicom, mpeg, quicktime
Models e.g. 3D, statistical
Software e.g. Java, C programs
Domain-specific e.g. FITS in astronomy, CIF in chemistry
Instrument-specific e.g. Olympus con-focal microscope
Not always in neat packages like books
9
CrossRef Annual Meeting ©2010, MIT

10. What Do Researchers Do With
Data?
 Analyze (e.g. process, visualize)
 Share
 Review (evaluate methods)
 Annotate
 Cite
 Re-use (reproduce results)
 Re-purpose (e.g. integrate)
CrossRef Annual Meeting ©2010, MIT

11. Data Sharing Innovations
 New-fangled Hybrid Articles
 Integrate text, data and tools
 Enhanced PDFs
 Linked Open Data
 Access to data via Web standards to
encourage large-scale interoperability
 “Data Papers”
CrossRef Annual Meeting ©2010, MIT

12. Issues in Data Curation
 Storage very large scale
 Metadata what standard to use?
 Provenance research methods
 Identifiers scalability, persistence
 Preservation see slide #5 on formats
 Sharing laws confusing, not interoperable
CrossRef Annual Meeting ©2010, MIT

13. Data Sharing Trends
“The NIH expects and supports the timely release
and sharing of final research data from NIH-
supported studies for use by other
researchers.” NIH grant proposal guide
Similar data management, sharing mandates from
US NSF, other funding agencies worldwide
Journals mandating deposit
(e.g. Journal of Evolutionary Biology)
13
CrossRef Annual Meeting ©2010, MIT

14. Data Interoperability
IPR and data licenses
 Lots of data not copyrightable since facts cannot be
copyrighted
 UK, EU, some other countries have sui generis
data rights
 Laws not “interoperable”
Big problem for international scientific
collaborations and data re-purposing
14
CrossRef Annual Meeting ©2010, MIT

15. BWIN presentation ©2010, MIT 15

16. Libraries and Data
Established curation for some data types
statistical (Harvard-MIT Data Center)
geospatial (Geodata Repository)
bioinformatics (via NLM NCBI)
digital media (e.g. images, videos)
datasets (IR digital archives)
16
CrossRef Annual Meeting ©2010, MIT

17. BWIN presentation ©2010, MIT
17

18. Libraries and Data
Applies to both faculty-authored and
externally-acquired data
 Consultation services (in-person, via Website)
 Liaise with data archives (e.g. ICPSR)
 Develop (meta)data standards (e.g. DDI)
 Manage and preserve data
18
CrossRef Annual Meeting ©2010, MIT

19. BWIN presentation ©2010, MIT 19

20. Robotics Data in DSpace@MIT
The Library:
 Defined local taxonomy for metadata values
 Customized metadata records
 Adapted/simplified deposit workflow
 Loaded data from previous repository
 Added CC0 licenses
Review of new deposits done by community
20
CrossRef Annual Meeting ©2010, MIT

21. CrossRef Annual Meeting ©2010, MIT

22. 22

23. Researcher’s Role: Data Provision
e.g. Sage Commons
“The Sage Commons is a novel information platform being built by an
international partnership of researchers and stakeholders to define the
molecular basis of disease and guide the development of effective
human therapeutics and diagnostics.
The Sage Commons will be used to integrate diverse molecular mega-data
sets, to build predictive bionetworks and to offer advanced tools proven
to provide unique new insights into human disease biology. Users will
also be contributors that advance the knowledge base and tools
through their cumulative participation.
The public access mission of the Sage Commons requires the development
of a new strategic and legal framework to protect the rights of
contributors while providing widespread access to integrative genomics
resources.”
23
CrossRef Annual Meeting ©2010, MIT

24. Library’s Role: Data
Curation
 Data organization and annotation
e.g. ontologies and metadata
 Data archiving, preservation
e.g. perpetual access
Outreach and support to local researchers
24
CrossRef Annual Meeting ©2010, MIT

25. Publisher’s Role: Data Accreditation
 Require data deposit to archives
 Publish data journals
 Manage peer review (quality control)
 Provide credit for data publishing
(evolution of promotion & tenure system)
25
CrossRef Annual Meeting ©2010, MIT

26. Data Papers Revisited
“a formal publication whose primary purpose is to
expose and describe data, as opposed to
analyze and draw conclusions from it.”
1. Organize peer-review, establish quality-control measures
2. Create citable entity
3. Establish cross-linking mechanisms with traditional papers, to enforce
separation of concerns (methodology vs analysis)
4. Specify required documentation to make data re-usable, re-purposable
5. Apply standard interoperable legal license
(CC0 or PDDL with normative attribution, CC-By with URI attribution)
6. Ensure archiving strategy in place
Jonathan Rees, Recommendations for independent scholarly publication of data sets, Creative
Commons Working Paper, March 2010,
http://neurocommons.org/report/data-publication.pdf
CrossRef Annual Meeting ©2010, MIT

27. Questions?
CrossRef Annual Meeting ©2010, MIT