Some Ideas on Making Research Data Discoverable and Usable: It s the Metadata, Stupid!

Size: px

Start display at page:

Download "Some Ideas on Making Research Data Discoverable and Usable: It s the Metadata, Stupid!"

Barbara Howard
5 years ago
Views:

1 Some Ideas on Making Research Data Discoverable and Usable: It s the Metadata, Stupid! Anita de Waard, VP Research Data Collaborations, Elsevier Research Data Services (VT) The Metadata [R]evolution: Transformative Opportunities September 18, 2013

2 Everybody s talking about research data: Roles and needs wrt Research Data: Share research outputs Demonstrate impact to public Data availability drives growth Demonstrate impact Guarantee permanence, discoverability Avoid fraud Gov Funding bodies Mike Huerta, Ass. Director NLM O of Health Info at NIH, 6/13: Today, the major public product of science are concepts, written down in papers. But tomorrow, data will be the main product of science. We will require scientists to track and share their data as least as well, if not better, than they are sharing their ideas today. Barbara Ransom, NSF Program Director Earth Sciences, 2/13: We re not going to spend any more money for you to go out and get more data! We want you first to show us how you re going to use all the data we paid y all to collect in the past! Archive Add curation Allow reuse Generate, track outputs Comply with mandates Ensure availability Archive, track, curate Support researcher/institution Derive credit Comply with mandates Discover and use Cite/acknowledge Data Repositories University management Librarians Researchers Todd Vision, DataDryad, OAI8, 6/23/13: We need to find a way to keep Dryad funded, and would love to hear your ideas about doing that. Phil Bourne, Associate Vice Chancellor, UCSD, 4/13: We are thinking about the university as a digital enterprise. Mara Saule, Dean University Libraries/CIO, UVM, 5/13: We need to do something about data. Nathan Urban, PI Urban Lab, CMU, 3/13: If we can share our data, we can write a paper that will knock everybody s socks off!

sustainability for highend repositories?

3 Where research data goes now: > 50 My Papers 2 M scientists 2 My papers/year Majority of data (90%?) is stored on local hard drives A small portion of data (1-2%?) stored in small, topic-focused data repositories PDB: How PetDB: do we ensure 88,3 k long-term 1,5 k sustainability for highend repositories? MiRB: 25k Some data (8%?) stored in large, generic data How do we get researchers repositories to curate, store and share their data? Dryad: 7,631 files TAIR: 72,1 k Dataverse: 0.6 My SedDB: 0.6 k What role do libraries/institution Institutional s play? Repositories

4 Research data management in action: Using antibodies and squishy bits Grad Students experiment and enter details into their lab notebook. The PI then tries to make sense of their slides, and writes a paper. End of story.

5 An attempt to get researchers to curate (but only partially share!) their data: de Waard, A., Burton, S. et al., 2013

What to do in the meantime: In 220 publications only 40% of antibodies, 40% of cell lines and 25% of constructs can be manually identified (Vasilevsky et al, submitted) 193 publications 49

6 What to do in the meantime: In 220 publications only 40% of antibodies, 40% of cell lines and 25% of constructs can be manually identified (Vasilevsky et al, submitted) 193 publications 49 publications 76 publications 214 publications 210 publicat Proposal (with NIH/NIF and Force11 Group): Adding minimal data standards Tool extracts likely reagents / resources User interface asks author to confirm or select

7 How can research databases become sustainable in the long term? 1. With IEDA: Building a database for lunar geochemistry Write joint report on building repository, curation costs and challenges 2. With WDS/RDA WG: Planning survey of cost recovery models Input/inspiration: ICPSR Sloane-funded project Sustaining Domain Repositories for Digital Data Developing overarching funding model with Todd Vision/DataDryad

8 Making lunar sample data usable:

A research database funding model: Acces s Data user Closed Publi c Limited Limited Academic Use/Limited & Flow of funds Service Private store Data publication Collaboration Conclave Subscriptio n

9 A research database funding model: Acces s Data user Closed Publi c Limited Limited Academic Use/Limited & Flow of funds Service Private store Data publication Collaboration Conclave Subscriptio n content Commercial overlay Flow of funds Data producer or sponsor or Examples Commercial and institutional storage Dryad, arxiv, PDB Many small operations, e.g. try-db.org, plhdb.org ICSP R, CERN -LHC KEGG GeoFacets Reaxys DRAFT - CC-BY-NC 2013, Todd Vision & Anita de Waard

Comparing data repository types: Repository Advantages Disadvantages Local data repository Generic data repository Institutional Repository

Tracking and compliance checks. Data can be reused. Credit! No one sees it. Not compliant with requirements Data can t be easily reused. Credit? Data can t easily be reused.

10 Comparing data repository types: Repository Advantages Disadvantages Local data repository Generic data repository Institutional Repository Domain-specific data repository Easy! No one steals your data. Not very hard to do. Have complied! Can use existing IR? Tracking and compliance checks. Data can be reused. Credit! No one sees it. Not compliant with requirements Data can t be easily reused. Credit? Data can t easily be reused. Credit? Lot of work for curators. Long-term sustainable? Effort, Reuse, Credit, Compliance Habit, Ease, Privacy, Control Higher quality metadata

11 Metadata madness WANT Domain-Specific Data Repository Domain of study: DIFFERENT Collaborators: AND Local Data Repository ALL Generic Data Repository Funding Agency: METADATA!!!! University: THEY Institutional Data Repository

12 Where do IRs/libraries fit in? Planning series of interviews at key institutions: What role do libraries/institutions play wrt research data management? What tools/metadata standards are used? What aspects of data deposition is the Research Office/IR/Institution interested in? How does this compare with what scientists want and do in their labs? Goal: share knowledge; establish plan of action

13 Principles of Elsevier RDS: Main goal: make research data optimally available, discoverable and reusable. Collaboration is tailored to partner s unique needs: Working with a few domain-specific and institutional repositories and institutions Aspects where collaboration is needed are discussed Collaboration plan is drawn up using SLA: agree on time, conditions, etc. 2013: series of pilots, studies and reports to enable feasibility study: What are key needs? Can Elsevier play a role: skillsets, partnerships? Is there a (transparent) business model for this?

14 In summary: If researchers start to curate and share their data And research databases become long-term sustainable we enable enrichment with high-quality metadata that makes research data truly discoverable and reusable. Many questions remain:? What role would the institution/library play?? How do we ensure interoperable metadata?? What are sustainable models, moving forward?? Is there a place for publishers, in all this?

15 Thank you! Collaborations and discussions gratefully acknowledged: CMU: Nathan Urban, Shreejoy Tripathy, Shawn Burton, Ed Hovy UCSD: Phil Bourne, Brian Shoettlander, David Minor, Declan Fleming, Ilya Zaslavsky NIF: Maryann Martone, Anita Bandrowski MSU: Brian Bothner OHSU: Melissa Haendel, Nicole Vasilevsky California Digital Library: Carly Strasser, John Kunze, Stephen Abrams Columbia/IEDA: Kerstin Lehnert, Leslie Hsu CNI: Clifford Lynch Harvard: Michael Kurtz, Chris Erdmann MIT: Micah Altman UVM: Mara Saurle

16 Your questions? Anita de Waard VP Research Data Collaborations, Elsevier Research Data Services (VT)

Research Elsevier

Research Elsevier Research Data @ Elsevier From generation through sharing and publishing to discovery IJsbrand Jan Aalbersberg SVP Journal and Data Solutions NDS, Boulder - June 12, 2014 Contributors: Anita de Waard Hylke