1. Toward Adoption of RDA Outcomes by US Ocean Science Data Repositories Cynthia Chandler, Bob Arko, Adam Shepherd

2. 2  BCO-DMO  Biological and Chemical Oceanography Data Management Office (WHOI)  Curation of marine ecosystem system data contributed by NSF funded investigators  R2R  Rolling Deck to Repository  Curation of routine, underway data from US academic fleet, and authoritative expedition catalog  Members of Marine Data Harmonization IG US Ocean Science Domain Repositories

3. 3  Awareness – “I just became aware that the output exists”  Interest – “I heard about the output, have learned about it, and now have an active interest”  Evaluation – “I’ve got a strong interest and am willing to commit to evaluating the output”  Trial – “I’ve evaluated the output, decided I like it, and am willing to give it a try.”  Adoption – “It really works!! I’ve decided to adopt it and make it part of the system, Yay!” Adoption Process Stages

4. 4  Data Citation  Data Type Registries  PID Information Types  Data Foundation & Terminology  Practical Policy  Metadata  Data Publishing (3 of 4 groups) Outcomes of Seven Groups Have Potential for Adoption by Ocean Science Data Repositories

5. 5  BCO-DMO: transition human -> machine clients  Ocean science is interdisciplinary  Data curation is distributed  RDA outcomes can help address challenges of  Interoperability between distributed systems in ocean sciences  Interoperability between different domains (natural, social)  Need solutions that scale for 'Big Data' (VARIETY, VERACITY, velocity, volume)  RDA outcomes developed and vetted by representatives from multiple domains What does RDA offer domain repositories?

6. 6 Data Citation (DC) of evolving data  DC goals: to create identification mechanisms that:  allow us to identify and cite arbitrary views of data, from a single record to an entire data set in a precise, machine-actionable manner  allow us to cite and retrieve that data as it existed at a certain point in time, whether the database is static or highly dynamic  DC outcomes: 13 recommendations and associated documentation  ensuring that data are stored in a versioned and timestamped manner  identifying data sets by storing and assigning persistent identifiers (PIDs) to timestamped queries that can be re-executed against the timestamped data store

7. 7 Description of Data Citation Outputs »» Data Versioning: For retrieving earlier states of datasets the data needs to be versioned. Markers shall indicate inserts, updates and deletes of data in the database. »» Data Timestamping: Ensure that operations on data are timestamped, i.e. any additions, deletions are marked with a timestamp. »» Data Identification: The data used shall be identified via a PID pointing to a time- stamped query, resolving to a landing page.

8. 8 Adoption of Data Citation Outputs  Evaluation  Evaluate recommendations  Try implementation in existing systems  Trial  BCO-DMO: R1-11 fit well with current architecture; R12 doable; test as part of DataONE node membership  R2R: curation of original field data and selected subset of post-field products (ship track); so no evolving data  Both working with DataONE as our aggregation system and service provider

9. 9 Data Type Registry (DTR) DTR mission: see if it is possible to make implicit assumptions about data contained in datasets explicit and programmatically share these assumptions using types and type registries DTR outcomes: 1 website and 1 API. The registry website provides a user interface for someone to describe both simple and complex data types used for data within a project. They can also search data types created by others. The API provides a way to programmatically interact with the registry including the ability to import data type descriptions.

10. 10 Description of DTR Outputs 1.Data Type RegistryData Type Registry  A website with a GUI that provides a way for an authorized someone to describe a data types used in data products. 2.Data Type Registry APIData Type Registry API  An API that among other things creates JSON representations of the information about data types. This is a pointer to the API specification implemented in the data type registry mentioned above.

11. 11 Adoption of DTR Outputs  Evaluation  evaluate the registry and API for use in existing data repositories  try using the prototype registry to record a set of data types and then provide some example code that uses the API to access the information in the data type registry  Trial  BCO-DMO, R2R, GeoLink? data type determined by instrument type  R2R already maintains a de facto library of file types for environmental sensor systems in the US research fleet, in collaboration with NCEI and Chronopolis. We could publish this as a formal Data Type Registry

12. 12 PID Information Type (PID-IT) PID-IT goal: Provide a way to harmonize PID information types (and associated information) that are associated PID across disciplines and PID providers. (Also to provide technical solutions) PID-IT persistent outcomes: Types for example use- cases have been registered in the type registry developed in this WG

13. 13 Description of PID-IT Outputs 1.Type Examples and Illustration Use Cases  These are examples of … 2.API Description  A description of the API used to access the PID registry created by this group 3.API Prototype Implementation  A working version of the API connected to the PID registry that has been created 4.Registry Prototype  The registry prototype itself 5.Client demonstrator GUIClient demonstrator GUI  Demonstration of the registry and it’s use via a graphical user interface developed by the group’s intern.

14. 14 Adoption of PID-IT Outputs  Evaluation  evaluate the client registry GUI  Trial  BCO-DMO and R2R PID systems in use: DOI (datasets and expeditions), ORCID, FundRef (US awards), ISNI (global organization), IGSN (global samples), re3data (repository), and domain-specific for instruments and measurements  Possible: R2R, BCO-DMO and NCEI (US ocean archive) joining DataONE; DataONE architecture is well-aligned with PID-IT approach, so perhaps DataONE adopts the PID-IT API and offers that as a service to the community

15. 15 Data Foundations and Terminology (DFT) DFT mission: to understand what the core of the RDA data domain is and then develop definitions of core terms based on data models. This effort is a part of the effort to form agreement on RDA culture. DFT persistent outcomes: 4 Documents and 1 Wiki Tool that summarize the work they have done on Terminology. The wiki tool is intended to be used by other RDA WGs and IGs to extend the terminology terms, etc. beyond those determined to date.

16. 16 Description of DFT Outputs 1.Overview Document – model descriptionsOverview Document  Report on the discussion about a large number of data models 2.Analysis & Synthesis DocumentAnalysis  Report on the analysis of the data models considered by the group 3.Term Snapshot Document  Report on a snapshot of core terms that have been identified 4.Use Cases (1), (2)(1)(2)  Use cases that describe how other working groups use the terms the group has been talked about 5.Semantic Media Wiki Term ToolSemantic Media Wiki Term Tool  Tool to capture initial list of terms and definitions for DFT WG discussions, open for others to use. (it is kind of persistent at this point) 6.Report of Interactions w others about terms  Summary of conversations with ~120 individuals about data in the context of DFT findings.

17. 17 Adoption of DFT Outputs  Evaluation  evaluate core terms in Semantic Media Wiki Term Tool for ocean science domain as they extend our current term reference source  Trial  BCO-DMO: map local system terms to the DFT terms; adding deployment type (cruise, dive, float, experiment),  R2R: already essentially follows this model; publishes Collections that represent a field expedition (research cruise), having Persistent Identifier (DOI).  Challenges  Need dereferenceable term URIs (DTR ?)  Does the Semantic Wiki Tool provide relationships or a way to describe them? Is each term a concept in an ontology (e.g. OWL file)? Governance?

18. 18  Interaction of DFT, PID-IT, DDRI and DTR ??  Registering terms/types at DTR (federated system of distributed, production ready, registries)  What is the appropriate ‘level’ for a registry?  Professional, domain-specific societies? (ASLO, AGU, EGU)  Institutional library?  Community organization (ESIP, OGC, ODIP) ?  Not having operational registries may hinder adoption Opportunity...

19. 19 Practical Policy (PP) PP goals: To enable sharing, revising, adapting, and re- using of computer actionable policies for sharing data, particularly in a data repository and to suggest a set of generic policies to be applied to our data; collect and register practical policies PP persistent outcomes: Practical Policy (PP) WG recommendation package of Policy Examples and Template Workbooks

20. 20 Description of Practical Policy Outputs 1.Policy TemplatePolicy Template  Template that includes a generic set of policies suggesting how they can be implemented within a data system. 2.ImplementationsImplementations  Policy descriptions and implementation details

21. 21 Adoption of Practical Policy Outputs  Evaluation  evaluate the policy template and implementation documents  Trial  Identify policies that should be documented  BCO-DMO and R2R are DOI Publication Agents, but not long-term archives.  Consider documenting our practices (for archive and replication) in a computer-actionable format, so data deposits (to NCEI, Chronopolis, DataONE) can be periodically verified as part of a self-audit process

22. 22 Metadata  Metadata group goals:  Set up a sustainable, community-driven RDA Metadata Standards Directory, designed for users rather than automated tools, that provides brief details for common research data.  Compile a set of use cases that analyze and document the various ways in which metadata can be used (e.g. for discovery, exchange, re- use, etc.)  Metadata group outcomes:  UK DCC Disciplinary Metadata Standards Catalogue http://www.dcc.ac.uk/resources/metadata-standards http://www.dcc.ac.uk/resources/metadata-standards  functional GitHub prototype directory with version control http://rd-alliance.github.io/metadata-directory/ http://rd-alliance.github.io/metadata-directory/

23. 23 Adoption of Metadata Group Outputs  Evaluation  Compare DCC lists with current practices  Identify standards where we currently have none  Identify mismatches and consider addressing them  Current Status  BCO-DMO: ISO 19115-2 (19139 compliant), DIF, CF via NVS, O&M, PROV, RDF, DCAT, Dublin Core, (OAI-ORE soon), (used to do FGDC, but dropped that recently in favor of ISO-19115)  R2R: all of the above plus DataCite and IGSN for samples

24. 24 Publishing Data Working Groups  Publishing Data Workflows  Publishing Data Services  Publishing Data Bibliometrics  Evaluate and Monitor:  R2R and BCO-DMO will evaluate these outcomes and look for ways to implement in our current architecture, or relevant communities (promoting recommended practices)  GeoLink (NSF EarthCube) using Linked Data (Semantic Web technologies) to connect data and publications  meaningful data use statistics would be very welcome

25. 25 Infrastructure Components  supporting the modern research endeavor, is like creating a quilt; a work of art created by a community of practitioners with a shared goal. Each member of the community lovingly, and laboriously designs and constructs their piece of the quilt.

26. 26 Creating the Infrastructure for a Domain  Putting the pieces together to create the ‘whole’ block for a domain.

27. 27 RDA  Combining the domains,  And adding the unifying framework,  to create the global research data quilt (RDA)

28. Thank you!

29. 29  Data Description Registry Interoperability (DDRI)  Infrastructure providers & data librarians to find connections across research data registries and create global views of research data.  Repository Audit and Certification DSA–WDS  A convergent DSA-WDS certification standard will help eliminate duplication of effort, increase certification procedure, coherence and compatibility thus benefitting researchers, data managers, librarians and scientific communities.  RDA/WDS Publishing Data Bibliometrics  RDA/WDS Publishing Data Services  universal interlinking service between data and scientific literature  RDA/WDS Publishing Data Workflows  Wheat Interoperability Others (6 other group outcomes available by Sep 2015)

30. 30 SLIDE TEMPLATE: 3 slides per working group

31. 31 Group name (GRP)  GRP goal:  Description  GRP outcomes:  Description

32. 32 Description of GRP Outputs 1.output  description 2.output  description

33. 33 Adoption of GRP Outputs  Suggestions  evaluate  try using the product  Test cases  BCO-DMO  R2R  GeoLink