1. A Funder's Perspective on Sustainability of Digital Data Repositories
Allen Dearry, PhD
Director, Office of Scientific Information Management
National Institute of Environmental Health Sciences, NIH
 SciDataCon, Denver, CO  September 13 , 2016 

2. The Challenge

3. This is expected to increase 50% this year alone.
National Center for Biotechnology Information
European Molecular Biology Laboratory-European Bioinformatics Institute
US NCBI holds 20 PB of data.
This is expected to increase 50% this year alone.
EMBL-EBI estimates that biological data double every months.
Dark data—12% of data described in published papers is in recognized archives.

4. BD2K Sustainability Workgroup
Function
Develop an NIH vision for economic, technical, and social stewardship of biomedical data repositories.
Goals
Goal 1: Define metrics for evaluation of biomedical data repositories and assess value.
Goal 2: Develop a sustainable lifecycle and coherent funding plan in support of biomedical research data.

5. What Solutions Are We Exploring?
Metrics for review and evaluation
Enhance efficiency and effectiveness of curation
International collaboration on business models
Pilots
Model Organism Databases (MODs)
Commons

6. Request for Information: Metrics to Assess Value of Biomedical Digital Repositories (NOT-OD-16-133)
Which data should be preserved and for how long?
Qualitative and quantitative metrics, such as
Utilization at multiple levels
Indicators of quality and impact
Quality of service
Infrastructure and governance
Case studies demonstrating value, e.g., What would happen in the absence of the repository?
Responses due September 30
Utilization at multiple levels (repository, dataset, data item)
Size and demand of community served
Indicators of repository quality and impact
Publications, citations, altmetrics, patents
Quality of service
Data quality measures; user support and training
Infrastructure and governance
Advisory board; legal structure
Qualitative metrics for the above categories, e.g., use cases/case studies
Case studies demonstrating value
If the repository weren’t available, how would that impact your work?

7. Interagency Workshop on Measuring the Impact of Data Repositories
Organized by Big Data Interagency Work Group
Planning group NIH, NSF, NIST, NARA, DOT, NTIS, DHS
December 8 and 9, Washington DC
Repository managers, data producers & users, funders, publishers, metrics/evaluation experts
Workshop Objectives
Identify current metrics, tools and methodologies for assessing and communicating impact of digital repositories.
Identify technical, social and financial obstacles.
Synthesize results into best practices for both near and long term success.

8. Big Data to Knowledge (BD2K) Enhancing the Efficiency and Effectiveness of Digital Curation for Biomedical Big Data (RFA-LM )
Efficient Tools
Automated or semi-automated approaches
Improve speed and accuracy
Support data annotation at points throughout the research lifecycle
Distributed, crowdsource approaches to curation
Tools and templates to facilitate consistent use of community-defined standards such as common data elements and standards used by archival resources such as GenBank, SRA, Biosample, etc.
Automated or semi-automated approaches to merging (harmonizing) disparate or heterogeneous data sets for purposes of new research.
Approaches that improve the speed and accuracy of extracting metadata information from text or other digital sources, and linking the information to a data set or other digital asset.
Approaches that support data annotation at points throughout the research lifecycle (data gathering, preparation of data for sharing, public sharing of data sets, submission or review of articles supported by data sets, etc.).
Distributed approaches to curation processes that increase the efficiency, completeness, accuracy or quality of the digital asset.

9. Sustainable Business Models for Data Repositories
Organized by OECD Global Science Forum
Cochair with Simon Hodson, CODATA, & Ingrid Dillo, DANS
Landscape survey, July-September
Broad spectrum of 60 worldwide repositories
Characteristics, metrics, income streams, future funding, alternative income, cost optimization
Workshops
1.1 Innovative income streams, November 3 Paris
1.2 Cost restraint, November 4 Paris
2.0 Business models, March 2017 Brussels
Session at SciDataCon, September Denver
Report and recommendations, April 2017

10. Future of Life Sciences and Biomedical Databases
Organized by International Human Frontier Science Program
Information gathering, June-September
20 life science repositories
Funding model, user community, usage, metrics, sustainability challenges, contingency plans
Workshop
Life sciences data resources and the future,” November 18-19, Strasbourg
International data resources, public & private funding agencies, scientific organizations, publishers
White paper
NIH, GA4GH, EBI, academic experts
Data management/curation, QA, IP, commons, DOI, FAIR, sustainable funding, improved efficiencies

11. Example: Model Organism Databases
Highly curated and valuable data
Siloed /Not interoperable
Cumbersome to compute over all the data
Costly to maintain as individual resources I

12. Pilot a new infrastructure model
SGD
FlyBase WB
MGD
ZFIN
GOC
Alliance Genomic Resources
SGD
FlyBase
WormBase
MGD
ZFin
GO Consortium
User confusion for lack of homogeneity
User access interfaces need different navigation skills and data access approaches for each resource
Semantic inconsistencies and different data structures for the same genomic entities
Analyses
human/model organism association for disease and phenotypes
functional annotation
Homology representation
MODs support biomedical research across NIH and international biomedical research
Aim is to support findable, accessible, interoperable and reusable (FAIR) model organism data facilitated by the NIH Commons platform
Different user access & analyses
Redundancy of operations
Standardize interfaces
Standardize curation, display of shared data

13. The Data Commons Framework
Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
Digital Object Compliance
App store/User Interface
Treats products of research - data, software, metadata, workflows, papers etc. - as digital objects
Digital objects exist in a shared virtual space
- Deposit, Manage, Find, Share, and Re-Use digital objects
Conforms to FAIR principles:
Findable
Accessible
Interoperable
Reusable
Detailed description of the Commons Framework can be found at :

14. Data Science at NIH Data Science at NIH allen.dearry@nih.gov 
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