biomedical and healthcare Data Discovery Index Ecosystem

Transcription

1 biomedical and healthcare Data Discovery Index Ecosystem Maryann E. Martone, Ph. D. University of California, San Diego

2 How is NIH Tackling Big Data? Associate Director for Data Science (ADDS) Scientific Data Council (SDC) Philip Bourne Big Data to Knowledge (BD2K) BD2K slides courtesy of Dr. Ron Margolis

3 BD2K: Big Data to Knowledge BD2K - a trans-nih initiative established to enable biomedical research as a digital research enterprise, to facilitate discovery and support new knowledge, and to maximize community engagement. BD2K aims to develop the new approaches, standards, methods, tools, software, and competencies that will enhance the use of biomedical Big Data by: Facilitating broad use of biomedical digital assets by making them discoverable, accessible, and citable Conducting research and developing the methods, software, and tools needed to analyze biomedical Big Data Enhancing training in the development and use of methods and tools necessary for biomedical Big Data science Supporting a data ecosystem that accelerates discovery as part of a digital enterprise 3

4 The Data Discovery Index (DDI) Data Catalog Data Discovery Index An index of data, not a repository... designed to enhance discovery of data through detailed indexing of shared datasets deposited at many sites but with sufficient metadata to allow for discoverability and accessibility and with identifiers to facilitate citation Should be developed in a framework that supports rapid innovation, testing, failure as a learning tool, and improvement of the concept Should enable trans-disciplinary data indexing and queries both simple and complex from users at all levels Combines intuitive searching and visualization tools to enable users to find datasets of interest and relevance to specific scientific questions Effective stewardship... to assure that the data remain of value to all stakeholders and users.

5 Data Discovery Index Coordination Consortium (DDICC) Designed to explore how best to increase: Discoverability Accessibility Citability of biomedical big data By posing and testing questions that will lead to an NIH DDI Through an integrated consortium centered on community engagement, including: Task forces, administrative supplements, pilot projects, and workshops to engage the community

6 biocaddie October,

7 biocaddie : Projected Deliverables Validation of approaches to foster discoverability of data Tools and means for citation Answers to key questions: What kind of identifier works best? How to foster requisite data sharing and allocation of credit? Best ways for engaging the community What is the role for existing efforts by data repositories, data integration efforts and publishers Prototype that integrates lessons learned, possible solutions and a path forward Integration with other BD2K efforts (e.g. Centers, Software Discovery Index) and the Commons

8 biocaddie: Dr. Lucila Ohno-Machado Steering Committee Chair: Ohno-Machado NIH: Ian Fore, Jennie Larkin, Ron Margolis Dawei Lin, Alison Yao (program officers) Task Force and Community Engagement leaders Management UCSD: Mahera (general) Farcas (technical) Community Engagement UCSD: Martone, Hagstrom Taskforce 1: Data Shop UT Houston: Xu Taskforce 2: Data Showcase U Michigan: Alter Taskforce 3: Data Market Oxford U: Sansone Supplements Collaborating PIs Pilot Projects Pilot Projects Pilot Projects NIH-funded projects Ontologies and Standards Visualization and User Needs Incentives and Publishing Data and Expert Knowledge

9 biocaddie as the DDI Pilot projects Test bed for possible solutions Task forces Identification of needs Workshops Community-based solutions Supplements Early adopters

10 Task Forces Data Shop Data Showcase Data Market Discoverability and indexing Data access; Meeting User Needs Citation and business cases Data producers and informaticians Data consumers Data disseminators

11 Data Shop Pilots Year 1 Linking Publications and Underlying Data Using NLP Hua Xu, UT Houston Rating Relationships in Imaging Reports: Machine vs. Crowdsourcing Approaches Ricky Taira, UCLA 11

12 Data Showcase Pilots Year 1 Recommendation System: Machine Learning and Crowdsourcing Xiaoqian Jiang, UCSD Intelligent Search and Visualization of Data Todd Johnson, UT Houston 12

13 Data Market Pilot Year 1 Citation and Data Access Metrics for a Molecular Data Bank and Related Resources Peter Rose, UCSD Chun-nan Hsu, UCSD 13

14 Early Adopters Cardiovascular research grid: Natural language processing for clinical and translational research Generation of a centralized and integrated resource for exposure data A Hub for the Nuclear Receptor Signaling Atlas

15 Community Engagement Community engagement is a key feature of DDICC approach, and the DDICC will be expected to foster the wide-scale collaborations and partnerships between and among key stakeholders to demonstrate feasibility of the concept.

16 Community Engagement: FORCE11 Future of Research Communications and e-scholarship

17 What is FORCE11? Future of Research Communications and E- Scholarship: A grass roots effort to accelerate the pace and nature of scholarly communications and e-scholarship through technology, education and community Why 11? We were born in 2011 in Dagstuhl, Germany Principles laid out in the FORCE11 Manifesto FORCE11 launched in July 2012

18 Who is FORCE11? Scholars Publishers Science Library and Information scientists Social Sciences Policy makers Humanities Tool builders Funders Anyone who has a stake in moving scholarly communication into the 21 st century

19 FORCE11 BioCADDIE CE Working Group Alejandra Gonzalez-Beltran University of Oxford Amarnath Gupta University of Clifornia, San Diego Anita de Waard Elsevier Research Data Services Carol Bean Stanford University Chris Mungall LBNL David Eichmann University of Iowa Eduard Hovy Carnetie Mellon University, Language Technologies Institute Harry Hochheiser University of Pittsburgh Ian Fore US National Cancer Institute, National Institutes of Health Jeffrey Grethe University of Clifornia, San Diego Mark Musen Mark Musen, Stanford University Maryann Martone University of California, San Diego Melissa Haendel Oregon Health & Science University Merce Crosas IQSS, Harvard University Michel Dumontier Stanford University Paolo Ciccarese Massachusetts General Hospital and Harvard Medical School Philippe Rocca-Serra Oxford e-research Centre, University of Oxford Puneet Kishor Creative Commons Satrajit Ghosh MIT Stephanie Hagstrom University of California, San Diego Susanna-Assunta Sansone University of Oxford and Nature Publishing Group Tim Clark Massachusetts General Hospital and Harvard Medical School William Gunn Mendeley Join us:

20 Community engagement supports the goals of biocaddie through... Performing requirements analysis Evaluating outcomes through broad testing Advancing new perspectives Participating in the task forces and pilot projects Engaging with other communities of practice Scholars Publishers Scienc e Library and Information scientists Social Science s Humanitie s Tool builders Funders Policy makers 20

21 Questions for community input What are possible models for a DDI? What are existing efforts, both national and international, that can be used to inform the creation of a DDI? How can we work with them? How much curation is needed and by whom? What are the use cases? Who needs a DDI? Researchers Clinicians Funders Administrators Industry What are the issues surrounding access to data? Where are the data? If PubMed had been built after the internet and search engines had matured, how would it have been built?

22 The Current Biomedical Data Ecosystem Publishers DDI Community Repositories Software General Specific Data Papers Figure s and tables Journals Supplem entary material Reposito ries Web of Data Specific Curation Analysis services Institutional Repository Single source Databases (1000 s) Consortia ELN Laboratories Web Sites LIMS Researchers Registries and Catalogs Institutional Repository Institutional Repository Libraries Institutional Repository Institutional Repository Clinicians Data Federations Data Federations Data Federations Governments Are these are interchangeable? Better coordination is needed Clinical Data Warehouses EHR Data Federations

23 What the Commons Is and Is Not The Commons is Not: A database A physical location New large infrastructure built by the government Owned by any one group The Commons is: A conceptual framework A collaboratory A set of shared rules

24 NIH Commons Pilots Testing the cloud as an environment for the Commons A business model for the commons

25 Join task forces Participate! Become a biocaddie collaborator Submit proposals for pilot projects Let us know about what you do Regular community webinar series Let us know where there are opportunities for interactions, e.g., joint workshops More information at 25

26 Formerly Beyond the PDF Conference/Unconference Brings together all stakeholder groups as equals: scholars, publishers, librarians, technologists First biocaddie community engagement workshop (pre-meeting)