Data Space Overview NSF Site Visit 8 February

Data. Space Overview – NSF Site Visit 8 February 2010 (v 8) 1

Vision • “To bring the dramatic benefits of the Web to scientists – comparable to the benefits the Web has had to commerce and other areas” • . . . Not just in the impact to science, but also a similar distributed federated ecosystem for: – Technology Infrastructure – Organizational Responsibilities • View: Research data-generating institutions and their libraries should play an active role in curating their researchers’ data – Financial and Technical Sustainability – Openness: 3 rd party extension and Open Source development • Support research across all domains, but initially: – Neuroscience – Biological Oceanography 2

Basic Data User Services: Discovery, Quality, Conversion, Integration Data Curation Services: Process, Catalog, Annotate, Preserve Data. Space High-Level Architecture Additional Data User Services : • Data Analytics • Data Visualization Distributed Data Management Services: Security, Replication, Administration Policy Management, Workflow Services Data. Space Services Global Network (Web) Local Network. . . Interface to Legacy Scientific Data Repositories Metadata Repository for Scientific Data Other USA Nodes 3 rd par Multiple Scientific Data Repositories (Data. Space Native Architecture) Basic Workflow Scientist Provides data, preliminary metadata MIT Node Curator Process and ingests data, complete metadata, and policies (e. g. retention) International Nodes 3 rd Party Specialized Data Services Data. Space User Searches (meta)data, accesses/integrates data, analyzes/visualizes data (via Data. Space data services or 3 rd party 3 data services)

Initial Scientific Domains Chosen • Neuroscience and Biological Oceanography – Sciences with complex interdisciplinary sub-domains – Different and diverse types of scientific data • Though some aspects of overlap (genetic data) – Faced with challenges related to • Data expression, encoding, sharing, integration, visualizing, and preserving • Difficult to perform research that crosses sub-domains or requires multi-source data – Can build on existing collections and collaborations • But must also address technical, social, and legal issues • Will bring in additional domains over time 4

Data. Space Organizational Structure (preliminary)

Some Key Goals for First Year • Complete hiring and staffing • Design and development of Data. Space v 1 (Interim architecture) – Build on existing software base (DSpace, Fedora) – Addition of initial Data. Space middleware • Ingest of initial Neuroscience and Biological Oceanography data – Selection/development of ontologies – Recording of metadata (including preservation policies, etc. ) • Establish operational Data. Space v 1 – Service models defined with partner nodes • Design of Data. Space long-term architecture – Initial results from research groups for v 2 • Initial results of Business Development Management Team • Educational and Outreach efforts (i-schools, OCW) 6

Sustainability Approach • Core to Financial Sustainability – Provide maximum value to science – Minimize cost to any one organization by broad distribution • Can actually reduce costs by eliminating duplication and inefficiencies – Build on the long-standing role and sustainability of libraries – Follows Web/Internet value (to both large and small orgs) • Worldwide infrastructure, costs widely shared • Technological Sustainability – Open Source software, multiple implementations possible, and encourage 3 rd party augmentation – Participation of commercial technology company partners • Some Resources: Data. Space Federation, Partner experiences, Business Development Management Team (working with MIT Entrepreneurship Center, E&I students, etc. ) 7

Some Key Features of the Data. Space Proposal • Distributed federated infrastructure for accessibility & long-term preservation – Address privacy, property and data rights, etc. with legal and policy framework • Builds on successful Dspace/Fedora platform • Proposes new top-level internet domain (". arc") • Addresses need for “temporal semantics” and other advanced metadata • Risk mitigation: Research risk: Personnel with extensive experience. Operational risk and sustainability: Distributed design and federated approach. • Public/Private Partnership: Corporate partners help build more sustainable ecosystem and ensure sustainability, MIT Entrepreneurship Center, etc. • Expert Advisory Board: Diverse fields (i. e. science, law, business, technology, libraries, and digital preservation) advise and promote the project • Advances scholarly communications through data/publication integration • Advances educational technology through data/courseware integration • Outreach to minority and pre-college student, underserved small and medium research groups. Data. Space will be a truly transformational project 8

Multi-disciplinary team of Principal Investigators • Hal Abelson, MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) • Ed De. Long, MIT Departments of Civil and Environment Engineering and Biological Engineering • John Gabrieli, MIT Department of Brain and Cognitive Sciences • Stuart Madnick, MIT Sloan School of Management & School of Engineering • Mac. Kenzie Smith, MIT Libraries • Marilyn T. Smith, Director, MIT Information Systems & Technology (IS&T) [replaces Jerry Grochow] 9

Diverse and Experienced Senior Personnel • Timothy Berners-Lee (W 3 C, WSRI) • Alon Halevy (Google) • Geneva Henry (Rice University) • Mei Hsu (HP) • David Karger (MIT) • Michele Kimpton (DSpace Foundation) • Thomas Malone (MIT) • Dejan Milojicic (HP) [replaces John Erickson] • Joe Pato (HP) • Terry Reese (Oregon State University) • Michael Siegel (MIT) • Stephen Todd (EMC) • Tyler Walters (Georgia Tech) • Danny Weitzner (W 3 C, WSRI) • Steve White (Microsoft) [addition to team] • John Wilbanks (Science Commons) • Wei Lee Woon (MIST, Abu Dhabi) 10

Advisory Board • Christine L. Borgman, Department of Information Studies, Graduate School of Education and Information Science, UCLA • Randy Buckner, Psychology, Harvard University • Scott Doney, Marine Chemistry & Geochemistry, Woods Hole Oceanographic Institution • Keith Jeffery, European Research Consortium of Informatics and Mathematics (ERCIM) and UK Rutherford Appleton Laboratory • Liz Lyon, UKOLN and UK Digital Curation Centre • Ed Roberts, Management of Technology, MIT Sloan School of Management and MIT Entrepreneurship Center • Pam Samuelson, School of Information and School of Law , UC Berkeley • Dan Schutzer, Financial Services Technical Consortium (FSTC) • Andrew Treloar, ARCHER Project, Australian National Data Service, Monash University, Australia • Wanda Orlikowski, Information Technologies and Organization Studies , MIT Sloan School of Management 11

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Backup Slides 13

1. New types of science enabled • Enhance scientific interdisciplinarity and innovation via standards-based data architecture and broad adoption • Disciplines: Neuroscience and Biological Oceanography (a) Science and education goals help – Library and Computer Science goals: minimize duplication of effort, maximize access to prior work, improve interoperation and quality – Education goals: disseminated through multiple means (OCW) to enable semantic tagging of data and reuse of data (b) Metrics of Success – Usage: number groups contributing and using, amount and diversity of data shared and used, etc. – Impact: Publications, discoveries 14

Neuroscience Domain • Address questions, such as “Variation of cognitive and emotions traits due to age? ” • Future requires access to large datasets, but – Broadly distributed across many organizations – Diverse types: DTI, f. MRI, structural MRI, VBM – Difficult to aggregate and annotate • Initial organizations include – Martinos Imaging Center (at MIT) – Center for Advanced Brain Imaging (Georgia Tech) – Collaboration with Microsoft 15

Biological Oceanography Domain • Address questions such as “How does change in ocean current cause proliferation of microbial groups that, in turn, influence flux of carbon into and out of the sea? ” • Need to interrelate diverse datasets – Scale: from genome to biomes – Types: 4 D physical and biological oceanographic, satellite, genomic, metagenomic, taxonomic, nutrient analysis, bio-optical – From diverse sources • Data. Space will enable research not possible today 16

2. Value to Previous Investments • For selected domains: Resources to organize, annotate, archive, and publish existing data – – Curated by partnership with library data curators Improve collaborations, e. g. , C-MORE (interrelate difficult) Address complex legal, political, and social realities Sustainability by providing significant new value to scientists (e. g. ease of search, data integration, reuse) (a) What data contributors gain from Data. Space – More efficiently archive and reutilize their own data – Able to utilize vast amounts of data from other sources – Over time, will be respected academic achievement (citations) (b) Investment utilized and enhanced – Significant prior R&D by team members, e. g. , Dspace, temporal semantics, data quality and provenance, policy and legal, etc 17

3. Barriers to Implementation and Adoption • In past, scientists often don’t participate because: – Insufficient time and expertise (which we address via better functionality and assistance from curators) – Insufficient value back (which we address through re-use, etc) • Some points: – Demonstrable ease-of-use and value • Especially sciences that are struggling with these problems • Examples from Neuroscience and Biological Oceanography – – – Dedicated data curators Data. Space Federation to represent collective needs Openness: encourages scientific innovation and evolution Support for policy and legal issues Team has experience evolving systems (W 3 C, Dspace, etc. ) 18

4. Cyberinfrastructure, Technical Sustainability • Much of Data. Space cyberinfrastructure builds on prior work (e. g. Dspace) and adds: (a) archive, (b) annotate to enable discovery and re-use, (c) interoperate with Ed Tech, “citizen science, ” etc. • Technical sustainability: Software free and open source – establish architecture and standards – Project will provide at least one reference implementation – Enable multiple implementations (including commercial) • Will develop cost and service models as exemplars Institutions already expand large amounts Data. Space will streamline, rationalize, distribute costs Libraries have stood the test of time Additional business models • 1 st Year Goal: Initial system and ingest of data, test interop – – 19

5. Manage Program, Providers, International • Experience with highly distributed projects (Dspace) • Management – see organization chart – Multiple levels and multiple sub-groups – Public/private partnership to insure industrial adoption and relevance to other sectors – Added management and data expertise from Advisory Board • Data providers and assured participation – Data initially from partners (Georgia Tech, MIT, OSU, Rice) • Already communicating with scientists – Then extend more broadly, initially to the DSpace community • International Counterparts: (1) direct collaboration (Dura. Space), (2) International partner (MIST), (3) International corporations (EMC, Google, HP, Microsoft), (4) Advisory Board, (5) indirect collaborations (C-MORE) 20