1. Wrap-Up – NSF Site Visit 8 February 2010
DataSpace
Welcome and Thank you.
Provided considerable material in Full Proposal with the 6 Appendices plus materials provided for Reverse Site Visit.
- Will not try to repeat all those materials
Instead will:
- Focus on the key points of our proposal
- Try to address issues raised in Panel feedback
- Introduce as many of our team (about 17) as possible – since that is important strength of our proposal
(v8)

2. Some Key Features of the DataSpace 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 to minimize base risk
Proposes new top-level internet domain (".arc")
Addresses need for “temporal semantics” and other advanced metadata
Experienced research team: Personnel with extensive experience and research and prototypes to be deployed.
Public/Private Partnership and Management School: Corporate partners to 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 (in cooperation with OCW), underserved small and medium research groups.
DataSpace will be a truly transformational project
Intellectual merit: distributed infrastructure for data archives and a model for institutionally-based support services for digital archiving, long-term preservation, and open use.
Scientific progress: Data initially from neuroscience and biological oceanography later others to insure interdisciplinarity and interoperability of the platform design. Support coordinated storage facilities, encoding standards, cataloging, curation, and retrieval methodologies leads to avoid needless duplication of effort and enablinginformation exchange and important discoveries.
Open Access: Tempered by demands of privacy, property rights, data rights, etc. with legal and policy framework
Exemplars: Archives at leading research universities , deployed and federated. Address data life-cycle issues across variety of disciplines and time span.
Some Unique issues incorporated: Builds on successful DSpace platform and open source model; proposes creation of new top-level internet domain (".arc"); addresses 'temporal semantics' recognizing that in addition that there will be changes in the meaning of the semantics of the data.
Diverse and expert personnel: From ten geographically distributed organizations, represent a diversity of skills (including life scientists, environmental scientists, computer scientists, library scientists and business management experts). Many are uniquely qualified experts in their specific fields.
Risk mitigation: Research risk: Personnel have extensive experience and results in the each of the research areas proposed. Operational risk and sustainability: Distributed design and federated approach to policy. For sustainability, takes the approach that research data-generating institutions should play an active role in curating their own data, and that cross-sector infrastructure adoption will spread costs and risk beyond research institutions.
Assessment: Assess the project technically, socially, and economically. Addresses the need for platform independence by designing standards and protocols, and by actively encouraging cross-sector adoption.
Public/Private Partnership: Brings significant technology corporate expertise into the research enterprise to insure realistic, real-world, scalable solutions and proven innovation-to-market strategies to minimize project risk.
Expert Advisory Board: Ten recognized experts from all fields represented in the project (i.e. science, law, business, technology, libraries, and digital preservation) will advise the project over its lifespan.
Organizational Structure and Plan for Expansion/Evolution: Management Board, Advisory Board, and DataSpace Business Development Management Team, and DataSpace Federation.
Broad impact: Model for archival data management in a variety of scientific communities. Incorporate data into coursework, such as Open CourseWare, and linking underlying data to published research articles. Outreach to minority and pre-college students to enhance and broaden their resources as well as underserved small research groups. Potential to improve the use of science by governmental decision makers. Preserving science data for the future can have huge benefits to society. DataSpace is capable of being a truly transformational project.

3. My dean wanted to address you, but we could not find a mutually feasible time. So he asked me to give you two messages:
Welcome to MIT.
He wanted to note that although Neurosciences and Biological Oceanography are our initial science data domains that the Sloan School has lots of social science data and anxious to become a user. One particularly interesting multi-disciplinary project is the Global Climate Change model which uses data from atmospheric and oceanographic sources combined with economic data – that would greatly benefit from DataSpace.