108  ·  Representative Documents:  Policies, Procedures, Guidelines
Johns Hopkins University
Data Conservancy: Objectives
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Objectives
In 2009, Data Conservancy answered the National Science Foundations call to create a world where “digital data are routinely deposited in
well-documented form, are regularly and easily consulted and analyzed by specialists and non-specialists alike, are openly accessible while suitably protected,
and are reliably preserved.”
In their charge to reach this goal, DC researchers have revealed that cross-disciplinary research cannot be accomplished by the imposition of technical
standards but rather through careful negotiation, long-term understanding, and localized demonstrations of benefits for current scientific problems. This
realization has prompted DC to acknowledge the size of the problem space and include proofs of concept as part of our prototyping.
Implementation
Data Conservancy (DC) utilizes a unique approach reflective of our overarching vision through four major components: diversity of domain sciences, data
preservation, educational programs, and library-led organizational framework.
1) Diversity of Domain Sciences By engaging a varied spectrum of science domains astronomy, earth sciences, life sciences, and social sciences,
DC is able to explore various types of data structures, deep investigations of the exemplar community of astronomy, and broad examinations of disciplines
typically described as “small science.” While this multi-disciplinary nature represents an important opportunity to comprehensively examine scientific needs,
identifying the optimal pathway for supporting and promoting interdisciplinary science requires additional research.
2) Data Preservation DC architecture maps onto the Open Archival Information System (OAIS) reference model for digital preservation and thus
allows the DC infrastructure to ingest data through multiple modes.
Preservation entails preparing content with appropriate representation information, context, metadata, and fixity such that someone other than the
producer of the data can access, use, and properly interpret them. It also ensures long-term preservation of data through not only the bits, but also format and
media migrations, or other actions consistent with an overall policy framework. DC’s approach should allow for remote curation of data, use of preservation
services from other providers, and flexibility as research results inform technical future infrastructure development.
3) Capacity Building DC emphasizes workforce development and broadening participation through a unique set of educational programs. DC
features a comprehensive set of institutes, summits, fellowships, and internships related to education and outreach. DC partners Illinois and UCLA have
developed new courses and curriculum around the paradigms of Data Conservancy. Additionally, DC utilizes data scientists as mentors for recent graduates,
post-docs, and Library staff persons acting as data scientists. This cluster of data scientists embedded in multiple science teams and the Library represents a
unique opportunity to learn lessons and apply findings from across the DC network.
4) Libraries as Cornerstone for Sustainable Infrastructure DC will represent a blueprint for research libraries in the data age. DC will bolster
the resource base and capacity of research libraries toward data curation. In cases where research libraries may not have local capacity, DC could provide
services on a fee basis thereby providing another revenue stream.
The Sheridan Libraries (SL) at Johns Hopkins University has been a leader of digital libraries and preservation for over a decade and has already led
a major long-term funding effort that has resulted in a draft sustainability plan, an initial service stack, and a business plan that has been submitted to JHU
central administration.
Vision for the Next Three Years
The first 18 months of DC were focused on prototyping, which have created the foundation for full-fledged preservation, improved conduct of science,
and developed greater insights into current science and frameworks for new forms of science. In the next three years, DC will:
Augment the open and flexible architecture for data curation and data synthesis.
Extend the current data model or define new data models.
Develop additional pilots and proofs of concept.
Research the full problem space of CI development and cross-disciplinary science.
Strengthen connection points between DC socio-technical research and infrastructure.
Create a DC operational environment that provides data management support.
Build capacity through continued community engagement of various stakeholders.
Expand upon initial sustainability planning through case studies and further market analysis.
The Data Conservancy is sponsored by the National Science Foundation under DataNET award OCI0830976.
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