Guest Editors' Introduction: Internet Access to Scientific Data

IEEE Internet Computing, 2005

Proc. Am. Soc. Info. Sci. Tech., 2006

Preservation of data for long-term use will require data management strategies that include curation and preservation planning and implementation. While data management and curatorial activities have been an integral part of some scientific domains for years (see for example, high energy particle physics), these are new concepts in other areas of science. Concepts such as provenance, representation for re-use, and work-flow capture are rarely understood, let alone addressed. By bringing together theories and best practices from archives, museum studies, and library and information science (LIS), it is possible to address these problems. on current research into scientific data management problems, this panel will consider questions about sharing and re-use of data, curation and preservation, and the intersection of scientific production and scholarly communication. Our research explores information work and problems across a range of scientific areas in the life and physical sciences, including genomics, neuroscience, ecology, and earth science. As more scientific work products are shifted to open or shared data collections (including archives, repositories and databases), we will need to understand how these systems are implemented and used to support collaboration and discovery, as well as scholarly and scientific communication.

2014

Science Data Repositories (SDRs) have been recognized as both critical to science, and undergoing a fundamental change. A websample study was conducted of 100 SDRs. Information on the websites and from administrators of the SDRs was reviewed to determine salient characteris-tics of the SDRs, which were used to classify SDRs into groups using a combination of cluster analysis and logis-tic regression. Characteristics of the SDRs were explored for their role in determining groupings and for their relationship to the success of SDRs. Four of these char-acteristics were identified as important for further investi-gation: whether the SDR was supported with grants and contracts, whether support comes from multiple spon-sors, what the holding size of the SDR is and whether a preservation policy exists for the SDR. An inferential framework for understanding SDR composition, guided by observations, characteristic collection and refinement and subsequent analysis on elements of group member-s...

IEEE/ACM Joint Conference on Digital Libraries, 2014

The promise of technology-enabled, data-intensive scholarship is predicated upon access to knowledge infrastructures that are not yet in place. Scientific data management requires expertise in the scientific domain and in organizing and retrieving complex research objects. The Knowledge Infrastructures project compares data management activities of four large, distributed, multidisciplinary scientific endeavors as they ramp their activities up or down; two are big science and two are small science. Research questions address digital library solutions, knowledge infrastructure concerns, issues specific to individual domains, and common problems across domains. Findings are based on interviews (n=113 to date), ethnography, and other analyses of these four cases, studied since 2002. Based on initial comparisons, we conclude that the roles of digital libraries in scientific data management often depend upon the scale of data, the scientific goals, and the temporal scale of the research projects being supported. Digital libraries serve immediate data management purposes in some projects and long-term stewardship in others. In small science projects, data management tools are selected, designed, and used by the same individuals. In the multi-decade time scale of some big science research, data management technologies, policies, and practices are designed for anticipated future uses and users. The need for library, archival, and digital library expertise is apparent throughout all four of these cases. Managing research data is a knowledge infrastructure problem beyond the scope of individual researchers or projects. The real challenges lie in designing digital libraries to assist in the capture, management, interpretation, use, reuse, and stewardship of research data.

Many environmental scientists today need to assemble, use, share and save data from a diverse set of sources. These "synthesis" efforts are often interdisciplinary and blend data from ground-based sensors, satellites, field observations, and the literature. At even moderate scales of both data size and diversity, the cost and time required to find, gather, collate, normalize, and customize data in order to build a synthesis dataset can be daunting at best.. By explicitly identifying and addressing the different requirements for each data role (author, curator, data valet, publisher, and consumer), our data management architecture for large-scale shared environmental data enables the creation of such synthesis datasets that continue to grow and evolve with new data, data annotations, participants, and use rules. We show the effectiveness of our approach in the context of the FLUXNET Carbon-Climate Synthesis Dataset, one of the largest ongoing biogeophysical field experiments.

2011

The role of libraries is to collect, preserve and disseminate the intellectual output of the society. This output includes books and serials as well as the digital versions of the same.

Journal of the American Society …, 2010

PLoS ONE, 2011

Background: Scientific research in the 21st century is more data intensive and collaborative than in the past. It is important to study the data practices of researchers -data accessibility, discovery, re-use, preservation and, particularly, data sharing. Data sharing is a valuable part of the scientific method allowing for verification of results and extending research from prior results.

Proc. Am. Soc. Info. Sci. Tech., 2007

The seemingly simple task of reusing data for science education relies on the presence of scientific data, scientists willing to share, infrastructure to provide access, and mechanisms to share between the two disparate communities of scientists and science students. What makes sharing between scientists and science students a special case of data sharing, is that all of the implicit knowledge attending the data must pass along this same vector. Our work at the Center for Embedded Networked Sensing studying aspects of this data reuse problem has shown us a rough outline of how the future of this data sharing will look. Our approach is to start from the prospective of the scientists, looking for opportunities to support scientific research, and then leveraging the data for reuse by education. The investment needed to capture high quality scientific data necessitates the consideration of reuse by the general population as well as other interested scientific parties.

Proceedings of the Association for Information Science and Technology, 2020

Scientific data repositories (SDRs) provide a way for scientists to share data through data deposition and reuse of deposited data. Over the last twenty-plus years, hundreds of scientific SDRs have become available. This research examines 132 SDRs. This study assesses if the information available in the SDRs aligned with what scientists need to determine data reusability and if the SDRs enforce FAIR principles.