Some challenges and issues in managing, and preserving access to, long-lived collections of digital scientific and technical data

Anderson, William L.

doi:10.2481/dsj.3.191

Cited by 25 publications

(15 citation statements)

References 10 publications

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“…“The retention of selected and appraised data raises other issues. These include maintenance of data quality, enforcement of data security, and, migration of data sets to current, available and maintainable, hardware and software systems.” (Anderson, 2004)…”

Section: Scientific Data Management Maturity Levelsmentioning

confidence: 99%

A capability maturity model for scientific data management: Evidence from the literature

Crowston

Qin

2011

Proc. Am. Soc. Info. Sci. Tech.

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In this paper, we propose a capability maturity model (CMM) for scientific data management (SDM) practices, with the goal of supporting assessment and improvement of these practices. The model describes key process areas and practices necessary for effective SDM. Appropriate SDM practices were identified by content analysis of papers about SDM and include both those specific SDM practices and generic process management practices. The CMM further characterizes organizations by the level of maturity of these processes, meaning the organizational capability to reliably perform SDM processes. The model may be useful to organizations in evaluating and planning improvements to their SDM practices. KeywordsScientific data management, capability maturity model.

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Section: Scientific Data Management Maturity Levelsmentioning

confidence: 99%

A capability maturity model for scientific data management: Evidence from the literature

Crowston

Qin

2011

Proc. Am. Soc. Info. Sci. Tech.

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“…Moreover, [12] points out that "metadata descriptions are as important as the data values in providing meaning to the data, and thereby enabling sharing and potential future useful access". Since there is no conceptual model for an experimental collection, metadata schemes for describing it are also lacking.…”

Section: Conceptual Model and Metadatamentioning

confidence: 99%

The Importance of Scientific Data Curation for Evaluation Campaigns

Agosti

Nunzio

Ferro

2007

Lecture Notes in Computer Science

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Information Retrieval system evaluation campaigns produce valuable scientific data, which should be preserved carefully so that they can be available for further studies. A complete record should be maintained of all analyses and interpretations in order to ensure that they are reusable in attempts to replicate particular results or in new research and so that they can be referred to or cited at any time. In this paper, we describe the data curation approach for the scientific data produced by evaluation campaigns. The medium/long-term aim is to create a large-scale Digital Library System (DLS) of scientific data which supports services for the creation, interpretation and use of multidisciplinary and multilingual digital content

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“…A guide to work done in digital archive preservation through CCSDS/ISO is provided at http://nssdc.gsfc.nasa.gov/nost/isoas/. The OAIS reference model has been widely adopted as a key framework in the discussion and implementation of digital preservation and management systems (Anderson, 2004).…”

Section: Data-intensive Missions and The New Data Environmentmentioning

confidence: 99%

eScience and archiving for space science

Eastman

Borne

Green³

et al. 2005

Data Sci. J.

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A confluence of technologies is leading towards revolutionary new interactions between robust data sets, state-ofthe-art models and simulations, high-data-rate sensors, and high-performance computing. Data and data systems are central to these new developments in various forms of eScience or grid systems. Space science missions are developing multi-spacecraft, distributed, communications-and computation-intensive, adaptive mission architectures that will further add to the data avalanche. Fortunately, Knowledge Discovery in Database (KDD) tools are rapidly expanding to meet the need for more efficient information extraction and knowledge generation in this data-intensive environment. Concurrently, scientific data management is being augmented by content-based metadata and semantic services. Archiving, eScience and KDD all require a solid foundation in interoperability and systems architecture. These concepts are illustrated through examples of space science data preservation, archiving, and access, including application of the ISO-standard Open Archive Information System (OAIS) architecture.A confluence of new technologies (internet, XML and Web Services, broadband networking, high-speed computation, distributed Grid computing, ontologies and semantic representation) is dramatically changing the data landscape. Distributed data and computing resources are more and more being linked together in virtual observatories and grid systems. Focusing only on possibilities emerging from virtual observatories (e.g., NVO, 2005), however, may distract us from the prime objective -support of science research.This confluence of new technologies provides a greatly enhanced synergism, illustrated in Figure 1, between robust data sets (Data), state-of-the-art models and simulations (Model), high-data-rate sensors (Sensor), and highperformance computing (HPC). In the late 20 th century, a major revolution in chaotic systems and nonlinear dynamics arose because of a new coupling of models and high-performance computing. Similarly, we expect that the emerging linkage of rich data sets, high-performance computing, models and sensors will lead to even greater scientific impact. Data-driven science is already advancing in numerous domains as a separate research discipline (e.g., Bioinformatics and Geographic Information Systems) in the same way that computational science has become an established research endeavor.The need for this Data-Model-HPC-Sensor synergism derives from the following set of drivers.

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Some challenges and issues in managing, and preserving access to, long-lived collections of digital scientific and technical data

Abstract: One goal of the Committee on Data for Science and Technology

Cited by 25 publications

References 10 publications

A capability maturity model for scientific data management: Evidence from the literature

A capability maturity model for scientific data management: Evidence from the literature

The Importance of Scientific Data Curation for Evaluation Campaigns

eScience and archiving for space science

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