Journal ArticleDOI
The central role of metadata in a science data literacy course
Jian Qin,John D'Ignazio +1 more
TLDR
An NSF-funded project at the Syracuse University School of Information Studies examined this changing environment and directed specific attention to digital data management practices and metadata proved to have a central role in how scientists operate in the e-science information environment.Abstract:
Science research is increasingly computer and network enabled and referred to as e-science. The change has had an impact on the information environment in which scientists across disciplines operate to conduct their research. This paper reports on an NSF-funded project at the Syracuse University School of Information Studies (2007–2009) that examined this changing environment and directed specific attention to digital data management practices. A local faculty survey of data management practices and attitudes was conducted, as was a scan of related courses at peer institutions. Knowledge about data management in e-science was used to design a new course addressing data-related literacy for science students and teach them skills for managing data created as part of the scientific research process. Throughout the project, metadata proved to have a central role in how scientists operate in the e-science information environment and to be a key component of data literacy in the e-science environment.read more
Citations
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Journal ArticleDOI
Application of STEM learning approach through simple technology to increase data literacy
Journal ArticleDOI
Pursuing Best Performance in Research Data Management by Using the Capability Maturity Model and Rubrics
TL;DR: The development of a capability maturity model (CMM) for RDM is described, which provides guidance on where effort is needed to improve the practice of RDM.
Research Data Curation Practices in Institutional Repositories and Data Identifiers
TL;DR: Time is essential information in research data, but identifiers are not yet assigned to the information, so the importance of all the changes that affect the digital objects is emphasized.
Journal ArticleDOI
The Data Engagement Opportunities Scaffold: Development and Implementation
Journal ArticleDOI
Curriculum Data Deep Dive: Identifying Data Literacies in the Disciplines
TL;DR: Klenke et al. as mentioned in this paper evaluated and examined Data Literacy (DL) in the supported disciplines of four liaison librarians at a large research university and found that the most common DLs being taught in courses are Reading, Interpreting, and Evaluating Data, and Using Data.
References
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Posted Content
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Lineage retrieval for scientific data processing: a survey
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TL;DR: Based on a comprehensive survey of lineage research and previous prototypes, a metamodel is presented to help identify and assess the basic components of systems that provide lineage retrieval for scientific data products.
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Scientific data management in the coming decade
Jim Gray,David T. Liu,Maria Nieto-Santisteban,Alexander S. Szalay,David J. DeWitt,Gerd Heber +5 more
TL;DR: In this article, the authors propose algorithms that can simultaneously deal with huge datasets and that can find very subtle effects, finding both needles in the haystack and finding very small haystacks that were undetected in previous measurements.
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A Case Study on the Use of Workflow Technologies for Scientific Analysis: Gravitational Wave Data Analysis
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Information Literacy, Statistical Literacy and Data Literacy
TL;DR: In this article, the evaluation of information is a key element in information literacy, statistical literacy and data literacy, and more attention is needed on how these three literacies relate and how they may be taught synergistically.