We must all accept that science is data and that data are science, and thus provide for, and justify the need for the support of, much-improved data curation. (Hanson, Sugden, & Alberts) 
 
Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data sharing practices in other fields vary widely. Moreover, research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice. © 2012 Wiley Periodicals, Inc.

科研数据共享的挑战 (The Conundrum of Sharing Research Data)

This is a thought piece on data-intensive science requirements for databases and science centers. It argues that peta-scale datasets will be housed by science centers that provide substantial storage and processing for scientists who access the data via smart notebooks. Next-generation science instruments and simulations will generate these peta-scale datasets. The need to publish and share data and the need for generic analysis and visualization tools will finally create a convergence on common metadata standards. Database systems will be judged by their support of these metadata standards and by their ability to manage and access peta-scale datasets. The procedural stream-of-bytes-file-centric approach to data analysis is both too cumbersome and too serial for such large datasets. Non-procedural query and analysis of schematized self-describing data is both easier to use and allows much more parallelism.

Scientific Data Management in the Coming Decade

Researchers increasingly need to integrate the disposition, management, and curation of their data into their current workflows. However, it is not yet clear to what extent faculty and students are sufficiently prepared to take on these responsibilities. This paper articulates the need for a data information literacy program (DIL) to prepare students to engage in such an "e-research" environment. Assessments of faculty interviews and student performance in a geoinformatics course provide complementary sources of information, which are then filtered through the perspective of ACRL's information literacy competency standards to produce a draft set of outcomes for a data information literacy program.

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Determining Data Information Literacy Needs: A Study of Students and Research Faculty

Abstract The growing importance of data in society in general and scientific domains in particular, mirrored in the Open Data initiative and in the advent of eScience, requires public, school and academic libraries to contribute to both data and information literacy, as part of their mission to further knowledge and innovation in their respective fields of action. No specific library standards have been proposed to date, however, and most research studies conducted adopt a partial view of data literacy, stressing only the components needed in any given context. The present paper aims to contribute to the advancement of data literacy with the proposal of a set of core competencies and contents that can serve as a framework of reference for its inclusion in libraries’ information literacy programs. The various definitions of data literacy are discussed, the coverage of the competencies listed in information literacy standards is described, and the competencies considered in the experiments conducted to date in education and libraries are identified. The conclusion drawn is that the model proposed can favour the development of data literacy support resources and services. Topics for further research are also specified.

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Incorporating Data Literacy into Information Literacy Programs: Core Competencies and Contents

Purpose – The role of data literacy is discussed in the light of such activities as data a quality, data management, data curation, and data citation. The differing terms and their relationship to the most important literacies are examined. The paper aims to discuss these issues. Design/methodology/approach – By stressing the importance of data literacy in fulfilling the mission of the contemporary academic library, the paper centres on information literacy, while the characteristics of other relevant literacies are also examined. The content of data literacy education is explained in the context of data-related activities. Findings – It can be concluded that there is a need for data literacy and it is advantageous to have a unified terminology. Data literacy can be offered both to researchers, who need to become data literate science workers and have the goal to educate data management professionals. Several lists of competencies contain important skills and abilities, many of them indicating the close r...

Data literacy: in search of a name and identity

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.

The central role of metadata in a science data literacy course

In spring 2008 and 2009, we offered a course “Scientific Data Management” to undergraduate and graduate students from science and technology majors with support from an NSF CCLI (award #0633447) grant. We actively advertised the course on campus and conducted outreach efforts to individual classes and faculty representing a wide range of science and technology disciplines. Each time the course was offered, we conducted preand post-course surveys to assess the instructional effectiveness and learning outcomes. This paper will describe our experience as well as lessons learned in three areas: science faculty perceptions on data management, effect of science curriculum structures on data literacy education, and changes of students’ perceptions and aptitudes, and discuss how the lessons learned will help shape our escience Librarianship curriculum development, which is being funded by IMLS.

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Lessons learned from a two-year experience in science data literacy education

https://asistdl.onlinelibrary.wiley.com/doi/pdf/10.1002/meet.2008.14504503139

Faculty data management practices: A campus-wide census of STEM departments†

La recherche d'information federee est une technologie qui permet de rechercher des materiaux en provenance de sources multiples a partir d'un seul point d'entree. Vendeurs de logiciels, concepteurs et fournisseurs de contenus se sont associes pour explorer le comportement et les attentes d'etudiants ayant eu a utiliser des ressources de bibliotheques pour rediger un devoir. Les resultats des entretiens devraient permettre d'ameliorer la technologie de recherche federee.

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Federated search observed in the context of student writing Taking steps towards improving user experience

Meta-analyses are studies that bring together data or results from multiple independent studies to produce new and over-arching findings. Current data curation systems only partially support meta-analytic research. Some important meta-analytic tasks, such as the selection of relevant studies for review and the integration of research datasets or findings, are not well supported in current data curation systems. To design tools and services that more fully support meta-analyses, we need a better understanding of meta-analytic research. This includes an understanding of both the practices of researchers who perform the analyses and the characteristics of the individual studies that are brought together. In this study, we make an initial contribution to filling this gap by developing a conceptual framework linking meta-analyses with data paths represented in published articles selected for the analysis. The framework focuses on key variables that represent primary/secondary datasets or derived socio-ecological data, contexts of use, and the data transformations that are applied. We introduce the notion of using variables and their relevant information (e.g., metadata and variable relationships) as a type of currency to facilitate synthesis of findings across individual studies and leverage larger bodies of relevant source data produced in small science research. Handling variables in this manner provides an equalizing factor between data from otherwise disparate data-producing communities. We conclude with implications for exploring data integration and synthesis issues as well as system development.

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John D'Ignazio

Papers

The central role of metadata in a science data literacy course

Lessons learned from a two-year experience in science data literacy education

Faculty data management practices: A campus-wide census of STEM departments†

Federated search observed in the context of student writing Taking steps towards improving user experience

Variables As Currency: Linking Meta-Analysis Research and Data Paths in Sciences