Library+

Merrill, Alex

doi:10.1080/15228959.2011.623598

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…Data literacy's goal is not different as it intends ultimately enable adequate research data management in a wide sense. Providing data literacy education is a service that also appears in the predictions about future directions for academic libraries (Merrill, 2011). Its importance is also underlined by expanding the view on what we mean by data (ACRL, 2013), and it receives accent as employers require data librarians that they fulfil the role of data literacy educators (Si et al, 2013).…”

Section: Data Literacy For Researchers and Librariansmentioning

confidence: 99%

Data literacy for researchers and data librarians

Koltay

2016

Journal of Librarianship and Information Science

126

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This paper describes data literacy and emphasizes its importance of data literacy. Data literacy is vital for researchers, who need to become data literate science workers and also for (potential) data management professionals. Its important characteristic is a close connection and similarity to information literacy. To support this argument, a review of literature was done on the importance of data, and the data-intensive paradigm of scientific research, researchers' expected and real behaviour, the nature of research data management, the possible roles of the academic library, data quality and data citation, Besides describing the nature of data literacy and enumerating the related skills, the application of phenomenographic approaches to data literacy and its relationship to the digital humanities have been identified as subjects for further investigations.

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Section: Data Literacy For Researchers and Librariansmentioning

confidence: 99%

Data literacy for researchers and data librarians

Koltay

2016

Journal of Librarianship and Information Science

126

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“…Last, but not least they can incorporate data literacy in their instructional programs and services. Data literacy also appears in the predictions about future directions for academic libraries in the Library 2.0 world (Merrill, 2011). Information professionals have begun to understand these needs.…”

Section: Research Data and The Librarymentioning

confidence: 99%

Data literacy: in search of a name and identity

Koltay

2015

Journal of Documentation

133

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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 relationship between data literacy and information literacy. It is vital to take a critical stance on hopes and fears, related to the promises of widespread ability of (big) data. Originality/value – The paper intends to be an add-on to the body of knowledge about information literacy and other literacies in the light of research data and data literacy.

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“…The reason consists in the DD versions use the block scan implemented by the CUB library instead of the device scan primitive as in [21]. Besides the block scan we use block load and block store from the CUB library [20] for loading a linear segment of items from memory into a blocked arrangement across a CUDA thread block. The efficiency is increased by the increased granularity I T E M S_P E R_T H RE AD.…”

Section: Domain Decompositionmentioning

confidence: 99%

“…Access optimization of this feature is a future work. One idea is to use the CUB block load primitive from CUB library [20] which provides collective data movement methods for loading a linear segment of items from memory into a blocked arrangement across a CUDA thread block.…”

Section: Cuda Implementationmentioning

confidence: 99%

A massively parallel Eikonal solver on unstructured meshes

Ganellari

Haase

Zumbusch

2018

Comput. Visual Sci.

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Algorithms for the numerical solution of the Eikonal equation discretized with tetrahedra are discussed. Several massively parallel algorithms for GPU computing are developed. This includes domain decomposition concepts for tracking the moving wave fronts in sub-domains and over the sub-domain boundaries. Furthermore a low memory footprint implementation of the solver is introduced which reduces the number of arithmetic operations and enables improved memory access schemes. The numerical tests for different meshes originating from the geometry of a human heart document the decreased runtime of the new algorithms.

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Library+

Cited by 6 publications

References 1 publication

Data literacy for researchers and data librarians

Data literacy for researchers and data librarians

Data literacy: in search of a name and identity

A massively parallel Eikonal solver on unstructured meshes

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