This paper provides an overview of the KRDS Benefit Analysis Toolkit. The Toolkit has been developed to assist curation activities by assessing the benefits associated with the long-term preservation of research data. It builds on the outputs of the Keeping Research Data Safe (KRDS) research projects and consists of two tools: the KRDS Benefits Framework, and the Value-chain and Benefits Impact tool. Each tool consists of a more detailed guide and worksheet(s). Both tools have drawn on partner case studies and previous work on benefits and impact for digital curation and preservation. This experience has provided a series of common examples of generic benefits that are employed in both tools for users to modify or add to as required.

Citation

Beagrie, N., Duke, M., Patel, M., Lyon, L., Hardman, C., Kalra, D., Lavoie, B. and Woollard, M., 2012. The KRDS Benefit Analysis Toolkit: Development and Application. International Journal of Digital Curation, 7 (2), pp. 64-67

This paper provides an overview of the KRDS Benefit Analysis Toolkit. The Toolkit has been developed to assist curation activities by assessing the benefits associated with the long-term preservation of research data. It builds on the outputs of the Keeping Research Data Safe (KRDS) research projects and consists of two tools: the KRDS Benefits Framework, and the Value-chain and Benefits Impact tool. Each tool consists of a more detailed guide and worksheet(s). Both tools have drawn on partner case studies and previous work on benefits and impact for digital curation and preservation. This experience has provided a series of common examples of generic benefits that are employed in both tools for users to modify or add to as required.

Citation

Beagrie, N., Duke, M., Patel, M., Lyon, L., Hardman, C., Kalra, D., Lavoie, B. and Woollard, M., 2012. The KRDS Benefit Analysis Toolkit: Development and Application. International Journal of Digital Curation, 7 (2), pp. 64-67

It proposes a model whereby authors publishing in an open access journal agree to license the text of their paper using a Creative Commons Attribution licence, and to apply the Creative Commons Zero (CC0) waiver and licence to the data published within and alongside it.

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This is a breath of fresh air. An EU report that does exactly what it says on the tin - in only 3 pages!

The LIBER e-Science working group held three workshops at conferences in 2011-2012. Their final report very succinctly summarises the findings of these and gives 10 prioritised recommendations.

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Researchers across a range of fields have been inspired by the possibilities of data-intensive research. In many cases, however, researchers find themselves unable to take part due to a lack of facilities, insufficient access to data, cultural disincentives, and a range of other impediments. In order to develop a deeper understanding of this, UKOLN, University of Bath and Microsoft Research have been collaborating on developing a Community Capability Model Framework (CCMF) designed to assist institutions, research funding-bodies and researchers to enhance the capability of their communities to perform data-intensive research. This paper explores the rationale for using capability modelling for informing the development of data-intensive research and outlines the main capability factors underlying the current version of the CCMF.

Citation

Lyon, L., Ball, A., Duke, M. and Day, M., 2012. Developing a Community Capability Model Framework for data-intensive research. In: Moore, R., Ashley, K. and Ross, S. (eds.), iPres 2012. Toronto: University of Toronto, Faculty of Information, pp. 9-16

Researchers across a range of fields have been inspired by the possibilities of data-intensive research. In many cases, however, researchers find themselves unable to take part due to a lack of facilities, insufficient access to data, cultural disincentives, and a range of other impediments. In order to develop a deeper understanding of this, UKOLN, University of Bath and Microsoft Research have been collaborating on developing a Community Capability Model Framework (CCMF) designed to assist institutions, research funding-bodies and researchers to enhance the capability of their communities to perform data-intensive research. This paper explores the rationale for using capability modelling for informing the development of data-intensive research and outlines the main capability factors underlying the current version of the CCMF.

Citation

Lyon, L., Ball, A., Duke, M. and Day, M., 2012. Developing a Community Capability Model Framework for data-intensive research. In: Moore, R., Ashley, K. and Ross, S. (eds.), iPres 2012. Toronto: University of Toronto, Faculty of Information, pp. 9-16

This guide describes the code underlying the RAIDmap Associative Tool. It also describes how it may be imported into an integrated development environment, and exported as an installation executable.

Citation

Ball, A. and Thangarajah, U., 2012. RAIDmap Application Developer Guide. Bath, UK: University of Bath

This document defines a set of metadata elements corresponding to information considered necessary for good data management, most easily provided close to the point of record creation, and unique to a data record, data case or data development process. The set corresponds to a subset of elements drawn from the PREMIS and DataCite metadata schemata. It is provided as a basis for the metadata collection functionality of the RAIDmap Associative Tool.

Citation

Ball, A., 2012. Minimum Mandatory Metadata Set for RAIDmap. Bath, UK: University of Bath