International Journal of Digital Curation
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Trust in Digital Repositories
Get PDFISO 16363:2012, Space Data and Information Transfer Systems - Audit and Certification of Trustworthy Digital Repositories (ISO TRAC), outlines actions a repository can take to be considered trustworthy, but research examining whether the repository’s designated community of users associates such actions with trustworthiness has been limited. Drawing from this ISO document and the management and information systems literatures, this paper discusses findings from interviews with 66 archaeologists and quantitative social scientists. We found similarities and differences across the disciplines and among the social scientists. Both disciplinary communities associated trust with a repository’s transparency. However, archaeologists mentioned guarantees of preservation and sustainability more frequently than the social scientists, who talked about institutional reputation. Repository processes were also linked to trust, with archaeologists more frequently citing metadata issues and social scientists discussing data selection and cleaning processes. Among the social scientists, novices mentioned the influence of colleagues on their trust in repositories almost twice as much as the experts. We discuss the implications our findings have for identifying trustworthy repositories and how they extend the models presented in the management and information systems literatures
Developing an Approach for Data Management Education: A Report from the Data Information Literacy Project
Get PDFThis paper describes the initial results from the Data Information Literacy (DIL) project designed to identify the educational needs of graduate students across a variety of science disciplines and respond with effective educational interventions to meet those needs. The DIL project consists of five teams in disparate disciplines from four academic institutions in the United States. The project teams include a data librarian, a subject-specialist or information literacy librarian, and a faculty member representing a disciplinary group of students. Interviews with the students and faculty members present a detailed snapshot of graduate student needs in data management education. Following our study, educational programs addressing identified needs will be delivered in the fall of 2012 and spring of 2013. Our findings from the project interviews are analyzed here, with a preview of the training approaches that will be taken by the five teams
Informative Provenance for Repurposed Data: A Case Study using Clinical Research Data
Get PDFThe task repurposing of heterogeneous, distributed data for originally unintended research objectives is a non-trivial problem because the mappings required may not be precise. A particular case is clinical data collected for patient care being used for medical research. The fact that research repositories will record data differently means that assumptions must be made as how to transform of this data. Records of provenance that document how this process has taken place will enable users of the data warehouse to utilise the data appropriately and ensure that future data added from another source is transformed using comparable assumptions. For a provenance-based approach to be reusable and supportable with software tools, the provenance records must use a well-defined model of the transformation process. In this paper, we propose such a model, including a classification of the individual ‘sub-functions’ that make up the overall transformation. This model enables meaningful provenance data to be generated automatically. A case study is used to illustrate this approach and an initial classification of transformations that alter the information is created
Data Management and Preservation Planning for Big Science
Get PDF‘Big Science’ - that is, science which involves large collaborations with dedicated facilities, and involving large data volumes and multinational investments – is often seen as different when it comes to data management and preservation planning. Big Science handles its data differently from other disciplines and has data management problems that are qualitatively different from other disciplines. In part, these differences arise from the quantities of data involved, but possibly more importantly from the cultural, organisational and technical distinctiveness of these academic cultures. Consequently, the data management systems are typically and rationally bespoke, but this means that the planning for data management and preservation (DMP) must also be bespoke.These differences are such that ‘just read and implement the OAIS specification’ is reasonable Data Management and Preservation (DMP) advice, but this bald prescription can and should be usefully supported by a methodological ‘toolkit’, including overviews, case-studies and costing models to provide guidance on developing best practice in DMP policy and infrastructure for these projects, as well as considering OAIS validation, audit and cost modelling.In this paper, we build on previous work with the LIGO collaboration to consider the role of DMP planning within these big science scenarios, and discuss how to apply current best practice. We discuss the result of the MaRDI-Gross project (Managing Research Data Infrastructures – Big Science), which has been developing a toolkit to provide guidelines on the application of best practice in DMP planning within big science projects. This is targeted primarily at projects’ engineering managers, but intending also to help funders collaborate on DMP plans which satisfy the requirements imposed on them
Data Intelligence Training for Library Staff
Get PDFThe Data Intelligence 4 Librarians course was developed by 3TU.Datacentrum at the end of 2011 to provide online resources and training for digital preservation practitioners, specifically for library staff. The course objectives are to transfer and exchange knowledge about data management, and to provide participants with the skills required to advise researchers or research groups on efficient and effective ways of adding value to their data. The paper describes the process of creating the course, the methodology and the results of the first pilot, which took place from February to June 2012. It also demonstrates the choices made during the design process and discusses the implications of the evaluation of the pilot course for further development: in particular, how the course might be expanded to more disciplines and other data repositories
EUDAT: A New Cross-Disciplinary Data Infrastructure for Science
Get PDFThe EUDAT project is a pan-European data initiative that started in October 2011. The project brings together a unique consortium of 25 partners – including research communities, national data and high performance computing (HPC) centres, technology providers, and funding agencies – from 13 countries. EUDAT aims to build a sustainable cross-disciplinary and cross-national data infrastructure that provides a set of shared services for accessing and preserving research data
Gathering Evidence of Benefits: A Structured Approach from the Jisc Managing Research Data Programme
Get PDFThe work of the Jisc Managing Research Data programme is – along with the rest of the UK higher education sector – taking place in an environment of increasing pressure on research funding. In order to justify the investment made by Jisc in this activity – and to help make the case more widely for the value of investing time and money in research data management – individual projects and the programme as a whole must be able to clearly express the resultant benefits to the host institutions and to the broader sector. This paper describes a structured approach to the measurement and description of benefits provided by the work of these projects for the benefit of funders, institutions and researchers. We outline the context of the programme and its work; discuss the drivers and challenges of gathering evidence of benefits; specify benefits as distinct from aims and outputs; present emerging findings and the types of metrics and other evidence which projects have provided; explain the value of gathering evidence in a structured way to demonstrate benefits generated by work in this field; and share lessons learned from progress to date
Towards a Unified University Infrastructure: The Data Management Roll-Out at the University of Oxford
Get PDFSince presenting a paper at the International Digital Curation Conference 2010 conference entitled ‘An Institutional Approach to Developing Research Data Management Infrastructure’, the University of Oxford has come a long way in developing research data management (RDM) policy, tools and training to address the various phases of the research data lifecycle. Work has now begun on integrating these various elements into a unified infrastructure for the whole university, under the aegis of the Data Management Roll-out at Oxford (Damaro) Project.This paper will explain the process and motivation behind the project, and describes our vision for the future. It will also introduce the new tools and processes created by the university to tie the individual RDM components together. Chief among these is the ‘DataFinder’ – a hierarchically-structured metadata cataloguing system which will enable researchers to search for and locate research datasets hosted in a variety of different datastores from institutional repositories, through Web 2 services, to filing cabinets standing in department offices. DataFinder will be able to pull and associate research metadata from research information databases and data management plans, and is intended to be CERIF compatible. DataFinder is being designed so that it can be deployed at different levels within different contexts, with higher-level instances harvesting information from lower-level instances enabling, for example, an academic department to deploy one instance of DataFinder, which can then be harvested by another at an institutional level, which can then in turn be harvested by another at a national level.The paper will also consider the requirements of embedding tools and training within an institution and address the difficulties of ensuring the sustainability of an RDM infrastructure at a time when funding for such endeavours is limited. Our research shows that researchers (and indeed departments) are at present not exposed to the true costs of their (often suboptimal) data management solutions, whereas when data management services are centrally provided the full costs are visible and off-putting. There is, therefore, the need to sell the benefits of centrally-provided infrastructure to researchers. Furthermore, there is a distinction between training and services that can be most effectively provided at the institutional level, and those which need to be provided at the divisional or departmental level in order to be relevant and applicable to researchers. This is being addressed in principle by Oxford’s research data management policy, and in practice by the planning and piloting aspects of the Damaro Project
Linking Data and Publications: Towards a Cross-Disciplinary Approach
Get PDFIn this paper, we tackle the challenge of linking scholarly information in multi-disciplinary research infrastructures. There is a trend towards linking publications with research data and other information, but, as it is still emerging, this is handled differently by various initiatives and disciplines. For OpenAIRE, a European cross-disciplinary publication infrastructure, this poses the challenge of supporting these heterogeneous practices. Hence, OpenAIRE wants to contribute to the development of a common approach for discipline-independent linking practices between publications, data, project information and researchers. To this end, we constructed two demonstrators to identify commonalities and differences. The results show the importance of stable and unique identifiers, and support a ‘by reference’ approach of interlinking research results. This approach allows discipline-specific research information to be managed independently in distributed systems and avoids redundant maintenance. Furthermore, it allows these disciplinary systems to manage the specialized structures of their contents themselves
Research Data Management Education for Future Curators
Get PDFScience has progressed by “standing on the shoulders of giants” and for centuries research and knowledge have been shared through the publication and dissemination of books, papers and scholarly communications. Moving forward, much of our understanding builds on (large scale) datasets, which have been collected or generated as part of the scientific process of discovery. How will this be made available for future generations? How will we ensure that, once collected or generated, others can stand on the shoulders of the data we produce?Educating students about the challenges and opportunities of data management is a key part of the solution and helps the researchers of the future to start to think about the problems early on in their careers. We have compiled a set of case studies to show the similarities and differences in data between disciplines, and produced a booklet for students containing the case studies and an introduction to the data lifecycle and other data management practices. This has already been used at the University of Southampton within the Faculty of Engineering and is now being adopted centrally for use in other faculties. In this paper, we will provide an overview of the case studies and the guide, and reflect on the reception the guide has had to date