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Purpose – The purpose of this paper is to present a case study of work performed at the London School of Hygiene and Tropical Medicine to set-up a Research Data Management Service and tailor it to the needs of health researchers. Design/methodology/approach – The paper describes the motivations for establishing the RDM Service and outlines the three objectives that were set to improve data management practice within the institution. Each of the objectives are explored in turn, stating how they were addressed. Findings – A university with limited resources can operate a RDM Service that pro-actively supports researchers wishing to manage research data by monitoring evolving support needs, identifying common trends and developing resources that will reduce the time investment needed. The institution-wide survey identified a need for guidance on developing data documentation and archiving research data following project completion. Analysis of ongoing support requests identifies a need for guidance on data management plans and complying with journal sharing requirements. Research limitations/implications – The paper provides a case study of a single institution. The results may not be generally applicable to universities that support other disciplines. Practical implications – The case study may be helpful in helping other universities to establish an RDM Service using limited resources. Originality/value – The paper outlines how the evolving data management needs of public health researchers can be identified and a strategy that can be adopted by an RDM Service to efficiently address these requirements.

The Data Documentation Initiative (DDI) specification has gone through significant development in recent years. Most Health and Demographic Surveillance System (HDSS) researchers in Low and Middle Income Countries (LMIC) are, however, unclear on how to apply it to their work. This paper sets out considerations that LMIC HDSS researchers need to make regarding DDI use. We use the Kisesa HDSS in Mwanza Tanzania as a prototype. First, we mapped the Kisesa HDSS data production process to the Generic Longitudinal Business Process Model (GLBPM). Next, we used existing GLBPM to DDI mapping to guide us on the DDI elements to use. We then explored implementation of DDI using the tools Nesstar Publisher for the DDI Codebook version and Colectica Designer for the DDI Lifecycle version. We found the amounts of metadata entry comparable between Nesstar Publisher and Colectica Designer when documenting a study from scratch. The majority of metadata had to be entered manually. Automatically extracted metadata amounted to at most 48% in Nesstar Publisher and 33% in Colectica Designer. We found Colectica Designer to have stiffer staff training needs and software costs than Nesstar Publisher. Our study shows that, at least for HDSS in LMIC, it is unlikely to be the amount of metadata entry that determines the choice between DDI Codebook and DDI Lifecycle but rather staff training needs and software costs. LMIC HDSS studies would need to invest in extensive staff training to directly start with DDI Lifecycle or they could start with DDI Codebook and move to DDI Lifecycle later.

It is well recognised that the period of time in which digital research may remain accessible is likely to be short in comparison to the period in which it will continue to hold intellectual value. Although many digital preservation strategies are effective for simple resources, it is not always possible to confirm that all of the significant properties - the characteristics that contribute to the intended meaning - have been maintained when stored in different formats and software environments. The paper outlines methodologies being developed by InterPARES, PLANETS and other projects in the international research community to support the decision-making process and highlights the work of four recent JISC-funded studies to specify the significant properties of vector images, moving images, software and learning objects.

This paper describes the FISHNet project, which developed a repository environment for the curation and sharing of data relating to freshwater science, a discipline whose research community is distributed thinly across a variety of institutions, and usually works in relative isolation as individual researchers or within small groups. As in other “small sciences”, these datasets tend to be small and “hand-crafted”, created to address particular research questions rather than with a view to reuse, so they are rarely curated effectively, and the potential for sharing and reusing them is limited. The paper addresses a variety of issues and concerns raised by freshwater researchers as regards data sharing, describes our approach to developing a repository environment that addresses these concerns, and identifies the potential impact within the research community of the system.

This paper describes the problems and explores potential solutions for providing long term storage and access to research outputs, focusing mainly on research data. The ready availability of cloud storage and compute services provides a potentially attractive option for curation and preservation of research information. In contrast to deploying infrastructure within an organisation, which normally requires long lead times and upfront capital investment, cloud infrastructure is available on demand and is highly scalable. However, use of commercial cloud services in particular raises issues of governance, cost-effectiveness, trust and quality of service. We describe a set of in-depth case studies conducted with researchers across the sciences and humanities performing data-intensive research, which demonstrate the issues that need to be considered when preserving data in the cloud. We then describe the design of a repository framework that addresses these requirements. The framework uses hybrid cloud, combining internal institutional storage, cloud storage and cloud-based preservation services into a single integrated repository infrastructure. Allocation of content to storage providers is performed using on a rules-based approach. The results of an evaluation of the proof-of-concept system are described.