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The Data Asset Framework (formerly the Data Audit Framework) provides organisations with the means to identify, locate, describe and assess how they are managing their research data assets. DAF combines a set of methods with an online tool to enable data auditors to gather this information. DAF will help ensure that research data produced in UK Higher Education Institutions is preserved and remains accessible in the long term.

Funding bodies increasingly require their grant-holders to produce and maintain Data Management Plans (DMPs), both at the bid-preparation stage and after funding has been secured. DMP Online has been developed by the Digital Curation Centre to enable you to build and edit DMPs according to the requirements stipulated by the major UK funders. The tool also contains helpful guidance and links for researchers and other data professionals.

The project's overall aim was to contribute to new models, workflows and tools for academic data sharing within a complex and dynamic information environment which included increased emphasis on stewardship of institutional knowledge assets of all types; new technologies to enhance e-Research; new research council policies and mandates; and the growth of the Open Access / Open Data movement.

"Change we are leading" is the theme of CLOUD 2011. Cloud Computing has become a scalable services consumption and delivery platform in the field of Services Computing. The technical foundations of Cloud Computing include Service-Oriented Architecture (SOA) and Virtualizations of hardware and software. The goal of Cloud Computing is to share resources among the cloud service consumers, cloud partners, and cloud vendors in the cloud value chain. The resource sharing at various levels results in various cloud offerings such as infrastructure cloud (e.g. hardware, IT infrastructure management), software cloud (e.g. SaaS focusing on middleware as a service, or traditional CRM as a service), application cloud (e.g. Application as a Service, UML modeling tools as a service, social network as a service), and business cloud (e.g. business process as a service).

The Information Retrieval area has a strong and long tradition dating back to the 1960s in producing and processing scientific data resulting from the experimental evaluation of search algorithms and search systems. This attitude towards evaluation has led to fast and continuous progress in the evolution of information retrieval systems and search engines. However, in order to make these data test collections understandable and usable they must be endowed with some auxiliary information, i.e., provenance, quality, context, etc. Therefore, there is a need for metadata models able to describe the main characteristics of evaluation data. In addition, in order to make distributed data collections accessible, sharable, and interoperable, there is a need for advanced data infrastructures. In contrast, the information retrieval area has barely explored and exploited the possibilities for managing, storing, and effectively accessing the scientific data produced during the evaluation studies by making use of the methods typical of the database and knowledge management areas. Over the years, the information retrieval area has produced a vast set of large test collections which have become the main benchmark tools of the area and ensure reproducible and comparable experiments. However, these same collections have not been organised into coherent and integrated infrastructures which make them accessible, searchable, citable, exploitable, and re-usable to all possibly interested researchers, developers, and user communities. It is thus time for these three communities - information retrieval, databases, and knowledge management - to join efforts, meet, and cooperate to address the problem of envisaging and designing useful infrastructures able to coherently manage pertinent data collections and sources of information, and so take concrete steps towards developing them. Indeed, the information retrieval experts need to recognise this need, while the database and knowledge

This Web page provides an overview of the outputs of projects in JISC's Managing Research Data programme. It includes links to project websites, how to guides, data management planning tools and guidance, case studies, reports, and training materials.

Experts exchange their practical knowledge and experiences on digital preservation. Day 1 - 19.10.2011 "GETTING READY FOR DIGITAL PRESERVATION" The necessary "preparations" and fundamental decisions are the most underestimated challenges of digital preservation. But especially those tasks are bumps in the road which, if they aren't addressed early on, will remain constant challenges through the process of digital preservation. For those reasons the first block of the conference is dedicated to different aspects of necessary preparations and fundamental decisions. Experiences made in daily work of libraries and archives are presented and ways of dealing with challenges are communicated to the attendees. The following questions should be answered in this block: * How can a meaningful contextual and technical selection of holdings for digital preservation be made? * Which collections and digital objects should be dealt with first? * What expectations do users have in digital preservation? What do those expectations mean for the digital preservation process? * Should objects be normalized before ingest / when entering the collection? If so, how? What are recommended formats? * What gaps exist between existing digital preservation systems and institutional requirements? * What risks exist for different types of data and material? * What does the implementation of digital preservation mean for an institution? Which steps need to be considered? What are the challenges? * Which aspects of digital preservation are unanswered as of today / what are the main areas of further development, research and action? Day 2 - 20.10.2011 THE INGEST PROCESS FOR DIFFERENT TYPES OF DIGITAL MATERIAL Ingest describes the entire process of information transfer into the digital archive. The focus of this presentation block will be practitioner reports about experiences made in the development and implementation of ingest workflows for differe

Known as "dropbox for scientists", it is a completely free way to transfer large files around the Web. It is a classic problem: you need to send files to someone, or they need to send them to you, and there's no way except email. But they are too large or your administrator won't let you transfer the files by email at all.

FigShare allows to share data, negative results and unpublished figures.

BuzzData lets you share data in a social network fashion. It is possible to publish and discuss your data with others

This collaborative project sought to promote research data management skills of postgraduate research students in the health studies discipline through a specially-developed training programme which focuses on qualitative, unstructured research data. The project was funded by JISC under their Managing Research Data (JISCMRD) Programme. The project ran from 1st October 2010 to 31st July 2011.

The DataTrain teaching materials have been designed to familiarise post-graduate students in good practice in looking after their research data. A central tenet is the importance of thinking about this in conjunction with the projected outputs and publication of research projects.

Free Cloud Computing and the Law toolkit for FE and HE professionals. Whether you work in a teaching, research, management or support capacity, the aim is to help you to make confident, informed decisions about implementing cloud computing solutions in your institution.

JISC's Managing Research Data programme has, with an investment of nearly £2M, funded a strand of eight Research Data Management Infrastructure (RDMI) projects to provide the UK Higher Education sector with examples of good research data management. The RDMI projects have identified requirements to manage data created by researchers within an institution, or across a group of institutions, and then piloted research data management infrastructures at institutional, departmental or research group level, to address these requirements. This report provides an analysis and synthesis of the benefits from this work identified by the eight RDMI projects in their benefits case studies, the benefits and enhancements that accrued to existing tools and methodologies from them, and the emerging business cases (as of June 2011) for sustainability being built by the RDMI projects.

CARDIO enables you to: 1. collaboratively assess data management requirements, activity, and capacity at your institution 2. build consensus between data creators, information managers and service providers 3. identify practical goals for improvement in data management provision and support; 4. identify operational inefficiencies and opportunities for cost saving; 5. make a compelling case to senior managers for investment in data management support