Overview of all keyword tags in articles

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This page provides an overview of 44 tags, ordered by trending factor. Column headings allow re-sorting by other criteria. In the expanding tab below you can adjust filters to display sub-sets of tags and narrow the focus to specific items of interest (see FAQs on filtering for usage tips). Select this link to remove all filters.

Term Brief description Charts

automatic metadata generation

Use cases for the automatic generation and use of metadata will be developed using an approach that maximises the use of current community knowledge. Through knowledge-gathering workshops, commissioned expert reports, and surveys of the wider community it will be possible to identify a number of key use cases defining how metadata can be automatically gathered and how that metadata will be used. This approach builds on existing expertise and allows the focus to be on gathering use cases, analysing them, identifying tools and services, prioritising them, and assessing the main costs and benefits. The project will deliver general guidance for service providers in HE, a synthesis of previous work on automated metadata generation and a set of recommendations on tools and services required in the future. Project start date: 2009-03-01. Project end date: 2009-08-31. (Excerpt from <a href="http://www.intrallect.com/index.php/intrallect/knowledge_base">this source</a>)

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bath information and data services

Bath Information and Data Services (BIDS) provided bibliographic database services to the academic community in the UK from 1991 to 2005. BIDS academic and scholarly journals services are now incorporated into IngentaConnect www.ingentaconnect.com (Excerpt from <a href="http://www.bids.ac.uk/info/fs_aboutbids.htm">this source</a>)

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data curation for e-science

The DTI and the Research Councils are committing &pound;118M to a government-industry programme on e-Science. The reason for this investment is that GRID technology is seen as the natural successor to the world wide web and the UK wants to take a leading role in order to develop solutions for its scientists and developing opportunities for its industry. The world wide web has revolutionised the way companies do business and fundamentally altered people's personal lives but it can no longer cope with the demands being placed on it by science. The world wide web allows very easy access to information, Grid allows that same easy access to computing power, data processing and communication of the results. The opportunities are immense, it will allow the efficient manipulation of vast amounts of information such as that contained in the human genome or the results from experiments in CERN's new Large Hadron Collider. It will also allow the ability to mine data again and again by comparing existing data sets collected for one purpose with new and previously unrelated information, so generating new knowledge. This consultancy will establish the current provision and future requirements for curation of primary research data being generated within e-science in the UK. This will include the e-science core programme but is anticipated to extend beyond this to other e-science research and primary research data. A consultancy report will provide a synthesis of findings and make recommendations for future action. The consultancy will support aims to manage JISC involvement in e-Science and the Research Grid, and to work in partnership to support the research community through activities such as its digital preservation programme. Project start date: 2003-02-01. Project end date: 2004-02-02. (Excerpt from <a href="https://pims.jisc.ac.uk/projects/view/931">this source</a>)

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data train project

The DataTrain project aims to build on findings and tools developed in the Incremental project (JISC 07/09 funding strand) by developing disciplinary focussed data management training modules for post-graduate courses in Archaeology and Social Anthropology at the University of Cambridge. To this end, the project will develop training modules for each of the two departments, and pilot these as part of the departments' postgraduate training provision in Spring of 2011. Beyond this, the modules would be embedded within research methods courses in each department. To extend its impact, the project would also make the training resources available through the University of Cambridge's institutional repository's support provision and via the Archaeology Data Service (ADS) and Digital Curation Centre (DCC). Project start date: 2010-08-01. Project end date: 2011-07-31. (Excerpt from <a href="http://www.lib.cam.ac.uk/preservation/datatrain/">this source</a>)

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data without boundaries

The Data without Boundaries û DwB û project exists to support equal and easy access to official microdata for the European Research Area, within a structured framework where responsibilities and liability are equally shared. Europe needs a comprehensive and easy-to-access research data infrastructure to be able to continuously produce cutting-edge research and reliable policy evaluations. (Excerpt from <a href="http://www.dwbproject.org/">this source</a>)

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datagovuk

data.gov.uk is a UK Government project to open up almost all non-personal data acquired for official purposes for free re-use. Sir Tim Berners-Lee and Professor Nigel Shadbolt are the two key figures behind the project. The beta version of data.gov.uk has been online since the 30 September 2009 and by January 2010 more than 2,400 developers had registered to test the site, provide feedback and start experimenting with the data. When the project was officially launched in January 2010 it contained 2,500 data sets and developers had already built a site that showed the location of schools according to the rating assigned to them by education watchdog Ofsted. (Excerpt from <a href="http://en.wikipedia.org/wiki/Data.gov.uk">Wikipedia article: Data.gov.uk</a>)

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datashare

DataShare, led by Edina, arises from an existing UK consortium of data support professionals working in departments and academic libraries in universities (Data Information Specialists Committee-UK), and builds on an international network with a tradition of data sharing and data archiving dating back to the 1960s in the social sciences. By working together across four universities and internally with colleagues already engaged in managing open access repositories for e-prints, this partnership will introduce and test a new model of data sharing and archiving to UK research institutions. By supporting academics within the four partner institutions who wish to share datasets on which written research outputs are based, this network of institution-based data repositories develops a niche model for deposit of 'orphaned datasets' currently filled neither by centralised subject-domain data archives/centres/grids nor by e-print based institutional repositories (IRs). The project's overall aim is to contribute to new models, workflows and tools for academic data sharing within a complex and dynamic information environment which includes increased emphasis on stewardship of institutional knowledge assets of all types; new technologies for doing e-Research; new research council policies and mandates; and the growth of the Open Access / Open Data movement. Project start date: 2007-03-01. Project end date: 2009-03-31. (Excerpt from <a href="http://www.disc-uk.org/datashare.html">this source</a>)

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dealing with data

UKOLN was asked to undertake a small-scale consultancy for JISC to investigate the relationships between data centres and institutions which may develop data repositories. The resulting direction-setting report will be used to advance the digital repository development agenda within the JISC Capital programme (2006 - 2009), to assist in the co-ordination of research data repositories and to inform an emerging Vision and Roadmap. The study includes a synthesis of some of the lessons learned from the projects within the Digital Repositories programme that were concerned with research data. Project start date: 2006-11-01. Project end date: 2007-05-31. (Excerpt from <a href="http://www.ukoln.ac.uk/projects/data-cluster-consultancy/">this source</a>)

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british oceanographic data centre

The British Oceanographic Data Centre (BODC) is a national facility for looking after and distributing data about the marine environment. BODC deal with a range of physical, chemical and biological data, which help scientists provide answers to both local questions (such as the likelihood of coastal flooding) and global issues (such as the impact of climate change). BODC is the designated marine science data centre for the UK’s Natural Environment Research Council (NERC). The centre provides a resource for science, education and industry, as well as the general public. BODC is hosted by the National Oceanography Centre (NOC), Liverpool. (Excerpt from <a href="http://en.wikipedia.org/wiki/British_Oceanographic_Data_Centre">Wikipedia article: British Oceanographic Data Centre</a>)

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codata

The Committee on Data for Science and Technology (CODATA) was established in 1966 as an interdisciplinary committee of the International Council for Science. It seeks to improve the compilation, critical evaluation, storage, and retrieval of data of importance to science and technology. The CODATA Task Group on Fundamental Constants was established in 1969. Its purpose is to periodically provide the international scientific and technological communities with an internationally accepted set of values of the fundamental physical constants and closely related conversion factors for use worldwide. The first such CODATA set was published in 1973, later in 1986, 1998, 2002 and the fifth in 2006. The latest version is Ver.6.0 called "2010CODATA" published on 2011-06-02. The CODATA recommended values of fundamental physical constants are published at the NIST Reference on Constants, Units, and Uncertainty. (Excerpt from <a href="http://en.wikipedia.org/wiki/CODATA">Wikipedia article: CODATA</a>)

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council of european social science data archives

CESSDA is an umbrella organisation for social science data archives across Europe. Since the 1970s the members have worked together to improve access to data for researchers and students. CESSDA research and development projects and Expert Seminars enhance exchange of data and technologies among data organisations. Preparations are underway to move CESSDA into a new organisation known as CESSDA European Research Infrastructure Consortium (CESSDA ERIC). (Excerpt from <a href="http://www.cessda.org/">this source</a>)

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datacite

DataCite is an international consortium which aims to improve data citation in order to: establish easier access to scientific research data on the Internet; to increase acceptance of research data as legitimate, citable contributions to the scientific record; and to support data archiving that will permit results to be verified and re-purposed for future study. (Excerpt from <a href="http://en.wikipedia.org/wiki/DataCite">Wikipedia article: DataCite</a>)

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uk data archive

The UK Data Archive is a national centre of expertise in data archiving in the United Kingdom (UK). It houses the largest collection of digital data in the social sciences and humanities in the UK. Located in Colchester, the UK Data Archive is a specialist centre of the University of Essex. It is funded by the Economic and Social Research Council (ESRC), the Joint Information Systems Committee (JISC) and the University of Essex. (Excerpt from <a href="http://en.wikipedia.org/wiki/UK_Data_Archive">Wikipedia article: UK Data Archive</a>)

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visual arts data service

VADS (Visual Arts Data Service) is a UK organisation that provides digital images and other visual arts resources free and copyright cleared for use in UK higher education and further education. It has provided services to the academic community for 11 years, and has built up a portfolio of visual art collections comprising over 100,000 images. VADS is based at the University for the Creative Arts at Farnham. (Excerpt from <a href="http://en.wikipedia.org/wiki/Visual_Arts_Data_Service">Wikipedia article: Visual Arts Data Service</a>)

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authority data

Functional Requirements for Authority Data (FRAD), formerly known as Functional Requirements for Authority Records (FRAR) is a conceptual entity-relationship model developed by the International Federation of Library Associations and Institutions (IFLA) for relating the data that are recorded in library authority records to the needs of the users of those records and facilitate and sharing of that data. (Excerpt from <a href="http://en.wikipedia.org/wiki/Functional_Requirements_for_Authority_Data"... article: FRAD</a>)

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bibliographic data

A bibliographic database is a database of bibliographic records, an organized digital collection of references to published literature, including journal and newspaper articles, conference proceedings, reports, government and legal publications, patents, books, etc. In contrast to library catalogue entries, a large proportion of the bibliographic records in bibliographic databases describe analytics (articles, conference papers, etc.) rather than complete monographs, and they generally contain very rich subject descriptions in the form of keywords, subject classification terms, or abstracts. (Excerpt from <a href="http://en.wikipedia.org/wiki/Bibliographic_database">Wikipedia article: Bibliographic data</a>)

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bibliographic database

A bibliographic database is a database of bibliographic records, an organized digital collection of references to published literature, including journal and newspaper articles, conference proceedings, reports, government and legal publications, patents, books, etc. In contrast to library catalogue entries, a large proportion of the bibliographic records in bibliographic databases describe analytics (articles, conference papers, etc.) rather than complete monographs, and they generally contain very rich subject descriptions in the form of keywords, subject classification terms, or abstracts. (Excerpt from <a href="http://en.wikipedia.org/wiki/Bibliographic_database">Wikipedia article: Bibliographic database</a>)

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big data

In information technology, big data consists of datasets that grow so large that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage, search, sharing, analytics, and visualizing. This trend continues because of the benefits of working with larger and larger datasets allowing analysts to "spot business trends, prevent diseases, combat crime." Though a moving target, current limits are on the order of terabytes, exabytes and zettabytes of data. Scientists regularly encounter this problem in meteorology, genomics, connectomics, complex physics simulations, biological and environmental research, Internet search, finance and business informatics. Data sets also grow in size because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, Radio-frequency identification readers, wireless sensor networks and so on." Every day, 2.5 quintillion bytes of data are created and 90% of the data in the world today was created within the past two years. (Excerpt from <a href="http://en.wikipedia.org/wiki/Big_data">Wikipedia article: Big data</a>)

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data

The term data refers to qualitative or quantitative attributes of a variable or set of variables. Data (plural of "datum") are typically the results of measurements and can be the basis of graphs, images, or observations of a set of variables. Data are often viewed as the lowest level of abstraction from which information and then knowledge are derived. Raw data, i.e. unprocessed data, refers to a collection of numbers, characters, images or other outputs from devices that collect information to convert physical quantities into symbols. (Excerpt from <a href="http://en.wikipedia.org/wiki/Data">Wikipedia article: Data</a>)

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data citation

Data citation refers to the practice of providing a reference to data in the same way as researchers routinely provide a bibliographic reference to printed resources. The need to cite data is starting to be recognised as one of the key practices underpinning the recognition of data as a primary research output rather than as a by-product of research. While data has often been shared in the past, it is rarely, if ever, cited in the same way as a journal article or other publication might be. If datasets were cited, they would achieve a validity and significance within the cycle of activities associated with scholarly communications and recognition of scholarly effort. (Excerpt from <a href="http://ands.org.au/guides/data-citation-awareness.html">this source</a>)

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