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pdf Digital Curation of Research Data experiences oh, Stefan Strathmann, Achim Osswald, Jens Ludwig Frecvente

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Digital Curation of Research Data_ experiences oh, Stefan Strathmann, Achim Osswald, Jens Ludwig.pdf

Digital Curation of Research Data  experiences oh, Stefan Strathmann, Achim Osswald, Jens Ludwig

Recently the government of the United Kingdom decided that publicly funded research publications and research data must be freely available and accessible to the public in all situations in which there are no copyright issues or other legal aspects that would prevent it. It is expected that the European Commission will publish similar requirements in the context of the next framework program Horizon 2020 starting at the end of 2013. The newly funded Research Data Alliance (RDA) is one example of the increasing interest in these topics worldwide. Ensuring that research data are accessible, sharable, and re-usable over time makes several further steps possible:

• Research data are documented and could therefore be validated. • Research data could be the basis for other and new research questions, since they could be an integral part of the (digital) research lifecycle from the very beginning. • Research data could be re-analysed by using new, innovative digital methods which were unknown at the moment of data acquisition. • Research data could be used by other disciplines, therefore encouraging interdisciplinary research.

pdf Five Steps to decide what data to keep - DCC Checklist for Appraising Research Data Frecvente

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Five Steps to decide what data to keep - Unknown.pdf

Five Steps to decide what data to keep - DCC Checklist for Appraising Research Data

This guide takes you through five steps:
1.     Consider potential reuse purposes - what aims could the data meet?
2.     Check for indications that it must be kept considering legal or policy compliance risks
3.     Identify which data should be kept as it may have long-term value
4.     Weigh up the costs - which data management costs have already been incurred and therefore contribute to its value, and how much more is planned and affordable? Where will the funds to pay these costs come from? Considering these questions will give you the cost element of your data appraisal and should help identify any need for external advice, e.g., on how to deal with any shortfall in the budget.
5.     Complete your data appraisal - this will list what data must, should or could be kept to fulfil potential reuse purposes. The appraisal should also summarise any actions needed to prepare the data for deposit, or the
justification for not keeping it.

This guide draws mainly on the existing DCC guide, How to Appraise and Select Research Data for Curation. NERC Data Value Checklist, and University of Bristol Research Data Evaluation Guide. Section 4 is adapted from the UK Data Service’s Data management costing tool and checklist.

pdf How to Cite Datasets and Link to Publications Frecvente

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How to Cite Datasets and Link to Publications - Alex Ball, Monica Duke.pdf

How to Cite Datasets and Link to Publications

The motivation to cite datasets arises from a recognition that data generated in the course of research are just as valuable to the ongoing academic discourse as papers and monographs. Scientific journals have traditionally supported research by disseminating knowledge in such detail that first, peer scientists could judge the strength of the conclusions based on the quality of the premises and research methods employed, and second, further investigations could be based upon it. In many disciplines, though, the paper alone is no longer sufficient for these purposes: the underlying data also need to be shared

pdf JRC Data Policy Frecvente

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JRC Data Policy - Grainne Mulhern.pdf

JRC Data Policy

The work on the JRC Data Policy followed the task identified in the JRC Management Plan 2014 to
develop a dedicated data policy to complement the JRC Policy on Open Access to Scientific
Publications and Supporting Guidance, and to promote open access to research data in the context
of Horizon 2020.


Important policy commitments and the relevant regulatory basis within the European Union and the
European Commission include: the Commission Decision on the reuse of Commission documents, 1
Commission communication on better access to scientific information 2 , Commission communication
on a reinforced European research area partnership for excellence and growth 3 , Commission
recommendation on access to and preservation of scientific information, 4 and the EU
implementation of the G8 Open Data Charter. 5
The objectives for adopting and implementing the JRC Data Policy include:

  • Share and use data on the basis of the JRC Open Data principles: fully, freely, openly and timely;
  • Establish a transparent mechanism to restrict access to data when sharing them on the basis of the JRC Open Data principles is impossible;
  • Provide a coordinated approach to the acquisition of data by the JRC;
  • Facilitate management, broaden access and use of the JRC data;
  • Reinforce goals of Horizon 2020;
  • Support EU implementation of the G8 Open Data Charter;
  • Continuously support evidence-based decision making and research;
  • Ensure that JRC data are made available through the EU Open Data Portal.


For a successful implementation process, the JRC Data Policy lays down implementation principles
with respect to responsible entities, overall implementation guidelines, and their individual
elements like data management plans, as well as the monitoring of its implementation.


1 2011/833/EU of 12 December 2011.
2 COM (2012) 401.
3 COM (2012) 392.
4 COM (2012) 4890.
5 31 October 2013. Online:
http://ec.europa.eu/information_society/newsroom/cf/dae/document.cfm?doc_id=3489.

pdf KE report incentives for sharing research data Frecvente

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KE report-incentives-for-sharing-researchdata - Van den Eynden, Libby Bishop.pdf

KE report incentives for sharing research data

This study, commissioned by Knowledge Exchange, has gathered evidence, examples and opinions on current and future incentives for research data sharing from the researcher’s point of view, in order to provide recommendations for policy and practice development on how best to incentivise data access and reuse.

pdf Managing and sharing data best practice for researchers: UK Data Archive Frecvente

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Managing and sharing data_ best practice for researchers. UK Data Archive - UK Data Archive.pdf

Managing and sharing data best practice for researchers: UK Data Archive

The information provided in this guide is designed to help researchers and data managers, across a wide range of
research disciplines and research environments, produce highest quality research data with the greatest potential for long-term use. Expertise for producing this guidance comes from the Data Support Service of the interdisciplinary Rural Economy and Land Use (Relu) Programme, the Economic and Social Data Service (ESDS) and the Data Management Planning for ESRC Research Data-rich Investments project (DMP-ESRC) project. All these initiatives involve close liaising with numerous researchers spanning the natural and social sciences and humanities.

pdf Open Research Data: Report to the Australian National Data Service (ANDS) Frecvente

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Open Research Data - John Houghton, Nicolas Gruen.pdf

Open Research Data: Report to the Australian National Data Service (ANDS)

This study offers conservative estimates of the value and benefits to Australia of making publicly-funded research data freely available, and examines the role and contribution of data repositories and associated infrastructure. It also explores the policy settings required to optimise research data sharing, and thereby increase the return on public investment in research.


The study’s focus is Australia’s Commonwealth-funded research and agencies. It includes research commissioned or funded by Commonwealth bodies as well as in-house research within research-oriented agencies wholly or largely funded by the Commonwealth. Government data or public sector information is a separate category of publicly-funded data – although there is some overlap at the margins (e.g. Commonwealth Government funding for Geoscience Australia).

pdf Paving the way for Data Centric, Open Science: An Example From the Social Sciences Frecvente

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Paving the way for Data-Centric, Open Science_ Ae From the Social Sciences 89-175-1-SM - Unknown.pdf

Paving the way for Data Centric, Open Science: An Example From the Social Sciences

INTRODUCTION Data has moved into the spotlight as an important scholarly output that should be
shared with the scientific community for replication and re-use in new contexts. This has a direct impact on
libraries, archives, and other service providers in the data curation and access landscape. DESCRIPTION OF
PROJECT The GESIS Data Archive for the Social Sciences (DAS) has been curating and disseminating social
science research data since 1960. The article presents tools, services, and strategies developed by the DAS to
support the research community in adequately responding to the legal, ethical, and practical challenges that
the transformation towards data-centric, open science presents. These include GESIS’s Secure Data Center,
the data publication platform “datorium” and a recent project to create a georeferencing service for survey
data. LESSONS LEARNED The experiences gained through these activities show that getting involved—now,
rather than further down the road—pays off in that it allows service providers to actively shape the ongoing
transformation. At the same time, by cooperating with suitable partners, the effort and investment of resources
can be kept at a manageable level for individual organizations.

pdf RECODE - Policy recommendations for open access to research data Frecvente

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Policy recommendations for open access to research data - RECODE.pdf

RECODE - Policy recommendations for open access to research data

On the basis of this work, RECODE identified two overarching issues in the mobilisation of open access to research data: a lack of a coherent open data ecosystem; and a lack of attention to the specificity of research practice, processes and data collections. These findings along with the horizontal analyses of the RECODE case studies in relation to the four grand challenges, have informed the following policy recommendations on open access to research data.

pdf TDM Copyright Exception Frecvente

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TDM-Copyright-Exception - Unknown.pdf

TDM Copyright Exception

A mandatory and non-overridable copyright exception for text and data mining (TDM) for both commercial and non-commercial activity is needed.
By modernising European copyright laws to support TDM, researchers will be enabled to make new discoveries and, in turn, to help drive science, competitiveness and innovation.