31 research outputs found

    A reform of science

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    Open Science concerns both the individual scientist, science in general and the access to knowledge all over the world. And the reform of science concerns libraries. Meet Jon Tennant – the only Englishman who doesn’t drink tea

    Show us the money! – mod større åbenhed i OA-publicering

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    At skabe større transparens i udgifterne er et nødvendigt skridt at tage, hvis vi vil kunne kæmpe for forskernes ret til at publicere OA under rimelige vilkår til rimelige priser

    Data fra verdens ældste landsby

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    Nyudviklet KUBIS-service på Det Kongelige Bibliotek giver forskere mulighed for at få opbevaret og formidlet deres data. Det er et arkæologisk udgravningsprojekt i Jordan, der ligger til grund for udviklingen af servicen – som i øvrigt er sket på baggrund af softwaren Harvard Dataverse Network. Et af perspektiverne er, at servicen letter forskernes samarbejde med andre forskere verden over

    Mere tid til bedre forskning

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    Forskningens og forskningsbibliotekernes landskab forandrer sig hastigt, og det stiller nye krav til bibliotekernes forskerservice. Men hvad findes der af services, hvordan kan en best practice se ud, og hvordan bliver bibliotekerne en mere integreret del af forskningens kredsløb. På KUBIS har man set på sagen

    Bliv Data Steward - en ny uddannelse

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    Data Stewardship og rollen som data steward er stadig under modning i Danmark og der er endnu ingen klar definition af rollen. En ny rapport identificerer imidlertid et bredt behov og en efterspørgsel på uddannelse og kvalifikationer inden for området data stewardship i Danmark, der går på tværs af den offentlige og private sektor

    Stort DEFF-projekt styrker bibliotekers forskningsanalyse

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    Når de sidste punktum sættes i det DEFF-støttede projekt Research Output & Impact – Analyzed & Visualised, ROI-AV, i slutningen af 2018, vil medarbejdere i DFFU landskabet være klædt på til at arbejde med og formidle visualisering af komplekse data, og der vil være udviklet en open source VIVO rapportgenerator til visse tidskrævende bibliometriske analyser

    Research data management challenges in citizen science projects and recommendations for library support services. A scoping review and case study.

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    Citizen science (CS) projects are part of a new era of data aggregation and harmonisation that facilitates interconnections between different datasets. Increasing the value and reuse of CS data has received growing attention with the appearance of the FAIR principles and systematic research data management (RDM) practises, which are often promoted by university libraries. However, RDM initiatives in CS appear diversified and if CS have special needs in terms of RDM is unclear. Therefore, the aim of this article is firstly to identify RDM challenges for CS projects and secondly, to discuss how university libraries may support any such challenges. A scoping review and a case study of Danish CS projects were performed to identify RDM challenges. 48 articles were selected for data extraction. Four academic project leaders were interviewed about RDM practices in their CS projects. Challenges and recommendations identified in the review and case study are often not specific for CS. However, finding CS data, engaging specific populations, attributing volunteers and handling sensitive data including health data are some of the challenges requiring special attention by CS project managers. Scientific requirements or national practices do not always encompass the nature of CS projects. Based on the identified challenges, it is recommended that university libraries focus their services on 1) identifying legal and ethical issues that the project managers should be aware of in their projects, 2) elaborating these issues in a Terms of Participation that also specifies data handling and sharing to the citizen scientist, and 3) motivating the project manager to good data handling practises. Adhering to the FAIR principles and good RDM practices in CS projects will continuously secure contextualisation and data quality. High data quality increases the value and reuse of the data and, therefore, the empowerment of the citizen scientists
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