The FAIR Guiding Principles for scientific data management and stewardship

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community

Tehnološki aspekti industrijske proizvodnje dječje hrane na bazi mlijeka

Idealna hrana za dojenčad - ženino mlijeko, nije uvijek praktički na raspolaganju ili ga nema u dovoljnoj količini. Kao najbolja i jedina zamjena tome mlijeku, odnosno kao sirovina za preradu u dojenačku i dječju hranu, služi kravlje mlijeko

Training in Data Curation as Service in a Federated Data Infrastructure - the FrontOffice-BackOffice Mode

The increasing volume and importance of research data leads to the emergence of research data infrastructures in which data management plays an important role. As a consequence, practices at digital archives and libraries change. In this paper, we focus on a possible alliance between archives and libraries around training activities in data curation. We introduce a so-called \emph{FrontOffice--BackOffice model} and discuss experiences of its implementation in the Netherlands. In this model, an efficient division of tasks relies on a distributed infrastructure in which research institutions (i.e., universities) use centralized storage and data curation services provided by national research data archives. The training activities are aimed at information professionals working at those research institutions, for instance as digital librarians. We describe our experiences with the course \emph{DataIntelligence4Librarians}. Eventually, we reflect about the international dimension of education and training around data curation and stewardship.Comment: TPDL 2013, accepted for workshop Education in Data Curation, preprin

De bekendheid van DANS en zijn diensten in 2013 en 2011 onder onderzoekers, promovendi en researchmaster studenten

In het najaar van 2011 heeft er een naambekendheidonderzoek plaatsgevonden ten behoeve van DANS. Dit onderzoek is in het najaar van 2013 herhaald. Dit in verband met de strategische prioriteit van DANS om haar naambekendheid te versterken. Deze doelstelling is voor de periode 2011 tot en met 2015 als volgt gekwantificeerd: In 2015 is de naambekendheid van DANS en zijn diensten onder de wetenschappelijke onderzoekers en researchmaster studenten aanzienlijk togenomenen en worden deze diensten ruim voldoende gewaardeerd. Het naambekendheidonderzoek is uitgevoerd onder drie doelgroepen in de wetenschap: -onderzoekers (UD’s, UHD’s, hoogleraren) -promovendi -researchmaster studenten. Ten opzichte van de nulmeting in 2011 zijn er in 2013 ook lectoren bevraagd en researchmaster studenten van de Universiteit Leiden. In dit rapport wordt verslag gedaan van de resultaten van het naambekendheidonderzoek van 2013 en worden de resultaten vergeleken met de nulmeting in 2011. Het onderzoek is uitgevoerd door Pleiade Management en Consultancy, onder supervisie van Ingrid Dillo en Heidi Berkhout

Building a Federated Infrastructure for Preservation of and Access to Research Data in the Netherlands: The Front Office-Back Office Model

A federated data infrastructure is emerging in The Netherlands on the basis of the collaborative model proposed in the Riding the Wave report as a framework for the scholarly information system of the future. This federated model is elaborated as a layered front office – back office model, in which university libraries, national data services and basic technical einfrastructure organizations work together. The responsibilities and functions performed by the various stakeholders involved in the federated infrastructure are clearly complementary. The costs and benefits are distributed efficiently over the stakeholder. The introduction of the model is timely, because many research organizations are currently developing data management policies. Therefore the federated infrastructure is attractive for all parties involved