Search

This poster presents the results of a student research project (Master in Information Science of HEG, HES-SO) performed in the frame of the consulting, training and teaching activities of the Swiss DLCM project. This work is dedicated to designing one or several e-learning models for the DLCM context. We first make an inventory of the e-learning resources in RDM already made available by Swiss and foreign institutions. This review allows us to set up a typology of e-learning resources in GDR and to identify some reusable and adaptable material for the DLCM e-learning course. The creation and the development of an e-learning course should follow a step-by-step process: analyse, design, test, produce, follow-up, which we adapt to the project environment and describe in detail. E-learning courses may take a whole variety of configurations, according to choices made among (to give only a few examples): hybrid course or full distant learning, free access or not, delivery of a certificate or not, tutored course or not; and to related questions such as: how much tutoring? By which means? etc. We discuss each element of configuration and make recommendations for the project specific context. Basing on typical profiles of the DLCM course audience and on an assessment of their needs, we suggest three options of training configurations: SPOC, MOOC and free training course. The complete report of this student project (in French) is available here: http://doc.rero.ch/record/328462?ln=fr

Dans ce chapitre, les auteurs décrivent le projet national de gestion du cycle de vie des données (DLCM), constitué par les équipes de bibliothèques, informatiques et services de recherche de huit institutions partenaires. Lancé officiellement en août 2015 et financé par le programme swissuniversities SUC P-2/ P-5, le projet vise à mettre en place des services destinés principalement aux chercheurs au niveau suisse pour leurs besoins les plus importants dans la gestion de leurs données. L'identification de ces besoins s'est basée sur une étude d'envergure de documents, ainsi que des entretiens semi-structurés, ce qui a conduit à répertorier des ‚use cases‘ principaux ainsi que des services de base, tels que : un point d'accès et de contact pour obtenir des informations, de la formation, et conseils personnalisés, ainsi que des solutions de gestion actives des données, y inclus des options de stockage de conservation à long terme, et leur publication selon des normes internationales. Un accent particulier a en outre été mis sur la pérénité des services en appliquant des méthodologies de modèles d'affaire inspirés des ‚lean startups‘ et en établissant des modèles de coûts.

This poster was presented during the 11th International Digital Curation Conference "Visible data, invisible infrastructure", 22 - 25 February 2016, in Amsterdam, Netherlands.

Les universités n'ont pas attendu les directives du Fonds National Suisse (FNS) pour se pencher sur la question de la préservation et de l'accessibilité des données scientifiques. Hors du milieu académique, d'autres acteurs, comme Docuteam Sàrl, se sont aussi spécialisés dans cette problématique.

Managing research data throughout its lifecycle ensures its long-term value and prevents data from falling into digital obsolescence. Proper data management is a key prere- quisite for effective data sharing throughout the scientific community. This, in turn, increases the visibility of scholarly work and is likely to increase citation rates. Many funding organizations prescribe the use of data management plans and insist on open access publication of the research results they funded. Even if a funding body does not explicitly demand data management, following professional curation and preserva- tion concepts has numerous advantages: • It greatly facilitates the reuse of research data. • As a result, this increases the impact of research results. • It saves precious research funds and ultimately natural and human resources by avoiding unnecessary dupli- cation of work. Today, the availability of well-managed data is part of good scientific practice and ensures the reproducibility of research results, a key requirement at the core of the research process. The following data management checklist is based on a generalised research data lifecycle, and is flexible enough to be applied to requirements from different funding organisations.