Suchergebnisse

Data discovery is important to facilitate data re-use. In order to help frame the development and improvement of data discovery tools, we collected a list of requirements and users' wishes. This paper presents the analysis of these 101 use cases to examine data discovery requirements; these cases were collected between 2019 and 2020. We categorized the information across 12 'topics' and eight types of users. While the availability of metadata was an expected topic of importance, users were also keen on receiving more information on data citation and a better overview of their field. We conducted and analysed a survey among data infrastructure specialists in a first attempt at ranking the requirements. Between these data professionals, these rankings were very different, excepting the availability of metadata and data quality assessment.

FAIRplus seeks to establish 'FAIRification' processes that can be used at scale to ensure FAIRness of IMI data. In order to establish, refine and validate FAIRplus FAIRification techniques, four pilot IMI datasets were selected in D1.1. This deliverable describes the outcomes achieved to date when FAIRifying these datasets using the newly established FAIRplus FAIRification process. The FAIRified pilot datasets are now listed in the IMI data catalogue, along with an evaluation of their level of FAIR after FAIRification processes were applied. In this deliverable, we show that FAIRplus FAIRification processes generally increase the level of FAIR for pilot datasets, although no datasets became "completely FAIR" as a result. Level of FAIR is quantified by a series of FAIR indicators, established from a FAIR assessment conducted on each dataset before and after FAIRification. The results of these assessments are linked from the IMI data catalogue. All processes used during FAIRification are documented as recipes in the FAIR Cookbook. ; This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 802750. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation and EFPIA companies.

FAIRplus seeks to establish 'FAIRification' processes that can be used at scale to ensure FAIRness of IMI data. In order to establish, refine and validate FAIRplus FAIRification techniques, four pilot IMI datasets were selected in D1.1. This deliverable describes the outcomes achieved to date when FAIRifying these datasets using the newly established FAIRplus FAIRification process. The FAIRified pilot datasets are now listed in the IMI data catalogue, along with an evaluation of their level of FAIR after FAIRification processes were applied. In this deliverable, we show that FAIRplus FAIRification processes generally increase the level of FAIR for pilot datasets, although no datasets became "completely FAIR" as a result. Level of FAIR is quantified by a series of FAIR indicators, established from a FAIR assessment conducted on each dataset before and after FAIRification. The results of these assessments are linked from the IMI data catalogue. All processes used during FAIRification are documented as recipes in the FAIR Cookbook. ; This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 802750. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation and EFPIA companies.