General Dataset Curation Guide (GDCG)_French

Abstract

Data collection and organization is one of the main tasks during research activities. In fact, most of projects results depend on the good management of data. However, “the long-term value of data can be affected, for better or worse, by how well those data are curated. Unfortunately, many valuable datasets are poorly curated, which contributes to errors, redundant effort, and obstacles to replication and use” (Ruggles, 2018). This, because is common to organize data in spreadsheets in a way which makes them easily understandable for the dataset author at that time, without following the machine-readable standards or considering any next research use. Due to this, there is then the need to review and adjust these datasets. This is one of the data curation roles. “Data curation activities enable data discovery and retrieval, maintain data quality, add value, and provide for re-use over time” (DH Curation Guide, 2017). Nowadays specific jobs related to data curation responsibilities are increasing (with title like “data curator” or “data curation specialist”) demonstrating the current importance of these skills. Thus, it will be important for anyone to have basic knowledge of this subject to be able during research activities, creating autonomously well curated datasets and this guide will support doing so.