Neuroscientific data - be they experimental or simulated data, models or software - are multimodal, heterogeneous, and organised in many different ways. Although most of these data use similar terms and concepts, it can be challenging to standardise these terms without losing the study-specific aspects of each data type. The process of organising, categorising and integrating data is called data curation. Data curations allows for the data to be found, compared and/or reused by other researchers. The EBRAINS Curation Service supports you in this process. Together, we will advance Open Science by making data "open".
"Open data" are shared without any restrictions on the reuse of data to promote transparency, accelerate scientific research, and encourage new collaborations. In some cases, there may be legitimate reasons to shield data from the public, for example, to protect sensitive data (e.g., safeguard the privacy of subjects), and to reduce the risk of being scooped by others. The “A” in the FAIR Data Guiding Principles (Wilkinson et al., 2016) stands for “Accessible under well-defined conditions”. The goal is to strive to make data “as open as possible, and as closed as necessary”, which ensures the safeguarding of data and prevents their misuse without compromising the possibility for reuse.
EBRAINS has clear guidelines on how data are shared. We ensure that all data are accompanied by licenses to allow for such restrictions; we have several options in place for releasing data under embargo until a scientific article is accepted for publication (read more); and we safeguard data via our access control solutions for sensitive human data (read more).
The FAIR guiding principles also serve to guide researchers in preparing data for data sharing and managing data and metadata appropriately. The EBRAINS Curation Service follows these FAIR principles in the curation of data and supports researchers and scientific publishers in their endeavours to improve the Findability, Accessibility, Interoperability, and Reusability (FAIRness) of their data.