Thales Sehn Körting

Is Deep Learning FAIR?



Deep Learning articles use benchmarks to measure the quality of the results. However, several benchmarks do not have the copyright of all data used. So, how to believe that every paper uses the same benchmark? From we have the description of the FAIR acronym Findable: The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers.  Accessible: Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation. Interoperable: The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. Reusable: The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings. From the article Implementing FAIR Data Pri