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 https://www.go-fair.org/fair-principles/ 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 Principles: The Role of Libraries (https://libereurope.eu/wp-content/uploads/2017/12/LIBER-FAIR-Data.pdf) we include the following additional description on the Reusable term: Data and collections have a clear usage licenses and provide accurate information on provenance.
Top-3 dataset for Deep Learning, based on a 25 list (https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/)
- From http://cocodataset.org/#termsofuse: The COCO Consortium does not own the copyright of the images.
- From http://image-net.org/download-faq: The images in their original resolutions may be subject to copyright, so we do not make them publicly available on our server.
- From https://storage.googleapis.com/openimages: While we tried to identify images that are licensed under a Creative Commons Attribution license, we make no representations or warranties regarding the license status of each image and you should verify the license for each image yourself.
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