Guidelines for ‘good data management,’ which improve the discovery and (re)use of scholarly data by humans and computers (e.g., machine learning, algorithms). These principles include:
– Findable: Data (or any digital object), metadata (i.e., information about that digital object), and infrastructure (e.g., data registered or indexed in a searchable resource) should be easy to find for both humans and computers
– Accessible: Once found, there should be clear means of accessing data, metadata, or infrastructure of interest
– Interoperable: Data should interoperate with applications or workflows for analysis, storage, and processing
– Reusable: Metadata and data should be well-described so that they can be (re)used, replicated, or combined in different settings.