3. Open Data
Open research data is data that can be freely accessed, reused, remixed and redistributed, for academic research and teaching purposes and beyond. Ideally, open data have no restrictions on reuse or redistribution, and are appropriately licensed as such. In exceptional cases, e.g. to protect the identity of human subjects, special or limited restrictions of access are set. Openly sharing data exposes it to inspection, forming the basis for research verification and reproducibility, and opens up a pathway to wider collaboration. At most, open data may be subject to the requirement to attribute and sharealike (see the Open Data Handbook).
Research data are often the most valuable output of many research projects, they are used as primary sources that underpin scientific research and enable derivation of theoretical or applied findings. In order to make findings/studies replicable, or at least reproducible or reusable in any other way, the best practice recommendation for research data is to be as open and FAIR as possible, while accounting for ethical, commercial and privacy constraints with sensitive data or proprietary data.
FAIR principles
In 2014, a core set of principles were drafted in order to optimise the reusability of research data, named the FAIR Data Principles. They represent a community-developed set of guidelines and best practices to ensure that data or any digital object are Findable, Accessible, Interoperable and Re-usable:
Findable: The first thing to be in place to make data reusable is the possibility to find them. It should be easy to find the data and the metadata for both humans and computers. Automatic and reliable discovery of datasets and services depends on machine-readable persistent identifiers (PIDs) and metadata.
Accessible: The (meta)data should be retrievable by their identifier using a standardised and open communications protocol, possibly including authentication and authorisation. Also, metadata should be available even when the data are no longer available.
Interoperable: The data should be able to be combined with and used with other data or tools. The format of the data should therefore be open and interpretable for various tools, including other data records. The concept of interoperability applies both at the data and metadata level. For instance, the (meta)data should use vocabularies that follow FAIR principles.
Re-usable: Ultimately, FAIR aims at optimising 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. Also, the reuse of the (meta)data should be stated with (a) clear and accessible license(s).
Distinct from peer initiatives that focus on the human scholar, the FAIR principles put a specific emphasis on enhancing the ability of machines to automatically find and use data or any digital object, in addition to supporting its reuse by individuals. The FAIR principles are guiding principles, not standards. FAIR describes qualities or behaviours that are required to make data maximally reusable (e.g., description, citation). Those qualities can be achieved by different standards.
This content has been adapted from the FOSTER Open Science Trainer Handbook, License: CC0 1.0.
In This Section
3.1. Repositories
3.2. Data Management Plans (DMP)
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