Research data

• Observational: data captured in real time. For instance, neuroimages, sample data, sensor data, questionnaire data
• Experimental: data captured on laboratory equipment. For instance, gene sequences, chromatograms, magnetic field data
• Simulation: data generated based on test models. For example, climatological, mathematical or economic models
• Derived or compiled: difficult-to-reproduce data. For instance, text and data mining, 3D models or compiled databases
• Reference: data conglomerate or dataset. For instance, databases of gene sequences, chemical structures or spatial data portals

Research data management (RDM) encompasses the organization, structure, storage and processing of the data used or generated during a research project.

• Data creation and planning their reuse
• Processing and organization: structuring and metadata
• Analysis
• Preservation – security, access, storage and retrieval
• Share: publish and be cited
• Reuse

• Validation of the results obtained.
• Data localization and comprehension.
• Reduces the duplication of data collection and the costs involved.
• Complies with the requisites of calls for research.
• Promotes scientific debate.
• Promotes innovation and new potential uses of the data.
• Encourages collaboration between data users and creators.
• Increases research impact and visibility.
• Increases your reputation when other people cite your work.

According to Article 12 of the Law on Intellectual Property, the databases “that by selection or arrangement of their content constitute intellectual creations” are protected by copyright. However, the raw data included in a database have no authorship and, therefore, are not intellectual property.

• Law on Intellectual Property
• Law 5/1998 of 6 March, incorporating into Spanish law Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases