Research Data Management (RDM)

Research data management (RDM) can be defined as a set of practices to handle information collected and created during research. It is ‘the compilation of many small practices that make your data easier to find, easier to understand, less likely to be lost, and more likely to be usable during a project or ten years later’.[1]​ T hese practices involve, but are not limited to, data management planning, documentation, organization, storage, dissemination and preservation.[2]​ Effective RDM is an ongoing process which is structured and aligned with the research context and disciplinary practices.

• What is RDM and why should that interest us?
• What are the RDM challenges in engineering?
• How to prepare a Research Data management plan?
• What are the recommended file formats?
• What are the FAIR principles?
• How to select the most appropriate repository? • Where can I learn more about RDM?
• RDM describes the organisation, storage, preservation, and sharing of data collected and used in a research project. • It involves decision about how data will be preserved and shared after the project is completed.

Ethics
-RDM is part of the responsible conduct of research, i.e. the practice of scientific inviestigation with integrity -Reproducibility crisis in science (well-managed and accessible data allows others to validate and replicate findings)

Requirements imposed by funders and publishers
-Open Science is one of the pillars of the Horizon Europe, the new EU framework programme for research and innovation

Saves times and resources (in the long run)
Horizon 2020 Framework Programme of the European Union; H2020 WIDESPREAD-2-Teaming: #739574 7 Why shoud this interest us? • A formal document that outlines how data are to be handled during a research project, and after the project is completed • What should a DMP include?
-Description of data to be collected/created -Standards/methodologies for data collection and management -Ethics and intellectual property right -Plans for data sharing and access -Strategy for long-term preservation • Useful resources: Digital Curation Centre Horizon 2020 Framework Programme of the European Union; H2020 WIDESPREAD-2-Teaming: #739574 9 Data management plan (DMP) 1. What data will you collect or created? How? 2. What documentation and metadata will accompany the data? 3. How will you manage any ethical and legal issues? 4. How will the data be stored and backed up during research? 5. How will you manage access and security? 6. How will you share the data? Are any restrictions required? 7. Who will be responsible for data management? What resources will you require to deliver your plan?

Documentation and metadata
• Metadata is "data about data" (Examples: persistent identifier such as DOI, pubication date, title, authors, description, keywords, licence, funding, related identifiers, etc.) • Documentation may also include details on the methodology used, analytical and procedural information, definition of variables, vocabularies, units of measurment, assumptions made, and the format and file type of the data • Existing community metadata standards: General (e.g. Dublin Core) or discipline specific (e.g. DDI); See RDA Metadata directory

www.innorenew.eu InnoRenew CoE
The FAIR data principles • Findable: metadata and data easy to find for both humans and computers • Accessible: users need to know how can they be accessed, possibly including authentication and authorisation • Interoperable: data can be integrated with other data in applications or workflows for analysis, storage, and processing • Reusable: metadata and data should be well-described so that they can be used in different settings

Ethics and legal compliance
• Review of research plans involving sensitive research on human subjects are submitted to the ethical commitee • Informed consent sought for data collection, processing and long-term preservation • Removal, aggregation, pseudoanonymisation, or anonymisation of direct and indircet identifiers in data files • Restriction of access do the data in cases, when anonymisation would hinder the reusability of data • Compliance with General Data Protection Regulation (GDPR) • Confidential information and trade secrets

Storage and backup
• Copyrigth and intelectual property rights (Consortium agreements) • Licences for reuse (e.g. Creative commons) • Restriction on reuse of third-party data • Data sharing restrictions (embargo periods) • Security measures and standards for confidential data

Selection and preservation
• What data must be retained/destroyed for contractual, legal, or regulatory purposes • Foreseeable research uses for the data (validation of research findings, conduct of new studies, teaching) • Length of retention and preservation • Repository or archive for data to be held • Costs (repository charges, time and effort to prepare te data for sharing/preservation)