Researcher

Chapter on storage, backup, recovery and security strategies for research data, to protect them against accidental loss and against unauthorised manipulation. Particularly when collecting (sensitive) personal data it is necessary to ensure that these data can only be accessed by those authorized to do so. 

Focus on:

• different storage solutions and their advantages and disadvantages
• storage strategy for research data
• backup and disaster recovery strategy to ensure that avoid data loss, e.g. through human error or hardware failure
• protect data against unauthorised access with strong passwords and encryption
• relevant DMP questions on these topics

Chapter on the data operations needed to prepare data files for analysis and data sharing, starting with data entry and coding of data files. Throughout the different phases of research data files will be edited numerous times. During this process, it is crucial to maintain the authenticity of research information contained in the data and prevent it from loss or deterioration, as well as a comprehensive approach to data quality.

Focus on:

• strategies to minimise errors during the processes of data entry and data coding
• the choice of file formats
• managing the integrity and authenticity of data during the research process
• a systematic approach to data quality
• DMP questions on these topics.

Chapter on how to properly organise and document data and metadata, discussing good practices in designing an appropriate data file structure, file naming and organising data within suitable folder structures; how organising data facilitates orientation in the data file, contributes to the understanding of the information contained and helps to prevent errors and misinterpretations. Also what counts as appropriate documentation of data, development of rich metadata to make data FAIR and standards to promote data sharing.

Focus on:

• elements which are important in setting up an appropriate structure for organising data for intended research work and data sharing
• overview of best practices in file naming and organising data files in a well-structured and unambiguous folder structure
• how comprehensive data documentation and metadata increases the chance data are correctly understood and discovered
• common metadata standards and their value
• relevant DMP questions on this topic.

Chapter introducing research data management and data management planning, explaining basic concepts on:

• research data, social science data, (sensitive) personal data and FAIR principles
• data management and data management plans (DMP)
• the content elements that make up a DMP

Practical tutorials to manage and handle research data for particular software packages: SPSS, R, ArcGIS and N-Vivo. Tutorials contain many practical exercises.

Detailed guidance for researchers, with model consent form and example consent forms, on how to consider future reuse of research data as part of consent procedures with research participants. To make sure that research data, in particular qualitative data, can be made available for future reuse, it is important that consent for future reuse of the data by other researchers is sought from participants. Participants should be informed how research data will be stored, preserved and used in the long-term, and how confidentiality can be protected when needed.

Guidance considers written and verbal consent, the timing of consent, examples consent forms and wording to use, etc. 

Set of hands-on exercises that can be used for data management and sharing training, developed from real-case datasets and scenarios. 

The Top 10 FAIR Data & Software Things are brief guides (stand alone, self paced training materials), called "Things", that can be used by the research community to understand how they can make their research (data and software) more FAIR. Each discipline/topic has its own specific list:

• Nanotechnology
• Astronomy
• Linked Open Data
• Imaging
• Music
• The European Open Science Cloud (EOSC)
• Oceanography
• Research Software
• Research Libraries
• Research Data Management Support
• International Relations
• Humanities: Historical Research
• Geoscience
• Biomedical Data Producers, Stewards, and Funders
• Biodiversity
• Australian Government Data/Collections
• Archaeology

Online data management guidance and resources for researchers, developed by the UK Data Service. This resource provides best practice guidance and advice, including examples, exercises, tools and templates. The focus is on the social sciences and research with human participants. Particular areas covered are:

• Data management planning
• Legal and ethical aspects of managing and sharing data
• IP Rights
• Documenting data
• Formatting data
• Storing data

Library Carpentry workshops teach people working in library- and information-related roles how to:

• Cut through the jargon terms and phrases of software development and data science and apply concepts from these fields in library tasks;
• Identify and use best practices in data structures;
• Learn how to programmatically transform and map data from one form to another;
• Work effectively with researchers, IT, and systems colleagues;
• Automate repetitive, error prone tasks.