E-learning module

Chapter on the process of data discovery and reuse, including evaluating data quality. For discovering high-quality data, curated lists of different types of social science data sources in Europe and around the world are presented.

Focus on:

• set up - and adjust - a search strategy to find suitable data for your research purposes
• social science data repositories as sources for discovering social science data
• data sources which CESSDA-experts recommend for selected research topics
• evaluating the quality and usefulness of data for secondary analysis
• different types and modes of access to data
• relevant DMP questions on this topic

High-quality data have the potential to be reused in many ways. This chapter explores options for archiving and publishing data as a strategy for FAIR data, considering repository solutions, access, use and citation of data.

Focus on:

• the difference between data archiving and data publishing
• the benefits of data publishing
• different data publication services, such as data journal, self-archiving, a data repository
• selecting a data repository which fits your research data's needs
• ways to promote published research data
• relevant DMP questions on these topics

Key legal and ethical considerations in creating shareable data. This chapter clarifies the different legal requirements of the European Union Member States, and the impact of the General Data Protection Regulation (GDPR) on research data management. It shows how sharing personal data can often be accomplished by using a combination of obtaining informed consent, data anonymisation and regulating data access. The supporting role of ethical review in managing legal and ethical obligations is also highlighted in this chapter.

Focus on:

• legal and ethical obligations towards research participants and the different legal requirements of EU Member States
• how protecting data properly protects against violating laws and promises made to participants
• the General Data Protection Regulation and its relevance in research
• informed consent, anonymisation and access controls to facilitate creating shareable data
• relevant elements in a consent form
• anonymisation techniques for quantitative and qualitative data
• relevant DMP questions on these topics

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.

This workshop uses a tabular interview dataset from the SAFI Teaching Database and teaches data cleaning, management, analysis and visualization. There are no pre-requisites, and the materials assume no prior knowledge about the tools. We use a single dataset throughout the workshop to model the data management and analysis workflow that a researcher would use.

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.