Govern and assess

This interactive on-line course prepared by the Social Science Research Council and the Qualitative Data Repository contains four modules about different aspects of RDM of qualitative data in Social Sciences. Each module is composed by multiple lessons and can also function as a stand-alone resource to be completed individually. Most lessons include associated readings, resources, exercises, and activities.

The knowledge base’s of the DataWiz is a complete RDM guideline for Psychology research to support or complement the use of the DataWiz data management tool. The content is structured in three sections: before, during and after data collection & analysis. The first section covers data management planning as well as the various legal and ethical aspects related to data management. The second section focuses on best practices and tips for handling and documenting data during research. Finally, the last section focuses on how to share and preserve data at the end of the project.

This guideline helps researchers to use OneNote as an Electronic Laboratory Notebook (ELN). It provides tips to adapt OneNote to an ELN workflow with a focus on the biomedical sciences, which can be adjusted to other nonscientific ELNs. It covers several topics such as how to structure and label experiments, data acquisition and representation. It also provides relevant recommendations on how to approach data storage and security to comply with applicable legislation.

The Library service of Standford University has compiled a series of case studies with examples of good and bad practices about different RDM topics, such as file organization and naming, data storage, metadata, spreadsheets and publishing data online. The examples explain the consequences of bad practices and provide solutions.

ANDS has published a comprehensive guide about best practice for the publication and sharing of research data (in the Australian context). The guide helps researchers with the publishing phase of senstitive data with a step-by-step approach which covers several phases of the research data lifecycle. These steps are summarized in a decision tree.

Ghent University has elaborated an encryption manual for researchers. It begins with basic information about what encryption is and when is it needed. Then, it describes different encryption strategies and frames them into different scenarios, and provides step by step instructions for each of these scenarios.

Utrecht University provides an overview of possible legal instruments and agreements that might be necessary to establish prior to data collection. The information is provided in a user friendly approach departing from the perspective of different stakeholders perspective: data subject, third party and data reuser. It then provides extended details of what it instrument entails with further guidance, templates or examples for each case.

This Open Source tool developed by the Institute of Formal and Applied Linguistics of Charles University guides the user through a decision tree approach to select a license for their research output. The tool considers scenarios where the user does not own copyright or similar rights in some of all of the constitutive parts of the dataset and provides further guidance based on whether those elements are clearly licensed or not.

The University of Ghent summarises all the RDM requirements per funder and grouped into four categories: during proposal stage, post-award data management plan, covering data management costs and data sharing and/or preservation requirements.

The University of Bath has elaborated a page with UKRI and other major funder requirements, together with university policy requirements and national relevant legislation. For each funder, information is given in a set of harmonized sections: providing a DMP, recovering data management costs, providing data access statements, data sharing and data retention.