Access control and management

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.

DataWiz is an automated assistant for data management in Psychology, developed by the Leibniz Institute for Psychology Information. It is a web-based application that supports researchers in planning their data management before the project starts and in managing their research data during the project. It provides functions to cover the entire research data lifecycle: data preparation, documentation and archiving. It also provides a digital collaborative working environment for you and your team. With DataWiz researchers can reduce the time spent on RDM, increase the quality and ensure the long-term reusability of research data.

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.

Utrecht University has created a simple decision-tree tool that helps researchers to choose a generic repository that best fits their needs. To do so, criteria such as restriction of access and cost of the repository are taken into account.

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.

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.