Version control tools & techniques handout

The Massachusetts Institute of Technology (MIT) has developed a series of file organization handouts. The handout for version control briefly summarise different techniques for version control and provides an overview of the main differences between automatic change log platforms and tools.

Version control with Git course

This course prepare by the Software Carpentry guides through how Git (and GitHub) can be used to manage versions during a project. It starts with the basics (setting up Git and creating a repository), and follows with practical guidelines to track changes, collaborate or resolve conflicts. It has also dedicated sections about the impact of version control on Open Science, licensing and citations.

Guidelines and examples of transcription of qualitative data

The UK Data Service has compiled a set of instructions and best practices to transcribe qualitative data from interviews. This guide seeks to provide advice to ensure methodological consistency and to increase the shareability and reuse of qualitative research data. It provides links to further instructions, examples and a template transcriber confidentiality agreement.

Data processing recommendations for Social Sciences

The CESSDA (Consortium of European Social Science Data Archives) Data Management Expert guide provides a specific chapter about processing data, which includes tips and examples on topics such as quantitative and qualitative coding, adequate weights of survey data and data quality assurance.

Legal instruments and agreements before collecting data

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.

License selector tool

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.

Identifying and assessing RDM costs

The OpenAire project has prepared a guideline to cost data management based on the work of the UK Data Service and the Landelijk Coördinatiepunt Research Data Management. For each data management phase, different activities are foreseen and a description and estimated costs are provided. The guidelines come with brief instructions on how to used them and a useful infographic about RDM and sharing data costs, prepared for OpenAire by the Digital Curation Centre.

Compilation of data sources per research discipline

The Southern Methodist University has compiled a list of recommended resources to find existing data, together with a list of sources categorized by research discipline.

Anticipating the costs of research data management

The University of Bristol has elaborated brief but complete guidelines to anticipate data management costs in a research project, so that these can be included in research funding applications. The guideline provides approximated costs of different activities at different stages of the research data lifecycle.

RDM requirements per research phase

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.