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Developing and applying appropriate methods to enable lines of enquiry to be formulated and pursued towards the research objectives, by assembling and integrating selected data, software, systems, or other resources, and enabling relevant knowledge and techniques to be applied in their analysis and transformation into research outputs.

Chemotion Electronic Laboratory Notebook

Chemotion is an Open Source Electronic Laboratory Notebook for chemical researchers. The Chemotion ELN is equipped with the basic functionalities necessary for the acquisition and processing of chemical data, in particular the work with molecular structures and calculations based on molecular properties. The ELN allows the search for molecules and reactions not only within the user’s data but also in conventional external sources as provided by SciFinder and PubChem. The ELN provides tools to share data in the Chemotion Data Repository. More information available at: Tremouilhac, P., Nguyen, A., Huang, Y. et al. Chemotion ELN: an Open Source electronic lab notebook for chemists in academia. J Cheminform 9, 54 (2017). https://doi.org/10.1186/s13321-017-0240-0

GeoDatabase (.gdb) Data Curation Primer

The Data Curation primers are documents used as a reference to curate research data within a specific discipline area or when using certain software or data types. They are developed during a series of workshops were attendees get input from a mentor of the Data Curator Network. The results are published in GitHub repositories. The GeoDatabase Data Curation Primer provides guidelines to manage and organize geographic data in geodatabases, to describe such data using geospatial metadata standards and which actions can be undertaken to preserve geospatial data in the long term.

Managing Qualitative Social Science Data An interactive online course

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.

Case studies: good and bad RDM practices

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.

The R workshops and the R café

Utrecht University organises regular workshops to teach R basics: data handling and visualisation, and making research reproducible with R and R Markdown. The R Café has a more informal set-up, where researchers with R programming skills can meet and learn from each other, or from prepared exercises.

Data Cleaning with Open Refine for Ecologists

Data Carpentry has developed this course of data pre-processing with Open Refine, an open tool to work with data. The course covers several topics such as error correction and data formatting and harmonization.

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.