Capture and process

Goodtables is a free online service for tabular data validation, developed by the Frictionless Data team of the Open Knowledge Foundation. This open source tool will check basic structural errors such as blank or duplicate rows, duplicate headers, whether all rows have the same number of columns, etc. The data can be validated by providing a URL to the file (e.g. link to GitHub repository) or by uploading a file. Several formats are admitted: csv, excel, LibreOffice, Data Package, etc. Besides, a data schema can be uploaded to enable further checks, such as whether the data type (e.g. date), format (e.g. YYYY-MM-DD) and possible data constrains (e.g. no later than 2000-01-01) are respected. Documentation about the tool is available at: http://docs.goodtables.io/index.html

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

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.

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.

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 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.

The University of North Caroline has developed a step-wise tutorial about metadata. It addresses what metadata is and why is it needed, explains the basic elements of metadata and how these are represemted in standards, as well as how controlled vocabularies are related to metadata. It finally provides a list of best practices resources for metadata.

The University of Helsinki created, upon request, a compact guide for researchers to help with research data documentation. It first introduces the basic elements of documentation, and then provides practical instructions and strategies to proceed with documentation during the research project, but also for the publishing phase.