Discipline agnostic

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

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.

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 University of Glasgow has prepared a detailed guideline on how to use Microsoft OneNote as a research notebook. It guides researchers from the initial steps of accessing the software and setting up a notebook, to more specific functionalities such as inserting tables, images, equations. It also provides information onhow to manage and share content.

The University of Glasgow run a work package between 2018 and 2019 to investigate the user requirements for Electronic Research Notebooks (ELNs). They organized a series of workshops to understand user needs, and run software trials to increase the interest and understand barriers inhibiting the uptake of ELNs. The results and conclusions of the exercise have now been published.

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 Australian National Data Service has developed three guides about Persistent Identifiers for three different types of users. The basic guide focus on awareness level and explains the basics about PIDs. The working level guide expands on the basic, going into technical and policy details. The expert level guide is intended to provide in-depth understanding for research administrator and technical staff on how to set up a PID infrastructure.

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.

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.

The Digital Curation Centre has prepared a tool to guide researchers in their preservation strategy. Through a series of checklist, researchers are guided to reflect on what purposes the data could serve in the future, to consider what requirements they need to fulfill and what should be preserved according to the data reuse potential and its replicability. Finally, the costs of the preservation are considered to make a final decision.