Findability

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.

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 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.

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.

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.

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.

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.