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Identifying the scope of research data services and stewardship activities and securing the resources to sustain these. Continually reviewing the business case considering the service value propositions, processes, and relevant costs and benefits, taking into account governance processes and timelines, and the need for cost recovery mechanisms to comply with funder requirements.

DataWiz Knowledge Base

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.

A quick guide for using Microsoft OneNote as an electronic laboratory notebook

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.

Guide to using OneNote as a Research Notebook

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.

Electronic Research Notebook Case Study

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.

Persistent Identifiers ANDS guides

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.

Five steps to decide what to keep

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.

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.

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.