ARDC curate a collection of resources for researchers, librarians and eResearch professionals. The topics covered by resources and guides range from ethics and sensitive data to managing data; from research data policy and licensing to training and teaching research data management skills.

Biomedical research today is not only rigorous, innovative and insightful, it also has to be organized and reproducible. With more capacity to create and store data, there is the challenge of making data discoverable, understandable, and reusable. Many funding agencies and journal publishers are requiring publication of relevant data to promote open science and reproducibility of research.

In order to meet to these requirements and evolving trends, researchers and information professionals will need the data management and curation knowledge and skills to support the access, reuse and preservation of data.

This course is designed to address present and future data management needs.

Ever need to help a researcher share and archive their research data? Would you know how to advise them on managing their data so it can be easily shared and re-used? This workshop will cover best practices for collecting and organizing research data related to the goal of data preservation and sharing. We will focus on best practices and tips for collecting data, including file naming, documentation/metadata, quality control, and versioning, as well as access and control/security, backup and storage, and licensing. We will discuss the library’s role in data management, and the opportunities and challenges around supporting data sharing efforts. Through case studies we will explore a typical research data scenario and propose solutions and services by the library and institutional partners. Finally, we discuss methods to stay up to date with data management related topics.

Abstract
Training materials. The DATUM for Health training programme covers both generic and discipline-specific issues, focusing on the management of qualitative, unstructured data, and is suitable for students at any stage of their PhD. It aims to provide students with the knowledge to manage their research data at every stage in the data lifecycle, from creation to final storage or destruction. They learn how to use their data more effectively and efficiently, how to store and destroy it securely, and how to make it available to a wider audience to increase its use, value and impact.

Data Carpentry trains researchers in the core data skills for efficient, shareable, and reproducible research practices. We run accessible, inclusive training workshops; teach openly available, high-quality, domain-tailored lessons; and foster an active, inclusive, diverse instructor community that promotes and models reproducible research as a community norm.

Data curation primers are peer-reviewed, living documents to provide practical and concise guides on curating a specific data type or format, or addressing a particular challenge in data curation work. All the primers are developed by Data Curation Network (DCN) which is a seed funding project from the Alfred P Sloan Foundation. The target audiences of primers are data curators and/or data librarians. To date, DCN has published more than 25 primers on database, Excel, netCDF, NVivo, R, SPSS, etc.

This is a workshop PowerPoint slides about data curation. It aims to instruct researchers (especially in the University of Hong Kong) how to manage their research data. Some basic theories and how research data are managed in Lifecycle are covered in it.

The Data Management Skillbuilding Hub is a repository for open educational resources regarding data management, meaning that it is a collection of learning resources freely contributed by anyone willing to share them. Materials such as lessons, best practices, and videos, are stored in the DataONEorg GitHub repository as well as searchable through the Data Management Training Clearinghouse. We invite you submit your own educational resources so that the Data Management Skillbuilding Hub can remain an up-to-date and sustainable educational tool for all to benefit from. You can easily contribute learning materials to the Skillbuilding Hub via GitHub online.

Data Tree is a free online course with all you need to know for research data management, along with ways to engage and share data with business, policymakers, media and the wider public. The self-paced training course will take 15 to 20 hours to complete in eight structured modules. The course is packed with video, quizzes and real-life examples of data management, along with valuable tips from experts in data management, data sharing and science communication. The training course materials will be available for structured learning, but also to dip into for immediate problem solving.

Data Tree is funded by the Natural Environment Research Council (NERC) through the National Productivity Investment Fund (NPIF), delivered by the Institute for Environmental Analytics and Stats4SD and supported by the Institute of Physics.

This workshop was designed to serve those who enrolled in the research postgraduate program at University of Hong Kong. The content of this workshop covers the relevant basic knowledge about data curation and research data management, including the value of data management, writing a data management plan and repository options. Most importantly, this workshop is aiming to facilitate the HKU RPG students by providing more necessary information related to them, which could be the easy but proper practice they could follow.

Effective Research Data Management (RDM) is a key component of research integrity and reproducible research, and its importance is increasingly emphasised by funding bodies, governments, and research institutions around the world. However, many researchers are unfamiliar with RDM best practices, and research support staff are faced with the difficult task of delivering support to researchers across different disciplines and career stages. What strategies can institutions use to solve these problems?

Engaging Researchers with Data Management is an invaluable collection of 24 case studies, drawn from institutions across the globe, that demonstrate clearly and practically how to engage the research community with RDM. These case studies together illustrate the variety of innovative strategies research institutions have developed to engage with their researchers about managing research data. Each study is presented concisely and clearly, highlighting the essential ingredients that led to its success and challenges encountered along the way. By interviewing key staff about their experiences and the organisational context, the authors of this book have created an essential resource for organisations looking to increase engagement with their research communities.

This handbook is a collaboration by research institutions, for research institutions. It aims not only to inspire and engage, but also to help drive cultural change towards better data management. It has been written for anyone interested in RDM, or simply, good research practice.

This lesson introduces undergraduates to personal digital archiving (PDA) as an instructional bridge to research data management.

PDA is the study of how people organize, maintain, use and share personal digital information in their daily lives. PDA skills closely parallel research data management skills, with the added benefit of being directly relevant to undergraduate students, most of whom manage complex personal digital content on a daily basis.

By teaching PDA, librarians encourage authentic learning experiences that immediately resonate with students' day-to-day activities. Teaching PDA builds a foundation of knowledge that not only helps students manage their personal digital materials, but can be translated into research data management skills that will enhance students' academic and professional careers.

The FOSTER portal is an e-learning platform that brings together the best training resources addressed to those who need to know more about Open Science, or need to develop strategies and skills for implementing Open Science practices in their daily workflows. Here you will find a growing collection of training materials. Many different users - from early-career researchers, to data managers, librarians, research administrators, and graduate schools - can benefit from the portal. In order to meet their needs, the existing materials will be extended from basic to more advanced-level resources. In addition, discipline-specific resources will be created.

The link takes users to "Research Data Management" topic. Howevere, there are other topics to explore.

Note: Unless otherwise stated, all materials created by the FOSTER consortium are licensed under a CREATIVE COMMONS ATTRIBUTION 4.0 INTERNATIONAL LICENSE.

"Learning Data Ethics for Data Sharing" is an Open Educational Resource (OER) for library students, data librarians, or researchers interested in learning how to ethically share data into data repositories. Often DEIA topics are discussed in terms of data collection or in regards to public use of data; this work attempts to target responsible ethics at the process that is occurring while preparing to share data, with the goal of creating FAIR access to the data and reusability for future research.

Additional documentation is available at https://osf.io/cb3sa/.

This module will look at emerging trends and best practice in data management, quality assessment and IPR issues. We will look at policies regarding data management and their implementation, particularly in the framework of a Research Infrastructure.

Learning Outcomes:
By the end of this module, you should be able to:
- Understand and describe the FAIR Principles and what they are used for
- Understand and describe what a Data Management Plan is, and how they are used
- Understand and explain what Open Data, Open Access and Open Science means for researchers
- Describe best practices around data management
- Understand and explain how Research Infrastructures interact with and inform policy on issues around data management

You can progress through this module in the order in which we present the various sections. However, this is merely a suggestion as to how you might approach this topic. You might choose to skip certain sections depending on your level of previous knowledge in that area. You can navigate this via the menu on the lefthand side.

Each section has a set of resources and tools that you might find useful, as well as a list of items that we recommend for further reading around the subject.

A class on setting up and managing research materials; caring for digital files to enable collaboration, sharing, and re-use; and helpful software/digital tools for organizing personal research files.

In January 2014, NIH launched a series of initiatives to enhance rigor and reproducibility in research. As a part of this initiative, NIGMS, along with nine other NIH institutes and centers, issued a funding opportunity announcement (FOA) RFA-GM-15-006 to develop, pilot, and disseminate training modules to enhance data reproducibility. This FOA was reissued in 2018 (RFA-GM-18-002).For the benefit of the scientific community, we will post the products of grants funded by these FOAs on this website as they become available. In addition, we are sharing here other relevant training modules developed, including courses developed from administrative supplements to NIGMS predoctoral T32 grants.

Après un premier Plan Stratégique Numerique, décliné de 2015 à 2020, l'UCLouvain se dote en 2021 d'un nouveau projet de transformation numérique à l'horizon du 600e anniversaire de l'institution. Il contribue à la mise en place d'objectifs d’ouverture (Open Science, Open Education, dynamique internationale via Circle U.) et d' innovation (transformation de l’environnement pédagogique). Prolongeant l’expérience numérique développée ces dernières années, ce projet (document PDF, sous licence CC-BY) vise à en pérenniser les résultats, en matière d'Open Education, d'Open Sience et de formation à distance.