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.

List of exercises, presentation slides, and poster on research data management and scholarly communication topics by Chealsye Bowley.

The FAIR Cookbook is created by researchers and data managers professionals, and is an online resource for the Life Sciences with recipes that help you to make and keep data Findable, Accessible, Interoperable and Reusable (FAIR).

The FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. However, the FAIR Principles are aspirational and generic. The FAIR Cookbook guides researchers and data stewards of the Life Science domain in their FAIRification journey; and also provides policy makers and trainers with practical examples to recommend in their guidance and use in their educational material.

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.

The Image Data Resource (IDR) is a public repository of reference image datasets from published scientific studies. IDR enables access, search and analysis of these highly annotated datasets. Datasets are usually CC0 or CC BY 4.0.

As rapidly changing technology enables researchers to collect large, complex datasets with relative ease, the need to effectively manage these data increases in kind. This is the first lesson in a series of education modules intended to provide a broad overview of various topics related to research data management. It covers: trends in data collection, storage and loss, the importance and benefits of data management, and an introduction to the data life cycle.

"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/.

Conversations regarding research data often intersect with questions related to ethical, legal, and policy issues for managing research data. This lesson will define copyrights, licenses, and waivers, discuss ownership and intellectual property, and describe some reasons for data restriction. After completing this lesson, participants will be able to identify ethical, legal, and policy considerations that surround the use and management of research data.

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.

What is metadata? Metadata is data (or documentation) that describes and provides context for data and it is everywhere around us. Metadata allows us to understand the details of a dataset, including: where it was collected, how it was collected, what gaps in the data mean, what the units of measurement are, who collected the data, how it should be attributed etc. By creating and providing good descriptive metadata for our own data, we enable others to efficiently discover and use the data products from our research. This lesson explores the importance of metadata to data authors, users of the data and organizations, and highlights the utility of metadata. It provides an overview of the different metadata standards that exist, and the core elements that are consistent across them. It guides users in selecting a metadata standard to work with and introduces the best practices needed for writing a high quality metadata record.

The Mozilla Science Lab is developing an Open Data Training Program. This repository will be where we build and share our curriculum and resources for open data.

The New England Collaborative Data Management Curriculum (NECDMC) project is led by the Lamar Soutter Library at the University of Massachusetts Medical School in partnership with several libraries in the New England region.

NECDMC is an instructional tool for teaching data management best practices to undergraduates, graduate students, and researchers in the health sciences, sciences, and engineering disciplines. Each of the curriculum’s seven online instructional modules aligns with the National Science Foundation’s data management plan recommendations and addresses universal data management challenges. Included in the curriculum is a collection of actual research cases that provides a discipline specific context to the content of the instructional modules. These cases come from a range of research settings such as clinical research, biomedical labs, an engineering project, and a qualitative behavioral health study. Additional research cases will be added to the collection on an ongoing basis. Each of the modules can be taught as a stand-alone class or as part of a series of classes. Instructors are welcome to customize the content of the instructional modules to meet the learning needs of their students and the policies and resources at their institutions

Through the Public Library Partnerships Project (PLPP), DPLA has been working with existing DPLA Service Hubs to provide digital skills training for public librarians and connect them sustainably with state and regional resources for digitizing, describing, and exhibiting their cultural heritage content.

During the project, DPLA collaborated with trainers at Digital Commonwealth, Digital Library of Georgia, Minnesota Digital Library, Montana Memory Project, and Mountain West Digital Library to write and iterate a workshop curriculum based on documented best practices. Through the project workshops, we used this curriculum to introduce 150 public librarians to the digitization process.

Now at the end of the project, we’ve made this curriculum available in a self-guided version intended for digitization beginners from a variety of cultural heritage institutions. Each module includes a video presentation, slides with notes in Powerpoint, and slides in PDF. Please feel free to share, reuse, and adapt these materials.

OpenAlex is a fully open catalog of the global research system. Its dataset describes scholarly entities and how those entities are connected to each other. OpenAlex provides documentation and guidance on how to use API to retrieve thier data. Thus, one can this resource to prepare an API workshop or for professional development.

The Open Research Toolkit was created by Christopher Eaker during Faculty Development Leave, Fall 2021. While this toolkit was designed for librarians for learning open research concepts and skills and teaching them at their institutions, it would be useful for anyone interested in learning more about open research. Any questions related to this content can be directed to the author.

The ORT YouTube Channel is found here: http://doi.org/10.7290/ORT_Videos

The Open Research Toolkit is an Open Educational Resource, and is available under Creative Commons Attribution 4.0 International (CC BY 4.0). You may re-use and copy information from this toolkit with attribution. In addition, some of the materials referenced in this toolkit (e.g. some materials linked to and created by others) might be copyright protected; that will be indicated as best as possible, but no guarantees are made as to accuracy of that information. The user should check restrictions of any material prior to reusing it.

You can become a more visible, effective and impactful researcher by sharing your research data and publications openly. In this course, you will learn the objectives, main concepts, and benefits of Open Science principles along with practices for open data management and open data sharing.

Since research increasingly relies on software which is used to model and simulate, and to deal with the ever growing volume of research data, the course will also introduce FAIR software practices.

You'll learn to establish links between publications, data, software and methods, how to attach a persistent identifier and metadata to your results, and methods for clarifying usage rights. You will also discover ways to apply these principles to your daily research and adapt existing routines. Finally, you'll uncover potential barriers to sharing research and discuss possible solutions.

This course will help you grasp the key principles of Open Science, with answers to questions like:

How can researchers effectively store, manage, and share research data?
What kinds of open access publishing are most effective?
How can researchers increase the visibility and impact of their research?
How can the use of social media contribute to the visibility and impact of research?
How can researchers be acknowledged for the research software they write?
You will apply the topics of the course to a variety of case studies on Open Science adoption, which you will then discuss among fellow students. You will also be presented with a hands-on guide to publishing your research with open access. This will help you to apply Open Science principles in your daily work. It will enable you to implement and benefit from the Open Science policies that are currently being developed by governments and research institutions.

This course is aimed at professionals. Those who will see the most benefit include academic researchers at different levels: PhD students, postdoctoral researchers, and professors; researchers working for governments; researchers working for commercial enterprises; MSc and BSc students interested to learn about the principles of Open Science.

The development of this course is supported by the VRE4EIC project with funding from the European Union's Horizon 2020 Research and Innovation Programme.

This lesson series is for the Openscapes Champions program, an open data science mentorship program for science teams.

Openscapes Champions is a professional development and leadership opportunity for teams to reimagine data analysis & stewardship as a collaborative effort, develop modern skills that are of immediate value to them, and cultivate collaborative and inclusive research communities. Cohorts are ~7 research teams (~35 total participants including team leads and members) that convene remotely to explore open data science tooling and practices together. This is a remote-by-design program since its launch in 2019.

The Series is written (and always improving) to be used as a reference, to teach, or as self-paced learning.

Openscapes is co-directed by Julia Stewart Lowndes and Erin Robinson. It is operated by the National Center for Ecological Analysis & Synthesis (NCEAS) and was incubated by a Mozilla Fellowship awarded to Lowndes in 2018.

Training Modules developed for Ocean Sciences Meeting 2020 and recorded for sharing at European Geosciences Union 2020 General Assembly.

Over the past ten years, researchers, institutional leaders and policymakers have begun to speak more and more about infrastructure. As more voices join the conversation, however, it can sometimes become more difficult, rather than less, to understand what exactly research infrastructure is and does. In particular in the humanities, and the digital humanities, the term is used to cover a lot of different projects, resources and approaches.

To address this gap, the PARTHENOS cluster of humanities research infrastructure projects has devised this series of training modules and resources for researchers, educators, managers, and policy makers who want to learn more about research infrastructures and the issues and methods around them.

The modules, which released on a rolling basis from late 2016, cover a wide range of awareness levels, requirements and topic areas within the landscape of research infrastructure. Parthenos provides training modules for independent learners and for instructors looking to incorporate this material into existing courses.