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.

Data Management Expert Guide
This guide is designed by European experts to help social science researchers make their research data Findable, Accessible, Interoperable and Reusable (FAIR).

You will be guided by different European experts who are - on a daily basis - busy ensuring long-term access to valuable social science datasets, available for discovery and reuse at one of the CESSDA social science data archives.

You can download the full DMEG for your personal study offline (DOI: 10.5281/zenodo.3820473). PDFs for every single chapter are also available for being printed as handouts for training.

Data Carpentry, Software Carpentry, and Library Carpentry are branches under The Carpentries known as a learning program to develop and teach workshops on the fundamental data and coding skills needed to conduct research. Participants can request to host a workshop at their institution or organization, attend a workshop, and/or involve by becoming a certified instructor, contributing in developing the content, or just simply support the programs. All lessons in either Carpentry branch can be used to teach introduction courses in data science/library information sciences.

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.

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.

A group of fourteen authors came together in February 2018 at the TIB (German National Library of Science and Technology) in Hannover to create an open, living handbook on Open Science training. High-quality trainings are fundamental when aiming at a cultural change towards the implementation of Open Science principles. Teaching resources provide great support for Open Science instructors and trainers. The Open Science training handbook will be a key resource and a first step towards developing Open Access and Open Science curricula and andragogies. Supporting and connecting an emerging Open Science community that wishes to pass on their knowledge as multipliers, the handbook will enrich training activities and unlock the community’s full potential.

In this first release of the Open Science Training Handbook, some initial feedback from the community is already included.

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.

How researchers structure their data varies by disciplines and research questions. Still, there are general guidelines for structuring data that make it more likely to be usable in the future. The following questions should be considered for any project that gathers data. These questions should be considered first at the planning stage, again as data is being gathered and stored, and once more prior to final deposit into a digital archive or repository.

1. What are the data organization standards for your field? For example, there are often standards for labeling data fields that will make your data machinereadable. There may also be specific variables and coding guidelines that you can use that will make your work interoperable with other datasets. Lastly, there may be accepted hierarchies and directory structures in your discipline that you can build upon.
2. What are the data export options in the software you are using? If using proprietary and/or highly specialized software to analyze large data sets, export the data in a format that is likely to be supported in the future, and that will be accessible from other software programs. This usually means choosing an open format that is not proprietary. Remember that you may not have access to the same software in the future, and not all software upgrades can read old file types.
3. What forms of the data will be needed for future access? Consider the various forms the data may take, and the scale of the data involved. You may need to preserve not only the underlying raw data, but also the resulting analyses you have created from it.

The Research Data Curation Bibliography includes over 750 selected English-language articles, books, and technical reports that are useful in understanding the curation of digital research data in academic and other research institutions.

The Research Data Curation and Management Bibliography includes over 800 selected English-language articles and books that are useful in understanding the curation of digital research data in academic and other research institutions. It covers topics such as research data creation, acquisition, metadata, provenance, repositories, management, policies, support services, funding agency requirements, open access, peer review, publication, citation, sharing, reuse, and preservation. Most sources have been published from January 2009 through December 2019; however, a limited number of earlier key sources are also included. The bibliography has links to included works. Abstracts are included in this bibliography if a work is under certain Creative Commons Attribution licenses. It is available as a 250-page PDF or a website.

MANTRA is a free, online non-assessed course with guidelines to help you understand and reflect on how to manage the digital data you collect throughout your research. It has been crafted for the use of post-graduate students, early career researchers, and also information professionals. It is freely available on the web for anyone to explore on their own.

Through a series of interactive online units you will learn about terminology, key concepts, and best practice in research data management.

There are eight online units in this course and one set of offline (downloadable) data handling tutorials that will help you:

1. Understand the nature of research data in a variety of disciplinary settings
2. Create a data management plan and apply it from the start to the finish of your research project
3. Name, organise, and version your data files effectively
4. Gain familiarity with different kinds of data formats and know how and when to transform your data
5. Document your data well for yourself and others, learn about metadata standards and cite data properly
6. Know how to store and transport your data safely and securely (backup and encryption)
7. Understand legal and ethical requirements for managing data about human subjects; manage intellectual property rights
8. Understand the benefits of sharing, preserving and licensing data for re-use
9. Improve your data handling skills in one of four software environments: R, SPSS, NVivo, or ArcGIS

Each unit takes up to one hour, plus time for further reading and carrying out the data handling exercises. In the units you will find explanations, descriptions, examples, exercises, and video clips in which academics, PhD students and others talk about the challenges of managing research data. The data handling tutorials assume some experience with each software environment and provide exercises in PDF along with open datasets to download and work through using your own installed software.

MANTRA modules and data handling exercises are available for download via Zenodo: https://doi.org/10.5281/zenodo.1035218

The purpose of this resource is explain Research Data Management (RDM) in a data intensive research landscape.

This webliography is intended for librarians seeking to enhance their own knowledge and assist peers in improving their data management awareness. The webliography is organized by content type, first with more foundational materials such as established data management curricula and then with current awareness and community materials such as social media.

The School of Data aims to make your learning experience as tailored as possible through independent learning modules. Learning modules are all stand-alone and can be taken in any order. To make your learning experience easier, we curated modules into a series of courses - with a focus on data basics as well as specific skills. When you identified the course you're interested in click on "Show Modules" to see all modules you might want to take.

This is a slide for the upcoming workshop on research data curation for HKU researchers. It covers topics on the concept and importance of data curation, "FAIR" principles, data curation practices using DMPTool, Dublin Core and Github, and security issues for research data management, including secure storage and privacy.

The Turing Way project is open source, open collaboration, and community-driven. We involve and support a diverse community of contributors to make data science accessible, comprehensible and effective for everyone. Our goal is to provide all the information that researchers and data scientists in academia, industry and the public sector need to ensure that the projects they work on are easy to reproduce and reuse.