This short course provides training materials about how to create a set of publication data, gather additional information about the data through an API (Application Programming Interface), clean the data, and analyze the data in various ways. Developing these skills will assist academic librarians who are:

Negotiating a renewal of a journal package or an open access publishing agreement,
Interested in which journals the institution's authors published in or which repositories the institution’s authors shared their works in,
Looking to identify publications that could be added to your repository,
Searching for authors who do or do not publish OA for designing outreach programs, or
Tracking how open access choices have changed over time.
After completing the lessons, the user will be able to gain an understanding of an institution’s publishing output, such as number of publications per year, open access status of the publications, major funders of the research, estimates of how much funding might be spent towards article processing charges (APCs), and more. The user will also be better prepared to think critically about institutional publishing data to make sustainable and values-driven scholarly communications decisions.

The course is presented in two sections. Section 1 describes how to build a dataset. Section 2 describes a free, open source tool for working with data. Examples of how to do analyses both in OpenRefine and Microsoft Excel are provided.

This short course was created for the Scholarly Communication Notebook. The file "Analyzing Institutional Publishing Output-A Short Course.docx" serves as a table of contents for the materials.

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.

There are several important elements to digital preservation, including data protection, backup and archiving. In this lesson, these concepts are introduced and best practices are highlighted with case study examples of how things can go wrong. Exploring the logistical, technical and policy implications of data preservation, participants will be able to identify their preservation needs and be ready to implement good data preservation practices by the end of the module.

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.

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.

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.

Understanding the types, processes, and frameworks of workflows and analyses is helpful for researchers seeking to understand more about research, how it was created, and what it may be used for. This lesson uses a subset of data analysis types to introduce reproducibility, iterative analysis, documentation, provenance and different types of processes. Described in more detail are the benefits of documenting and establishing informal (conceptual) and formal (executable) workflows.

Data citation is a key practice that supports the recognition of data creation as a primary research output rather than as a mere byproduct of research. Providing reliable access to research data should be a routine practice, similar to the practice of linking researchers to bibliographic references. After completing this lesson, participants should be able to define data citation and describe its benefits; to identify the roles of various actors in supporting data citation; to recognize common metadata elements and persistent data locators and describe the process for obtaining one, and to summarize best practices for supporting data citation.

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.

When entering data, common goals include creating data sets that are valid, have gone through an established process to ensure quality, are organized, and reusable. This lesson outlines best practices for creating data files. It will detail options for data entry and integration, and provide examples of processes used for data cleaning, organization and manipulation.

Data management planning is the starting point in the data life cycle. Creating a formal document that outlines what you will do with the data during and after the completion of research helps to ensure that the data is safe for current and future use. This lesson describes the benefits of a data management plan (DMP), outlines the components of a DMP, details tools for creating a DMP, provides NSF DMP information, and demonstrates the use of an example DMP.

The ESIP Federation, in cooperation with NOAA and the Data Conservancy, seeks to share the community's knowledge with scientists who increasingly need to be better data managers, as well as to support workforce development for new data management professionals. Over the next several years, the ESIP Federation expects to evolve training courses which seeks to improve the understanding of scientific data management among scientists, emerging scientists, and data professionals of all sorts.

All courses are available under a Creative Commons Attribution 3.0 license that allows you to share and adapt the work as long as you cite the work according to the citation provided. Please send feedback upon the courses to shortcourseeditors@esipfed.org.

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.

Quality assurance and quality control are phrases used to describe activities that prevent errors from entering or staying in a data set. These activities ensure the quality of the data before it is collected, entered, or analyzed, as well as actively monitoring and maintaining the quality of data throughout the study. In this lesson, we define and provide examples of quality assurance, quality control, data contamination and types of errors that may be found in data sets. After completing this lesson, participants will be able to describe best practices in quality assurance and quality control and relate them to different phases of data collection and entry.

When first sharing research data, researchers often raise questions about the value, benefits, and mechanisms for sharing. Many stakeholders and interested parties, such as funding agencies, communities, other researchers, or members of the public may be interested in research, results and related data. This lesson addresses data sharing in the context of the data life cycle, the value of sharing data, concerns about sharing data, and methods and best practices for sharing data.

Some research funders have a mandate for data resulting from their funded research to be shared. This presentation provides a general definition of data sharing and how scholars can identify and follow data sharing mandates.

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.

Those workshops help to gain new skills in research data management. Created by MIT Libraries, under CC-BY license, others can adapt and utilize this resources to develop thier own slides in teaching data management.

The ETD+ Virtual Workshop Series, taught by Dr. Katherine Skinner, is a set of free introductory training resources on crucial data curation and digital longevity techniques. Focusing on the Electronic Thesis and Dissertation (ETD) as a mile-marker in a student’s research trajectory, it provides in-time advice to students and faculty about avoiding common digital loss scenarios for the ETD and all of its affiliated files.

About the ETDplus Project
The ETDplus project is helping institutions ensure the longevity and availability of ETD research data and complex digital objects (e.g., software, multimedia files) that comprise an integral component of student theses and dissertations. The project was generously funded by the Institute of Museum and Library Services (IMLS) and led by the Educopia Institute, in collaboration with the NDLTD, HBCU Alliance, bepress, ProQuest, and the libraries of Carnegie Mellon, Colorado State, Indiana State, Morehouse, Oregon State, Penn State, Purdue, University of Louisville, University of Tennessee, the University of North Texas, and Virginia Tech.

Acknowledgements
This project was made possible in part by the Institute of Museum and Library Services.