A Data Management Plan (DMP) is a formal document outlining how you will handle data during your research project and after the project is complete.
Data Management for Researchers by A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Planning for data management covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. Documenting your data - an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. Organizing your data - explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. Improving data analysis - covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data - many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage - deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data - digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data - addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data - as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it.
Data Management for Librarians by Libraries organize information and data is information, so it is natural that librarians should help people who need to find, organize, use, or store data. Data Management will guide readers through: 1.Understanding data management basics and best practices. 2.Using the reference interview to help with data management 3.Writing data management plans for grants. 4.Starting and growing a data management service. 5.Finding collaborators inside and outside the library. 6.Collecting and using data in different disciplines.
Meryl Brodsky: Communication, Information Studies
Adriana Cásarez: US History
Hannah Chapman Tripp: Biosciences, Neuroscience
Elle Covington: Educational Psychology, Kinesiology & Health Education, Social Work
Larayne Dallas: Engineering
Lydia Fletcher: Physics, Math, Astronomy, Computer Science
Susan Kung: Manager, Archive of the Indigenous Languages of Latin America
Susan Macicak: Linguistics
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