All Learning Resources
USGS Data Management Training Modules – Planning for Data Management Part II
This is one of six interactive modules created to help researchers, data stewards, managers and the public gain an understanding of the value of data management in science and provide best practices to perform good data management within their organization. By the end of this course you should know the difference between data management plans and project plans; you should know how to use the DMPTool to create a data management plan; and you should understand the basic information that should go into a data management plan.
Template Research Data Management workshop for STEM researchers
These materials are designed as a template for an introductory Research Data Management workshop for STEM postgraduate students and Early Career Researchers. The workshop is interactive and is designed to be run for 2-3 hours depending on which sections of the workshop are delivered. As it is a template workshop there is a lot of material to cover all disciplines, it is unlikely that all sections would be of interest to any one group of researchers. The sections are:
Backup and file sharing
How to organise your data well
Personal and sensitive data
Data Management Plans
The workshop works best when adapted for a particular discipline and with a maximum of 30 participants. This workshop was developed for the Data Champions programme at the University of Cambridge and is an adaptation of workshops which are run on a regular basis for PhD students and Postdoctoral researchers. If you would like any more information please email firstname.lastname@example.org and we would be happy to answer any questions that you have.
Data Rescue: Packaging, Curation, Ingest, and Discovery
Data Conservancy was introduced to Data Rescue Boulder through our long-time partner Ruth Duerr of Ronin Institute. Through our conversations, we recognized that Data Rescue Boulder has a need to process large number of rescued data sets and store them in more permanent homes. We also recognized that Data Conservancy along with Open Science Framework have the software infrastructure to support such activities and bring a selective subset of the rescued data into our own institution repository. We chose the subset of data based on a selection from one of the Johns Hopkins University faculty members.
This video shows one of the pathways through which data could be brought into a Fedora-backed institutional repository using our tools and platforms
Data Conservancy screen cast demonstrating integration between the Data Conservancy Packaging Tool, the Fedora repository, and the Open Science Framework. Resources referenced throughout the screen cast are linked below.
DC Package Tool GUI
DC Package Ingest
- Package Ingest release page
- Fedora API Extension Architecture Home, GitHub, and Docker-based demo
- API-X funding provided by IMLS grant #LG-70-16-0076-16
Fedora OSF Storage Provider
(under development as of April 2017)
LEARN Toolkit of Best Practice for Research Data Management
The LEARN Project's Toolkit of Best Practice for Research Data Management expands on the issues outlines in the LERU Roadmap for Research Data (2013). It is freely downloadable, and is a deliverable for the Eruopean Commission. It includes:
- 23 Best-Practice Case Studies from institutions around the world, drawn from issues in the original LERU Roadmap;
- 8 Main Sections, on topics such as Policy and Leadership, Open Data, Advocacy and Costs;
- One Model RDM Policy, produced by the University of Vienna and accompanied by guidance and an overview of 20 RDMpolicies across Europe;
- An Executive Briefing in six languages, aimed at senior institutional decision makers.
The Executive Briefing of the LEARN Toolkit is available in English, Spanish, German, Portuguese, French and Italian translations.
Planning for Software Reproducibility and Reuse
Many research projects depend on the development of scripts or other software to collect data, perform analyses or simulations, and visualize results. Working in a way that makes it easier for your future self and others to understand and re-use your code means that more time can be dedicated to the research itself, rather than troubleshooting hard-to-understand code, resulting in more effective research. In addition, by following some simple best practices around code sharing, the visibility and impact of your research can be increased. In this introductory session, you will:
- learn about best practices for writing, documenting (Documentation), and organizing code (Organization & Automation),
- understand the benefits of using version control (Version Control & Quality Assurance),
- learn about how code can be linked to research results and why (Context & Credit),
- understand why it is important to make your code publishable and citable and how to do so (Context & Credit),
- learn about intellectual property issues (Licensing),
- learn about how and why your software can be preserved over time (Archiving).
USGS Data Management Training Modules – Planning for Data Management
This is one of six interactive modules created to help researchers, data stewards, managers and the public gain an understanding of the value of data management in science and provide best practices to perform good data management within their organization. In this module, we will provide an overview of data management plans. First, we will define and describe Data Management Plans, or DMPs. We will then explain the benefits of creating a DMP. Finally, we will provide instructions on how to prepare a DMP, including covering key components common to most DMPs.
USGS Data Management Training Modules – the Value of Data Management
This is one of six interactive modules created to help researchers, data stewards, managers and the public gain an understanding of the value of data management in science and provide best practices to perform good data management within their organization. In this module, you will learn how to: 1. Describe the various roles and responsibilities of data management. 2. Explain how data management relates to everyday work and the greater good. 3. Motivate (with examples) why data management is valuable. These basic lessons will provide the foundation for understanding why good data management is worth pursuing.
23 (research data) Things
23 (research data) Things is self-directed, online learning for anybody who wants to know more about research data. Each of the 23 Things offers a variety of learning opportunities with activities at three levels of complexity: ‘Getting started’, ‘Learn more’ and ‘Challenge me’. All resources used in the program are online and free to use.
Structuring and Documenting a USGS Public Data Release
This tutorial is designed to help scientists think about the best way to structure and document their USGS public data releases. The ultimate goal is to present data in a logical and organized manner that enables users to quickly understand the data. The first part of the tutorial describes the general considerations for structuring and documenting a data release, regardless of the platform being used to distribute the data. The second part of the tutorial describes how these general consideration can be implemented in ScienceBase. The tutorial is designed for USGS researchers, data managers, and collaborators, but some of the content may be useful for non-USGS researchers who need some tips for structuring and documenting their data for public distribution.
New England Collaborative Data Management Curriculum
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.
Unidata Data Management Resource Center
In this online resource center, Unidata provides information about evolving data management requirements, techniques, and tools. They walk through common requirements of funding agencies to make it easier for researchers to fulfill them. In addition, they explain how to use some common tools to build a scientific data managment workflow that makes the life of an investigator easier, and enhances access to their work. The resource center provides information about: 1) Agency Requirements, 2) Best Practices, 3) Tools for Managing Data, 4) Data Management Plan Resources, 5) Data Archives, and 6) Scenarios and Use Case.
Data Management Planning Part 1: overview and a USGS program experience
Emily Fort of the USGS presents an introduction to data management planning and a USGS program experience.
Data Management Planning Part 2: theory and practice in research data management
Steve Tessler and Stan Smith present an example of a data management planning strategy for USGS science centers.
Introduction to the ISO 19115-2 Metadata Standard - DISL Data Management Metadata Training Webinar Series - Part 2
Begins with a brief overview of how the components of the ISO 19115-2 metadata standard are organized, followed by an example completed metadata record. Overview of how to use NOAA NCEI's ISO workbooks and EDM Wiki as resources for writing ISO metadata. The video is 34 minutes.
Introduction to Data Documentation - DISL Data Management Metadata Training Webinar Series - Part 1
Introduction to data documentation (metadata) for science datasets. Includes basic concepts about metadata and a few words about data accessibility. Video is about 23 minutes.
LP DAAC Data Recipes
A collection of tutorials that describe how to use Earth science data from NASA's Land Processes Distributed Active Archive Center (LP DAAC) using easily available tools and commonly used formats for Earth science data. These tutorials are available to assist those wishing to learn or teach how to obtain and view these data.