All Learning Resources

Dash is a self-service tool for researchers to select, describe, identify, upload, update, and share their research data. 

For more information about Dash go to ​https://cdlib.org/services/uc3/dryad/.   Dash is the front end to the Dryad repository platform.

This self-paced, online training introduces several fundamental concepts of lidar and demonstrates how high-accuracy lidar-derived elevation data support natural resource and emergency management applications in the coastal zone.  Note: requires Adopbe Flash Plugin.

Learning objectives:

• Define lidar
• Select different types of elevation data for specific coastal applications
• Describe how lidar are collected
• Identify the important characteristics of lidar data
• Distinguish between different lidar data products
• Recognize aspects of data quality that impact data usability
• Locate sources of lidar data
• Discover additional information and additional educational resources

The Handbook provides an internationally authoritative and practical guide to the subject of managing digital resources over time and the issues in sustaining access to them. A key knowledge base for digital preservation, peer-reviewed and freely accessible to all. It will be of interest to all those involved in the creation and management of digital materials.
The contents page provides an "at a glance" view of the major sections and all their component topics. You can navigate the Handbook by clicking and expanding the "Explore the Handbook" navigation bar or by clicking links in the contents page.
The contents are listed hierarchically and indented to show major sections and sub-sections. Landing pages provide overviews and information for major sections with many sub-sections.
Contents:
-Introduction
-Digital preservation briefing [landing page]
-Getting started
-Institutional strategies [landing page]
-Organizational activities [landing page]
-Technical solutions and tools [landing page]
-Content-specific preservation [landing page]
-Glossary

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. This module covers metadata for research data. The USGS Data Management Training modules were funded by the USGS Community for Data Integration and the USGS Office of Organizational and Employee Development's Technology Enabled Learning Program in collaboration with Bureau of Land Management, California Digital Library, and Oak Ridge National Laboratory. Special thanks to Jeffrey Morisette, Dept. of the Interior North Central Climate Science Center; Janice Gordon, USGS Core Science Analytics, Synthesis, and Libraries; National Indian Programs Training Center; and Keith Kirk, USGS Office of Science Quality Information.

Presentation given by Esther Plomp at the "FAIR Data - the Key to Sustainable Research" seminar at Tartu University Library on the 9th of April 2019.

Data Champions are experts in data management who share their experience with their group/department members. They are volunteers that act as advocates for good data management and sharing practises and they help their Faculty’s Data Steward with disciplinary specific understandings of Research Data Management (RDM). The Data Champion programme started in September 2018 at the TU Delft and is open to researchers from all levels (PhD students to professors) as well as support staff (data managers, software developers and technicians). As the Data Champions are members of all faculties and various departments of Delft University of Technology (TU Delft) they form a network across the university campus. The Champions are invited for meetings where they interact with each other and share their experiences, such as achievements and problems that they encounter in managing data and software. They are also encouraged to participate at a national and international level by being informed on current trends in data management and there is a travel grant available that allows them to participate in RDM events, trainings and workshops. At TU Delft they are actively working together with the Data Stewards on RDM policy development as well as involved in more practical activities such as coding support and Software Carpentry workshops. These activities increase the visibility and impact of the Data Champions, recognise their data management efforts, and offer them opportunities to learn new skills which they can share with their local community members.

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:
Introduction
Backup and file sharing
How to organise your data well
Data Tools
Personal and sensitive data
Data sharing
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 [email protected] and we would be happy to answer any questions that you have.

Presentation given about Data Stewardship at TU Delft and Data Championship at Cambridge University at Dutch LCRDM (Landelijk Coördinatiepunt Research Data Management) Data Steward meeting 1st December 2017.  Topics covered include suggestions by data stewards about how to approach and persuade researchers to engage in data management and stewardship activities.  

What is R?

R is an [Open Source](https://opensource.org) programming language that is specifically designed for data analysis and visualization. It consists of the [core R system](https://cran.r-project.org) and a collection of (currently) over [13,000 packages](http://cran.cnr.berkeley.edu) that provide specialized data manipulation, analysis, and visualization capabilities. R is an implementation of the *S* statistical language developed in the mid-1970s at Bell Labs, with the start of development in the early 1990s and a stable beta version available by 2000. R has been under continuous development for over 25 years and has hit major development [milestones](https://en.wikipedia.org/wiki/R )(programming_language) over that time.

R syntax is relatively straightforward and is based on a core principle of providing reasonable default values for many functions, and allowing a lot of flexibility and power through the use of optional parameters.

Library Carpentry is an open education volunteer network and lesson organization dedicated to teaching librarians data and software skills. The goal is to help librarians better engage with constituents and improve how they do their work. This presentation will serve as an introduction on how Library Carpentry formed in 2015, evolved as a global community of library professionals and will continue as a future sibling of the Carpentries, an umbrella organization of distinct lesson organizations, such as Data and Software Carpentry. We’ll cover existing collaborative lesson development, curricula coverage, workshop activities and the global instructor community. We’ll then talk about the future coordinating activities led by the UC system to align and prepare for a merging with Data and Software Carpentry.

This workshop uses a tabular ecology dataset from the Portal Project Teaching Database and teaches data cleaning, management, analysis, and visualization. There are no pre-requisites, and the materials assume no prior knowledge about the tools. We use a single dataset throughout the workshop to model the data management and analysis workflow that a researcher would use.
Lessons:

• Data Organization in Spreadsheets
• Data Cleaning with OpenRefine
• Data Management with SQL
• Data Analysis and Visualization in R
• Data Analysis and Visualization in Python

The Ecology workshop can be taught using R or Python as the base language.
Portal Project Teaching Dataset: the Portal Project Teaching Database is a simplified version of the Portal Project Database designed for teaching. It provides a real-world example of life-history, population, and ecological data, with sufficient complexity to teach many aspects of data analysis and management, but with many complexities removed to allow students to focus on the core ideas and skills being taught.
 

This video tutorial provides information about using ScienceBase as a platform for data release. We will describe the data release workflow and demonstrate, step-by-step, how to complete a data release in ScienceBase.

This introductory course will help you to understand what open science is and why it is something you should care about. You'll get to grips with the expectations of research funders and will learn how practising aspects of open science can benefit your career progression. Upon completing this course, you will:

• understand what Open Science means and why you should care about it
• be aware of some of the different ways to go about making your own research more open over the research lifecycle
• understand why funding bodies are in support of Open Science and what their basic requirements are 
• be aware of the pontential benefits of practicing open science 

It is important to remember that Open Science is not different from traditional science. It just means that you carry out your research in a more transparent and collaborative way. Open Science applies to all research disciplines. While Open Science is the most commonly used term, you may also hear people talking about Open Scholarship or Open Research in the Arts and Humanities.

This training module provides you with instructions on how to deploy B2SAFE and B2STAGE with iRODS4. It also shows you how to use these services. Moreover, the module provides hands-on training on Persistent Identifiers, more specifically Handle v8 and the corresponding B2HANDLE python library.

B2SAFE is a robust, safe and highly available service which allows community and departmental repositories to implement data management policies on their research data across multiple administrative domains in a trustworthy manner.

B2STAGE is a reliable, efficient, light-weight and easy-to-use service to transfer research data sets between EUDAT storage resources and high-performance computing (HPC) workspaces.

Please consult the user documentation on the services for a general introduction, if needed, before following the contents of this git repository. This training material foresees two types of trainees: those who want to learn how to use the EUDAT B2SAFE and B2STAGE services; and those who prefer to deploy and integrate these services. Following the full, in-depth tutorial will allow you to understand how the components of a service are combined and thus enables you to also extend the integration of services at the low-level (technology-level rather than API level). Following just the "use" part of the training will familiarise you with the APIs of the services, but not with the underlying technology and its wiring.

The guidelines available from this web page cover a number of topics related to research data management.  The guidelinesare targeted to researchers wishing to submit data to the Finnish Social Science Data Archive, but may be helpful to other social scientists interested in practices related to research data management with the understanding that the guidelines refer to the situation in Finland, and may not be applicable in other countries due to differences in legislation and research infrastructure.
High level topics (or chapters) covered include:
- Data management planning (the data, rights, confidentiality and data security, file formats and programs, documentation on data processing and content, lifecycle, data management plan models)
- Copyrights and agreements
- Processing quantitative data files
- Processing qualitiative data files
- Anonymisation and personal data including policies related to ethical review of human sciences
- Data description and metadata
- Physical data storage
- Examples 
The guidelines are also available in FSD's Guidelines in DMPTuuli, a data management planning tool for Finnish research organisations. It provides templates and guidance for making a data management plan (DMP).
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HydroShare is an online, collaborative system for sharing and publishing a broad set of hydrologic data types, models, and code. It enables people to collaborate seamlessly in a high performance computing environment, thereby enhancing research, education, and application of hydrologic knowledge. HydroShare is being developed by a team from the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) supported by National Science Foundation awards ACI-1148453 and ACI-1148090.

The introduction to HydroSHare inlcude a Getting Started guide, a Frequently Asked Questions guide, and a number of videos on topics such as: 
- Collaborate Data and Model Sharing using HydroShare
- Delineate Watersheds and Perform Hydrologic Terrain Analysis with HydroShare and CyberGIS
- Share, Publish and execute your SWAT models with HydroShare and SWATShare

CUAHSI is an organization representing more than 130 U.S. universities and international water-science-related organizations and is sponsored by the National Science Foundation to provide infrastructure and services to advance the development of hydrologic science and education in the United States.

The guidelines available from this web page cover a number of topics related to Data Management. Many of the resources and information found in this guide have been adapted from the UK Data Archive and the DH Curation Guide. The guidelines are targeted to researchers wishing to submit data to the social science research data, and would be useful to new data curators and data librarians in the Arts & Humanities as well.  Each section has useful references for further study, if desired.
What You Will Find in This Guide:
-How to Document and Format your Data
-Examples of Data Management Plans (DMP) and Data Curation Profiles (DCP)
-Tools to Help You Create DMPs and DCPs
-California Digital Library Data Repositories
-Where to Get Help on Campus
-A list of Federal Funding Agencies and Their Data Management Requirements
-A Description of Data Curation for the Humanities and What Makes Humanities Data Unique
-Information on Data Representation
-Resources on Data Description Standards

ResearchVault is a secure computing environment where scientists and collaborators can conduct research on restricted and confidential data.

ResearchVault (also known as ResVault) is designed to act as a workstation that is secure and pre-approved with the capacity for large-scale data storage and computation. Researchers can:

Securely store restricted data like:

• electronic protected health information (ePHI) (HIPAA)
• export-controlled data (ITAR/EAR)
• student data (FERPA)
• controlled unclassified information (CUI)
• intellectual property data (IP)

Store and work with larger data sets than is possible on a regular workstation
Perform work on stored data sets with familiar software tools running on virtual machines located in the UF data center
Concurrently run more programs than on a regular workstation
Display work results on a graphical interface that is securely transmitted to remote devices such as desktops, laptops, or iPads
Work collaboratively with other researchers on the same data sets using different workstations.

The system is modeled on a bank vault where you receive:

An individual deposit box with secure storage for valuables
Privacy from other users and bank staff
A secure area within the vault to privately access your valuables

ResVault is available to University of Florida faculty and students.  People not associated with UF can be sponsored by faculty at UF.  Training materials available from the ResVault home page include an introductory / overview video, and a recording of a training session on the research administration for restricted data that was given on Oct 12, 2018. The recording is available from the UF Media Website. It describes how the requirement for using special IT infrastructure is handled and how the right environment for each project is determined, as well as the training requirements for project participants.

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.

This workshop was presented at NN/LM MAR Research Data Management Symposium: Doing It Your Way: Approaches to Research Data Management for Libraries.  Powerpoint slides are available for download.  files include a biophysics case study.

Terms of Access:  There is 1 restricted file in this dataset which may be used;  however, you are asked not to share the Mock lab notebook. It is completely fictitious. Users may request access to files.

This online toolkit provides training and educational resources for data discovery, management, and curation across the globe, in support of an international collaborative effort to enable open access to scientific data.  Tools within the toolkit include:
- DDOMP Researcher Guide which has resources and tips for creating a successful DDOMP (data management plan)
- Data Management Training including webinars, courses, certifications, and literature on data management topics
- Best Practices & Standards which provide guidelines for effective data management.
Video tutorials about each of these tools are available at: https://www.youtube.com/watch?v=2qQeDCB3XhU&list=PLq4USJIxTB6TYUgkJ0OX3W... 
Other capacity building tools include a Data Skills Curricula Framework to enhance information management skills for data-intensive science which was developed by the Belmont Forum’s e-Infrastructures and Data Management (e-I&DM) Project to improve data literacy, security and sharing in data-intensive, transdisciplinary global change research.  More information about the curricula framework including a full report and an outline of courses important for researchers doing data-intensive research can be found at:  https://www.belmontforum.org/resources/outline-data-skills-curricula-fra... .