All Learning Resources

  • Hivebench Electronic Lab Notebook

    The time it takes to prepare, analyze and share experimental results can seem prohibitive, especially in the current, highly competitive world of biological research. However, not only is data sharing mandated by certain funding and governmental bodies, it also has distinct advantages for research quality and impact. Good laboratory practices recommend that all researchers use electronic lab notebooks (ELN) to save their results. This resource includes numerous short video demonstrations of Hivebench:

    • Start using Hivebench, the full demo
    • Creating a Hivebench account
    • Managing protocols & methods
    • Storing experimental findings in a notebook
    • Managing research data
    • Doing research on iPhone and iPad
    • Editing experiments
    • Collaborating with colleagues
    • Searching for results
    • Staying up to date with the newsfeed
    • Planning experiments with the calendar
    • Using open science protocols
    • Mendeley Data Export
    • Managing inventory of reagents
    • Signing and counter signing experiments
    • Archiving notebooks
    • How to keep data alive when researchers move on? Organizing data, methods, and protocols.
  • Remote Sensing for Monitoring Land Degradation and Sustainable Cities Sustainable Development Goals (SDGs) [Advanced]

    The Sustainable Development Goals (SDGs) are an urgent call for action by countries to preserve our oceans and forests, reduce inequality, and spur economic growth. The land management SDGs call for consistent tracking of land cover metrics. These metrics include productivity, land cover, soil carbon, urban expansion, and more. This webinar series will highlight a tool that uses NASA Earth Observations to track land degradation and urban development that meet the appropriate SDG targets. 

    SDGs 11 and 15 relate to sustainable urbanization and land use and cover change. SDG 11 aims to "make cities and human settlements inclusive, safe, resilient, and sustainable." SDG 15 aims to "combat desertification, drought, and floods, and strive to achieve a land degradation neutral world." To assess progress towards these goals, indicators have been established, many of which can be monitored using remote sensing. 

    In this training, attendees will learn to use a freely-available QGIS plugin, Trends.Earth, created by Conservation International (CI) and have special guest speakers from the United Nations Convention to Combat Desertification (UNCCD) and UN Habitat. Trends.Earth allows users to plot time series of key land change indicators. Attendees will learn to produce maps and figures to support monitoring and reporting on land degradation, improvement, and urbanization for SDG indicators 15.3.1 and 11.3.1. Each part of the webinar series will feature a presentation, hands-on exercise, and time for the speaker to answer live questions. 

    Learning Objectives: By the end of this training, attendees will: 

    • Become familiar with SDG Indicators 15.3.1 and 11.3.1
    • Understand the basics on how to compute sub indicators of SDG 15.3.1 such as: productivity, land cover, and soil carbon 
    • Understand how to use the Trends.Earth Urban Mapper web interface
    • Learn the basics of the Trends.Earth toolkit including: 
      • Plotting time series 
      • Downloading data
      • Use default or custom data for productivity, land cover, and soil organic carbon
      • Calculate a SDG 15.3.1 spatial layers and summary table 
      • Calculate urban change metrics
      • Create urban change summary tables

    Course Format: This training has been developed in partnership with Conservation International, United Nations Convention to Combat Desertification (UNCCD), and UN Habitat. 

    • Three, 1.5-hour sessions that include lectures, hands-on exercises, and a question and answer session
    • The first session will be broadcast in English, and the second session will contain the same content, broadcast in Spanish (see separate record for Spanish version at: 


    Each part of 3 includes links to the recordings, presentation slides, exercises and Question & Answer Transcripts.   

  • Agency Requirements: NSF Data Management Plans

    This training module is part of the Federation of Earth Science Information Partners (or ESIP Federation's) Data Management for Scientists Short Course.  The subject of this module is "NSF Data Management Plans".  The module was authored by Ruth Duerr from the National Snow and Ice Data Center in Boulder, Colorado.  Besides the ESIP Federation, sponsors of this Data Management for Scientists Short Course are the Data Conservancy and the United States National Oceanic and Atmospheric Administration (NOAA).

    If you’ve done any proposal writing for the National Science Foundation (NSF), you know that NSF now requires that all proposals be accompanied by a data management plan that can be no longer than two pages.   The data management plans are expected to respond to NSF’s existing policy on the dissemination and sharing of research results.  You can find a description of this policy in the NSF Award and Administration Guide to which we provide a link later in this module. In addition, we should note that the NSF’s proposal submission system, Fastlane, will not accept a proposal that does not have a data management plan attached as a supplementary document.

    Individual directorates may have specific guidance for data management plans. For example, the Ocean Sciences Division specifies that data be available within two years after acquisition. Specifications for some individual directorates may provide a list of places where you must archive your data and what you should do if none of the archives in the list can take your data. They may also have additional requirements for both annual and final reporting beyond the general case requirements from NSF.  In addition, individual solicitations may have program specific guidelines to which you need to pay attention.  This module is available in both presentation slide and video formats.

  • Intro to Data Management

    This guide will provide general information about data management, including an overview of Data Management Plans (DMPs), file naming conventions, documentation, security, backup, publication, and preservation. We have included the CMU data life cycle to put the pieces in context in the Data 101 section.
    The CMU Libraries provides research data management resources for guidance on data management, planning, and sharing for researchers, faculty, and students.

  • Content-based Identifiers for Iterative Forecasts: A Proposal

    Iterative forecasts pose particular challenges for archival data storage and retrieval. In an iterative forecast, data about the past and present must be downloaded and fed into an algorithm that will output a forecast data product. Previous forecasts must also be scored against the realized values in the latest observations. Content-based identifiers provide a convenient way to consistently identify input and outputs and associated scripts. These identifiers are:
    (1) location-agnostic – they don’t depend on a URL or other location-based authority (like DOI)
    (2) reproducible – the same data file always has the same identifier
    (3) frictionless – cheap and easy to generate with widely available software, no authentication or network connection
    (4) sticky – the identifier cannot become unstuck or separated from the content
    (5) compatible – most existing infrastructure, including DataONE, can quite readily use these identifiers.

    In this webinar, the speaker will illustrate an example iterative forecasting workflow. In the process, he will highlight some newly developed R packages for making this easier.

  • Supporting Researchers in Discovering Data Repositories

    How do researchers go about identifying a repository to preserve their data? Do they have all the information they need to make an informed decision? Are there resources available to help?
    There is a myriad of repositories available to support data preservation and they differ across multiple axes. So which one is right for your data? The answer is large, ‘it depends’. But this can be frustrating to a new researcher looking to publish data for the first time. What questions need to be asked to detangle these dependencies and where can a researcher go for answers?
    Conversations and sessions at domain conferences have consistently suggested that researchers need more support in navigating the landscape of data repositories and with support from ESIP Funding Friday, we sought to do that. In this webinar, we will introduce a resource under development that aims to serve as a gateway for information about repository selection. With links to existing resources, games, and outreach materials, we aim to facilitate the discovery of data repositories and we welcome contributions to increase the value of this resource.

  • A FAIR Afternoon: On FAIR Data Stewardship for Technology Hotel (/ETH4) beneficiaries

    FAIR data awareness event for Enabling Technology Hotels 4ed. One of the aims of the Enabling Technologies Hotels programme, is to promote the application of the FAIR data principles in research data stewardship, data integration, methods, and standards. This relates to the objective of the national plan open science that research data have to be made suitable for re-usability.

    With this FAIR data training, ZonMw and DTL aim to help researchers (hotel guests and managers) that have obtained a grant in the 4th round of the programme to apply FAIR data management in their research.

  • RDM Onboarding Checklist

    Research Data Management is essential for responsible research and should be introduced when starting a new project or joining a new lab. Managing data across a project and/ or a team allows for accurate communication about that project. This session will review the important steps for onboarding new employees/trainees to a lab or new projects. The key takeaway from this session will be how to incorporate these steps within your individual project or lab environment. While the principles are general, these documents focus on Harvard policies and resources. Internal and external links have been provided throughout the document as supplementary resources, including a glossary of terms. 

    There are 2 checklists as follow: 
    The RDM Onboarding Checklist: Abridged Version serves as a condensed version of the comprehensive checklist described above. This version is intended to be used as an actionable checklist, employed after reviewing the onboarding processes and resources provided in the comprehensive checklist.
    The RDM Onboarding Checklist: Comprehensive Version serves as a general, research data management-focused guide to employee/trainee onboarding as they join a new lab or begin new projects (follow one or both of these as they apply to your situation). This comprehensive version is provided as an initial introduction to the onboarding process and to the breadth of available resources; this version is intended to be reviewed first, prior to utilizing the abridged version.

     Learning Objectives:

    • Become familiar with the research data lifecycle
    • Understand the details and requirements at each stage of data management onboarding
    • Engage with best practices to enhance your current and future research
    • Receive resources and contacts for future help
  • Workshop On Data Management Plans For Linguistic Research

    The rising tide of data management and sharing requirements from funding agencies, publishers, and institutions has created a new set of pressures for researchers who are already stretched for time and funds. While it can feel like yet another set of painful hurdles, in reality, the process of creating a Data Management Plan (DMP) can be a surprisingly useful exercise, especially when done early in a project’s lifecycle. Good data management practiced throughout one’s career, can save time, money, and frustration, while ultimately helping increase the impacts of research.
    This 1-day workshop will involve lecture and discussion around concepts of data management throughout the data lifecycle (from data creation, storage, and analysis to data sharing, archiving, and reusing), as well as related issues such as intellectual property, copyright, open access, data citation, attribution, and metrics. Participants will learn about data management best practices and useful tools while engaging in activities designed to produce a DMP similar to those desired by the NSF Behavioral and Cognitive Sciences Division (for example, Linguistics, Documenting Endangered Languages), as well as other federal agencies such as NEH.
  • Genomics Workshop

    Getting Started

    This lesson assumes no prior experience with the tools covered in the workshop. However, learners are expected to have some familiarity with biological concepts, including nucleotide abbreviations and the concept of genomic variation within a population. 
    Workshop Overview.  Workshop materials include a recommendation for a dataset to be used with the lesson materials.

    Project organization and management:
    Learn how to structure your metadata, organize and document your genomics data and bioinformatics workflow, and access data on the NCBI sequence read archive (SRA) database.
    Introduction to the command line:
    Learn to navigate your file system, create, copy, move, and remove files and directories, and automate repetitive tasks using scripts and wildcards.
    Data wrangling and processing:
    Use command-line tools to perform quality control, align reads to a reference genome, and identify and visualize between-sample variation.
    Introduction to cloud computing for genomics:
    Learn how to work with Amazon AWS cloud computing and how to transfer data between your local computer and cloud resources.


  • EC FAIR How-to Series: Getting a DOI for Your Data

    Identifiers are very important as a means to make research data more FAIR (Findable Accessible Interoperable Reusable). This quick reference guide briefly describes why, when, where and how to acquire a digital object identifier (DOI) for research data.    The guide is targeted to Earth Science researchers, but should be useful for other researchers and their support staff as well.  It is part of the EarthCub How-to Series that is designed to provide targeted, practical lessons on making research data FAIR.  

  • EarthCube FAIR How-to Series: Choosing a Repository for Your Data

    This quick reference guide briefly describes why, when, where and how to choose a FAIR-enabled data repository for research data.    The guide is targeted to Earth Science researchers, but should be useful for other researchers and their support staff as well.  It is part of the EarthCub How-to Series that is designed to provide targeted, practical lessons on making research data FAIR.  

  • Biological Observation Data Standardization - A Primer for Data Managers

    Lots of standards exist for use with biological data but navigating them can be difficult for data managers who are new to them. The Earth Science Information Partners (ESIP) Biological Data Standards Cluster developed this primer for managers of biological data to provide a quick, easy resource for navigating a selection of the standards that exist. The goal of the primer is to spread awareness about existing standards and is intended to be shared online and at conferences to increase the adoption of standards for biological data and make them FAIR.

  • Data Management Support for Researchers

    Tips and advice from a variety of researchers, data managers, and service providers, to help with data management. Titles include:

    • Sharing data: good for science, good for you
    • What support needs to be provided to assist researchers with data management?
    • How can choices about data capture open up, or limit, opportunities for researchers?
    • What should researchers do to help their data survive?
    • Why should researchers share their data?
    • How can repositories and data centres help researchers?
  • USGS Data Templates Overview

    Creating Data Templates for data collection, data storage, and metadata saves time and increases consistency. Utilizing form validation increases data entry reliability.
    Topics include:

    • Why use data templates?
    • Templates During Data Entry - how to design data validating templates 
    • After Data Entry - ensuring accurate data entry
    • Data Storage and Metadata
    • Best Practices
      • Data Templates
      • Long-term Storage
    • Tools for creating data templates
    • Google Forms 
    • Microsoft Excel
    • Microsoft Access
    • OpenOffice - Calc


  • USGS Data Management Plans

    The resources in this section will help you understand how to develop your DMP. The checklist outlines the minimum USGS requirements. The FAQ and DMP Writing Best Practices list below will help you understand other important considerations when developing your own DMP. To help standardize or provide guidance on DMPs, a science center or funding source may choose to document their own Data Management strategy. A number of templates and examples are provided.  This page also includes resources related to the overall research data lifecycle that will help put data management plans in the context of the research done.  Information is provided that identifies what the U.S. Geological Survey Manual requires.

  • United Nations Online Access to Research in Environment (UN OARE) Training Materials

    Here you can find training modules on information management training topics that help you learn not only how to open journals and download full-text articles from the OARE website, but also how to use OARE’s search databases to find articles about specific topics in thousands of scientific journals from major publishers around the world.  Topics include:  searching strategies for finding scientific research using environmental issues, accessing full-text articles, e-journals, e-books, and other internat resources such as indexes for searching EBSCO, SCOPUS (Elsevier), environmental gateways and other portals.  Downloadable powerpoint slides are available for each topic along with a workbook for most of the modules.  

  • FAIR Webinar Series

    This webinar series explores each of the four FAIR principles (Findable, Accessible, Interoperable, Reusable) in depth - practical case studies from a range of disciplines, Australian and international perspectives, and resources to support the uptake of FAIR principles.

    The FAIR data principles were drafted by the FORCE11 group in 2015. The principles have since received worldwide recognition as a useful framework for thinking about sharing data in a way that will enable maximum use and reuse.  A seminal article describing the FAIR principles can also be found at:

    This series is of interest to those who work with creating, managing, connecting and publishing research data at institutions:
    - researchers and research teams who need to ensure their data is reusable and publishable
    - data managers and researchers
    - Librarians, data managers and repository managers
    - IT who need to connect Institutional research data, HR and other IT systems

  • ANDS Guide to Persistent Identifiers: Awareness Level

    A persistent identifier (PID) is a long-lasting reference to a resource. That resource might be a publication, dataset or person. Equally it could be a scientific sample, funding body, set of geographical coordinates, unpublished report or piece of software. Whatever it is, the primary purpose of the PID is to provide the information required to reliably identify, verify and locate it. A PID may be connected to a set of metadata describing an item rather than to the item itself.
    The contents of this page are:
     What is a persistent identifier?
    Why do we need persistent identifiers?
    How do persistent identifiers work?
    What needs to be done, by whom?

    Other ANDS Guides are available at the working level and expert level from this page.

  • ANDS Guides to Persistent Identifiers: Working Level

    This module is to familiarize researchers and administrators with persistent identifiers as they apply to research. It gives an overview of the various issues involved with ensuring identifiers provide ongoing access to research products. The issues are both technical and policy; this module focuses on policy issues. 
    This guide goes through the same issues as the ANDS guide Persistent identifiers: awareness level, but in more detail. The introductory module is not a prerequisite for this module.
    The contents of this page are:
    Why persistent identifiers?
    What is an Identifier?
    Data and Identifier life cycles
    What is Identifier Resolution?

    Other ANDS Guides on this topic at the awareness level and expert level can be found from this page.

  • ANDS Guides to Persistent identifiers: Expert Level

    This module aims to provide research administrators and technical staff with a thorough understanding of the issues involved in setting up a persistent identifier infrastructure. It provides an overview of the types of possible identifier services, including core services and value-added services. It offers a comprehensive review of the policy issues that are involved in setting up persistent identifiers. Finally, a glossary captures the underlying concepts on which the policies and services are based.

    Other ANDS Guides on this topic are available for the awareness level and the working level from this page.

  • 23 (research data) Things

    23 (research data) Things is self-directed learning for anybody who wants to know more about research data. Anyone can do 23 (research data) Things at any time.  Do them all, do some, cherry-pick the Things you need or want to know about. Do them on your own, or get together a Group and share the learning.  The program is intended to be flexible, adaptable and fun!

    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.

  • 'Good Enough' Research Data Management: A Brief Guide for Busy People

    This brief guide presents a set of good data management practices that researchers can adopt, regardless of their data management skills and levels of expertise.

  • De bonnes pratiques en gestion des données de recherche: Un guide sommaire pour gens occupés (French version of the 'Good Enough' RDM)

    Ce petit guide présente un ensemble de bonnes pratiques que les chercheurs peuvent adopter, et ce, indépendamment de leurs compétences ou de leur niveau d’expertise.