All Learning Resources
'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.
How to Make a Data Dictionary
A data dictionary is critical to making your research more reproducible because it allows others to understand your data. The purpose of a data dictionary is to explain what all the variable names and values in your spreadsheet really mean. This guide gives examples and instruction on how to asemble a data dictionary.
Smithsonian Libraries: Describing Your Project : Citation Metadata
Smithsonian Libraries Metadata Guide.
The overall description for your project could be referred to as project metadata, citation metadata, a data record, a metadata record, or a dataset record. The information supplied in the project description should be sufficient to enable you and others to find and properly cite your data.
A metadata record gives the basic who, what, where, and when of the data. It is a high level description that others can use to cite your data. It may be submitted with a dataset as a separate file when deposited in a repository, or displayed in the repository with data entered into a form.
Metacat Administrator's Guide
Metacat is a repository for data and metadata (documentation about data) that helps scientists find, understand and effectively use data sets they manage or that have been created by others. Thousands of data sets are currently documented in a standardized way and stored in Metacat systems, providing the scientific community with a broad range of science data that–because the data are well and consistently described–can be easily searched, compared, merged, or used in other ways.
This Metacat Administrator's Guide includes instruction on the following topics:
Chapter 1: Introduction
Chapter 2: Contributors
Chapter 3: License
Chapter 4: Downloading and installing Metacat
Chapter 5: Configuring Metacat
Chapter 6: DataONE Member Node Support
Chapter 7: Accessing and submitting Metadata and data
Chapter 8: Metacat indexing
Chapter 9: Modifying and creating themes
Chapter 10: Metacat authentication mechanism
Chapter 11: Metacat's use of Geoserver
Chapter 12: Replication
Chapter 13: Harvester and harvest list editor
Chapter 14: OAI protocol for metadata harvesting
Chapter 15: Event logging
Chapter 16: Enabling web searches: sitemaps
Chapter 17: Appendix: Metacat properties
Chapter 18: Appendix: Development issues
QGIS - for Absolute Beginners
This video is a complete rundown of the basics in QGIS, a free GIS software package designed as an alternative to ArcMap.
QGIS is a user friendly Open Source Geographic Information System (GIS) licensed under the GNU General Public License. QGIS is an official project of the Open Source Geospatial Foundation (OSGeo). It runs on Linux, Unix, Mac OSX, Windows and Android and supports numerous vector, raster, and database formats and functionalities.
QGIS Training Manual
A training manual written by the QGIS Development Team. It includes instruction on the basic use of the QGIS interface, applied applications, and other basic operations. Topics include: general tools, QGIS GUI, working with projections, raster and vector data, managing data sources and integration with GRASS GIS. Examples are given of working with GPS and OGC data. A list of plugins is also included.
QGIS aims to be a user-friendly GIS, providing common functions and features. The initial goal of the project was to provide a GIS data viewer. QGIS has reached the point in its evolution where it is being used by many for their daily GIS data-viewing needs. QGIS supports a number of raster and vector data formats, with new format support easily added using the plugin architecture.
Training Materials for Data Management in Reclamation
This document (downloadable from this landing page) provides supplementary educational materials focused upon US Bureau of Reclamation (USBR) approaches to data management that use and expand upon a number of USGS training modules on data management. The USBR supplementary materials include:
- A discussion of the Reclamation data lifecycle
- A Reclamation data management plan template
- Examples of Reclamation data management best practice
- Lessons learned from various USBR data management efforts.
Introduction to Data Management Plans
Video presentation and slides introducing the concept of Data Management Plans given by Dr. Andrew Stephonson at the Research Resource Forum at Northwestern University in 2016. Dr. Stephenson is Distinguished Professor of Biology and Associate Dean for Research and Graduate Education in the Eberly College of Science at Penn State. As an active researcher, he has generated and collected data for many years and served on many a panel reviewing grant proposals. From his perspective, data management plans make good sense. In the following video, he describes the elements of a DMP and why they are important. The video presentation is available at: https://www.youtube.com/watch?v=uHyDzt6E3qU
This presentation is part of a Data Management Plan Tutorial prepared by the Penn State University Libraries and contains the following modules:
- Introduction to Data Management Plans
- Why Do You Need a Data Management Plan?
- Components of a Typical Plan
- Tools and Other Resources for Data Management Planning
- Part 1: Data and Data Collection
- Part 2: Documenting the Data
- Part 3: Policies for Data Sharing and Access
- Part 4: Reuse and Redistribution of Data
- Part 5: Long-Term Preservation and Archiving of Data
- Next Steps to Take
The entire Data Management Plan tutorial can be found at: https://www.e-education.psu.edu/dmpt
- Introduction to Data Management Plans
Python for Data Management
This training webinar for Python is part of a technical webinar series created by the USGS Core Science Analytics, Synthesis, and Library section to improve data managers’ and scientists' skills with using Python in order to perform basic data management tasks.
Who: These training events are intended for a wide array of users, ranging from those with little or no experience with Python to others who may be familiar with the language but are interested in learning techniques for automating file manipulation, batch generation of metadata, and other data management related tasks.
Requirements: This series will be taught using Jupyter notebook and the Python bundle that ships with the new USGS Metadata Wizard 2.x tool.
- Working with Local Files
- Batch Metadata Handling
- Using the USGS ScienceBase Platform with PySB
Why should you worry about good data management practices?
To prepare data for archival it must be organized in well-formatted, described, and documented datasets. Benefits of good data management include:
Spend less time doing data management and more time doing research
Easier to prepare and use data for yourself
Collaborators can readily understand and use data files
Long-term (data publication)
Scientists outside your project can find, understand, and use your data to address broad questions
You get credit for archived data products and their use in other papers
Sponsors protect their investment
This page provides an overview of data management planning and preparation. It offers practical methods to successfully share and archive your data at the ORNL DAAC. Topics include: Best Practices for Data Management, Writing Data Management Plans including examples of data management plans, How-to's amd Resources.
Data Management Resources (University of Arizona Research Data Management Services)
The information on this website is intending to provide information on developing data management plans now being required by some federal agencies and to support researchers in the various stages of the research cycle. Topics covered include:
- Research Data Lifecycle
- Data Management Plans with funding requirements from many agencies
- Sharing Data
- Managing Data
Workshops and tutorials are available as recordings, slides, and exercises on topics such as: Data Literacy for Postdocs, Increasing Openness and Reproducibility using the OSF, and Research Data Life Cycle.
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.
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: https://www.nature.com/articles/sdata201618.
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
Introduction, FAIR Principles and Management Plans
A presentation on FAIR Data and Software, and FAIR Principles and Managment Plans, which occured during a Carpentries-Based Workshop in Hannover, Germany, Jul 9-13 2018.
Tools for Version Control of Research Data
Research data tend to change over time (get expanded, corrected, cleaned, etc.). Version control is the management of changes to data or documents. This talk addresses why version control is a crucial component of research data management and introduces software tools that are available for this purpose. This workshop was part of the Conference Connecting Data for Research held at VU University in Amsterdam.
Top 10 FAIR Data & Software Things
The Top 10 FAIR Data & Software Global Sprint was held online over the course of two-days (29-30 November 2018), where participants from around the world were invited to develop brief guides (stand alone, self paced training materials), called "Things", that can be used by the research community to understand FAIR in different contexts but also as starting points for conversations around FAIR. The idea for "Top 10 Data Things" stems from initial work done at the Australian Research Data Commons or ARDC (formerly known as the Australian National Data Service).
The Global Sprint was organised by Library Carpentry, Australian Research Data Commons and the Research Data Alliance Libraries for Research Data Interest Group in collaboration with FOSTER Open Science, OpenAire, RDA Europe, Data Management Training Clearinghouse, California Digital Library, Dryad, AARNet, Center for Digital Scholarship at the Leiden University, and DANS. Anyone could join the Sprint and roughly 25 groups/individuals participated from The Netherlands, Germany, Australia, United States, Hungary, Norway, Italy, and Belgium. See the full list of registered Sprinters.
Sprinters worked off of a primer that was provided in advance together with an online ARDC webinar introducing FAIR and the Sprint titled, "Ready, Set, Go! Join the Top 10 FAIR Data Things Global Sprint." Groups/individuals developed their Things in Google docs which could be accessed and edited by all participants. The Sprinters also used a Zoom channel provided by ARDC, for online calls and coordination, and a Gitter channel, provided by Library Carpentry, to chat with each other throughout the two-days. In addition, participants used the Twitter hashtag #Top10FAIR to communicate with the broader community, sometimes including images of the day.
Participants greeted each other throughout the Sprint and created an overall welcoming environment. As the Sprint shifted to different timezones, it was a chance for participants to catch up. The Zoom and Gitter channels were a way for many to connect over FAIR but also discuss other topics. A number of participants did not know what to expect from a Library Carpentry/Carpentries-like event but found a welcoming environment where everyone could participate.
Essentials 4 Data Support
Essentials 4 Data Support is an introductory course for those people who (want to) support researchers in storing, managing, archiving and sharing their research data.
Essentials 4 Data Support is a product of Research Data Netherlands.
Guidelines for Effective Data Management Plans
Data Management Plans
Federal funding agencies are increasingly recommending or requiring formal data management plans with all grant applications. To help researchers meet those requirements, ICPSR offers these guidelines. Based on our Data Management Plan Web site, this document contains a framework, example data management plans, links to other resources, and a bibliography of related publications. ICPSR also hosts a blog on data management plans.
Framework for Creating a Data Management Plan
Data Mangeme Plan Resources & Examples
Resources for Development
Templates and Tools
Guidance on Funder Requirements
Good Practice Guidance.
We hope you find this information helpful as you craft a data management plan. Please contact us at firstname.lastname@example.org with any comments or suggestions.
Data Management Plan - Data Management Guides
A collection of online data management guides, data management planning tools, guidelines from funding agencies, and data management plan examples for researchers and librarians. This page also contains a link to various courses and tutorials on research data management for health science librarians and researchers at: https://nnlm.gov/data/courses-and-workshops .
Columbia Research Data Management Tutorials and Templates
The ReaDI Program has created several tutorials and templates to aid in the management of data during the collection phase of research and preparing for publication. Tutorial topics include: Good Laboratory Notebook Practices, Laboratory Notebook Checklist, Best Practices for Data Management When Using Instrumentation, and Guidelines on the Organization of Samples in a Laboratory. Downloadable templates are available on related topics, such as data to figure map templates.
The Research and Data Integrity (ReaDI) program is designed to enhance data management and research integrity at Columbia University. The ReaDI program provides resources, outreach and consultation services to researchers at all stages in their careers. Many of the resources are applicable to researchers at any institution.
How-to Guides to Managing a Research Project
These guides are designed to mirror the lifecycle of your research project. They provide support at its various stages. Topics include:
- Creating & analysing data
- Choosing file formats
- Data discovery & re-use
- Storing & preserving data
- Sharing data
- Handling sensitive & personal information
- Planning ahead for Data Management
- Software sustainability, preservation and sharing.
Data Management Guidelines
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
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).
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.
CURATE! The Digital Curator Game
The CURATE game is designed to be used as an exercise that prompts players to put themselves into digital project scenarios in order to address issues and challenges that arise when institutions engage with digital curation and preservation.
Developed as a means to highlight the importance of training in digital curation among practitioners and managers working in libraries, museums and cultural heritage institutes, the game has been used as a self-assessment tool, a team-building exercise and a training tool for early career students.
The CURATE game package includes:
- Welcome to CURATE Presentation
- Game Board (PDF)
- Game Cards (PDF)
- About the Game (PDF)
- Rules (PDF)
- Record Sheet & Closing Questions (PDF)
- Frequently Asked Questions (DoC)
Research data management training modules in Archaeology (Cambridge)
Looking after digital data is central to good research. We all know of horror stories of people losing or deleting their entire dissertation just weeks prior to a deadline! But even before this happens, good practice in looking after research data from the beginning to the end of a project makes work and life a lot less stressful. Defined in the widest sense, digital data includes all files created or manipulated on a computer (text, images, spreadsheets, databases, etc). With publishing and archiving of research increasingly online we all have a responsibility to ensure the long-term preservation of archaeological data, while at same time being aware of issues of sensitive data, intellectual property rights, open access, and freedom of information. The DataTrain teaching materials have been designed to familiarise post-graduate students in good practice in looking after their research data. A central tenet is the importance of thinking about this in conjunction with the projected outputs and publication of research projects. The eight presentations, followed by group discussion and written exercises, follow the lifecycle of digital data from pre-project planning, data creation, data management, publication, long-term preservation and lastly to issues of the re-use of digital data. At the same time the course follows the career path of researchers from post-graduate research students, through post-doctoral research projects, to larger collaborative and inter-disciplinary projects. The teaching material is targeted at co-ordinators of Core Research Skills courses for first year post-graduate research students in archaeology. The material is open access and you are invited to re-use and amend the content as best suits the requirements of your university department. The complete course is designed to run either as a four hour half-day workshop, or 2 x 2 hour classes. Alternatively, individual modules can be slotted into existing data management and core research skills teaching.
Ten Simple Rules for Creating a Good Data Management Plan
Research papers and data products are key outcomes of the science enterprise. Governmental, nongovernmental, and private foundation sponsors of research are increasingly recognizing the value of research data. As a result, most funders now require that sufficiently detailed data management plans be submitted as part of a research proposal. A data management plan (DMP) is a document that describes how you will treat your data during a project and what happens with the data after the project ends. Such plans typically cover all or portions of the data life cycle—from data discovery, collection, and organization (e.g., spreadsheets, databases), through quality assurance/quality control, documentation (e.g., data types, laboratory methods) and use of the data, to data preservation and sharing with others (e.g., data policies and dissemination approaches). The article also includes a downloadable image that illustrates the relationship between hypothetical research and data life cycles and highlights the links to the rules presented in this paper.
Research Data Services Guides in Support of Data Management
Research Data Services is a collaboration between the University of Iowa Libraries, the Office of the Vice President of Research and Economic Development, Information Technology Services, and other campus offices, to support researchers' data management needs. The guides that are part of these Services include answers to key questions, but may also include short videos on the following topics:
- Data Management Plans
- Data Organization and Documentation
- Data Repositories
- Other University of Iowa services and resources available as well as external tools, websites, and repositories that may be useful.
Data Champions: leading the way to proper research data management
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.
Photogrammetry Workshop UNM GEM Lab
This course provides an introduction to photgrammetry with a full set of data to utilize in building a Digital Elevation Model using Agisoft Photoscan. The course uses a gitHub repository to grow the workshop into a full featured course on the applications of modern remote sensing and photogrammetry techniques in and for the environmental and geosciences.
Coffee and Code: Natural Language Processing with Python
Github repository for this workshop: https://github.com/unmrds/cc-nlp https://github.com/unmrds/cc-nlp
The processing and analysis of natural languages is a core requirement for extracting structured information from spoken, signed, or written language and for feeding that information into systems or processes that generate insights from, or responses to provided language data. As languages that are naturally evolved and not designed for a specific purpose natural languages pose significant challenges when developing automated systems.
Natural Language Processing - the class of activities in which language analysis, interpretation, and generation play key roles - is used in many disciplines as is demonstrated by this random sample of recent papers using NLP to address very different research problems:
"Unsupervised entity and relation extraction from clinical records in Italian" (1)
"Candyflipping and Other Combinations: Identifying Drug–Drug Combinations from an Online Forum" (2)
"How Can Linguistics Help to Structure a Multidisciplinary Neo Domain such as Exobiology?" (3)
"Bag of meta-words: A novel method to represent document for the sentiment classification" (4)
"Information Needs and Communication Gaps between Citizens and Local Governments Online during Natural Disasters" (5)
"Mining the Web for New Words: Semi-Automatic Neologism Identification with the NeoCrawler" (6)
"Distributed language representation for authorship attribution" (7)
"Toward a computational history of universities: Evaluating text mining methods for interdisciplinarity detection from PhD dissertation abstracts" (8)
"Ecological momentary interventions for depression and anxiety" (9)
Coffee and Code: Reproducibility and Communication
This workshop provides an introduction to reproducibility and communication of research using notebooks based on RStudio and Jupyter Notebooks. The development of effective documentation and accesible and reusable methods in scientific analysis can make a significant contribution to the reproducibility and understanding of a research activity. The integration of executable code with blocks of narrative content within notebook systems such as those provided in RStudio and the Jupyter Notebook (and Lab) software environments provides a streamined way to bring these minimum components (data, metadata, code, and software) into a package that can be easily shared with others for review and reuse.
This workshop will provide:
- A high-level introduction to the notebook interfaces provided for R and Python through the RStudio and Jupyter Notebook environments.
- An introduction to Markdown as a language supported by both systems for adding narrative content to notebooks
- Sample notebooks illustrating structure, content, and output options
From the master page for this resource, the Reproducibility and Communication Using Notebooks ipynb file provides more information about what is covered in this workshop.
Coffee and Code: NoSQL
Introduction to NoSQL
In previous sessions we have looked at use cases for relational database management systems (RDBMS), which predominantly make use of SQL. Today's session provides an overview of NoSQL databases. NoSQL can be understood to mean "no SQL" or, alternatively, "not only SQL." NoSQL databases are non-relational, which in the simplest terms means they are not made up of tables.
Topics we will cover include:
- Differences between SQL and NoSQL databases
- Types of NoSQL databases and their use cases
- Document database basics with MongoDB
- Graph database basics with Neo4j
Coffee and Code: Content Platform
UNM RDS Content Platform for the Coffee & Code Workshop Series
This repository contains the needed code to replicate the presentation and playground environments used for the UNM Research Data Services (RDS) Coffee & Code workshop series. The materials in this repository leverage Docker as a platform for developing and deploying portable containers that support individual applications. In the case of the Coffee & Code instruction platform, the applications that are integrated into the system include:
- Jupyter Notebooks as a presentation, demonstration, and experimentation environment (based on the datascience-notebook container with the addition of Pandoc and LaTeX)
- A web-based RStudio environment (based on the rocker/rstudio with the addition of the R dplyr, ggplot2, ggrepel)
- Installed tools within the Jupyter Notebook platform include:
- Pandoc & LaTeX
- BASH shell
Coffee and Code: Introduction to Version Control
This is a tutorial about version control, also known as revision control, a method for tracking changes to files and folders within a source code tree, project, or any complex set of files or documents.
Also see Advanced Version Control, here: https://github.com/unmrds/cc-version-control/blob/master/03-advanced-ver...
Coffee and Code: Advanced Version Control
Learn advanced version control practices for tracking changes to files and folders within a source code tree, project, or any complex set of files or documents.
This tutorial builds on concepts taught in "Introduction to Version Control," found here: https://github.com/unmrds/cc-version-control/blob/master/01-version-cont....
Git Repository for this Workshop: https://github.com/unmrds/cc-version-control
Coffee and Code: Introduction to Database Design
In this session, we are going to dig a little deeper into databases as representions of systems and processes. A database with a single table may not feel or function much differently from a spreadsheet. Much of the benefit of using databases results from designing them as models of complex systems in ways that spreadsheets just can't do:
- Inventory control and billing
- Human resources
- Blogging platforms
There will be some more advanced SQL statements this time, though we will still be using SQLite. Concepts which will be discussed and implemented in our code include
- Entities and attributes
Coffee and Code: Write Once Use Everywhere (Pandoc)
Pandoc (http://pandoc.org) is a document processing program that runs on multiple operating systems (Mac, Windows, Linux) and can read and write a wide variety of file formats. In many respects, Pandoc can be thought of as a universal translator for documents. This workshop focuses on a subset of input and output document types, just scratching the surface of the transformations made possible by Pandoc.
Click 00-Overview.ipynb on the provided GitHub page or go directly to the overview, here:
How to motivate researcher engagement?
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.
Open Data Management in Agriculture and Nutrition Online Course
This free online course aims to strengthen the capacity of data producers and data consumers to manage and use open data in agriculture and nutrition. One of the main learning objectives is for the course to be used widely within agricultural and nutrition knowledge networks, in different institutions. The course also aims to raise awareness of different types of data formats and uses, and to highlight how important it is for data to be reliable, accessible and transparent.
The course is delivered through Moodle e-learning platform. Course units include:
Unit 1: Open data principles (http://aims.fao.org/online-courses/open-data-management-agriculture-and-...)
Unit 2: Using open data (http://aims.fao.org/online-courses/open-data-management-agriculture-and-...)
Unit 3: Making data open (http://aims.fao.org/online-courses/open-data-management-agriculture-and-...)
Unit 4: Sharing open data (http://aims.fao.org/online-courses/open-data-management-agriculture-and-...)
Unit 5: IPR and Licensing (http://aims.fao.org/online-courses/open-data-management-agriculture-and-...)
By the end of the course, participants will be able to:
- Understand the principles and benefits of open data
- Understand ethics and responsible use of data
- Identify the steps to advocate for open data policies
- Understand how and where to find open data
- Apply techniques to data analysis and visualisation
- Recognise the necessary steps to set up an open data repository
- Define the FAIR data principles
- Understand the basics of copyright and database rights
- Apply open licenses to data
The course is open to infomediaries which includes ICT workers, technologist - journalists, communication officers, librarians and extensionists; policy makers, administrators and project managers, and researchers, academics and scientists working in the area of agriculture, nutrition, weather and climate, and land data.