All Learning Resources
DataONE Data Management Module 04: Data Entry and Manipulation
When entering data, common goals include: creating data sets that are valid, have gone through an established process to ensure quality, are organized, and reusable. This lesson outlines best practices for creating data files. It will detail options for data entry and integration, and provide examples of processes used for data cleaning, organization and manipulation and includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise, handout, and supporting data files.
DataONE Data Management Module 05: Data Quality Control and Assurance
Quality assurance and quality control are phrases used to describe activities that prevent errors from entering or staying in a data set. These activities ensure the quality of the data before it is collected, entered, or analyzed, as well as actively monitoring and maintaining the quality of data throughout the study. In this lesson, we define and provide examples of quality assurance, quality control, data contamination and types of errors that may be found in data sets. After completing this lesson, participants will be able to describe best practices in quality assurance and quality control and relate them to different phases of data collection and entry. This 30-40 minute lesson includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.
DataONE Data Management Module 06: Data Protection and Backups
There are several important elements to digital preservation, including data protection, backup and archiving. In this lesson, these concepts are introduced and best practices are highlighted with case study examples of how things can go wrong. Exploring the logistical, technical and policy implications of data preservation, participants will be able to identify their preservation needs and be ready to implement good data preservation practices by the end of the module. This 30-40 minute lesson includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.
DataONE Data Management Module 07: Metadata
What is metadata? Metadata is data (or documentation) that describes and provides context for data and it is everywhere around us. Metadata allows us to understand the details of a dataset, including: where it was collected, how it was collected, what gaps in the data mean, what the units of measurement are, who collected the data, how it should be attributed etc. By creating and providing good descriptive metadata for our own data, we enable others to efficiently discover and use the data products from our research. This lesson explores the importance of metadata to data authors, users of the data and organizations, and highlights the utility of metadata. It provides an overview of the different metadata standards that exist, and the core elements that are consistent across them; guiding users in selecting a metadata standard to work with and introduces the best practices needed for writing a high quality metadata record.
This 30-40 minute lesson includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise, handout, and supporting data files.
DataONE Data Management Module 08: Data Citation
Data citation is a key practice that supports the recognition of data creation as a primary research output rather than as a mere byproduct of research. Providing reliable access to research data should be a routine practice, similar to the practice of linking researchers to bibliographic references. After completing this lesson, participants should be able to define data citation and describe its benefits; to identify the roles of various actors in supporting data citation; to recognize common metadata elements and persistent data locators and describe the process for obtaining one, and to summarize best practices for supporting data citation. This 30-40 minute lesson includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.
DataONE Data Management Module 09: Analysis and Workflows
Understanding the types, processes, and frameworks of workflows and analyses is helpful for researchers seeking to understand more about research, how it was created, and what it may be used for. This lesson uses a subset of data analysis types to introduce reproducibility, iterative analysis, documentation, provenance and different types of processes. Described in more detail are the benefits of documenting and establishing informal (conceptual) and formal (executable) workflows. This 30-40 minute lesson includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.
DataONE Data Management Module 10: Legal and Policy Issues
Conversations regarding research data often intersect with questions related to ethical, legal, and policy issues for managing research data. This lesson will define copyrights, licenses, and waivers, discuss ownership and intellectual property, and describe some reasons for data restriction. After completing this lesson, participants will be able to identify ethical, legal, and policy considerations that surround the use and management of research data. The 30-40 minute lesson includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.
NASA Earthdata Webinar Series
Monthly webinars on discovery and access to NASA Earth science data sets, services and tools. Webinars are archived on YouTube from 2013 to the present. Presenters are experts in different domains within NASA's Earth science research areas and are usually affiliated with NASA data centers and / or data archives. Specific titles for the current year's webinars can be found from the main page, but can also be found from separate pages for each year. These webinars are available to assist those wishing to learn or teach how to obtain and view these data.
NASA Earthdata Video Tutorials
Short video tutorials on topics related to available NASA EOSDIS data products, various types of data discovery, data access, and data tool demonstrations such as the Panoply tool for creating line plots. Videos accessible on YouTube from listing on main webinars and tutorials page. These tutorials are available to assist those wishing to learn or teach how to obtain and view these data.
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.
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.
NSIDC DAAC Data Recipes
A collection of tutorials, called "data recipes" that describe how to use Earth science data from NASA's National Snow and Ice Data Center (NSIDC) 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.
An overview of the EDI data repository and data portal
The Environmental Data Initiative (EDI) data repository is a metadata-driven archive for environmental and ecological research data described by the Ecological Metadata Language (EML). This webinar will provide an overview of the PASTA software used by the repository and demonstrate the essentials of uploading a data package to the repository through the EDI Data Portal.
FAIR Self-Assessment Tool
The FAIR Data Principles are a set of guiding principles in order to make data findable, accessible, interoperable and reusable (Wilkinson et al., 2016). Using this tool you will be able to assess the 'FAIRness' of a dataset and determine how to enhance its FAIRness (where applicable).
This self-assessment tool has been designed predominantly for data librarians and IT staff but could be used by software engineers developing FAIR Data tools and services, and researchers provided they have assistance from research support staff.
You will be asked questions related to the principles underpinning Findable, Accessible, Interoperable and Reusable. Once you have answered all the questions in each section you will be given a ‘green bar’ indicator based on your answers in that section, and when all sections are completed, an overall 'FAIRness' indicator is provided.
Webinar: Jupyter as a Gateway for Scientific Collaboration and Education
Project Jupyter, evolved from the IPython environment, provides a platform for interactive computing that is widely used today in research, education, journalism, and industry. The core premise of the Jupyter architecture is to design tools around the experience of interactive computing, building an environment, protocol, file format and libraries optimized for the computational process when there is a human in the loop, in a live iteration with ideas and data assisted by the computer.
The Jupyter Notebook, a system that allows users to compose rich documents that combine narrative text and mathematics together with live code and the output of computations in any format compatible with a web browser (plots, animations, audio, video, etc.), provides a foundation for scientific collaboration. The next generation of the Jupyter web interface, JupyterLab, will combine in a single user interface not only the notebook but multiple other tools to access Jupyter services and remote computational resources and data. A flexible and responsive UI allows the user to mix Notebooks, terminals, text editors, graphical consoles and more, presenting in a single, unified environment the tools needed to work with a remote environment. Furthermore, the entire design is extensible and based on plugins that interoperate via open APIs, making it possible to design new plugins tailored to specific types of data or user needs.
JupyterHub enables Jupyter Notebook and JupyterLab to be used by groups of users for research collaboration and education. We believe JupyterHub provides a foundation on which to build modern scientific gateways that support a wide range of user scenarios, from interactive data exploration in high-level languages like Python, Julia or R, to the education of researchers and students whose work relies on traditional HPC resources.
The presenter discusses the benefits and applications of Jupyter Notebooks.
Scroll to the bottom of the page to view the webinar. Presentation slides are also available on the same page.
R Class for Seismologists
The IRIS Data Management Center (DMC) archives and distributes data to support the seismological research community. The class described here introduces DMC and other seismologists to the R statistical programming language and its use with seismological data available from DMC web services. The capabilities of the seismicRoll, IRISSeismic and IRISMustangMetrics packages developed as part of the MUSTANG project will be demonstrated.
Class materials are broken up into nine separate lessons that assume some experience coding but not necessarily any familiarity with R. Lessons are presented in sequential order and assume the student already has R and RStudio installed on their computer. Autodidacts new to R should take about 20-30 hrs to complete the course. The target audience for these materials consists of IRIS DMC employees or graduate students with a degree in the natural sciences and some experience using scientific software such as MATLAB or Python.
Research Lifecycle at University of Central Florida
Short video discussing the "Research Lifecycle at University of Central Florida," a useful diagram for understanding the typical flow of a research project.
Research Data Management Community Training
Good research data management is of great importance for high-quality research. Implementing professional research data management from the start helps to avoid problems in the data creation and curation phases.
- Definition(s) of RDM
- Benefits and Advantages of RDM
- Research Data Life-Cycle
- Structure and components of RDM
- Recommended literature
Access Policies and Usage Regulations: Licenses
The webinar about licensing and policy will look into why it is important that research data are provided with licenses.
- Benefits of sharing research data
- Types of licenses
- Data ownership and reuse
- Using creative commons in archiving research data
During the workshop, participants will acquire a basic knowledge of data licensing.
Introduction to R
Learn the basics of reproducible workflows in R using a USGS National Water Information System (NWIS) dataset.
Instructions for accessing the dataset are provided within the tutorial.
Clean your taxonomy data with the taxonomyCleanr R package
Taxonomic data can be messy and challenging to work with. Incorrect spelling, the use of common names, unaccepted names, and synonyms, contribute to ambiguity in what a taxon actually is. The taxonomyCleanr R package helps you resolve taxonomic data to a taxonomic authority, get accepted names and taxonomic serial numbers, as well as create metadata for your taxa in the Ecological Metadata Language (EML) format.
Postgres, EML and R in a data management workflow
Metadata storage and creation of Ecological Metadata Language (EML) can be a challenge for people and organizations who want to archive their data. A workflow was developed to combine efficient EML record generation (using the package developed by the R community) with centrally-controlled metadata in a relational database. The webinar has two components: 1) a demonstration of metadata storage and management using a relational database, and 2) discussion of an example EML file generation workflow using pre-defined R functions.
Experimental Design Assistant
The Experimental Design Assistant (EDA) (https://eda.nc3rs.org.uk) is a free web-based tool that was developed by the NC3Rs (https://www.nc3rs.org.uk). It guides researchers through the design and analysis of in vivo experiments. The EDA allows users to build a stepwise visual representation of their experiment, providing feedback and dedicated support for randomization, blinding and sample size calculation. This demonstration will provide an introduction to the tool and provide guidance on getting started. Ultimately, the use of a tool such as the EDA will lead to carefully designed experiments that yield robust and reproducible data using the minimum number of animals consistent with scientific objectives.
Open Science and Innovation
This course helps you to understand open business models and responsible research and innovation (RRI) and illustrates how these can foster innovation. By the end of the course, you will:
- Understand key concepts and values of open business models and responsible research and innovation
- Know how to plan your innovation activities
- Be able to use Creative Commons licenses in business
- Understand new technology transfer policies with the ethos of Open Science
- Learn how to get things to market faster
Data Management using NEON Small Mammal Data
Undergraduate STEM students are graduating into professions that require them to manage and work with data at many points of a data management lifecycle. Within ecology, students are presented not only with many opportunities to collect data themselves but increasingly to access and use public data collected by others. This activity introduces the basic concept of data management from the field through to data analysis. The accompanying presentation materials mention the importance of considering long-term data storage and data analysis using public data.
Licensing your research outputs is an important part of practicing Open Science. After completing this course, you will:
- Know what licenses are, how they work, and how to apply them
- Understand how different types of licenses can affect research output reuse
- Know how to select the appropriate license for your research
Make EML with R and share on GitHub
Introduction to the Ecological Metadata Language (EML). Topics include:
- Use R to build EML for a mock dataset
- Validate EML and write to file
- Install Git and configure to track file versioning in RStudio
- Set up GitHub account and repository
- Push local content to GitHub for sharing and collaboration
Access the rendered version of this tutorial here:https://cdn.rawgit.com/EDIorg/tutorials/2002b911/make_eml_with_r/make_em...
Florilege, a new database of habitats and phenotypes of food microbe flora
This tutorial explains how to use the “Habitat-Phenotype Relation Extractor for Microbes” application available from the OpenMinTeD platform. It also explains the scientific issues it addresses, and how the results of the TDM process can be queried and exploited by researchers through the Florilège application.
In recent years, developments in molecular technologies have led to an exponential growth of experimental data and publications, many of which are open, however accessible separately. Therefore, it is now crucial for researchers to have bioinformatics infrastructures at their disposal, that propose unified access to both data and related scientific articles. With the right text mining infrastructures and tools, application developers and data managers can rapidly access and process textual data, link them with other data and make the results available for scientists.
The text-mining process behind Florilege has been set up by INRA using the OpenMinTeD environment. It consists in extracting the relevant information, mostly textual, from scientific literature and databases. Words or word groups are identified and assigned a type, like “habitat” or “taxon”.
Sections of the tutorial:
1. Biological motivation of the Florilege database
2. Florilège Use-Case on OpenMinTeD (includes a description of how to access the Habitat-Phenotype Relation Extractor for Microbes application)
3. Florilege backstage: how is it build?
4. Florilège description
5. How to use Florilege ?
Best Practice in Open Research
This course introduces some practical steps toward making your research more open. We begin by exploring the practical implications of open research, and the benefits it can deliver for research integrity and public trust, as well as benefits you will accrue in your own work. After a short elaboration of some useful rules of thumb, we move quickly onto some more practical steps towards meeting contemporary best practice in open research and introduce some useful discipline-specific resources. Upon completing this course, you will:
- Understand the practical implications of taking a more open approach to research
- Be prepared to meet expectations relating to openness from funders, publishers, and peers
- Be able to reap the benefits of working openly
- Have an understanding of the guiding principles to follow when building openness into your research workflow
- Know about some useful tools and resources to help you embed Open Science into work research practices
Managing and Sharing Research Data
Data-driven research is becoming increasingly common in a wide range of academic disciplines, from Archaeology to Zoology, and spanning Arts and Science subject areas alike. To support good research, we need to ensure that researchers have access to good data. Upon completing this course, you will:
- Understand which data you can make open and which need to be protected
- Know how to go about writing a data management plan
- Understand the FAIR principles
- Be able to select which data to keep and find an appropriate repository for them
- Learn tips on how to get maximum impact from your research data
GeoNode for Developers Workshop
GeoNode is a web-based application and platform for developing geospatial information systems (GIS) and for deploying spatial data infrastructures (SDI). It is designed to be extended and modified and can be integrated into existing platforms.
This workshop covers the following topics:
- GeoNode in development mode, how to
- The geonode-project to customize GeoNode
- Change the look and feel of the application
- Add your own app
- Add your own models, view, and logic
- Build your own APIs
- Add a third party app
- Deploy your customized GeoNode
To access geonode-project on GitHub, go to https://github.com/GeoNode/geonode-project .
Science Impact of Sustained Cyberinfrastructure: The Pegasus Example
This talk is the first in a series of NSF's Office of Advanced Cyberinfrastructure (OAC) webinars. Dr. Deelman describes the challenges of developing and sustaining cyberinfrastructure capabilities that have impact on scientific discovery and that innovate in the changing cyberinfrastructure landscape. The recent multi-messenger observation triggered by LIGO and VIRGO’s first detection of gravitational waves produced by colliding neutron stars is a clear display of the increasing impact of dependable research cyberinfrastructure (CI) on scientific discovery.
Today’s cyberinfrastructure—hardware, software, and workforce—underpins the entire scientific workflow, from data collection at instruments, through complex analysis, to simulation, visualization, and analytics. The Pegasus project in an example of a cyberinfrastructure effort that enables LIGO and other communities to accomplish their scientific goals. In addition, it delivers robust automation capabilities to researchers at the Southern California Earthquake Center (SCEC) studying seismic phenomena, to astronomers seeking to understand the structure of the universe, to material scientists developing new drug delivery methods, and to students seeking to understand human population migration.
Environmental Data Initiative Five Phases of Data Publishing Webinar - What are metadata and structured metadata?
Metadata are essential to understanding a dataset. The talk covers:
- How structured metadata are used to document, discover, and analyze ecological datasets.
- Tips on creating quality metadata content.
- An introduction to the metadata language used by the Environmental Data Initiative, Ecological Metadata Language (EML). EML is written in XML, a general purpose mechanism for describing hierarchical information, so some general XML features and how these apply to EML are covered.
This video in the Environmental Data Initiative (EDI) "Five Phases of Data Publishing" tutorial series covers the third phase of data publishing, describing.
Environmental Data Initiative Five Phases of Data Publishing Webinar - Make metadata with the EML assembly line
High-quality structured metadata is essential to the persistence and reuse of ecological data; however, creating such metadata requires substantial technical expertise and effort. To accelerate the production of metadata in the Ecological Metadata Language (EML), we’ve created the EMLassemblyline R code package. Assembly line operators supply the data and information about the data, then the machinery auto-extracts additional content and translates it all to EML. In this webinar, the presenter will provide an overview of the assembly line, how to operate it, and a brief demonstration of its use on an example dataset.
This video in the Environmental Data Initiative (EDI) "Five Phases of Data Publishing" tutorial series covers the third phase of data publishing, describing.
Environmental Data Initiative Five Phases of Data Publishing Webinar - Creating "clean" data for archiving
Not all data are easy to use, and some are nearly impossible to use effectively. This presentation lays out the principles and some best practices for creating data that will be easy to document and use. It will identify many of the pitfalls in data preparation and formatting that will cause problems further down the line and how to avoid them.
This video in the Environmental Data Initiative (EDI) "Five Phases of Data Publishing" tutorial series covers the second phase of data publishing, cleaning data. For more guidance from EDI on data cleaning, also see "How to clean and format data using Excel, OpenRefine, and Excel," located here: https://www.youtube.com/watch?v=tRk01ytRXjE.
Environmental Data Initiative Five Phases of Data Publishing Webinar - How to clean and format data using Excel, OpenRefine, and Excel
This webinar provides an overview of some of the tools available for formatting and cleaning data, guidance on tool suitability and limitations, and an example dataset and instructions for working with those tools.
This video in the Environmental Data Initiative (EDI) "Five Phases of Data Publishing" tutorial series covers the second phase of data publishing, cleaning data.
For more guidance from EDI on data cleaning, also see " Creating 'clean' data for archiving," located here: https://www.youtube.com/watch?v=gW_-XTwJ1OA.
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.
Intro to SQL for Data Science
The role of a data scientist is to turn raw data into actionable insights. Much of the world's raw data—from electronic medical records to customer transaction histories—lives in organized collections of tables called relational databases. Therefore, to be an effective data scientist, you must know how to wrangle and extract data from these databases using a language called SQL (pronounced ess-que-ell, or sequel). This course teaches you everything you need to know to begin working with databases today!
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.
- 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