All Learning Resources

This presentation covers updates and enhancements to the DMPonline  DMPonline helps you to create, review, and share data management plans that meet institutional and funder requirements. Six areas are discussed:

1. Usability improvements
2. Lifecycle and review
3. API for systems integration
4. Institutional enhancements 
5. Locale-aware support
6. Maintenance

Introduction to the Global Biodiversity Information Facility (GBIF), including lessons and achievements from the first ten years of using GBIF. This presentation was part of the Conference Connecting Data for Research held at VU University in Amsterdam. Topics include:

• What is biodiversity?
• Biodiversity data
• History of GBIF
• GBIF usage trends
• Primary GBIF data
• Using GBIF
• Darwin Core
• Integrated Publishing Toolkit (IPT)

Part of the Embracing Data Management, Bridging the Gap Between Theory and Practice workshop, held in Brussels, this talk provides an introduction to open access publishing. Topics include:

• Benefits
• Philosophy
• Requirements
• Preprints and postprints
• Tips

An overview of research management tools:

• Re3data to find repositories
• FAIRsharing and RDA Data Standards Catalogue
• DMPonline for writing data management plans
• OpenAIRE for managing outputs and reporting results

Data often have a longer lifespan than the research project that creates them. Researchers may continue to work on data after funding has ceased, follow-up projects may analyse or add to the data, and data may be re-used by other researchers.

Adhering to a well-defined research data lifecycle results in organised, well documented, preserved and shared data that advance scientific inquiry and increase opportunities for learning and innovation.

This course provides an in-depth overview of the application of Geographic Information Systems (GIS) to the marine environment using QGIS. All resources can be accessed from the provided URL. 

Topic 1:
Aims and Objectives:

• Provide an introduction to GIS for marine applications
• Focus on some publicly available marine datasets
• Show the potential applications of GIS for the marine environment

Learning Outcomes:

• Knowledge and understanding of GIS, spatial data, raster, and vector models
• Core tasks involved in the GIS analysis process including data acquisition, management, manipulation and analysis, and presentation and output
• Core functionality of QGIS Desktop and QGIS Browser
• Creating and editing spatial data
• Appreciation of coastal and marine GIS data applications

Topic 2: Introduction to Marine GIS

• A brief explanation of GIS
• GIS components
• GIS data models
• Marine GIS applications
• Spatial analysis

Topic 3: Introduction to QGIS

• Why QGIS?
• Exercise: Introduction To QGIS

Topic 4: View data in QGIS Desktop

• QGIS provides several windows, convenient for the user
• Exercise

Topic 5: Map Projections and Coordinate Systems

• Geographic Coordinate Systems
• Coordinate Systems And Map Projections
• Video: Map projections 
• Exercise

Topic 6: Create Base Map in QGIS

• Define the Area of Interest (AOI)
• Exercise

Topic 7: Creating Data Collection from the World Ocean Database

• Exercise: Obtaining Marine Data from the World Ocean Database

Topic 8: Introduction to Ocean Data View

• Video: Ocean Data View (ODV) by Reiner Schlitzer
• Exercise: Creating Data Collection from the World Ocean Database
• Exercise: Export Marine Data from the Ocean Data View

Topic 9: Working with Spreadsheet Data

• Exercise: Adding Spreadsheet Data

Topic 10: Edit Data in QGIS

• Exercise

Topic 11: Edit Data: Area of Interest and Analysis Mask

• Exercise

Topic 12: Interpolating surfaces

• Map Interpolation
• Interpolating Surfaces: Summary 
• Exercise: Interpolate to Raster

Topic 13: Rendering Raster Data

• Exercise

Topic 14: Raster Calculator

• Exercise: Using the Raster Calculation

Topic 15: Working with NetCDF

• Exercise

Topic 16: Plotting Vector Arrows from U and V Component Grids

• Marine Geospatial Ecology Tools (MGET)

Topic 17: Downloading species observations from the Ocean Biogeographic Information System (OBIS)

• Exercise: Downloading OBIS Data in CSV Format

Topic 18: Creating KML files for Google Earth

• Example KML document
• Exercise

Topic 19: Publication Quality Maps

• Exercise: Create Publication Quality Maps

Everybody wants to play FAIR, but how do we put the principles into practice?

There is a growing demand for quality criteria for research datasets. The presenters argue that the DSA (Data Seal of Approval for data repositories) and FAIR principles get as close as possible to giving quality criteria for research data. They do not do this by trying to make value judgments about the content of datasets, but rather by qualifying the fitness for data reuse in an impartial and measurable way. By bringing the ideas of the DSA and FAIR together, we will be able to offer an operationalization that can be implemented in any certified Trustworthy Digital Repository. In 2014 the FAIR Guiding Principles (Findable, Accessible, Interoperable and Reusable) were formulated. The well-chosen FAIR acronym is highly attractive: it is one of these ideas that almost automatically get stuck in your mind once you have heard it. In a relatively short term, the FAIR data principles have been adopted by many stakeholder groups, including research funders. The FAIR principles are remarkably similar to the underlying principles of DSA (2005): the data can be found on the Internet, are accessible (clear rights and licenses), in a usable format, reliable and are identified in a unique and persistent way so that they can be referred to. Essentially, the DSA presents quality criteria for digital repositories, whereas the FAIR principles target individual datasets. In this
webinar the two sets of principles will be discussed and compared and a tangible operationalization will be presented.

Learn how and why to group works that have been added to your ORCID (Open Researcher and Contributor ID) record from different sources, so that they are displayed together on your record.

The DMP Assistant is a bilingual tool to assist in the preparation of a Data Management Plan (DMP). This tool, which is based on international standards and best practices in data management, guides the researcher step by step through the key questions to develop his plan. DMP Assistant is powered by an open source application called DMPOnline, which is developed by the Digital Curation Centre (DCC).  Site registration is required.  Data management planning templates are available for the DMP Assistant after registration and sign in.

In the digital era, documenting and sharing our scientific data is growing increasingly important as an integral part of the scientific process. Data Management not only makes our data resources available for others to build upon, but it also enables data syntheses and new analyses that hold the potential for significant scientific advancement. Effective data management begins during the planning stages of a project and continues throughout the research process from field and/or laboratory work, through analysis, and culminating with scientific literature and data publication. By planning ahead, and following some best practices along the way, the process of data management can be simple and relatively low-effort, enabling rapid contribution and publication of data in the appropriate data systems at the conclusion of a project.

IEDA offers a variety of tools to support investigators along the full continuum of their data management efforts:  Links to these tools and resources are available from the landing page for this resource.

Pre-Award

• IEDA Data Discovery Tools
• IEDA Data Management Plan (DMP) Tool

Research & Analysis

• Register sample-based data sets and samples 
  • Register sample metadata and get a unique sample identifier (IGSN)
  • Download Templates for Analytical Data
  • Learn about contributing Analytical Data to the EarthChem Library
• Register sensor-based data sets and samples 
  • Contribute sensor data files (e.g. geophysical data) and supporting metadata to MGDS
• IEDA Analysis Tools
  • GeoMapApp earth science exploration and visualization application 
    • Analyze your own geospatial data within the context of other available datasets

Synthesis & Publication

• Register final data sets with IEDA 
• Publish your data 
  • Publishing your data with a DOI ensures that it can be directly referenced in your paper and cited by others.
• IEDA Data Compliance Reporting (DCR) Tool 
  • Rapidly generate a Data Compliance Report (DCR) based on your NSF award number to demonstrate that your data are registered with IEDA systems and you are compliant with NSF Data Policies.

A collection of tutorials, called "data recipes" that describe how to use Earth science data from NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) using easily available tools and commonly used formats for Earth science data focusing on biogeochemical dynamics data.  These tutorials are available to assist those wishing to learn or teach how to obtain and view these data. 

A brief tutorial that shows how to upload, preview, and publish from iData. To use the accompanying My Geo Hub tutorial exercises, go to https://mygeohub.org/resources/1217. Note that the number before each step is the time on the YouTube video where it shows how each step is done. Also, note that the video does not contain audio content.

The System for Earth Sample Registration (SESAR) operates a registry that distributes the International Geo Sample Number IGSN. SESAR catalogs and preserves sample metadata profiles, and provides access to the sample catalog via the Global Sample Search.

MySESAR provides a private working space in the System for Earth Sample Registration. This tutorial will introduce you to how to manage samples in MySESAR, including how to search the sample catalog, how to view and edit samples, how to print labels, how to group samples and how to transfer ownership of samples. For details relating to sample registration, please see tutorials for individual sample and batch sample registration here: http://www.geosamples.org/help/registration.

MySESAR allows you to:

• obtain IGSNs for your samples by registering them with SESAR.
• register samples one at a time by entering metadata into a web form.
• register multiple samples by uploading metadata in a SESAR spreadsheet form.
• generate customized SESAR spreadsheet forms.
• view lists of samples that you registered.
• edit sample metadata profiles.
• upload images and other documents such as field notes, maps, or links to publications to a sample profile.
• restrict access to metadata profiles of your samples.
• transfer ownership of a sample to another SESAR user.

A brief tutorial that shows how to share a GeoBuilder session. ​The GeoBuilder tool provides a wizard type interface that guides users through several steps for loading, selecting, configuring and analyzing geo-referenced tabular data. To use the accompanying My Geo Hub tutorial exercises, go to https://mygeohub.org/resources/1219. Note that the number before each step is the time on the YouTube video where it shows how each step is done. Also, note that the video does not contain audio content.

For more information about GeoBuilder, go to ​https://mygeohub.org/resources/geobuilder.

The focus of this workshop is on working with genomics data and data management and analysis for genomics research. It covers data management and analysis for genomics research including best practices for the organization of bioinformatics projects and data, use of command line utilities, use of command line tools to analyze sequence quality and perform variant calling, and connecting to and using cloud computing.
Lessons:

• Project organization and management
• Introduction to the command line
• Data wrangling and processing
• Introduction to cloud computing for genomics
• Data analysis and visualization in R *beta*

Over the past 5+ years, many of Southern California’s active faults have been scanned with airborne lidar through various community and PI-data collection efforts (e.g., the B4 Project, EarthScope, and the post-El Mayor–Cucapah earthquake). All of these community datasets are publicly available (via OpenTopography: https://www.opentopography.org) and powerfully depict the effect of repeated slip along these active faults as well as surface processes in a range of climatic regimes. These datasets are of great interest to the Southern California Earthquake Center (SCEC) research and greater academic communities and have already yielded important new insights into earthquake processes in southern California.

This is a short course on LiDAR technology, data processing, and analysis techniques. The foci of the course are fault trace and geomorphic mapping applications, integration with other geospatial data, and data visualization and analysis approaches. Course materials include slide presentations, video demonstrations, and text-based software application tutorials.

Digital projects – science gateways, data repositories, educational websites, and others—have a few things in common. They can deliver a great deal of value to users – by sharing widely sophisticated tools, large data sets, or access to computing capacity among those in the academic sector who really need them to advance their work.  But they share something else in common, too: They are devilishly hard to run in a way that permits ongoing growth and expansion.

In this webinar, Nancy Maron, a lead instructor in the Science Gateways Bootcamp, introduces participants to the key elements of sustainability planning – the building blocks for developing Science Gateways that have the best chance for ongoing growth.

The webinar will introduce sustainability models and share some key tactics for identifying the models that are most likely to work for your gateway. We will touch upon funding models, the competitive environment, and audience assessment, to show how these need to be considered in tandem with any plan.

The growing size and complexity of high-value scientific datasets are pushing the boundaries of traditional models of data access and discovery. Many large datasets are only accessible through the systems on which they were created or require specialized software or computational resources for re-use. In response to this growing need, the National Data Service (NDS) consortium is developing the Labs Workbench platform, a scalable, web-based system intended to support turn-key deployment of encapsulated data management and analysis tools to support exploratory analysis and development on cloud resources that are physically "near" the data and associated high-performance computing (HPC) systems.  The Labs Workbench may complement existing science gateways by enabling exploratory analysis of data and the ability for users to deploy and share their own tools. The Labs Workbench platform has also been used to support a variety training and workshop environments.

This webinar includes a demonstration of the Labs Workbench platform and a discussion of several key use cases. A presentation of findings from the recent Workshop on Container Based Analysis Environments for Research Data Access and Computing further highlight compatibilities between science gateways and interactive analysis platforms such as Labs Workbench.
 

This course will provide learners with an introduction to research data management and sharing. After completing this course, learners will understand the diversity of data and their management needs across the research data lifecycle, be able to identify the components of good data management plans and be familiar with best practices for working with data including the organization, documentation, and storage and security of data. Learners will also understand the impetus and importance of archiving and sharing data as well as how to assess the trustworthiness of repositories.

Note: The course is free to access. However, if you pay for the course, you will have access to all of the features and content you need to earn a Course Certificate from Coursera. If you complete the course successfully, your electronic Certificate will be added to your Coursera Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. Note that the Course Certificate does not represent official academic credit from the partner institution offering the course.
Also, note that the course is offered on a regular basis. For information about the next enrollment, go to the provided URL.