All Learning Resources

  • PARTHENOS E-Humanities and E-Heritage Webinar Series

    The PARTHENOS eHumanities and eHeritage Webinar Series provides a lens through which a more nuanced understanding of the role of Digital Humanities and Cultural Heritage research infrastructures in research can be obtained.  Participants of the PARTHENOS Webinar Series will delve into a number of topics, technologies, and methods that are connected with an “infrastructural way” of engaging with data and conducting humanities research.

    Topics include: theoretical and practical reflections on digital and analogue research infrastructures; opportunities and challenges of eHumanities and eResearch; finding, working and contributing to Research Infrastructure collections; standards; FAIR principles; ontologies; tools and Virtual Research Environments (VREs), and; new publication and dissemination types.  

    Slides and video recordings of the webinars can be found from the "Wrap Up & Materials" pages at the landing page for each webinar's separate listing/linking that can be found on this series landing page.  

    Learning Objectives: 
    Each webinar of the PARTHENOS Webinar Series has an individual focus and can be followed independently.  Participants who follow the whole series will gain a complete overview on the role and value of Digital Humanities and Cultural Heritage Research Infrastructures for research, and will be able to identify Research Infrastructures especially valuable for their research and data.
     

  • Analyzing Documents with TF-IDF

    This lesson focuses on a core natural language processing and information retrieval method called Term Frequency - Inverse Document Frequency (tf-idf). You may have heard about tf-idf in the context of topic modeling, machine learning, or other approaches to text analysis. Tf-idf comes up a lot in published work because it’s both a corpus exploration method and a pre-processing step for many other text-mining measures and models.

    Looking closely at tf-idf will leave you with an immediately applicable text analysis method. This lesson will also introduce you to some of the questions and concepts of computationally oriented text analysis. Namely, this lesson addresses how you can isolate a document’s most important words from the kinds of words that tend to be highly frequent across a set of documents in that language. In addition to tf-idf, there are a number of computational methods for determining which words or phrases characterize a set of documents, and I highly recommend Ted Underwood’s 2011 blog post as a supplement.

    Suggested Prior Skills
    - Prior familiarity with Python or a similar programming language. Code for this lesson is written in Python 3.6, but you can run tf-idf in several different versions of Python, using one of several packages, or in various other programming languages. The precise level of code literacy or familiarity recommended is hard to estimate, but you will want to be comfortable with basic types and operations. To get the most out of this lesson, it is recommended that you work your way through something like Codeacademy’s “Introduction to Python” course, or that you complete some of the introductory Python lessons on the Programming Historian.
    - In lieu of the above recommendation, you should review Python’s basic types (string, integer, float, list, tuple, dictionary), working with variables, writing loops in Python, and working with object classes/instances.
    - Experience with Excel or an equivalent spreadsheet application if you wish to examine the linked spreadsheet files. You can also use the pandas library in python to view the CSVs.

  • Temporal Network Analysis with R

    This tutorial introduces methods for visualizing and analyzing temporal networks using several libraries written for the statistical programming language R. With the rate at which network analysis is developing, there will soon be more user-friendly ways to produce similar visualizations and analyses, as well as entirely new metrics of interest. For these reasons, this tutorial focuses as much on the principles behind creating, visualizing, and analyzing temporal networks (the “why”) as it does on the particular technical means by which we achieve these goals (the “how”). It also highlights some of the unhappy oversimplifications that historians may have to make when preparing their data for temporal network analysis, an area where our discipline may actually suggest new directions for temporal network analysis research.

    One of the most basic forms of historical argument is to identify, describe, and analyze changes in a phenomenon or set of phenomena as they occur over a period of time. The premise of this tutorial is that when historians study networks, we should, insofar as it is possible, also be acknowledging and investigating how networks change over time.

    Lesson Goals
    In this tutorial you will learn:
    -The types of data necessary to model a temporal network
    -How to visualize a temporal network using the NDTV package in R
    -How to quantify and visualize some important network-level and node-level metrics that describe temporal networks using the TSNA package in R.

    Prerequisites:
    This tutorial assumes that you have:
    - a basic familiarity with static network visualization and analysis, which you can get from excellent tutorials on the Programming Historian such as From Hermeneutics to Data to Networks: Data Extraction and Network Visualization of Historical Sources and Exploring and Analyzing Network Data with Python
    - RStudio with R version 3.0 or higher
    - A basic understanding of how R can be used to modify data. You may want to review the excellent tutorial on R Basics with Tabular Data found at:  https://programminghistorian.org/en/lessons/r-basics-with-tabular-data.

  • File Naming Convention Worksheet

    This worksheet walks researchers through the process of creating a file naming convention for a group of files. This process includes: choosing metadata, encoding and ordering the metadata, adding version information, and properly formatting the file names. Two versions of the worksheet are available: a Caltech Library branded version (PDF) and a generic editable version (MS Word).

  • Data Science Training Camp at Woods Hole Oceanographic Institution: Syllabus and slide presentations in 2020

    With data and software increasingly recognized as scholarly research products, and aiming towards open science and reproducibility, it is imperative for today's oceanographers to learn foundational practices and skills for data management and research computing, as well as practices specific to the ocean sciences. This educational package was developed as a data science training camp for graduate students and professionals in the ocean sciences and implemented at the Woods Hole Oceanographic Institution (WHOI) in 2019 and 2020. Here we provide materials for the 2020 camp.  Contents of this package include the syllabus and slide presentations for each of the four modules:
    1 "Good enough practices in scientific computing,"
    2 Data management,
    3 Software development and research computing,
    and 4 Best practices in the ocean sciences.
    The 3rd module is split into two parts. We also include a poster presented at the 2020 Ocean Science Meeting, which has some results from pre- and post-surveys.
     

  • Project Close-Out Checklist for Research Data

    The close-out checklist describes a range of activities for helping ensure that research data are properly managed at the end of a project or at researcher departure. Activities include: making stewardship decisions, preparing files for archiving, sharing data, and setting aside important files in a "FINAL" folder. Two versions of the checklist are available: a Caltech Library branded version (PDF) and a generic editable version (MS Word).

  • Efficient BIM Data Management & Quality Control of Revit Projects

    This AGACAD webinar provides guidance to speedy building design, facility management, and BIM data analysis in Revit projects. The contents include:
    • Manage BIM data in your Revit model and set LOD
    • Review, change & easily update BIM Data in your Revit projects
    • Find and modify any element parameters in BIM model with ease
    • Use formulas to make your own data tables
    • Insert elements into your project using various predefined rules
    • Set up and control LOD requirements based on standards, specifications, or framework agreed upon by the IPD team
    • Ensure that BIM models fit the agreed standards.

  • Top 5 Workflows for Precise BIM Data Management

    Do you have the need to easily rename families in your Revit project to match standards?  Do you find it hard to edit and control revisions within Revit?  Do you need accurate Quantity Take-Off information from your Revit model?  How about the need to edit parameter information more easily than a Revit Schedule?  Tired of assigning View Templates and managing view properties manually? Review this webcast as we cover these examples and more, utilizing a powerful Revit add-on application from Ideate Software called Ideate BIMLink.  It’s precise, fast, and easy Data Management of your BIM information.

  • Visualizing Data with Bokeh and Pandas

    The ability to load raw data, sample it, and then visually explore and present it is a valuable skill across disciplines. In this tutorial, you will learn how to do this in Python by using the Bokeh and Pandas libraries. Specifically, we will work through visualizing and exploring aspects of WWII bombing runs conducted by Allied powers, i.e., the WW II THOR dataset (Theater History of Operations Reports (THOR).
    At the end of the lesson you will be able to:
    -Load tabular CSV data
    -Perform basic data manipulation, such as aggregating and sub-sampling raw data
    -Visualize quantitative, categorical, and geographic data for web display
    -Add varying types of interactivity to your visualizations

    Prerequisites
    -This tutorial can be completed using any operating systems. It requires Python 3 and a web browser. You may use any text editor to write your code.
    -This tutorial assumes that you have a basic knowledge of the Python language and its associated data structures, particularly lists.
    -If you work in Python 2, you will need to create a virtual environment for Python 3, and even if you work in Python 3, creating a virtual environment for this tutorial is good practice.

  • Introduction To MySQL With R

    MySQL is a relational database used to store and query information. This lesson will use the R language to provide a tutorial and examples to:
    -Set up and connect to a table in MySQL.
    -Store records to the table.
    -Query the table.
    In this tutorial you will make a database of newspaper stories that contain words from a search of a newspaper archive. The program will store the title, date published and URL of each story in a database. They’ll use another program to query the database and look for historically significant patterns. Sample data will be provided from the Welsh Newspapers Online newspaper archive. They are working toward having a list of stories they can query for information. At the end of the lesson, they will run a query to generate a graph of the number of newspaper stories in the database to see if there is a pattern that is significant.

    To do this lesson you will need a computer where you have permission to install software such as R and RStudio, if you are not running that already. In addition to programming in R, they will be installing some components of a database system called MySQL which works on Windows, Mac and Linux.

    Some knowledge of installing software as well as organizing data into fields is helpful for this lesson which is of medium difficulty.

  • Dealing with Big Data and Network Analysis Using Neo4j

    In this lesson, you will learn how to use a graph database to store and analyze complex networked information. Networks are all around us. Social scientists use networks to better understand how people are connected. This information can be used to understand how things like rumors or even communicable diseases can spread throughout a community of people.
    This tutorial will focus on the Neo4j graph database and the Cypher query language that comes with it.
    -Neo4j is a free, open-source graph database written in java that is available for all major computing platforms.
    -Cypher is the query language for the Neo4j database that is designed to insert and select information from the database.
    By the end of this lesson you will be able to construct, analyze, and visualize networks based on big — or just inconveniently large — data. The final section of this lesson contains code and data to illustrate the key points of this lesson.

  • SPSS Data Curation Primer

    This data curation primer primarily discusses .sav and .por files. SPSS Statistics (.sav): Data files saved in IBM SPSS Statistics format. Portable (.por): Portable format that can be read by other versions of IBM SPSS Statistics and versions on other operating systems.
    This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #1 co-located with the Digital Library Federation (DLF) Forum 2018 in Las Vegas, Nevada on October 17-18, 2018.
    Table of Contents:
    -Description of Format
    -Example Data
    -Start the Conversation: Broad Questions and Clarifications on Research Data
    -Key Questions
    -Key Clarifications
    -Applicable Metadata Standards, Recommended Elements, and Readme File
    -Tutorials
    -Software
    -Preservation Actions
    -FAIR Principles & SPSS
    -Format Use
    -Documentation of Curation Process
    -Appendix A: Other SPSS File Formats
    -Appendix B: Project Level or Study Level Metadata
    -Appendix C: DDI Metadata
    -Appendix D: Dictionary Schema
    -Bibliography

    Other Data Curation Primers can be found at:  https://conservancy.umn.edu/handle/11299/202810.  Interactive primers available for download and derivatives at: https://github.com/DataCurationNetwork/data-primers.

     

  • STL Data Curation Primer

    An STL file stores information about 3D models. It is commonly used for printing 3D objects. The STL format approximates 3D surfaces of a solid model with oriented triangles (facets) of different size and shape (aspect ratio) in order to achieve a representation suitable for viewing or reproduction using digital fabrication. This format describes only the surface geometry of a three-dimensional object without any representation of color, texture, or other common model attributes. These files are usually generated as an end product of a 3D modeling or spatial capture process. The purpose of this primer is to guide a data curator through the curation process for STL files.

    This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #2 held at Johns Hopkins University on April 17-18, 2019.
    The full set of Data Curation Primers can be found at:https://conservancy.umn.edu/handle/11299/202810
    Interactive primers available for download and derivatives at:https://github.com/DataCurationNetwork/data-primers
     

  • The Paper and The Data: Authors, Reviewers, and Editors Webinar on Updated Journal Practices for Data (and Software)

    The Paper and The Data workshop was first presented at the Ocean Science meeting held in February 2020.  Following that conference it was updated and presented as five recorded modules for the purpose of sharing broadly. The workshop consists of 5 modules on topics related to the new practices of publishers for journals for the improvement of data and software sharing that are targeted to journal authors, reviewers, and editors.  Both slides and video presentations of the slides are available.  Modules include:

    Module 1: Introduction 

    • Challenges with Accessing Data
    • AGU Data Position Statement
    • Recommendations from the NAS
    • Updated Journal Guidelines
    • Benefits for Sharing Data/Software



    Module 2: Data

    •   What Data  
    •   What Repository
    •   Availability Statement
    •   Citation
    •   Examples


    Module 3: Software 

    •  What Software
    •   Availability Statement
    •   Citation
    •   Github?  Nope.  But now what?
    •   Examples 


    Module 4: Peer Review 

    •   Recommendation from AGU
    •   Examples



    Module 5: Persistent Identifiers 

    •   ORCID, DOI…
    •   PID Graph

  • GeoJSON Data Curation Primer

    GeoJSON is a geospatial data interchange format for encoding vector geographical data structures, such as point, line, and polygon geometries, as well as their non-spatial attributes. The purpose of this primer is to guide a data curator through the curation process for GeoJSON files. Key questions for curation review:
    ● Are coordinates listed in the following format: [longitude, latitude, elevation] 
    ● Can the file be opened in a text editor and viewed in QGIS 
    ● Does the file pass validation 
    ● Is there associated metadata/README.md files
    This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #2 held at Johns Hopkins University on April 17-18, 2019.
    The full set of Data Curation Primers can be found at:https://conservancy.umn.edu/handle/11299/202810
    Interactive primers available for download and derivatives at:https://github.com/DataCurationNetwork/data-primers
     

  • Confocal Microscopy Data Curation Primer

    The purpose of this primer is to guide a data curator through the curation process for confocal images. Confocal microscopy is a type of microscopy technique to image objects that are too small to view with the unassisted human eye. Primary fields in which confocal microscopy is used are:  Biology, health, engineering, chemistry.  This primer describes the image specifics, as well as what details and metadata from the instrumentation and experiment are needed to understand the images and use them for further research or educational purposes.
    This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #2 held at Johns Hopkins University on April 17-18, 2019.
    The full set of Data Curation Primers can be found at:https://conservancy.umn.edu/handle/11299/202810
    Interactive primers available for download and derivatives at:https://github.com/DataCurationNetwork/data-primers
  • R Data Curation Primer

    The purpose of this primer is to guide a data curator through the curation process for text files with a “.R” extension that contain code for executing programs in the R language.
    Key questions for curation review
    -What is the purpose of the file?
    -Are any data associated with the file?
    -Are the referenced data present at the indicated location? 
    This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #2 held at Johns Hopkins University on April 17-18, 2019.
    The full set of Data Curation Primers can be found at:https://conservancy.umn.edu/handle/11299/202810
    Interactive primers available for download and derivatives at:https://github.com/DataCurationNetwork/data-primers
     

  • Tableau Data Curation Primer

    Tableau Software is a proprietary suite of products for data exploration, analysis, and visualization with an initial concentration in business intelligence. This primer focuses on the Tableau workbook files – .twb and .twbx – produced using Tableau Desktop. Like Microsoft Excel, Tableau Desktop uses a workbook and sheet file structure. Workbooks can contain worksheets, dashboards, and stories.
    Key questions for curation review
    ● Can the Tableau workbook file be opened?
    ● If the Tableau workbook is provided as a .twb file, is there an accompanying data source file or data extract?
    ● Is there documentation for how to navigate and work with the Tableau workbook?
    ● Is there an accompanying snapshot to show how a workbook, dashboard, or story view should be rendered?

    This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #2 held at Johns Hopkins University on April 17-18, 2019.
    The full set of Data Curation Primers can be found at:https://conservancy.umn.edu/handle/11299/202810
    Interactive primers available for download and derivatives at:https://github.com/DataCurationNetwork/data-primers

  • PDF Data Curation Primer

    The purpose of this primer is to guide a data curator through the curation process for Portable Document Format (PDF) files. As a highly-used document publication format, PDF documents represent considerable bodies of important information globally and have become commonly used for publishing data and related files.
    This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #2 held at Johns Hopkins University on April 17-18, 2019.

    The full set of Data Curation Primers can be found at:https://conservancy.umn.edu/handle/11299/202810
    Interactive primers available for download and derivatives at:https://github.com/DataCurationNetwork/data-primers
     

  • Atlas.ti Data Curation Primer

    Altas.ti is a software application that allows researchers to analyze qualitative data in a systematic and transparent way, increasing the validity of results (Friese 2019). ATLAS.ti handles different types of data that are kept in a project. The project files can contain text documents, images, audio recordings, videos, pdf files, geodata, Twitter data, citations from Evernote and reference managers, and survey data. The purpose of this primer is to guide a data curator through the curation process for Altas.ti files.
    Key questions for curation review
    -What ATLAS.ti version was used?
    -Can other researchers open the project without the ATLAS.ti?
    -Does the project include metadata/documentation/codebook?
    -Are there consent forms/participation agreements? Is there sensitive information that can compromise human subjects’ rights?
    -Are there associated data that has been exported (i.e. result reports, codebook) outside the project?

    This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #2 held at Johns Hopkins University on April 17-18, 2019.
    The full set of Data Curation Primers can be found at:https://conservancy.umn.edu/handle/11299/202810
    Interactive primers available for download and derivatives at:https://github.com/DataCurationNetwork/data-primers

  • Data Management and Reporting: BCO-DMO Data Management Services and Best Practices

    The University-National Oceanographic Laboratory System (UNOLS) hosted an Early Career Chief Scientist Training Workshop in June 2019. The goal of this workshop was to help early-career marine scientists plan and write effective cruise proposals, develop collaborative sampling strategies and plans, become familiar with shipboard equipment and sampling at sea, and communicate major findings through the writing of manuscripts and cruise reports. This presentation provides information on data management and reporting best practices for chief scientists. It includes information on the National Science Foundation (NSF) data policy requirements, writing a Data Management Plan (DMP), the data lifecycle, data publication, and shipboard data management recommendations.

  • Remote Sensing for Freshwater Habitats [Intermediate]

    Freshwater habitats play an important role in ecological function and biodiversity. Remote sensing of these ecosystems is primarily tied to observations of the drivers of biodiversity and ecosystem health. Remote sensing can be used to understand things like land use and land cover change in a watershed, habitat connectivity along a water body, water body location and extent, and water quality parameters. This webinar series will guide participants through using NASA Earth observations for habitat monitoring, specifically for freshwater fish and other species. The training will also provide a conceptual overview, as well as the tools and techniques for applying landscape environmental variables to genetic and habitat diversity in species. 

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


    • understand the limitations of using remote sensing for freshwater habitats
    • find data and models that can be used in their landscape genetics and habitat monitoring work
    • see how remote sensing can be used for habitat restoration, ecological assessments, and climate change assessments relating to freshwater systems
    • be able to use the Riverscape Analysis Project decision support system
    • be familiar with the Freshwater Health Index


    Course Format: 


    • Three, one-hour parts that include lectures, demonstrations, and question & answer sessions
    • This training will only be broadcast in English
    • A certificate of completion will be available to participants who attend all parts and complete all homework assignments. Note: certificates of completion only indicate the attendee participated in all aspects of the training, they do not imply proficiency on the subject matter, nor should they be seen as a professional certification.


    Prerequisites: Please complete ARSET's Fundamentals of Remote Sensing or have equivalent knowledge. Attendees that do not complete the prerequisite may not be prepared for the pace of the training. 

    Part One: Review of Aquatic Remote Sensing & Freshwater Habitats
    As a result of this part of the webinar series, attendees will be able to: 


    • identify which NASA satellites & sensors can be used for freshwater monitoring
    • understand the limitations of remote sensing of freshwater habitats
    • find data and models they can use in landscape genetics and habitat monitoring work


    Part Two: Overview of the Riverscape Analysis Project (RAP)
    As a result of this part of the webinar series, attendees will be able to: 


    • understand using remote sensing for habitat restoration, ecological assessments, and climate change assessments relating to freshwater systems through case studies
    • use the RAP decision-support system for accessing, downloading, and applying remote sensing data


    Part Three: Overview of the Freshwater Health Index (FHI)
    As a result of this part of the webinar series, attendees will be able to: 


    • understand how to evaluate freshwater ecosystem health
    • have the ability to use the FHI data and tools to assess freshwater ecosystem health
    • identify potential uses of the FHI for their work and decision-making
    • Use the FHI to identify vulnerabilities to degradation and/or climate change, as well as opportunities for improvement of infrastructure development within a basin


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

  • Teledetección para el Monitoreo de los ODS sobre la Degradación de Tierras y Ciudades Sostenibles

    Los Objetivos de Desarrollo Sostenible (ODS) son un llamado urgente a la acción a todos los países para preservar nuestros océanos y bosques, reducir la desigualdad y fomentar el crecimiento económico. Los ODS sobre la gestión de tierras exigen un seguimiento consistente de las métricas de la cobertura terrestre. Estas métricas incluyen productividad, cobertura terrestre, carbono en el suelo, expansión urbana y más. Esta serie de webinars resaltará una herramienta que utiliza observaciones de la tierra de la NASA para monitorear la degradación de las tierras y el desarrollo urbano que cumplen las metas de los ODS apropiados.  

    Los ODS 11 y 15 tratan la urbanización sostenible así como el uso y los cambios en la cobertura terrestre. El ODS anhela “lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles.” El ODS 15 promueve “luchar contra la desertificación, la sequía y las inundaciones y procurar lograr un mundo con una degradación neutra del suelo." Para evaluar el progreso hacia estos fines, hay indicadores establecidos, muchos de los cuales se pueden monitorear mediante la teledetección.

    En esta capacitación los/las participantes aprenderán a utilizar un plugin de QGIS libremente disponible, Trends.Earth, creado por Conservation International (CI). Trends.Earth permite a los usuarios diagramar series temporales de indicadores clave de cambios en la cobertura terrestre. Los/las participantes aprenderán a producir mapas y figuras para apoyar el seguimiento y la información sobre la degradación de tierras, mejoras y urbanización para los indicadores de los ODS 15.3.1 y 11.3.1. Cada parte de esta serie contendrá una presentación, un ejercicio práctico y tiempo para hacerle preguntas en vivo al presentador/ la presentadora.  

    Objetivos de Aprendizaje:

    Durante esta capacitación, usted hará lo siguiente:

    • Se familiarizará con los Indicadores de los ODS 15.3.1 y 11.3.1
    • Llegará a entender lo básico de cómo computar los sub-indicadores del ODS 15.3.1 como productividad, cobertura terrestre y carbono del suelo
    • Aprenderá a utilizar la interfaz en línea Trends.Earth Urban Mapper
    • Aprenderá lo básico del conjunto de herramientas (Toolkit) de Trends.Earth incluyendo:
      • Diagramación de series temporales
      • Descarga de datos
      • Cómo utilizar los datos preconfigurados o personalizados para productividad, cobertura terrestre y carbono orgánico del suelo
      • Cómo calcular capas espaciales y una tabla de resumen para el ODS 15.3.1
      • Cómo calcular métricas de cambios urbanos
      • Cómo crear tablas de resumen para cambios urbanos


    Formato del Curso:

    • Esta capacitación ha sido desarrollada en colaboración con Conservation International
    • Tres sesiones de una hora y media cada una que incluyen presentaciones, ejercicios prácticos y una sesión de preguntas y respuestas
    • La primera sesión se transmitirá en inglés y la segunda sesión tendrá el mismo contenido transmitido en español.
    • Habrá un certificado de finalización disponible para quienes asistan a las tres sesiones y completen la tarea asignada, la cual se basará en las presentaciones del webinar. Nota: los certificados de finalización sólo indican que el poseedor participó en todos los aspectos de la capacitación, no implican proficiencia en el material de esta, ni se deben ver como una certificación profesional.


    Prima Parte

    En esta sesión aprenderán acerca del marco de los ODS y la coordinación entre agencias a nivel mundial; se familiarizarán con el ODS 15, Meta 15.3 e Indicador 15.3.1; aprenderán sobre el concepto de la productividad primaria neta y cómo monitorear esa métrica con datos por teledetección; también aprenderemos cómo visualizar e interpretar datos por teledetección asociados con el ODS 15 dentro de una herramienta para QGIS desarrollada por Conservation International llamada Trends.Earth como un ejercicio práctico.

    • Ver grabación »
      • Diapositivas de la Presentación »
      • Ejercicio 1 (subindicadores) »
      • Ejercicio 1.2 (descargar resultados) »
      • Transcripción de preguntas y respuestas »


    Segunda Parte

    En esta sesión, aprenderán acerca de los cambios en la cobertura terrestre y el carbono orgánico del suelo y cómo monitorear esas métricas mediante la teledetección; aprenderán acerca de los requisitos en cuanto a la presentación de informes para el ODS 15; además,visualizarán e interpretarán datos por teledetección locales asociados con el ODS dentro de Trends.Earth.

    • Ver grabación »
      • Diapositivas de la Presentación »
      • Ejercicio 2 »
      • Transcripción de preguntas y respuestas »


    Tercera Parte

    En esta sesión aprenderán acerca del ODS 11, Meta 11.3 e Indicador 11.3.1; aprenderán acerca de las entradas necesarias para calcular el Indicador 11.3.1 y visualizarán e interpretarán el mapeo de áreas urbanas dentro de Trends.Earth.

    • Ver grabación »
      • Diapositivas de la Presentación »
      • Ejercicio 3 »
      • Tarea (completar hasta el 6 de agosto) »
      • Transcripción de preguntas y respuestas »

  • SAR y sus Aplicaciones para la Cobertura Terrestre [Avanzado]

    Esta capacitación se basará en los conocimientos y las habilidades adquiridas en capacitaciones anteriores de ARSET sobre radar de apertura sintética (synthetic aperture radar o SAR). Las presentaciones y demostraciones se enfocarán en aplicaciones para la agricultura y para desastres. Los participantes aprenderán a utilizar imágenes SAR 1) para caracterizar inundaciones con Google Earth Engine 2) y para aplicaciones en la agricultura incluyendo estimación de la humedad del suelo e identificación de cultivos.

    Objetivos de Aprendizaje: Para la conclusión de esta capacitación, los participantes podrán:

    1. analizar datos SAR en Google Earth Engine para el mapeo de inundaciones
    2. generar análisis de la humedad del suelo
    3. identificar diferentes tipos de cultivos


    Formato del Curso: Dos partes de dos horas cada una

    • Cada parte incluirá una
    • presentación teórica del tema seguida por una demostración
    • Esta capacitación también está disponible en inglés. Por favor visite la página de inscripciones en inglés para más información.
    • Habrá un certificado de finalización disponible para los participantes que asistan a las dos sesiones y completen la tarea, la cual estará basada en las sesiones del webinar.
    • Nota: los certificados de finalización indican únicamente que el poseyente participó en todos los aspectos de la capacitación, no implican competencia en la temática ni se deben ver como una certificación profesional.



    Prerequisites:

    Los prerrequisitos no son obligatorios para esta capacitación, pero quienes no los completen podrían no estar lo suficientemente preparados para esta capacitación


    Inscripciones:
    Debido a la demanda anticipada, por favor inscríbase solo para la sesión en español o la sesión en inglés.

    Parte Uno: SAR para el Mapeo de Inundaciones Utilizando Google Earth Engine
    Esta parte estará enfocada en el uso de Google Earth Engine (GEE) para mapear inundaciones utilizando imágenes SAR de Sentinel-1. La primera parte de la sesión cubrirá los principios básicos de SAR relacionados a las inundaciones. El resto de la sesión estará enfocada en una demostración de cómo utilizar GEE para generar mapas de inundación con Sentinel-1.
    Parte Dos: SAR para el Monitoreo Agrícola
    Esta parte estará enfocada en el uso de SAR para monitorear diferentes aspectos relacionados con la agricultura, extendiendo los conocimientos adquiridos en la sesión de SAR para la agricultura del 2018. El resto de la sesión estará enfocada en el uso de SAR para estimar la humedad del suelo e identificar diferentes tipos de cultivos. La Dra. Heather McNairn, de Agriculture and Agri-Food Canadá, será la presentadora de esta sesión.
     

  • SAR for Disasters and Hydrological Applications [Advanced]

    This training builds on the skills taught from previous ARSET SAR training in terms of the use of Google Earth Engine for flood mapping of radar data. This training presents two new topics; the use of InSAR for characterizing landslides and the generation of a digital elevation model (DEM).
    Learning Objectives: By the end of this training, attendees will be able to:

    • Create a flood map using Google Earth Engine
    • Generate a map characterizing areas where landslides have occurred
    • Generate a digital elevation model (DEM)


    Course Format: 

    • This webinar series will consist of three, two-hour parts
    • Each part will include a presentation on the theory of the topic followed by a demonstration and exercise for attendees. 
    • This training is also available in Spanish. Please visit the Spanish page for more information.
    • A certificate of completion will also be available to participants who attend all sessions and complete the homework assignment, which will be based on the webinar sessions. Note: certificates of completion only indicate the attendee participated in all aspects of the training, they do not imply proficiency on the subject matter, nor should they be seen as a professional certification.



    Prerequisites: 
    Prerequisites are not required for this training, but attendees that do not complete them may not be adequately prepared for the pace of the training. 



    Part One: SAR for Flood Mapping Using Google Earth Engine
    This session will focus on the use of the Google Earth Engine (GEE) to generate a flood map utilizing SAR images from Sentinel-1. The first part of this session will cover the basic principles of radar remote sensing related to flooding. The remaining time in the session will be dedicated to a demonstration on how to use GEE to generate flood extent products with Sentinel-1 and how to integrate socioeconomic data into the flood map to identify areas at risk.
    Part Two: Interferometric SAR for Landslide Observations
    Featuring guest speaker Dr. Eric Fielding from JPL, this session is focused on landslide observations utilizing and building on InSAR skills from the previous three SAR webinar series. The first part of the session will cover the physics of InSAR as related to landslides. The remainder will be focused on how to generate and interpret the derived landslide product.
     Part Three: Generating a Digital Elevation Model (DEM)
    Featuring guest speaker Nicolás Grunfeld Brook, from Argentina’s CONAE, participants will learn how to generate a digital elevation model (DEM) through InSAR techniques. The first part of the session will cover the physics behind using two SAR phase images to generate a DEM. The remainder of the time will focus on how to generate a DEM.

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

  • SAR para Desastres y Aplicaciones Hidrológicas [Avanzado]

    Esta capacitación se basará en las capacidades de utilizar Google Earth Engine para el mapeo de inundaciones a partir de datos de radar enseñadas en capacitaciones ARSET de SAR anteriores. Esta capacitación presenta dos temas nuevos; el uso de InSAR para la caracterización de derrumbes y la generación de un modelo de elevación digital (digital elevation model o DEM).

    Objetivos de Aprendizaje: Para la conclusión de esta capacitación, los participantes podrán:

    • Crear un mapa de inundación utilizando Google Earth Engine
    • Generar un mapa que caracteriza las zonas donde ocurrieron derrumbes
    • Generar un modelo de elevación (digital elevation model o DEM)


    Formato del Curso: 

    • Tres partes de dos horas cada una
    • Cada parte incluirá una presentación teórica del tema seguida por una demostración y un ejercicio para quienes asistan. 
    • Esta página también está disponible en inglés. Por favor visite la página de inscripciones en inglés para más información. 
    • Habrá un certificado de finalización disponible para los participantes que asistan a todas las sesiones y completen la tarea, la cual estará basada en las sesiones del webinar. Nota: los certificados de finalización indican únicamente que el poseyente participó en todos los aspectos de la capacitación, no implican competencia en la temática ni se deben ver como una certificación profesional.


    Prerrequisitos:
    Los prerrequisitos no son obligatorios para esta capacitación, pero quienes no los completen podrían no estar lo suficientemente preparados para esta. 


    Primera Parte: SAR para el Mapeo de Inundaciones Utilizando Google Earth Engine
    Esta sesión estará enfocada en el uso de of Google Earth Engine (GEE) para generar un mapa de inundación utilizando imágenes SAR de Sentinel-1.  La primera parte de la sesión cubrirá los principios básicos de SAR relacionados con las inundaciones. El resto de la sesión será dedicada a una demostración de cómo utilizar GEE para generar productos relevantes a la extensión de inundaciones y cómo integrar datos socioeconómicos al mapeo de inundaciones para identificar áreas en peligro. 

    Segunda Parte: SAR Interferométrico para la Observación de Derrumbes
    Dirigida por el presentador invitado, el Dr. Eric Fielding de JPL, esta sesión se enfocará en la observación de derrumbes. Desarrollará las capacidades con InSAR enseñadas en las tres anteriores series de webinars de SAR. La primera parte de la sesión cubrirá la física de InSAR relacionada con los derrumbes. El resto se enfocará en cómo generar e interpretar el producto derrumbes derivado.

    Tercera Parte: Generación de un Modelo de Elevación Digital (Digital Elevation Model o DEM)
    A cargo de un presentador invitado de la agencia espacial argentina, CONAE, los participantes aprenderán cómo generar un modelo de elevación digital (DEM) a través de técnicas de InSAR.  La primera parte de la sesión cubrirá la física de utilizar dos imágenes de fase de SAR para generar un DEM. El resto del tiempo se enfocará en cómo generar un DEM.

     

  • Using the UN Biodiversity Lab to Support National Conservation and Sustainable Development Goals [Introductory]

    As we enter the fourth industrial revolution, technology is revolutionizing our ability to map nature. Satellite data provide a bird’s eye, yet incredibly detailed view of the Earth’s surface in real-time, while drones and mobile apps enable local communities and indigenous peoples to map their knowledge of local ecosystems. To support policymakers to develop data-driven sustainable development solutions, UNDP, the United Nations Environment Programme (UNEP), and the Secretariat of the Convention on Biological Diversity (CBD)  launched UN Biodiversity Lab, with funding from the GEF and support from MapX, UNEP World Conservation Monitoring Centre, Global Resource Information Database - Geneva, and NASA. The UN Biodiversity Lab is an online platform that allows policymakers and other stakeholders to access global data layers, upload national datasets, and analyze these datasets in combination to provide key information on the CBD’s Aichi Biodiversity Targets and on the nature-based Sustainable Development Goals. Already in use by over 50 countries, as well as utilized as the key decision support system for two NASA-funded applied science projects, the UN Biodiversity Lab has high potential to be scaled up to reach new ministries and countries and stakeholder groups.

    There is a global demand for more NASA ARSET training focused on biodiversity, conservation, the UN Sustainable Development Goals (SDGs), and how to link NASA satellite data to ecological and human-influenced systems. This training aims to fill that gap by extending the influence of this NASA-supported tool and increasing its dissemination, use, and overall success. UN Biodiversity Lab makes global datasets on biodiversity and sustainable development easily accessible, supporting our broad audience.

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

    • Understand key global biodiversity and sustainable development policy instruments (CBD, UN Framework Convention on Climate Change (UNFCCC), the 2030 Agenda for Sustainable Development) as they relate to conservation efforts
    • Have knowledge of spatial data on biodiversity and sustainable development, including data generated by NASA projects
    • Be familiar with the UN Biodiversity Lab structure, data, and tools
    • Have the ability to apply UN Biodiversity Lab tools to their region of interest
    • Utilize case study examples from multiple partner countries as a context for their work


    Course Format: 

    • Three, 1.5-hour sessions offered in English, French, and Spanish
    • A certificate of completion will also be available to participants who attend all sessions and complete the homework assignments, which will be based on the webinar sessions. Note: certificates of completion only indicate the attendee participated in all aspects of the training, they do not imply proficiency on the subject matter, nor should they be seen as a professional certification. 


    Prerequisites:
     Attendees that do not complete the required prerequisites may not be adequately prepared for the pace of the training.


    Part One: Introduction to Spatial Data and Policies for Biodiversity (
    Part Two: UN Biodiversity Lab: Introduction and Training 
    Part Three: How are Countries Using Spatial Data to Support Conservation of Nature? 

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

  • Utiliser le UN Biodiversity Lab pour soutenir les objectifs nationaux de conservation et de développement durable [d’introduction]

    Alors que nous entrons dans la quatrième révolution industrielle, la technologie révolutionne notre capacité à cartographier la nature. Les données spatiales fournissent une vue d’ensemble, mais également une vue incroyablement détaillée de la surface de la Terre en temps réel, tandis que les drones et les applications mobiles permettent aux communautés locales et aux peuples autochtones de cartographier leurs connaissances des écosystèmes locaux. Pour aider les décideurs à élaborer des solutions de développement durable fondées sur des données, le PNUD, le Programme des Nations Unies pour l'environnement (PNUE) et le Secrétariat de la Convention sur la diversité biologique (CDB) ont lancé le UN Biodiversity Lab, avec un financement du FEM et le soutien de MapX, le Centre mondial de surveillance de la conservation du PNUE (UNEP-WCMC), le Global Resource Information Database – Geneva et la NASA. Le UN Biodiversity Lab est une plateforme en ligne qui permet aux décideurs et autres parties prenantes d'accéder aux couches de données mondiales, de télécharger des ensembles de données nationaux et d'analyser ces ensembles de données en combinaison pour fournir des informations clés sur les objectifs d'Aichi pour la biodiversité de la CDB et sur les objectifs de développement durable fondés sur la nature. Déjà utilisé par plus de 50 pays, et utilisé comme système clé d'aide à la décision pour deux projets de science appliquée financés par la NASA, le UN Biodiversity Lab a un fort potentiel d'être étendu pour atteindre de nouveaux ministères et pays et groupes de parties prenantes.

    Il existe une demande mondiale pour plus de formations ARSET de la NASA axées sur la biodiversité, la conservation, les objectifs de développement durable (ODD) des Nations Unies et la façon de relier les données spatiales de la NASA à des systèmes écologiques et influencés par l'homme. Cette formation vise à combler cette lacune en étendant l'influence de cet outil soutenu par la NASA et en augmentant sa diffusion, son utilisation et son succès global. Le UN Biodiversity Lab rend les ensembles de données mondiaux sur la biodiversité et le développement durable facilement accessibles, soutenant notre large public.



    Objectifs d’apprentissage: À la fin de cette formation, les participants:

    • Comprendront les principaux instruments de politique mondiale sur la biodiversité et le développement durable (CDB, Convention-cadre des Nations Unies sur les changements climatiques (CCNUCC), le Programme de développement durable à l'horizon 2030) en ce qui concerne les efforts de conservation
    • Connaîtront les données spatiales sur la biodiversité et le développement durable, y compris les données générées par les projets de la NASA
    • Connaîtront la structure, les données et les outils du UN Biodiversity Lab
    • Auront la capacité d'appliquer les outils du UN Biodiversity Lab à leur région d'intérêt
    • Utiliseront des exemples d'études de cas de plusieurs pays partenaires comme contexte pour leur travail


    Format du cours:
    Trois sessions de une heure et demie, dispensées en anglais, français et espagnol

    Pré-requis:
    Les participants qui ne remplissent pas les conditions préalables requises peuvent ne pas être convenablement préparés au rythme de la formation.
    Principes fondamentaux des données spatiales (en anglais) » 

    Partie 1: Introduction aux données spatiales et aux politiques de biodiversité 
    Partie 2: UN Biodiversity Lab: Introduction et formation  
    Partie 3: Comment les pays utilisent-ils les données spatiales pour soutenir la conservation de la nature ? 

  • Utilizando el UN Biodiversity Lab para Apoyar los Objetivos Nacionales de Conservación y Desarrollo Sostenible [Introductoria]

    A inicios de la cuarta revolución industrial, la tecnología está revolucionando nuestra capacidad de mapear la naturaleza. Los datos satelitales proporcionan una vista panorámica pero a la vez increíblemente detallada de la superficie de la Tierra en tiempo real mientras que los drones y las aplicaciones móviles permiten que las comunidades locales y los pueblos indígenas mapeen su conocimiento de ecosistemas locales. Para poder ayudar a los formuladores de políticas a desarrollar soluciones para el desarrollo sostenible basadas en datos y políticas enfocadas, el UNDP, el Programa de las Naciones Unidas para el Medio Ambiente (UNEP por sus siglas en inglés) y la Secretaría del Convenio sobre la Diversidad Biológica (CDB) lanzaron el UN Biodiversity Lab con financiación del GEF y apoyo de MapX, el Centro de Monitoreo de la Conservación Mundial del UNEP, la Base de Datos Mundial sobre Recursos de Información – Ginebra y la NASA. El UN Biodiversity Lab es una plataforma en línea que permite a los formuladores de políticas y otras partes interesadas acceder a capas de datos a nivel mundial, cargar conjuntos de datos nacionales y analizar estos conjuntos de datos en combinación para brindar información clave sobre los Objetivos Aichi para la Biodiversidad del CDB y sobre los Objetivos de Desarrollo Sostenible relacionados con la naturaleza. Ya lo están utilizando en más de 50 países, incluso como el principal sistema de apoyo a la toma de decisiones para dos proyectos de ciencias aplicadas financiados por la NASA. El UN Biodiversity Lab tiene un alto potencial de ser escalado para llegar a nuevos ministerios y países y grupos de partes interesadas. 


    Existe una demanda a nivel mundial de más capacitaciones NASA ARSET enfocadas en la biodiversidad, conservación, los Objetivos de Desarrollo Sostenible (ODS) de la ONU y sobre cómo conectar datos de satélites de la NASA con sistemas ecológicos y aquellos que han sido influidos por la actividad humana. Esta capacitación pretende llenar este vacío extendiendo la influencia de esta herramienta apoyada por la NASA y fomentando su diseminación, utilización y éxito general. El  UN Biodiversity Lab hace conjuntos de datos mundiales sobre la biodiversidad y el desarrollo sostenible fácilmente accesibles, apoyando a nuestro público variado.



    Objetivos de Aprendizaje: Para la conclusión de esta capacitación, los/las participantes podrán:

    • Entender instrumentos políticos claves para la diversidad biológica global y el desarrollo sostenible (CDB, Convención Marco De Las Naciones Unidas Sobre el Cambio Climático (UNFCCC), la Agenda 2030 para el Desarrollo Sostenible) en lo que se refieren a campañas de conservación.
    • Adquirir conocimiento sobre datos espaciales sobre la diversidad biológica y el desarrollo sostenible, incluso datos generados por proyectos de la NASA
    • Estar familiarizados con la estructura, datos y herramientas del UN Biodiversity Lab
    • Tener la capacidad de aplicar las herramientas del UN Biodiversity Lab a su región de interés
    • Utilizar ejemplos de casos de estudio de múltiples países colaboradores como contexto para su trabajo


    Formato del Curso:

    • Tres sesiones de una hora y media cada una ofrecidas en inglés, francés y español
    • Habrá un certificado de finalización disponible para los participantes que asistan a todas las sesiones y completen las tareas, la cual estará basada en las sesiones del webinar. Nota: los certificados de finalización indican únicamente que el poseyente participó en todos los aspectos de la capacitación, no implican competencia en la temática ni se deben ver como una certificación profesional.


    Prerrequisitos: 
    Los participantes que no completen los prerrequisitos podrían no estar lo suficientemente preparados para el ritmo de la capacitación.

    Fundamentos de la Percepción Remota (Teledetección) Diapositivas de la Presentación »

    Primera Parte: Introducción a Datos Espaciales y Políticas para la Diversidad Biológica
    Segunda Parte: El UN Biodiversity Lab 
    Tercera Parte: Casos de Uso por Países 
     

  • Satellite Remote Sensing for Agricultural Applications[Introductory]

    Since the launch of NASA’s first Landsat mission in 1972, satellite imagery has been used for global agricultural monitoring, providing one of the longest operational applications for the Landsat program. Although satellite observations of land began with agricultural monitoring, only in recent years has agricultural remote sensing seen reinvigoration among space agencies, national ministries of agriculture, and global initiatives. To monitor agricultural systems, NASA utilizes satellite observations to assess a wide variety of geophysical and biophysical parameters, including precipitation, temperature, evapotranspiration, soil moisture, and vegetation health.

    Past ARSET webinars on land and water resources covered remote sensing-derived parameters relevant to agriculture within a broader scope. This 4-part introductory webinar will focus on data products, data access, and case-studies demonstrating how remote sensing data can be used for decision-making among the agriculture and food security communities.



    This training will address how to use remote sensing data for agriculture monitoring, specifically drought and crop monitoring. The webinar will also provide end-users the ability to evaluate which regions of the world agricultural productivity are above or below long-term trends. This informs decisions pertaining to market stability and humanitarian relief.

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

    • Identify which satellites and sensors can be used for agricultural applications
    • Understand the limitations of remote sensing and modeled data for agriculture and food security
    • Acquire specific remote sensing data products that are appropriate for their work
    • Apply remote sensing techniques to crop monitoring, drought, and humanitarian relief


    course Format: 

    • Four online, 1.5-hour parts with sessions offered twice a day
    • A certificate of completion will also be available to participants who attend all sessions and complete the homework assignment, which will be based on the webinar sessions. Note: certificates of completion only indicate the attendee participated in all aspects of the training, they do not imply proficiency on the subject matter, nor should they be seen as a professional certification.


     Prerequisites:
     Attendees who have not completed the following may not be prepared for the pace of the training:
    Fundamentals of Remote Sensing  

    Part 1: Overview of Agricultural Remote Sensing
    This section will cover the ARSET Program and give a general overview of remote sensing as it pertains to agriculture. This part will include the history of Earth observations (EO) for agriculture, satellites and sensors that can be used, the limitations of satellite data, an introduction of NASA HARVEST, examples of current EO applications in agriculture, and a Q&A session.

    Supplementary Materials:
    NASA Satellites and Sensors Relevant for Agriculture »

    Fact Sheets:

    • Air Quality
    • Vegetation
    • Water Availability
    • Water Quality


    Part 2: Soil Moisture for Agricultural Applications
    This part of the training provides an overview of SMAP and case studies for agricultural applications and an overview of soil moisture and shallow groundwater from the Land Data Assimilation System (LDAS), as well as a Q&A session.

    Part 3: Earth Observations for Agricultural Monitoring
    This section will cover previous ARSET training that relates to agricultural monitoring and present case studies of EO being used for agricultural monitoring. There will also be a Q&A session.

    Part 4: Evapotranspiration (ET) & Evaporative Stress Index (ESI) for Agricultural Applications
    This section includes a presentation from guest speaker Dr. Christopher Hain, along with an overview and case studies of ET and ESI in agricultural applications. This section will conclude with a Q&A session.

    Each part of 4 includes links to the recordings, presentation slides,  and Question & Answer Transcripts.
     

  • Using Earth Observations to Monitor Water Budgets for River Basin Management II [Advanced]

    Rivers are a major source of fresh water. They support aquatic and terrestrial ecosystems, provide transportation, generate hydropower, and when treated, provide drinking and agricultural water. Estimating and monitoring water budgets within a river basin is required for sustainable management of water resources and flooding within watersheds. This advanced-level webinar series will focus on the use of NASA Earth observations and Earth system-modeled data for estimating water budgets in river basins.
    Past ARSET training on monitoring water budgets for river basins focused on data sources relevant for river basin monitoring and management and provided case studies for estimating the water budget of a watershed using remote sensing products. This advanced webinar will include lectures and hands-on exercises for participants to estimate water budgets for a given river basin.

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


    • Identify and access remote sensing and Earth system-modeled data for estimating water budgets in a river basin
    • Explain the uncertainties involved in estimating water budgets for river basins
    • Replicate the steps for estimating water budgets for a river basin and sub-watersheds using remote sensing products and GIS


    Course Format: 


    • Three, two-hour webinars 
    • A certificate of completion will also be available to participants who attend all sessions and complete the homework assignment, which will be based on the webinar sessions.
    • NOTE: Certificates of completion only indicate participation in all aspects of the training.
    • They do not imply proficiency on the subject matter, nor should they be seen as a professional certification.



    Prerequisites: Attendees who have not completed the following may not be prepared for the pace of the training:



    Portions of the series will include data import to QGIS. If you wish to follow along with those steps, please install using the instructions here:



    Part 1: Review and Access of Earth Observations and Earth System-Modeled Data for River Basin Monitoring and Management


    This session will provide an overview of data sources relevant to estimating water budgets for a river basin. There will be a demonstration and guided exercise to download water budget component data to estimate the water budget of a given watershed using remote sensing products.


    Part 2: Water Budget Estimation using Remote Sensing Observations
    This session will include a demonstration and step-by-step exercise to estimate an integrated water budget over a river basin using Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation data, Atmosphere Land Exchange Inverse (ALEXI) evapotranspiration data, and Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data, all analyzed with QGIS.
    Part 3: Water Budget Estimation using the Global Land Data Assimilation Model
    The final session will include a demonstration and step-by-step exercise to estimate water budgets at a sub-watershed level within a river basin using water budget components from the latest version of the Global Land Data Assimilation System (GLDAS v2.2), which includes assimilation of groundwater data.
     


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

  • An Inside Look at how NASA Measures Air Pollution [Introductory]

    Would you like to learn how to access and visualize NASA satellite imagery? With the world’s eyes and media coverage turned to recent global changes in air pollution from the economic downturn, this two-part webinar series provides a primer for the novice and a good refresher course for all others. You will learn which pollutants can be measured from space, how satellites make these measurements, the do’s and don’ts in interpreting satellite data, and how to download and create your own visualizations.

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

    • List the pollutants that can be observed by NASA satellites
    • Find and download imagery for NO2 and aerosols/particles
    • Describe the capabilities and limitations of NASA NO2 and aerosol measurements



    Prerequisites: Fundamentals of Remote Sensing (recommended but not required)

    Part One: Nitrogen Dioxide (NO2)
    • What is NO2?
    • NASA Remote Sensing Basics
    • Interpreting NO2 Imagery: Dos and Don’ts
    • Downloading Data and Creating Imagery

    Part Two: Particulate Matter (Aerosols)
    • What are Aerosols?
    • Interpreting Aerosol Imagery: Dos and Don’ts
    • A Tour of NASA Resources for Generating Your Own Visualizations

    Each part of 2 includes links to the recordings, presentation slides,  and Question & Answer Transcripts.

  • Un Vistazo a Cómo la NASA Mide la Contaminación del Aire [Introductorio]

    ¿Le gustaría saber cómo acceder y visualizar imágenes satelitales de la NASA? La reciente disminución de la contaminación atmosférica a nivel mundial debido al bajón económico ha capturado la atención del mundo entero y recibido mucha cobertura mediática. Inspirándose en ello, esta serie de dos webinars imparte conocimiento fundamental para novatos y sirve de curso de repaso para los demás. Ud. aprenderá cuáles son los contaminantes que se pueden medir desde el espacio, cómo los satélites hacen estas mediciones, lo que se debe hacer y no se debe hacer al momento de interpretar datos satelitales y cómo descargar y crear sus propias visualizaciones. 

    Objetivos de Aprendizaje:Al finalizar esta capacitación, los/las participantes podrán:

    • Nombrar los contaminantes que pueden ser observados por satélites de la NASA
    • Encontrar y descargar imágenes para NO2 y aerosoles/partículas
    • Describir las capacidades y limitaciones de las mediciones de NO2 y aerosoles de la NASA



    Prerrequisitos: Fundamentos de la Teledetección (Percepción Remota) -  recomendado pero no obligatorio

    Parte 1: Dióxido de Nitrógeno (NO2)

    • ¿Qué es el NO2?
    • Conceptos Básicos de la Teledetección de la NASA 
    • Interpretación de Imágenes de NO2: Qué hacer y qué no hacer
    • Descargar Datos y Crear Imágenes


    Parte 2: Partículas (Aerosoles)

    • ¿Qué son los Aerosoles?
    • Interpretación de Imágenes de Aerosoles: Qué hacer y qué no hacer
    • Un Recorrido por los Recursos de la NASA para Generar sus Propias Visualizaciones

  • Earth Observations for Disaster Risk Assessment & Resilience [Introductory]

    According to a UN report, between 1998 and 2017, the U.S. alone lost $944.8 billion USD from disasters. Between 1878 and 2017, losses from extreme weather events rose by 251 percent. It is critical to developing disaster management strategies to reduce and mitigate disaster risks. A major factor in regional risk assessment is evaluating the vulnerability of lives and property to disasters. Environmental information about disasters, their spatial impact, and their temporal evolution can plan an important role as well.
    This webinar series will focus on Earth observation (EO) data useful for disaster risk assessment. The series will cover disasters including tropical cyclones, flooding, wildfires, and heat stress. The training will also include access to socioeconomic and disaster damage data. Sessions 3 & 4 will cover case studies and operational applications of EO for disaster risk assessment.

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


    • learn about available NASA remote sensing and socioeconomic data and how to combine them for assessing risk
    • understand how to apply these data for assessing risk from floods and tropical cyclones in specific regions
    • learn how operational agencies are using NASA data for risk management



    Course Format:


    • Four, two-hour parts that include lectures, demonstrations, and question and answer sessions
    • Both Session A & B will be broadcast in English
    • A certificate of completion will also be available to participants who attend all four parts and complete all homework assignments. Note: certificates of completion only indicate the attendee participated in all aspects of the training, they do not imply proficiency on the subject matter, nor should they be seen as a professional certification.



    Prerequisites: 



    Part One: NASA Remote Sensing and Socioeconomic Data for Disaster Risk Assessment Attendees will learn basic concepts and definitions in disaster risk management. Attendees will also learn about the types of satellites and socioeconomic data available through NASA for disaster risk management.


    Part Two: Assessing the Risk of Floods and Cyclones Using NASA Data Attendees will learn a methodology for analyzing remote sensing and socioeconomic data to assess flood and cyclone risk. Examples will be shown for an urban area (Houston, TX, USA) and a country (Mozambique). These case studies will use both historical and forecast data.

    Part Three: Disaster Risk Assessment Case Studies Using Remote Sensing Data This will cover two case studies for using remote sensing data. One on how New York state is using NASA data for heatwave risk assessment, another on the freely available online tools from the World Resources Institute for visualizing NASA remote sensing and socioeconomic data.

    Part Four: Operational Application of Remote Sensing for Disaster Management The Pacific Disaster Center will describe the data, applications, and strategies they use for disaster risk reduction, response, and relief operations.




    Each part of 4 includes links to the recordings, presentation slides,  and Question & Answer Transcripts.

  • Remote Sensing for Conservation & Biodiversity [Introductory]

    The United Nations Millennium Ecosystem Assessment states: “ecosystems are critical to human well-being - to our health, our prosperity, our security, and to our social and cultural identity.” Conservation and biodiversity management play important roles in maintaining healthy ecosystems. Earth observations can help with these efforts. This online webinar series introduces participants to the use of satellite data for conservation and biodiversity applications. The series will highlight specific projects that have successfully used satellite data. Examples include:

    • monitoring chimpanzee habitat loss
    • decreasing whale mortality
    • detecting penguins
    • monitoring wildfires
    • biodiversity observation networks


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

    • be able to outline uses of remote sensing for habitat suitability, species population dynamics, and monitoring wildfires
    • learn about the Group on Earth Observations Biodiversity Observation Network (GEOBON), Marine Biodiversity Observation Network (MBON), and essential biodiversity variables


    Course Format: 

    • Two, one hour sessions
    • The same session will be broadcast at both times, both in English


    Prerequisites: Fundamentals of Remote Sensing or equivalent knowledge
    If you do not complete the prerequisite, you may not be adequately prepared for the pace of the training.

    Session One: Remote Sensing for Conservation 
    This session will focus on remote sensing for habitat suitability, species population dynamics, and monitoring wildfires.

    Session Two: Remote Sensing for Biodiversity 
    This session will focus on the Group on Earth Observations Biodiversity Observation Network (GEOBON), Marine Biodiversity Observation Network (MBON), and essential biodiversity variables.

    Each part of 2 includes links to the recordings, presentation slides, exercises, and Question & Answer Transcripts, in English and in Spanish.  There is no link to a landing page in Spanish for this resource.   

  • An Introduction to Humanities Data Curation

    This webpage is a compilation of articles that address aspects of data curation in the digital humanities. The goal of it is to direct readers to trusted resources with enough context from expert editors and the other members of the research community to indicate how these resources might help them with their own data curation challenges.
    Each article provides a short introduction to a topic and a list of linked resources. Structuring articles in this way acknowledges the many excellent resources that already exist to provide guidance on subjects relevant to curation such as data formats, legal policies, description, and more.
    The table of contents:
    -An Introduction to Humanities
    -Data Curation-Classics, “Digital Classics” and Issues for Data Curation
    -Data Representation
    -Digital Collections and Aggregations
    -Policy, Practice, and Law
    -Standards

  • 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.

    Content page: ​https://github.com/NEONScience/NEON-Data-Skills/blob/master/tutorials/te...

  • Introduction To The Principles Of Linked Open Data

    This lesson offers a brief and concise introduction to Linked Open Data (LOD). No prior knowledge is assumed. Readers should gain a clear understanding of the concepts behind linked open data, how it is used, and how it is created. The tutorial is split into five parts, plus further reading:
    -Linked open data: what is it?
    -The role of the Uniform Resource Identifier (URI)
    -How LOD organizes knowledge: ontologies
    -The Resource Description Framework (RDF) and data formats
    -Querying linked open data with SPARQL
    -Further reading and resources
    The tutorial should take a couple of hours to complete, and you may find it helpful to re-read sections to solidify your understanding. Technical terms have been linked to their corresponding page on Wikipedia, and you are encouraged to pause and read about terms that you find challenging. After having learned some of the key principles of LOD, the best way to improve and solidify that knowledge is to practice. This tutorial provides opportunities to do so. By the end of the course, you should understand the basics of LOD, including key terms and concepts.
    In order to provide readers with a solid grounding in the basic principles of LOD, this tutorial will not be able to offer comprehensive coverage of all LOD concepts. The following two LOD concepts will not be the focus of this lesson:
    -The semantic web and semantic reasoning of datasets. A semantic reasoner would deduce that George VI is the brother or half-brother of Edward VIII, given the fact that a) Edward VIII is the son of George V and b) George VI is the son of George V. This tutorial does not focus on this type of task.
    -Creating and uploading linked open datasets to the linked data cloud. Sharing your LOD is an important principle, which is encouraged below. However, the practicalities of contributing your LOD to the linked data cloud are beyond the scope of this lesson. Some resources that can help you get started with this task are available at the end of this tutorial.

    This tutorial is also available in Spanish at:  https://programminghistorian.org/es/lecciones/introduccion-datos-abiertos-enlazados

  • From Hermeneutics To Data To Networks: Data Extraction And Network Visualization Of Historical Sources

    Network visualizations can help humanities scholars reveal hidden and complex patterns and structures in textual sources. This tutorial explains how to extract network data (people, institutions, places, etc) from historical sources through the use of non-technical methods developed in Qualitative Data Analysis (QDA) and Social Network Analysis (SNA), and how to visualize this data with the platform-independent and particularly easy-to-use Palladio.

    This tutorial will focus on data extraction from unstructured text and shows one way to visualize it using Palladio. It is purposefully designed to be as simple and robust as possible. For the limited scope of this tutorial it will suffice to say that an actor refers to the persons, institutions, etc. which are the object of study and which are connected by relations. Within the context of a network visualization or computation (also called graph), we call them nodes and we call the connections ties. In all cases it is important to remember that nodes and ties are drastically simplified models used to represent the complexities of past events, and in themselves do not always suffice to generate insight. But it is likely that the graph will highlight interesting aspects, challenge your hypothesis and/or lead you to generate new ones. Network diagrams become meaningful when they are part of a dialogue with data and other sources of information.

    Topics include:  

    • Introduction
    • About the case study
    • Developing a coding scheme
    • Visualize network data in Palladio
    • The added value of network visualizations
    • Other network visualization tools to consider

    This tutorial is also available in Spanish at:  https://programminghistorian.org/es/lecciones/creando-diagramas-de-redes-desde-fuentes-historicas.

  • Ocean Data Management for Researchers

    This training course is aimed at researchers at the post-graduate level and provides a comprehensive introduction to a variety of marine datasets and formats and the use of software for synthesis and analysis of marine data. The importance of good research data management practices and the role of researchers will also be highlighted. Personal projects are presented by the students at the end of the course. 

    To acquire Certificates of Participation, this course required an application and once approved, member login.  Guest access is available to review course slides, video presentations, exercises, class activities and supplementary materials.  

    Aims and Objectives
    -Provide an introduction to the use of software for synthesis and analysis of marine data
    -Introduction to the FAIR Guiding Principles for scientific data management and stewardship
    -Understand best practice for management and analysis of marine data
     
    The learning outcomes of this course include:
    -Knowledge and understanding of the importance of management of ocean data
    -Experience in the use of data analysis and visualization tools
    -Recognize the importance of good research data management practice
    -Awareness of European based marine research projects and data repositories

    Preparation 
    Participants must download the latest version of ODV (5.1.5) from https://odv.awi.de/software/download/ and install the software on their laptops. If not already done, participants must register as non-commercial users before getting access to the software.
    Participants also must download the course material package from https://drive.google.com/file/d/1SojaNEPE3uI5zUN2gib7SfPI8f319ILQ/view?u... unzip to the desktop.