All Learning Resources

  • 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.
     

  • Forest Mapping and Monitoring with SAR Data [Advanced]

    Measurements of forest cover and change are vital to understanding the global carbon cycle and the contribution of forests to carbon sequestration. Many nations are engaged in international agreements, such as the Reducing Emissions from Deforestation and Degradation (REDD+) initiative, which includes tracking annual deforestation rates and developing early warning systems of forest loss. Remote sensing data are integral to data collection for these metrics, however, the use of optical remote sensing for monitoring forest health can be challenging in tropical, cloud-prone regions.
    Radar remote sensing overcomes these challenges because of its ability to “see” the surface through clouds or regardless of day or night conditions. In addition, the radar signal can penetrate through the vegetation canopy and provide information relevant to structure and density. Although the capabilities and benefits of SAR data for forest mapping and monitoring are known, it is underutilized operationally due to data complexities and limited user-friendly tutorials.


    This advanced webinar series will introduce participants to 1.) SAR time series analysis of forest change using Google Earth Engine (GEE), 2.) land cover classification with radar and optical data with GEE, 3.) mapping mangroves with SAR, and 4.) forest stand height estimation with SAR. Each training will include a theoretical portion describing the use of SAR for landcover mapping as related to the focus of the session followed by a demonstration that will show participants how to access, download, and analyze SAR data for forest mapping and monitoring. These demonstrations will use freely-available, open-source data, and software.

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

    • Interpret radar data for forest mapping
    • Understand how radar data can be applied to land cover mapping
    • Become familiar with open source tools used to analyze radar data
    • Conduct a land cover classification with radar and optical data
    • Map mangrove forests with radar data
    • Understand how forest stand height can be mapped using radar data
    • Apply SAR time-series analysis to map forest change
    • Learn about upcoming radar missions at NASA


    Course Format: 

    • Four parts with sessions offered in English and Spanish
    • Four exercises
    • One Google Form homework


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



    Part 1: Time Series Analysis of Forest Change

    • Introduction to analysis and interpretation of SAR data for forest mapping
    • Exercise: Time Series of Forest Change using GEE
    • Q&A


    Part 2: Land Cover Classification with Radar and Optical Data

    • Review of the unique attributes of radar and optical data as related to forest mapping and how they can be complementary
    • Classification algorithms and improvements with optical imagery
    • Exercise: Land Cover Classification with Radar and Optical using GEE
    • Q&A


    Part 3: Mangrove Mapping

    • Introduction to analysis and interpretation of SAR data for mangrove mapping
    • Exercise: Mapping Mangroves with the Sentinel Toolbox
    • Q&A


    Part 4: Forest Stand Height (with Guest Speaker Paul Siqueria)

    • Introduction to the use of SAR data for mapping forest stand height
    • Applications and looking forward to NISAR 2022
    • Demo: Estimating Forest Stand Height
    • Q&A


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

  • Mapeo y Monitoreo de los Bosques con Datos SAR [Avanzado]

    Esta capacitación avanzada cubrirá los siguientes temas 1) análisis del cambio en los bosques con datos SAR multi-temporales utilizando Google Earth Engine (GEE); 2) la clasificación de la cobertura terrestre con datos SAR y ópticos utilizando GEE; 3) el mapeo de manglares con SAR; 4) y la estimación de la altura de los bosques utilizando SAR. Cada sesión incluirá una porción teórica describiendo el uso de SAR para el mapeo de la cobertura relevante el enfoque de la sesión, seguida por una demostración de cómo acceder, descargar y analizar datos SAR para el mapeo y monitoreo del bosque. Estas demostraciones utilizan datos y software de libre acceso y de fuente abierta.

    Objetivos de Aprendizaje: 

    • Para la conclusión de esta capacitación, los participantes podrán:
    • Interpretar datos radar para el mapeo de los bosques
    • Entender cómo se puede aplicar datos radar para el mapeo de la cobertura terrestre
    • Estar familiarizados con herramientas de fuente abierta para analizar datos radar
    • Realizar una clasificación de la cobertura terrestre con datos radar y ópticos
    • Mapear manglares con datos radar
    • Entender cómo la altura de los rodales de los bosques se puede mapear con datos radar
    • Aplicar análisis de series temporales SAR para mapear cambios en los bosques
    • Aprender sobre futuras misiones radar de la NASA


    Formato del Curso: 

    • Cuatro partes con sesiones disponibles en inglés y español
    • Cuatro ejercicios
    • Una tarea en Google Form
    • 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.



    Prerequisitos: 
    Completar los Fundamentos de la Percepción Remota (Teledetección), Introducción al Radar de Apertura Sintética y SAR y sus Aplicaciones para la Cobertura Terrestre o tener experiencia equivalente. Los participantes que no completen los prerrequisitos podrían no estar lo suficientemente preparados para el ritmo de la capacitación.
    Software instrucciones
    Puede seguir las demostraciones utilizando el software enumerado a continuación. Las grabaciones de cada parte estarán disponibles en YouTube dentro de 24 horas después de cada demostración para que usted pueda repasarlas a su propio ritmo.
    Primera Parte: SAR para el Mapeo de Inundaciones Utilizando Google Earth Engine
    Google Earth Engine
    Segunda Parte: SAR Interferométrico para la Observación de Derrumbes
    Tercera Parte: Generación de un Modelo de Elevación Digital (Digital Elevation Model o DEM)
    Para ambas partes, los presentadores utilizarán el Sentinel-1 Toolbox

    Primera Parte: Análisis del Cambio en los Bosques con Datos SAR Multi-Temporales
    • Introducción al análisis e interpretación de datos SAR para el mapeo de los bosques 
    • Ejercicio: Datos SAR multi-temporales para el análisis del cambio en los bosques usando GEE 
    • Sesión de preguntas y respuestas

    Segunda Parte: Clasificación de la Cobertura Terrestre con Datos SAR y Ópticos
    • Repaso de las caracteristicas de los datos SAR y ópticos relevantes al mapeo de bosques y cómo se pueden complementar entre sí
    • Algoritmos para la clasificación con imágenes ópticas 
    • Ejercicio: Clasificación de la cobertura terrestre con datos SAR y opticos usando GEE 
    • Sesión de preguntas y respuestas

    Tercera Parte: Mapeo de Manglares
    • Introducción al análisis e interpretación de datos SAR para el mapeo de manglares 
    • Ejercicio: El mapeo de Manglares con el Sentinel Toolbox 
    • Sesión de preguntas y respuestas

    Cuarta Parte: Estimación de la Altura de los Bosques con SAR (Presentador Invitado el Dr. Paul Siqueira)
    • Introducción al uso de datos SAR para estimar la altura de los bosques
    • Aplicaciones y a la espera de NISAR en el 2022 
    • Demo: Estimacion de la altura de los bosques 
    • Sesión de preguntas y respuestas

  • Groundwater Monitoring using Observations from NASA’s Gravity Recovery and Climate Experiment (GRACE) Missions [Introductory]

    Groundwater makes up roughly 30% of global freshwater. It also provides drinking water for the world’s population and irrigation for close to 1/3rd of global agricultural land. Because of this level of reliance, monitoring groundwater is crucial for water resources and land management. The Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) missions from NASA and the German Research Centre for Geosciences (GFZ) provide large-scale terrestrial water storage estimation from mid-2000 to present. The mission uses twin satellites to accurately map variations in the Earth's gravity field and surface mass distribution.


     


    GRACE observations have been used for detecting groundwater depletion and for drought and flood predictions. This lightning-style training is designed to answer the demand and interest from the applications community in technologies that can be used to support water resources management. The webinar will provide an overview of the GRACE missions, groundwater data availability, and their applications in the monitoring and management of water resources. This lightning webinar will also serve as the foundation for the upcoming advanced webinar: Using Earth Observations to Monitor Water Budgets for River Basin Management II.

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


    • Access GRACE data and analyze regional groundwater changes


    course Format: 


    • A single, 1.5-hour webinar that includes a lecture and a question & answer session
    • No certificate of completion will be available for this webinar


    Prerequisites:
     Fundamentals of Remote Sensing   

    Agenda:
    • Introduction to GRACE and GRACE-FO
    • Data Format, Variables, and Resolution
    • GRACE Data Access
    • Q&A Session

  • Understanding Phenology with Remote Sensing [Introductory]

    This training will focus on the use of remote sensing to understand phenology: the study of life-cycle events. Phenological patterns and processes can vary greatly across a range of spatial and temporal scales and can provide insights about ecological processes like invasive species encroachment, drought, wildlife habitat, and wildfire potential. This training will highlight NASA-funded tools to observe and study phenology across a range of scales. Attendees will be exposed to the latest in phenological observatory networks and science, and how these observations relate to ecosystem services, the carbon cycle, biodiversity, and conservation.

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

    • Summarize NASA satellites and sensors that can be used for monitoring global phenology patterns
    • Outline the benefits and limitations of NASA data for phenology
    • Describe the multi-scalar approach to vegetation life cycle analyses
    • Compare and contrast data from multiple phenology networks
    • Evaluate various projects and case-study examples of phenological data


    Course Format: 

    • Three, one-hour sessions


    Prerequisites: Attendees who have not completed the course(s) below may be unprepared for the pace of this training.
    Fundamentals of Remote Sensing  

    Part 1: Overview of Phenology and Remote Sensing

    • Introduction to NASA data and Phenology
    • Land Surface Phenology from MODIS and VIIRS


    Part 2: Scales of Phenology

    • Resolving challenges associated with variability in space, time, and resolution for phenology research and applications
    • USA-National Phenology Network (NPN) and The National Ecological Observatory Network (NEON) 
    • Phenocam: Near-surface phenology
    • Conservation Science Partners


    Part 3: Utility and Advantage of Multi-Scale Analysis

    • Field-based phenology and gridded products
    • Case-study examples:
    • Integration of PhenoCam near-surface remote sensing and satellite phenological data
    • Greenwave modeling
    • Urbanization and plant phenology


    Each part of 3 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.

  • Centre Of Excellence Ocean Data Management

    This course provides a comprehensive introduction to a wide variety of earth science datasets, formats and analysis software. Students will learn and practice methods using a common ocean area, and they are expected to create a personal project of data products for a marine region of their own choosing. Personal projects are presented by the students at the end of the course.  This course requires either guest or POGO Scholar login, and is hosted on a Moodle platform.

    Aims and Objectives:
    -Recognize the importance of good research data management practice
    -Provide an introduction to the use of free software for synthesis of marine data and analyses
    -Creation and use of multi-parameter marine data collections to prepare and publish standard data products
    -Develop marine data and products from multiple sources using selected software programs

    Course overview
    1. Course outline and summary
    2. Pre-course reading (optional)
    3. Introduction to IODE and data management
    4. Research Data Management
    5. Ocean Data Collections using Ocean Data View
    6. Introduction to Marine Metadata
    7. Managing Operational Data using Integrated Data Viewer
    8. Marine GIS operations using Saga
    9. Student Project - Marine data products for selected project areas

     

  • Train-the-Trainer Concept on Research Data Management

    Within the project FDMentor, a German Train-the-Trainer Programme on Research Data Management (RDM) was developed and piloted in a series of workshops. The topics cover many aspects of research data management, such as data management plans and the publication of research data, as well as didactic units on learning concepts, workshop design, and a range of didactic methods.
    After the end of the project, the concept was supplemented and updated by members of the Sub-Working Group Training/Further Education (UAG Schulungen/Fortbildungen) of the DINI/Nestor Working Group Research Data (DINI/Nestor-AG Forschungsdaten). The newly published English version of the Train-the-Trainer Concept contains the translated concept, the materials, and all methods of the Train-the-Trainer Programme. Furthermore, additional English references and materials complement this version.
    This document is primarily intended for trainers who want to conduct a Train-the-Trainer workshop on research data management. It contains background knowledge on the PowerPoint slides and teaching scripts as well as further information on the individual subject areas required for reuse and implementation of a two-day workshop of seven and a half hours a day.

    Each unit of this guide contains information about how to teach the unit including the unit's learning objectives, key aspects, contents, didactic methods and exercises, training materials, addiitional sources, template, and teaching scripts.

    Topics of the units inlclude orientation, didactic approach, digital research data, research data policies, data management plans, order and structure, documentation and metadata, storage and backup, long term archiving, access control, formal framework, data publication, re-use of research data, legal aspects, institutional infrastructure, training exercises, concept development and didactic methods.  
     

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

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

  • Environmental Data Initiative Five Phases of Data Publishing Webinar - What are metadata and structured metadata?

    Metadata are essential to understanding a dataset. The talk covers:

    • How structured metadata are used to document, discover, and analyze ecological datasets.
    • Tips on creating quality metadata content.
    • An introduction to the metadata language used by the Environmental Data Initiative, Ecological Metadata Language (EML). EML is written in XML, a general purpose mechanism for describing hierarchical information, so some general XML features and how these apply to EML are covered.

    This video in the Environmental Data Initiative (EDI) "Five Phases of Data Publishing" tutorial series covers the third phase of data publishing, describing.

     

  • Environmental Data Initiative Five Phases of Data Publishing Webinar - Creating "clean" data for archiving

    Not all data are easy to use, and some are nearly impossible to use effectively. This presentation lays out the principles and some best practices for creating data that will be easy to document and use. It will identify many of the pitfalls in data preparation and formatting that will cause problems further down the line and how to avoid them.

    This video in the Environmental Data Initiative (EDI) "Five Phases of Data Publishing" tutorial series covers the second phase of data publishing, cleaning data. For more guidance from EDI on data cleaning, also see "How to clean and format data using Excel, OpenRefine, and Excel," located here: ​https://www.youtube.com/watch?v=tRk01ytRXjE.

  • Environmental Data Initiative Five Phases of Data Publishing Webinar - How to clean and format data using Excel, OpenRefine, and Excel

    This webinar provides an overview of some of the tools available for formatting and cleaning data,  guidance on tool suitability and limitations, and an example dataset and instructions for working with those tools.

    This video in the Environmental Data Initiative (EDI) "Five Phases of Data Publishing" tutorial series covers the second phase of data publishing, cleaning data.

    For more guidance from EDI on data cleaning, also see " Creating 'clean' data for archiving," located here:  https://www.youtube.com/watch?v=gW_-XTwJ1OA.

  • Introduction to Scientific Visualization

    Scientific Visualization transforms numerical data sets obtained through measurements or computations into graphical representations. Interactive visualization systems allow scientists, engineers, and biomedical researchers to explore and analyze a variety of phenomena in an intuitive and effective way. The course provides an introduction to the principles and techniques of Scientific Visualization. It covers methods corresponding to the visualization of the most common data types, as well as higher-dimensional, so-called multi-field problems. It combines a description of visualization algorithms with a presentation of their practical application. Basic notions of computer graphics and human visual perception are introduced early on for completeness. Simple but very instructive programming assignments offer a hands-on exposure to the most widely used visualization techniques.

    Note that the lectures, demonstration, and tutorial content require a Purdue Credentials,Hydroshare, or CILogon account.

    Access the CCSM Portal/ESG/ESGC Integration slide presentation at  https://mygeohub.org/resources/50/download/ccsm.pdf. The CCSM/ESG/ESGC collaboration provides a semantically enabled environment that includes modeling, simulated and observed data, visualization, and analysis.
    Topics include:

    • CCSM Overview
    • CCSM on the TeraGrid
    • Challenges
    • Steps in a typical CCSM Simulation
    • Climate Modeling Portal: Community Climate System Model (CCSM) to simulate climate change on Earth
    • CCSM Self-Describing Workflows 
    • Provenance metadata collection
    • Metadata

     

  • 23 (research data) Things

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

    Each of the 23 Things offers a variety of learning opportunities with activities at three levels of complexity: ‘Getting started’, ‘Learn more’ and ‘Challenge me’. All resources used in the program are online and free to use.

  • Introduction: FAIR Principles and Management Plans

    This presentation introducing the FAIR (Findable Accessible Interoperable Re-usable) data principles and management plans is one of 9 webinars on topics related to FAIR Data and Software that was offered at a Carpentries-based Workshop in Hannover, Germany, Jul 9-13 2018.  Presentation slides are also available in addition to the recorded presentation.

    Other topics included in the series include:
    - Findability of Research Data and Software through PIDs and FAIR
    - Accessibility through Git, Python Funcations and Their Documentation
    - Interoperability through Python Modules, Unit-Testing and Continuous Integration
    - Reusability through Community Standards, Tidy Data Formats and R Functions, their Documentation, Packaging, and Unit-Testing
    - Reusability:  Data Licensing
    - Reusability:  Software Licensing
    - Reusability:  Software Publication
    - FAIR Data and Software - Summary

    URL locations for the other modules in the webinar can be found at the URL above.
     

  • IOCCP & BONUS INTEGRAL Training Course on "Instrumenting our oceans for better observation: a training course on a suite of biogeochemical sensors"

    Building on the success of prior training courses, the International Ocean Carbon Coordination Project (IOCCP) and EU BONUS INTEGRAL Project (Integrated carboN and TracE Gas monitoRing for the bALtic sea) organized an international training course on "Instrumenting our ocean for better observation:a training course on a suite of biogeochemical sensors." The course was held on June 10-19, 2019 at the Sven Lovén Center for Marine Sciences, in Kristineberg, Sweden. This course responded to the growing demand of the global ocean observing system and the marine biogeochemistry community for expanding the correct usage and generation of information from a suite of autonomous biogeochemical sensors.

    The goal of the course was to train the new generation of marine biogeochemists in the use of a suite of biogeochemical sensors and to assure the best possible quality of the data produced. This intensive training course provided trainees with lectures and hands-on field and laboratory experience with sensors (deployment, interfacing, troubleshooting, and calibration), and provided in-depth knowledge on data reduction and quality control as well as data management. This course also offered an overview of the use of remote sensing, modeling, and intelligent data extrapolation techniques.

    It provides a comprehensive set of training materials divided into several topics. The course materials include video-recorded lectures and/or lecture slideshows in PDF supplemented with links and references to various materials such as manuals, guides, and best practices. 

    Note:  please explore the contents of this course as a self-learning course. Note however that the contents of this training course were designed for a face to face context. As such, some features (assignments, discussion fora, etc) may not work properly and we cannot ensure tutor support. For any queries please contact [email protected] and we will do our best to redirect you to an expert that can assist you. Thank you for your understanding.
    Topic 1: Scientific importance of instrumenting our ocean
    Topic 2: Coordinated global observing networks for marine biogeochemistry
    Topic 3: Sensors inside out
    Topic 4: Interfacing sensors
    Topic 5: Calibration and validation: what are the needs?
    Topic 6: The carbonate system: assessing and controlling measurement uncertainty in estimating the seawater CO2 system
    Topic 7: Equilibrator-based surface measurements
    Topic 8: How to choose the right sensor depending on your circumstances?
    Topic 9: Theory of data processing
    Topic 10: Combining remote sensing and in situ biogeochemical observations
    Topic 11: How to take care of data?
    Topic 12: Modelling for best observations design
    Topic 13: "Smart" data extrapolation
    Topic 14: From surface measurements to ocean-atmosphere fluxes
    Topic 15: Emerging technologies
    Topic 16: Ocean Best Practices (OBP) Initiative and Repository
     

  • MBON Pole to Pole Of The Americas: Tools For The Analysis Of Biodiversity Data Using OBIS And Remote Sensing Data

    The Marine Biodiversity Observation Network (MBON) Pole to Pole organized a second Marine Biodiversity Workshop - From the Sea to the Cloud - after a successful first workshop held during the 2018 AmeriGEOSS Week in Brazil. This activity advanced the implementation of the MBON Pole to Pole network by enhancing knowledge on field data collection methods and the use of informatic technologies for data management and analysis.

    Github site: marinebon.github.io/p2p-mexico-workshop/index.html

    The purpose was to continue the development of a community of practice dedicated to understanding change in marine biodiversity and generating knowledge and products that inform conservation and management strategies of marine living resources by engaging researchers, managers, and policy-makers with interest in biodiversity monitoring and data synthesis and analysis. During this workshop, participants:
    -Advanced already agreed on field sampling protocols for rocky shores and sandy beaches;
    -Manipulated tabular and spatial data already collected at their study sites for standardized data formats using Darwin Core vocabularies and quality controls;
    -Developed specific vocabularies for flora and fauna of rocky shore and sandy beach measured during field surveys;
    -Published survey datasets to the Ocean Biogeographic Information System (OBIS) using tools for sharing data;
    -Advanced knowledge on data science tools (R, Rmarkdown, Github) to mine data, visualize and analyze, and produce reproducible research documents with interactive visualizations onto the web.

    The MBON Pole to Pole workshops are designed to:

    • enhance coordination of data collection among nations;
    • improve the collection of harmonized data, developing data standards and methodologies for data management and dissemination without compromising national concerns;
    • support the integration of biodiversity information with physical and chemical data over time (status and trends); and
    • generates products needed for informed policy and management of the ocean.
    The workshop targeted investigators and resource managers dedicated to studying and conserving biodiversity of invertebrates in two important coastal habitats: rocky shore intertidal zone and sandy beaches. This activity targeted participants from all nations in the Americas, from pole to pole.

    Note:  please explore the contents of this course as a self-learning course. Note however that the contents of this training course were designed for a face to face context. As such, some features (assignments, discussion fora, etc) may not work properly and we cannot ensure tutor support. For any queries please contact [email protected] and we will do our best to redirect you to an expert that can assist you. Thank you for your understanding.

  • Ocean-Colour Data In Climate Studies

    The course will deliver training in ocean-colour data and their applications in climate studies. Remote sensing experts from the Plymouth Marine Laboratory (PML) will guide students through a combination of lectures and computer-based exercises covering the following topics:
    -Introduction to ocean colour;
    -Modelling primary production;
    -Ocean colour applications for ecosystem state assessment;
    -Climate impacts and feedbacks;
    -Ocean colour in data assimilation;
    -Dataset archive, management, visualization, and analysis.
    Objectives
    The objectives and learning outcomes of this course are for students to be able to:
    • Understand the fundamentals of ocean colour;
    • Understand the principles for modeling primary production, for detecting phytoplankton size structure, Harmful Algal Blooms, and for estimating phytoplankton phenology;
    • Conduct research with ocean-colour data for ecosystem state assessment, model validation, data assimilation, and climate research;
    • Plan requirements for large datasets processing, management and archiving;
    • Apply tools and statistical methods for visualization and analysis of ocean-colour data with their associated uncertainty.

  • Research Design, Data Management & Data Communication in Marine Sciences - Module 3 - Data Communication

    The contents of the course are:
    1 - Scientific paper writing
    2 - Communication with the media
    3 - Communication via online platforms: Academic sites and profiling
    4 - Communication via online platforms: social media
    5.1 - Visualizations (option 1)
    5.2 - Presentations & public speaking (option 2)
    5.3 - Storytelling (option 3)
    6 – Assignments

     

  • Data Visualization of Marine Met Data (using FERRET)

    Data visualization is the science of describing the significance of data by placing it in a visual context. In the current scenario it is very useful for dealing with marine met data as patterns, trends, and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software.
    The current course will demonstrate the use of open-source software FERRET for the generation of NetCDF data and visualize various types of plots, save and reuse them at a later stage. The course is designed to be a mix of both practical and theoretical sessions.
    Aims and Objectives:
    -Provide exposure to Data Visualization using FERRET.
    -Generation of scripts to visualize various types of data sets (1D, 2D, 3D, etc.).
    -Perform data analysis, generate added-value products.
     Learning Outcomes:
    -Knowledge and understanding of FERRET software.
    -Generation of different types of JNL scripts for visualization and analysis.
    -Able to visualize data set viz., in situ, remote sensing, and model outputs.
    -Tools for visualizing different types of ocean data and data products.
    Course Pre-requisites:
    -Candidates should have knowledge of data formats in which most of the oceanographic data sets are available;
    -Candidates should be preferably working in institutions responsible for the management of oceanographic and/or atmospheric data;

    Note:  please explore the contents of this course as a self-learning course. Note however that the contents of this training course were designed for a face to face context. As such, some features (assignments, discussion fora, etc) may not work properly and we cannot ensure tutor support. For any queries please contact [email protected] and we will do our best to redirect you to an expert that can assist you. Thank you for your understanding.

  • Health And Medical Short Bites #1 - Funders And Publishers

    This short webinar is the first of the fifth in the Health and Medical Short Bites webinar series which aims to support better management and publication of Health and Medical data. 

    You can find the information from the Funder perspective: Dr. Wee-Ming Boon speaks about NHMRC's statement on data sharing And Jeremy Kenner Reviews the National Statement on Ethical Conduct in Human Research. 
    A Publisher perspective on data: Peter D’Onghia, Senior Journal Publishing Manager at Wiley, has a portfolio of journals in health and life sciences and will discuss the new Wiley data policies.

    Find other parts of this webinar:
    Health and Medical Short Bites #2 - Storing and publishing health and medical data

    Health and medical data #3 - Ethics, legal issues, and data sharing

    Health & Medical Data Short Bites #4 - Patient views on data sharing

    Health & Medical Data Short Bites #5 - Data linkage and the Australian Health Thesaurus

    Recording, slides, transcripts, and links are available for all health and medical webinars.

  • Health And Medical Short Bites #2 - Storing And Publishing Health And Medical Data

    This short webinar is the second of the fifth in the Health and Medical Short Bites webinar series which aims to support better management and publication of Health and Medical data.

    Kate LeMay covered more general storage and repository options for health and medical data including Institutional, Discipline, Non-specific repositories, and Jeff Christiansen introduced Med.data.edu.au is a national facility to provide petabyte-scale research data storage, and related high-speed networked computational services, to Australian medical and health research organizations. Find data, Use data, and Store data.
     

    Find other parts of this webinar:

    Health And Medical Short Bites #1 - Funders And Publishers

    Health and medical data #3 - Ethics, legal issues, and data sharing

    Health & Medical Data Short Bites #4 - Patient views on data sharing

    Health & Medical Data Short Bites #5 - Data linkage and the Australian Health Thesaurus

    Recordings, slides, transcripts and links for all healthand medical webinars are available.  

  • Health and Medical Data #3 - Ethics, Legal Issues and Data Sharing

    This short webinar is the third of the fifth in the Health and Medical Short Bites webinar series which aims to support better management and publication of Health and Medical data.

    The legal framework around privacy in Australia is complex and differs between states. Many Acts regulate the collection, use, disclosure and handling of private data. Principles to follow around sensitive data include the management of personal information openly and transparently, only collecting necessary information, and adequate de-identification of data when possible. There are also many ethical considerations around the management and sharing of sensitive data. Informed consent by research participants is essential for the collection, use, and sharing of sensitive data. Storage, access, de-identification, and plans for sharing are very important considerations.
    Topics include:
    1) Legal issues and data sharing
    2) Ethics and data sharing

    Find other parts of this webinar:
    Health and Medical Short Bites #1 - Funders and publishers 

    Health and Medical Short Bites #2 - Storing and publishing health and medical data

    Health & Medical Data Short Bites #4 - Patient views on data sharing

    Health & Medical Data Short Bites #5 - Data linkage and the Australian Health Thesaurus

    Recordings, slides, transcripts and links are available for all webinars in this series.

  • Health & Medical Data Short Bites #4 - Patient Views on Data Sharing

    This short webinar is the fourth in the Health and Medical Short Bites webinar series which aims to support better management and publication of Health and Medical data.

    Informed consent by research participants is essential for the collection, use, and sharing of sensitive data. Storage, access, de-identification, and plans for sharing are important considerations when gaining patient consent.

    Find other parts of this webinar:
    Health and Medical Short Bites #1 - Funders and publishers 

    Health and Medical Short Bites #2 - Storing and publishing health and medical data

    Health and medical data #3 - Ethics, legal issues, and data sharing

    Health & Medical Data Short Bites #5 - Data linkage and the Australian Health Thesaurus

    Recordings, slides, transcripts and links are available for all health and medical webinars.  

  • Health & Medical Data Short Bites #5 - Data linkage and the Australian Health Thesaurus

    This short webinar is the fifth in the Health and Medical Short Bites webinar series which aims to support better management and publication of Health and Medical data.

     Topics and Speakers include:
     1) Data linkage: processes, how it’s done, and data availability statements. Dr. Trisha Johnston, Director, Statistical Analysis Linkage Team, Queensland Department of Health Data points out that linkage is an efficient way to enhance existing data to increase its usefulness for informing population health and clinical research, policy development, and health service planning, management, monitoring, and evaluation.
    2) Australian Health Thesaurus James Humffray, Manager of the Australian Health Thesaurus talks about how Health Direct uses the Thesaurus to improve the user’s search experience by auto-suggestions of search terms, alternative terms or synonyms to find content, ranking in search results of the most relevant content, facets or filters to narrow down a user’s search results.

    Find other parts of this webinar:
    Health and Medical Short Bites #1 - Funders and publishers 

    Health and Medical Short Bites #2 - Storing and publishing health and medical data

    Health and medical data #3 - Ethics, legal issues, and data sharing

    Health & Medical Data Short Bites #4 - Patient views on data sharing

    Recordings, slides, transcripts, and links are available for all health and medical webinars.