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Luego aprenderás cómo instalar y trabajar con MALLET, una caja de herramientas para procesamiento de lenguajes naturales (PLN) que sirve para realizar este tipo de análisis. MALLET requiere que se modifique una variable de entorno (esto es, configurar un atajo para que la computadora sepa en todo momento dónde encontrar el programa MALLET) y que se trabaje con la línea de comandos (es decir, tecleando comandos manualmente en vez de hacer clic en íconos o menús).", "abstract_format": "filtered_html", "subject": "Arts and Humanities", "keywords": [ "Data access methods", "Data modeling", "Data skills education", "Digital humanities", "Humanities data", "Programming", "Software management" ], "license": "", "usage_info": "", "citation": "Shawn Graham, Scott Weingart, y Ian Milligan, \"Introducci\u00f3n a Topic Modeling y MALLET\", traducido por Ulrike Henny-Krahmer, The Programming Historian en espa\u00f1ol 2 (2018), https://doi.org/10.46430/phes0040.", "locator_data": "10.46430/phes0040", "locator_type": "DOI", "publisher": "The Programming Historian", "version": "", "created": "2021-06-01T17:00:29", "published": "2012-09-02T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "es", "languages_secondary": [ "en" ], "ed_frameworks": [], "target_audience": [ "Early-career research scientist", "Graduate student", "Librarian", "Mid-career research scientist", "Research faculty", "Research scientist", "Software engineer", "Technology expert group", "Undergraduate student" ], "purpose": "Instruction - detailed information about aspects or processes related to data management or data skills.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "Nhoebelheinrich", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "af3793fd-2008-315f-9f10-4a79adef3b9f", "contributors": [], "contributor_orgs": [], "score": 1.5265268, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "EC FAIR How-to Series: Getting a DOI for Your Data", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:59:03Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://library.ucsd.edu/dc/object/bb10665684", "access_cost": 0, "submitter_name": "Nancy J Hoebelheinrich", "submitter_email": "njhoebel@gmail.com", "authors": [ { "givenName": "Nancy J.", "familyName": "Hoebelheinrich", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Nancy J. Hoebelheinrich" ], "author_org": { "name": "Knowledge Motifs LLC", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Nancy J. Hoebelheinrich", "org": "Knowledge Motifs LLC", "email": "" }, "abstract_data": "Identifiers are very important as a means to make research data more FAIR (Findable Accessible Interoperable Reusable). This quick reference guide briefly describes why, when, where and how to acquire a digital object identifier (DOI) for research data.    The guide is targeted to Earth Science researchers, but should be useful for other researchers and their support staff as well.  It is part of the EarthCub How-to Series that is designed to provide targeted, practical lessons on making research data FAIR.  ", "abstract_format": "filtered_html", "subject": "", "keywords": [ "Accessible data - FAIR Data Principle", "Findable data - FAIR Data Principle", "Persistent Identifiers (PID)" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "Hoebelheinrich, Nancy J. (2021). EC FAIR How-to Series: Getting a DOI for Your Data. In EarthCube Organization Materials. UC San Diego Library Digital Collections. https://doi.org/10.6075/J06973F4", "locator_data": "10.6075/J06973F4", "locator_type": "DOI", "publisher": "EarthCube", "version": "", "created": "2021-11-05T15:04:02", "published": "2021-05-22T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [ { "name": "FAIR Data Principles", "nodes": [ { "description": "", "name": "Accessible" }, { "description": "", "name": "Findable" }, { "description": "", "name": "Interoperable" }, { "description": "", "name": "Re-usable" } ], "type": "framework" } ], "target_audience": [ "Early-career research scientist", "Mid-career research scientist" ], "purpose": "Instruction - detailed information about aspects or processes related to data management or data skills.", "completion_time": "Up to 1 hour", "media_type": "Static Image - a single visual representation that does not change. Example: a poster explicating a concept.", "lr_type": "Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.", "creator": "Nhoebelheinrich", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "91d35f96-d883-342a-a3e9-442fda2abff8", "contributors": [], "contributor_orgs": [ { "name": "EarthCube", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Funding and sponsorship" } ], "score": 1.5479954, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "EarthCube FAIR How-to Series: Choosing a Repository for Your Data", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:59:03Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://library.ucsd.edu/dc/object/bb74830103", "access_cost": 0, "submitter_name": "Nancy J. Hoebelheinrich", "submitter_email": "nhoebel@kmotifs.con", "authors": [ { "givenName": "Nancy J.", "familyName": "Hoebelheinrich", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Nancy J. Hoebelheinrich" ], "author_org": { "name": "Knowledge Motifs LLC", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Nancy J. Hoebelheinrich", "org": "Knowledge Motifs LLC", "email": "" }, "abstract_data": "This quick reference guide briefly describes why, when, where and how to choose a FAIR-enabled data repository for research data.    The guide is targeted to Earth Science researchers, but should be useful for other researchers and their support staff as well.  It is part of the EarthCub How-to Series that is designed to provide targeted, practical lessons on making research data FAIR.  ", "abstract_format": "filtered_html", "subject": "", "keywords": [ "Data reuse - Core Trustworthy Data Repositories Requirements", "Data storage" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "Hoebelheinrich, Nancy J. (2021). EC FAIR How-to Series: Choosing a Repository for Your Data. In EarthCube Organization Materials. UC San Diego Library Digital Collections. https://doi.org/10.6075/J02J6BQD", "locator_data": "10.6075/J02J6BQD", "locator_type": "DOI", "publisher": "EarthCube", "version": "", "created": "2021-11-06T13:11:47", "published": "2021-05-11T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [ { "name": "FAIR Data Principles", "nodes": [ { "description": "", "name": "Accessible" }, { "description": "", "name": "Findable" }, { "description": "", "name": "Re-usable" } ], "type": "framework" } ], "target_audience": [ "Early-career research scientist", "Mid-career research scientist" ], "purpose": "Instruction - detailed information about aspects or processes related to data management or data skills.", "completion_time": "Up to 1 hour", "media_type": "Static Image - a single visual representation that does not change. Example: a poster explicating a concept.", "lr_type": "Images / Visuals - media, including but not limited to pictures, graphics, diagrams, figures, illustrations, charts, and maps.", "creator": "Nhoebelheinrich", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "d57952b5-fc5a-3045-86bc-d93c5acb75fb", "contributors": [], "contributor_orgs": [ { "name": "EarthCube", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Funding and sponsorship" } ], "score": 1.5479954, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Supporting Researchers in Discovering Data Repositories", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:59:00Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://www.dataone.org/webinars/repo-discovery/", "access_cost": 0, "submitter_name": "Zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Amber", "familyName": "Budden", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Erin", "familyName": "McLean", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Amber Budden", "Erin McLean" ], "author_org": { "name": "", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "How do researchers go about identifying a repository to preserve their data? Do they have all the information they need to make an informed decision? Are there resources available to help?
\r\nThere is a myriad of repositories available to support data preservation and they differ across multiple axes. So which one is right for your data? The answer is large, ‘it depends’. But this can be frustrating to a new researcher looking to publish data for the first time. What questions need to be asked to detangle these dependencies and where can a researcher go for answers?
\r\nConversations and sessions at domain conferences have consistently suggested that researchers need more support in navigating the landscape of data repositories and with support from ESIP Funding Friday, we sought to do that. In this webinar, we will introduce a resource under development that aims to serve as a gateway for information about repository selection. With links to existing resources, games, and outreach materials, we aim to facilitate the discovery of data repositories and we welcome contributions to increase the value of this resource.", "abstract_format": "filtered_html", "subject": "", "keywords": [ "Data discovery and identification - Core Trustworthy Data Repositories Requirements", "Data management", "Data preservation", "Data reuse - Core Trustworthy Data Repositories Requirements", "Data storage" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "DataONE", "version": "", "created": "2021-02-22T11:35:35", "published": "2021-01-12T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Early-career research scientist", "Graduate student", "Librarian", "Publisher", "Repository manager", "Research scientist", "Undergraduate student" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "Up to 1 hour", "media_type": "Event - time-based happening that is portrayed or covered by the learning resource, e.g., a webinar.", "lr_type": "Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "ca9d6303-95de-3c60-a19d-66b607844873", "contributors": [], "contributor_orgs": [], "score": 1.5155138, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "RDM Onboarding Checklist", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:59Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://datamanagement.hms.harvard.edu/plan/rdm-onboarding-checklist", "access_cost": 0, "submitter_name": "Zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Julie", "familyName": "Goldman", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Sarah", "familyName": "Hauserman", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Meghan", "familyName": "Kerr", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Julie Goldman", "Sarah Hauserman", "Meghan Kerr" ], "author_org": { "name": "Harvard Medical School", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "Research Data Management is essential for responsible research and should be introduced when starting a new project or joining a new lab. Managing data across a project and/ or a team allows for accurate communication about that project. This session will review the important steps for onboarding new employees/trainees to a lab or new projects. The key takeaway from this session will be how to incorporate these steps within your individual project or lab environment. While the principles are general, these documents focus on Harvard policies and resources. Internal and external links have been provided throughout the document as supplementary resources, including a glossary of terms. 
\r\n
\r\nThere are 2 checklists as follow: 
\r\nThe RDM Onboarding Checklist: Abridged Version serves as a condensed version of the comprehensive checklist described above. This version is intended to be used as an actionable checklist, employed after reviewing the onboarding processes and resources provided in the comprehensive checklist.
\r\nThe RDM Onboarding Checklist: Comprehensive Version serves as a general, research data management-focused guide to employee/trainee onboarding as they join a new lab or begin new projects (follow one or both of these as they apply to your situation). This comprehensive version is provided as an initial introduction to the onboarding process and to the breadth of available resources; this version is intended to be reviewed first, prior to utilizing the abridged version.
\r\n
\r\n Learning Objectives:\r\n\r\n", "abstract_format": "filtered_html", "subject": "", "keywords": [ "Data backup", "Data communication", "Data management", "Data management planning", "Data policy", "Data publication", "Data research", "Data sharing", "Data storage", "File naming", "Metadata standards", "Research lifecycle", "Researcher engagement", "Version control" ], "license": "Standard YouTube License", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "Harvard Medical School", "version": "", "created": "2021-02-08T15:28:31", "published": "2020-09-14T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data manager", "Data professional", "Early-career research scientist", "Funding organization", "Librarian", "Mid-career research scientist", "Repository manager", "Research faculty", "Research scientist" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "Up to 1 hour", "media_type": "Collection - a group or set of items that comprise a single learning resource, e.g., a PDF version of a slide presentation, an audio file of the presentation and a textual representation of the oral transcription of the presentation.", "lr_type": "Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "ea45507f-2c11-3f2d-b800-74d3f9bdd89a", "contributors": [], "contributor_orgs": [], "score": 1.5239732, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Content-based Identifiers for Iterative Forecasts: A Proposal", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:55Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://www.dataone.org/webinars/iterative-forecasts/", "access_cost": 0, "submitter_name": "zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Carl", "familyName": "Boettiger", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Carl Boettiger" ], "author_org": { "name": "University of California, Berkeley", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "Iterative forecasts pose particular challenges for archival data storage and retrieval. In an iterative forecast, data about the past and present must be downloaded and fed into an algorithm that will output a forecast data product. Previous forecasts must also be scored against the realized values in the latest observations. Content-based identifiers provide a convenient way to consistently identify input and outputs and associated scripts. These identifiers are:
\r\n(1) location-agnostic – they don’t depend on a URL or other location-based authority (like DOI)
\r\n(2) reproducible – the same data file always has the same identifier
\r\n(3) frictionless – cheap and easy to generate with widely available software, no authentication or network connection
\r\n(4) sticky – the identifier cannot become unstuck or separated from the content
\r\n(5) compatible – most existing infrastructure, including DataONE, can quite readily use these identifiers.
\r\n
\r\nIn this webinar, the speaker will illustrate an example iterative forecasting workflow. In the process, he will highlight some newly developed R packages for making this easier.", "abstract_format": "filtered_html", "subject": "Physical Sciences and Mathematics: Environmental Sciences", "keywords": [ "Data archiving", "Data skills education", "Data storage", "Persistent Identifiers (PID)", "Programming", "R software" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "DataONE", "version": "", "created": "2020-11-12T10:35:13", "published": "2020-10-13T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data manager", "Data professional", "Data supporter", "Early-career research scientist", "Graduate student", "Librarian", "Mid-career research scientist", "Repository manager", "Research scientist", "Undergraduate student" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "Up to 1 hour", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "38de3dde-104f-3cff-a0a9-7036e7c53cb9", "contributors": [], "contributor_orgs": [], "score": 1.5190574, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Remote Sensing for Conservation & Biodiversity [Introductory]", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:50Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://appliedsciences.nasa.gov/what-we-do/capacity-building/arset", "access_cost": 0, "submitter_name": "zohreh mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Cindy", "familyName": "Schmidt", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Amber", "familyName": "McCullum", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Cindy Schmidt", "Amber McCullum" ], "author_org": { "name": "NASA Applied Remote Sensing Training Program (ARSET)", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Brock Blevins", "org": "NASA Applied Remote Sensing Training Program (ARSET)", "email": "" }, "abstract_data": "The United Nations Millennium Ecosystem Assessment states: \u0093ecosystems are critical to human well-being - to our health, our prosperity, our security, and to our social and cultural identity.\u0094 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\u00a0use of satellite data for\u00a0conservation\u00a0and biodiversity\u00a0applications. The series will\u00a0highlight\u00a0specific projects that\u00a0have successfully used\u00a0satellite data. Examples include: \n \nLearning Objectives:\u00a0By the end of this training, attendees will:\u00a0 \n \n \nCourse Format:\u00a0 \n \n \nPrerequisites:\u00a0Fundamentals of\u00a0Remote Sensing\u00a0or equivalent knowledge
\nIf you do\u00a0not complete the prerequisite, you may not be adequately prepared for the pace of the training.
\n
\nSession One: Remote Sensing for Conservation\u00a0
\nThis session will focus on remote sensing for habitat suitability, species population dynamics, and monitoring wildfires.
\n
\nSession Two: Remote Sensing for Biodiversity\u00a0
\nThis session will focus on the Group on Earth Observations Biodiversity Observation Network (GEOBON), Marine Biodiversity Observation Network (MBON), and essential biodiversity variables.
\n
\nEach part of 2 includes links to the recordings, presentation slides, exercises, and Question & Answer Transcripts, in English and in Spanish.\u00a0 There is no link to a landing page in Spanish for this resource.\u00a0 \u00a0", "abstract_format": "filtered_html", "subject": "Education: Science and Mathematics Education", "keywords": [ "Biodiversity data", "Climate data", "Conservation", "Environmental change records", "Environmental management", "Land management", "Landcover applications", "Remote sensing", "Satellite imagery" ], "license": "Creative Commons Attribution 2.0 Generic - CC BY 2.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "NASA Applied Remote Sensing Training Program (ARSET)", "version": "", "created": "2020-10-15T13:19:06", "published": "2019-01-24T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [ "es" ], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data policymaker", "Data professional", "Early-career research scientist", "Educator", "Graduate student", "Mid-career research scientist", "Research scientist", "Technology expert group", "Undergraduate student" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Presentation - representation of the particular way in which an author shows, describes or explains one or more concepts, e.g., a set of Powerpoint slides.", "lr_type": "Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "91c0cd7b-33bb-34b6-8483-354ff82ff872", "contributors": [], "contributor_orgs": [], "score": 1.5214843, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "An Inside Look at how NASA Measures Air Pollution [Introductory]", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:48Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://appliedsciences.nasa.gov/what-we-do/capacity-building/arset", "access_cost": 0, "submitter_name": "zohreh mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Melanie", "familyName": "Follette-Cook", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Ana", "familyName": "Prados", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Pawan", "familyName": "Gupta", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Melanie Follette-Cook", "Ana Prados", "Pawan Gupta" ], "author_org": { "name": "NASA Applied Remote Sensing Training Program (ARSET)", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Brock Blevins", "org": "NASA Applied Remote Sensing Training Program (ARSET)", "email": "" }, "abstract_data": "Would you like to learn how to access and visualize NASA satellite imagery? With the world\u0092s 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\u0092s and don\u0092ts in interpreting satellite data, and how to download and create your own visualizations.
\n
\nLearning Objectives:\u00a0By the end of this training, attendees will be able to: \n \n
\nPrerequisites:\u00a0Fundamentals of Remote Sensing\u00a0(recommended but not required)
\n
\nPart One: Nitrogen Dioxide (NO2)
\n\u0095 What is NO2?
\n\u0095 NASA Remote Sensing Basics
\n\u0095 Interpreting NO2 Imagery: Dos and Don\u0092ts
\n\u0095 Downloading Data and Creating Imagery
\n
\nPart Two: Particulate Matter (Aerosols)
\n\u0095 What are Aerosols?
\n\u0095 Interpreting Aerosol Imagery: Dos and Don\u0092ts
\n\u0095 A Tour of NASA Resources for Generating Your Own Visualizations
\n
\nEach part of 2 includes links to the recordings, presentation slides,\u00a0 and Question & Answer Transcripts.", "abstract_format": "filtered_html", "subject": "Education: Science and Mathematics Education", "keywords": [ "Air quality data", "Climate data", "Data analysis", "Environmental change records", "Environmental management", "Geospatial data", "Remote sensing", "Satellite imagery", "User guides for Earth science data" ], "license": "Creative Commons Attribution 2.0 Generic - CC BY 2.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "NASA Applied Remote Sensing Training Program (ARSET)", "version": "", "created": "2020-10-08T10:46:51", "published": "2020-05-26T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [ "es" ], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data policymaker", "Data professional", "Early-career research scientist", "Educator", "Graduate student", "Mid-career research scientist", "Research scientist", "Technology expert group", "Undergraduate student" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Presentation - representation of the particular way in which an author shows, describes or explains one or more concepts, e.g., a set of Powerpoint slides.", "lr_type": "Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "db85a104-81cd-3ad3-880c-07650aec6fc1", "contributors": [], "contributor_orgs": [], "score": 1.5214843, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Un Vistazo a C\u00f3mo la NASA Mide la Contaminaci\u00f3n del Aire [Introductorio]", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:48Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://appliedsciences.nasa.gov/what-we-do/capacity-building/arset", "access_cost": 0, "submitter_name": "zohreh mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Melanie", "familyName": "Follette-Cook", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Ana", "familyName": "Prados", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Pawan", "familyName": "Gupta", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Melanie Follette-Cook", "Ana Prados", "Pawan Gupta" ], "author_org": { "name": "NASA Applied Remote Sensing Training Program (ARSET)", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Brock Blevins", "org": "NASA Applied Remote Sensing Training Program (ARSET)", "email": "" }, "abstract_data": "\u00bfLe gustar\u00eda saber c\u00f3mo acceder y visualizar im\u00e1genes satelitales de la NASA? La reciente disminuci\u00f3n de la contaminaci\u00f3n atmosf\u00e9rica a nivel mundial debido al baj\u00f3n econ\u00f3mico ha capturado la atenci\u00f3n del mundo entero y recibido mucha cobertura medi\u00e1tica. Inspir\u00e1ndose en ello, esta serie de dos webinars imparte conocimiento fundamental para novatos y sirve de curso de repaso para los dem\u00e1s. Ud. aprender\u00e1 cu\u00e1les son los contaminantes que se pueden medir desde el espacio, c\u00f3mo los sat\u00e9lites hacen estas mediciones, lo que se debe hacer y no se debe hacer al momento de interpretar datos satelitales y c\u00f3mo descargar y crear sus propias visualizaciones.\u00a0
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\nObjetivos de Aprendizaje:Al finalizar esta capacitaci\u00f3n, los/las participantes podr\u00e1n: \n \n
\nPrerrequisitos:\u00a0Fundamentos de la Teledetecci\u00f3n (Percepci\u00f3n Remota)\u00a0-\u00a0 recomendado pero no obligatorio
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\nParte 1: Di\u00f3xido de Nitr\u00f3geno (NO2) \n \nParte 2: Part\u00edculas (Aerosoles) \n \n \n", "abstract_format": "filtered_html", "subject": "Education: Science and Mathematics Education", "keywords": [ "Air quality data", "Environmental change records", "Environmental management", "Geospatial data", "Remote sensing", "Satellite imagery", "User guides for Earth science data" ], "license": "Creative Commons Attribution 2.0 Generic - CC BY 2.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "NASA Applied Remote Sensing Training Program (ARSET)", "version": "", "created": "2020-10-08T11:14:16", "published": "2020-05-26T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "es", "languages_secondary": [ "en" ], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data policymaker", "Data professional", "Early-career research scientist", "Educator", "Graduate student", "Mid-career research scientist", "Research scientist", "Technology expert group", "Undergraduate student" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Presentation - representation of the particular way in which an author shows, describes or explains one or more concepts, e.g., a set of Powerpoint slides.", "lr_type": "Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "67c4bfcd-a957-3489-b355-8b9661a04e16", "contributors": [], "contributor_orgs": [], "score": 1.5214843, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Earth Observations for Disaster Risk Assessment & Resilience [Introductory]", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:48Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://appliedsciences.nasa.gov/what-we-do/capacity-building/arset", "access_cost": 0, "submitter_name": "zohreh mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Amita", "familyName": "Mehta", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Sean", "familyName": "McCartney", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Amita Mehta", "Sean McCartney" ], "author_org": { "name": "NASA Applied Remote Sensing Training Program (ARSET)", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Brock Blevins", "org": "NASA Applied Remote Sensing Training Program (ARSET)", "email": "" }, "abstract_data": "
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.
\nThis 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.
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\nLearning Objectives:\u00a0By the end of this training, attendees will:\u00a0
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\nCourse Format:
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\nPrerequisites:\u00a0
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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.
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\nPart 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.
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\nPart 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.
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\nPart 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.
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\nEach part of 4 includes links to the recordings, presentation slides,\u00a0 and Question & Answer Transcripts.", "abstract_format": "filtered_html", "subject": "Education: Science and Mathematics Education", "keywords": [ "Climate data", "Climate resilience", "Climate risk assessment applications", "Data analysis", "Disaster applications", "Earth observation data", "Environmental change records", "Environmental management", "Remote sensing", "Satellite imagery", "Sustainable Development Goals (SDGs)", "User guides for Earth science data" ], "license": "Creative Commons Attribution 2.0 Generic - CC BY 2.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "NASA Applied Remote Sensing Training Program (ARSET)", "version": "", "created": "2020-10-14T10:51:53", "published": "2019-08-15T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data policymaker", "Data professional", "Early-career research scientist", "Educator", "Graduate student", "Mid-career research scientist", "Research scientist", "Technology expert group", "Undergraduate student" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Presentation - representation of the particular way in which an author shows, describes or explains one or more concepts, e.g., a set of Powerpoint slides.", "lr_type": "Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "7d09a16e-a46b-3c32-a375-4a26e15e8337", "contributors": [], "contributor_orgs": [], "score": 1.5214843, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] } ] }