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Pres. No.Poster/Demo TitleNameAbstractContact InfoLive Q&A Times
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1Radiant MLHub: An Open Repository for Geospatial Machine Learning Training DataBooth, KevinRadiant MLHub contains over 20 high-quality geospatial machine learning datasets for applications such as land cover classification, crop detection, flood monitoring, and building detection. In this tutorial you will learn how to register for Radiant MLHub and download datasets via our website, API, and Python client.kevin@radiant.earthWed. 7/21 6-7:30 pm ET
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2Graphing the USGSBristol, SkyUSGS has lots of different catalogs with information about who we are, where we are, what we do, and what we produce. They are all in different forms and structures that are nominally all linked together in some way, but they are functionally disconnected and can't be queried as a whole. In trying to answer things like, "what is the current USGS capacity for climate change science and how could we contribute to an ARPA-C in the US?," we've built a graph from the contents of all those catalogs, pointing out all the areas where they need to improve.sbristol@usgs.govWed. 7/21 6-7:30 pm ET
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3Gridded Environmental Data in the Cloud: NCEI Data Access PerspectiveCapece, MarkThe development of affordable cloud storage has revolutionized data storage and distribution, offering innate durability and nearly unlimited capacity. As the NOAA National Centers for Environmental Information migrates on-premises data, software, and applications to the cloud, it is important for these resources to evolve to leverage the benefits of scalability and agility in the cloud. Here we illustrate how existing services can be built in AWS to work with traditional gridded data in S3 object storage, as well as introduce new technologies and tools for converting to and working with zarr-formatted environmental data.mark.capece@noaa.govWed. 7/21 6-7:30 pm ET
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4Climate Change and Fast FashionChandler, PatrickIn this research project, we interrogate fast fashion in the 21st century in the context of a changing climate, by assessing emergent trends in sustainable fashion as an alternative consumption pathway through the annual ‘Trash the Runway’ event in Boulder, Colorado. We interviewed and surveyed designers and analyzed workshops and activities that led up to their annual fashion show. We also surveyed and interviewed students at the University of Colorado Boulder who worked with designers to produce short films about them and their work. The project provides youth – who are often marginalized in decision-making processes – a literal stage to suggest policy and behavior changes to address climate change and sustainability. We found that designers expressed reticence before the workshops and events to speak about climate change in everyday life, yet their design work creatively spoke powerfully for them, and they expressed less discomfort after the experience while they advanced their skillset as climate communicators.patrick.chandler@colorado.eduWed. 7/21 6-7:30 pm ET
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5Cloud-based Data Match-Up Service (CDMS)Chung, NgaThe Cloud-based Data Match-Up Service (CDMS) is a collaborative effort between NASA JPL, COAPS, NCAR, and Saildrone. CDMS is an extension of the Distributed Oceanographic Match-Up Service (DOMS) which was funded by the NASA AIST program. CDMS will provide a mechanism for users to input a series of geospatial references for satellite observations and receive the in situ or satellite observations that are matched to the primary satellite data within selectable temporal and spatial search domains.

The software stack that enables CDMS match-up capability is available via the Apache Science Data Analytics Platform (SDAP), which is an Apache incubator project. Under the ACCESS program, the team plans to deliver a production-ready match-up capability that fully leverages cloud-native services.		nga.t.chung@jpl.nasa.govWed. 7/21 6-7:30 pm ET
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6ARM Data: Exploring the Process of Evaluation Data Migration and Metadata ManagementCollier, HannahAtmospheric Radiation Measurement (ARM), a U.S. Department of Energy (DOE) scientific user facility, is a key geophysical data source for climate research. The ARM Data Center (ADC) holds over 3 PB of data. Among these data are “evaluation” datasets, which are created by computational algorithms that are in the early stages of development. These data products are made available for scientists to use in a “beta testing” mode that encourages users to provide feedback for continual improvement in the quality and scope of the dataset. In an effort to ensure these data adhere to the highest level of findable, accessible, interoperable, and reusable (FAIR) principles, we have implemented ARM standard naming conventions, file formatting, and metadata. Through these efforts, data will be easier for the user community to find, utilize and evaluate through the ARM Data Discovery Portal. This presentation will detail the ongoing process of transitioning legacy evaluation data to a new data archive, present an updated workflow to process new evaluation datasets, and highlight products available to the scientific user community.collierhr@ornl.govWed. 7/21 6-7:30 pm ET
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7Creating a knowledge graph to connect scientific publications and datasets for improving discovery of GES DISC’s data and servicesCrosby, NathanielThe NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) archives and distributes to the public hundreds of Earth Science data collections. These collections are used in research, resulting in thousands of scientific papers published each year. As new users come to GES DISC for the data, it is important for them to understand how these data were used in the prior research. For this we are creating the Knowledge Graph that connects research paper citation and the data collection metadata. The relationships created in the graph have potential for the Web applications that utilize this information to directly connect the paper research to the GES DISC datasets and services. We will demonstrate these relationships using the Web application prototype.nathaniel.r.crosby@nasa.govWed. 7/21 6-7:30 pm ET
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8Automated classification of scientific publications linked to GES DISC datasetsDayal, RohanThe data collections archived and distributed by the GES DISC NASA data center are widely utilized for various Earth Science studies. As these collections are created, many research works are published regarding the collections, algorithms, validations and applications. Since GES DISC collects these publications and provides their citations for the users, it is helpful to categorize them based on how they relate to the datasets they are associated with. Specifically, whether the publication that is linked to GES DISC dataset is using it for applicational research, or if it describes the algorithm for dataset creation, or the validation of the dataset, or provides the general overview of the data collection. Currently, this process requires simple manual labelling, and as such, may be possible to solve via automation. To approach this problem, we developed machine learning classifiers to predict the category a publication belongs to. We used manually labeled publications as training data for supervised machine learning algorithms: Random Forest and Naive Bayes. We achieved classification accuracy that is substantially better than the baseline accuracy, thus greatly improving the efficiency of the publication internal analysis.rohan.o.dayal@nasa.govWed. 7/21 6-7:30 pm ET
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9Collaborative Technology Infusion: A Use Case on Persistent Data DisseminationDowns, RobertInnovative adoption and infusion of technology presents challenges to organizations, often involving disruption of existing processes to establish new capabilities. Collaboration can enable innovative adoption and infusion of technology by enabling collaborative organizations to build on their respective experiences with the technology that is targeted for adoption. A use case about the adoption of Digital Object Identifiers (DOIs) at the NASA Socioeconomic Data and Applications Center (SEDAC) offers insight into the dynamics of collaborative technology infusion during stages that include Identifying and Sharing Needs, Matchmaking, Nurturing Infusion, Integration with the System, and Evaluation and Enhancement.rdowns@ciesin.columbia.eduWed. 7/21 6-7:30 pm ET
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10Developing guideline for code sharing towards reproducibilityRao, Yuhan & Erdmann, ChrisReproducibility is the key for sustainable development of earth and space science (ESS) data applications, especially for more complex technologies like machine learning. We are proposing this collaborative effort to develop a community guideline on code sharing for ESS. We will use this poster as a teaser to collect community inputs for the community guideline building off the success of NeurIPS’s recommendations, templates, and resources.yrao5@ncsu.edu, cerdmann@agu.orgWed. 7/21 6-7:30 pm ET
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11Plant Distribution, Maintenance, and Flammability in Santa BarbaraFauss, KristinaThe purpose of this two-part study is to better understand the vegetation near homes in Santa Barbara County’s wildland urban interface (WUI) communities (i.e., in “defensible space” areas within 100 ft of people’s homes), how residents interact with and value that vegetation, and the flammability of commmon and desireable plants under typical maintenance conditions. Using a digital survey and brief interviews, we aim to answer what plant species exist within 100 feet of homes, how these plants are maintained, and the ecosystem and human services they provide.​ Then we will to choose plants and test conditions for small scale flammability testing based off survey results. We hypothesize that residents will value certain traits (flowering, or drought tolerance, for example) over others and plan to compare these preferences with flammability measured by small scale combustion experiments.kfauss@ucsb.edu Wed. 7/21 6-7:30 pm ET
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12Aligning Earth Science Data with Community Resilience NeedsGregg, ChristineHistorically, Earth Science data have been made by and for scientists, but society now expects more access to and value from data and information. Earth Science can offer societal benefit by contributing to the resilience of communities, but there are many complex challenges that must be overcome, including inequity in the scientific process, gaps in data ethics and governance, mismatches of scale and focus, and a shortage of actionable information for communities. The ESIP Community Resilience cluster aims to support the Earth Science data community’s understanding of the challenges place-based communities face when applying Earth Science data to their resiliency efforts and seeks to offer a starting point to address the challenges presented.cgregg@umich.eduWed. 7/21 6-7:30 pm ET
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13ezEML - EDI's online metadata editorGries, CorinnaEDI developed a simple online editor to create metadata in the Ecological Metadata Language (EML) format. It automatically reads table headings and guides users through the most commonly used fields in EML. The user may then submit the data and metadata to EDI or download the package and submit to a different data repository.cgries@wisc.eduWed. 7/21 6-7:30 pm ET
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14The Application of AI/ML with Data StewardshipHood, CarrollRiverside is working with Kitware to apply AI/ML constructs to data stewardship value streams. We are currently developing proof-of-concept demonstrations that are related to both data ingest and data discovery/access. We Intend to provide these demonstrations to NOAA as potential enhancements to current operational procedures.carroll.hood@riverside.comWed. 7/21 6-7:30 pm ET
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15AMP: An Automated Metadata PipelineHuffer, BethWe are in the final phase of development of AMP under a grant from NASA's Earth Science Technology Office. The AMP platform combines semantics and machine learning to auto-generate robust, semantically consistent metadata records that can be understood by both human and machine, and are compliant NASA's UMM-Var schema. We use the metadata records and the data processing pipeline to drive a scalable data discovery and acquisition service that finds and delivers on-demand, variable-level data from multiple, heterogeneous collections in CSV format.beth@lingualogica.netWed. 7/21 6-7:30 pm ET
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16An Automated Approach to Labelling Datasets in Earth Science PublicationsJahoda, EdwardMost datasets used in Earth Science publications are simply not cited or cited incorrectly. Thus, there is no direct link between datasets and the scientific publications that reference them. An automated approach is desired to identify the datasets used in Earth Science publication. This researched explored the use of the Supervised Machine Learning and Earth Data Search Common Metadata Repository (CMR) queries as a way to identify datasets.edward.jahoda@gmail.comWed. 7/21 6-7:30 pm ET
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17Delivering Trusted Geospatial & Data Across platforms in a Collaborative EnvironmentJones, DaveDave will briefly show how GeoCollaborate can share trusted data across platforms and deliver data when it is needed, to whom it is needed and for how long it is needed. All to advance situational awareness and decision making to protect lives and propertydave@stormcenter.comWed. 7/21 6-7:30 pm ET
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18Enabling informed climate decisions through data integrationsKearns, EdwardFirst Street is quantifying and communicating the risk from climate-driven flood and wildfire risk, using a combination of direct engagement through FloodFactor.com, integration with consumer-facing information systems, and bulk delivery of data to expert users. We will demo such an integration and describe the information being made available through these mechanisms.ed@firststreet.orgWed. 7/21 6-7:30 pm ET
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19Usage-Based Discovery ToolLynnes, ChristopherConventional dataset discovery interfaces rely on descriptions of the datasets to guide users to the most appropriate dataset for the intended use. Usage-based Discovery is an innovative tool that directly connects datasets to known use cases (either Applications or Research Papers). This enables a dead simple and intuitive User Interface and Experience that we demonstrate herein.christopher.s.lynnes@nasa.govWed. 7/21 6-7:30 pm ET
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20Implications of the Data-Centric Nature of Climate Science for AI & MLMcGinnis, SethClimate science prioritizes the production and dissemination of data to enhance its value as evidence. The re-use of data in this way depends on how it is packaged. A comparison of the influence of Big Data in biology versus climate science reveals potential hazards associated with the categorization of phenomena. To avoid undesirably constraining downstream research, the development of ontologies and training datasets for machine learning needs to be an open community effort.mcginnis@ucar.eduWed. 7/21 6-7:30 pm ET
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21Search Enhancements using Natural Language Processing TechniquesMehrabian, ArminNASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of the 12 NASA Science Mission Directorate Data Centers with the common goal of providing earth science data, information, and services to the earth science data community. However, with the growth of archiving more than 1,500 data collections covering multiple disciplines, data discovery through our search engine is the primary tool for our users to interact, find, and access our data. Current search approaches are largely focused on hard-matching of keywords in the search query with dataset metadata. Here we propose to expand the search by introducing a complementary natural language processing (NLP) search. At the heart of our proposed NLP search, we trained a joint embedding using scientific text corpus and a curated set of dataset metadata. The embedding learns the association between words in our dataset metadata and those of the scientific text corpus. This enables us to go beyond simple hard-matching of a query and dataset metadata and have a notion of “similarity” between the search query and the datasets. We further integrated our NLP search into the ElasticSearch (ES) framework leveraging similarity search capabilities offered through the “dense_vector” field type. Our preliminary evaluations show that our proposed NLP search has the potential to be utilized to complement the existing search engine and serve as a base for a dataset recommendation system.armin.mehrabian@nasa.govWed. 7/21 6-7:30 pm ET
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22Identifying Vulnerable Healthcare Facilities Due to Effects of PrecipitationMunasinghe, ThilankaPoster : Environmental factors such as extreme precipitation pose increased flood risk, thereby increasing the vulnerability of healthcare facilities in these areas. Our work explores the effect of extreme daily and monthly precipitation, collected from NASA Global Precipitation Mission (GPM), on the risk of healthcare facilities vulnerability.munast@rpi.eduWed. 7/21 6-7:30 pm ET
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23Advancing the ESIP-E2SIP partnership: Australasian Earth and Environmental Science Information PartnersMurdoch, ClareE2SIP provides a forum for the Australian Earth and environmental science data and informatics community to discuss and engage in local and international collaboration on eResearch infrastructures. After a quiet 2020, as a result of the impact of COVID-19, the group is looking to rekindle activities in facilitating and coordinating collaboration with relevant ESIP clusters. This poster outlines E2SIP collaboration to date and next steps.clare.l.murdoch@gmail.com
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24Australian Bushfire in 2020: Accessing MERRA-2 Data in GES DISC Remotely through OPeNDAP & Calculating Statistics with Python3Pan, XiaohuaThe evolution and transport of thick haze from the 2020 Australia bushfire is tracked using the NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data archived at the NASA GES DISC data center. The MERRA-2 provides global data at 0.5 x 0.625 spatial resolution since the year 1980. In this use case, with xarray, a python3 library, we remotely accessed the hourly aerosol optical depth (AOD) and PM2.5 data through the OpeNDAP service provided by GES DISC. We also derived weekly from hourly ones.Xiaohua.Pan@nasa.govWed. 7/21 6-7:30 pm ET
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25SensorProb - A Database-driven System for Automating Corrections to Streaming DataPorter, JohnSensorProb is a web-based system for logging problems with streaming sensors. It can produce a variety of reports, but also use code generation to produce code that can be used to automate the production of Level 1 (errors flagged or removed) data from raw data. This demo will include how problems are entered and the code that is generated.jhp7e@virginia.eduWed. 7/21 6-7:30 pm ET
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26Approaches to Quality Control and Assurance in a Meteorological Station NetworkPorter, JohnDuring the summer of 2021 we have been going back over 30+ years of data from a small network of meteorological stations to resolve existing quality issues caused by partial or complete sensor failures. Once detected, issues are input into a database that then uses code-generation to flag, remove or comment on issues. The poster will discuss some of the automated or semi-automated methodologies we have found to be most effective and future plans for gap filling.jhp7e@virginia.eduWed. 7/21 6-7:30 pm ET
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27Data Discovery Interface from ARM Data CenterPrakash, GiriThe video tutorial will allow users to experience the latest data discovery interface developed by the Atmospheric Radiation Measurement (ARM) Data Center for searching over 11,000 data products from the data archive. The video will show novel data discovery concepts, data quality details, data ordering capabilities.palanisamyg@ornl.govWed. 7/21 6-7:30 pm ET
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28Data Discovery for the Next-generation Data Interoperability - a Case Study from ARM Data CenterPrakash, GiriThe Atmospheric Radiation Measurement (ARM) Data Center archives and distributes a wide range of atmospheric data from sensors deployed in various locations. The ARM Data Center recently developed a new data discovery interface using modern web services and data science concepts. This poster will discuss novel data discovery concepts such as data epochs and a recommender system to drill down vast amounts of scientific data.palanisamyg@ornl.govWed. 7/21 6-7:30 pm ET
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29High Altitude Soil Testing: Increasing Accessibility of Data from Remote LocationsRubalcaba, JustinWe have developed a prototype of a High Altitude Soil Testing (HAST) device capable of being deployed in remote locations for long periods of time. The device will record soil temperature and humidity that will be logged on board, as well as transmitted periodically over the Iridium satelite network to a remote host. Access to data without needing to physically retrieve it will allow for semi-realtime analysis of weather patterns and trends associated with climate change in high altitude environments that are acutely affected by changes in precipitation and temperature.jrubalcaba@mtech.eduWed. 7/21 6-7:30 pm ET
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30PyRain: Handling high-resolution datasets for data-driven weather forecastingSchroeder de Witt, ChristianCurrent data loading and processing pipelines for deep learning on geospatial datasets tend to hit barriers when moving to ultra-high resolution datasets and complex sample construction processes across multiple variable types and temporal slices. In particular, mainstream dataloading approaches based on HDF storage (or NetCDF) do usually require writing out training samples apriori, which is clearly infeasible - or at least wildly impracticable - if the original dataset is already ranging in the TB regime and/or sample architecture and variable selection are part of the research process. We discuss how we overcame such limitations in RainBench, a benchmark suite for data-driven global weather forecasting, and introduce the audience to PyRain and lessons learned for next-generation geospatial dataloading pipelines.christian.schroeder@stcatz.ox.ac.ukWed. 7/21 6-7:30 pm ET
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31RainBench: Towards Global Precipitation Forecasting from Satellite ImageryExtreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts, to mitigate against such events. However, there is currently no benchmark dataset dedicated to the study of global precipitation forecasts. In this paper, we introduce RainBench, a new multi-modal benchmark dataset for data-driven precipitation forecasting. It includes simulated satellite data, a selection of relevant meteorological data from the ERA5 reanalysis product, and IMERG precipitation data. We also release PyRain, a library to process large precipitation datasets efficiently. We present an extensive analysis of our novel dataset and establish baseline results for two benchmark medium-range precipitation forecasting tasks. Finally, we discuss existing data-driven weather forecasting methodologies and suggest future research avenues.
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32ESDSWG Community Development Best Practices Working Group: Community Guide ProgressSiarto, JeffThe Community Development Best Practices Working Group (CDBP) aims to promulgate best practices for fostering community for the development of open-source toolsets and packages especially within the NASA Earth Science community.
CDBP will continue and refine the work on the EOSDIS (Earth Observing System Data and Information System) General Open Source Software Collaboration and Contribution Process guide and add sections on community development best-practices. This poster will highlight the work done so far and solicit feedback on sections, writing, and purpose to inform the completion of the guide.		jeff@element84.comWed. 7/21 6-7:30 pm ET
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33Automated Classification of GES DISC User Support Tickets (ACOUSTICS)Smith, SamuelThe NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is a leading data center that provides earth science data, information, and services to users all around the world. In order to further improve the GES DISC data services, user data and service analytics have been collected over the past year. In this project, we are analyzing the categorizing user support tickets through GES DISC’s tracking tool system. In particular, we experimented with machine learning and natural language processing practices to classify a ticket as one of four categories: findability, accessibility, interoperability, or reusability (i.e., F.A.I.R). This entails pre-processing the textual data, extracting features, and evaluating classification algorithms. The goal of this work is to use this model to classify historical tickets to gain a broader understanding of the GES DISC user needs.samuel.c.smith-1@nasa.govWed. 7/21 6-7:30 pm ET
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34Helmholtz Metadata Collaboration (HMC) - Integrating Large-Infrastructure DataSoeding, EmanuelThe Helmholtz Association is Germanys framework for large-scale infrastructures, like polar programs, traffic and areospace or particle accelerators. The Helmholtz Metadata Collaboration is tasked to connect and integrate the Helmholtz Associations data products into the ongoing global activities, like the EOSC and other coordinated programs. HMC is working on a concept to address the I and R of FAIR, making these data sets interoperable and reusable. To achieve this we plan to upgrade our data infrastructures with consistent semantic concepts and implement technical features like FAIR Digital Objects. We thus strive to "turn FAIR in to reality" at Helmholtz and beyond.esoeding@geomar.deWed. 7/21 6-7:30 pm ET
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35AGU Data Leadership Strategic DirectionStall, ShelleyAGU’s Data Leadership program focuses on the value of research data and software as an important contribution to science and as affirmed in AGU Position Statement on Data -- A World Heritage. In collaboration with the AGU community and organizations such as ESIP, AGU works towards the culture change needed for the preservation of research data and software to be a common activity that is well supported by curation resulting in credit and attribution. Join us to learn more about AGU’s Data Leadership program and how to be involved.sstall@agu.orgWed. 7/21 6-7:30 pm ET
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36Improvements of searching GES DISC datasets with a knowledge graph Improving Earth Science dataset search with publications content via Knowledge Graph linkageStoyanova, KristinaThe NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) archives a large number of Earth observational datasets. Thousands of the publications are created each year based on these datasets. The content of these publications can be used for discovery of the datasets based on the characteristics of applicational research. We leverage the content of these publications to retrieve the information about phenomena and domains where measurements from the datasets were utilized through linking these publications and dataset in Knowledge Graph. We retrieve phenomena and domain information using SWEET ontology and produce the set of keywords that are linked to the datasets. Further, we evaluate this link strength according to the frequency of dataset usage in the papers mentioning these keywords. We demonstrate how this linkage can improve dataset search by comparing the search results obtained from Common Metadata Repository (CMR) search and the publications based data.kristina.a.stoyanova@nasa.govWed. 7/21 6-7:30 pm ET
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37Geoweaver TutorialSun, ZihengThe automation of full stack workflow has become viral since the Earth data volume expontionally increases and the complexity of Earth system models and algorithms gets more difficult to manage and faciliate. The latest development in AI/ML technique brings a lot of new opportunities to significantly improve the accuracy, increase the model resilience and intelligence, and reduce the overall cost. However, managing and automating AI experiments is a grand challenge for the entire Earth science community. Geoweaver is a software developed to tackle this problem. We will demonstrate how to use Geoweaver to create AI workflow in one place and run the processes on various distributed platforms, separate code from computing resources for resilience, record the provenance of every workflow execution, and share and reuse workflows to boost knowledge accumulation and discovery.zsun@gmu.eduWed. 7/21 6-7:30 pm ET
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38Soil data harmonization as a community activityTodd-Brown, KatherineData harmonization is often a thankless task done via manual data transcription. How can we reimagine this as a team science activity? In this poster we will present current examples of data harmonization projects in soil science and contrast this will a new team-based approach.ktoddbrown@ufl.eduWed. 7/21 6-7:30 pm ET
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39Wildfire data and information interoperability across fire management phasesWee, BrianWildfire data and information should ideally be reusable and repurposable across different fire management phases. For example, infrastructure that is vulnerable to wildfire-induced floods identified during the active-fight fighting phase should be easily discoverable to city managers weeks or even months later, when heavy rains on burn areas may trigger catastrophic debris-flow that threaten lives. The Agriculture and Climate Cluster and the Semantic Harmonization Cluster are examining how formally encoded knowledge about disasters like wildfires can be used to enable applications (including AI/ML applications) that result in wildfire data and information interoperability across fire management phases.bwee@massiveconnections.comWed. 7/21 6-7:30 pm ET
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40The ESIP/RDA Earth Space and Environmental Science Interest Group: enabling global coordinationWyborn, LesleyThe Earth, space, and environmental science communities are developing, through multiple international efforts, both general and domain-specific leading practices for data management, infrastructure development, vocabularies, and common data/digital services. The ESIP/RDA Earth Space and Environmental Science Interest Group (ESES-IG) was formed in 2018 to try to work towards coordinating and harmonizing these efforts to reduce possible duplication, increase efficiency, share use cases, and promote partnerships and adoption in the community. The ESES-IG offers the opportunity for ESIP clusters to expose their work to a wider international audience and seek additional feedback.lesley.wyborn@anu.edu.auWed. 7/21 6-7:30 pm ET
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41Linked-data And Networked DRoneS: Tooling for FAIR drone dataWyngaard, JaneLinked-data And Networked DRoneS [LANDRS] is a project working to build core open source tooling to enable scientific data captured using drones to be easily and practically made Findable Accessible Interoperable and Reusable [FAIR]. The work has been driven and directed by extensive community input and is leveraging the rapidly maturing set of semantic web and open application programming interface software tool stacks. While COVID has limited community involvement remote input is very welcome. Primarily core components created and under development to-date include: (i) an aligned an ontology ready LANDRS vocabulary for the annotation of required metadata regard the drone, flights, project, sensors, and environmental conditions, (ii) an onboard drone data annotation application and API, (iii) a ground based data archive and publication services similarly accessible and searchable via an API.
jane.wyngaard@uct.ac.zaWed. 7/21 5-6:30 pm ET
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