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1 | Join us for the ESIP Meeting Research Showcase Poster & Demo Event in-person on Wednesday July 20th (5:30-7:30 pm ET) | |||||||||||||||||
2 | Name | Poster/Demo Title | Abstract | |||||||||||||||
3 | Ali, Sahara | SICNet - A Spatiotemporal Deep Neural Network for Arctic Sea Ice Forecasting | Arctic amplification has altered the climate patterns both regionally and globally, resulting in more frequent and more intense extreme weather events in the past few decades. The essential part of Arctic amplification is the unprecedented sea ice loss as demonstrated by satellite observations. Accurately forecasting Arctic sea ice from sub-seasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven approaches to study sea ice variations, we propose SICNet – a UNet-based based spatiotemporal deep learning model for forecasting Arctic sea ice concentration (SIC) at greater lead times. The model uses an encoder-decoder architecture with skip connections to regenerate spatial maps at future timesteps. Using monthly satellite retrieved sea ice data from NSIDC as well as atmospheric and oceanic variables from ERA5 reanalysis product during 1979-2021, we show that our proposed model provides promising predictive performance for per-pixel SIC forecasting at long lead times. This will substantially improve our ability in predicting future Arctic sea ice changes, which is fundamental for forecasting transportation routes, resource development, coastal erosion, threats to Arctic coastal communities and wildlife. | Poster | ||||||||||||||
4 | Blythe, Jonathan | Configured, Not Coded: Rethinking How the General Public Discovers Environmental Science | To better serve the public, the Bureau of Ocean Energy Management’s Environmental Studies Program (ESP) is rebuilding its science dissemination tool, the Environmental Studies Program Information System (ESPIS). The target audience for this tool are users with little knowledge of ocean science and the engaged government agencies. Built on the ArcGIS Online (AGOL) portal/hub environment, the tool uses large format infographic cards to give users options to browse among four science topics: Physical, Chemical, Biological, and Social Sciences. Under each science topic, the user can select from a dozen or more themes. Under each theme, the user will find a templated theme page that uses website widgets to search, sort, and filter curated lists of research projects, products, and applications. This buildable, sustainable interface enhances the public’s discovery of environmental science information. | Poster & Demo | ||||||||||||||
5 | Collier, Hannah | Using Metadata Standards to Simplify Discovery and Navigation of Results | The Atmospheric Radiation Measurement (ARM) Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. Data producers and repositories are currently faced with the challenges of making ever larger, more numerous, and increasingly heterogeneous collections of data available to users. Beyond simply making the content available, it is necessary to simplify the discovery and navigation of relevant results for easier access. In particular, general searches are typically met with an overwhelming number of results which are difficult to evaluate for relevance. In this presentation, we share the work that ARM Data Center has accomplished in improving the search and discovery experience of such users. | Poster | ||||||||||||||
6 | Gearon, James (Jake) | Global Calculation of Equilibrium Response Time for Closed-Lakes | Lakes are commonly heralded as “sentinels of climate change”— natural systems that record the interplay of biological, geological, and chemical processes that occur in their catchments. Hydrologically closed lakes, or those without drainage outlets, have lake levels fluctuate more than hydrologically open lakes because variations in lake inflow can only be compensated by a change in in lake surface area. As a result, closed lakes are sensitive to their catchment-level climate regimes as their equilibrium water levels are a direct result of only the shape of the lake basin (hypsometry), basin-level precipitation (influx), and basin-level evaporation (outflux) balance. The rate at which a closed lake attains a new hydrologic equilibrium is governed by an e-folding timescale, or a physical speed limit on how fast the lake can respond to a change in climate. For large, deep, closed lakes, values can be 100s of years (Lake Titicaca = ~ 522.2 yr), for shallow, wide lakes, values can be as small as ten years (Great Salt Lake = ~ 9.1 yr) or even less than five years (Lake Eyre = 3.2 yr). This means that two lakes with the same volume and climate regime can have equilibrium response times that differ by an order of magnitude, obscuring climate attribution. Determining empirical values of the world’s hydrologically closed lakes is therefore critical for predicting changes to the water storage budget into the next century. To that end, we calculate a global dataset of values for 27,288 lakes by harmonizing five separate global lake datasets. Equilibrium response time values roughly correlate to the timescale of the descending limb of area-normalized hydrographs, indicating their predictive utility. | |||||||||||||||
7 | Hogenson, Kirk | Get HyP3! Cloud-native SAR processing for everyone | HyP3 is an On Demand processing service for processing Synthetic Aperture Radar (SAR) imagery that addresses common issues with SAR data | |||||||||||||||
8 | Huang, Thomas | Emerging Big Data Platform for Water Cycle and Flood Detection | The idea behind Digital Twin (DT) is to establish a virtual representation of a system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making. Earth System Digital Twin (ESDT) is an emerging concept that mirrors the Earth Science System to not only understand the current condition of our environment or climate, but also to be able to learn from the environment by analyzing changes and automatically acquire new data to improve its prediction and forecast (Fuller et al. 2020). The NASA Advanced Information Systems Technology (AIST)’s Integrated Digital Earth Analysis System (IDEAS) project is to establish a comprehensive science platform for our decision makers with science-driven solutions to tackle the global and local impacts due to climate change. For validation and demonstration of IDEAS architecture, the project tackles one of the most fundamental Earth Science challenges related to water cycle science and flood detection and monitoring. | |||||||||||||||
9 | Jarboe, Nicholas | The Modular FIESTA Software Stack for the Quick Stand-Up of FAIR, Sample Based Data Repositories | The Framework of Integrated Earth Science and Technology Applications (FIESTA, https://earthref.org/FIESTA/ ) is a containerized set of services designed for reuse which enables the quick stand-up of sample-based geoscience subdomain data repositories. Journals, funding agencies, and scientific organizations have been increasingly promoting and requiring the data that supports scientific works to be FAIR (Findable, Accessible, Interoperable, Reusable). FIESTA is a system that can dramatically reduce the cost and time needed to create a domain-specific data repository. A community needs to come together and develop a data model for their field and then, using FIESTA, they can quickly build a FAIR data repository that has these features built-in: ORCID iD for identity authentication and secure login, quick text searches using Elasticsearch, complex searches using ranges over multiple data columns, the ability to combine search results from multiple datasets into a single file download, schema.org/JSON-LD headers for every dataset for indexing by EarthCube's GeoCODES and Google Dataset Search, a data DOI minted for each data publication, data validation based on the data model, customizable website homepage layout showing the most recent data publications and community events, a private workspace where researchers can upload data before publication with the option to share with colleagues, journal editors, or reviewers. | |||||||||||||||
10 | Kale, Amruta | Provenance documentation to enable explainable and trustworthy AI | TBD | |||||||||||||||
11 | Li, Chenhao | Toward trust-based recommender systems for open data: A literature review | While the number of open data portals and the volume of shared data have increased significantly, most open data portals still use keywords and faceted models as their primary methods for data search and discovery. There should be opportunities to incorporate more intelligent functions to facilitate the data flow between data portals and end-users. | |||||||||||||||
12 | LI, Wenjia | Constructing a Stratigraphic Knowledge Graph (StraKG) with Multi-source Data to Understand the Earth’s Rock Layer | Knowledge graph; Earth’s rock layer; Multi-source data; Relationship extraction; Data mining | |||||||||||||||
13 | Pan, Xiaohua | From Data to Application and User Need: Perspectives Gained from Curating the NASA MERRA-2 Products | We will share a broad perspective based on our experience in curating MERRA-2 products such as what hurdles users might face in using this data product, and how they apply the data, and we will also update the status on cloud migration | |||||||||||||||
14 | Razin, Naufal | AI-Readiness of the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset | The Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED) is a dataset of satellite passive microwave observations of tropical cyclones with auxiliary data. The dataset has the potential to advance tropical cyclone research to improve tropical cyclone forecasting. Here, we evaluate the AI-readiness of the dataset and outline future work towards ensuring easier application of AI approaches to the dataset. | |||||||||||||||
15 | Slocum, Chris | Community Roadmap for Promoting AI Proficiency in NOAA and Beyond | NOAA is realizing the benefits of Artificial Intelligence in core mission areas of environmental observation, data ingest and management, weather and space weather forecasting, ocean forecast and prediction, and marine ecosystem and fisheries management. To promote AI adoption and proficiency, the NOAA Center for AI (NCAI) is facilitating a community roadmap for providing NOAA-mission area specific training and resource development. | |||||||||||||||
16 | Smith, Deborah | CASEI: Ensuring All Can Access NASA Airborne and Field Data and Information | CASEI, the The Catalog of Archived Suborbital Earth Science Investigations provides a 1-stop shop for accessing detailed metadata about NASA airborne and field campaigns. Since data from campaigns are spread between many of the NASA Distributed Active Archive Centers (DAACs), searching for data can be difficult. The Airborne Data Management Group (ADMG) works to ensure users have easy access no matter where data are located. CASEI has been built to user needs. Come see how easy it is to find information and data using CASEI. | |||||||||||||||
17 | Speed, Cole | Enhancing usability and utility of USGS 3D Elevation Program (3DEP) lidar data and web services with Jupyter Notebooks | The 3D Elevation Program (3DEP), managed by the U.S. Geological Survey (USGS), is an ongoing effort to acquire quality level 2 or better light detecting and ranging (lidar) data over the conterminous United States, Hawaii, and US Territories to meet the growing need for high-resolution 3-D representations of Earth's surface, vegetation, and other constructed features. Since its operational start in 2015, over 1800 3DEP projects have been acquired, amounting to > 42 trillion lidar points covering an area > 6.5 million sq. km. The resulting data are publicly and freely available in Entwine Point Tile (EPT) format hosted on Amazon Web Services (AWS). While the volume of available 3DEP lidar data is substantial, documented workflows and best practices for most effectively utilizing these cloud-hosted resources are underdeveloped. In collaboration with the USGS, OpenTopography is working to develop well-documented and customizable Python-based workflows for programmatically accessing, processing, and visualizing 3DEP data and derivative products for a variety of use-cases. These workflows are in Jupyter Notebook format and will aim to enhance usability of 3DEP resources for USGS scientists and other users of point cloud data across the geospatial community. We seek and welcome additional input from this community related to specific use-cases and applications where there is currently need for high resolution elevation products. | |||||||||||||||
18 | Stevens, Tyler | GCMD Keyword and ESDIS Standards Coodination Office (ESCO) Keyword Coordination Activities | The Global Change Master Directory (GCMD) Keywords, initiated over twenty years ago, are a hierarchical set of controlled Earth Science vocabularies that help ensure Earth science data, services, and variables are described in a consistent and comprehensive manner and allow for the precise searching of metadata and subsequent retrieval of data, services, and variables. GCMD keywords are periodically analyzed for relevancy and will continue to be refined and expanded in response to user needs. The periodic analysis is a result of successful coordination with the ESDIS Standards Coordination Office (ESCO), which is responsible for standards activities across ESDIS, and assists with providing valuable stakeholder and subject matter expert (SME) feedback on multiple sets of GCMD vocabularies. The GCMD team, working in coordination with the ESCO, has conducted several keyword reviews recently, including Ocean keywords, a complete reorganization of the Platform keywords, Atmospheric Composition Variable Standard Names, and the Machine Learning (ML) Hub model keywords. The ESCO is also turning to the GCMD to discuss how the keyword review process can be improved and more streamlined. In addition to the ESCO, keyword requests and feedback are also received through the new GCMD Keyword Forum. | |||||||||||||||
19 | Sun, Ziheng | Geoweaver | Scientific productivity is a long-time issue across all the cyberinfrastructure-backed research areas. It is a known issue that many research groups have low productivity due to the lack of FAIR principles in practice for their workflows and data products. Non-sharable and non-reusable code and datasets drive students and researchers to repeat the same data retrieval and cleaning procedure repeatedly, wasting numerous hours on the same steps by new onboarded members or even the original contributor researcher who forgot and cannot reproduce their results after a while. Geoweaver, a GUI-based scientific workflow management system, is developed to address this low-FAIRness-caused productivity issue while reducing the learning costs for researchers with a less technical background. | |||||||||||||||
20 | Swanson, Eric | Separating the land from the sea: image segmentation in support of coastal hazards research and community early warning systems | Predictions of total water level (TWL) are necessary for long-term coastal planning and early warning systems including the USGS/NOAA Total Water Level and Coastal Change Forecast. TWL is challenging to monitor, but coastal imaging cameras are a scalable monitoring solution. The goal of this project is to replace laborious hand-digitization with a robust automated method. TWL extraction methods will be trained and then tested using existing imagery collected in several diverse coastal settings. The methods developed will have add-on applications to other imagery collected, including image to stage estimates, flood detection, and satellite shoreline detection. | |||||||||||||||
21 | Thornton, Michele | Daymet: Open Source Science Leverages Standardized Interoperable Data | Daymet is a fine-scaled gridded dataset of daily surface weather variables with data staring in 1980 to present. Researchers leverage standard file formats and interoperable accessibility to derive higher-level and custom research projects that are shared through open source software and data products. | |||||||||||||||
22 | Walker, Marguerite (Mimi) | Wildland Firefighting...How Science and Technology are helping my sons and teaching my students! | I won a FUNding Friday Award in 2021 and would like to present the work I did with my students using the tools presented as well as the new GOES satellite that my daughter researched for the Science Fair. | |||||||||||||||
23 | Wen, Tao | A High-throughput Cloud-based Open-Source Platform for Monitoring Stream Water Quality | We developed a river-network-based geospatial-analysis tool – GeoNet to automatically detect stream water quality impairments for the community. This tool is currently available for the community applications as a GitHub repository and an R package. We also developed a web-based R Shiny app to demonstrate this tool. | |||||||||||||||
24 | Yang, Chaowei | An open-source repository for earth science data processing | Will introduce a repository with approximately 50 open source packages for processing (such as collocating, calibration, downscaling, upscaling, classification, mining) earth science data. training.stcenter.net | |||||||||||||||
25 | Zhang, Jiyin | Knowledge Graphs in Geoscience Data Analytics: A Literature Review | Knowledge graph technology has attracted broad attention in the last decade. We performed a bibliometric analysis of knowledge graphs and geoscience-related literature to demonstrate the annual growth and research trends on this topic. In addition, the knowledge graph concepts adopted in geoscience data research are also studied. | |||||||||||||||
26 | Rao, Yuhan "Douglas" | Are you data AI-Ready? A community-driven AI-readiness checkliat | Data readiness cluster worked with the environmental data and AI/ML community to develop an AI-readiness checklist to assess the level of readiness of open environmental data for AI/ML development. We will present the checklist and the cluster's effort to develop a pilot collection of AI-ready data for climate applications. | |||||||||||||||
27 | Hsu, Leslie | Communication for Technology Infusion | Project representatives from three different informatics seed funding programs will be in attendance for conversations about their projects. Find these attendeesfrom ESIP Lab, NASA (name) , and USGS Community for Data Integration during the poster session and ask them about their work. | |||||||||||||||
28 | Bugbee, Kaylin | TBD | TBD | |||||||||||||||
29 | Child, Andrew | Centralized project-specific metadata platforms: toolkit provides new perspectives on open data management within multi-institution and multidisciplinary research projects | A central component of open data management, especially on collaborative, multidisciplinary, and multi-institutional research projects, is documentation of complete and accurate metadata, workflow, and source code in addition to raw data and data products in order to uphold FAIR (Findable, Accessible, Interoperable, Reusable) principles. Although best practice in data/metadata management is to use established internationally accepted metadata schemata, many of these standards are discipline-specific making it difficult to catalog multidisciplinary data and data products in a way that is easily findable and accessible. Consequently, scattered and incompatible metadata records create a barrier to scientific innovation, as researchers are burdened to find and link multidisciplinary datasets. One possible solution to increase data findability, accessibility, interoperability, reproducibility, and integrity within multi-institutional and interdisciplinary projects is a centralized and integrated data management platform. Overall, this type of interoperable framework supports reproducible open science and its dissemination to various stakeholders and the public in a FAIR manner by providing direct access to raw data and linking protocols, metadata and supporting workflow materials. | |||||||||||||||
30 | Lafia, Sara | Detecting Specimen Citations in Scientific Literature | Assessing the reach and impact of natural history collections requires reviewing scientific literature for citations to specimens, which can be labor-intensive. This project curates and analyzes a bibliography of literature citing specimens from the University of Michigan Museum of Zoology. We demonstrate a machine learning approach we are developing to detect specimen citations in scientific literature with the goal of enabling comprehensive studies of specimen reuse patterns across multiple natural history collections. | |||||||||||||||
31 | Mehrabian, Armin | Utilizing Publications and Graph-Assisted Vector Search for DAAC Search Engines | Data discovery is an essential function of a data active archive center (DAAC). Search engines are the primary means by which users can find and explore relevant data that they need from To enhance data discovery of DAAC search engines, we propose using publications in conjunction with graph and vector search technologies. | |||||||||||||||
32 | Stall, Shelley | Your Open Science Journey: Walking, Driving, or Public Transit | The practice of Open Science is journey not a destination. You can see the sites by walking, get there faster by driving, or keep your carbon footprint lower by taking public transit. Join us for traveling advice. | |||||||||||||||
33 | Lindsay Barbieri | Agriculture and Climate Information Fellowship Outcomes: ISO Smart Farming Data Standards and SDGs and Adapting Systematic Review Methodologies for Climate-Smart Data | This poster will present two different projects that have been a part of my Agriculture and Climate Information Fellowship with ESIP. 1) Outcomes from work with International Organization for Standardization (ISO) Smart Farming Strategic Advisory Group. Collecting and accessing usable data is at the heart of both Smart Farming and the Sustainable Development Goals (SDGs), and data standardization is important for both. We examine all 230 SDG indicators, which are the metrics by which the goals are being measured, and determine which indicators are aligned with Smart Farming information practices. We assess which data standards (if any) are currently being used by the custodian agencies who are in charge of collecting these data for the UN (e.g. FAO) and match the gaps in data standards with potential useful standards from the International Organization of Standardization (ISO). 2) Outcomes from work with a team from USDA and Cornell to use Systematic Review methodology applied to data searches. We demonstrate the use of Systematic Review methods for amassing climate-smart agricultural datasets for synthesis, and use this process to determine what needs to be adapted from systematic reviews to look at datasets instead of publications and to assess gaps and opportunities for data infrastructure to support these methods. | |||||||||||||||
34 | Huang, Qian | Wildfire Smoke Impacts on COVID-19 Cases and Deaths: A preliminary analysis in California | Wildfires change the weather conditions and environment significantly, facilitating the transmission of infectious diseases. And its smoke increases the risk for respiratory, cardiovascular, endocrine, and nervous system diseases. This paper examines the relationship between wildfire allied pollutants, using Air Quality Index (AQI) values from the EPA, and the dynamics of confirmed cases, deaths, and testing of COVID-19 for California from January 26th to November 14th, 2020. This study first describes the pattern of the newly confirmed cases per week across the state and further compares them to AQI changes. The newly confirmed COVID-19 cases remained under 20,000 until Memorial Day weekend (epi week 23), then exponentially increased during the early summer, slowly declining thereafter, and increasing again since late October. The state average AQI value per epi week remained under 40 until epi week 33 where it began to increase greatly in the next few weeks, peaking in epi week 37 (September 6th- 12th) at the time when several large wildfires were reported. These two contradicted trends indicated California’s coronavirus testing capability decreased with the increased frequency and size of wildfires. Second, the paper suggests that the average AQI values are significantly positively correlated with the COVID-19 cases and mortalities per 100,000 population, supporting the hypothesis that wildfire-allied pollutants have a vital impact on the COVID-19 cases and deaths in California. | |||||||||||||||
35 | Segessenman, Daniel | Macrostrat Data Platform of rocks and their characteristics | Macrostrat is a platform for the aggregation and distribution of geological data relevant to the spatial and temporal distribution of sedimentary, igneous, and metamorphic rocks as well as data extracted from them (e.g. paleomagnetic or carbon isotope data). It is linked to the xDD (formerly GeoDeepDive) digital library and machine reading system, and it aims to become a community resource for the addition, editing, and distribution of new stratigraphic, lithological, environmental, and economic data. Macrostrat powers the mobile app Rock'd, a companion for geologists in the field, educators looking to provide real-world data for students, and for anyone interested in geology. I will demonstrate how to interact with the Macrostrat data platform, figure out what data it contains you might be interested in, and how to extract that data via its API (Application Programming Interface). | |||||||||||||||
36 | Chung, Nga | Cloud-based Data Match-Up Service (CDMS) | There is a need in the oceanographic community for a generalized match-up capability that is publicly accessible and provides flexibility and reproducibility for use cases ranging from Ocean Science to satellite retrieval calibration and validation. The Cloud-based Data Match-Up Service (CDMS) is a collaborative effort between NASA JPL, COAPS, NCAR, and Saildrone to address this need. This talk will demonstrate the present and planned capabilities of CDMS. CDMS is an extension of the Distributed Oceanographic Match-Up Service (DOMS) which was funded by the NASA AIST program, with additional support now under NASA ACCESS to deliver a production-ready match-up capability that fully leverages cloud-native services and is ready for infusion at data archive centers and with other projects. CDMS provides a mechanism for users to input a series of geospatial references for satellite datasets and receive the in situ or satellite observations that are collocated based on provided temporal and spatial ranges. CDMS eliminates the need for one-off match-up programs that require satellite and in situ data to be housed locally as the computation occurs in the cloud and supports connectivity to remote data providers via a common set of interfaces and protocols. CDMS exposes a number of HTTP API endpoints, allowing users to execute match-up requests from their desired programming environment. The software stack that enables the CDMS match-up capability is available via the Apache Science Data Analytics Platform (SDAP) which is an Apache incubator project. | |||||||||||||||
37 | Levy, Stuart | Data Fusion Visualization for NASA CAMP2Ex Field Campaign | NASA aircraft-based field campaigns, such as CAMP2Ex, deploy many instruments to study atmospheric properties -- particulates, chemistry, cloud droplet sizes and so on. Data users are accustomed to viewing and analyzing the archived results from individual instruments. But fusion is good! Data could be more useful - and archives more valuable - if corresponding measures across instruments could be conveniently indexed and presented together. (Where sulfate levels are high, is there a shift in droplet sizes, or in precipitation, or in incoming sunlight?) We made a prototype synoptic visualization of one day's flight, animating 70+ aircraft and satellite measurements in 2D and 3D, and look forward to building better tools. | |||||||||||||||
38 | Bagstad, Kenneth | Artificial Intelligence for Environment & Sustainability: a platform for semantically interoperable data and models | Interoperability is the ability of independently produced data and models to seamlessly work together with minimal effort, and is one of the key tenets espoused by the FAIR Principles. Interoperable data and models offer the promise of faster, cheaper, and higher-quality assessments that systematically reuse preexisting science rather than continually reinventing the wheel. Yet despite a decade of advances in open science, widespread achievement of high-level semantic interoperability remains stalled. This demo will introduce the Artificial Intelligence for Environment and Sustainability (ARIES) Project, which is making environmental data and models interoperable for people and machines through semantics and machine reasoning. Insights from this work offer a path forward for widespread use of semantically interoperable data and models in the environmental and Earth Science modeling communities. | |||||||||||||||
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