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Tracking and Establishing Provenance of Earth Science Datasets: A NASA-based Example
Information quality is of paramount importance to science. Accurate, scientifically vetted and statistically meaningful and, ideally, reproducible information engenders scientific trust and research opportunities. Therefore, so-called Highly Influential Scientific Assessments (HISA) such as the U.S. Third National Climate Assessment undergo a very rigorous process to ensure transparency and credibility. As an activity to support the transparency of such reports, the U.S. Global Change Research Program has developed the Global Change Information System (GCIS). Specifically related to the transparency of NCA3, a recent activity was carried out to trace the provenance as completely as possible for all figures in the NCA3 report that predominantly used NASA data. This paper discusses lessons learned from this activity that trace the provenance of NASA figures in a major HISA-class pdf report.
Tracking and Establishing Provenance of Earth Science Datasets: A NASA-Based Example
Information quality is of paramount importance to science. Accurate, scientifically vetted and statistically meaningful and, ideally, reproducible information engenders scientific trust and research opportunities. Not surprisingly, federal bodies (e.g., NASA, NOAA, USGS) have very strictly affirmed the importance of information quality in their product requirements. So-called Highly Influential Scientific Assessments (HISA) such as The Third US National Climate Assessment (NCA3) published in 2014 undergo a very rigorous review process to ensure transparency and credibility. To support the transparency of such reports, the U.S. Global Change Research Program (USGCRP) has developed the Global Change Information System (GCIS). A recent activity was performed to trace the provenance as completely as possible for all NCA3 figures that were predominantly based on NASA data. This poster presents the mechanics of that project and the lessons learned from that activity.
Lessons Learned from Successful Earth Science Research-to-Applications Efforts
No abstract available
Earth Science Project Office (ESPO) Field Experiences During ORACLES, ATom, KORUS and POSIDON
Very often, scientific field campaigns entail years of planning and incur substantial cost, especially if they involve the operation of large research aircraft in remote locations. Deploying and operating these aircrafts even for short periods of time poses challenges that, if not addressed properly, can have significant negative consequences and potentially jeopardize the success of a scientific campaign. Challenges vary from country to country and range from safety, health, and security risks to differences in cultural and social norms. Our presentation will focus on sharing experiences on the ESPO 2016 conducted field campaigns ORACLES, ATom, KORUS and POSIDON. We will focus on the best practices, lessons learned, international relations and coordination aspects of the country-specific experiences. This presentation will be part of the ICARE Conference (2nd International Conference on Airborne Research for the Environment (ICARE 2017) that will focus on "Developing the infrastructure to meet future scientific challenges". This unique conference and gathering of facility support experts will not only allow for dissemination and sharing of knowledge but also promote collaboration and networking among groups that support scientific research using airborne platforms around the globe.
Crosscutting Airborne Remote Sensing Technologies for Oil and Gas and Earth Science Applications
Airborne imaging spectroscopy has evolved dramatically since the 1980s as a robust remote sensing technique used to generate 2-dimensional maps of surface properties over large spatial areas. Traditional applications for passive airborne imaging spectroscopy include interrogation of surface composition, such as mapping of vegetation diversity and surface geological composition. Two recent applications are particularly relevant to the needs of both the oil and gas as well as government sectors: quantification of surficial hydrocarbon thickness in aquatic environments and mapping atmospheric greenhouse gas components. These techniques provide valuable capabilities for petroleum seepage in addition to detection and quantification of fugitive emissions. New empirical data that provides insight into the source strength of anthropogenic methane will be reviewed, with particular emphasis on the evolving constraints enabled by new methane remote sensing techniques. Contemporary studies attribute high-strength point sources as significantly contributing to the national methane inventory and underscore the need for high performance remote sensing technologies that provide quantitative leak detection. Imaging sensors that map spatial distributions of methane anomalies provide effective techniques to detect, localize, and quantify fugitive leaks. Airborne remote sensing instruments provide the unique combination of high spatial resolution (<1 m) and large coverage required to directly attribute methane emissions to individual emission sources. This capability cannot currently be achieved using spaceborne sensors. In this study, results from recent NASA remote sensing field experiments focused on point-source leak detection, will be highlighted. This includes existing quantitative capabilities for oil and methane using state-of-the-art airborne remote sensing instruments. While these capabilities are of interest to NASA for assessment of environmental impact and global climate change, industry similarly seeks to detect and localize leaks of both oil and methane across operating fields. In some cases, higher sensitivities desired for upstream and downstream applications can only be provided by new airborne remote sensing instruments tailored specifically for a given application. There exists a unique opportunity for alignment of efforts between commercial and government sectors to advance the next generation of instruments to provide more sensitive leak detection capabilities, including those for quantitative source strength determination.
Large-Scale Labeled Datasets to Fuel Earth Science Deep Learning Applications
No abstract available
NASA Earth Science Disasters Program Response Activities During Hurricanes Harvey, Irma and Maria in 2017
No abstract available
An Innovative Infrastructure with a Universal Geo-Spatiotemporal Data Representation Supporting Cost-Effective Integration of Diverse Earth Science Data
The SpatioTemporal Adaptive Resolution Encoding (STARE) is a unifying scheme encoding geospatial and temporal information for organizing data on scalable computing/storage resources, minimizing expensive data transfers. STARE provides a compact representation that turns set-logic functions into integer operations, e.g. conditional sub-setting, taking into account representative spatiotemporal resolutions of the data in the datasets. STARE geo-spatiotemporally aligns data placements of diverse data on massive parallel resources to maximize performance. Automating important scientific functions (e.g. regridding) and computational functions (e.g. data placement) allows scientists to focus on domain-specific questions instead of expending their efforts and expertise on data processing. With STARE-enabled automation, SciDB (Scientific Database) plus STARE provides a database interface, reducing costly data preparation, increasing the volume and variety of interoperable data, and easing result sharing. Using SciDB plus STARE as part of an integrated analysis infrastructure dramatically eases combining diametrically different datasets.
NASA Earth Science Disasters Program Response Activities During Hurricanes Harvey, Irma, and Maria in 2017
No abstract available
NASA's Collaborative Metadata Curation Activity to Improve Earth Science Data Discovery
No abstract available
Methodology to Build Scalable Knowledge Graphs for Earth Science
No abstract available
Using a Knowledge Graph to Discover Earth Science Information
Knowledge graphs link key entities within a specific domain to other entities via relationships. Researchers are able to mine these relationships from numerous sources to infer new knowledge. Text extraction from peer-reviewed papers and scientific reports are untapped resources that can be leveraged by knowledge graphs to accelerate scientific discovery.
A Metadata Curation Approach to Improve the Discoverability and Accessibility of NASA Earth Science Data
No abstract available
NASA Earth Science Activities Supporting Analysis and Response to Hurricanes Florence and Michael
No abstract available