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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Storage of Physical Sample Metadata in the Astrobiology Habitable Environments Database (AHED)

The National Aeronautics and Space Administration has begun an effort to store, curate, and publish information about physical samples collected and analyzed in conjunction with NASA-funded astrobiology research. Astrobiology is a multidisciplinary area of scientific research being conducted by collaborating teams of biologists, chemists, geologists, atmospheric scientists, oceanographers, astrophysicists, astronomers, and other specialists. Astrobiology studies the origin, evolution, and distribution of life in the Universe. NASA uses the results of astrobiology research to focus its future missions on targets of opportunity for the discovery of life off Earth. Astrobiology researchers conduct both field-based and laboratory-based research, during which physical samples are collected, processed, and catalogued. The cataloguing practices employed by different teams of astrobiologists vary widely, and there are no specific standards available to guide the collection and recording of astrobiology sample data. The disparity in data collection approaches and the lack of a centralized sample repository makes it difficult for astrobiology teams to share data and benefit from resultant synergies.To facilitate data sharing within the astrobiology community, NASA is developing a prototype database the Astrobiology Habitable Environments Database (AHED) and an associated set of data collection templates. The database will store information about samples, along with associated measurements and analyses, including information about biological cultures enriched or isolated from samples, and the results of analyses performed on the samples (e.g., via spectrography, microscopy, etc.). In addition, the system will store contextual information about field sites where samples were collected, the instruments or equipment used for analysis, and people and institutions involved in their collection. AHED is being implemented on top of Open Data Repository's Data Publisher [1], an open source software platform for the publication of scientific datasets. The data collection templates under development represent an initial attempt to propose a set of metadata for capture and storage within AHED. The design of these templates is being conducted by a consolidated group of astrobiologists from active research teams at NASA Ames Research Center, assisted by data science and software engineering specialists. These initial templates must be vetted with the broader astrobiology community through a defined process to ensure that they meet community needs. Each template captures a different type of data collection record. For each template, we are developing a list of fields to be captured, including a set of required entry fields, a set of recommended but optional fields, and a set of discretionary fields. A datatype selected from a variety of text and numeric types is specified for each field. Included is a 'choice' type that restricts user input to an enumerated list of values. Many of the fields and field values capture information of particular interest to the astrobiology community, and are intended to facilitate search and retrieval of relevant data across multiple datasets.

Keller, Rich↗
NASA NTRS2009

Predicting the Sunspot Cycle

The 11-year sunspot cycle was discovered by an amateur astronomer in 1844. Visual and photographic observations of sunspots have been made by both amateurs and professionals over the last 400 years. These observations provide key statistical information about the sunspot cycle that do allow for predictions of future activity. However, sunspots and the sunspot cycle are magnetic in nature. For the last 100 years these magnetic measurements have been acquired and used exclusively by professional astronomers to gain new information about the nature of the solar activity cycle. Recently, magnetic dynamo models have evolved to the stage where they can assimilate past data and provide predictions. With the advent of the Internet and open data policies, amateurs now have equal access to the same data used by professionals and equal opportunities to contribute (but, alas, without pay). This talk will describe some of the more useful prediction techniques and reveal what they say about the intensity of the upcoming sunspot cycle.

Hathaway, David H.↗
NASA NTRSDate not supplied

Earth Science and Weather Research: A Satellite’s View of Our Dynamic Planet

The earth and its phenomena affect every aspect of our daily lives. NASA satellite earth observation platforms make it possible for earth scientists to develop detection algorithms, improve prediction, and understand the climatologies of earth phenomena even in places where traditional observations are inconsistent or unavailable, and use these observations to monitor our changing Earth system. NASA is committed to making its data open and accessible to the public. Learn how to access NASA’s earth observation data for your community and around the world, and ways to engage your community to learn more about understanding and conserving our dynamic home planet.

Sarah D Bang↗
NASA NTRSDate not supplied

Opening Historical Airborne Data to Present Day Researchers

For more than 50 years, NASA has flown airborne sensors to carry out research, validate satellite sensors, and test new instrument capabilities. Data collected prior to 2000 are typically analog and difficult to locate and use. The Airborne Data Management Group (ADMG) facilitates rescue of these valuable data to ensure easier discovery, access, and use. But opening historical data comes at a cost of both time and money. Careful decisions are required in assessing the return on investment. - Is there interest in the science community? - Are there government data requirements? - What is the temporal / spatial value of the data? - Can data be transformed to a digital format? - What is cost of transformation? - What time period is needed for rescue? Converting the data to today’s digital storage standards increases value and provides data access. The addition of metadata makes the data easier to search for.

Deborah Smith↗
NASA NTRS2015

NASA'S Earth Science Data Stewardship Activities

NASA has been collecting Earth observation data for over 50 years using instruments on board satellites, aircraft and ground-based systems. With the inception of the Earth Observing System (EOS) Program in 1990, NASA established the Earth Science Data and Information System (ESDIS) Project and initiated development of the Earth Observing System Data and Information System (EOSDIS). A set of Distributed Active Archive Centers (DAACs) was established at locations based on science discipline expertise. Today, EOSDIS consists of 12 DAACs and 12 Science Investigator-led Processing Systems (SIPS), processing data from the EOS missions, as well as the Suomi National Polar Orbiting Partnership mission, and other satellite and airborne missions. The DAACs archive and distribute the vast majority of data from NASA’s Earth science missions, with data holdings exceeding 12 petabytes The data held by EOSDIS are available to all users consistent with NASA’s free and open data policy, which has been in effect since 1990. The EOSDIS archives consist of raw instrument data counts (level 0 data), as well as higher level standard products (e.g., geophysical parameters, products mapped to standard spatio-temporal grids, results of Earth system models using multi-instrument observations, and long time series of Earth System Data Records resulting from multiple satellite observations of a given type of phenomenon). EOSDIS data stewardship responsibilities include ensuring that the data and information content are reliable, of high quality, easily accessible, and usable for as long as they are considered to be of value.

metadata↗
NASA NTRSDate not supplied

The Radiation Biology Ontology: A New Tool Supporting FAIR Principles Across Radiation Biology Facilitating Data Discovery and Integration

Development of the Radiation Biology Ontology (RBO) was motivated by the need for a comprehensive, well-structured ontology for encoding radiation biology metadata. The primary use-cases were archiving data in the STORE database (https://www.storedb.org/), the repository for the RadoNorm Project, and in GeneLab (https://genelab.nasa.gov), NASA’s ‘omics database. The scope of radiobiology research ranges from physics to radiation oncology to socio-legal studies; no existing ontology has the necessary breadth or depth. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR radiation biology data.

ontology↗
NASA NTRSDate not supplied

National Aeronautics and Space Administration (NASA) Agency Report WGISS-51

The Committee on Earth Observation Satellites (CEOS) strives to enhance international coordination and data exchange and to optimize societal benefit. CEOS contributes to NASA’s core mission and is critical to NASA’s Earth Science program and to the future of Earth observations community as a whole because it advances mission planning, interagency coordination and technical implementation. Within NASA, the CEOS Working Group on Information System and Services (WGISS) is a forum for the Earth Sciences Data Systems (ESDS) Program to collaborate with other international and domestic agencies in the development of Earth observation data systems and services. NASA leads the development and demonstration of multiple prototypes supporting CEOS and Group on Earth Observations (GEO) requirements. NASA’s participation in WGISS influences NASA’s Earth Observing System Data and Information System’s (EOSDIS) ability to make high-quality data products available to the broad science community both nationally and internationally. Combined with NASA’s free and open data policy, EOSDIS’s involvement in WGISS is essential to widespread use of research satellite measurements. This presentation focuses on an overview and recent status of NASA’s EOSDIS.

Andrew Mitchell↗
NASA NTRSDate not supplied

NASA Agency Report WGISS 52

The Committee on Earth Observation Satellites (CEOS) strives to enhance international coordination and data exchange and to optimize societal benefit. CEOS contributes to NASA’s core mission and is critical to NASA’s Earth Science program and to the future of Earth observations community as a whole because it advances mission planning, interagency coordination and technical implementation. Within NASA, the CEOS Working Group on Information System and Services (WGISS) is a forum for the Earth Sciences Data Systems (ESDS) Program to collaborate with other international and domestic agencies in the development of Earth observation data systems and services. NASA leads the development and demonstration of multiple prototypes supporting CEOS and Group on Earth Observations (GEO) requirements. NASA’s participation in WGISS influences NASA’s Earth Observing System Data and Information System’s (EOSDIS) ability to make high-quality data products available to the broad science community both nationally and internationally. Combined with NASA’s free and open data policy, EOSDIS’s involvement in WGISS is essential to widespread use of research satellite measurements. This presentation focuses on an overview and recent status of NASA’s EOSDIS.

Andrew Mitchell↗
NASA NTRS2021

Future SAR Imaging Systems: Goals, Plans, Challenges and Opportunities

Synthetic Aperture Radar (SAR) Earth observation data are becoming increasingly ubiquitous as new spaceborne systems become operational and their data are made available to scientists and applications users. The characteristic of active sensors like SAR to be able to observe Earth independent of weather or solar illumination, coupled with regular data acquisition, fosters reliability and encourages the investment in algorithm and product development toward a beneficial result. As SAR systems typically contain proprietary or nationally important technologies, civilian SAR systems are typically developed with a national focus, or in the case of the European Union, with the Union’s focus. As a result, when viewed from a global perspective, SAR programs can be generally viewed as independent developments, each with their own requirements, schedules, development approaches, and data policies. At the same time, these systems can be expensive, and particularly in an era of increasingly open data policies, coordination of programs could reduce redundancy in observations, increase sampling density and measurement diversity, and improve dependability of data streams in the long term. Since 2018, agency representatives from NASA, ESA, DLR, JAXA, ISRO, ASI, and CONAE have been evaluating the possibilities for programmatic and technical coordination of future SAR systems, data sharing, and scientific exploitation. In this paper, we describe the work in discovering trends and possibilities associated with flight systems, by evaluating current and future plans for SAR systems around the world, and identifying opportunities for coordination.

Zink, Manfred↗
NASA NTRS2017

A View from Above: Earth Observations

Since the 1960s, satellites have been looking down at the Earth to monitor weather patterns and track severe storms, observe how our land surface is changing and responding to hydrometerological extremes, and even to sense how the Earth's crust is deforming from earthquakes and volcanoes. Space and airborne platforms can provide unique views of the disaster lifecycle, informing pre-event mitigation and preparedness, emergency response following an event, and monitoring longer-term recovery. These remotely-sensed data, products and models can provide a global perspective to see beyond administrative boundaries, reach remote places where in situ observations are di cult or non-existent, and provide the necessary context and situational awareness to aid in disaster response. So how do these platforms work? Instruments aboard satellites use different portions of the electromagnetic spectrum to passively or actively observe energy across a range of wavelengths, which can be turned into meaningful data on geophysical, atmospheric, and hydrological variables. e US has had a broad range of Earth observation (EO) platforms delivering open data for scientific research and societal benefits for decades. e Landsat programme, a joint initiative between the US Geological Survey (USGS) and NASA, has the world's longest continuous collection of space-based satellite imagery of the Earth, extending from 1972 to present. e Landsat satellites provide visible, near infrared, and thermal data that are used to support emergency response and disaster relief by mapping changes in water during floods, and dramatic land surface changes, including those resulting from landslides, wild res, severe weather, volcanic plumes, and dust storms.

FEMA↗
NASA NTRSDate not supplied

A Cloud-Based Operational Surface Water Extent Mapping Service from Sentinel-1 SAR

With its weather independence and day-and-night capabilities, SAR has long been known as a useful data set for flood monitoring. The recently launched Sentinel-1 (S1) C-band sensors, with their regularly acquired, free-and-open data, have finally elevated SAR to a relevant data source in operational hazard response. Leveraging these capabilities of S1, this poster introduces the HYDRO30 product, a 30-m resolution surface water extent product derived from dual-pol S1 SAR data. To enable automatic and near real-time product generation, HYDRO30 is embedded in the HydroSAR service, a cloud-based production pipeline developed by the University of Alaska Fairbanks in collaboration with the NASA Alaska Satellite Facility DAAC and the NASA Marshall and Goddard Space Flight Centers.

Franz J Meyer↗
NASA NTRS1983

Implications of information from LANDSAT-4 for private industry

The broader spectral coverage and higher resolution of LANDSAT-4 Thematic Mapper (TM) data open the door for identification from space of spectral phenomena associated with mineralization and microseepage of hydrocarbon. Digitally enhanced image products generated from TM data allow the mapping of many major and minor structural features that mark or influence emplacement of mineralization and accumulation of hydrocarbons. These improvements in capabilities over multispectral scanner data should accelerate the acceptance and integration of satellite data as a routinely used exploration tool that allows rapid examination of large areas in considerable detail. Imagery of Southern Ontario, Canada as well as of Cement, Oklahoma and Death Valley, California is discussed.

Everett, J. R.↗
NASA NTRS2012

NAND Flash Qualification Guideline

Better performing Forward Error Correction on the forward link along with adequate power in the data open an uplink operations trade space that enable missions to: Command to greater distances in deep space (increased uplink margin). Increase the size of the payload data (latency may be a factor). Provides space for the security header/trailer of the CCSDS Space Data Link Security Protocol. Note: These higher rates could be used for relief of emergency communication margins/rates and not limited to improving top-end rate performance. A higher performance uplink could also reduce the requirements on flight emergency antenna size and/or the performance required from ground stations. Use of a selective repeat ARQ protocol may increase the uplink design requirements but the resultant development is deemed acceptable, due the factor of 4 to 8 potential increase in uplink data rate.

Floating Gate Memory↗
NASA NTRS2012

Replacing the CCSDS Telecommand Protocol with Next Generation Uplink

Better performing Forward Error Correction on the forward link along with adequate power in the data open an uplink operations trade space that enable missions to: Command to greater distances in deep space (increased uplink margin) Increase the size of the payload data (latency may be a factor) Provides space for the security header/trailer of the CCSDS Space Data Link Security Protocol Note: These higher rates could be used for relief of emergency communication margins/rates and not limited to improving top-end rate performance. A higher performance uplink could also reduce the requirements on flight emergency antenna size and/or the performance required from ground stations. Use of a selective repeat ARQ protocol may increase the uplink design requirements but the resultant development is deemed acceptable, due the factor of 4 to 8 potential increase in uplink data rate.

Low Density Parity Check (LDPC)↗
NASA NTRS2018

The Planetary Materials Database

NASA provides funds for a variety of research programs whose principal focus is to collect and analyze terrestrial analog materials. These data are used to (1) understand and interpret planetary geology; (2) identify and characterize habitable environments and pre-biotic/biotic processes; (3) interpret returned data from present and past missions; and (4) evaluate future mission and instrument concepts prior to selection for flight. Data management plans are now required for these programs, but the collected data are still not generally available to the community. There is also little possibility to re-analyze the collected materials by other techniques, since there is no requirement to archive collected samples. The Planetary Materials Database (PMD) is a central, high-quality, long-term data repository, which aims to promote the field of astrobiology and increase scientific returns from NASA funded research by enabling data sharing, collaboration and exposure of non-NASA scientists to NASA research initiatives and missions. The PMD is a linked collection of databases developed using the Open Data Repository (ODR) system. The PMD will include detailed descriptions of terrestrial analog planetary materials as well as data from the instruments used in their analysis. The goal is to provide example patterns/spectra/analyses, etc. and background information suitable for use by the Space Science community. An early example showing the utility of these databases (although not in the ODR format) is the RRUFF mineral database. RRUFF, comprising 4,000+ pure mineral standards, is the most popular and widely used dataset of minerals and receives more than 180,000 queries per week from geologists and mineralogists worldwide. The PMD will be patterned after the CheMin database [3], a resource that contains all of the data collected by the MSL CheMin XRD instrument on Mars. Raw and processed CheMin data can be viewed, downloaded, reprocessed and reanalyzed using cloud-based “applications” linked to the data.

Blake, David↗
NASA NTRSDate not supplied

Advancing Open Science in Atmospheric Research: Integrating Data Usability and Machine Learning

In the dynamic realm of atmospheric sciences, the convergence of data science methodologies and open data marks a transformative era, driving research advancements and nurturing aspiring scientists. This abstract highlights two pivotal projects that epitomize open science principles, aligning seamlessly with the session's objective of interdisciplinary synergy and the cultivation of emerging talent. As a NASA-certified data center, our foremost endeavor focuses on enhancing the visibility and traceability of NASA datasets within atmospheric science research. This initiative not only elevates these datasets' prominence but also establishes a robust framework ensuring their credibility in scholarly discourse. By bridging the gap between data sources and research publications, this project serves as an educational catalyst, nurturing a new generation of scholars in open collaboration and dataset authenticity. Concurrently, our second project pioneers an early warning system for flooding events, utilizing machine learning algorithms to predict flooded fractions. Through multi-source data fusion and predictive modeling, this initiative goes beyond forecasting; it embodies the core of open science by enabling proactive risk mitigation strategies. This project not only advances atmospheric sciences but also fosters an environment where young scholars engage in practical, data-driven solutions. These intertwined projects exemplify the fusion of data science with open data solutions, ensuring both the usability of quality datasets and the cultivation of scientific knowledge among emerging scholars. By spotlighting these impactful use cases, our aim is to foster discussions emphasizing the importance of open collaboration, data integrity, and the nurturing of scientific talent in atmospheric sciences." "In the dynamic realm of atmospheric sciences, the convergence of data science methodologies and open data marks a transformative era, driving research advancements and nurturing aspiring scientists. This abstract highlights two pivotal projects that epitomize open science principles, aligning seamlessly with the session's objective of interdisciplinary synergy and the cultivation of emerging talent. As a NASA-certified data center, our foremost endeavor focuses on enhancing the visibility and traceability of NASA datasets within atmospheric science research. This initiative not only elevates these datasets' prominence but also establishes a robust framework ensuring their credibility in scholarly discourse. By bridging the gap between data sources and research publications, this project serves as an educational catalyst, nurturing a new generation of scholars in open collaboration and dataset authenticity. Concurrently, our second project pioneers an early warning system for flooding events, utilizing machine learning algorithms to predict flooded fractions. Through multi-source data fusion and predictive modeling, this initiative goes beyond forecasting; it embodies the core of open science by enabling proactive risk mitigation strategies. This project not only advances atmospheric sciences but also fosters an environment where young scholars engage in practical, data-driven solutions. These intertwined projects exemplify the fusion of data science with open data solutions, ensuring both the usability of quality datasets and the cultivation of scientific knowledge among emerging scholars. By spotlighting these impactful use cases, our aim is to foster discussions emphasizing the importance of open collaboration, data integrity, and the nurturing of scientific talent in atmospheric sciences.

Jennifer Wei↗
NASA NTRSDate not supplied

Improving Satellite-Based Hotspot Detection Through Deep Learning-Enabled Smoke Recognition

While geostationary satellites, such as the GOES-R series, provide wildland fire hotspot readings at a high temporal resolution, they are prone to false negative readings and decreased confidence. One cause of decreased hotspot confidence is cloud contamination. Smoke produced from wildfire is often misinterpreted as cloud contamination, resulting in inaccurate and unsure sensor readings. To this end, we built a deep learning image segmentation model to identify smoke and cloud in true color satellite images. The model is pre-trained using self-supervised learning on over 10,000 GOES-R images to learn the underlying structure of satellite imagery. Then, the model is fine-tuned on a set of 130 labeled documents using supervised learning. The resulting model performs multi-class image segmentation with 85% accuracy and runs in under a minute on a standard personal computer. When paired alongside hotspot data, the model’s outputs can help increase confidence in wildfire location by identifying cases of cloud contamination that are due to smoke. The resulting model can be deployed in a stand-alone application or bundled in an Open Data Integration for wildland fire management (ODIN) application.

Earth observation↗
NASA NTRS2016

Toward a New Generation of Agricultural System Data, Models, and Knowledge Products: State of Agricultural Systems Science

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.

Livestock models↗