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DOE OSTI2023

National Critical Minerals Data Dashboard

The Critical Minerals (CM) Data Dashboard provides data-driven insights to help unlock the potential of conventional and unconventional CM resources in the United States. The dashboard focuses on 12 CM essential for producing zero-emission transportation and clean power technology. More information about these CM and potential resources is available in our CM Story Map. Dashboard data sets can be filtered geographically using the options provided along the top of the dashboard. Data sets can also be filtered by various attributes using the options in the expandable side panel on the left side of the screen. The data sets in this dashboard were compiled from multiple sources including the USGS, USEPA, OSMRE, and DHS., Users are encouraged to provide feedback/suggestions on the function and content of the dashboard, as well as any issues that may arise.

Conventional Resources↗

DOE OSTI2025

Future Intensity‐Duration‐Frequency Curves of Extreme Precipitation in the Midwest United States From Convection‐Permitting Modeling

Abstract During the last four decades, global warming has statistically significant intensified extreme precipitation events in the Midwestern United States (defined here as the region covering Illinois, Indiana, Ohio, and Kentucky), leading to increased risks to human life, property, and infrastructure. To enable climate change adaptation and resilience across various economic and social sectors in this region, updated information about future climate changes, specifically at finer spatial scales, is essential. Leveraging a new 150‐year dynamical downscaling data set at convection‐permitting resolution, this study introduces a framework to construct the projected future intensity‐duration‐frequency (IDF) curves of heavy precipitation, which are prominent tools for infrastructure design and water resources management. This framework generates IDF curves at both sub‐daily and multi‐day duration utilizing hourly in situ observations as well as quantile‐based statistical techniques in bias‐correction and return levels selection. The assumption of non‐stationarity in the distribution parameter fitting process is also implemented in this workflow. Compared to historical IDF curves for 1980–2022, future projected IDF curves for 2058–2100 under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios indicate an average intensity increase of approximately 15% and 25%, respectively, across 74 stations, considering both annual and seasonal timescales. Future projections suggest that extreme precipitation events may become more severe across six investigated return periods, with longer return periods showing a greater increase. The frequency of future extreme precipitation events in the Midwest region is also projected to double. Furthermore, current results reveal spatial heterogeneity of future trends across stations owing to the high‐resolution input data set. Plain Language Summary This study investigates the evolving nature of extreme precipitation events in the Midwestern United States under a changing climate. By leveraging a high‐resolution dynamical downscaling data set, we construct projected intensity‐duration‐frequency (IDF) curves for future extreme rainfall events. These curves serve as vital tools for infrastructure planning and water resource management. Our analysis reveals a significant increase in both the intensity and frequency of extreme precipitation events in the region. Future projected IDF curves for the late century indicate an average intensity increase of approximately 15%–25% compared to historical values. Moreover, the frequency of such events is expected to double. Spatial heterogeneity in future trends is observed across different stations within the Midwest, highlighting the importance of high‐resolution modeling in capturing localized climate variability. These findings underscore the urgent need for climate adaptation strategies to mitigate the increasing risks associated with extreme precipitation events in the region. Key Points This study introduces a workflow to construct future intensity‐duration‐frequency (IDF) curves over the Midwest United States using a new convection‐permitting modeling data set The current IDF construction workflow reproduces well the historical observed IDF 30 curves in summer months with median relative errors of 2.4% among 74 stations and 6 investigated durations The projected IDF curves show diverse future trends of extreme precipitation across stations, with intensity increases of approximately 15% and 25% under RCP4.5 and RCP8.5 climate scenarios, respectively, and a doubling of frequency on average

Nguyen, Trung↗

NASA NTRS1998

Locating Biodiversity Data Through The Global Change Master Directory

The Global Change Master Directory (GCMD) presently holds descriptions for almost 7000 data sets held worldwide. The directory's primary purpose is for data discovery. The information provided through the GCMD's Directory Interchange Format (DIF) is the set of information that a researcher would need to determine if a particular data set could be of value. By offering data set descriptions worldwide in many scientific disciplines - including meteorology, oceanography, ecology, geology, hydrology, geophysics, remote sensing, paleoclimate, solar-terrestrial physics, and human dimensions of climate change - the GCMD simplifies the discovery of data sources. Direct linkages to many of the data sets are also provided. In addition, several data set registration tools are offered for populating the directory. To search the directory, one may choose the Guided Search or Free-Text Search. Two experimental interfaces were also made available with the latest software release - one based on a keyword search and another based on a graphical interface. The graphical interface was designed in collaboration with the Human Computer Interaction Laboratory at the University of Maryland. The latest version of the software, Version 6, was released in April, 1998. It features the implementation of a scheme to handle hierarchical data set collections (parent-child relationships); a hierarchical geospatial location search scheme; a Java-based geographic map for conducting geospatial searches; a Related-URL field for project-related data set collections, metadata extensions (such as more detailed inventory information), etc.; a new implementation of the Isite software; a new dataset language field; hyperlinked email addresses, and more. The key to the continued evolution of the GCMD is in the flexibility of the GCMD database, allowing modifications and additions to made relatively easily to maintain currency, thus providing the ability to capitalize on current technology while importing all existing records. Changes are discussed and approved through an online "interoperability" forum. The next major release of the GCMD is scheduled for early 1999 and will include the incorporation of a new matrix-based interface, a rapid valids-based query system; improvement in the operations facility - important for future distributed options; new streamlined code for greater performance and maintainability; improvements in the handling of seven current fields proposed through the interoperability forum (at no expense to the data providers); and the release of DOCmorph, a more robust version of DIFmorph to translate many 'standards' multi-directionally. Issues and actions will also be addressed.

Olsen, Lola M.↗

DOE OSTI2020

Super resolution for root imaging

Premise High‐resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above‐ground plant attributes. However, the acquisition of high‐resolution images of plant roots is more challenging than above‐ground data collection. An effective super‐resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. Methods We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non‐plant‐root images, (ii) training with plant‐root images, and (iii) pretraining the model with non‐plant‐root images and fine‐tuning with plant‐root images. The architectures of the SR models were based on two state‐of‐the‐art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. Results In our experiments, we observed that the SR models improved the quality of low‐resolution images of plant roots in an unseen data set in terms of the signal‐to‐noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non‐root data sets. Discussion The incorporation of a deep learning–based SR model in the imaging process enhances the quality of low‐resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal‐to‐noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.

Ruiz‐Munoz, Jose F.↗

NASA NTRS1981

Ultimate limits to error probabilities for ionospheric models based on solar geophysical indices and how these compare with the state of the art

An ideal model based on a given set of geophysical indices is defined as a model that provides a least squares fit to the data set as a function of the indices considered. Satellite measurements of electron content for three stations at different magnetic latitudes were used to provide such data sets which were each fitted to the geophysical indices. The magnitude of the difference between the measured value and the derived equation for the data set was used to estimate the probability of making an error greater than a given magnitude for such an ideal model. Atmospheric Explorer C data is used to examine the causes of the fluctuations and suggestions are made about how real improvements can be made in ionospheric forecasting ability. Joule heating inputs in the auroral electrojets are related to the AL and AU magnetic indices. Magnetic indices based on the time integral of the energy deposited in the electrojets are proposed for modeling processes affected by auroral zone heating.

Nisbet, J. S.↗

NASA NTRS2011

Combining Satellite and in Situ Data with Models to Support Climate Data Records in Ocean Biology

The satellite ocean color data record spans multiple decades and, like most long-term satellite observations of the Earth, comes from many sensors. Unfortunately, global and regional chlorophyll estimates from the overlapping missions show substantial biases, limiting their use in combination to construct consistent data records. SeaWiFS and MODIS-Aqua differed by 13% globally in overlapping time segments, 2003-2007. For perspective, the maximum change in annual means over the entire Sea WiFS mission era was about 3%, and this included an El NinoLa Nina transition. These discrepancies lead to different estimates of trends depending upon whether one uses SeaWiFS alone for the 1998-2007 (no significant change), or whether MODIS is substituted for the 2003-2007 period (18% decline, P less than 0.05). Understanding the effects of climate change on the global oceans is difficult if different satellite data sets cannot be brought into conformity. The differences arise from two causes: 1) different sensors see chlorophyll differently, and 2) different sensors see different chlorophyll. In the first case, differences in sensor band locations, bandwidths, sensitivity, and time of observation lead to different estimates of chlorophyll even from the same location and day. In the second, differences in orbit and sensitivities to aerosols lead to sampling differences. A new approach to ocean color using in situ data from the public archives forces different satellite data to agree to within interannual variability. The global difference between Sea WiFS and MODIS is 0.6% for 2003-2007 using this approach. It also produces a trend using the combination of SeaWiFS and MODIS that agrees with SeaWiFS alone for 1998-2007. This is a major step to reducing errors produced by the first cause, sensor-related discrepancies. For differences that arise from sampling, data assimilation is applied. The underlying geographically complete fields derived from a free-running model is unaffected by solar zenith angle requirements and obscuration from clouds and aerosols. Combined with in situ dataenhanced satellite data, the model is forced into consistency using data assimilation. This approach eliminates sampling discrepancies from satellites. Combining the reduced differences of satellite data sets using in situ data, and the removal of sampling biases using data assimilation, we generate consistent data records of ocean color. These data records can support investigations of long-term effects of climate change on ocean biology over multiple satellites, and can improve the consistency of future satellite data sets.

Gregg, Watson↗

NASA NTRS1989

Data acquisition for a real time fault monitoring and diagnosis knowledge-based system for space power system

The design and implementation of the real-time data acquisition and processing system employed in the AMPERES project is described, including effective data structures for efficient storage and flexible manipulation of the data by the knowledge-based system (KBS), the interprocess communication mechanism required between the data acquisition system and the KBS, and the appropriate data acquisition protocols for collecting data from the sensors. Sensor data are categorized as critical or noncritical data on the basis of the inherent frequencies of the signals and the diagnostic requirements reflected in their values. The critical data set contains 30 analog values and 42 digital values and is collected every 10 ms. The noncritical data set contains 240 analog values and is collected every second. The collected critical and noncritical data are stored in separate circular buffers. Buffers are created in shared memory to enable other processes, i.e., the fault monitoring and diagnosis process and the user interface process, to freely access the data sets.

Wilhite, Larry D.↗

NASA NTRS1990

Interannual differences of Geosat altimeter heights and sea level - The importance of a datum

Sea surface height data from the Geosat altimeter are compared with island sea level data from 18 gages in the western and central tropical Pacific during December 1986 to November 1987. Care was taken to ensure that the two data sets are referenced to the same mean surface. This was done by requiring that both data sets have a zero mean sea level over the period April 1985 to April 1986. When the annual means are computed at each station in the later time period, it is found that the annual mean sea surface height values have drifted away from the corresponding sea level values by as much as 16 cm. Further, the pattern of differences that develop between the two data sets is not random but is spatially coherent with a strong east-west gradient. These observations illustrate the necessity for sea surface height data to be referred to a well-defined zero point, a datum, in order to reliably monitor interannual changes in the sea surface topography. Until these differences can be eliminated, it will be necessary to use tide gage data as benchmarks for the altimeter sea surface height data.

Wyrtki, Klaus↗

NASA NTRS2000

BOREAS RSS-17 Stem, Soil, and Air Temperature Data

The BOREAS RSS-17 team collected several data sets in support of its research in monitoring and analyzing environmental and phenological states using radar data. This data set consists of tree bole and soil temperature measurements from various BOREAS flux tower sites. Temperatures were measured with thermistors implanted in the hydroconductive tissue of the trunks of several trees at each site and at various depths in the soil. Data were stored on a data logger at intervals of either 1 or 2 hours. The majority of the data were acquired between early 1994 and early 1995. The primary product of this data set is the diurnal stem temperature measurements acquired for selected trees at five BOREAS tower sites. The data are provided in tabular ASCII format. The data files are available on a CD-ROM (see document number 20010000884) or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Zimmerman, Reiner↗

NASA NTRS2011

SPASE: The Connection Among Solar and Space Physics Data Centers

The Space Physics Archive Search and Extract (SPASE) project is an international collaboration among Heliophysics (solar and space physics) groups concerned with data acquisition and archiving. Within this community there are a variety of old and new data centers, resident archives, "virtual observatories", etc. acquiring, holding, and distributing data. A researcher interested in finding data of value for his or her study faces a complex data environment. The SPASE group has simplified the search for data through the development of the SPASE Data Model as a common method to describe data sets in the various archives. The data model is an XML-based schema and is now in operational use. There are both positives and negatives to this approach. The advantage is the common metadata language enabling wide-ranging searches across the archives, but it is difficult to inspire the data holders to spend the time necessary to describe their data using the Model. Software tools have helped, but the main motivational factor is wide-ranging use of the standard by the community. The use is expanding, but there are still other groups who could benefit from adopting SPASE. The SPASE Data Model is also being expanded in the sense of providing the means for more detailed description of data sets with the aim of enabling more automated ingestion and use of the data through detailed format descriptions. We will discuss the present state of SPASE usage and how we foresee development in the future. The evolution is based on a number of lessons learned - some unique to Heliophysics, but many common to the various data disciplines.

Thieman, James R.↗

NASA NTRS2010

Large Scale Covariability in the Aerosol-SST-Precipitation Relation: A Satellite Perspective

Although large scale aerosol effects on precipitation have attracted increasing attention in recent years, significant uncertainties still remain in the current understanding of'such effects. One of the factors contributing to these uncertainties is the availability of aerosol data sets that are sufficiently long, stable, and comprehensive to be used for climate analysis. Such data sets are crucial for any long-term studies that are needed to demonstrate the statistical significance of large scale aerosol effects on precipitation when compared to the effects from other climate factors, such as remote or local sea surface temperature (SST) variability (ENSO. etc.). A longterm consistent aerosol product that complements the existing MODIS and AVHRR aerosol products is therefore needed to minimize aerosol data uncertainty issues for such Studies. The SeaWiFS instrument was primarily designed to measure ocean color, and it has been the mayor source for providing a comprehensive global data set of such measurements since its launch in 1997. Besides being used in this capacity, however, the well-calibrated set of radiances measured in the wavelength range from the visible (0.41 m) to the near infrared (0.87 m) is also well suited to creating long-term climate data records of aerosols. Recently, a new 13-year SeaWiFS aerosol optical depth dataset has been developed that expands its capacity to be used for such atmospheric studies. This SeaWiFS aerosol product provides us with an unprecedented opportunity to examine the covariability of aerosol, SST and precipitation on both a regional and global scale. We also use concurrent independent SST (NOAA Optimum Interpolation, OI, SST V2) and precipitation (GPCP V2) datasets for this study. We will present and discuss results from this study over several aerosol hot spot regions ( -,North Atlantic. South Atlantic, Western Pacific, Indian Ocean etc.) during the SeaWIFS data period (1997 -- current) from this study will be presented and discussed. These results will highlight the value and impact of this unique product to climate studies

Hsu, N. Christina↗

NASA NTRS2014

Analysis of Inter-Moss Loops in the Solar Region with IRIS and SDO AIA: Automatic Event Detection and Characterization

The Interface Region Imaging Spectrograph (IRIS), launched in the summer of 2013, is designed specifically to observe and investigate the transition region and adjacent layers of the solar atmosphere, obtaining images in high spatial, temporal, and spectral resolution. Our particular work is focused on the evolution of inter-moss loops, which have been detected in the lower corona by the Atmospheric Imaging Assembly (AIA) and the High-Resolution Coronal Imager (Hi- C), but are known to have foot points below the transition region. With the high-resolution capabilities of IRIS and its Si IV pass band, which measures activity in the upper chromosphere, we can study these magnetic loops in detail and compare their characteristic length and time scales to those obtained from several AIA image sets, particularly the 171, 193, and 211 pass bands. By comparing the results between these four data sets, one can potentially establish a measure of the ionization equilibrium for the location in question. To explore this idea, we found a large, sit-and-stare observation within the IRIS database that fit our specifications. This data set contained a number of well-defined inter-moss loops (by visual inspection) with a cadence less than or equal to that of AIA (approximately 12 seconds). This particular data set was recorded on October 23, 2013 at 07:09:30, lasting for 3219 seconds with a field of view of 120.6 by 128.1 arcseconds, centered on -53.9 by 59.1 arcseconds from disk center. For ease of comparison, the AIA data has been interpolated to match the IRIS cadence and resolution. In the main portion of the poster, we demonstrate the detection of events, the information collected, and the immediate results to the right, showing the progress of an event with green as the start, blue as the peak, and red as the end. Below here, we demonstrate how pixels are combined to form groups. The 3D results are shown to the right.

event detection↗

NASA NTRS2016

Analysis of Inter-Moss Loops in the Solar Region with IRIS and SDO AIA: Automatic Event Detection and Characterization

IRIS↗

DOE OSTI2021

Creating a Tools Ecosystem for Cross-Discipline Environmental Data Reuse

Reusing data is difficult even within well-defined science communities and only gets worse when combining data from multiple communities and disciplines. Through the lens of current work on constructing an environmental epidemiological data set from multiple disciplinary sources, we demonstrate the need for a new tool ecosystem to support heterogeneous Big Data science. Extending existing community standards for schemas and/or data formats through human auditing and wrangling of the data is not feasible at scale. This work therefore suggests new approaches for the multi-disciplinary communities to build a shared tool ecosystem for big data. We discuss both the larger context of data wrangling of epidemiological data sets for novel artificial intelligence algorithms and the specific lessons from working with these multi-disciplinary data sets. Adopting a more model-driven, automatable approach promises not only better efficiency but also removes key sources of human-generated errors and promotes reuse and reproducibility of science data.

Logan, Jeremy↗

NASA NTRS1984

Planetary Data Definition

Planetary data include all of those data which have resulted from measurements made by the instruments carried aboard planetary exploration spacecraft, and (for our purposes) exclude observations of Moon and Earth. The working, planetary data base is envisioned to contain not only these data, but also a wide range of supporting measurements such as calibration files, navigation parameters, spacecraft engineering states, and the various Earth-based and laboratory measurements which provide the planetary research scientist with historical and comparative data. No convention exists across the disciplines of the planetary community for defining or naming the various levels through which data pass in the progression from a sensed impulse at the spacecraft to a reduced, calibrated, and/or analyzed element in a planetary data set. Terms such as EDR (experiment data record), RDR (reduced data record), and SEDR (supplementary experiment data record) imply different meanings depending on the data set under consideration. The development of standard terminology for the general levels of planetary data is necessary.

Source record↗

NASA NTRS1993

Direct measurements of tropospheric ozone from TOMS data

In the past year, we have made measurements of the tropospheric total column of ozone during the biomass burning season in Africa (August to October). Fishman et. al. had reported previously that by taking a time average of the low spatial resolution data from TOMS (Total Ozone Mapping Spectrometer) on Nimbus-7 (referred to as the Grid-T data set), during the biomass burning season in Africa, a plume of ozone extends from the East coast of Africa into the Atlantic. In this report, we present an analysis that we have made using the measured TOMS radiances taken from the High Density TOMS data set (referred as the HDT data set), which examines this plume in more detail.

Hudson, Robert D.↗

NASA NTRS1976

User's guide: DATEXT

A guide is presented for a computer program which reads multispectral scanner data from a universal format tape and outputs an intermediate data set in card image format for use as an input data set in various data analysis development programs.

Coberly, W. A.↗

NASA NTRS1976

IBIS - A geographic information system based on digital image processing and image raster datatype

IBIS (Image Based Information System) is a geographic information system which makes use of digital image processing techniques to interface existing geocoded data sets and information management systems with thematic maps and remotely sensed imagery. The basic premise is that geocoded data sets can be referenced to a raster scan that is equivalent to a grid cell data set. The first applications (St. Tammany Parish, Louisiana, and Los Angeles County) have been restricted to the design of a land resource inventory and analysis system. It is thought that the algorithms and the hardware interfaces developed will be readily applicable to other Landsat imagery.

Bryant, N. A.↗