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System uses pattern recognition and interactive data handling techniques applied to remotely sensed data. Basic analysis concept consists of locating data points which are believed to be representative of classes of interest.
Averaged radiance data obtained over the oceans from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) are used to produce an empirical correction algorithm for the polarization mixing which is inherent to the SMMR because of its fixed multichannel receiving horn and its scanning antenna dish. The algorithm, developed without the use of an ocean/atmosphere radiance model, also properly accounts for off center and scan independent polarization mixing, which were discovered after launch as a result of analyzing the aforementioned data. The radiance averaging consisted of collecting data for each beam position of each of the ten radiance channels of SMMR (nominal horizontal and vertical polarizations of the five SMMR wavelengths, 0.81, 1.4, 1.7, 2.8, and 4.6 cm) for about 300 orbits, subdividing the global ocean radiance data into 10 deg latitude bands and ascending (daytime) and descending (nighttime) orbits. This served to smooth out atmospheric and ocean surface variations in order to perform the polarization mixing analysis.
The objective initially proposed was to analyze shuttle remote manipulator (SRM) performance data collected during a Shuttle Flight. The data was to consist of video TRAC data collected via a video recorder. Unfortunately, the flight never collected the data due to higher priority experiments superseding it. As a result, the research team at Texas A&M was directed to work on several other pressing issues regarding the TRAC sensor. All but one of these issues were reported earlier in the form of periodic status reports. In fulfillment of the grant conditions, the last issue investigated is being reported as the final report. Ordinarily, a TRAC sensor determines the orientation of an object by analyzing the image reflected from a mirror target. The concern addressed is to develop a method for using the TRAC sensor when the target does not reflect a usable image.
This document is the proceedings from the fourth annual 'Space and Earth Science Data Compression Workshop,' which was held on April 2, 1994, at the University of Utah in Salt Lake City, Utah. This workshop was held in cooperation with the 1994 Data Compression Conference, which was held at Snowbird, Utah, March 29-31 1994. The Workshop explored opportunities for data compression to enhance the collection and analysis of space and Earth science data. It consisted of 13 papers presented in 4 sessions. The papers focus on data compression research that is integrated into, or has the potential to be integrated into, a particular space and/or Earth science data information system. Presenters were encouraged to take into account the scientist's data requirements, and the constraints imposed by the data collection, transmission, distribution, and archival system.
During the past few years we have conducted work on several different topics, as reflected by our publications. As one of the Co-Project scientists for The Cirrus Regional Study of Tropical Anvils and Cirrus Layers - Florida Area Cirrus Experiment (CRYSTAL FACE) we worked to help design the mission and then conduct it in the field. Another major activity during the past two years has been to pull together various groups to formulate plans for follow on missions to CRYSTAL FACE. We organized a workshop at the University of Colorado during the summer of 2003 to assess the best locations for future missions. Working with a group of about 10 scientists from around the country we prepared a science-planning document (Tropical Composition, Cloud and Climate Coupling Experiment (TC(sup 4)) that outlined the rationale, locations, strategy to accomplish the goals, and possible payloads for a set of three tropical missions. We also prepared background materials for various NRAs being prepared at NASA Headquarters for missions in Costa Rica, Darwin and Guam. In conjunction with the group at NASA Ames we have helped build a new numerical model for deep convection and have applied that model to simulate the CRYSTAL data. Our goal in particular has been to better understand how convection distributes water vapor isotopes. CRYSTAL observations of water isotopes are very different from those suggested by previous workers who assumed the isotopes would obey Rayleigh fractionation. The water isotope study has several implications. First it is a check on the realism of the deep convection model. Second, the isotopes are a measure of the precipitation removal in the atmosphere. Hence they provide a constraint on a parameter that is difficult to otherwise measure. Finally it has been suggested that isotopes may be the key to unraveling the water transport into the stratosphere and upper troposphere. Such transport is critical both for the radiation balance and for stratospheric chemistry. Ours is the first model that is able to treat this transport. Our initial results have just been submitted to Geophys. Res. Lett (Smith et al., 2005). Essentially we are able to explain the vertical profiles of isotopes in the tropical tropopause transition layer. We are also able to account for stratospheric humidity and isotope abundances with this model. We have also been heavily involved in trying to improve our understanding of nitric acid condensation on ice. Gao et al (2004) have shown that water supersaturations above ice occur when the atmosphere is supersaturated with respect to nitric acid trihydrate. As one of the co-authors of that work, we suggested the mechanism that may explain why this is occurring. Essentially, ice does not like to grow near unit supersaturation, but does so because the water molecules can find sites on the ice surface to attach themselves to before they fly off the ice surface. This phenomena was well known in the 1960s when it was a source of debate about whether condensation and evaporation coefficients for ice would be the same. Evaporation does not require any molecular orientation, while condensation does, so it was possible that the coefficients would differ. They don't differ because the water molecules rapidly move across the surface and find places to attach. Nitric acid may be occupying these preferred sites and therefore the water molecules can't find a desirable place to attach. We anticipate that this research will be the subject of laboratory work during the coming few years. Another possibility that has been suggested is that cubic ice is forming in clouds. We have measured the vapor pressure of cubic ice, and plan to publish that result in the next few months. We have also been working on additional aspects of the condensation of nitric acid on ice. With Y. Kondo we studied the condensation of NOy on ice using the SOLVE data. Gamblin et al. have continued this work. The CRYSTAL NOy and HNO, groups have shown th their data can be fit using standard Langmuir isotherms as suggested in some, but not all, laboratory studies. We have found in the SOLVE data set that this is not the case. Moreover some laboratory studies show there are important kinetic effects that may be occurring in the atmosphere limiting the transfer of nitric acid to the ice. The SOLVE data seem consistent with these studies. We are currently re-analyzing the CRYSTAL data to look for these kinetic effects. There are a number of implications of these studies. One of the more interesting is that the nitric acid coating on ice can be used as a cloud clock to determine how long the cloud parcel has been in existence. We have also been involved with several laboratory studies. We have worked to improve the database on ice optical constants, which are critical for remote sensing. We have also studied the ways in which ice nucleates on clays. We suspect now that the standard theories used for depositional ice nucleation are completely incorrect. Further work will be needed to develop a new theory.
One of the biggest challenges in developing scientific instruments is not just the build and testing of an instrument, but the method for processing the data and producing a consistent, well characterized data product that can be confidently used by the public and scientific community. Raw data products are frequently an array of numbers that are a read-out of voltages. The challenge is to convert these arrays into meaningful information as well as remove noise and interferences. Because this can be a tedious and time-consuming, the goal is to automate the process so that data ca be processed rapidly and be available in real-time for event monitoring. Here we present a rapid analysis method for Laser Heterodyne Radiometer (LHR) data that can be used to analyze data taken from a range of LHR observation modes (column, limb, etc.). This online tool uses the versatile Planetary Spectrum Generator (PSG) and allows LHR users to quickly analyze their own data using a NASA Goddard Space Flight Center (GSFC) monitored capability to ensure quality and reproducibility in the data products. Background: Development of a miniaturized LHR (mini-LHR) as a ground instrument for measuring carbon dioxide (CO 2 ) and methane (CH 4 ) in the atmospheric column started in 2009 with the commercial availability of distributive feedback lasers (DFB) in wavelengths that could measure absorption of gases in the near-IR. As new DFB lasers, detectors and hardware emerged at different wavelengths, additional gases were added to the capability. The mini-LHR was adapted into an occultation-viewing CubeSat with Lawrence Livermore National Laboratory (LLNL) for observing CO 2 , CH 4 , and H 2 O in the limb and was launched in 2019. Other versions of the LHR technology have been designed for observation of water vapor in the lunar exosphere as well as observations of trace gases in planetary atmospheres and plumes from icy moons. The PSG is an online tool developed at GSFC that can be used for synthesizing Earth and planetary spectra (atmospheres and surfaces) over a broad range of wavelengths (0.1 μm to 100 mm) for any observatory, orbiter or lander. Spectra are simulated by combining several state-of-the-art radiative transfer models, spectroscopic databases, planetary databases, as well as modern-era retrospective analysis for research and applications, version 2 (MERRA-2) data set which provides meteorological inputs such as modeled surface pressure for calculating dry-air columns.
Omics data are essential for understanding the myriad and complex effects of space environments on humans. To assure maximum benefit from these kinds of data, the NASA Human Research Program Data Management Plan stipulates that human omics data should be archived within and accessed through the NASA Life Sciences Portal (NLSP). The NLSP has the capability to acquire and provision access to omics (and other kinds of) research results for individual and ad-hoc groups of subjects at the direction of institutional review boards, or other authorizing bodies or individuals, per institutional, program and investigation-specific policies and procedures. However, because some single-subject omics data, like CT scans and other kinds of large, complex biomedical data, could be used to identify heretofore unknown risks to the subject’s health, or, in certain cases, be used to identify a subject, NASA Policy Directive 7170.1 describes various policies regarding the management of and access to “research genetic testing” data, which includes many kinds of omics data. For example, NPD 7170.1 prohibits access to human research genetic data by NASA personnel who make employment decisions for the subjects from whom the data were obtained. To meet the objective of acquiring research omics data for NLSP in compliance with the policies in NPD 7170.1 and other applicable NASA policies, we designed NOMADS (the NLSP Omics Multimodal Acquisition of Data System), a new component that supports the transfer of large research data files, including research genetic testing data, using one of several different transfer mechanisms. The choice of mechanism is made by the submitter of the data, with guiding information from the system, and is likely to often be determined in large part by the nature and source location of the data. For example, for small files where the source data files are not already stored in a cloud storage system, users are likely to prefer to transfer their data to the NLSP via a web browser. Conversely, for large sets of files already organized and stored in a cloud storage system, users may opt for NOMAD’s cloud-to-cloud transfer method. All omics datasets targeted for the NASA Life Sciences Data Archive must pass a variety of quality checks to ensure data integrity and adherence to the standards defined by the LSDA Data Submission Guidelines (DSG) (see https://nlsp.nasa.gov/explore/lsdahome/datasubmit). These include requirements that data are consistent with open standards established by the omics community. Non-compliant data will not be accepted however archivists are available to advise submitters on how to revise data submissions and re-submit until compliance is achieved. Following compliance with the LSDA DSG, omics data next undergo a variety of additional quality checks to ensure the data meet omics community standards. Domain specific Omics data quality control tools and techniques are continually evolving and linked to the advancements in omics assays utilized and thus, the tools and techniques utilized by the LSDA for data quality control and validation will need to be sustained accordingly. All human omics data will be access controlled according to the policies described above, and requiring IRB approval for any additional access grants once the data are acquired (including access for analysis using the NLSP workspace tools).
Omics data are essential for understanding the myriad and complex effects of space environments on humans. To assure maximum benefit from these kinds of data, the NASA Human Research Program Data Management Plan stipulates that human omics data should be archived within and accessed through the NASA Life Sciences Portal (NLSP). The NLSP has the capability to acquire and provision access to omics (and other kinds of) research results for individual and ad-hoc groups of subjects at the direction of institutional review boards, or other authorizing bodies or individuals, per institutional, program and investigation-specific policies and procedures. However, because some single-subject omics data, like CT scans and other kinds of large, complex biomedical data, could be used to identify heretofore unknown risks to the subject’s health, or, in certain cases, be used to identify a subject, NASA Policy Directive 7170.1 describes various policies regarding the management of and access to “research genetic testing” data, which includes many kinds of omics data. For example, NPD 7170.1 prohibits access to human research genetic data by NASA personnel who make employment decisions for the subjects from whom the data were obtained. To meet the objective of acquiring research omics data for NLSP in compliance with the policies in NPD 7170.1 and other applicable NASA policies, we designed NOMADS (the NLSP Omics Multimodal Acquisition of Data System), a new component that supports the transfer of large research data files, including research genetic testing data, using one of several different transfer mechanisms. The choice of mechanism is made by the submitter of the data, with guiding information from the system, and is likely to often be determined in large part by the nature and source location of the data. For example, for small files where the source data files are not already stored in a cloud storage system, users are likely to prefer to transfer their data to the NLSP via a web browser. Conversely, for large sets of files already organized and stored in a cloud storage system, users may opt for NOMAD’s cloud-to-cloud transfer method. All omics datasets targeted for the NASA Life Sciences Data Archive must pass a variety of quality checks to ensure data integrity and adherence to the standards defined by the LSDA Data Submission Guidelines (DSG) (see https://nlsp.nasa.gov/explore/lsdahome/datasubmit). These include requirements that data are consistent with open standards established by the omics community. Non-compliant data will not be accepted however archivists are available to advise submitters on how to revise data submissions and re-submit until compliance is achieved. Following compliance with the LSDA DSG, omics data next undergo a variety of additional quality checks to ensure the data meet omics community standards. Domain specific Omics data quality control tools and techniques are continually evolving and linked to the advancements in omics assays utilized and thus, the tools and techniques utilized by the LSDA for data quality control and validation will need to be sustained accordingly. All human omics data will be access controlled according to the policies described above, and requiring IRB approval for any additional access grants once the data are acquired (including access for analysis using the NLSP workspace tools).
The Landsat global consolidated data archive now exceeds 50 years. In recognition of the need for consistently processed data across the Landsat satellite series, the U.S. Geological Survey (USGS) initiated collection-based processing of the entire archive that was processed as Collection 1 in 2016. In preparation for the data from the now successfully launched Landsat 9, the USGS reprocessed the Landsat archive as Collection 2 in 2020. This paper describes the rationale for, and the contents and advancements provided by Collection 2, and highlights the differences between the Collection 1 and Collection 2 products. Notably, the Collection 2 products have improved geolocation and, for the first time, the USGS provides a global inventory of Level 2 surface reflectance and surface temperature products. Also for the first time, the USGS used a commercial cloud computing architecture to efficiently process the archive and enable direct cloud access of the Landsat products. The paper concludes with discussion of likely improvements expected in Collection 3 in preparation for the Landsat Next mission that is planned for launch in the early 2030s.
We perform a general test of the ΛCDM and wCDM cosmological models by comparing constraints on the geometry of the expansion history to those on the growth of structure. Specifically, we split the total matter energy density, Ω M , and (for wCDM) dark energy equation of state, w, into two parameters each: one that captures the geometry, and another that captures the growth. We constrain our split models using current cosmological data, including type Ia supernovae, baryon acoustic oscillations, redshift space distortions, gravitational lensing, and cosmic microwave background (CMB) anisotropies. We focus on two tasks: (i) constraining deviations from the standard model, captured by the parameters ΔΩ M ≡ Ω M grow -Ω M geom and Δw ≡ w grow -w geom , and (ii) investigating whether the S 8 tension between the CMB and weak lensing can be translated into a tension between geometry and growth, i.e. ΔΩ M ≠ 0, Δw ≠ 0. In both the split ΛCDM and wCDM cases, our results from combining all data are consistent with ΔΩ M = 0 and Δ w = 0. If we omit BAO/RSD data and constrain the split wCDM cosmology, we find the data prefers Δw < 0 at 3.6σ significance and ΔΩ M > 0 at 4.2σ evidence. We also find that for both CMB and weak lensing, ΔΩ M and S 8 are correlated, with CMB showing a slightly stronger correlation. Furthermore, the general broadening of the contours in our extended model does alleviate the S 8 tension, but the allowed nonzero values of ΔΩ M do not encompass the S 8 values that would point toward a mismatch between geometry and growth as the origin of the tension.
SUMMARY Surface wave retrieval from ambient noise records using seismic interferometry techniques has been widely used for multiscale shear wave velocity (Vs) imaging. One key step during Vs imaging is the generation of dispersion spectra and the extraction of a reliable dispersion curve from the retrieved surface waves. However, the sparse array geometry usually affects the ability for high-frequency (>1 Hz) seismic signals’ acquisition. Dispersion measurements are degraded by array response due to sparse sampling and often present smeared dispersion spectra with sidelobe artefacts. Previous studies usually focus on interferograms’ domain (e.g. cross-correlation function) and attempt to enhance coherent signals before dispersion measurement. We propose an alternative technique to explicitly deblur dispersion spectra through use of a phase-weighted slant-stacking algorithm. Numerical examples demonstrate the strength of the proposed technique to attenuate array responses as well as incoherent noise. Three different field examples prove the flexibility and superiority of the proposed technique: the first data set consists of ambient noise records acquired using a nodal seismometer array; the second data set utilizes distributed acoustic sensing (DAS) and a marine fibre-optic cable to acquire a similar ambient noise data set; the last data set is a vibrator-based active-source surface wave data. The enhanced dispersion measurements provide cleaner and higher-resolution spectra without distortions which will assist both human interpreters as well as ML algorithms in efficiently picking curves for subsequent Vs inversion.
In fall 2020, the Colorado Energy Office, as part of the State of Colorado’s “Can Do Colorado” initiative, initiated a project aimed at encouraging energy-efficient transportation during the COVID-19 pandemic. The initial mini-pilot provided e-bikes to 13 low-income households under an individual ownership model. This report assesses the impact of providing this additional mobility option on the travel behavior of participants. It also outlines the lessons learned from deploying a continuous monitoring platform to track the travel behavior. These lessons will influence the evaluation component for the full pilot, which will cover multiple geographic regions, starting in summer 2021, and run for 2 years. The continuous data collection was enabled by a customized version of the open-source e-mission platform, called CanBikeCO, configured with a behavioral gamification feature. The Colorado Energy Office used this system to collect a unique data set consisting of 3 months of partially automated travel diaries, combining sensed and surveyed data and linked with demographic information, from 12 participants. The data collection process worked well overall: users generally liked the app, appreciated the game, and did not complain about battery life. The long tracking period introduced behavioral challenges in user engagement, which we plan to address using repeated patterns and automated status checks for the full pilot. The analysis results, based on the subset of trips with user-reported labels (68%), indicate that the e-bike was the dominant commute mode share (31%), in sharp contrast to the census bicycle commute mode share (<1%). E-bike trips primarily replaced single-occupancy vehicle (SOV) trips (28%), followed closely by walking (24%) and regular bike (20%). The nonmotorized mode replacement corresponds to lower travel time and increased productivity enabled by the program. The emissions impact analysis of the program, computed using trip-level energy intensity factors, indicates savings of 1,367 lbs. of CO 2 . Although the results are strongly positive, the narrow demographic profile of study participants, their limited mobility alternatives, and nonuniform labeling indicate caution in broader interpretation. These preliminary results do suggest that such programs, supported by real-time education and support from program managers, can simultaneously meet equity and sustainability goals. The planned full pilot, addressing the data collection challenges and broadening the geographic scope, will provide additional insights into the generality of this approach.
Big Bang nucleosynthesis provides the earliest probe of standard model physics, at a time when the universe was less than 1000 seconds old. It determines the abundances of the lightest nuclides, which give rise to the subsequent history of the visible matter in the universe. This work derives new 7 Be(n,p) 7 Li thermonuclear reaction rates based on all available experimental information. This reaction sensitively impacts the primordial abundances of 7 Be and 7 Li during big bang nucleosynthesis. We critically evaluate all available data and disregard experimental results that are questionable. For the nuclear model, we adopt an incoherent sum of single-level, two-channel, R -matrix approximation expressions, which are implemented into a hierarchical Bayesian model, to analyze the remaining six data sets we deem most reliable. In the fitting of the data, we consistently model all known sources of uncertainty, including discrepant absolute normalizations of different data sets, and also take the variation of the neutron and proton channel radii into account, hence providing less biased estimates of the 7 Be(n,p) 7 Li thermonuclear rates. From the resulting posteriors, we extract R -matrix parameters ($E_r, γ^2_n, γ^2_p$) and derive excitation energies and partial and total widths. Our fit is sensitive to the contributions of the first three levels above the neutron threshold. Reaction rates were computed by integrating 10,000 samples of the reduced cross section. Our 7 Be(n,p) 7 Li thermonuclear rates have uncertainties between 1.5% and 2.0% at temperatures of ≤1 GK. Finally, we compare our rates to previous results and find that the 7 Be(n,p) 7 Li rates most commonly used in big bang simulations have uncertainties that are too optimistic.
Microcomputer controlled system gather current and voltage data. System consists of light source, microcomputer, programable dc power supply, analog/ digital interface, and data storage display equipment. Applies series of test loads to cell via programable dc power supply to obtain I/V characteristic curve and key cell-peformance parameter. Apparatus and programming technique are applicable to devices such as batteries and sensors.
A commercial three-component laser Doppler anemometer (LDA) has been used to acquire a detailed set of three-dimensional mean and fluctuating velocity measurements in a low-speed air jet entering a stagnant ambient, over the first 15 jet exit diameters along the jet trajectory. These data are consistent with previous measurements in axisymmetric, turbulent jets. Mean velocity and Reynolds stress data approach a self-preserving behavior by x/D equal to about 15. However, the RMS turbulence fluctuations were not self-preserving at this axial location, as expected based upon previous experimental studies. These data confirm the ability to obtain reliable three-dimensional velocity data using the present three-dimensional LDA system.
We present infrared images of Cen A (NGC 5128) in the J, H, and K bands. The infrared morphology is primarily determined by the presence of a thin absorptive warped disk. By integrating the light of the underlying prolate galaxy through such a disk, we construct models which we compare with infrared and X-ray data. The geometry of the warped disk needed to fit the IR data is consistent with a warped disk which has evolved as a result of differential precession in a prolate potential. The disk has an inclination, with respect to the principal axis of the underlying elliptical galaxy, that is higher at larger radii than in the inner region. A scenario is proposed where a small gas-rich galaxy infalling under the force of dynamical friction is tidally stripped. Stripping occurs at different times during its infall. The orientation of the resulting gas disk depends upon the angular momentum of the infalling galaxy. We find that the resulting precession angle of the disk is well described by the precession model, but that the inclination angle may vary as a function of radius. We propose an orbit for the infalling galaxy that is consistent with the geometry of the warped disk needed to fit our infrared data, and rotation observed in the outer part of the galaxy.
The Telecommand Data Routing and Channel Services defined by the Consultative Committee for Space Data Systems (CCSDS) are flexible enough to support a myriad of commanding models. Because the standard is so broad, the traditional approach has been to implement only the portion of the standard needed by the particular spacecraft being tested/operated. Tasked with providing Telecommand Services for an entire class of spacecraft, where each spacecraft may choose any valid CCSDS commanding model, NASA Code 584 designed a common architecture capable of handling the full CCSDS protocol. The solution uses another CCSDS standard - the Standard Formatted Data Unit (SFDU) as the interface to the Telecommand Services. SFDUs provide a consistent way of labelling data objects, as well as allowing data objects to encapsulate other data objects. The resulting interface is: - Flexible: The full Data Routing and Channel Services are available via a single interface. The client (i.e. the command source) may enter commands at any layer within the protocol stack, specify any of the data aggregation or segmentation methods, and dynamically set any configuration parameter defined in the standard. - Object-oriented: Each object specifies both the data and the actions to be performed with the data. An object may contain other objects. - Expandable: New capabilities are added by defining new objects. Objects pass thru the protocol layers until they reach the applicable layer. The resulting design is: - Modular: The logic for each protocol layer is contained in a separate Application Program Interface (API). The objects used for the external interface are also used for communication between layers. - Distributable: The design can be split along any layer boundary for distribution across multiple machines. The objects ensure data consistency across platforms. This paper describes the SFDU-based interface and the resulting protocol implementation. The implementation is currently used by NASA (National Aeronautics and Space Administration) for integration & test of the microwave Anisotropy Probe (MAP) and Earth observer-I (EO-l) spacecraft. It will be used for post-launch operations of these spacecraft as well as the Imager for Magnetopause to Aurora Global Exploration (IMAGE) spacecraft.
The Tropospheric Ozone Lidar Network (TOLNet) has generated over a decade of ozone vertical profile data products over North America and contributed to several air quality focused field studies. The science value of the TOLNet data has been demonstrated in numerous peer-reviewed publications on air quality and ozone relevant research. As the broad scientific community has moved towards adopting FAIR Principles to make data more findable, accessible, interoperable, and (re)usable, the TOLNet team has been consistently making data more FAIR. This effort has many challenges, partially reflecting on the FAIR principles being domain agnostic while the implementation needs to be domain specific. The FAIR principles declare the dependence on the community standards, domain-relevant metadata, and rich metadata. This presentation uses the TOLNet data and data system as an example to explore the best practices to implement FAIR principle. Particularly, we will examine the metadata and the “richness” to support findability and usability as well as machine-to-machine actionability via API. Last year, as part of our FAIR journey, we launched the TOLNet website (https://tolnet.larc.nasa.gov/) and the API (https://tolnet.larc.nasa.gov/api/). Part of this process included extracting and cataloging metadata across the entire TOLNet mission timeframe. This enabled users to search through the mission by various metadata criteria, improving the findability and accessibility. And computers could connect directly to the TOLNet API to extract both metadata and data, providing a level of interoperability never present before for TOLNet data. On top of that, all new TOLNet data is now automatically validated using the API to ensure it complies with GEOMS standards, aiding in reusability. It takes both technology and scientists working together to make progress. The next step is to evaluate the current TOLNet offerings against NASA’s Practical Guide for Open, Free & FAIR NASA Earth Science Data Products (https://doi.org/10.5067/DOC/ESCO/ESDSWG-0002V1).