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DOE OSTI2023

Approaches for Synthesis and Deployment of Controller Models on Automated Vehicles for Car-following in Mixed Autonomy

This paper describes the software design patterns and vehicle interfaces that were employed to transition vehicle controllers from simulation environments to open-road field experiments. The approach relies on a life cycle that utilizes model-based design and code generation, along with agile software development, and both software and hardware-in-the-loop testing, with additional safety margins. Autonomous designs should consider the dynamics of mixed autonomy in traffic to safely operate among humans. The software that provides a vehicle’s behavior intelligence is often developed through simulation, which may have a mismatch between dynamics, or as a result of a reinforcement learning workflow, which may be a black box with challenges to analyze. In each of these cases, it is important to have research interfaces that provide strongly typed data streams accessible to researchers who are not software experts while continuing to satisfy safety and liveness constraints. This paper describes how we design the hardware platform interfaces and software design process for a mixed autonomy traffic experiment with a leader-follower scenario. Controller synthesis for these vehicles requires clearly articulated vehicle interfaces and software design patterns for successful onboard deployment. Testing strategies for such controllers are also described before algorithms are transitioned to full-scale field experiments with safety operators for the vehicles. Testing strategies include software-in-the-loop simulation testing, hardware-in-the-loop simulation, ghost-car testing, and read-only testing in live traffic. With our approach, we were not only able to validate our controller synthesized in scripts and simulation, but also able to scale deployment to multiple vehicles.

Bhadani, Rahul↗

DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

DNA-PAINT Imaging with Hydrogel Imprinting and Clearing

Hydrogel-embedding is a versatile technique in fluorescence microscopy, offering stabilization, optical clearing, and the physical expansion of biological specimens. DNA-PAINT is a super-resolution microscopy approach based on the diffusion and transient binding of fluorescently labeled oligos, but its feasibility in hydrogels has not yet been explored. In this study, we demonstrate that polyacrylamide hydrogels support sufficient diffusion for effective DNA-PAINT imaging. Using acrydite-anchored oligonucleotides imprinted from patterned DNA origami nanostructures and microtubule filaments in fixed cells, we find that hydrogel embedding preserves docking strand positioning at the nanoscale. Sample clearing via protease treatment had minor structural effects on the microtubule structure and enhanced diffusion and accessibility to hydrogel-imprinted docking strands. Our work demonstrates promising potential for diffusion and binding-based fluorescence imaging applications in hydrogel-embedded samples.

DNA origami↗

DOE OSTI2026

The U.S. Agrivoltaic Shading Tool: A National-Scale Interface for Modeling Light and Shade Patterns in Ten Common Agrivoltaic Configurations

Agrivoltaic systems are dual-use configurations that co-locate agriculture and photovoltaic (PV) infrastructure and require careful design to balance crop performance and energy generation. A critical element of agrivoltaic design is the spatial and temporal distribution of irradiance and shade within and around PV arrays. To support research, planning, and stakeholder decision-making, we introduce the U.S. Agrivoltaic Shading Tool, a novel web-based application that delivers high-resolution irradiance and photosynthetically active radiation (PAR) modeling for ten standardized PV configurations across the conterminous United States. The tool leverages the National Laboratory of the Rockies (NLR) System Advisor Model (SAM) to perform detailed irradiance simulations, using meteorological data from the National Solar Radiation Database (NSRDB). Outputs include seasonal, monthly, weekly, and diurnal patterns of available sunlight, amount of shade, irradiance, and PAR at ground level within agrivoltaic system footprints. For a user's selected location, these results are visualized through interactive visualizations, heatmaps, and time-series plots, designed to be accessible to both technical and non-technical users. In addition to facilitating rapid spatial exploration of agrivoltaic light environments, the tool will offer seamless integration with the InSPIRE Agrivoltaics Design and Analysis Model (ADAM). This optional workflow will allow users to port selected site and configuration parameters into a more advanced modeling environment for further customization of structural layouts, crop-system compatibility, power generation, and technoeconomic performance. Finally, to promote open science, the entire dataset will be hosted and available for open access through the OpenEI platform. By standardizing and disseminating high-quality irradiance data and design tools, the U.S. Agrivoltaic Shading Tool supports a wide range of users, including researchers, landowners, energy developers, and policymakers, in evaluating the agronomic and energetic feasibility of agrivoltaic systems across the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1993

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a multispectral imaging radiometer scheduled to fly in Earth orbit in 1998 on NASA's Earth Observation System platform. The instrument will have 14 spectral bands from the visible to thermal infrared wavelength regions with high spectral and spatial resolution and with along-track stereoscopic capability. ASTER imagery will be used to study such phenomena as Earth surface properties, elements of the surface heat balance, cloud cover characteristics, glacier and sea ice extent, patterns of vegetation and land use, volcanoes, coral reefs and coastal processes, geology and topography, and hydrology. ASTER will have three separate radiometer subsystems, each with a swath width of 60 km. Any point on the globe will be accessible at least once every 16 days for the short wavelength infrared and thermal infrared subsystems, and once every five days for the visible and near infrared subsystem. Instrument and spacecraft resources are allocated to support an 8 percent average duty cycle, corresponding to over 700 60 by 60-km scenes per day. ASTER data will be acquired and processed according to specific user requirements over its five-year mission.

Kahle, Anne B.↗

DOE OSTI2023

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Abstract Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Protein folds vs. protein folding: Differing questions, different challenges

We report protein fold prediction using deep-learning artificial intelligence (AI) has transformed the field of protein structure prediction. By combining physical and geometric constraints—and especially patterns extracted from the Protein Data Bank —these machine learning algorithms can predict protein structures at or near atomic resolution and do so in seconds. Today, these computational methods have now solved more than 200 million protein structures, which are accessible from the AlphaFold Protein Structure Database. This accomplishment seems all the more remarkable because few thought it possible or saw it coming. Deservedly, deep-learning AI was named Science magazine’s 2021 “breakthrough of the year”. Clearly, deep-learning AI represents a major advance in protein fold prediction.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2009

The Role of Flux Transfer Events in Solar Wind-Magnetosphere Coupling

Recent global MHD simulations of Flux Transfer Events (FTE's)[Dorelli and Bhattachar ee, JGR, 114, 2009] demonstrate that they are topologically complex flux ropes which extend large distances away from the subsolar magnetopause. Thus, FTE's represent a significant perturbation to the magnetopause magnetic field topology. Specifically, prior to the formation of the first FTE of the simulation, the dayside magnetopause has a relatively simple topology consisting of a single magnetic separator draping northward (even for southward IMF) over the dayside magnetopause (in a manner similar to the "overdraping" which occurs under northward IMF conditions). After the first FTE forms, however, the magnetopause becomes topologically more complex, consisting of multiple separators and braided flux domains. One consequence of this topological complexity is that open flux tubes have direct access to the magnetosphere near the subsolar region (such direct access is not possible during steady separator reconnection). Since the magnetopause topology affects the mapping of the solar wind electric field to the magnetosphere, an interesting question arises: What is the effect of FTE generation on the global magnetospheric convection pattern? In this talk, we address this question by exploring the differences between magnetospheric and ionospheric convection before and after the formation of the first FTE of the simulation.

Dorelli, John C.↗

DOE OSTI2016

Intermediate-Scale Hydraulic Fracturing in a Deep Mine - kISMET Project Summary 2016

In support of the U.S. DOE SubTER Crosscut initiative, we established a field test facility in a deep mine and designed and carried out in situ hydraulic fracturing experiments in the crystalline rock at the site to characterize the stress field, understand the effects of rock fabric on fracturing, and gain experience in monitoring using geophysical methods. The project also included pre- and post-fracturing simulation and analysis, laboratory measurements and experiments, and we conducted an extended analysis of the local stress state using previously collected data. Some of these activities are still ongoing. The kISMET (permeability (k) and Induced Seismicity Management for Energy Technologies) experiments meet objectives in SubTER’s “stress” pillar and the “new subsurface signals” pillar. The kISMET site was established in the West Access Drift of SURF 4850 ft (1478 m) below ground (on the 4850L) in phyllite of the Precambrian Poorman Formation. We drilled and cored five near-vertical boreholes in a line on 3 m spacing, deviating the two outermost boreholes slightly to create a five-spot pattern around the test borehole centered in the test volume at ~1528 m (5013 ft). Laboratory measurements of core from the center test borehole showed P-wave velocity heterogeneity along each core indicating strong, fine-scale (~1 cm or smaller) changes in the mechanical properties of the rock. The load-displacement record on the core suggests that the elastic stiffness is anisotropic. Tensile strength ranges between 3-7.5 MPa and 5-12 MPa. Permeability measurements are planned, as are two types of laboratory miniature hydraulic fracturing experiments to investigate the importance of rock fabric (anisotropy and heterogeneity) on near-borehole hydraulic fracture generation. Pre-fracturing numerical simulations with INL’s FALCON discrete element code predicted a fracture radius of 1.2 m for a corresponding injection volume of 1.2 L for the planned fractures, and negligible microseismicity. Field measurements of the stress field by hydraulic fracturing showed that the minimum horizontal stress at the kISMET site averages 21.7 MPa (3146 psi) pointing approximately N-S (356 degrees azimuth) and plunging slightly NNW at 12°. The vertical and horizontal maximum stress are similar in magnitude at 42-44 MPa (6090-6380 psi) for the depths of testing which averaged approximately 1530 m (5030 ft). Hydraulic fractures were remarkably uniform suggesting core-scale and larger rock fabric did not play a role in controlling fracture orientation. Monitoring using ERT and CASSM in the four monitoring boreholes, and passive seismic accelerometer-based measurements in the West Access Drift, was carried out during the generation of a larger fracture (so-called stimulation test) at a depth of 40 m below the invert. ERT was not able to detect the fracture created, nor were the accelerometers in the drift, but microseismicity was detected for first (deepest) hydraulic-fracturing stress measurement. The CASSM data have not yet been analyzed. Analytical solutions suggest fracture radius of the large fracture (stimulation test) was more than 6 m, depending on the unknown amount of leak-off. The kISMET results for stress state are consistent with large-scale mid-continent estimates of stress. Currently we are using the orientation of the stress field we determined to interpret a large number of borehole breakouts recorded in nearby boreholes at SURF to generate a more complete picture of the stress field and its variations at SURF. The efforts on the project have prompted a host of additional follow-on studies that we recommend be carried out at the kISMET site.

02 PETROLEUM↗

DOE OSTI2024

Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research

Introduction The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped. Methods To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources. Results Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments. Discussion We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Genetics & Heredity↗

DOE OSTI2016

Nephron segment specific microRNA biomarkers of pre-clinical drug-induced renal toxicity: Opportunities and challenges

Drug-induced nephrotoxicity is a common drug development complication for pharmaceutical companies. Sensitive, specific, translatable and non-invasive biomarkers of renal toxicity are urgently needed to diagnose nephron segment specific injury. The currently available gold standard biomarkers for nephrotoxicity are not kidney-specific, lack sensitivity for early detection, and are not suitable for renal damage localization (glomerular vs tubulointerstitial injury). MicroRNAs (miRNAs) are increasingly gaining momentum as promising biomarkers of various organ toxicities, including drug induced renal injury. This is mostly due to their stability in easily accessible biofluids, ease of developing nucleic acids detection compared to protein detection assays, as well as their interspecies translatability. Increasing concordance of miRNA findings by standardizing methodology most suitable for their detection and quantitation, as well as characterization of their expression pattern in a cell type specific manner, will accelerate progress toward validation of these miRNAs as biomarkers in pre-clinical, and clinical settings. This review aims to highlight the current pre-clinical findings surrounding miRNAs as biomarkers in two important segments of the nephron, the glomerulus and tubules. - Highlights: • miRNAs are promising biomarkers of drug-induced kidney injury. • Summarized pre-clinical miRNA biomarkers of drug-induced nephrotoxicity. • Described the strengths and challenges associated with miRNAs as biomarkers.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Nanoscale measurement of Nernst effect in two-dimensional charge density wave material 1T-TaS 2

Advances in nanoscale material characterization on two-dimensional van der Waals layered materials primarily involve their optical and electronic properties. The thermal properties of these materials are harder to access due to the difficulty of thermal measurements at the nanoscale. In this work, we create a nanoscale magnetothermal device platform to access the basic out-of-plane magnetothermal transport properties of ultrathin van der Waals materials. Specifically, the Nernst effect in the charge density wave transition metal dichalcogenide 1T-TaS 2 is examined on nano-thin flakes in a patterned device structure. It is revealed that near the commensurate charge density wave (CCDW) to nearly commensurate charge density wave (NCCDW) phase transition, the polarity of the Nernst effect changes. Since the Nernst effect is especially sensitive to changes in the Fermi surface, this suggests that large changes are occurring in the out-of-plane electronic structure of 1T-TaS 2 , which are otherwise unresolved in just in-plane electronic transport measurements. This may signal a coherent evolution of out-of-plane stacking in the CCDW! NCCDW transition.

36 MATERIALS SCIENCE↗

NASA NTRS1995

Geophysical phenomena classification by artificial neural networks

Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

Gough, M. P.↗

NASA NTRS2014

Design and Control of Modular Spine-Like Tensegrity Structures

We present a methodology enabled by the NASA Tensegrity Robotics Toolkit (NTRT) for the rapid structural design of tensegrity robots in simulation and an approach for developing control systems using central pattern generators, local impedance controllers, and parameter optimization techniques to determine effective locomotion strategies for the robot. Biomimetic tensegrity structures provide advantageous properties to robotic locomotion and manipulation tasks, such as their adaptability and force distribution properties, flexibility, energy efficiency, and access to extreme terrains. While strides have been made in designing insightful static biotensegrity structures, gaining a clear understanding of how a particular structure can efficiently move has been an open problem. The tools in the NTRT enable the rapid exploration of the dynamics of a given morphology, and the links between structure, controllability, and resulting gait efficiency. To highlight the effectiveness of the NTRT at this exploration of morphology and control, we will provide examples from the designs and locomotion of four different modular spine-like tensegrity robots.

Modular↗

DOE OSTI2021

Indexing habitat opportunity for juvenile anadromous fishes in tidal-fluvial wetland systems

In intertidal wetlands, annual habitat use by migrating juvenile salmon is limited by both physical and biological constraints. Physical limits are set by inundation patterns and water quality parameters, while biological bounds are determined by species specific migration timing and seasonal residency behaviors. We developed a new metric called the fish habitat opportunity index (FHOI) to quantify the total time per calendar year that salmon could access intertidal sites, based on periods of inundation and juvenile salmon seasonal migration. The index was applied using data collected at a recently reconnected tidal freshwater wetland area during 2006-2009. We found the wetland to be accessible to different salmon species for different lengths of time and periods of the year. On average, chum salmon (Oncorhynchus keta) had remarkably consistent opportunities to use marsh habitat over the 4 years sampled, while opportunities were more variable for Chinook (O. tshawytscha), and coho salmon (O. kisutch). Inundation varied nonlinearly with tidal height, and fish had access to the productive marsh edge for approximately 40-50% of the time during periods of access. The FHOI is widely applicable to wetland systems throughout the tidal fluvial continuum, and provides a metric for assessing fish habitat opportunity that is more realistic than simple wetted area calculations. Finally, the FHOI affords a method of a priori comparison among potential restoration sites to help resource managers predict the likely relative benefit to juvenile salmon.

wetland restoration, estuary, habitat opportunity,↗

DOE OSTI2015

New information on the occurrence of the O(6) symmetry in nuclei

New γγ-coincidence relations and E2 decay transition rates in the isotopes 194,196 Pt have been deduced from γ-ray spectroscopy experiments using the Gammasphere spectrometer in projectile-Coulomb excitation reactions of beams of 194,196 Pt ions provided by the ATLAS accelerator facility. The results give access to observables that are crucial for a classification of excited quadrupole-collective states in terms of quantum numbers associated with the analytically solvable O(6) dynamical symmetry of the interacting boson model. The data on 196 Pt corroborate the qualitative pattern of excitation energies and E2 transition rates expected from the O(6) solution but the excitation energies significantly deviate from it on a quantitative level.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

The cis-regulatory codes of response to combined heat and drought stress in Arabidopsis thaliana

Abstract Plants respond to their environment by dynamically modulating gene expression. A powerful approach for understanding how these responses are regulated is to integrate information about cis-regulatory elements (CREs) into models called cis-regulatory codes. Transcriptional response to combined stress is typically not the sum of the responses to the individual stresses. However, cis-regulatory codes underlying combined stress response have not been established. Here we modeled transcriptional response to single and combined heat and drought stress in Arabidopsis thaliana. We grouped genes by their pattern of response (independent, antagonistic and synergistic) and trained machine learning models to predict their response using putative CREs (pCREs) as features (median F-measure = 0.64). We then developed a deep learning approach to integrate additional omics information (sequence conservation, chromatin accessibility and histone modification) into our models, improving performance by 6.2%. While pCREs important for predicting independent and antagonistic responses tended to resemble binding motifs of transcription factors associated with heat and/or drought stress, important synergistic pCREs resembled binding motifs of transcription factors not known to be associated with stress. These findings demonstrate how in silico approaches can improve our understanding of the complex codes regulating response to combined stress and help us identify prime targets for future characterization.

Azodi, Christina B.↗

NASA NTRS2013

Feeding People's Curiosity: Leveraging the Cloud for Automatic Dissemination of Mars Images

Smartphones and tablets have made wireless computing ubiquitous, and users expect instant, on-demand access to information. The Mars Science Laboratory (MSL) operations software suite, MSL InterfaCE (MSLICE), employs a different back-end image processing architecture compared to that of the Mars Exploration Rovers (MER) in order to better satisfy modern consumer-driven usage patterns and to offer greater server-side flexibility. Cloud services are a centerpiece of the server-side architecture that allows new image data to be delivered automatically to both scientists using MSLICE and the general public through the MSL website (http://mars.jpl.nasa.gov/msl/).

Cloud Computing↗