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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

URBANopt™ District Energy Systems HVAC Measures (DES_HVAC) [SWR-21-30]

DES_HVAC is a repository of a Parametric Analysis Tool (PAT) project and for scripts related to modeling of different HVAC systems for buildings in a district served by a district thermal energy system. The Measures directory contains the new measures developed to extract data from the simulations. Update these measures and not the measures in the Test Suite measures directory. PAT should be used to update the measures in the Test Suite project. The Test directory contains a PAT project for testing the functionality of the measures. The PAT project is named HVAC_Test and can be opened in PAT > 3.x.

Allen, Amy↗
DOE OSTI2023

ORNL_AISD_DL-HLgap

This dataset provides supplementary molecular dataset of Deep Learning Workflow for the Inverse Design of Molecules with Specific Optoelectronic Properties. The dataset comprises three main directories such as GDB-9_dataset, Low_HL_Gap_dataset, and High_HL_Gap_dataset which individually has csv files, smiles_txt files, pdb files and xyz files containing information of molecular structures, properties and coordinates generated from deep learning workflow using generative model, surrogate model and DFTB calculation results. GDB-9_dataset contains the molecular data extracted from the original GDB-9 dataset with additional data of DFTB HL gap, surrogate HL gap and molecular property analysis. (the number of atoms, aromaticity and double bond equivalent) Low_HL_Gap_dataset and High_HL_Gap_dataset contains series of dataset for different generations with further split to train and test dataset that were obtained from the iterative workflow described in the manuscript. Additional directory Chemiscope_visualization in Low_HL_Gap_dataset directory contains compressed json files to visualize molecules using chemiscope.org page or application to help readers examine generated molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Models, data, and scripts associated with “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning”

This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.

13C↗
DOE OSTI2024

Data associated with the manuscript “Radiative impact of record-breaking wildfires from integrated ground-based data” collected in Richland, Washington in September 2020

This data package is associated with the publication “Radiative impact of record-breaking wildfires from integrated ground-based data” submitted to Nature Scientific Reports (Kassianov et al., 2024). Data from ground-based measurements of shortwave and spectrally resolved irradiance and aerosol optical depth (AOD) in the visible and near-infrared spectral ranges were assessed to quantify the radiative impact of the September 2020 wildfires that occurred in the Western United States. Data were collected in September 2020 by several ground-based instruments at the Atmospheric Measurements Laboratory (AML) located in Richland, Washington (46.3451, -119.2792). These data include (1) Aerosol Optical Depth (AOD); (2) spectrally resolved and shortwave (SW) irradiances; (3) backscatter profiles; (4) total sky images; and (5) near-surface ambient air temperatures.The data package consists of five sub-directories: (1) “AML_Ceilometer_”; (2)” AML_CSPHOT_”; (3) “AML_MFRSR_irradiances_”; (4) “AML_SW_irradiances_and_Temp_”; (5) “AML_TSI_images_”; and 6 files stored at the directory level, including the readme, file-level metadata file, and data dictionary. The file-level metadata file (the file ending in “_flmd.csv”) lists all files contained in this data package and descriptions for each. The data dictionary (the file ending in “_dd.csv”) describes each tabular column header’s unit, definition, and structure. Below are descriptions of each sub-directory:“AML_Ceilometer_” includes ceilometer data collected at the AML. These files contain the corresponding narratives of data. Details related to the ceilometer data can be found in Morris (2016). “AML_CSPHOT_” includes ascii files with high-temporal resolution (about 10-15 min) AML CSPHOT data and their daily-averaged counterparts. These two files contain the corresponding narratives of data. Details related to the CSPHOT data can be found in Gregory (2011). “AML_MFRSR_irradiances_” includes ascii files with the AML MFRSR-measured diffuse, normal, and total spectrally resolved irradiance. Details related to the MFRSR data can be found in Hodges and Michalsky (2016) and Koontz et al. (2013). “AML_SW_irradiances_+_Temp_” includes near-surface ambient air temperature and SW irradiances, namely direct normal, diffuse hemispherical, and total hemispheric (global), measured at the AML. These files also incorporate the corresponding narratives of data. Details related to the SW irradiances can be found in Andreas et al. (2018). “AML_TSI_images_” includes Total Sky Images (TSIs) collected at the AML. Details related to the TSI data can be found in Morris (2005).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data and scripts associated with “Allometric scaling of hyporheic respiration across basins in the Pacific Northwest USA"

This data package is associated with the publication “Allometric scaling of hyporheic respiration across basins in the Pacific Northwest USA” submitted to JGR-Biogeosciences (Regier et al. 2025).This study used reach-scale modeled estimates of hyporheic aerobic respiration made by the River Corridor Model (Fang et al. 2020) and watershed characteristics across the Willamette and Yakima River basins to explore potential allometric scaling (i.e., power-law relationships between size and function) of cumulative hyporheic respiration across catchment-to-basin scales. Scaling was explored quantitatively via the R2, slope, and y-intercept of relationships between cumulative hyporheic respiration and watershed area, divided into hyporheic exchange flux (HEF) quantiles. We also explored relationships between allometric scaling and other watershed characteristics through linear regression, spatial patterns, and mutual information analyses. Our results also suggest variability of hyporheic respiration allometry for middle exchange flux quantiles, and in relation to land-cover. Our findings provide initial evidence that allometric scaling may be useful for predicting hyporheic biogeochemical dynamics across watersheds from reach to basin scales. This data package is associated with the GitHub repository found at https://github.com/peterregier/rc_wrb_yrb_scaling. The data package is organized into several key directories. The “data” folder contains multiple CSV files, including landscape heterogeneity, scaling analysis, and watershed boundary data. The “figures” folder has all figure files in both PDF and PNG formats. Core analysis scripts and figure generation scripts are in the “scripts” directory, systematically numbered for sequential execution. The root directory includes essential project files; please see the file ending in “flmd.csv” for a list and description of all files contained in this data package and the file ending in “dd.csv” for data dictionaries used to describe tabular column headers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

CSEM, Bottomhole Pressure, Salinity, and Flow-Rate Data Collected in Sep. 2020 for Proj. DE-FE0031785

This dataset consists of two directories: first directory named as EM for the controlled source electromagnetic (CSEM) survey data that are zipped per day for September 22, 23, 24, 25, and 27, 2020, and second directory named as "Bottomhole Pressure, Salinity, and Flow-Rate" for the simultaneous collection of bottomhole pressure and salinity and pump flow rate during the CSEM surveys.

CSEM↗
DOE OSTI2023

CSEM, BHP, Salinity, Flow-Rate, Fiber, and Tilt Data Collected in Jan. 2022 for Proj. DE-FE0031785

This dataset consists of four directories: first directory named as EM for the controlled source electromagnetic (CSEM) survey data that are zipped per day for January 18-21, 22, 23, 24, 25, 26, 27, 28, 29, 30, and 31, 2022; second directory named as "Fiber" for the fiber optic slow-strain and microseismic data that are zipped per well; the third file that contains bottomhole pressure (BHP) and salinity and pump flow rate data during the CSEM surveys; and the fourth file that contained tiltmeter data.

CSEM↗
DOE OSTI2020

Guide for Using ENDF/B-VIII.0 Nuclear Data with MCNP

This report provides guidance for using the ENDF/B-VIII.0 nuclear data that were released in 2018 and 2020 with MCNP. Appendix A: Obtaining and installing the new ENDF/B-VIII.0 ACE nuclear data. Appendix B. Obtaining & using XSDIR files that include ENDF/B-VIII.0 data. Appendix C: ENDF/B-VIII.0-based ACE files for neutron cross-sections. A listing of the available temperatures and ZA numbers for neutron cross-sections (ACE files with suffix .nnC). Appendix D: ENDF/B-VIII.0-based ACE files for thermal scattering law (TSL) data, $S$(α,β). A listing of the available thermal scattering $S$(α,β) data (ACE files with suffix .nnT) The next section below provides guidance for modifying existing $\textit{mcnp}$ input files to use the ENDF/B-VIII.0 data. Detailed results from running several benchmark suites for nuclear criticality safety with both ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear data are reported in. It is important to note that this report deals only with TSL data released in 2020. With the original release of ENDF/B-VIII.0 in 2018, the ACE files for neutron cross-sections were contained in a directory called Lib80X and the ACE files for TSL data were contained in a directory called ENDF80SaB. A few years later, it was determined that many of the TSL data files had been generated with faulty data or with incorrect processing, and a new set of ACE files for TSL data was created, with the corrected files contained in directory ENDF80SaB2.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

MODFLOW6 models used to evaluate potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, Southern Nevada

The hydrograph for well ER-5-3-2 in Frenchman Flat, southern Nevada, has previously unexplained water-level fluctuations. Four, three-dimensional, groundwater models (MODFLOW 6) were developed to evaluate potential stresses and hydrologic conditions affecting the well ER-5-3-2 hydrograph. Four model scenarios were developed that simulated: (1) wellbore leakage without recharge, (2) wellbore leakage with recharge, (3) shallow (low transmissivity) and deep (high transmissivity) carbonate rocks, and (4) lateral heterogeneity of carbonate rocks. Input and output files for the four model scenarios are in the model and output directories, respectively. Hydraulic conductivity, specific storage, and wellbore-leakage rates (when simulated) were estimated with parameter estimation (PEST) by minimizing a weighted composite, sum-of-squares objective function. The objective function was informed by measurement and Tikhonov regularization observations. Measurement observations included drawdowns from the constant-rate aquifer test and water-level altitudes measured in well ER-5-3-2 from 2001-2021. Tikhonov regularization informed hydraulic conductivity and specific storage parameters that were insensitive to measurement observations, where homogeneity was the preferred relation. Batch files, executables, and MODFLOW 6, PEST, and post-processing utilities are in the ancillary directory. Supplementary data also are included in the ancillary directory, including site information, high-frequency water-level and aquifer-test data, transmissivity estimates, water-chemistry data, and water-temperature analyses. This USGS data release contains data, analyses, and model files for the simulations and analysis results described in U.S. Geological Survey Scientific Investigations Report (https://doi.org/10.3133/sir20225132).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

JOBRUNNER

SF-23-071 Jobrunner is a command line tool to manage and deploy computing jobs, organize complex workloads, and enforce a directory based hierarchy to enable reuse of files and bash scripts within a project. Organization details of a directory tree are encoded in Jobfiles which serve as an index of files/scripts, and indicate their purpose when deploying or setting up a job. It is a flexible tool that allows users to design 16 their own directory structure, perserve their design, and maintain 17 consistency with increase in complexity of the project.

DHRUV, AKASH VIJAYKUMAR↗
DOE OSTI2024

Synthetic Streamflow Datasets to Support Emulation of Water Allocations via LSTM

This archive is the data companion to the bonney_et-al_2026_erc metarepo which generates synthetic data, trains an LSTM model, and generates performance metrics on the trained model. While the generation of the synthetic data is fully reprodicible, it is a computationally expensive process. This data archive contains the synthetic datasets needed for training and testing an LSTM model and reproduction of figures and tables. In addition, supplemenatary data products generating and visualizing results is also included, such as geospatial data for the basin. Contents There are two high level directories: `WRAP_archive/` and `repo_data/`. The `WRAP_archive` directory contains compressed intermediate dataproducts from the dataset generation workflow (marked as "I_Dataset_Generation" in the metarepo). These data products are not required by any scripts in the metarepo, but they are archived as they are expensive to generate and may have useful information for other analyses. The `repo_data` directory contains the necessary data for reproducing the workflow in the metarepo and should be decompressed and moved into the top level of the metarepo. Additional details are provided in README.md.

drought↗
NASA NTRS1992

A trace-driven analysis of name and attribute caching in a distributed system

This paper presents the results of simulating file name and attribute caching on client machines in a distributed file system. The simulation used trace data gathered on a network of about 40 workstations. Caching was found to be advantageous: a cache on each client containing just 10 directories had a 91 percent hit rate on name look ups. Entry-based name caches (holding individual directory entries) had poorer performance for several reasons, resulting in a maximum hit rate of about 83 percent. File attribute caching obtained a 90 percent hit rate with a cache on each machine of the attributes for 30 files. The simulations show that maintaining cache consistency between machines is not a significant problem; only 1 in 400 name component look ups required invalidation of a remotely cached entry. Process migration to remote machines had little effect on caching. Caching was less successful in heavily shared and modified directories such as /tmp, but there weren't enough references to /tmp overall to affect the results significantly. We estimate that adding name and attribute caching to the Sprite operating system could reduce server load by 36 percent and the number of network packets by 30 percent.

Shirriff, Ken W.↗
NASA NTRS1995

SEDAC information gateway plan V(1)

This annual update of the Information Gateway Plan incorporates changes recommended by the Socioeconomic Data and Applications Center (SEDAC) User Working Group (UWG) and reflects comments and suggestions from users, collaborators, and the Contracting Officer Technical Representative (COTR). The Information Gateway Plan is a concise and specific plan that outlines SEDAC activities and services in support of the earth and social sciences and other user communities. The SEDAC Information Gateway effort is a primary means by which the Earth Observing System Data and Information System (EOSDIS) can link meaningfully with a broad range of social science data sources and users in ways that lead to tangible benefits to the American people. The SEDAC Information Gateway provides interdisciplinary access to socioeconomic and physical science data and information resources held by SEDAC and numerous other institutions and networks around the world. The Plan describes the areas of research of earth scientists and socioeconomic scientists where interchange of data and information is most needed. It sets guidelines for the continued development of SEDAC's directory of social science datasets and establishes priorities for efforts to make data held by SEDAC or accessible through SEDAC available to the user community. The Plan also describes the means by which the SEDAC user community can access information products specified by the SEDAC Data and Applications Development Plan (DADP). Among other major activities, SEDAC will continue to enhance and operate a directory capability, interoperable with the Global Change Master Directory, that provides the socioeconomic community with information about earth science products and the earth science research community with information about socioeconomic data. The Information Gateway also serves as a unique and powerful access pathway for a wide range of users and potential users of socioeconomic and earth science data, including especially remote sensing data.

Chen, Robert S.↗
NASA NTRS1996

DIstributed VIRtual System (DIVIRS) Project

The development of Prospero moved from the University of Washington to ISI and several new versions of the software were released from ISI during the contract period. Changes in the first release from ISI included bug fixes and extensions to support the needs of specific users. Among these changes was a new option to directory queries that allows attributes to be returned for all files in a directory together with the directory listing. This change greatly improves the performance of their server and reduces the number of packets sent across their trans-pacific connection to the rest of the internet. Several new access method were added to the Prospero file method. The Prospero Data Access Protocol was designed, to support secure retrieval of data from systems running Prospero.

Schorr, Herbert↗
NASA NTRS2001

In Interactive, Web-Based Approach to Metadata Authoring

NASA's Global Change Master Directory (GCMD) serves a growing number of users by assisting the scientific community in the discovery of and linkage to Earth science data sets and related services. The GCMD holds over 8000 data set descriptions in Directory Interchange Format (DIF) and 200 data service descriptions in Service Entry Resource Format (SERF), encompassing the disciplines of geology, hydrology, oceanography, meteorology, and ecology. Data descriptions also contain geographic coverage information, thus allowing researchers to discover data pertaining to a particular geographic location, as well as subject of interest. The GCMD strives to be the preeminent data locator for world-wide directory level metadata. In this vein, scientists and data providers must have access to intuitive and efficient metadata authoring tools. Existing GCMD tools are not currently attracting. widespread usage. With usage being the prime indicator of utility, it has become apparent that current tools must be improved. As a result, the GCMD has released a new suite of web-based authoring tools that enable a user to create new data and service entries, as well as modify existing data entries. With these tools, a more interactive approach to metadata authoring is taken, as they feature a visual "checklist" of data/service fields that automatically update when a field is completed. In this way, the user can quickly gauge which of the required and optional fields have not been populated. With the release of these tools, the Earth science community will be further assisted in efficiently creating quality data and services metadata. Keywords: metadata, Earth science, metadata authoring tools

Pollack, Janine↗
NASA NTRS2004

Metadata Authoring with Versatility and Extensibility

NASA's Global Change Master Directory (GCMD) assists the scientific community in the discovery of and linkage to Earth science data sets and related services. The GCMD holds over 13,800 data set descriptions in Directory Interchange Format (DIF) and 700 data service descriptions in Service Entry Resource Format (SERF), encompassing the disciplines of geology, hydrology, oceanography, meteorology, and ecology. Data descriptions also contain geographic coverage information and direct links to the data, thus allowing researchers to discover data pertaining to a geographic location of interest, then quickly acquire those data. The GCMD strives to be the preferred data locator for world-wide directory-level metadata. In this vein, scientists and data providers must have access to intuitive and efficient metadata authoring tools. Existing GCMD tools are attracting widespread usage; however, a need for tools that are portable, customizable and versatile still exists. With tool usage directly influencing metadata population, it has become apparent that new tools are needed to fill these voids. As a result, the GCMD has released a new authoring tool allowing for both web-based and stand-alone authoring of descriptions. Furthermore, this tool incorporates the ability to plug-and-play the metadata format of choice, offering users options of DIF, SERF, FGDC, ISO or any other defined standard. Allowing data holders to work with their preferred format, as well as an option of a stand-alone application or web-based environment, docBUlLDER will assist the scientific community in efficiently creating quality data and services metadata.

Pollack, Janine↗
NASA NTRS2010

Automated Computer Access Request System

The Automated Computer Access Request (AutoCAR) system is a Web-based account provisioning application that replaces the time-consuming paper-based computer-access request process at Johnson Space Center (JSC). Auto- CAR combines rules-based and role-based functionality in one application to provide a centralized system that is easily and widely accessible. The system features a work-flow engine that facilitates request routing, a user registration directory containing contact information and user metadata, an access request submission and tracking process, and a system administrator account management component. This provides full, end-to-end disposition approval chain accountability from the moment a request is submitted. By blending both rules-based and rolebased functionality, AutoCAR has the flexibility to route requests based on a user s nationality, JSC affiliation status, and other export-control requirements, while ensuring a user s request is addressed by either a primary or backup approver. All user accounts that are tracked in AutoCAR are recorded and mapped to the native operating system schema on the target platform where user accounts reside. This allows for future extensibility for supporting creation, deletion, and account management directly on the target platforms by way of AutoCAR. The system s directory-based lookup and day-today change analysis of directory information determines personnel moves, deletions, and additions, and automatically notifies a user via e-mail to revalidate his/her account access as a result of such changes. AutoCAR is a Microsoft classic active server page (ASP) application hosted on a Microsoft Internet Information Server (IIS).

Snook, Bryan E.↗