Rights in data clauses and their impact on information functions of government
Data rights problems and government information systems with emphasis on computer software acquisition and dissemination
Mc Coy, G. T.↗
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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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Special Collection on Open Collaboration Across Geosciences
Join open writing teams to collaborate on commentaries for a special collection describing approaches that embody synthesis, cross-disciplinary integration, and open science across geosciences.
58 GEOSCIENCES↗
IM3 Open Source Data Center Atlas
IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830
Mongird, Kendall [Pacific Northwest National Labor↗
IM3 Open Source Data Center Atlas
IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Data items will occasionally be removed from OSM if they are misidentified, if they no longer exist, if they are duplicates of another item, or similar. For that reason, updated versions of this database may not contain all data center locations included in previous versions. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830
Mongird, Kendall [Pacific Northwest National Labor↗
Empowering Open Science with the Science Discovery Engine
: This presentation will describe the work to date in building the SDE as well as what the team has learned about the SMD ecosystem, information curation, and data governance. A short demonstration of the SDE will be presented, and an overview of near- and long-term goals for future development will be shared. Community feedback will be welcomed about the interface, content, and other features to help inform actions to maximize the SDE’s performance and usability. Whether users aim to discover Earth-like atmospheres on planets outside of our solar system or better understand the impacts of solar energy on our own planet, the Science Discovery Engine provides a means for scientists and all curious individuals to find content to further their understanding of science across all time and space scales.
Emily Foshee↗
Manufacturing Technology Information Analysis Center: Knowledge Is Strength
The Center's primary function is to facilitate technology transfer within DoD, other government agencies and industry. The DoD has recognized the importance of technology transfer, not only to support specific weapon system manufacture, but to strengthen the industrial base that sustains DoD. MTIAC uses an experienced technical staff of engineers and information specialists to acquire, analyze, and disseminate technical information. Besides ManTech project data, MTIAC collects manufacturing technology from other government agencies, commercial publications, proceedings, and various international sources. MTIAC has various means of disseminating this information. Much of the technical data is on user accessible data bases. The Center researches and writes a number of technical reports each year and publishes a newsletter monthly. Customized research is performed in response to specific inquiries from government and industry. MTIAC serves as a link between Government and Industry to strengthen the manufacturing technology base through the dissemination of advanced manufacturing information.
Safar, Michal↗
Supporting Responsible Machine Learning in Heliophysics
Over the last decade, Heliophysics researchers have increasingly adopted a variety of machine learning methods such as artificial neural networks, decision trees, and clustering algorithms into their workflow. Adoption of these advanced data science methods had quickly outpaced institutional response, but many professional organizations such as the European Commission, the National Aeronautics and Space Administration (NASA), and the American Geophysical Union have now issued (or will soon issue) standards for artificial intelligence and machine learning that will impact scientific research. These standards add further (necessary) burdens on the individual researcher who must now prepare the public release of data and code in addition to traditional paper writing. Support for these is not reflected in the current state of institutional support, community practices, or governance systems. We examine here some of these principles and how our institutions and community can promote their successful adoption within the Heliophysics discipline.
Machine learning↗
Transportation Big Data: Unbiased Analysis and Tools to Inform Sustainable Transportation Decisions
Today, transportation operation and energy systems data are generated at an unprecedented scale. The U.S. Department of Energy's National Renewable Energy Laboratory (NREL) is the go-to source for expertise in providing data and analysis to inform industry and government transportation decision making. The lab's teams of data experts and engineers are mining and analyzing large sets of complex data -- or 'big data' -- to develop solutions that support the research, development, and deployment of market-ready technologies that reduce fuel consumption and greenhouse gas emissions.
30 DIRECT ENERGY CONVERSION↗
Waste-to-Energy Technical Assistance for Local Governments
The goal of this project is to mobilize data, information, and knowledge generated about organic waste streams to local governments and support their decision-making. It builds on previous work related to wet organic waste resources (food waste, sludge, manure, waste fats and oils). The project provides an improved understanding of local waste challenges and priorities to inform BETO's R&D strategies and decision-making. It also supports local governments' goals and plans related to sustainable waste management, enables energy and/or resource recovery projects development at municipal level, and facilitates public-private partnerships. A key challenge is associated with more activities requested by entities than program allows which is mitigated by prioritization by requesting agency. All FY21 and FY22 TA requests have been completed. Major accomplishments include: 1). 34 requests managed in the past two years, 2) Distilled key challenges faced by communities. Key outcomes include strong community participation since inception of the program, dedicated program web site, deliverables tailored to communities' needs, and distribution materials (e.g., fact sheets, brochures) for a broader audience.
BIOMASS FUELS↗
Image Processing
The Computer Graphics Center of North Carolina State University uses LAS, a COSMIC program, to analyze and manipulate data from Landsat and SPOT providing information for government and commercial land resource application projects. LAS is used to interpret aircraft/satellite data and enables researchers to improve image-based classification accuracies. The system is easy to use and has proven to be a valuable remote sensing training tool.
Source record↗
The NASA Scientific and Technical Information (STI) Program's Implementation of Open Archives Initiation (OAI) for Data Interoperability and Data Exchange
Interoperability and data-exchange are critical for the survival of government information management programs. E-government initiatives are transforming the way the government interacts with the public. More information is to be made available through web-enabled technologies. Programs such as the NASA's Scientific and Technical Information (STI) Program Office are tasked to find more effective ways to disseminate information to the public. The NASA STI Program is an agency-wide program charged with gathering, organizing, storing, and disseminating NASA-produced information for research and public use. The program is investigating the use of a new protocol called the Open Archives Initiative (OAI) as a means to improve data interoperability and data collection. OAI promotes the use of the OAI harvesting protocol as a simple way for data sharing among repositories. In two separate initiatives, the STI Program is implementing OAI In collaboration with the Air Force, Department of Energy, and Old Dominion University, the NASA STI Program has funded research on implementing the OAI to exchange data between the three organizations. The second initiative is the deployment of OAI for the NASA technical report server (TRS) environment. The NASA TRS environment is comprised of distributed technical report servers with a centralized search interface. This paper focuses on the implementation of OAI to promote interoperability among diverse data repositories.
Rocker, JoAnne↗
A Database of Interplanetary and Interstellar Dust Detected by the Wind Spacecraft
It was recently discovered that the WAVES instrument on the Wind spacecraft has been detecting, in situ, interplanetary and interstellar dust of approximately 1 micron radius for the past 22 years. These data have the potential to enable advances in the study of cosmic dust and dust-plasma coupling within the heliosphere due to several unique properties: the Wind dust database spans two full solar cycles; it contains over 107,000 dust detections; it contains information about dust grain direction of motion; it contains data exclusively from the space environment within 350 Earth radii of Earth; and it overlaps by 12 years with the Ulysses dust database. Further, changes to the WAVES antenna response and the plasma environment traversed by Wind over the lifetime of the Wind mission create an opportunity for these data to inform investigations of the physics governing the coupling of dust impacts on spacecraft surfaces to electric field antennas. A Wind dust database has been created to make the Wind dust data easily accessible to the heliophysics community and other researchers. This work describes the motivation, methodology, contents, and accessibility of the Wind dust database.
interplaneary dust↗
Expanding geothermal resource utilization through directed research, education, and public outreach: Final Technical Report
The University of Nevada, Reno (UNR) conducts research and outreach activities that will lead to increased utilization of geothermal resources in the western US. The Great Basin Center for Geothermal Energy (GBCGE) is working in partnership with US industry to establish geothermal energy as a sustainable, environmentally sound, economically competitive contributor to energy supply in the western US. Task 1 involves conducting geoscience and engineering research and developing technology to improve the assessment, exploration, and stimulation of geothermal resources. Subtask projects were selected based on peer review of proposals submitted to the GBCGE from Nevada System of High Education (NSHE) institutions for short project development and seed awards intended to develop background and establish viability of approaches for future activities. Task 2 includes project management and organization of workshops periodically requested by DOE and others to satisfy other mission goals of the GBCGE and the DOE geothermal program. GBCGE supports interaction with national and international geothermal organizations, with brochures, presentations, and materials describing GBCGE accomplishments and current research. We continue to maintain and develop an internet-based information system that makes geothermal data and information available to industry, government, and academic stakeholders for exploration and development of geothermal resources. This award also partially supported post-doctoral scholar Drew Siler and research scientist Betsy Littlefield Pace whose effort is included under developing future research projects. Task 2 also focuses on education and outreach through a competitive graduate fellowship program. The budget is for two-year stipends for three graduate students to work collaboratively with GBCGE faculty on Master’s or PhD degrees in geoscience and engineering fields. This grant supported three MS students in full for two years toward the degree and contributed bridge money for four other students to finish their degrees. In total, eight graduate degrees were supported by this grant, either through the faculty seed grants or the fellowship program.
15 GEOTHERMAL ENERGY↗
High-Reliability Systems and the Control of National Security Data and Information
High-reliability systems are characterized by catastrophic implications in the event of failure. These implications can include substantive damage to the environment, social order, and loss of life. Examples of high-reliability systems include nuclear submarines, nuclear reactors, the electric grid, and nuclear weapons. Due to the catastrophic implications of failure, there are heightened awareness and control mechanisms surrounding related data and information. However, defining the difference between data and information is often ambiguous across scholarly disciplines and in United States policy and legislation. For high-reliability systems, the implications of ambiguity between data and information may affect the security of United States interests and even cost lives. For security, data are raw facts or figures without context, while information is the compilation or articulation of data that forms context. Security depends on clarity in the differences between data and information and how to control them. Control is necessary to ensure that data and information are not unintentionally released to foreign governments, the public, or those without need-to-know. A primary concern in the practice of security is the control of data to avoid the unintended conversion to information. Intra-institutionally, this control is highly complex given the amalgam of legacy data systems and the numerous and constantly evolving nature of modern data systems that were not necessarily designed to be integrated. The complexity of this concern is augmented when institutions are part of interinstitutional collaborations or networks of public-private partnerships that share data and information. Additionally, institutions that share data as a function of policy and legislative action— particularly formally integrated data and information system infrastructures—may be at higher security risk. This paper will present an intra-institutional paradigm that utilizes and integrates concepts from numerous disciplines to frame a critical and underspecified practical issue in security—controlling for the unintended conversion of data to information.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
High-Reliability Systems and the Control of National Security Data and Information
High-reliability systems are characterized by catastrophic implications in the event of failure. These implications can include substantive damage to the environment, social order, and loss of life. Examples of high-reliability systems include nuclear submarines, nuclear reactors, the electric grid, and nuclear weapons. Due to the catastrophic implications of failure, there are heightened awareness and control mechanisms surrounding related data and information. However, defining the difference between data and information is often ambiguous across scholarly disciplines and in United States policy and legislation. For high-reliability systems, the implications of ambiguity between data and information may affect the security of United States interests and even cost lives. For security, data are raw facts or figures without context, while information is the compilation or articulation of data that forms context. Security depends on clarity in the differences between data and information and how to control them. Control is necessary to ensure that data and information are not unintentionally released to foreign governments, the public, or those without need-to-know. A primary concern in the practice of security is the control of data to avoid the unintended conversion to information. Intra-institutionally, this control is highly complex given the amalgam of legacy data systems and the numerous and constantly evolving nature of modern data systems that were not necessarily designed to be integrated. The complexity of this concern is augmented when institutions are part of inter-institutional collaborations or networks of public-private partnerships that share data and information. Additionally, institutions that share data as a function of policy and legislative action—particularly formally integrated data and information system infrastructures—may be at higher security risk. This paper will present an intra-institutional paradigm that utilizes and integrates concepts from numerous disciplines to frame a critical and underspecified practical issue in security—controlling for the unintended conversion of data to information.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
A Nuclear Security Enterprise Study of High-Reliability Systems, Collaboration, and Data
It may seem simple and trivial, but defining the difference between data and information is contested and has implications that may affect the security of United States interests and even cost lives. For security, data are raw facts or figures without context, while information is the compilation or articulation of data that forms context. Security depends on clarity in the differences between data and information and controlling them. Control is necessary to ensure that data and information are not inadvertently released to foreign governments, the public, or those without Need-to-Know. A primary concern in the practice of security is the control of data to avoid the inadvertent conversion to sensitive information. The complexity of this concern is further augmented when institutions are part of tightly coupled networks that informally share data and information. Additionally, those that share data as a function of legislative action—and/or formally integrate data and information system infrastructures—may be a higher security risk. This paper will present a case study that utilizes elements of literature from Knowledge Management and networks to tell a story of an issue in security—specifically, controlling the conversion of data to information.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001–2019)
Harnessing the fire data revolution, i.e., the abundance of information from satellites, government records, social media, and human health sources, now requires complex and challenging data integration approaches. Defining fire events is key to that effort. In order to understand the spatial and temporal characteristics of fire, or the classic fire regime concept, we need to critically define fire events from remote sensing data. Events, fundamentally a geographic concept with delineated spatial and temporal boundaries around a specific phenomenon that is homogenous in some property, are key to understanding fire regimes and more importantly how they are changing. Here, we describe Fire Events Delineation (FIRED), an event-delineation algorithm, that has been used to derive fire events (N = 51,871) from the MODIS MCD64 burned area product for the coterminous US (CONUS) from January 2001 to May 2019. The optimized spatial and temporal parameters to cluster burned area pixels into events were an 11-day window and a 5-pixel (2315 m) distance, when optimized against 13,741 wildfire perimeters in the CONUS from the Monitoring Trends in Burn Severity record. The linear relationship between the size of individual FIRED and Monitoring Trends in Burn Severity (MTBS) events for the CONUS was strong (R2 = 0.92 for all events). Importantly, this algorithm is open-source and flexible, allowing the end user to modify the spatio-temporal threshold or even the underlying algorithm approach as they see fit. We expect the optimized criteria to vary across regions, based on regional distributions of fire event size and rate of spread. We describe the derived metrics provided in a new national database and how they can be used to better understand US fire regimes. The open, flexible FIRED algorithm could be utilized to derive events in any satellite product. We hope that this open science effort will help catalyze a community-driven, data-integration effort (termed OneFire) to build a more complete picture of fire.
54 ENVIRONMENTAL SCIENCES↗
NASA/DOD Aerospace Knowledge Diffusion Research Project. Report 13: Source selection and information use by US aerospace engineers and scientists: Results of a telephone survey
A telephone survey of U.S. aerospace engineers and scientists belonging to the Society of Automotive Engineers (SAE) was conducted between December 4, 1991 and January 5, 1992. The survey was undertaken to (1) validate the telephone survey as an appropriate technique for collecting data from U.S. aerospace engineers and scientists; (2) collect information about how the results of NASA/DoD aerospace research are used in the R&D process; (3) identify those selection criteria which affect the use of federally-funded aerospace R&D; and (4) obtain information that could be used to develop a self-administered mail questionnaire for use with the same population. The average rating of importance of U.S. government technical reports was 2.5 (on a 4-point scale); The mean/median number of times U.S. government technical reports were used per 6 months was 8/2. Factors scoring highest for U.S. government technical reports were technical accuracy (2.9), reliable data and technical information (2.8), and contains comprehensive data and information (2.7) on a 4-point system. The factors scoring highest for influencing the use of U.S. government technical reports were relevance (3.1), technical accuracy (3.06), and reliable data/information (3.02). Ease of use, familiarity, technical accuracy, and relevance correlated with use of U.S. government technical reports. Survey demographics, survey questionnaire, and the NASA/DoD Aerospace Knowledge Diffusion Research Project publications list are included.
Pinelli, Thomas E.↗