The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

Starting in 2015 NREL has presented the Annual Technology Baseline (ATB) in an Excel workbook that contains detailed cost and performance data, both current and projected, for renewable and conventional technologies. The workbook includes a spreadsheet for each technology. This version of the workbook provides the final updates to data for the 2021 ATB. In 2019 and 2020, NREL has also provided selected data in Tableau workbooks and structured summary csv files. The data for 2015 - 2020 is located on https://data.nrel.gov. In 2021 and going forward, the data is cloud optimized and provided in the OEDI data lake. A website documents this and future data at https://atb.nrel.gov.

These data provide the 2022 update of the Electricity Annual Technology Baseline (ATB). Starting in 2015 NREL has presented the ATB, consisting of detailed cost and performance data, both current and projected, for electricity generation and storage technologies. The ATB products now include data (Excel workbook, Tableau workbooks, and structured summary csv files), as well as documentation and user engagement via a website, presentation, and webinar. Starting in 2021, the data are cloud optimized and provided in the OEDI data lake. The data for 2015 - 2020 are can be found on the NREL Data Search Page. The website documentation can be found on the ATB Website.

Data for 2019 annual market performance review

Various data for 2020 annual market performance review

The TidGen Power System generates emission-free electricity from tidal currents and connects directly into existing grids using smart grid technology. The power system consists of three major subsystems: shore-side power electronics, mooring system, and turbine generator unit (TGU) device. This submission includes field Test Plans for subsystem and system tests.

The TidGen Power System generates emission-free electricity from tidal currents and connects directly into existing grids using smart grid technology. The power system consists of three major subsystems: shore-side power electronics, mooring system, and turbine generator unit (TGU) device. This submission includes the Advanced TidGen cost and cost of energy metrics after critical design review for BP1, and a complete LCOE content model and LCOE reporting according to DOE guidance for the baseline system and the system with advanced technology integrated. A revised LCOE content model is also included, with more relevant market array assumptions. Additionally, this submission includes a complete system overview and component overview content models. The LCOE Content Model provides data submitters with an easy and consistent means of uploading data that can be used to calculate the levelized cost of energy for MHK devices. Data represents the design completed for the Critical Design Review conducted at ORPC in December, 2017. All values are for a single device. Note that with substantial fixed costs, larger arrays will greatly reduce LCOE. For an array in Admiralty Inlet producing 136,000 MWh, 270 devices with an array CAPEX of $540,260,052 and an array OPEX of $39,959,207 would result in an LCOE of $722/MWh.

This dataset provides the Sydney Metropolitan and Outer Metropolitan Bus Service Contract on time running results from 2010 onward. Until June 2017, Transport for NSW monitored on-time running through monthly surveys conducted over a sample period at the start of the trip. As of July 2017, PTIPS replaced manual surveys as the source of information for these on-time running results. The definition of "on time" is provided in the bus contracts which can be found here: [https://www.transport.nsw.gov.au/operations/buses-and-coaches/bus-contracts](https://www.transport.nsw.gov.au/operations/buses-and-coaches/bus-contracts)

The data herein contains all data collected and used for the Performance Characterization Testing and Model Calibration of a Vertical Axis Hydrokinetic Turbine. The data includes performance data and durability data for the Hydrokinetic Turbine. The device performance data contains shaft RPM, turbine RPM, power output, flow velocity, pressure, and pressure drop across the turbine. The mechanical durability data includes stress and strain at varied depths and velocities. There is also an FEA analysis included. This TEAMER project was awarded to Emrgy, Inc.in collaboration with Alden Research Laboratory LLC.

Dixie Valley Binary Plant data points for January - March 6 2014. Used to demonstrate the techno-economic feasibility of utilizing the available unused heat to generate additional electric power from a binary power plant. Data includes Brine Flow, Brine Return Temperature, Brine Supply Temperature, Generation Output, Net Generation, Parasitic Loss, and Dry Bulb Temperature.

Formal Report: Dixie Valley Binary DOE Report February 2014. Includes summary of data, operations, outages, and curtailments in order to support the techno-economic feasibility of utilizing the available unused heat to generate additional electric power from a binary power plant from the low-temperature brine at the Dixie Valley Geothermal Power Plant.

This webpage contains sources of (1) Data tables of federal agency energy and water consumption; (2) Interactive graphics associated with most data tables; (3) Energy costs by end-use sector and efficiency investment information; (4) Progress toward key goals outlined in the National Energy Conservation Policy Act, as amended (42 U.S.C. 8253-8258); Energy Policy Act of 2005 (42 U.S.C. 15852); (5) Historical data tables of agency energy use and costs by facility and mobility sectors by energy type are also available for fiscal year (FY) 1975 through FY 2020

A shapefile of gridded squares for French urban centre 200 metre grid with following fields:id: Identifier unique to this urban centre population: Global Human Settlement Layer population estimate within 200 metre cellgeometry: Grid polygons within_uc: Whether the 200 metre cell is within the defined urban centreaccess_pop: The median sum population that can be reached from the centroid of the 200 metre cell within a 45 minute commute by walking, bus and trainproxim_pop: The sum proximal population within an 11.25 kilometre radius of the centroid of the 200 metre celltrans_perf: The transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nm: Name of the urban centrecountry_nm: Name of the country that the urban centre belongs to

This calculator is a handy tool for interested parties to estimate two key life cycle metrics, fossil energy consumption (Etot) and greenhouse gas emission (ghgtot) ratios, for geothermal electric power production. It is based solely on data developed by Argonne National Laboratory for DOE's Geothermal Technologies office. The calculator permits the user to explore the impact of a range of key geothermal power production parameters, including plant capacity, lifetime, capacity factor, geothermal technology, well numbers and depths, field exploration, and others on the two metrics just mentioned. Estimates of variations in the results are also available to the user.

Integrated Approach Towards the Application of Horizontal Wells to Improve Waterflooding Performance; Annual Report, 1994

The Key Roads Performance report outlines the time it takes to travel along major road routes in NSW during peak periods. The trips described in this report cover many major roads in Sydney, Newcastle, and Wollongong. Other key roads in NSW will be added in future reports as the report is refined, and additional roads may be included to address community needs. For each trip, this report provides the travel time during the AM and PM peak periods and a detailed description of the trip. The report is issued monthly, starting with October 2018 until June 2019. For more information visit [RMS Key Performance Report](https://www.rms.nsw.gov.au/about/corporate-publications/roads-report.html)

This submission includes all the data to support an LCOE baseline assessment for the Resolute Marine Energy (RME) Surge WEC device.

Quantitative polymerase chain reaction (qPCR) has become a frequently used technique for quantifying enterococci in recreational surface waters, but there are several methodological options. Here we evaluated how three method permutations, type of mastermix, sample extract dilution and use of controls in results calculation, affect method reliability among multiple laboratories with respect to sample interference. Multiple samples from each of 22 sites representing an array of habitat types were analyzed using EPA Method 1611 and 1609 reagents with full strength and five-fold diluted extracts. The presence of interference was assessed three ways: using sample processing and PCR amplifications controls; consistency of results across extract dilutions; and relative recovery of target genes from spiked enterococci in water sample compared to control matrices with acceptable recovery defined as 50 to 200%. Method 1609, which is based on an environmental mastermix, was found to be superior to Method 1611, which is based on a universal mastermix. Method 1611 had over a 40% control assay failure rate with undiluted extracts and a 6% failure rate with diluted extracts. Method 1609 failed in only 11% and 3% of undiluted and diluted extracts analyses. Use of sample processing control assay results in the delta-delta Ct method for calculating relative target gene recoveries increased the number of acceptable recovery results. Delta-delta tended to bias recoveries from apparent partially inhibitory samples on the high side which could help in avoiding potential underestimates of enterococci - an important consideration in a public health context. Control assay and delta-delta recovery results were largely consistent across the range of habitats sampled, and among laboratories. The methodological option that best balanced acceptable estimated target gene recoveries with method sensitivity and avoidance of underestimated enterococci densities was Method 1609 without extract dilution and using the delta-delta calculation method. The applicability of this method can be extended by the analysis of diluted extracts to sites where interference is indicated but, particularly in these instances, should be confirmed by augmenting the control assays with analyses for target gene recoveries from spiked target organisms. This dataset is associated with the following publication: Haugland , R., S. Siefring , M. Varma , K. Oshima , M. Sivaganesan , Y. Cao, M. Raith, J. Griffith, S. Weisberg, R. Noble, A.D. Blackwood, J. Kinzelman, T. Anan'eva, R. Bushon, E. Stelzer, V. Harwood, K. Gordon, and C. Sinigalliano. Multi-laboratory survey of qPCR enterococci analysis method performance in U.S. coastal and inland surface waters. JOURNAL OF MICROBIOLOGICAL METHODS. Elsevier Science Ltd, New York, NY, USA, 123(1): 114-125, (2016).

The net zero tracker aims to increase transparency and accountability of net zero targets pledged by nations, states and regions, cities and companies. They collect data on targets set and on many factors that indicate the integrity of those targets — essentially, how serious the entity setting the target is about meaningfully cutting its net emissions to zero.

The University of Alaska Fairbanks (UAF) Alaska Hydrokinetic Energy Research Center was tasked with developing a real-time data telemetry / remote power generation system to monitor frazil ice conditions in the Kvichak River in support of the U.S. Department of Energy funded "Next Generation MHK River Power System Optimized for Performance, Durability and Survivability" project. A real-time telemetry system was requested because of the short time span between the end of the frazil ice season when the instruments would be recovered, limited vessel availability and the project end-date. To meet the project objectives, UAF designed and assembled a remote power/real-time data telemetry system that included an auto start propane generator, a small PV array, a small battery bank and line-of-sight radios as well as two sonar systems to monitor river velocity and water column acoustic backscatter strength. Both sonars included internal batteries for powering the instruments in case of failure of the shore based power system. The sonars, deployed in ~5 m of water on the bed of the Kvichak River, adjacent to the Village of Igiugig, Alaska were tethered to shore via a waterproof armored cable that conveyed power to the subsurface instruments and data from the instruments to the shore based telemetry system. The instruments were programmed to record data internally as well as to transmit data serially over the cables to the shore based system. The system was in-place between November, 2016 and June, 2017. While the real-time data telemetry system was not successful and the remote power generation power system was only partially successful, the system design included sufficient redundant power in the form of internal instrument batteries to enable the collection of nearly three months of overlapping velocity and backscatter data (from November through February) and a record of acoustic backscatter strength spanning the entire ~150 day frazil ice season between November, 2016 and ~April, 2017. This submission includes the overwinter ice study plan, dataset, and final report. The dataset includes modeled water velocity, discharge, and measured water velocity and acoustic backscatter strength in winter 2016-17 from the Kvichak River at the Village of Igiugig, Alaska, USA.

In this report, GIPSA provides an overview of the Packers and Stockyards Program (PSP), its unit level activities, and management. It analyzes the economic state of the livestock and poultry industries. It describes changing business practices. It also identifies market operations or activities that appear to raise concerns under the Packers and Stockyards Act, 1921.

The U.S. Department of Energy Geothermal Vision (GeoVision) Study is currently looking at the potential to increase geothermal deployment in the U.S. and to understand the impact of this increased deployment. This paper reviews 31 performance, cost, and financial parameters as input for numerical simulations describing GDH system deployment in support of the GeoVision effort. The focus is on geothermal district heating (GDH) systems using hydrothermal and Enhanced Geothermal System resources in the U.S.; ground-source heat pumps and heat-to-electricity conversion technology were excluded. Parameters investigated include: 1) capital and operation and maintenance costs for both subsurface and surface equipment; 2) performance factors such as resource recovery factors, well flow rates, and system efficiencies; and 3) financial parameters such as inflation, interest, and tax rates. Current values as well as potential future improved values under various scenarios are presented. Sources of data considered include academic and popular literature, software tools such as GETEM and GEOPHIRES, industry interviews, and analysis conducted by other task forces for the GeoVision Study, e.g., on the drilling costs and reservoir performance.

The NREL PVDAQ is a large-scale time-series database containing system metadata and performance data from a variety of experimental PV sites and commercial public PV sites. The datasets are used to perform on-going performance and degradation analysis. Some of the sets can exhibit common elements that effect PV performance (e.g. soiling). The dataset consists of a series of files devoted to each of the systems and an associated set of metadata information that explains details about the system hardware and the site geo-location. Some system datasets also include environmental sensors that cover irradiance, temperatures, wind speeds, and precipitation at the site.

Information used to determine the effectiveness of Agriculture programs offered by FSA as to enrollments, approvals, funds disbursements, collections and business analytics.

The work evaluates, develops and demonstrates flexible, scalable mineral extraction technology for geothermal brines based upon solid phase sorbent materials with a specific focus upon rare earth elements (REEs). The selected organic and inorganic sorbent materials demonstrated high performance for collection of trace REEs, precious and valuable metals. The nanostructured materials typically performed better than commercially available sorbents. Data contains organic and inorganic sorbent removal efficiency, Sharkey Hot Springs (Idaho) water chemistry analysis, and rare earth removal efficiency from select sorbents. The Sharkley Hot Springs water chemistry presented includes spiked levels of REEs.

The aim of this study is to understand how commercially available elastomers perform under geothermal well-like conditions and make recommendations to the community based on these results. This poster highlights the mechanical performance of several elastomers (including EPDM, Viton A, Viton B, FFKM, and FEPM) after aging at elevated temperature, pressure, and in well brine and drilling solutions.

This dataset captures the following public transport performance reports. Each resource is displayed as an interactive chart with filter options. The [TfNSW passenger travel](https://www.transport.nsw.gov.au/data-and-research/passenger-travel#Performance_reports) provides more information. * Sydney Metropolitan and Outer Metropolitan Bus Service Contract on time running results * Ferries service Reliability and On-Time Running Results * NSW Trains service reliability and punctuality results on all regional lines in New South Wales. * Sydney Trains and NSW TrainLink (Intercity) performance reports * New Customer On-Time measure – measuring our customer’s experience * Sydney Network - Historical Trains Punctuality Performance report * Performance Reporting for the Sydney Light Rail network Use the **GO TO RESOURCE** Option to view the individual reports.

Surfactant flooding is flexible because of the ability to optimize formulations for a wide range of reservoir conditions and crude oil types. The objective for this work was to determine if the addition of alkaline additives will allow the design of surfactant formulations that are effective for the recovery of crude oil, while, at the same time, maintaining the surfactant concentration at a much lower level than has previously been used for micellar flooding. Specifically, the focus of the work was on light, midcontinent crudes that typically have very low acid contents. These oils are typical of much of the midcontinent resource. The positive effect of alkaline additives on the phase behavior of the surfactant formulations and acidic crude oils is well known. The extension to nonacidic and slightly acidic oils is not obvious. Three crude oils, a variety of commercial surfactants, and several alkaline additives were tested. The oils had acid numbers that ranged from 0.13, which is quite low, to less than 0.01 mg KOH/g of oil. Alkaline additives were found to be very effective in recovering Delaware-Childers (OK) oil at elevated temperatures, but much less effective at reservoir temperatures. Alkaline additives were very effective with Teapot Dome (WY) oil. With Teapot Dome oil, surfactant/alkali systems produced ultralow IFT values and recovered 60% of the residual oil that remained after waterflooding. The effect of alkaline additives on recovering Hepler (KS) oil was minimal. The results of this work indicate that alkaline additives do have merit for use in surfactant flooding of low acid crude oils; however, no universal statement about applicability can be made. Each oil behaves differently, with this treatment, and the effect of alkaline additives must be determined (at reservoir conditions) for each oil. 23 refs., 13 figs., 3 tabs

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in France, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods: For more information please visit: ·        Python Package: https://github.com/datasciencecampus/transport-network-performance ·        Docker Image: https://github.com/datasciencecampus/transport-performance-docker  Known Limitations/Caveats: These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below. Urban Centre and Population Estimates: ·        Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details. Public Transit Schedule Data (GTFS): ·        Does not include effects due to delays (such as congestion and diversions). ·        Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning.  Transport Network Routing: ·        “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary. Please also visit the Python package and Docker Image GitHub issues pages for more details.   How to Contribute: We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in Great Britain, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods: For more information please visit: ·        Python Package: https://github.com/datasciencecampus/transport-network-performance ·        Docker Image: https://github.com/datasciencecampus/transport-performance-docker  Known Limitations/Caveats: These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below. Urban Centre and Population Estimates: ·        Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details. Public Transit Schedule Data (GTFS): ·        Does not include effects due to delays (such as congestion and diversions). ·        Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning.  Transport Network Routing: ·        “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary. Please also visit the Python package and Docker Image GitHub issues pages for more details.   How to Contribute: We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

Preliminary System Design Package for the Triton-C WEC, including a report and CAD drawings pertaining to the overall preliminary design, system arrangement, surface float hull, and surface float arrangement.

This webpage contains U.S. Department of Energy performance reports for current and past years.

A shapefile of gridded squares for UK urban centre 200 metre grid with following fields:id: Identifier unique to this urban centre population: Global Human Settlement Layer population estimate within 200 metre cellgeometry: Grid polygons within_uc: Whether the 200 metre cell is within the defined urban centreaccess_pop: The median sum population that can be reached from the centroid of the 200 metre cell within a 45 minute commute by walking, bus and trainproxim_pop: The sum proximal population within an 11.25 kilometre radius of the centroid of the 200 metre celltrans_perf: The transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nm: Name of the urban centrecountry_nm: Name of the country that the urban centre belongs to

Wind turbine condition monitoring (CM) can potentially help the wind industry reduce turbine downtime and operation and maintenance (O&M) cost. NREL CM research has investigated various condition-monitoring techniques such as acoustic emission (AE specifically stress wave), vibration, electrical signature, lubricant and debris monitoring based on the Gearbox Reliability Collaborative dynamometer and field tests, and other test turbines and resources accessible by NREL. During the past several years, NREL CM research has shown that there are very few validation and verification efforts on commercial wind turbine CM systems. One of the reasons might be limited benchmarking datasets accessible by stakeholders. To fill this gap, NREL executed a data collection effort. The targeted users of these datasets include those investigating vibration-based wind turbine CM research, evaluating commercially available vibration-based CM systems, or testing prototyped vibration-based CM systems. NREL collected data from a healthy and a damaged gearbox of the same design tested by the GRC. Vibration data were collected by accelerometers along with high-speed shaft RPM signals during the dynamometer testing. The healthy gearbox was only tested in the dynamometer. The damaged gearbox was first tested in the dynamometer and later sent to a wind farm close to NREL for field testing. In the field test, it experienced two loss-of-oil events that damaged its internal bearings and gear elements. The gearbox was brought back to NREL and it was retested in the dynamometer with CM systems deployed under controlled loading conditions that would not cause catastrophic failure of the gearbox. The objective of releasing these datasets to the public along with information about the real damage that occurred to the damaged gearbox is to provide the wind industry with some benchmarking datasets. These datasets will benefit research, development, validation, verification, and advancement of vibration-based wind condition-monitoring techniques. By accessing this data you acknowledge the terms outlined in the "License Information" document. Please contract Shawn Sheng (NREL) if you have any questions on the data or would like to collaborate on publications based on the datasets.