In this dataset, for each measurement (a measuring instrument at one location), the indicators V50, V85, number of vehicles and infringement rate per direction are given. The individual journeys can be found in the data set Individual Measurements (https://data.bs.ch/explore/dataset/100097) Data are exclusively statistical surveys. These stand not in connection with fines or criminal charges Persecution.The statistical velocity measurements are used to: Cantonal Police Basel-Stadt to check the speed and the Road safety (e.g. safety on pedestrian strips) at the relevant Location. The results are used to decide in which locations There is a need for action in the form of speed controls. Each Statistical device has a single point geometry and is usually with two Directions (direction 1 and 2).Note: The Measurements are not necessarily representative for the whole year and must be Context of the survey date. In addition, certain Measurements during extraordinary traffic management (e.g. Traffic levied as a result of construction site activities, etc.). Tampering with devices can lead to incorrect measurements. To The following data sets are available for speed monitoringIndividual measurements from 2021: https://data.bs.ch/explore/dataset/100097Einzelmessungen to 2020: https://data.bs.ch/explore/dataset/100200Kennzahlen per measurement location (this dataset): https://data.bs.ch/explore/dataset/100112Die Measuring locations are also published on the Geoportal Basel-Stadt: https://map.geo.bs.ch/s/geschwindigkeit

This digital dataset describes the location of 21 extensometers used for observations of subsidence in the Central Valley Hydrologic Model (CVHM). The Central Valley encompasses an approximate 50,000 square- kilometer region of California. The complex hydrologic system of the Central Valley is simulated using the USGS numerical modeling code MODFLOW-FMP (Schmid and others, 2006). This simulation is referred to here as the CVHM (Faunt, 2009). Utilizing MODFLOW-FMP, the CVHM simulates groundwater and surface-water flow, irrigated agriculture, land subsidence, and other key processes in the Central Valley ]on a monthly basis from 1961-2003. The total active modeled area is 20,334 square-miles. Water levels, water-level altitude changes, and water-level and potentiometric-surface altitude maps; streamflows; boundary flows; subsidence; groundwater pumpage; water use; and water-delivery observations were used to constrain parameter estimates throughout the calibration of the CVHM. Measured compaction from data collected by extensometers in the valley was used as a subsidence calibration target. Subsidence monitoring observations can provide valuable information about hydrologic parameters such as elastic and inelastic skeletal specific storage. The CVHM was adjusted to fit the range of measured compaction at the extensometer sites utilizing UCODE-2005 (Poeter and others, 2006) and manual calibration. The calibration target was the measured compaction from several extensometers in the region. Compaction though delayed drainage and re-pressurizing of aquitards was not simulated. The CVHM is the most recent regional-scale model of the Central Valley developed by the U.S. Geological Survey (USGS). The CVHM was developed as part of the USGS Groundwater Resources Program (see "Foreword", Chapter A, page iii, for details).

DEPRECATED: This data package was erroneously published and contains several issues. For limno run hole locations, refer to knb-lter-mcm.39.23 (or later). For lake level measurement locations, refer to knb-lter-mcm.68.14 (or later).

   These are data about the dive holes and lakes limno holes, dive holes, lake level measurement locations

The dataset consists of site identification, location, temperature and CO2 flux from diffuse emission measurement at the Tiptop fire. A total of 40 CO2 flux measurements were made at 27 locations, including five points (seven measurements) outside of the active coal fire area.

1. This dataset describes the spatial location data of the river flow observation stations established by the Water Resources Agency to provide government agencies and private organizations, groups, or academic units commissioned by government agencies with the information. The fields display the station name, and the spatial data is presented in point form, with a total of 104 data entries.2. All river flow stations under the Water Resources Agency have adopted automatic recording to observe water levels, and then convert them to flow rates based on the water level-flow rate curve.3. This data only includes basic station information and does not include the actual observation results over time.4. When opening this file using Google Earth software, clicking on the point information will provide a link to flow data, which will open in a new browser window to the Water Resources Agency's Geographic Information Storage Center website, offering more comprehensive functions and queries, including spatial information, temporal data, and environmental analysis.5. Due to the lack of precise ortho-rectification in the base map images provided by Google, there may be some errors when overlaying layers.

This data set is related to Traffic. History of traffic data since 2013, indicating the latter for each measurement point, the passing vehicles. The infrastructure of measurement points, available in the city of Madrid corresponds to: 7,360 vehicle detectors with the following characteristics: 71 include number plate reading devices 158 have optical machine vision systems with control from the Mobility Management Center 1,245 are specific to fast roads and access to the city and the rest of the 5,886, with basic traffic light control systems. More than 4,000 measuring points : 253 with systems for speed control, characterization of vehicles and double reading loop 70 of them make up the stations of taking specific seats of the city. Automatic control systems of all the information obtained from the detectors with continuous contrast with expected behavior patterns, as well as the follow-up of the instructions marked by the Technical Committee for Standardization AEN/CTN 199; and in particular SC3 specific applications relating to “Detectors and data collection stations” and SC15 relating to “Data quality”. In this same portal you can find other related data sets such as: Traffic. Real-time traffic data . With real-time information (updated every 5 minutes) Traffic. Map of traffic intensity plots, with the same information in KML format, and with the possibility of viewing it in Google Maps or Google Earth. And other traffic-related data sets. You can search for them by putting the word 'Traffic' in the search engine (top right).

According to a survey conducted in September 2020, ** percent of Indonesian respondents stated that the workplaces they visited had implemented hand-washing as one of their COVID-19 safety measures. As of October 13, 2020, the total number of COVID-19 cases in Indonesia amounted to ******* and the number had been increasing since Indonesia had its first COVID-19 case in March 2020.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

This dataset contains identifiers, metadata, and a map of the locations where field measurements have been conducted at the East River Community Observatory located in the Upper Colorado River Basin, United States. This is version 3.0 of the dataset and replaces the prior version 2.0, which should no longer be used (see below for details on changes between the versions). Dataset description: The East River is the primary field site of the Watershed Function Scientific Focus Area (WFSFA) and the Rocky Mountain Biological Laboratory. Researchers from several institutions generate highly diverse hydrological, biogeochemical, climate, vegetation, geological, remote sensing, and model data at the East River in collaboration with the WFSFA. Thus, the purpose of this dataset is to maintain an inventory of the field locations and instrumentation to provide information on the field activities in the East River and coordinate data collected across different locations, researchers, and institutions. The dataset contains (1) a README file with information on the various files, (2) three csv files describing the metadata collected for each surface point location, plot and region registered with the WFSFA, (3) csv files with metadata and contact information for each surface point location registered with the WFSFA, (4) a csv file with with metadata and contact information for plots, (5) a csv file with metadata for geographic regions and sub-regions within the watershed, (6) a compiled xlsx file with all the data and metadata which can be opened in Microsoft Excel, (7) a kml map of the locations plotted in the watershed which can be opened in Google Earth, (8) a jpeg image of the kml map which can be viewed in any photo viewer, and (9) a zipped file with the registration templates used by the SFA team to collect location metadata. The zipped template file contains two csv files with the blank templates (point and plot), two csv files with instructions for filling out the location templates, and one compiled xlsx file with the instructions and blank templates together. Additionally, the templates in the xlsx include drop down validation for any controlled metadata fields. Persistent location identifiers (Location_ID) are determined by the WFSFA data management team and are used to track data and samples across locations. Dataset uses: This location metadata is used to update the Watershed SFA’s publicly accessible Field Information Portal (an interactive field sampling metadata exploration tool; https://wfsfa-data.lbl.gov/watershed/), the kml map file included in this dataset, and other data management tools internal to the Watershed SFA team. Version Information: The latest version of this dataset publication is version 3.0. The latest version contains a breaking change to the Location Map (EastRiverCommunityObservatory_Map_v3_0_20220613.kml), If you had previously downloaded the map file prior to version 3.0, it will no longer work. Use the updated Location Map (EastRiverCommunityObservatory_Map_v3_0_20220613.kml) in this version of the dataset. This version also contains a total of 51 new point locations, 8 new plot locations, and 1 new geographic region. Additionally, it corrects inconsistencies in existing metadata. Refer to methods for further details on the version history. This dataset will be updated on a periodic basis with new measurement location information. Researchers interested in having their East River measurement locations added in this list should reach out to the WFSFA data management team at wfsfa-data@googlegroups.com. Acknowledgements: Please cite this dataset if using any of the location metadata in other publications or derived products. If using the location metadata for the NEON hyperspectral campaign, additionally cite Chadwick et al. (2020). doi:10.15485/1618130.

All groundwater locations that are included in the Flanders Subsurface Database and for which measurements are publicly available can be viewed via this map layer. The possible measurements are: level measurements groundwater samples + analyzes Different types of groundwater locations have been defined: (wells and natural extractions). And also different filter types (pump filters, infiltration filters, reversible pump/infiltration filters, level filters, natural filters (sources and ponds). These locations are maintained for various purposes, including: - the extraction wells and monitoring wells of operators, these are entered on the basis of the file of the groundwater permit - the wells of the VMM groundwater monitoring networks - the measuring wells of INBO and other nature organizations

Traffic measurements taken on 10 April 2025. It collected data about the number of vehicles, pedestrians - children and adults, and cyclists, nearby school that is Warsaw's 2nd pilot location in REALLOCATE.

In this map you can see the measurement locations sampled by RIVM for the research into the derivation of the final background value PFAS in landbed. The map shows measurement values of PFOS in the deeper soil layer (50-100 cm) in potentially affected areas. Because the derivation of the final background values is based on the top layer of the uninfluenced soil, these measurements are not included in the derivation of the final background value.

According to a survey conducted in September 2020, **** percent of Indonesian respondents stated that the public service places they visited had implemented mask-wearing as one of their COVID-19 safety measures. As of October 13, 2020, the total number of COVID-19 cases in Indonesia amounted to ******* and the number had been increasing since Indonesia had its first COVID-19 case in March 2020.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Scientists use imaging to identify objects of interest and infer properties of these objects. The locations of these objects are often measured with error, which when ignored leads to biased parameter estimates and inflated variance. Current measurement error methods require an estimate or knowledge of the measurement error variance to correct these estimates, which may not be available. Instead, we create a spatial Bayesian hierarchical model that treats the locations as parameters, using the image itself to incorporate positional uncertainty. We lower the computational burden by approximating the likelihood using a noncontiguous block design around the object locations. We use this model to quantify the relationship between the intensity and displacement of hundreds of atom columns in crystal structures directly imaged via scanning transmission electron microscopy (STEM). Atomic displacements are related to important phenomena such as piezoelectricity, a property useful for engineering applications like ultrasound. Quantifying the sign and magnitude of this relationship will help materials scientists more precisely design materials with improved piezoelectricity. A simulation study confirms our method corrects bias in the estimate of the parameter of interest and drastically improves coverage in high noise scenarios compared to non-measurement error models.

1. This dataset describes the spatial locations of river flow observation stations managed by the Water Resources Agency, and is provided for use by government agencies, private organizations, groups commissioned by government agencies, and academic units. The field is displayed as the name of the discontinued observation station, and the geographic spatial data is in point form, with a total of 339 records. 2. All river flow stations under the Water Resources Agency have adopted automatic recording to observe water levels, and then converted them into flow rates according to the water level-flow rate curve. 3. This data only includes basic information of the observation stations, excluding the actual observation results of time series data. 4. When opening this file using Google Earth software, clicking on the point information will provide a link to the flow data, opening a separate browser window to the Water Resources Agency Geographic Information Repository website, which provides more functionality and queries, including spatial information, time series data, and environmental analysis. 5. Due to the base map imagery provided by Google not undergoing precise orthorectification, some errors may occur during layer stacking.

Mouth-to-ear (M2E) latency describes the time it takes speech input in a voice communication transmit device to be output from a receiving device, and has been identified as a key component of quality of experience (QoE) in communications. NIST's PSCR division developed a method to measure and quantify the M2E latency of any communications system transmitting audio, with specific emphasis on push to talk (PTT) devices. This measurement method is the first step in establishing QoE key performance indicators (KPI) for mission critical voice (MCV) and a measurement system to quantify these QoE KPIs. Additional measurement methods will be established and published in the near future. The measurement system provides a fair platform for the comparisons of M2E latency across radio communications technologies. Both single and two location measurement systems were developed. The single location measurement system is a simpler setup ideal for measurements performed in a single, controlled setting. The two location system allows for the measurement of M2E latency between devices in two distinct locations and adds the capability to see potential effects of distance and signal propagation on the latency a user experiences. Example measurements of the M2E latency of VHF and UHF land mobile radios (LMR) operating in both direct mode and in trunked mode were performed. These tests demonstrated that both the single and two location tests return consistent measurement results.

The patterns of interest and their prevalence measure values in the entire study region with neighborhood distance D = 300 m.

• Significantly different (p≤0.002) from winter 2009 or (for Mus m 1) from previous measurement.†Correlation between the 2 measurements (spring 2009 and winter 2010).

Locations of projects in the County funded by Measure A funds.

Periodic vegetation measurements over a period starting in 1960. The measurements were carried out per site on a grid of 1 km by 1 km, in which an estimate was made of the percentage of area per crop type. The data is used in a vegetation measurement viewer and dashboard. The data comes from ecological research and consultancy firm Van der Goes en Groot.

The data is aggregated based on measurement point (PQ number). The number of measurements over the years differs per measurement point. This is stored in the field 'PQ number repetitions'.

This dataset does not contain the measurement results, only the measurement locations. The measurement results are shielded internally within the province of Zuid-Holland because of information about protected plant species.

This map shows all measuring points of the Province of Zeeland. The measuring points consist of the slipperiness measuring network, motor vehicle counting points (traffic counting), speed measuring points, bicycle counting points and agricultural counting points. The dataset contains more information about the data, such as the measurement method, energy supply, location description and more.