IMS: Inductive Monitoring System The Inductive Monitoring System (IMS) is a tool that uses a data mining technique called clustering to extract models of normal system operation from archived data. IMS works with vectors of data values. IMS analyzes data collected during periods of normal system operation to build a system model. It characterizes how the parameters relate to one another during normal operation by finding areas in the vector space where nominal data tends to fall. These areas are called nominal operating regions and correspond to clusters of similar points found by the IMS clustering algorithm. These nominal operating regions are stored in a knowledge base that IMS uses for real-time telemetry monitoring or archived data analysis. During the monitoring operation, IMS reads real-time or archived data values, formats them into the predefined vector structure, and searches the knowledge base of nominal operating regions to see how well the new data fits the nominal system characterization. For each input vector, IMS returns the distance that vector falls from the nearest nominal operating region. Data that matches the normal training data well will have a deviation distance of zero. If one or more of the data parameters is slightly outside of expected values, a small non-zero result is returned. As incoming data deviates further from the normal system data, indicating a possible malfunction, IMS will return a higher deviation value to alert users of the anomaly. IMS also calculates the contribution of each individual parameter to the overall deviation, which can help isolate the cause of the anomaly.

This is version v3.3.0.2022f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data.

The quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information.

The data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format "station_code"_HadISD_HadOBS_19310101-20230101_v3.3.1.2022f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.

To keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.

For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/

References: When using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference) :

Dunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note.

Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.

Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012

Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1

For a homogeneity assessment of HadISD please see this following reference

Dunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. "Pairwise homogeneity assessment of HadISD." Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.

This dataset provides values for INDUSTRY VALUE ADDED ANNUAL PERCENT GROWTH WB DATA.HTML. reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The ASA Statement on p-Values: Context, Process, and Purpose

Building permits, values by activity sector, seasonally adjusted and unadjusted data for Canada, monthly data from 1948 to today.

Abstract. The present paper tests the structure and invariance of the Functional Theory of Human Values across 20 countries (N = 21,362). This theory proposes that values have the functions of guiding behaviour and expressing needs. The interplay between these two functions produces six subfunctions that in turn produce distinct content. These subfunctions are operationalised in the Basic Values Survey with three items each, forming an 18-item measure. Although this measure has been used for more than two decades, studies examining its psychometric properties in multiple-group data are scarce. Using multidimensional scaling (MDS), it was found that values were organised in a bidimensional space according to the hypothesised degree of congruence between subfunctions. Also, Confirmatory Factor Analysis (CFA) with a Bayes estimator and approximate zero cross-loadings and residual correlations supported the six-factor structure. A strict CFA with Robust-ML estimator did not support the model. Metric invariance was supported for all the items, except religiosity, using the alignment method and approximate Bayesian invariance.

This is supplementary material for the manuscript entitled "Toward Business Models for a Meta-Platform: Exploring Value Creation in the Case of Data Marketplaces." This paper explores value creation of a meta-platform in the case of data marketplaces. We interviewed fourteen data-sharing consultants and six meta-platform experts. This data consists of : 1) an overview of participants of our twenty semi-structured interviews, 2) four possible scenarios of a meta-platform for data marketplaces, 3) the interview protocol, and 4) the relation between data structure and interview excerpts. The type of the data is a PDF document.

This dataset contains raw data for all of the variables measured for the iUTAH GAMUT Network climate site near Trial Lake (PR_TL_C). Each file contains a calendar year of data. The file for the current year is updated on a daily basis. The data values were collected by a variety of sensors at 15 minute intervals. The file header contains detailed metadata for the site and the variable and method of each column.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

The University Center for Human Values series is a book series. It includes 8 books, written by 7 different authors.

Public participation geographic information systems (PPGIS) is increasingly used in coastal settings to inform natural resource management and spatial planning. Social Values for Ecosystem Services (SolVES), a PPGIS tool that systematizes the mapping and modeling of social values and cultural ecosystem services, is promising for use in coastal settings but has seen relatively limited applications relative to other PPGIS approaches; it has also to our knowledge not yet been applied in a barrier island setting. In this study, we surveyed two visitor groups and residents living near Cape Lookout National Seashore (North Carolina, USA) to understand social values they hold for the area in the context of the park’s management needs. We developed social-value models to evaluate differences between three user groups and evaluate how respondents’ experiences, attitudes, and recreational activities influence the locations they value and their most strongly held value types, which included aesthetic, recreation, biodiversity, future, therapeutic, and historic values. We found that accessibility, user types and the seasonality of major recreational activities, and the linear configuration of the barrier island system at Cape Lookout are important influences on the social values held by visitors and residents. The modeling approaches provide a variety of information relevant to management at Cape Lookout and can inform the design of future PPGIS studies in coastal and marine settings.

This dataset (located by latitude and longitude) is a subset of the geochemical dataset found in Chap. D, Appendix 8, Disc 1, and used in this study of the Upper Freeport coal bed. That dataset is a compilation of data from the U.S. Geological Survey's (USGS) National Coal Resources Data System (NCRDS) USCHEM (U.S. geoCHEMical), The Pennsylvania State University (PSU), the West Virginia Economic and Geological Survey (WVGES), and the Ohio Division of Geological Survey (OHGS) coal quality databases as well as published U.S. Bureau of Mines (USBM) data. The metadata file for the complete dataset is found in Chap. D, Appendix 9, Disc 1 (please see it for more detailed information on this geochemical dataset). This subset of the geochemical data for the Upper Freeport coal bed includes ash yield, sulfur content, SO2 value, gross calorific value, arsenic content and mercury content for these records, as well as the ranking of these values, which is described later under the attributes in this metadata file. Analytical techniques are described in the references in Chap. D, Appendix 10, Disc 1. The analytical data are stored as text fields because many of the parameters contain letter qualifiers appearing after the numerical data values. The following is a list of the possible qualifier values: L - less than, G - greater than, N - not detected, or H - interference that cannot be easily resolved. Not all of these codes may be in this database.

Full edition for scientific use. The COVID-19 pandemic has led to various Online-Panel studies to study the social effects of the crisis. The Values in Crisis study stands out in three central aspects. First, it offers a differentiated perspective to study how basic values of Austrian citizens change during times of crises. Second, the VIC study is a global survey project (see https://doi.org/10.11587/LIHK1L) which enables to compare the effects of the pandemic in Austria with other countries in Europe and other world regions. Third, the study represents a comparative longitudinal study, measuring the effects of the pandemic in three survey waves called “inmidst the crisis”, “end at sight” and “overcoming the pandemic”. This new dataset “Values in Crisis Austria – Wave 1, Wave 2 and Wave 3 combined” includes the Austrian data of the first wave (May 2020), the second wave (March/April 2021) and the third wave (July 2022). It is possible to analyse the data of the three waves separately or to conduct longitudinal analysis (based on n = 747 respondents who took part in all three surveys).The study investigates basic values (measured with classical value concepts such as the Inglehart Index and the short Portraits Values Questionnaire by Shalom Schwartz) the exposure to the crisis, attitudes towards politics and perceptions on public health and social and economic consequences of the pandemic. We used the international master questionnaire of the comparative study, but we added in all three waves various Austrian specific questions about attitudes towards public restrictions, towards social distancing or Austrian views of a post-corona society. In wave 3 we included also questions on perceptions of social integration, susceptibility to COVID-related conspiracy theories as well as items on political ideology and ethnocentrism.

This layer shows education level for adults 25+. Counts broken down by sex. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized by the percentage of adults (25+) who were not high school graduates. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B15002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

No description is available. Visit https://dataone.org/datasets/d6c80824bfba806f24a5a1b0b9be62c0 for complete metadata about this dataset.

Export value index (2000 = 100) in Mauritius was reported at 73.8 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Mauritius - Export value index (2000 = 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Saudi Arabia GDP: 2010p: Producer Values data was reported at 693,343.896 SAR mn in Sep 2023. This records a decrease from the previous number of 703,735.527 SAR mn for Jun 2023. Saudi Arabia GDP: 2010p: Producer Values data is updated quarterly, averaging 646,167.552 SAR mn from Mar 2010 (Median) to Sep 2023, with 55 observations. The data reached an all-time high of 750,749.255 SAR mn in Dec 2022 and a record low of 474,160.072 SAR mn in Jun 2010. Saudi Arabia GDP: 2010p: Producer Values data remains active status in CEIC and is reported by General Authority for Statistics. The data is categorized under Global Database’s Saudi Arabia – Table SA.A019: GDP: by Industry: 2010 Price.

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset. General Description The monthly aggregated water vapor dataset is derived from MCD19A2 v061. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:

Monthly time-series:Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the TMWM algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values. Yearly time-series:Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data. Long-term data (2000-2022):Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations. Data Details

Time period: 2000–2002 Type of data: Water vapor column above the ground (0.001cm) How the data was collected or derived: Derived from MCD19A2 v061 using Google Earth Engine. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the Scikit-map Python package. Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation. Limitations or exclusions in the data: The dataset does not include data for Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000) Spatial resolution: 1/120 d.d. = 0.008333333 (1km) Image size: 43,200 x 17,924 File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:

Technical issues and questions about the code: GitLab Issues General questions and comments: LandGIS Forum Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:

generic variable name: wv = Water vapor variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment Spatial support: 1km Depth reference: s = surface Time reference begin time: 20000101 = 2000-01-01 Time reference end time: 20021231 = 2002-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.4326 = EPSG:4326 Version code: v20230619 = 2023-06-19 (creation date)

This dataset is about: Color L*a*b values of sediment core SO106-220KG. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.761568 for more information.

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