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Providing a detailed breakdown of the fields in the dataset:
Url
: Direct URL to the watch page on TheWatchPages.comBrand
: Name of the watch brand (e.g., "Linde Werdelin")WatchName
: Full model name (e.g., "Spidospeed Monochrome")Price
: Listed price in USD (e.g., "$16,684")DetailInfo
: Detailed watch specifications in JSON format, including:
Category
: Tags representing the watch category or collection (e.g., "Home, Brands, Linde Werdelin")Image
: URL to the watch imageWatchDescription
: Description of the watch (if available)UniqId
: Unique identifier for each recordScrapedAt
: Timestamp of when the record was scraped
Note: This dataset is no longer updated but is being kept for historical reference. For the Department of Public Health’s current COVID-19 testing information at any time, please see the Testing section of https://www.chicago.gov/coronavirus.
Locations offering COVID-19 testing in Chicago.
For a map of these locations, see https://data.cityofchicago.org/d/j2wj-wjrp.
For more information on COVID-19 testing, see https://www.chicago.gov/city/en/sites/covid-19/home/managing-your-health.html?#tab-shouldtest.
For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.
This dataset includes data on how all datasets, stories and derived views (tabular views, visualizations and measures) on a domain are being accessed by users.
The following usage types are included in the Access Type column:Data are updated by a system process at least once a day.
Please see Site Analytics: Asset Access for more detail.
In November 2024, Danish internet users who visited the online search platform Google.dk viewed an average of 6.03 pages during each visit. Social media platforms Facebook and Instagram topped the list with users seeing an average of 9.73 pages during each session. Pornhub.com came in third, with users browsing an average of 8.26 pages per session.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset is about books. It has 1 row and is filtered where the book is Site-seeing : a visual approach to Web usability. It features 7 columns including author, publication date, language, and book publisher.
NOTE: This dataset has been retired and marked as historical-only. Select locations offering COVID-19 vaccination in Chicago. This is not an exhaustive list of all providers currently offering COVID-19 vaccine. Many providers are not listed on the map because they will start by providing vaccine to their current patients before expanding to others. Your first contact should be your health care provider, including your primary care provider, health clinic, or hospital where you have gotten medical care in the past. Check your provider’s website, electronic application (e.g., MyChart), or office recorded message for details on vaccine availability. Providers are also reaching out directly to schedule appointments with their existing patients, prioritizing those who are older with more underlying conditions. Check the details associated with each provider prior to calling or showing up at the office or pharmacy. There is a phased roll-out of the COVID-19 vaccine in Chicago with a very limited supply, so certain groups are prioritized. Currently, vaccination is available by appointment only for eligible individuals. The vaccine will be offered at no cost to all Chicagoans who want it, but patience is needed while vaccine quantities increase. Learn more about Chicago's vaccination phases and who is currently eligible here: https://www.chicago.gov/city/en/sites/covid19-vaccine/home/vaccine-distribution-phases.html For a map of these locations, see https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Locations-Map/4shi-izjg The City of Chicago does not endorse any of the listed organizations. This list is provided only as a convenience. See the full disclaimer: https://www.chicago.gov/content/dam/city/sites/covid-19-vaccine/Documents/Disclaimer.pdf For more information on COVID-19 vaccine, see https://www.chicago.gov/city/en/sites/covid19-vaccine/home.html. To get the latest updates on Chicago's vaccination plan and be notified when different groups are eligible to receive COVID-19 vaccine, sign up for Chi COVID Coach: https://covidcoach.chicago.gov/ For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.
This spatial dataset contains the digital boundaries of Sites of Special Scientific Interest (SSSIs) in Wales. SSSIs cover a wide range of habitats from small fens, bogs and riverside meadows to sand-dunes, woodlands and vast tracts of uplands. Most are in private ownership, although some are owned and managed by local wildlife trusts, or other voluntary conservation bodies.
Notification of an SSSI under the Wildlife and Countryside Act 1981 has since been amended by the Countryside and Rights of Way Act 2001, which brought about numerous changes in the way SSSI are notified managed and protected.
In order to ensure consistent, favourable long-term management of these sites, Natural Resources Wales (NRW) with landowners have prepared management plans for all SSSI in Wales. Local planning authorities are required to consult NRW before allowing any development to proceed that may affect an SSSI. Water, gas and electricity companies must also do the same.
SSSIs have been designated, from 1949 to the present day, and are on-going. The data has been held digitally since the mid-1990s. This data has been checked by relevant NRW staff. Please refer to the designation map as the legal definitive boundary. For large SSSIs that were captured digitally and have been printed on a smaller scale map than OS MasterMap, please refer to the OS MasterMap edition at time of capture to view the definitive boundary.
Public view of the site analytics dataset
Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.
Please see the DERF website for more information - https://dnr.wi.gov/Aid/DERF.htmlTo view more contamination data layers, see the RR Sites Map at https://dnr.wi.gov/topic/Brownfields/rrsm.html, or for specific site information search BRRTS on the Web at https://dnr.wi.gov/botw/SetUpBasicSearchForm.do
URL from idinfo/citation in CSDGM metadata.
For further information about Park and Ride sites - see the iTravelYork website
Ground validation and accuracy assessment sites. See the individual layers for a full description. Dataset Citation: Kendall, M., B. Costa, S. McKagan, L. Johnston, and D. Okano. 2017. Benthic Habitat Maps of Saipan Lagoon. NOAA Technical Memorandum NOS NCCOS 229. Silver Spring, MD. 79 pp.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Data associated with main analysis and figures presented in manuscript titled: "Long-term organic farming and floral diversity promotes stability of bee communities in agroecosystems." Dataset titled: "Bloom_BetaDiverstiy_GPSPointsAndSiteCharacteristics_DataFinal.csv" are associated with the map of site locations shown in Figure 1. These data are also given in Table 2 (note no data are associated with Table 1). GPS points have been jittered to protect the identity of farmer collaborators. Exact locations are available upon request and after consideration by the lead author. No data are associated with Figure 2. The four datasets tilted: "Bloom_BetaDiversity_GeometricRemovalXXX_DataFinal.csv" are files associated with the geometric species removal analysis accompanying Figure 3, where XXX is the scale (local or landscape) and term (species loss or removal) (see manuscript for details). Column definitions include: SiteID - the site for which the statistic was generated (local level only); SiteID1 - the first site involved in the pairwise comparison which generated the statistic (landscape level only); SiteID2 - the second site involved in the pairwise comparison which generated the statistic (landscape level only); year1 - the year (e.g., 20XX) for which the sample was taken at the site given in SiteID1 which then generated the statistic (landscape level only); year2 - the year (e.g., 20XX) for which the sample was taken at the site given in SiteID2 which then generated the statistic (landscape level only); variable - species removed at random; sim (replacement) or sne (loss) - the statistic with no species removed; value - the statistics with the species removed; vec - the color relating the number of species removed (see Figure 3 caption for column species number relationships). The dataset title: "Bloom_BetaDiverstiy_SADS_DataFinal.xlsx" contains three sheets each associated with a sample year (2014, 2015, 2016). Within each sheet are the vectors of abundance values and species names used to create species abundance models plotted in Figure 4. The datasets titled: "Bloom_BetaDiverstiy_LocalLevelRegressions_DataFinal.csv" and "Bloom_BetaDiverstiy_LandscapeLevelRegressions_DataFinal" contain statistics used to create Figure 5. The column name definitions that have not been previously given are as follows: detlaaic - difference in species abundance model fit (for local level); Years.Since.Transition - years since transitioning to organic farming scaled to enhance regression model fitting; sor - the overall Sorenson's beta diversity term for bees across years or sites (depends on scale see file name); sim - the species replacement term from the additive partition of Sorenson's beta diversity for bees across years or sites (depends on scale see file name); nes - the species loss term from the additive partition of Sorenson's beta diversity for bees across years or sites (depends on scale see file name); X.sor/X.sim/S.nes - where X is l and p for landscape and plant beta diversity for each term at the site across years or sites (depends on scale see file name); diff_years - differences in times since transitions to organic agriculture scaled to enhance regression model fitting (landscape level only); diff_do_new - difference in species abundance model fit (for landscape level); p.all - multiplication of the p.sim and l.nes terms for plotting the interaction shown in Figure 5d. The dataset titled: "Bloom_BetaDiverstiy_Jackknife_DataFinal.xlsx" contains 6 sheets corresponding to Figure 6 panels "a-f" in linear order. Column definitions include: pvalue - the pvalue found when the variable was removed from the site by variable matrix and used to create the histograms; variable - the variable that was removed from the site by variable matrix. Variables can be bee species, landscape classes, or plants given by their unique common name. The final 3 datasets are titled: "Bloom_BetaDiverstiy_BeeSiteXSpeciesMatrix_DataFinal.csv", "Bloom_BetaDiverstiy_LandscapeSiteXClassMatrix_DataFinal.csv", and "Bloom_BetaDiverstiy_PlantSiteXCommonNameMatrix_DataFinal.csv." These datasets contain the matrices used to generate these bee, landscape, and plant beta diversity metrics used for our analysis.
Access counts for Open Data Portal (assets where the URL includes the domain data.mesaaz.gov)
This dataset includes data on how all datasets, stories and derived views (tabular views, visualizations and measures) on a domain are being accessed by users.
The following usage types are included in the Access Type column:Data are updated by a system process at least once a day.
Please see Site Analytics: Asset Access for more detail.
This data release includes water-quality data collected at 38 sites in central and eastern Massachusetts from April 2018 through May 2019 by the U.S. Geological Survey to support the implementation of site-dependent aluminum criteria for Massachusetts waters. Samples of effluent and receiving surface waters were collected monthly at four wastewater-treatment facilities (WWTFs) and seven water-treatment facilities (WTFs) (see SWQ_data_and_instantaneous_CMC_CCC_values.txt). The measured properties and constituents include pH, hardness, and filtered (dissolved) organic carbon, which are required inputs to the U.S. Environmental Protection Agency's Aluminum Criteria Calculator version 2.0. Outputs from the Aluminum Criteria Calculator are also provided in that file; these outputs consist of acute (Criterion Maximum Concentration, CMC) and chronic (Criterion Continuous Concentration, CCC) instantaneous water-quality values for total recoverable aluminum, calculated for monthly samples at selected ambient sites near each of the 11 facilities. Quality-control data from blank, replicate, and spike samples are provided (see SWQ_QC_data.txt). In addition to data tables, the data release includes time-series graphs of the discrete water-quality data (see SWQ_plot_discrete_all.zip). For pH, time-series graphs also are provided showing pH from the discrete monthly water-quality samples as well as near-continuous pH measured at one surface-water site at each facility (see SWQ_plot_contin_discrete_pH.zip). The near-continuous pH data, along with all of the discrete water-quality data except the quality-control data, are also available online from the U.S. Geological Survey's National Water Information System (NWIS) database (https://nwis.waterdata.usgs.gov/nwis).
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This repository provides data and code to support the replication of the paper "Heritage site-seeing through the visitor’s lens on Instagram".
This statistic presents the monuments or places to see at least once in your lifetime according to French people in 2019. It reveals that 14 percent of respondents chose the Egyptian pyramids as the monuments people should visit in their lifetime. The Eiffel Tower was mentioned by seven percent of the interviewees.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Gross primary production, simulated by the P-model for each FLUXNET 2015 Tier 1 site. The model was driven by site-specific meteorological forcing and MODIS FPAR, extracted for the pixel corresponding to the site location. The CSV files contain simulated GPP values from different model setups conducted with the P-model and used for the publication Stocker et al. Geosci. Mod. Dev. (in review). One file is given for each temporal aggregation level (daily, 8-daily, annual, spatial [= mean annual value by site], and mean seasonal cycle [= mean per day-of-year]. Each file contains output from all model setups presented in Stocker et al. (2019), as given by column setup. The data differs slightly for each file: Daily gpp_pmodel_fluxnet2015_stocker19gmd_daily.csv: sitename: A character specifying the site ID following the naming given by FLUXNET 2015. date: YYYY-MM-DD), date_start (in _8daily, YYYY-MM-DD specifying the first day of the respective 8-day period), year (in _annual, YYYY), doy (in _meanseason, specifying the day-of-year), gpp: Simulated gross primary production, in units of g C m-2 d-1 setup: A character specifying the model setup name used in Stocker et al. (2019). See also below. 8-daily gpp_pmodel_fluxnet2015_stocker19gmd_8daily.csv: sitename: A character specifying the site ID following the naming given by FLUXNET 2015. date_start : YYYY-MM-DD specifying the first day of the respective 8-day period gpp: Simulated gross primary production, in units of g C m-2 d-1 setup: A character specifying the model setup name used in Stocker et al. (2019). See also below. Annual gpp_pmodel_fluxnet2015_stocker19gmd_annual.csv: sitename: A character specifying the site ID following the naming given by FLUXNET 2015. year: YYYY gpp: Simulated gross primary production, in units of g C m-2 yr-1 setup: A character specifying the model setup name used in Stocker et al. (2019). See also below. Spatial gpp_pmodel_fluxnet2015_stocker19gmd_spatial.csv: sitename: A character specifying the site ID following the naming given by FLUXNET 2015. gpp: Simulated gross primary production, in units of g C m-2 yr-1 setup: A character specifying the model setup name used in Stocker et al. (2019). See also below. Mean seasonal cycle gpp_pmodel_fluxnet2015_stocker19gmd_meanseason.csv: sitename: A character specifying the site ID following the naming given by FLUXNET 2015. doy: day-of-year gpp: Simulated gross primary production, in units of g C m-2 d-1 setup: A character specifying the model setup name used in Stocker et al. (2019). See also below.
The boundaries of all sites that held a Green Flag or Green Flag Community Award in the 2019-2020 Award Year.
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Providing a detailed breakdown of the fields in the dataset:
Url
: Direct URL to the watch page on TheWatchPages.comBrand
: Name of the watch brand (e.g., "Linde Werdelin")WatchName
: Full model name (e.g., "Spidospeed Monochrome")Price
: Listed price in USD (e.g., "$16,684")DetailInfo
: Detailed watch specifications in JSON format, including:
Category
: Tags representing the watch category or collection (e.g., "Home, Brands, Linde Werdelin")Image
: URL to the watch imageWatchDescription
: Description of the watch (if available)UniqId
: Unique identifier for each recordScrapedAt
: Timestamp of when the record was scraped