📜 Dataset Overview

This dataset contains 3,400 records of fashion retail sales, capturing various details about customer purchases, including item details, purchase amounts, ratings, and payment methods. It is useful for analyzing customer buying behavior, product popularity, and payment preferences.

📂 Dataset Details

🔍 Key Insights

• The dataset contains 3,400 transactions.
• Missing values are present in:
  • Purchase Amount (USD): 650 missing values
  • Review Rating: 324 missing values
• Payment Method includes multiple categories, allowing analysis of payment trends.
• Date Purchase is in DD-MM-YYYY format, which can be useful for time-series analysis.
• The dataset can help analyze sales trends, customer preferences, and payment behaviors in the fashion retail industry.

📊 Potential Use Cases

• Sales Analysis: Understanding which fashion items are selling the most.
• Customer Insights: Analyzing purchase behaviors and spending patterns.
• Trend Forecasting: Identifying seasonal trends in fashion retail.
• Payment Method Preferences: Understanding how customers prefer to pay.

This dataset is about: (Table 1) Estimates of missing sediment between each Core of ODP Site 128-798. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.776579 for more information.

On the continental scale, climate is an important determinant of the distributions of plant taxa and ecoregions. To quantify and depict the relations between specific climate variables and these distributions, we placed modern climate and plant taxa distribution data on an approximately 25-kilometer (km) equal-area grid with 27,984 points that cover Canada and the continental United States (Thompson and others, 2015). The gridded climatic data include annual and monthly temperature and precipitation, as well as bioclimatic variables (growing degree days, mean temperatures of the coldest and warmest months, and a moisture index) based on 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and absolute minimum and maximum temperatures for 1951-1980 interpolated from climate-station data (WeatherDisc Associates, 1989). As described below, these data were used to produce portions of the "Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America" (hereafter referred to as "the Atlas"; Thompson and others, 1999a, 1999b, 2000, 2006, 2007, 2012a, 2015). Evolution of the Atlas Over the 16 Years Between Volumes A & B and G: The Atlas evolved through time as technology improved and our knowledge expanded. The climate data employed in the first five Atlas volumes were replaced by more standard and better documented data in the last two volumes (Volumes F and G; Thompson and others, 2012a, 2015). Similarly, the plant distribution data used in Volumes A through D (Thompson and others, 1999a, 1999b, 2000, 2006) were improved for the latter volumes. However, the digitized ecoregion boundaries used in Volume E (Thompson and others, 2007) remain unchanged. Also, as we and others used the data in Atlas Volumes A through E, we came to realize that the plant distribution and climate data for areas south of the US-Mexico border were not of sufficient quality or resolution for our needs and these data are not included in this data release. The data in this data release are provided in comma-separated values (.csv) files. We also provide netCDF (.nc) files containing the climate and bioclimatic data, grouped taxa and species presence-absence data, and ecoregion assignment data for each grid point (but not the country, state, province, and county assignment data for each grid point, which are available in the .csv files). The netCDF files contain updated Albers conical equal-area projection details and more precise grid-point locations. When the original approximately 25-km equal-area grid was created (ca. 1990), it was designed to be registered with existing data sets, and only 3 decimal places were recorded for the grid-point latitude and longitude values (these original 3-decimal place latitude and longitude values are in the .csv files). In addition, the Albers conical equal-area projection used for the grid was modified to match projection irregularities of the U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977) from which plant taxa distribution data were digitized. For the netCDF files, we have updated the Albers conical equal-area projection parameters and recalculated the grid-point latitudes and longitudes to 6 decimal places. The additional precision in the location data produces maximum differences between the 6-decimal place and the original 3-decimal place values of up to 0.00266 degrees longitude (approximately 143.8 m along the projection x-axis of the grid) and up to 0.00123 degrees latitude (approximately 84.2 m along the projection y-axis of the grid). The maximum straight-line distance between a three-decimal-point and six-decimal-point grid-point location is 144.2 m. Note that we have not regridded the elevation, climate, grouped taxa and species presence-absence data, or ecoregion data to the locations defined by the new 6-decimal place latitude and longitude data. For example, the climate data described in the Atlas publications were interpolated to the grid-point locations defined by the original 3-decimal place latitude and longitude values. Interpolating the data to the 6-decimal place latitude and longitude values would in many cases not result in changes to the reported values and for other grid points the changes would be small and insignificant. Similarly, if the digitized Little (1971, 1976, 1977) taxa distribution maps were regridded using the 6-decimal place latitude and longitude values, the changes to the gridded distributions would be minor, with a small number of grid points along the edge of a taxa's digitized distribution potentially changing value from taxa "present" to taxa "absent" (or vice versa). These changes should be considered within the spatial margin of error for the taxa distributions, which are based on hand-drawn maps with the distributions evidently generalized, or represented by a small, filled circle, and these distributions were subsequently hand digitized. Users wanting to use data that exactly match the data in the Atlas volumes should use the 3-decimal place latitude and longitude data provided in the .csv files in this data release to represent the center point of each grid cell. Users for whom an offset of up to 144.2 m from the original grid-point location is acceptable (e.g., users investigating continental-scale questions) or who want to easily visualize the data may want to use the data associated with the 6-decimal place latitude and longitude values in the netCDF files. The variable names in the netCDF files generally match those in the data release .csv files, except where the .csv file variable name contains a forward slash, colon, period, or comma (i.e., "/", ":", ".", or ","). In the netCDF file variable short names, the forward slashes are replaced with an underscore symbol (i.e., "_") and the colons, periods, and commas are deleted. In the netCDF file variable long names, the punctuation in the name matches that in the .csv file variable names. The "country", "state, province, or territory", and "county" data in the .csv files are not included in the netCDF files. Data included in this release: - Geographic scope. The gridded data cover an area that we labelled as "CANUSA", which includes Canada and the USA (excluding Hawaii, Puerto Rico, and other oceanic islands). Note that the maps displayed in the Atlas volumes are cropped at their northern edge and do not display the full northern extent of the data included in this data release. - Elevation. The elevation data were regridded from the ETOPO5 data set (National Geophysical Data Center, 1993). There were 35 coastal grid points in our CANUSA study area grid for which the regridded elevations were below sea level and these grid points were assigned missing elevation values (i.e., elevation = 9999). The grid points with missing elevation values occur in five coastal areas: (1) near San Diego (California, USA; 1 grid point), (2) Vancouver Island (British Columbia, Canada) and the Olympic Peninsula (Washington, USA; 2 grid points), (3) the Haida Gwaii (formerly Queen Charlotte Islands, British Columbia, Canada) and southeast Alaska (USA, 9 grid points), (4) the Canadian Arctic Archipelago (22 grid points), and (5) Newfoundland (Canada; 1 grid point). - Climate. The gridded climatic data provided here are based on the 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and include annual and monthly temperature and precipitation. The CRU CL 2.0 data were interpolated onto the approximately 25-km grid using geographically-weighted regression, incorporating local lapse-rate estimation and correction. Additional bioclimatic variables (growing degree days on a 5 degrees Celsius base, mean temperatures of the coldest and warmest months, and a moisture index calculated as actual evapotranspiration divided by potential evapotranspiration) were calculated using the interpolated CRU CL 2.0 data. Also included are absolute minimum and maximum temperatures for 1951-1980 interpolated in a similar fashion from climate-station data (WeatherDisc Associates, 1989). These climate and bioclimate data were used in Atlas volumes F and G (see Thompson and others, 2015, for a description of the methods used to create the gridded climate data). Note that for grid points with missing elevation values (i.e., elevation values equal to 9999), climate data were created using an elevation value of -120 meters. Users may want to exclude these climate data from their analyses (see the Usage Notes section in the data release readme file). - Plant distributions. The gridded plant distribution data align with Atlas volume G (Thompson and others, 2015). Plant distribution data on the grid include 690 species, as well as 67 groups of related species and genera, and are based on U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977), regional atlases (e.g., Benson and Darrow, 1981), and new maps based on information available from herbaria and other online and published sources (for a list of sources, see Tables 3 and 4 in Thompson and others, 2015). See the "Notes" column in Table 1 (https://pubs.usgs.gov/pp/p1650-g/table1.html) and Table 2 (https://pubs.usgs.gov/pp/p1650-g/table2.html) in Thompson and others (2015) for important details regarding the species and grouped taxa distributions. - Ecoregions. The ecoregion gridded data are the same as in Atlas volumes D and E (Thompson and others, 2006, 2007), and include three different systems, Bailey's ecoregions (Bailey, 1997, 1998), WWF's ecoregions (Ricketts and others, 1999), and Kuchler's potential natural vegetation regions (Kuchler, 1985), that are each based on distinctive approaches to categorizing ecoregions. For the Bailey and WWF ecoregions for North America and the Kuchler potential natural vegetation regions for the contiguous United States (i.e.,

Abstract: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.

Source: Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France

Data Set Information: This archive contains 2075259 measurements gathered in a house located in Sceaux (7km of Paris, France) between December 2006 and November 2010 (47 months). Notes:

(global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3. The dataset contains some missing values in the measurements (nearly 1,25% of the rows). All calendar timestamps are present in the dataset but for some timestamps, the measurement values are missing: a missing value is represented by the absence of value between two consecutive semi-colon attribute separators. For instance, the dataset shows missing values on April 28, 2007.

Attribute Information:

date: Date in format dd/mm/yyyy time: time in format hh:mm:ss global_active_power: household global minute-averaged active power (in kilowatt) global_reactive_power: household global minute-averaged reactive power (in kilowatt) voltage: minute-averaged voltage (in volt) global_intensity: household global minute-averaged current intensity (in ampere) sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered). sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light. sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.

Relevant Papers: N/A

Citation Request: This dataset is made available under the “Creative Commons Attribution 4.0 International (CC BY 4.0)” license

Appendix files associated with the PhD thesis "Chromatin organization and gene regulation in granulocytic cells" by Elsie Carlota JacobsonSupplementary table 1. Significantly differentially expressed genes. All genes with significantly differential gene expression (false discovery rate (FDR) <0.05) between either 5µm pores compared to control, 14µm pores compared to control, or 5µm pores compared to 14µm pores. Sets refer to those described in Chapter 2. HUGO gene names, Ensembl gene names and IDs are provided as identifiers. Columns suffixed with ‘_5’ represent results from 5µm pore compared to control. Columns suffixed with ‘_14’ represent results from 14µm pore compared to control. Columns suffixed with ‘_5v14’ represent results from 5µm pore compared to 14µm pore. The ‘padj’ shows the FDR adjusted p-value. Supplementary table 2. PC1 values and correlation between the interaction patterns of 100kb bins. Each 100kb bin has both a PC1 value indicating compartment status, and an interaction pattern with every other bin in the chromosome. When bins have a low correlation (R<0.6) and an opposite PC1 value, this is considered a disruption to the compartment status of this region. Calculated using the runHiCpca.pl and getHiCcorrDiff.pl scripts in HOMER. Regions with missing values, and chromosomes with PC1 values indicating chromosome arm, are excluded from this table. Supplementary table 3. Significantly differentially expressed genes in promyelocytes treated with TNF-a, differentiated into granulocytes and macrophages, and granulocytes treated with TNF-a. All genes with significantly differential gene expression (false discovery rate (FDR) <0.05) in promyelocytes treated with TNF-a, differentiated into granulocytes and macrophages, and granulocytes treated with TNF-a. Categories refer to those described in Chapter 3. Ensembl gene names and IDs are provided as identifiers. Columns suffixed with ‘_proTNF’ represent results from undifferentiated promyelocytic cells treated with TNF-a. Columns suffixed with '_granTNF' represent results from all-trans retinoic acid (ATRA) differentiated granulocytic cells treated with TNF-a. Columns suffixed with ‘_ATRA’ represent results from cells differentiated into granulocytes with ATRA. Columns suffixed with ‘_TPA’ represent results from cells differentiated into macrophages with 12-O-tetradecanoylphorbol-13-acetate (TPA). The ‘padj’ shows the FDR adjusted p-value. Supplementary table 4. Hic-breakfinder filtered results for all samples. This table shows all breakpoints over 10Mb apart. The log-odds score indicates the strength of the call. The bias value predicts which coordinate is closest to the breakpoint. A “+” bias indicates the “end” coordinate is closest to the breakpoint, while a “-” bias indicates the “start” coordinate is closest to the breakpoint. The resolution indicates the maximum resolution the breakpoint was identified at, as hic_breakfinder identifies breakpoints at multiple resolutions. The sample column indicates which dataset the breakpoint was identified in. Supplementary table 5. STAR-Fusion filtered results for all samples. This table has the gene fusion predictions for all samples of the 17 fusions identified in all replicates of at least one condition. Each row provides the STAR-Fusion results for a single RNA-seq library, thus each gene fusion may fill up to 29 rows. The first three columns indicate the dataset, condition, and replicate of the library. The remaining columns include detailed information about the predicted fusion as outputted by STAR-Fusion, including specific breakpoint locations and the number of junction and spanning reads. Supplementary table 6. HiCUP QC results for HL-60/S4-HindIII.

Dataset name: asppl-dataset.csv

Version: 1.0

Dataset period: 06/07/2018- 05/25/2021

Dataset Characteristics: Multivalued

Number of Instances: 4861

Number of Attributes: 33

Missing Values: Yes

Area(s): Health and education

Sources:

• Primary: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1];

• Secondary:

  1. Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2];

  2. National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and

  3. Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4].

Description: The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.

Analysis of ‘COVID-19 Reported Patient Impact and Hospital Capacity by State Timeseries’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/187ac6e5-efdc-465a-aaf7-9ffbc1f6ffeb on 11 February 2022.

--- Dataset description provided by original source is as follows ---

The following dataset provides state-aggregated data for hospital utilization in a timeseries format dating back to January 1, 2020. These are derived from reports with facility-level granularity across three main sources: (1) HHS TeleTracking, (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities and (3) National Healthcare Safety Network (before July 15).

The file will be updated regularly and provides the latest values reported by each facility within the last four days for all time. This allows for a more comprehensive picture of the hospital utilization within a state by ensuring a hospital is represented, even if they miss a single day of reporting.

No statistical analysis is applied to account for non-response and/or to account for missing data.

The below table displays one value for each field (i.e., column). Sometimes, reports for a given facility will be provided to more than one reporting source: HHS TeleTracking, NHSN, and HHS Protect. When this occurs, to ensure that there are not duplicate reports, prioritization is applied to the numbers for each facility.

On April 30, 2021, this data set has had the following fields added: previous_day_admission_adult_covid_confirmed_18-19 previous_day_admission_adult_covid_confirmed_18-19_coverage previous_day_admission_adult_covid_confirmed_20-29_coverage previous_day_admission_adult_covid_confirmed_30-39 previous_day_admission_adult_covid_confirmed_30-39_coverage previous_day_admission_adult_covid_confirmed_40-49 previous_day_admission_adult_covid_confirmed_40-49_coverage previous_day_admission_adult_covid_confirmed_40-49_coverage previous_day_admission_adult_covid_confirmed_50-59 previous_day_admission_adult_covid_confirmed_50-59_coverage previous_day_admission_adult_covid_confirmed_60-69 previous_day_admission_adult_covid_confirmed_60-69_coverage previous_day_admission_adult_covid_confirmed_70-79 previous_day_admission_adult_covid_confirmed_70-79_coverage previous_day_admission_adult_covid_confirmed_80+ previous_day_admission_adult_covid_confirmed_80+_coverage previous_day_admission_adult_covid_confirmed_unknown previous_day_admission_adult_covid_confirmed_unknown_coverage previous_day_admission_adult_covid_suspected_18-19 previous_day_admission_adult_covid_suspected_18-19_coverage previous_day_admission_adult_covid_suspected_20-29 previous_day_admission_adult_covid_suspected_20-29_coverage previous_day_admission_adult_covid_suspected_30-39 previous_day_admission_adult_covid_suspected_30-39_coverage previous_day_admission_adult_covid_suspected_40-49 previous_day_admission_adult_covid_suspected_40-49_coverage previous_day_admission_adult_covid_suspected_50-59 previous_day_admission_adult_covid_suspected_50-59_coverage previous_day_admission_adult_covid_suspected_60-69 previous_day_admission_adult_covid_suspected_60-69_coverage previous_day_admission_adult_covid_suspected_70-79 previous_day_admission_adult_covid_suspected_70-79_coverage previous_day_admission_adult_covid_suspected_80+ previous_day_admission_adult_covid_suspected_80+_coverage previous_day_admission_adult_covid_suspected_unknown previous_day_admission_adult_covid_suspected_unknown_coverage

On June 30, 2021, this data set has had the following fields added: deaths_covid deaths_covid_coverage

On September 13, 2021, this data set has had the following fields added: on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses, on_hand_supply_therapeutic_b_bamlanivimab_courses, on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses, previous_week_therapeutic_a_casirivimab_imdevimab_courses_used, previous_week_therapeutic_b_bamlanivimab_courses_used, previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used

On September 17, 2021, this data set has had the following fields added: icu_patients_confirmed_influenza, icu_patients_confirmed_influenza_coverage, previous_day_admi

--- Original source retains full ownership of the source dataset ---

Additional file 1. Table with p-values for pairwise comparisons between the tested classification approaches on the 5 biologic datasets.

Vehicle travel time and delay data on sections of road in Hamilton City, based on Bluetooth sensor records. To get data for this dataset, please call the API directly talking to the HCC Data Warehouse: https://api.hcc.govt.nz/OpenData/get_traffic_link_stats?Page=1&Start_Date=2021-06-02&End_Date=2021-06-03. For this API, there are three mandatory parameters: Page, Start_Date, End_Date. Sample values for these parameters are in the link above. When calling the API for the first time, please always start with Page 1. Then from the returned JSON, you can see more information such as the total page count and page size. For help on using the API in your preferred data analysis software, please contact dale.townsend@hcc.govt.nz. NOTE: Anomalies and missing data may be present in the dataset. Column_InfoLink_Id, int : Unique link identifierTravel_Time, int : Average travel time in seconds to travel along the linkAverage_Delay, int : Average travel delay in seconds, calculated as the difference between the free flow travel time and observed travel timeDate, varchar : Starting date and time for the recorded delay and travel time, in 15 minute periods Relationship This table reference to table Traffic_Link Analytics For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here. Disclaimer Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works. Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data. While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data: ‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

Vehicle travel time and delay data on routes in Hamilton City, based on Bluetooth sensor records. To get data for this dataset, please call the API directly talking to the HCC Data Warehouse: https://api.hcc.govt.nz/OpenData/get_traffic_route_stats?Page=1&Start_Date=2021-06-02&End_Date=2021-06-03. For this API, there are three mandatory parameters: Page, Start_Date, End_Date. Sample values for these parameters are in the link above. When calling the API for the first time, please always start with Page 1. Then from the returned JSON, you can see more information such as the total page count and page size. For help on using the API in your preferred data analysis software, please contact dale.townsend@hcc.govt.nz. NOTE: Anomalies and missing data may be present in the dataset. Column_InfoRoute_Id, int : Unique route identifierTravel_Time, int : Average travel time in seconds to travel along the routeDelay, int : Average travel delay in seconds, calculated as the difference between the free flow travel time and observed travel timeExcess_Delay, int : Excess Delay is similar to Delay, but it ignores recurring (expected) delays associated with peak times of dayDate, varchar : Starting date and time for the recorded delay and travel time, in 15 minute periods Relationship This table reference to table Traffic_Route Analytics For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here. Disclaimer Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works. Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data. While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data: ‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

Pedestrian count at counters in Hamilton City. To get data for this dataset, please call the API directly talking to the HCC Data Warehouse: https://api.hcc.govt.nz/OpenData/get_Pedestrian_count?Page=1&Start_Date=2020-10-01&End_Date=2020-10-02. For this API, there are three mandatory parameters: Page, Start_Date, End_Date. Sample values for these parameters are in the link above. When calling the API for the first time, please always start with Page 1. Then from the returned JSON, you can see more information such as the total page count and page size. For help on using the API in your preferred data analysis software, please contact dale.townsend@hcc.govt.nz. NOTE: Anomalies and missing data may be present in the dataset. Column_InfoCounter_Id, int : Unique identifier of the counterCount_Datetime, varchar : Start of the time interval that the count was recorded forCount_Number, int : Volume of pedestrians recorded for the given time interval Relationship This table reference to table Pedestrian_Counter_Information Analytics For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here. Disclaimer Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works. Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data. While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data: ‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

Recorded volume data at SCATS intersections or pedestrian crossings in Hamilton. To get data for this dataset, please call the API directly talking to the HCC Data Warehouse: https://api.hcc.govt.nz/OpenData/get_traffic_signal_detector_count?Page=1&Start_Date=2020-10-01&End_Date=2020-10-02. For this API, there are three mandatory parameters: Page, Start_Date, End_Date. Sample values for these parameters are in the link above. When calling the API for the first time, please always start with Page 1. Then from the returned JSON, you can see more information such as the total page count and page size. For help on using the API in your preferred data analysis software, please contact dale.townsend@hcc.govt.nz. NOTE: Anomalies and missing data may be present in the dataset. Column_InfoSite_Number, int : SCATS ID - Unique identifierDetector_Number, int : Detector number that the count is recorded toDate, datetime : Start of the 15 minute time interval that the count was recorded forCount, int : Number of vehicles that passed over the detector Relationship This table reference to table Traffic_Signal_Detector Analytics For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here. Disclaimer Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works. Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data. While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data: ‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

The watershed data management (WDM) database SC18.WDM is updated with the processed data for the period October 1, 2018, through September 30, 2019. The precipitation data are collected from a tipping-bucket rain-gage network and the hydrologic data (stage and discharge) are collected at USGS streamflow-gaging stations in and around DuPage County, Illinois. Hourly precipitation and hydrologic data for the period October 1, 2018, through September 30, 2019, are processed following the guidelines described in Bera (2014) and Murphy and Ishii (2006) and appended to SC18.WDM and renamed as SC19.WDM. Meteorological data (wind speed, solar radiation, air temperature, dewpoint temperature, and potential evapotranspiration) from October 1, 2018, through September 30, 2019, are copied from ARGN19.WDM and appended to SC19.WDM. Data in dataset number (DSN) 107 and 801–810 are used in comparisons of precipitation data. DSN 107 contains hourly precipitation data collected at Argonne National Laboratory at Argonne, Illinois. DSN 801-810 contains the processed Next Generation Weather Radar (NEXRAD)-multisensor precipitation estimates (MPE) data from 10 NEXRAD–MPE subbasins in the Salt Creek watershed as described in Bera and Ortel (2018). Data in these DSNs are not quality-assured and quality-controlled. The data are downloaded and uploaded daily into a WDM database that is used for the real-time streamflow simulation system. Data from DSN 107 and 801-810 are copied from this WDM and stored in SC19.WDM. DSN 107 and 801-810 are updated with the data through September 30, 2019. Data in DSN 5400 (water-surface elevation at the quarry) and 5700 (water surface elevation at Thorndale) are updated through September 30, 2019, similarly (Murphy and Ishii, 2006). The WDM database that is used for the real-time streamflow simulation system has added six hourly data series with new dataset numbers 7020-7025. These data series are used for storing the HSPF computed snow variables for snow depth and snow water equivalent. The new data series with the DSN 7020-7025 are added in SC19.WDM similarly and no data is added to any of these data series. The detail of the attributes for each of these data series are given in the table, attribute-table (available in csv format). Errors have been found in each of ARGNXX.WDM prior to Water Year (WY) 2023. XX represents last two digits of a WY. A WY is the 12-month period, October 1 through September 30, in which it ends. SC19.wdm contains erroneous meteorological data and related flag values thereby. SC19.WDM is removed. User is advised to download SC22.WDM from https://doi.org/10.5066/P14D6FRA. SC22.WDM (Bera, 2024b) contains corrected meteorological data from ARGN23.WDM (Bera, 2024a) for the period from January 1, 1997, through September 30, 2022. This database file also contains the quality-assured and quality-controlled hydrologic data for the period January 1, 1997, through September 30, 2022, processed following the guidelines documented in Bera (2014). While SC19.WDM is available from the author, all the records in SC19.WDM can be found in SC22.WDM as well. The complete list of missing precipitation data periods and the nearby stations used to fill in those missing periods from October 1, 2018, through September 30, 2019, is given in the table, missing_data.csv. The list of snow affected days of precipitation data and the missing and estimated period of the stage and flow data in SC22.WDM database during the period October 1, 2018, through September 30, 2019, are given in the USGS annual Water Data Report at https://waterdata.usgs.gov/nwis. To open the WDM database SC22.WDM user needs to install Sara Timeseries utility described in the section "Related External Resources". First posted - February 26, 2021 (available from author) References Cited: Bera, M., 2024a, Meteorological Database, Argonne National Laboratory, Illinois: U.S. Geological Survey data release, https://doi.org/10.5066/P146RBHK. _ 2024b, Watershed Data Management (WDM) Database (SC22.WDM) for Salt Creek Streamflow Simulation, DuPage County, Illinois, January 1, 1997, through September 30, 2022: U.S. Geological Survey, https://doi.org/10.5066/P14D6FRA. Bera, M., and Ortel, T.W., 2018, Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system: U.S. Geological Survey Open-File Report 2017–1159, 16 p., https://doi.org/10.3133/ofr20171159. Bera, M., 2014, Watershed Data Management (WDM) database for Salt Creek streamflow simulation, DuPage County, Illinois, water years 2005–11: U.S. Geological Survey Data Series 870, 18 p., http://dx.doi.org/10.3133/ds870. Murphy, E.A., and Ishii, A.L., 2006, Watershed Data Management (WDM) database for Salt Creek streamflow simulation, DuPage County, Illinois: U.S. Geological Survey Open-File Report 2006–1248, 34 p. Sara Timeseries utility at https://www.respec.com/product/modeling-optimization/sara-timeseries-utility/.

1 Introduction The Peatland Decomposition Database (PDD) stores data from published litterbag experiments related to peatlands. Currently, the database focuses on northern peatlands and Sphagnum litter and peat, but it also contains data from some vascular plant litterbag experiments. Currently, the database contains entries from 34 studies, 2,160 litterbag experiments, and 7,297 individual samples with 117,841 measurements for various attributes (e.g. relative mass remaining, N content, holocellulose content, mesh size). The aim is to provide a harmonized data source that can be useful to re-analyse existing data and to plan future litterbag experiments. The Peatland Productivity and Decomposition Parameter Database (PPDPD) (Bona et al. 2018) is similar to the Peatland Decomposition Database (PDD) in that both contain data from peatland litterbag experiments. The differences are that both databases partly contain different data, that PPDPD additionally contains information on vegetation productivity, which PDD does not, and that PDD provides more information and metadata on litterbag experiments, and also measurement errors. 2 Updates Compared to version 1.0.0, this version has a new structure for table experimental_design_format, contains additional metadata on the experimental design (these were omitted in version 1.0.0), and contains the scripts that were used to import the data into the database. 3 Methods 3.1 Data collection Data for the database was collected from published litterbag studies, by extracting published data from figures, tables, or other data sources, and by contacting the authors of the studies to obtain raw data. All data processing was done with R (R version 4.2.0 (2022-04-22)) (R Core Team 2022). Studies were identified via a Scopus search with search string (TITLE-ABS-KEY ( peat* AND ( 'litter bag' OR 'decomposition rate' OR 'decay rate' OR 'mass loss')) AND NOT ('tropic*')) (2022-12-17). These studies were further screened to exclude those which do not contain litterbag data or which recycle data from other studies that have already been considered. Additional studies with litterbag experiments in northern peatlands we were aware of, but which were not identified in the literature search were added to the list of publications. For studies not older than 10 years, authors were contacted to obtain raw data, however this was successful only in few cases. To date, the database focuses on Sphagnum litterbag experiments and not from all studies that were identified by the literature search data have been included yet in the database. Data from figures were extracted using the package ‘metaDigitise’ (1.0.1) (Pick, Nakagawa, and Noble 2018). Data from tables were extracted manually. Data from the following studies are currently included: Farrish and Grigal (1985), Bartsch and Moore (1985), Farrish and Grigal (1988), Vitt (1990), Hogg, Lieffers, and Wein (1992), Sanger, Billett, and Cresser (1994), Hiroki and Watanabe (1996), Szumigalski and Bayley (1996), Prevost, Belleau, and Plamondon (1997), Arp, Cooper, and Stednick (1999), Robbert A. Scheffer and Aerts (2000), R. A. Scheffer, Van Logtestijn, and Verhoeven (2001), Limpens and Berendse (2003), Waddington, Rochefort, and Campeau (2003), Asada, Warner, and Banner (2004), Thormann, Bayley, and Currah (2001), Trinder, Johnson, and Artz (2008), Breeuwer et al. (2008), Trinder, Johnson, and Artz (2009), Bragazza and Iacumin (2009), Hoorens, Stroetenga, and Aerts (2010), Straková et al. (2010), Straková et al. (2012), Orwin and Ostle (2012), Lieffers (1988), Manninen et al. (2016), Johnson and Damman (1991), Bengtsson, Rydin, and Hájek (2018a), Bengtsson, Rydin, and Hájek (2018b), Asada and Warner (2005), Bengtsson, Granath, and Rydin (2017), Bengtsson, Granath, and Rydin (2016), Hagemann and Moroni (2015), Hagemann and Moroni (2016), B. Piatkowski et al. (2021), B. T. Piatkowski et al. (2021), Mäkilä et al. (2018), Golovatskaya and Nikonova (2017), Golovatskaya and Nikonova (2017). 4 Database records The database is a ‘MariaDB’ database and the database schema was designed to store data and metadata following the Ecological Metadata Language (EML) (Jones et al. 2019). Descriptions of the tables are shown in Tab. 1. The database contains general metadata relevant for litterbag experiments (e.g., geographical, temporal, and taxonomic coverage, mesh sizes, experimental design). However, it does not contain a detailed description of sample handling, sample preprocessing methods, site descriptions, because there currently are no discipline-specific metadata and reporting standards. Table 1: Description of the individual tables in the database. Name Description attributes Defines the attributes of the database and the values in column attribute_name in table data. citations Stores bibtex entries for references and data sources. citations_to_datasets Links entries in table citations with entries in table datasets. custom_units Stores custom units. data Stores measured values for samples, for example remaining masses. datasets Lists the individual datasets. experimental_design_format Stores information on the experimental design of litterbag experiments. measurement_scales, measurement_scales_date_time, measurement_scales_interval, measurement_scales_nominal, measurement_scales_ordinal, measurement_scales_ratio Defines data value types. missing_value_codes Defines how missing values are encoded. samples Stores information on individual samples. samples_to_samples Links samples to other samples, for example litter samples collected in the field to litter samples collected during the incubation of the litterbags. units, unit_types Stores information on measurement units. 5 Attributes Table 2: Definition of attributes in the Peatland Decomposition Database and entries in the column attribute_name in table data. Name Definition Example value Unit Measurement scale Number type Minimum value Maximum value String format 4_hydroxyacetophenone_mass_absolute A numeric value representing the content of 4-hydroxyacetophenone, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA 4_hydroxyacetophenone_mass_relative_mass A numeric value representing the content of 4-hydroxyacetophenone, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA 4_hydroxybenzaldehyde_mass_absolute A numeric value representing the content of 4-hydroxybenzaldehyde, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA 4_hydroxybenzaldehyde_mass_relative_mass A numeric value representing the content of 4-hydroxybenzaldehyde, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA 4_hydroxybenzoic_acid_mass_absolute A numeric value representing the content of 4-hydroxybenzoic acid, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA 4_hydroxybenzoic_acid_mass_relative_mass A numeric value representing the content of 4-hydroxybenzoic acid, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA abbreviation In table custom_units: A string representing an abbreviation for the custom unit. gC NA nominal NA NA NA NA acetone_extractives_mass_absolute A numeric value representing the content of acetone extractives, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA acetone_extractives_mass_relative_mass A numeric value representing the content of acetone extractives, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA acetosyringone_mass_absolute A numeric value representing the content of acetosyringone, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA acetosyringone_mass_relative_mass A numeric value representing the content of acetosyringone, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA acetovanillone_mass_absolute A numeric value representing the content of acetovanillone, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA acetovanillone_mass_relative_mass A numeric value representing the content of acetovanillone, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA arabinose_mass_absolute A numeric value representing the content of arabinose, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA arabinose_mass_relative_mass A numeric value representing the content of arabinose, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA ash_mass_absolute A numeric value representing the content of ash (after burning at 550°C). 4 g ratio real 0 Inf NA ash_mass_relative_mass A numeric value representing the content of ash (after burning at 550°C). 0.05 g/g ratio real 0 Inf NA attribute_definition A free text field with a textual description of the meaning of attributes in the dpeatdecomposition database. NA NA nominal NA NA NA NA attribute_name A string describing the names of the attributes in all tables of the dpeatdecomposition database. attribute_name NA nominal NA NA NA NA bibtex A string representing the bibtex code used for a literature reference throughout the dpeatdecomposition database. Galka.2021 NA nominal NA NA NA NA bounds_maximum A numeric value representing the minimum possible value for a numeric attribute. 0 NA interval real Inf Inf NA bounds_minimum A numeric value representing the maximum possible value for a numeric attribute. INF NA interval real Inf Inf NA bulk_density A numeric value representing the bulk density of the sample [g cm-3]. 0,2 g/cm^3 ratio real 0 Inf NA C_absolute The absolute mass of C in the sample. 1 g ratio real 0 Inf NA C_relative_mass The absolute mass of C in the sample. 1 g/g ratio real 0 Inf NA C_to_N A numeric value representing the C to N ratio of the sample. 35 g/g ratio real 0 Inf NA C_to_P A numeric value representing the C to P ratio of the sample. 35 g/g ratio real 0 Inf NA Ca_absolute The

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides state-aggregated data for hospital utilization in a timeseries format dating back to January 1, 2020. These are derived from reports with facility-level granularity across three main sources: (1) HHS TeleTracking, (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities and (3) National Healthcare Safety Network (before July 15).

The file will be updated regularly and provides the latest values reported by each facility within the last four days for all time. This allows for a more comprehensive picture of the hospital utilization within a state by ensuring a hospital is represented, even if they miss a single day of reporting.

No statistical analysis is applied to account for non-response and/or to account for missing data.

The below table displays one value for each field (i.e., column). Sometimes, reports for a given facility will be provided to more than one reporting source: HHS TeleTracking, NHSN, and HHS Protect. When this occurs, to ensure that there are not duplicate reports, prioritization is applied to the numbers for each facility.

On April 27, 2022 the following pediatric fields were added: