Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation mean tidal range (i.e. Mean Range of Tides, MN) in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New Jersey, USA. MN was based on the calculated difference in height between mean high water (MHW) and mean low water (MLW) using the VDatum (v3.5) software (http://vdatum.noaa.gov/). The input elevation was set to zero in VDatum to calculate the relative difference between the two datums. As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey has started a Wetland Synthesis Project to expand National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study area.

Descriptive statistics of dependent variables mean(SE) across range of speeds.

This derived dataset contains basic statistical products derived from the eReefs CSIRO hydrodynamic model v2.0 outputs at both 1 km and 4 km resolution and v4.0 at 4 km for both a daily and monthly aggregation period. The statistics generated are daily minimum, maximum, mean and range. For monthly aggregations there are monthly mean of the daily minimum, maximum and range, and the monthly minimum, maximum and range. The dataset only calculates statistics for the temperature and water elevation (eta).

These are generated by the AIMS eReefs Platform (https://ereefs.aims.gov.au/). These statistical products are derived from the original hourly model outputs available via the National Computing Infrastructure (NCI) (https://thredds.nci.org.au/thredds/catalogs/fx3/catalog.html).

The data is re-gridded from the original curvilinear grid used by the eReefs model into a regular grid so the data files can be easily loaded into standard GIS software. These products are made available via a THREDDS server (https://thredds.ereefs.aims.gov.au/thredds/) in NetCDF format and
This data set contains two (2) products, based on the periods over which the statistics are determined: daily, and monthly.

Method:
Data files are processed in two stages. The daily files are calculated from the original hourly files, then the monthly files are calculated from the daily files. See Technical Guide to Derived Products from CSIRO eReefs Models for details on the regridding process.

Data Dictionary:

Daily statistics:
The following variables can be found in the Daily statistics product:

- temp_mean: mean temperature for each grid cell for the day.
- temp_min: minimum temperature for each grid cell for the day.
- temp_max: maximum temperature for each grid cell for the day.
- temp_range: difference between maximum and minimum temperatures for each grid cell for the day.

- eta_mean: mean surface elevation for each grid cell for the day.
- eta_min: minimum surface elevation for each grid cell for the day.
- eta_max: maximum surface elevation for each grid cell for the day.
- eta_range: difference between maximum and minimum surface elevation for each grid cell for the day.

Depths:

Depths at 1km resolution: -2.35m, -5.35m, -18.0m, -49.0m
Depths are 4km resolution: -1.5m, -5.55m, -17.75m, -49.0m

* Monthly statistics:

The following variables can be found in the Monthly statistics product:

- temp_min_min: the minimum value of the "temp_min" variable from the Daily statistics product. This equates to the minimum temperature for each grid cell for the corresponding month.
- temp_min_mean: the mean value of the "temp_min" variable from the Daily statistics product. This equates to the mean minimum temperature for each grid cell for the corresponding month.
- temp_max_max: the maximum value of the "temp_max" variable from the Daily statistics product. This equates to the maximum temperature for each grid cell for the corresponding month.
- temp_max_mean: the mean value of the "temp_max" variable from the Daily statistics product. This equates to the mean maximum temperature for each grid cell for the corresponding month.
- temp_mean: the mean value of the "temp_mean" variable from the Daily statistics product. This equates to the mean temperature for each grid cell for the corresponding month.
- temp_range_mean: the mean value of the "temp_range" variable from the Daily statistics product. This equates to the mean range of temperatures for each grid cell for the corresponding month.
- eta_min_min: the minimum value of the "eta_min" variable from the Daily statistics product. This equates to the minimum surface elevation for each grid cell for the corresponding month.
- eta_min_mean: the mean value of the "eta_min" variable from the Daily statistics product. This equates to the mean minimum surface elevation for each grid cell for the corresponding month.
- eta_max_max: the maximum value of the "eta_max" variable from the Daily statistics product. This equates to the maximum surface elevation for each grid cell for the corresponding month.
- eta_max_mean: the mean value of the "eta_max" variable from the Daily statistics product. This equates to the mean maximum surface elevation for each grid cell for the corresponding month.
- eta_mean: the mean value of the "eta_mean" variable from the Daily statistics product. This equates to the mean surface elevation for each grid cell for the corresponding month.
- eta_range_mean: the mean value of the "eta_range" variable from the Daily statistics product. This equates to the mean range of surface elevations for each grid cell for the corresponding month.

Depths:
Depths at 1km resolution: -2.35m, -5.35m, -18.0m, -49.0m
Depths are 4km resolution: -1.5m, -5.55m, -17.75m, -49.0m

What does this dataset show:

The temperature statistics show that inshore areas along the coast get significantly warmer in summer and cooler in winter than offshore areas. The daily temperature range is lower in winter with most areas experiencing 0.2 - 0.3 degrees Celsius temperature change. In summer months the daily temperature range approximately doubles, with up welling areas in the Capricorn Bunker group, off the outer edge of the Prompey sector of reefs and on the east side of Torres Strait seeing daily temperature ranges between 0.7 - 1.2 degree Celsius.

Limitations:

This dataset is based on spatial and temporal models and so are an estimate of the environmental conditions. It is not based on in-water measurements, and thus will have a spatially varying level of error in the modelled values. It is important to consider if the model results are fit for the intended purpose.

Change Log:
2025-10-29: Updated the metadata title from 'eReefs AIMS-CSIRO Statistics of hydrodynamic model outputs' to 'Daily and monthly minimum, maximum and range of eReefs hydrodynamic model outputs - temperature, water elevation (AIMS, Source: CSIRO)'. Improve the introduction text. Corrected deprecated link to NCI THREDDS. Added a description of what the dataset shows.

Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation of mean tidal range (i.e. Mean Range of Tides, MN) in the Fire Island National Seashore and central Great South Bay salt marsh complex, based on conceptual marsh units defined by Defne and Ganju (2018). MN was based on the calculated difference in height between mean high water (MHW) and mean low water (MLW) using the VDatum (v3.5) database ( http://vdatum.noaa.gov/ ). Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the Fire Island National Seashore and central Great South Bay salt marshes, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. References: Defne, Z., and Ganju, N.K., 2018, Conceptual marsh units for Fire Island National Seashore and central Great South Bay salt marsh complex, New York: U.S. Geological Survey data release, https://doi.org/10.5066/P95U2MQ7.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Data Source: This dataset was compiled from multiple data sources

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About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in South Range, MI, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 8,987, while the mean income for the highest quintile (20% of households with the highest income) is 139,129. This indicates that the top earners earn 15 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 218,142, which is 156.79% higher compared to the highest quintile, and 2427.31% higher compared to the lowest quintile.

https://i.neilsberg.com/ch/south-range-mi-mean-household-income-by-quintiles.jpeg" alt="Mean household income by quintiles in South Range, MI (in 2022 inflation-adjusted dollars))">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range median household income. You can refer the same here

Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation of mean tidal range (i.e. Mean Range of Tides, MN) in the Plum Island Estuary and Parker River (PIEPR) salt marsh complex based on conceptual marsh units defined by Defne and Ganju (2018). MN was based on the calculated difference in height between mean high water (MHW) and mean low water (MLW) using the VDatum (v3.5) database ( http://vdatum.noaa.gov/ ). Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the Plum Island Estuary and Parker River salt marsh complex, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. Mean elevation of marsh units is planned to be an underlying parameter in the synthesis of these factors. References: Defne, Z., and Ganju, N.K., 2018, Conceptual marsh units for Plum Island Estuary and Parker River salt marsh complex, Massachusetts: U.S. Geological Survey data release, https://doi.org/10.5066/P9XF54QF

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The dataset is an extract for the Hunter subregion of the soil thickness data from the ASRIS Continental-scale soil property predictions 2001. The source data are the Surface of predicted Thickness of soil layer 1 (A Horizon - top-soil) surface for the intensive agricultural areas of Australia. Data modelled from area based observations made by soil agencies both State and CSIRO and presented as .0.01 degree grid cells.

The dataset consists of statistics for soils depths (MIN, MAX, RANGE, MEAN, STD, MEDIAN) for each of the simulation catchments in the AWRA-L model. The soil thickness data were resampled to the model grid (BILO cells - 0.05 degree grid cells) and the catchments are defined by the BILO cells which fall within them. The gauging station ID in the spreadsheet defines the gauges which were used to define the upstream catchment area.

Purpose

Used to define soils thickness in the AWRA-L model.

Dataset History

The soil thickness data were resampled to the model grid (BILO cells - 0.05 degree grid cells). Statistics for soils depths (MIN, MAX, RANGE, MEAN, STD, MEDIAN) for each of the simulation catchments in the AWRA-L model were calculated using the Zonal Statistics as Table tool within ArcGIS with the simulation catchments used as the zone dataset. The output table was used to populate the excel spreadsheet with the Station ID and catchments areas added.

Dataset Citation

Bioregional Assessment Programme (XXXX) HUN AWRA-L ASRIS soil properties v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/d8091c0a-5fdc-4f6a-8b61-b1e6cc7c3ace.

Dataset Ancestors

• Derived From HUN AWRA-R simulation nodes v01

• Derived From Bioregional Assessment areas v06

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Bioregional Assessment areas v04

• Derived From Gippsland Project boundary

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From BA All Regions BILO cells in subregions shapefile

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Bioregional Assessment areas v03

• Derived From Bioregional Assessment areas v05

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From National Surface Water sites Hydstra

• Derived From ASRIS Continental-scale soil property predictions 2001

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From Victoria - Seamless Geology 2014

• Derived From HUN AWRA-R simulation catchments v01

• Derived From Climate model 0.05x0.05 cells and cell centroids

• About Dataset Safa S. Abdul-Jabbar, Alaa k. Farhan

• Context This is the first Dataset for various ordinary patients in Iraq. The Dataset provides the patients’ Cell Blood Count test information that can be used to create a Hematology diagnosis/prediction system. Also, this Data was collected in 2022 from Al-Zahraa Al-Ahly Hospital. These data can be cleaned & analyzed using any programming language because it is provided in an excel file that can be accessed and manipulated easily. The user just needs to understand how rows and columns are arranged because the data was collected as images(CBC images) from the laboratories and then stored the extracted data in an excel file.  Content This Dataset contains 500 rows. For each row (patient information), there are 21 columns containing CBC test features that can be described as follows:

• ID: Patients Identifier

• WBC: White Blood Cell, Normal Ranges: 4.0 to 10.0, Unit: 10^9/L.

• LYMp: Lymphocytes percentage, which is a type of white blood cell, Normal Ranges: 20.0 to 40.0, Unit: %

• MIDp: Indicates the percentage combined value of the other types of white blood cells not classified as lymphocytes or granulocytes, Normal Ranges: 1.0 to 15.0, Unit: %

• NEUTp: Neutrophils are a type of white blood cell (leukocytes); neutrophils percentage, Normal Ranges: 50.0 to 70.0, Unit: %

• LYMn: Lymphocytes number are a type of white blood cell, Normal Ranges: 0.6 to 4.1, Unit: 10^9/L.

• MIDn: Indicates the combined number of other white blood cells not classified as lymphocytes or granulocytes, Normal Ranges: 0.1 to 1.8, Unit: 10^9/L.

• NEUTn: Neutrophils Number, Normal Ranges: 2.0 to 7.8, Unit: 10^9/L.

• RBC: Red Blood Cell, Normal Ranges: 3.50 to 5.50, Unit: 10^12/L

• HGB: Hemoglobin, Normal Ranges: 11.0 to 16.0, Unit: g/dL

• HCT: Hematocrit is the proportion, by volume, of the Blood that consists of red blood cells, Normal Ranges: 36.0 to 48.0, Unit: %

• MCV: Mean Corpuscular Volume, Normal Ranges: 80.0 to 99.0, Unit: fL

• MCH: Mean Corpuscular Hemoglobin is the average amount of haemoglobin in the average red cell, Normal Ranges: 26.0 to 32.0, Unit: pg

• MCHC: Mean Corpuscular Hemoglobin Concentration, Normal Ranges: 32.0 to 36.0, Unit: g/dL

• RDWSD: Red Blood Cell Distribution Width, Normal Ranges: 37.0 to 54.0, Unit: fL

• RDWCV: Red blood cell distribution width, Normal Ranges: 11.5 to 14.5, Unit: %

• PLT: Platelet Count, Normal Ranges: 100 to 400, Unit: 10^9/L

• MPV: Mean Platelet Volume, Normal Ranges: 7.4 to 10.4, Unit: fL

• PDW: Red Cell Distribution Width, Normal Ranges: 10.0 to 17.0, Unit: %

• PCT: The level of Procalcitonin in the Blood, Normal Ranges: 0.10 to 0.28, Unit: %

• PLCR: Platelet Large Cell Ratio, Normal Ranges: 13.0 to 43.0, Unit: %

• Acknowledgements We thank the entire Al-Zahraa Al-Ahly Hospital Hospital team, especially the hospital manager, for cooperating with us in collecting this data while maintaining patients' confidentiality.

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

Summary statistics (mean, standard deviation, median, interquartile range, number of subjects) for “ln_adducts” in cases, controls, and total population.

Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation of mean tidal range (i.e. Mean Range of Tides, MN) in the Assateague Island National Seashore and Chincoteague Bay based on conceptual marsh units defined by Defne and Ganju (2018). MN was based on the calculated difference in height between mean high water (MHW) and mean low water (MLW) using the VDatum (v3.5) database ( http://vdatum.noaa.gov/ ). Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the Assateague Island National Seashore and Chincoteague Bay salt marshes, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. Mean elevation of marsh units is planned to be an underlying parameter in the synthesis of these factors. References: Defne, Z., and Ganju, N.K., 2018, Conceptual marsh units for Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia: U.S. Geological Survey data release, https://doi.org/10.5066/P92ZW4D9.

UWB Positioning and Tracking Data Set UWB positioning data set contains measurements from four different indoor environments. The data set contains measurements that can be used for range-based positioning evaluation in different indoor environments. # Measurement system The measurements were made using 9 DW1000 UWB transceivers (DWM1000 modules) connected to the networked RaspberryPi computer using in-house radio board SNPN_UWB. 8 nodes were used as positioning anchor nodes with fixed locations in individual indoor environment and one node was used as a mobile positioning tag. Each UWB node is designed arround the RaspberryPi computer and are wirelessly connected to the measurement controller (e.g. laptop) using Wi-Fi and MQTT communication technologies. All tag positions were generated beforehand to as closelly resemble the human walking path as possible. All walking path points are equally spaced to represent the equidistand samples of a walking path in a time-domain. The sampled walking path (measurement TAG positions) are included in a downloadable data set file under downloads section. # Folder structure Folder structure is represented below this text. Folder contains four subfolders named by the indoor environments measured during the measurement campaign and a folder raw_data where raw measurement data is saved. Each environment folder has a anchors.csv file with anchor names and locations, .json file data.json with measurements, file walking_path.csv file with tag positions and subfolder floorplan with floorplan.dxf (AutoCAD format), floorplan.png and floorplan_track.jpg. Subfolder raw_data contains raw data in subfolders named by the four indor environments where the measurements were taken. Each location subfolder contains a subfolder data where data from each tag position from the walking_path.csv is collected in a separate folder. There is exactly the same number of folders in data folder as is the number of measurement points in the walking_path.csv. Each measurement subfolder contains 48 .csv files named by communication channel and anchor used for those measurements. For example: ch1_A1.csv contains all measurements at selected tag location with anchor A1 on UWB channel ch1. The location folder contains also anchors.csv and walking_path.csv files which are identical to the files mentioned previously. The last folder in the data set is the technical_validation folder, where results of technical validation of the data set are collected. They are separated into 8 subfolders: - cir_min_max_mean - los_nlos - positioning_wls - range - range_error - range_error_A6 - range_error_histograms - rss The organization of the data set is the following: data_set + location0 - anchors.csv - data.json - walking_path.csv + floorplan - floorplan.dxf - floorplan.png - floorplan_track.jpg - walking_path.csv + location1 - ... + location2 - ... + location3 - ... + raw_data + location0 + data + 1.07_9.37_1.2 - ch1_A1.csv - ch7_A8.csv - ... + 1.37_9.34_1.2 - ... + ... + location1 + ... + location2 + ... + location3 + ... + technical validation + cir_min_max_mean + positioning_wls + range + range_error + range_error_histograms + rss - LICENSE - README # Data format Raw measurements are saved in .csv files. Each file starts with a header, where first line represents the version of the file and the second line represents the data column names. The column names have a missing column name. Actual column names included in the .csv files are: TAG_ID ANCHOR_ID X_TAG Y_TAG Z_TAG X_ANCHOR Y_ANCHOR Z_ANCHOR NLOS RANGE FP_INDEX RSS RSS_FP FP_POINT1 FP_POINT2 FP_POINT3 STDEV_NOISE CIR_POWER MAX_NOISE RXPACC CHANNEL_NUMBER FRAME_LENGTH PREAMBLE_LENGTH BITRATE PRFR PREAMBLE_CODE CIR (starts with this column; all columns until the end of the line represent the channel impulse response) # Availability of CODE Code for data analysis and preprocessing of all data available in this data set is published on GitHub: https://github.com/KlemenBr/uwb_positioning.git The code is licensed under the Apache License 2.0. # Authors and License Author of data set in this repository is Klemen Bregar, klemen.bregar@ijs.si. This work is licensed under a Creative Commons Attribution 4.0 International License. # Funding The research leading to the data collection has been partially funded from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116, the Slovenian Research Agency under Grant numbers P2-0016, J2-2507 and bilateral project with Grant number BI-ME/21-22-007.

EPIC Ocean Surface PAR The EPIC observations of the Earth’s surface lit by the Sun made 13 times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean photosynthetically available radiation (PAR) at the ice-free ocean surface. PAR is defined as the quantum energy flux from the Sun in the 400-700 nm range. Daily mean PAR is the 24-hour averaged planar flux in that spectral range reaching the surface. It is expressed in E.m-2.d-1 (Einstein per meter squared per day). The factor required to convert E.m-2 d-1 units to mW.cm-2.μm-1 units is equal to 0.838 to an inaccuracy of a few percent regardless of meteorological conditions. The EPIC daily mean PAR product is generated on Plate Carrée (equal-angle) grid with 18.4 km resolution at the equator and on 18.4 km equal-area grid, i.e., the product is compatible with Ocean Biology Processing Group ocean color products.The EPIC PAR algorithm uses a budget approach, in which the solar irradiance reaching the surface is obtained by subtracting from the irradiance arriving at the top of the atmosphere (known) the irradiance reflected to space (estimated from the EPIC Level 1b radiance data), taking into account atmospheric transmission (modeled). Clear and cloudy regions within a pixel do not need to be distinguished, which dismisses the need for often-arbitrary assumptions about cloudiness distribution and is therefore adapted to the relatively large EPIC pixels. A daily mean PAR is estimated on the source grid for each EPIC instantaneous daytime observation, assuming no cloudiness change during the day, and the individual estimates are remapped and weight-averaged using the cosine of the Sun zenith angle. In the computations, wind speed, surface pressure, and water vapor amount are extracted from NECP Reanalysis 2 data, aerosol optical thickness and angstrom coefficient fromMERRA-2 data, and ozone amount from EPIC Level 2 data. Areas contaminated by sun glint are excluded using a threshold on sun glint reflectance calculated using wind data. Ice masking is based on NSIDC near real time ice fraction data. Details about the algorithm are given in Frouinet al., (2018). Figure A1 gives an example of EPIC daily mean PAR product. Date is March 20, 2018(equinox); land is in black and sea ice in white. Values range from a few E.m-2.d-1at high latitudes to about 58 E.m-2.d-1 at equatorial and tropical latitudes, with atmospheric perturbances modulating the surface PAR field especially at middle latitudes. The EPIC ocean surface PAR products are available at the Atmospheric Science Data Center (ASDC) at NASA Langley Research Center: https://asdc.larc.nasa.gov. 4. Reference Robert Frouin, Jing Tan, Didier Ramon, Bryan Franz, Hiroshi Murakami, 2018: Estimating photosynthetically available radiation at the ocean surface from EPIC/DSCOVR data, Proc. SPIE 10778, Remote Sensing of the Open and Coastal Ocean and Inland Waters, 1077806 (24 October 2018); doi: 10.1117/12.2501675. Changes from version 1 1) Algorithm (consistent with PACE) Updated the calculation of atmospheric reflectance, gaseous transmittance, and atmospheric transmittance using LUTs method so that calculations are accurate at high Sun and view zenith angles; Updated the calculation of surface albedo (based on Jin et al., 2011); Updated the calculation of cloud/surface layer albedo. 2)Ancillary data Changed the sources of the ancillary data including wind speed, surface pressure, and water vapor from NCEP to MERRA2; Added cloud fraction from MERRA2, which is needed for computing direct/diffuse ratio hence surface albedo.

E-commerce Sales Prediction Dataset

This repository contains a comprehensive and clean dataset for predicting e-commerce sales, tailored for data scientists, machine learning enthusiasts, and researchers. The dataset is crafted to analyze sales trends, optimize pricing strategies, and develop predictive models for sales forecasting.

📂 Dataset Overview

The dataset includes 1,000 records across the following features:

📊 Data Summary

General Properties

Date: - Range: 01-01-2023 to 12-31-2023. - Contains 1,000 unique values without missing data.

Product_Category: - Categories: Electronics (21%), Sports (21%), Other (58%). - Most common category: Electronics (21%).

Price: - Range: From 244 to 999. - Mean: 505, Standard Deviation: 290. - Most common price range: 14.59 - 113.07.

Discount: - Range: From 0.01% to 49.92%. - Mean: 24.9%, Standard Deviation: 14.4%. - Most common discount range: 0.01 - 5.00%.

Customer_Segment: - Segments: Regular (35%), Occasional (34%), Other (31%). - Most common segment: Regular.

Marketing_Spend: - Range: From 2.41k to 10k. - Mean: 4.91k, Standard Deviation: 2.84k.

Units_Sold: - Range: From 5 to 57. - Mean: 29.6, Standard Deviation: 7.26. - Most common range: 24 - 34 units sold.

📈 Data Visualizations

The dataset is suitable for creating the following visualizations: - 1. Price Distribution: Histogram to show the spread of prices. - 2. Discount Distribution: Histogram to analyze promotional offers. - 3. Marketing Spend Distribution: Histogram to understand marketing investment patterns. - 4. Customer Segment Distribution: Bar plot of customer segments. - 5. Price vs Units Sold: Scatter plot to show pricing effects on sales. - 6. Discount vs Units Sold: Scatter plot to explore the impact of discounts. - 7. Marketing Spend vs Units Sold: Scatter plot for marketing effectiveness. - 8. Correlation Heatmap: Identify relationships between features. - 9. Pairplot: Visualize pairwise feature interactions.

💡 How the Data Was Created

The dataset is synthetically generated to mimic realistic e-commerce sales trends. Below are the steps taken for data generation:

1. Feature Engineering:

   • Identified key attributes such as product category, price, discount, and marketing spend, typically observed in e-commerce data.
   • Generated dependent features like units sold based on logical relationships.

2. Data Simulation:

   • Python Libraries: Used NumPy and Pandas to generate and distribute values.
   • Statistical Modeling: Ensured feature distributions aligned with real-world sales data patterns.

3. Validation:

   • Verified data consistency with no missing or invalid values.
   • Ensured logical correlations (e.g., higher discounts → increased units sold).

Note: The dataset is synthetic and not sourced from any real-world e-commerce platform.

🛠 Example Usage: Sales Prediction Model

Here’s an example of building a predictive model using Linear Regression:

Written in python

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Load the dataset
df = pd.read_csv('ecommerce_sales.csv')

# Feature selection
X = df[['Price', 'Discount', 'Marketing_Spend']]
y = df['Units_Sold']

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Model training
model = LinearRegression()
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Evaluation
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f'Mean Squared Error: {mse:.2f}')
print(f'R-squared: {r2:.2f}')

This dataset contains the customer's data from a loan company known as Prosper. This dataset comprises of 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, and many others.

Definition of Variables:

ListingKey: Unique key for each listing, same value as the 'key' used in the listing object in the API. ListingNumber: The number that uniquely identifies the listing to the public as displayed on the website. ListingCreationDate: The date the listing was created. CreditGrade: The Credit rating that was assigned at the time the listing went live. Applicable for listings pre-2009 period and will only be populated for those listings. Term: The length of the loan expressed in months. LoanStatus: The current status of the loan: Cancelled, Chargedoff, Completed, Current, Defaulted, FinalPaymentInProgress, PastDue. The PastDue status will be accompanied by a delinquency bucket. ClosedDate: Closed date is applicable for Cancelled, Completed, Chargedoff and Defaulted loan statuses. BorrowerAPR: The Borrower's Annual Percentage Rate (APR) for the loan. BorrowerRate: The Borrower's interest rate for this loan. LenderYield: The Lender yield on the loan. Lender yield is equal to the interest rate on the loan less the servicing fee. EstimatedEffectiveYield: Effective yield is equal to the borrower interest rate (i) minus the servicing fee rate, (ii) minus estimated uncollected interest on charge-offs, (iii) plus estimated collected late fees. Applicable for loans originated after July 2009. EstimatedLoss: Estimated loss is the estimated principal loss on charge-offs. Applicable for loans originated after July 2009. EstimatedReturn: The estimated return assigned to the listing at the time it was created. Estimated return is the difference between the Estimated Effective Yield and the Estimated Loss Rate. Applicable for loans originated after July 2009. ProsperRating (numeric): The Prosper Rating assigned at the time the listing was created: 0 - N/A, 1 - HR, 2 - E, 3 - D, 4 - C, 5 - B, 6 - A, 7 - AA. Applicable for loans originated after July 2009. ProsperRating (Alpha): The Prosper Rating assigned at the time the listing was created between AA - HR. Applicable for loans originated after July 2009. ProsperScore: A custom risk score built using historical Prosper data. The score ranges from 1-10, with 10 being the best, or lowest risk score. Applicable for loans originated after July 2009. ListingCategory: The category of the listing that the borrower selected when posting their listing: 0 - Not Available, 1 - Debt Consolidation, 2 - Home Improvement, 3 - Business, 4 - Personal Loan, 5 - Student Use, 6 - Auto, 7- Other, 8 - Baby&Adoption, 9 - Boat, 10 - Cosmetic Procedure, 11 - Engagement Ring, 12 - Green Loans, 13 - Household Expenses, 14 - Large Purchases, 15 - Medical/Dental, 16 - Motorcycle, 17 - RV, 18 - Taxes, 19 - Vacation, 20 - Wedding Loans BorrowerState: The two letter abbreviation of the state of the address of the borrower at the time the Listing was created. Occupation: The Occupation selected by the Borrower at the time they created the listing. EmploymentStatus: The employment status of the borrower at the time they posted the listing. EmploymentStatusDuration: The length in months of the employment status at the time the listing was created. IsBorrowerHomeowner: A Borrower will be classified as a homowner if they have a mortgage on their credit profile or provide documentation confirming they are a homeowner. CurrentlyInGroup: Specifies whether or not the Borrower was in a group at the time the listing was created. GroupKey: The Key of the group in which the Borrower is a member of. Value will be null if the borrower does not have a group affiliation. DateCreditPulled: The date the credit profile was pulled. CreditScoreRangeLower: The lower value representing the range of the borrower's credit score as provided by a consumer credit rating agency. CreditScoreRangeUpper: The upper value representing the range of the borrower's credit score as provided by a consumer credit rating agency. FirstRecordedCreditLine: The date the first credit line was opened. CurrentCreditLines: Number of current credit lines at the time the credit profile was pulled. OpenCreditLines: Number of open credit lines at the time the credit profile was pulled. TotalCreditLinespast7years: Number of credit lines in the past seven years at the time the credit profile was pulled. OpenRevolvingAccounts: Number of open revolving accounts at the time the credit profile was pulled. OpenRevolvingMonthlyPayment: Monthly payment on revolving accounts at the time the credit profile was pulled. InquiriesLast6Months: Number of inquiries in the past six months at the time the cre...

The Mean Layer Temperature - NOAA CDR V5.0 is a monthly global dataset with 2.5°à 2.5° grid resolution covering the period from November 1978 to present. The dataset measures mean layer atmospheric temperatures from the lower-troposphere to the lower-stratosphere. The dataset was inter-calibrated and merged from three generations of microwave sounders, MSU, AMSU-A, and ATMS, with 16 polar-orbiting satellites including TIROS-N, NOAA-6, NOAA-7, NOAA-8, NOAA-9, NOAA-10, NOAA-11, NOAA-12, NOAA-14, NOAA-15, NOAA-18, NOAA-19, MetOp-A, Aqua, SNPP, and NOAA-20. The dataset includes temperature mid-troposphere (TMT, MSU channel 2 merged with AMSU-A channel 5 and ATMS channel 6), temperature upper-troposphere (TUT, MSU channel 3 merged with AMSU-A channel 7 and ATMS channel 8), temperature lower-stratosphere (TLS, MSU channel 4 merged with AMSU-A channel 9 and ATMS channel 10), and temperature lower-troposphere (TLT, derived from combinations of TMT, TUT, and TLS). TLT, TMT, TUT, and TLS measure layer temperatures peaking roughly at 3km, 5km, 10km, and 17km, respectively, above the Earth's surface. Features in the dataset development include a use of backward merging approach, development of an observation- and semi-physically-based algorithm for diurnal drift adjustment, and removal of spurious calibration drifting errors in NOAA-15, NOAA-14, NOAA-12, and NOAA-11 through recalibration. Satellite microwave sounding observations in stable sun-synchronous orbits (Aqua, MetOp-A, SNPP, NOAA-20) were used as a reference in the backward merging process. Bias corrections and satellite recalibration have resulted in inter-consistent CDR records for reliable climate change investigation.

This dataset contains maps showing the principal attributes of tides around the Australian coast. It has been derived from data published in the Australian National Tide Tables.

Format: shapefile.

Quality - Scope: Dataset. External accuracy: +/- one degree. Non Quantitative accuracy: Data are assumed to be correct. Three datasets describe tidal information around Australia:

Cover_Name, Item_Name, Item_Description:

TIDEMAX, MAX_TIDE_(M), Maximum tidal range in metres.

Conceptual consistency: Coverages are topologically consistent. No particular tests conducted by ERIN. Completeness omission: Complete for the Australian continent. Lineage: ERIN: Data was projected to geographics using the WGS84 datum and spheroid, to be compatible for the Australian Coastal Atlas. The digital datsets were attributed using the information held in the legend (.key) files.

CSIRO: All CAMRIS data were stored in VAX files, MS-DOS R-base files and as a microcomputer dataset accessible under the LUPIS (Land Use Planning Information System) land allocation package. CAMRIS was established using SPANS Geographic Information System (GIS) software running under a UNIX operating system on an IBM RS 6000 platform. A summary follows of processing completed by the CSIRO: 1. r-BASE: Information imported into r-BASE from a number of different sources (ie Digitised, scanned, CD-ROM, NOAA World Ocean Atlas, Atlas of Australian Soils, NOAA GEODAS archive and The Complete Book of Australian Weather). 2. From the information held in r-BASE a BASE Table was generated incorporating specific fields. 3. SPANS environment: Works on creating a UNIVERSE with a geographic projection - Equidistant Conic (Simple Conic) and Lambert Conformal Conic, Spheroid: International Astronomical Union 1965 (Australia/Sth America); the Lower left corner and the longitude and latitude of the centre point. 4. BASE Table imported into SPANS and a BASE Map generated. 5. Categorise Maps - created from the BASE map and table by selecting out specified fields, a desired window size (ie continental or continent and oceans) and resolution level (ie the quad tree level). 6. Rasterise maps specifying key parameters such as: number of bits, resolution (quad tree level 8 lowest - 16 highest) and the window size (usually 00 or cn). 7. Gifs produced using categorised maps with a title, legend, scale and long/lat grid. 8. Supplied to ERIN with .bil; .hdr; .gif; Arc export files .e00; and text files .asc and .txt formats. 9. The reference coastline for CAMRIS was the mean high water mark (AUSLIG 1:100 000 topographic map series).

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1940 to present".

WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32

This repository contains the WiFi CSI human presence detection and activity recognition datasets proposed in [1].

Datasets

• DP_LOS - Line-of-sight (LOS) presence detection dataset, comprised of 392 CSI amplitude spectrograms.
• DP_NLOS - Non-line-of-sight (NLOS) presence detection dataset, comprised of 384 CSI amplitude spectrograms.
• DA_LOS - LOS activity recognition dataset, comprised of 392 CSI amplitude spectrograms.
• DA_NLOS - NLOS activity recognition dataset, comprised of 384 CSI amplitude spectrograms.

Table 1: Characteristics of presence detection and activity recognition datasets.

Data Format

Each dataset employs an 8:1:1 training-validation-test split, defined in the provided label files trainLabels.csv, validationLabels.csv, and testLabels.csv. Label files use the sample format [i c], with i corresponding to the spectrogram index (i.png) and c corresponding to the class. For presence detection datasets (DP_LOS , DP_NLOS), c in {0 = "no presence", 1 = "presence in room 1", ..., 5 = "presence in room 5"}. For activity recognition datasets (DA_LOS , DA_NLOS), c in {0="no activity", 1="walking", and 2="walking + arm-waving"}. Furthermore, the mean and standard deviation of a given dataset are provided in meanStd.csv.

Download and Use
This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].

[1] Strohmayer, Julian, and Martin Kampel. "WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32" International Conference on Computer Vision Systems. Cham: Springer Nature Switzerland, 2023.

BibTeX citation:

@inproceedings{strohmayer2023wifi,
 title={WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32},
 author={Strohmayer, Julian and Kampel, Martin},
 booktitle={International Conference on Computer Vision Systems},
 pages={41--50},
 year={2023},
 organization={Springer}
}