About this dataset

Context

The dataset tabulates the population of Village Of Four Seasons by race. It includes the population of Village Of Four Seasons across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Village Of Four Seasons across relevant racial categories.

Key observations

The percent distribution of Village Of Four Seasons population by race (across all racial categories recognized by the U.S. Census Bureau): 96% are white, 0.39% are Black or African American, 0.19% are American Indian and Alaska Native, 0.96% are some other race and 2.46% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Village Of Four Seasons
• Population: The population of the racial category (excluding ethnicity) in the Village Of Four Seasons is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Village Of Four Seasons total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Village Of Four Seasons Population by Race & Ethnicity. You can refer the same here

Provide the main financial data indicators and the latest five seasons indicator values of credit unions according to the season.

The All Season Sunroom market is experiencing a transformative phase, driven by an increasing consumer desire for versatile living spaces that seamlessly blend indoor and outdoor environments. As homeowners seek to maximize their living square footage, sunrooms have become a favored choice, providing a year-round sa

Analysis of ‘Barclays Premiere League for last 12 seasons’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lumierebatalong/english-premiere-league-team-datasets on 28 January 2022.

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

Context

Barclay premier league is the best league in the world 💯 . It has 20 teams that qualified for the title. Among these 20 teams there are 5 teams which have already won the title in the last 12 seasons namely Man City, Liverpool, Man United, Chelsea, Leicester with two outsiders Arsenal and Tottenham. Who is your favorite team and how can you predict their title victory for the current or next season? The ball is in your camp 👀 .

Content

Notes for Football Data

All data is in csv format, ready for use within standard spreadsheet applications. Please note that some abbreviations are no longer in use and refer to data collected in earlier seasons. Each data contains last 12 seasons of English Premier League.

Key to results data:

Div = League Division Date = Match Date (dd/mm/yy) Time = Time of match kick off HomeTeam = Home Team AwayTeam = Away Team FTHG and HG = Full Time Home Team Goals FTAG and AG = Full Time Away Team Goals FTR and Res = Full Time Result (H=Home Win, D=Draw, A=Away Win) HTHG = Half Time Home Team Goals HTAG = Half Time Away Team Goals HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics (where available) Attendance = Crowd Attendance Referee = Match Referee HS = Home Team Shots AS = Away Team Shots HST = Home Team Shots on Target AST = Away Team Shots on Target HHW = Home Team Hit Woodwork AHW = Away Team Hit Woodwork HC = Home Team Corners AC = Away Team Corners HF = Home Team Fouls Committed AF = Away Team Fouls Committed HFKC = Home Team Free Kicks Conceded AFKC = Away Team Free Kicks Conceded HO = Home Team Offsides AO = Away Team Offsides HY = Home Team Yellow Cards AY = Away Team Yellow Cards HR = Home Team Red Cards AR = Away Team Red Cards

I remove some features.

Acknowledgements

This dataset contains data for last 12 seasons of English Premier League. The dataset is sourced from http://www.football-data.co.uk/ website and contains various statistical data such as final and half time result, corners, yellow and red cards etc

Inspiration

Can you explain why Man United has not won the title for last 12 seasons?. Can you predict the victory of your favorite team in every championship game?.

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

This data set unlies the following publication: Gade & Metz: "Competition, drought, season length? Disentangling key factors for local adaptation in two Mediterranean annuals across combined macroclimatic and microclimatic aridity gradients"

This dataset provides daily and annual air temperature, river water level, and leaf drop dates coincident with the moose (Alces alces) hunting season (September) for the area surrounding the rural communities of Nulato, Koyukuk, Kaltag, Galena, Ruby, Huslia, and Hughes in interior Alaska, USA, over the period 2000-2016. The main objective of the study was to assess how the environmental conditions impacted the success of hunters who rely on moose as a subsistence resource.

This Kaggle dataset comes from an output dataset that powers my March Madness Data Analysis dashboard in Domo. - Click here to view this dashboard: Dashboard Link - Click here to view this dashboard features in a Domo blog post: Hoops, Data, and Madness: Unveiling the Ultimate NCAA Dashboard

This dataset offers one the most robust resource you will find to discover key insights through data science and data analytics using historical NCAA Division 1 men's basketball data. This data, sourced from KenPom, goes as far back as 2002 and is updated with the latest 2025 data. This dataset is meticulously structured to provide every piece of information that I could pull from this site as an open-source tool for analysis for March Madness.

Key features of the dataset include: - Historical Data: Provides all historical KenPom data from 2002 to 2025 from the Efficiency, Four Factors (Offense & Defense), Point Distribution, Height/Experience, and Misc. Team Stats endpoints from KenPom's website. Please note that the Height/Experience data only goes as far back as 2007, but every other source contains data from 2002 onward. - Data Granularity: This dataset features an individual line item for every NCAA Division 1 men's basketball team in every season that contains every KenPom metric that you can possibly think of. This dataset has the ability to serve as a single source of truth for your March Madness analysis and provide you with the granularity necessary to perform any type of analysis you can think of. - 2025 Tournament Insights: Contains all seed and region information for the 2025 NCAA March Madness tournament. Please note that I will continually update this dataset with the seed and region information for previous tournaments as I continue to work on this dataset.

These datasets were created by downloading the raw CSV files for each season for the various sections on KenPom's website (Efficiency, Offense, Defense, Point Distribution, Summary, Miscellaneous Team Stats, and Height). All of these raw files were uploaded to Domo and imported into a dataflow using Domo's Magic ETL. In these dataflows, all of the column headers for each of the previous seasons are standardized to the current 2025 naming structure so all of the historical data can be viewed under the exact same field names. All of these cleaned datasets are then appended together, and some additional clean up takes place before ultimately creating the intermediate (INT) datasets that are uploaded to this Kaggle dataset. Once all of the INT datasets were created, I joined all of the tables together on the team name and season so all of these different metrics can be viewed under one single view. From there, I joined an NCAAM Conference & ESPN Team Name Mapping table to add a conference field in its full length and respective acronyms they are known by as well as the team name that ESPN currently uses. Please note that this reference table is an aggregated view of all of the different conferences a team has been a part of since 2002 and the different team names that KenPom has used historically, so this mapping table is necessary to map all of the teams properly and differentiate the historical conferences from their current conferences. From there, I join a reference table that includes all of the current NCAAM coaches and their active coaching lengths because the active current coaching length typically correlates to a team's success in the March Madness tournament. I also join another reference table to include the historical post-season tournament teams in the March Madness, NIT, CBI, and CIT tournaments, and I join another reference table to differentiate the teams who were ranked in the top 12 in the AP Top 25 during week 6 of the respective NCAA season. After some additional data clean-up, all of this cleaned data exports into the "DEV _ March Madness" file that contains the consolidated view of all of this data.

This dataset provides users with the flexibility to export data for further analysis in platforms such as Domo, Power BI, Tableau, Excel, and more. This dataset is designed for users who wish to conduct their own analysis, develop predictive models, or simply gain a deeper understanding of the intricacies that result in the excitement that Division 1 men's college basketball provides every year in March. Whether you are using this dataset for academic research, personal interest, or professional interest, I hope this dataset serves as a foundational tool for exploring the vast landscape of college basketball's most riveting and anticipated event of its season.

About this dataset

Context

The dataset tabulates the Village Of Four Seasons population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Village Of Four Seasons across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Village Of Four Seasons was 2,496, a 1.88% increase year-by-year from 2022. Previously, in 2022, Village Of Four Seasons population was 2,450, an increase of 0.66% compared to a population of 2,434 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Village Of Four Seasons increased by 1,009. In this period, the peak population was 2,496 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Village Of Four Seasons is shown in this column.
• Year on Year Change: This column displays the change in Village Of Four Seasons population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Village Of Four Seasons Population by Year. You can refer the same here

This Resource serves to explain and contain the methodology, R codes, and results of the PRISM freshwater supply key indicator analysis for my thesis. For more information, see my thesis at the USU Digital Commons.

Freshwater availability in the state can be summarized using streamflow, reservoir level, precipitation, and temperature data. Climate data for this study have a period of record greater than 30 years, preferably extending beyond 1950, and are representative of natural conditions at the county-level.

Oregon State University, Northwest Alliance for Computational Science and Engineering PRISM precipitation and temperature gridded data are representative of statewide, to county-level, from 1895-2015. These data are available online from the PRISM Climate Group. Using the R ‘prism’ package, monthly PRISM 4km raster grids were downloaded. Boundary shapefiles of Utah state, and each county, were obtained online from the Utah Geospatial Resource Center webpage. Using the R ‘rgdal’ and ‘sp’ packages, these shapefiles were transformed from their native World Geodetic System 1984 coordinate system to match the PRISM BIL raster’s native North American Datum 1983 coordinate system. Using the R ‘raster’ package, medians of PRISM precipitation grids at each spatial area of interest were calculated and summed for water years and seasons. Medians were also calculated for PRISM temperature grids and averaged over water years and seasons. For analysis of single months, the median results were used for all PRISM indicators. Seasons were analyzed for the calendar year which they are in, Winter being the first season of each year. Freshwater availability key indicators were non-parametrically separated per temporal/spatial delineation into quintiles representing Very Wet/Very High/Hot (top 20% of values), Wet/High/Hot (60-80%), Moderate/Mid-level (40-60%), Dry/Low/Cool (20-40%), to Very Dry/Very Low/Cool (bottom 20%). Each quintile bin was assigned a rank value 1-5, with ‘5’ being the value of the top quintile, in preparation for the Kendall Tau-b correlation analysis. These results, along with USGS irrigation withdrawal and acreage data, were loaded into R. State-level quintile results were matched according to USGS report year. County quintile results were matched with corresponding USGS irrigation withdrawal and acreage county-level data per report year for all other areas of interest. Using the base R function cor(), with the “kendall” method selected (which is, by default, the Kendall Tau-b calculation), relationship correlation matrices were produced for all areas of interest. The USGS irrigation withdrawal and acreage data correlation analysis matrices were created using the R ‘corrplot’ package for all areas of interest.

See Word file for an Example PRISM Analysis, made by Alan Butler at the United States Bureau of Reclamation, which was used as a guide for this analysis.

Abstract

The main objective of the Seasonal Agricultural Survey is to provide timely, accurate, reliable and comprehensive agricultural statistics that describe the structure of agriculture in Rwanda mainly in terms of land use, crop area, yield and crop production to monitor current agricultural and food supply conditions and to facilitate evidence-based decision making for the development of the agricultural sector.

The National Institute of Statistics of Rwanda (NISR) has been conducting seasonal agricultural survey since 2012 for the estimation of the national agricultural crop area and production estimates. In 2022/2023 agricultural year, the NISR conducted Seasonal Agricultural Survey (SAS) covering the three agricultural seasons. The SAS provides information used as a tool to assist in addressing key agricultural issues and information needs that will inform policymakers and other stakeholders and allow more effective identification of priority intervention needs.

Geographic coverage

National coverage allowing district-level estimation of key indicators

Analysis unit

Small scale agricultural farms and large scale farms

Universe

The SAS 2023 targeted potential agricultural land and large-scale farmers

Kind of data

Sample survey data [ssd]

Sampling procedure

The total country land was classified into five strata, of which four are agricultural, while the remaining stratum is designated for land not suitable for agriculture. The four agricultural strata are: dominant hill crop land, dominant wetland crops, dominant rangeland, and mixed stratum, all considered suitable for agriculture. The fifth stratum comprises non-agricultural land, including areas occupied by water bodies, forestry plantations, settlements, parks, and protected marshland not utilized for agriculture. The sampling frame excludes land areas covered by tea plantation farms. In 2023 agricultural year, the total sample used was 1200 segments. At first stage,1200 segments were selected and allocated at district level based on the power allocation approach (Bankier, 1988). Sampled segments inside each district were distributed among strata with a proportional-to-size criterion.

At the second stage, 25 sample points were systematically selected, following a special distance of 60 meters between points. For every sample point, a corresponding farm or plot is identified, and the operator is interviewed. The farms therefore constitute the sampling units within each segment. Enumerators locate every sample point, delineate plots in which the sample points fall using high accurate GPS devices and then collect information on land use and other related information. Sampling weights are calculated and applied to the sample data to obtain stratum-level estimates. District estimates are then derived by aggregating the estimates from all strata within the district.

Data collection was done in 1200 segments and 345 large scale farmers holdings for Season A and B, whereas in Season C data was collected in 1769 sites potential to grow season C crops in addition to 513 segments, response rate was 100% of the sample.

During the SAS 2023 exercise, data collection covered three main agricultural seasons A, B and C and was conducted into two separate phases in each season: A. The first phase, known as screening activity (post-planting phase), consists of visiting all sampled segments and demarcating all plots with sampled points with the aim of covering the information related to land area, planted crops and land use.

B. The second phase involves capturing of production data by visiting sampled agricultural plots identified from screening activity as well as all large-scale farmers. To ensure the smooth completion of the SAS workload, NISR employed 137 Enumerators and 23 Team Leaders. All fieldwork staff hold a degree in agriculture sciences and were consistently trained by NISR headquarter staff before starting data collection in each season. Moreover, higher-level supervision was organized and done by staff from NISR who frequently visited the field teams during each phase of data collection to ensure the quality of collected data. For Season A, data collection started on 4th December 2022 and ended on 16th February 2023. For Season B, data collection started on 2nd May 2023 and ended on 30th June 2023. For Season C, data collection started on 10th September 2023 and ended on 30th September 2023.

Mode of data collection

Computer Assisted Personal Interview [capi]

Context

A single table of the prominent data regarding all Formula E races, derived from Wikipedia race reports.

Content

This data set is merely a single CSV file, backed with all the files I used to create it. This is taken purely from Wikipedia race reports, with some R code to parse the relevant results tables and clean things up.

So while 57 files are available (as of Version 1), the main output file, as shown in the preview is the intended data set to use.

It has not been denormalized, so in it we have race, driver, team, and results information. Race: season, race number, race date, and race name Driver: name Team: car number, team name from Wikipedia, continuity-based team name Results: two forms of rank, grid start, number of laps, report time/retirement message, the points awarded, and the three categories of points

Acknowledgements

Wikipedia's race reports are consistent enough that a couple hours of cleanup was all that was needed to derive this data set. A big thanks is owed to the contributors there. Motorsports Stats information is a bit more expansive and possibly simpler to parse, but I used Wikipedia to keep licensing as simple as possible.

Inspiration

The inspiration for adding this to Kaggle was that it begs a comparison to Formula 1. @vopani has posted the ergast.com data set, and its accessibility had me able to work with the data enough to do some simple predictions. I have not found a Formula E data set that provides the results in one place. Unfortunately I don't know of a source for lap times at all. But with Formula E continually branding themselves as one of the most unpredictable championships in racing, putting this data in Kaggle seemed useful. It's my first true data set, and it's nice to give back to a community I've been part of for so long.

So I aim to add a few notebooks here soon to start this out. I also aim to manually keep it updated through the flurry of Berlin races to finish Season 6, ideally the night following each race using hand-entered results.

Data that is available that I have chosen not to use would be a deeper dive into Qualifying results, and potentially practice times. The qualifying results are already in the HTML pages I've posted here, they'd just need to be parsed. But even with that data in hand with the F1 data set, I have yet to use it other than pre-penalty grid positions. For those that don't know, Formula E's qualifying introduces a negative feedback loop, in that the top 6 of the Championship are forced to qualify in the first group, where the track is frequently very dirty/dusty and has less grip. It is rare that a driver from Group 1 makes it to super pole. And listening to the commentators, they frequently will comment on who "looked fast in practice" so if you had that information it might help predict race finish.

Linked to this record are a report providing further details about the project, as well as the data from the project.

Public Summary Regions of Antarctica are undergoing significant change in response to the Earth's changing climate. This project will provide a state of the art contemporary insight into the changing behaviour of the Totten drainage basin in East Antarctica - an area of vital importance in understanding ice/ocean/atmosphere and climate interactions in the Australian region of Antarctica. We will estimate the contribution of the Totten Glacier drainage basin to present-day sea level rise and simultaneously provide a critical validation of the European Space Agency (ESA) CryoSat-2 satellite mission over this region.

Project #3121 investigated the mass balance of the Totten basin and provided an Australian contribution to the validation of CryoSat-2 data over Law Dome and the Totten Glacier. With field seasons in 2010/11 and 2011/12, the project gathered a range of in situ data using field and airborne data collection techniques. These data include geodetic quality GPS observations from up to 6 quasi-permanent GPS sites from which ice velocity, tropospheric water vapour and in some cases, tidal motion are derived. These sites were equipped with temperature and atmospheric pressure sensors, and in some cases, acoustic snow accumulation sensors. GPS equipped skidoo surveys were undertaken over the survey region on Law Dome to facilitate the generation of a validation surface to compare against airborne LiDAR and ASIRAS based DEMs. In the 2011/12 season, AWI collaborators achieved 4 days of survey flights in Polar-6, obtaining LiDAR and ASIRAS data over specific flight lines spanning Law Dome and the Totten Glacier.

Project objectives: This project will provide a state-of-the-art contemporary insight into the most recent changes in the surface elevation of the Totten drainage basin in East Antarctica, whilst simultaneously providing a critical and unique contribution to the calibration and validation of the new European Space Agency (ESA) CryoSat-2 satellite mission and the Australian Antarctic Division (AAD) LiDAR/RADAR system. The present-day mass balance change of Antarctica plays a key role in understanding the effects of global warming on the Earth system, in particular the contribution of melting Antarctic ice to present-day sea level rise. The Totten Glacier is known to be undergoing significant surface lowering and is perhaps the most significant basin in the East Antarctic (e.g., Shepherd and Wingham, 2007). The basin itself drains approximately 1/8th of the East Antarctic Ice Sheet (EAIS) and, as a marine-based system, is analogous to the West Antarctic Ice Sheet (WAIS) whose changing mass balance dominates the Antarctic contribution to global sea level rise(Lemke et al., 2007). The TOT-Cal project will independently lead Australian research in understanding the contribution of Antarctic ice to changing sea-levels by focusing new data on this key drainage basin of international scientific interest. Importantly, this region can be reached with relative ease by AAD logistics - it is located literally at the doorstep of the Australian Casey station, in close proximity to the Wilkins intercontinental airstrip. With international interest focused on this region, this project provides a showcase of AAD short-stay logistics in support of vital time-critical research and a major new ESA satellite mission that will undoubtedly play a major role in cryospheric science into the future.

The TOT-Cal project will draw upon key resources and personnel within the University of Tasmania (UTAS), Australian National University (ANU), Laboratoire d'Etudes en Geophysique et Oceanographie Spatiales (LEGOS, France), Scripps Institution of Oceanography (SIO, USA) and the AAD, requiring the collection and analysis of field based, airborne and satellite data over a multi-season campaign. It builds upon and extends related past, existing and planned Australian Antarctic Science (AAS), Australian Research Council (ARC) and International Polar Year (IPY) projects, addressing three specific questions:

1) What is the present-day mass balance of the Totten drainage basin and what is its contribution to global sea level change? This will be assessed through a combination of airborne LiDAR/RADAR observations, satellite altimetry observations including Seasat (1978), Geosat (1985-1989), ERS-1 (1992-1996), ERS-2 (1995-2005), Envisat-RA2 (2002 to present), ICESat (2003-present) and CryoSat-2 (expected launch 2009), space gravity observations (GRACE), along with ground-based validation experiments.

2) What are the accuracies and uncertainty characteristics of the altimetry measurement systems? (In other words, what is the expected accuracy of the altimetry-derived mass balance estimates?) With an emphasis on the new CryoSat-2 and AAD LiDAR/RADAR systems, this will be assessed through repeated ground and airborne experiments, providing direct contribution to the CryoSat-2 international Calibration, Validation and Retrieval Team (CVRT), whilst also providing an important cross-calibration of synchronous ICESat, Envisat and CryoSat-2 data. Of particular focus will be the understanding of the different surface interactions between the incident radar and laser waveforms (both satellite and airborne) with the surface snow/ice characteristics (topography, firn, seasonal changes, etc).

3) What is the magnitude of the present-day Glacial Isostatic Adjustment (GIA) in the region that needs to be removed from the space-based geodetic observations in order to estimate mass balance using a space geodetic approach? Present uncertainty in the magnitude of GIA is a dominant error source in the mass balance error budget and requires an analysis of recent models and in-situ geodetic evidence in order to fully understand and minimise this error contribution.

Each of the objectives set out above will be assessed with data acquired over the coming three summer seasons, leading into participating in the larger period of logistics support around the Totten Glacier in 2011/12. This also enables this project to provide state-of-the-art estimates of surface lowering to the Australian AAD/ACECRC modelling team (R.Warner et al) for integration into dynamic ice models in the subsequent years of this project. These estimates will be fundamental in improving conventional forward ice models which to date, are not able to predict the observed changes in the Totten Glacier (van der Veen et al. 2008). The timing of the work outlined in this proposal is critical given the CryoSat-2 launch (expected late 2009) and the impending conclusion of the GRACE mission, this research needs to be undertaken now for the field seasons indicated in order to maximise the scientific impact and provide the necessary complement to other planned AAS projects that will operate over the same future field seasons.

Public summary of the season progress: 2010/11 was the first field season for this project. Valuable GPS field data were acquired in the Law Dome and Totten Glacier regions to assist with providing an Australian contribution to the validation of the CryoSat-2 ice monitoring satellite mission, and to further understand ice shelf/ocean interactions and climate change in this region. Planned airborne surveys by the German AWI Polar-5 aircraft were unable to be completed due to poor weather. Collaboration with the 'Investigating the Cryospheric Evolution of the Central Antarctic Plate' project (ICECAP - UTexas) yielded important airborne scanning laser altimeter elevation data over the Law Dome site.

In the 2019/2020 winter season, there were 280 ski areas and 922 lifts in Canada. While the number of national skiers totaled 4.31 million, the five-year average skier visits in the country reached 18.52 million in 2019/2020.

The dataset contains data pertaining to key result areas, match statistics and betting odds for Barclays' premier league 2018/19 season. Column description provided in Discussion section.

Seasons 52 is an American restaurant and wine bar chain owned by Darden Restaurants, founded in 2003. The restaurant focuses on offering a casually sophisticated dining experience with a menu that changes seasonally to reflect fresh, seasonal ingredients. The business model emphasizes health-conscious dining by using fresh, seasonal produce and sustainable seafood. This approach appeals to diners looking for healthier options and supports sustainable practices. You can download the complete list of key information about Seasons 52 locations, contact details, services offered, and geographical coordinates, beneficial for various applications like store locators, business analysis, and targeted marketing. The Seasons 52 data you can download includes:

Identification & Location:


  store_number, store_namestore_type, store_location, address, address_line_2, city, state, zip_code, latitude, longitude, country_code, county, geo_accuracy,country


Contact Information:


  phone_number


Operational Details & Services:


  store_hours

This product shows the snow cover duration for a hydrological year. Its beginning differs from the calendar year, since some of the precipitation that falls in late autumn and winter falls as snow and only drains away when the snow melts in the following spring or summer. The meteorological seasons are used for subdivision and the hydrological year begins in autumn and ends in summer. The snow cover duration is made available for three time periods: the snow cover duration for the entire hydrological year (SCD), the early snow cover duration (SCDE), which extends from autumn to midwinter (), and the late snow cover duration (SCDL), which in turn extends over the period from mid-winter to the end of summer. For the northern hemisphere SCD lasts from September 1st to August 31st, for the southern hemisphere it lasts from March 1st to February 28th/29th. The SCDE lasts from September 1st to January 14th in the northern hemisphere and from March 1st to July 14th in the southern hemisphere. The SCDL lasts from January 15th to August 31st in the northern hemisphere and from July 15th to February 28th/29th in the southern hemisphere. The “Global SnowPack” is derived from daily, operational MODIS snow cover product for each day since February 2000. Data gaps due to polar night and cloud cover are filled in several processing steps, which provides a unique global data set characterized by its high accuracy, spatial resolution of 500 meters and continuous future expansion. It consists of the two main elements daily snow cover extent (SCE) and seasonal snow cover duration (SCD; full and for early and late season). Both parameters have been designated by the WMO as essential climate variables, the accurate determination of which is important in order to be able to record the effects of climate change. Changes in the largest part of the cryosphere in terms of area have drastic effects on people and the environment. For more information please also refer to:

   Dietz, A.J., Kuenzer, C., Conrad, C., 2013. Snow-cover variability in central Asia
             between 2000 and 2011 derived from improved MODIS daily snow-cover products. International Journal of Remote Sensing 34, 3879–3902.
             https://doi.org/10.1080/01431161.2013.767480
   Dietz, A.J., Kuenzer, C., Dech, S., 2015. Global SnowPack: a new
             set of snow cover parameters for studying status and dynamics of the planetary snow cover extent. Remote Sensing Letters 6,
             844–853. https://doi.org/10.1080/2150704X.2015.1084551
   Dietz, A.J., Wohner, C., Kuenzer, C., 2012. European
             Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products. Remote Sensing 4.
             https://doi.org/10.3390/rs4082432
   Dietz, J.A., Conrad, C., Kuenzer, C., Gesell, G., Dech, S., 2014. Identifying
             Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data. Remote Sensing 6. https://doi.org/10.3390/rs61212752

             Rößler, S., Witt, M.S., Ikonen, J., Brown, I.A., Dietz, A.J., 2021. Remote Sensing of Snow Cover Variability and
             Its Influence on the Runoff of Sápmi’s Rivers. Geosciences 11, 130. https://doi.org/10.3390/geosciences11030130

Residents Travel Survey: Trips, overnight stays, average length and expenditure by main travel reason. Monthly. National.

The State of the Climate is a collection of periodic summaries recapping climate-related occurrences on both a global and national scale. The State of the Climate Monthly Overview - Hurricanes & Tropical Storms report focuses primarily on storms and conditions that affect the U.S. and its territories, in Atlantic and Pacific basins. The report places each basin's tropical cyclone activity in a climate-scale context. Key statistics (dates, strengths, landfall, energy, etc.) for major cyclone activity in other basins is occasionally presented. Reports began in June 2002. The primary Atlantic hurricane season (June-November) is covered each year; other months are included as storm events warrant. An annual summary is available from 2002. These reports are not updated in real time.

The database contains several datasets and files with NBA statistical data spanning four seasons (2015-2016 to 2018-2019). These datasets were procured from the Basketball Reference database (https://www.basketball-reference.com/), a publicly accessible source of NBA data.

The main file, `dat.cleaned.csv`, includes the Win/Loss records for all thirty NBA teams, along with box scores and advanced statistics. The data captured over the four seasons correspond to about 4,920 regular-season games. A distinguishing feature of this dataset is the repeated measurements per player within a team across the seasons. However, it's important to note that these repeated measurements are not independent, necessitating the use of hierarchical modelling to properly handle the data.

Two sets of additional text files (`per_2017.txt`, `per_2018.txt`, `rpm_2017.txt`, `rpm_2018.txt`) provide specific metrics for player performance. The 'PER' files contain the Athlete Efficiency Rating (PER) for the years 2017 and 2018. The 'RPM' files contain the ESPN-developed score called Real Plus-Minus (RPM) for the same years.

However, potential biases or limitations within the datasets should be acknowledged. For instance, the Basketball Reference website might not include data from some matches or may exclude certain variables, potentially affecting the quality and accuracy of the dataset.

Trichosurus_vulpecula_variables_dataOrigin (i.e., wildlife collection) and registration numbers of all specimens used in the analyses, together with all extracted environmental covariates