This Website Statistics dataset has four resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file.

• Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year.

• Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year.

• Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year.

• Dataset Statistics: This dataset shows cumulative totals for Datasets on the Lincolnshire Open Data site that have also been published on the national Open Data site Data.Gov.UK - see the Source link.

  Note: Website and Webpage statistics (the first three resources above) show only UK users, and exclude API calls (automated requests for datasets). The Dataset Statistics are confined to users with javascript enabled, which excludes web crawlers and API calls.

These Website Statistics resources are updated annually in January by the Lincolnshire County Council Business Intelligence team. For any enquiries about the information contact opendata@lincolnshire.gov.uk.

This online application gives manufacturers the ability to compare Iowa to other states on a number of different topics including: business climate, education, operating costs, quality of life and workforce.

Considering dataset description

The user wants an improved description of the "online_shoppers_intention" dataset. I'll base the rewrite on metadata like 12,330 sessions, collected over a year, and incorporate details about the dataset’s purpose, data components, and possible uses. I could consider writing it as a cohesive paragraph or bullet points. I'll need to include citations too—let's search for further details on OpenML. Once I find that, I'll craft a clearer, more thorough description.

Thought for 5 seconds

Overview The Online Shoppers Purchasing Intention dataset captures 12,330 distinct web‐session records collected over a one‐year span from an e-commerce site, with each session belonging to a different visitor to prevent user‐ or campaign-specific biases. Originally published in 2017 and licensed under CC BY 4.0, it was curated by Sakar et al. for benchmarking classifiers on independent and identically distributed tabular data.

Features

• Numerical (10):

  • Administrative, Informational, ProductRelated (counts of pages visited) and their corresponding _Duration fields (total time in seconds spent on those pages).
  • BounceRates, ExitRates (average session‐level bounce and exit rates) and PageValues (average monetary value of pages preceding a purchase).
  • SpecialDay (normalized [0 – 1] indicator of how close the visit was to major shopping holidays, e.g. Valentine’s Day).

• Categorical (7):

  • Month (Aug – Sep), OperatingSystems (8 codes), Browser (13 codes), Region (9 codes), TrafficType (20 codes), VisitorType (“New_Visitor,” “Returning_Visitor,” “Other”), and Weekend (True/False).

Target and Class Distribution

• Revenue (False/True) denotes whether the session ended in a purchase.
• Of the 12,330 sessions, 84.5 % (10,422) did not result in revenue, while 15.5 % (1,908) did.

Intended Use This dataset is ideal for developing and comparing binary classification models—ranging from multilayer perceptrons and LSTM networks to tree-based methods—to predict online purchasing intention in a controlled, time-invariant setting.

Nursing Home Compare has detailed information about every Medicare and Medicaid nursing home in the country. A nursing home is a place for people who can’t be cared for at home and need 24-hour nursing care. These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.

Online Sports Betting

A State-by-State Comparison

About this dataset

How prevalent is sports betting across the United States? This dataset provides information on the legal status of sports betting, revenue generated by sports betting, the number of sports betting outlets, and more. Use this dataset to compare the revenue generated by sports betting across different states

How to use the dataset

This dataset can be used to understand the prevalence of sports betting across the United States and to compare the revenue generated by sports betting across states.

Research Ideas

• Understand the prevalence of sports betting across the United States and to compare the revenue generated by sports betting across states
• Understand how the legal status of sports betting affects revenue generated
• Understand how the number of sports betting outlets affects revenue generated

Columns

File: New Jersey.csv | Column name | Description | |:------------------|:--------------------------------------------------------------| | date | The date of the data. (Date) | | New Jersey | The amount of money bet on sports in New Jersey. (Numeric) | | Pennsylvania | The amount of money bet on sports in Pennsylvania. (Numeric) | | Delaware | The amount of money bet on sports in Delaware. (Numeric) | | Mississippi | The amount of money bet on sports in Mississippi. (Numeric) | | Nevada | The amount of money bet on sports in Nevada. (Numeric) | | Rhode Island | The amount of money bet on sports in Rhode Island. (Numeric) | | West Virginia | The amount of money bet on sports in West Virginia. (Numeric) | | Arkansas | The amount of money bet on sports in Arkansas. (Numeric) | | New York | The amount of money bet on sports in New York. (Numeric) | | Iowa | The amount of money bet on sports in Iowa. (Numeric) | | Indiana | The amount of money bet on sports in Indiana. (Numeric) | | Oregon | The amount of money bet on sports in Oregon. (Numeric) | | New Hampshire | The amount of money bet on sports in New Hampshire. (Numeric) | | Michigan | The amount of money bet on sports in Michigan. (Numeric) | | Montana | The amount of money bet on sports in Montana. (Numeric) | | Colorado | The amount of money bet on sports in Colorado. (Numeric) | | Washington DC | The amount of money bet on sports in Washington DC. (Numeric) | | Illinois | The amount of money bet on sports in Illinois. (Numeric) | | Tennessee | The amount of money bet on sports in Tennessee. (Numeric) |

File: PopulationStates.csv | Column name | Description | |:--------------|:----------------------------------------------------| | State | The state in which the data was collected. (String) |

File: homeless.csv | Column name | Description | |:----------------|:----------------------------------------------------| | year | The year the data was collected. (Integer) | | unsheltered | The number of people who are unsheltered. (Integer) |

File: income.csv | Column name | Description | |:------------------|:--------------------------------------------------------------| | Pennsylvania | The amount of money bet on sports in Pennsylvania. (Numeric) | | Delaware | The amount of money bet on sports in Delaware. (Numeric) | | Mississippi | The amount of money bet on sports in Mississippi. (Numeric) | | Nevada | The amount of money bet on sports in Nevada. (Numeric) | | Rhode Island | The amount of money bet on sports in Rhode Island. (Numeric) | | West Virginia | The amount of money bet on sports in West Virginia. (Numeric) | | Arkansas | The amount of money bet on sports in Arkansas. (Numeric) | | New York | The amount of money bet on sports in New York. (Numeric) | | Iowa | The amount of money bet on sports in Iowa. (Numeric) | | Indiana | The amount of money bet on sports in Indiana. (Numeric) | | New Hampshire | The amount of money bet on sports in New Hampshire. (Numeric) | | Michigan | The amount of money bet on sports in Michigan. (Numeric) | | Colorado | The amount of money bet on sports in Colorado. (Numeric) | | Washington DC | The amount of money bet on sports in Washington DC. (Numeric) | | Illinois | The amount of money bet on sports in Illinois. (Nume...

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

CMAQv5.1 with a new dust module IMPROVE sitex files containing 24-hr (every 3rd day) paired model/ob data for the IMPROVE network. This dataset is associated with the following publication: Foroutan, H., J. Young, S. Napelenok, L. Ran, W. Appel, R. Gilliam, and J. Pleim. Development and evaluation of a physics-based windblown dust emission scheme implemented in the CMAQ modeling system. Journal of Advances in Modeling Earth Systems. John Wiley & Sons, Inc., Hoboken, NJ, USA, 9(1): 585-608, (2017).

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OECD Revenue Statistics: Comparative Tables Introduction

The OECD Revenue Statistics database provides detailed and internationally comparable data on the taxes and social contributions paid by businesses and individuals in OECD countries. The data is collected annually from national governments and covers a wide range of taxes, including personal income tax, corporate income tax, social security contributions, and value-added tax.

Data

The database is divided into two main parts:

Part 1: Revenue by Level of Government This part of the database provides data on the total revenue collected by each level of government (central, state, and local) in each OECD country. The data is broken down by type of tax and by source of revenue (e.g., taxes on income, profits, and capital gains; taxes on goods and services; social security contributions).

Part 2: Revenue by Tax Type This part of the database provides data on the revenue collected from each type of tax in each OECD country. The data is broken down by level of government and by source of revenue.

Uses

The OECD Revenue Statistics database can be used for a variety of purposes, including:

Cross-country comparisons of tax levels and structures The database can be used to compare the tax levels and structures of different OECD countries. This information can be used by policymakers to assess the effectiveness of their tax systems and to identify potential areas for reform.

Analysis of the impact of tax policies The database can be used to analyze the impact of tax policies on economic growth, income distribution, and other outcomes. This information can be used by policymakers to design tax policies that are more effective and efficient.

Research on tax policy The database can be used by researchers to study the effects of tax policy on a variety of economic outcomes. This research can help to inform the design of tax policy and to improve our understanding of the economic effects of taxation.

Conclusion

The OECD Revenue Statistics database is a valuable resource for policymakers, researchers, and anyone interested in the taxation of businesses and individuals in OECD countries. The database provides detailed and internationally comparable data on a wide range of taxes, making it an essential tool for understanding the tax systems of OECD countries.

Data Access

The OECD Revenue Statistics database is available online to subscribers. Subscribers can access the data through the OECD's website.

Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.

DATAANT provides the ability to extract data from any website using its web scraping service.

Receive raw HTML data by triggering the API or request a custom dataset from any website.

Use the received data for: - data analysis - data enrichment - data intelligence - data comparison

The only two parameters needed to start a data extraction project: - data source (website URL) - attributes set for extraction

All the data can be delivered using the following: - One-Time delivery - Scheduled updates delivery - DB access - API

All the projects are highly customizable, so our team of data specialists could provide any data enrichment.

The data we used for this study include species occurrence data (n=15 species), climate data and predictions, an expert opinion questionnaire, and species masks that represented the model domain for each species. For this data release, we include the results of the expert opinion questionnaire and the species model domains (or masks). We developed an expert opinion questionnaire to gather information regarding expert opinion regarding the importance of climate variables in determining a species geographic range. The species masks, or model domains, were defined separately for each species using a variation of the “target-group” approach (Phillips et al. 2009), where the domain was determine using convex polygons including occurrence data for at least three phylogenetically related and similar species (Watling et al. 2012). The species occurrence data, climate data, and climate predictions are freely available online, and therefore not included in this data release. The species occurrence data were obtained primarily from the online database Global Biodiversity Information Facility (GBIF; http://www.gbif.org/), and from scientific literature (Watling et al. 2011). Climate data were obtained from the WorldClim database (Hijmans et al. 2005) and climate predictions were obtained from the Center for Ocean-Atmosphere Prediction Studies (COAPS) at Florida State University (https://floridaclimateinstitute.org/resources/data-sets/regional-downscaling). See metadata for references.

Popular Products from NewChic.com E-Commerce Website

Product, Brand, and User Interaction Analytics

By Jeffrey Mvutu Mabilama [source]

About this dataset

This dataset offers an inside look at the popular products of the widely-known e-commerce website NewChic.com. With a snapshot from early August 2020, it provides insights into user tastes, which categories attract buyers, trends in fashion and more!

Using this data, you can gain a better understanding of customer preferences for products and optimize your stock accordingly. You can use it to explore what styles are in trend right now (and how long lasting they are) and find out which niches may be most productive. Moreover, you can segment user tastes based on demographic information such as age groups or geographic locations by analyzing SimilarWeb's traffic data for NewChic.

Are you ready to discover new ways to run your business? Take advantage of this resource now and make smarter decisions about product selection and seasonality – the market is constantly changing so don't miss any opportunity that passes by!

More Datasets

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How to use the dataset

This dataset is an excellent resource for anyone interested in understanding the trends and popularity of products sold by e-commerce website NewChic.com. It contains records of customers’ interactions with products, as well as individual product listings with their associated IDs, prices, discounts, images, brand name, likes count and more. In this guide we explore how to use this data to understand customer behaviour and make informed decisions about the effectiveness of sales and promotional activities.

Exploring the Data: Before diving into the analysis process it is important to understand what you have at your disposal - information that is relevant to customer behaviour on a particular e-commerce website. This dataset provides a comprehensive overview that contains detailed product descriptions as well as popularity metrics such as likes counted from customers who have interacted with them online or offline. Firstly browse through categories from which different types of products have been classified into – apparel & accessories being perhaps some of the most popular ones based on historical purchase behaviour seen in other platforms too; other interesting options could include lifestyle items such as electronic gadgets or art prints among many others. It is also noteworthy to note that the dataset includes details like inventory status (how many pieces remain) which allows us to compare promotional efforts in comparison with inventory size - essential knowledge while deciding how much resources should be allocated towards individual marketing campaigns or discounts etcetera without diluting margins too much over time forcing artificial demand increases simply because stock needs clearing out fast but doesn’t necessarily indicate genuine interest from customers which would leadto sustainable long term engagement opportunities

Processing & Analysing Data: Now begins the real work! Using different methods including descriptive statistics/visualizations & predictive modelling techniques benchmark performance/stock levels against user ratings collected by customers allows insights into customer approval ratings giving quick actionable ideas on where adjustments may need attention so changes can be necessary quickly while avoiding any further stock damage due massive overhauls like introducing completely new styles/products leading greater expenses then required initially so instead focus energies toward aligning existing repertoire while focusing positive public opinion providing value at same time reducing overall costs saving vital resources better engage elsewhere later Additionally analyse past purchase histories similar items helps develop effective plans tweak look structure features down level send more appropriate recommendations instead just trying guess previously failed giving real world tangible results making improvements bottom line profit margin every step way monitoring adjustments dynamically drive fresh continual improvement ways Thereafter when try determine whether current instructions still driving shift user engagement could dampen beginning success measure returns alignment adjust accordingly ensure always stay relevant continuously

Research Ideas

• Use this dataset to compare the popularity of different products across countries, and discover which countries have an affinity to particular styles.
• Identify popular brands in different product categories, then use this insigh...

This dataset contains statistics related to the Unleashed website (http://uladl.com). Unleashed is an open data competition, an initiative of the Office for Digital Government, Department of the Premier and Cabinet. The data is used to monitor the level of engagement activity with the audience, and make the communication effective in regards to the event.

PIECE is a plant gene structure comparison and evolution database with 25 species. Annotated genes extracted from the species are classified based on the Pfam motif and phylogenetic trees are reconstructed for each gene category integrating exon-intron and protein motif information. Resources in this dataset:Resource Title: Web Page. File Name: Web Page, url: https://probes.pw.usda.gov/piece/index.php

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

Detailed online statistics for player Master Knight Kuba from world Monza. View daily activity and session history.

Detailed online statistics for player Acta New Haven from world Tornabra. View daily activity and session history.

Detailed online statistics for player Nin from world Antica. View daily activity and session history.

Breedbase is a web-based, comprehensive breeding management and analysis software. It can be used to design field layouts, collect phenotypic information using tablets, support the collection of genotyping samples in a field, store large amounts of high density genotypic information, and provide Genomic Selection related analyses and predictions. Breedbase supports the Breeding Application Programming Interface (BrAPI) standard which defines data objects and methods for exchanging breeding data. The Breedbase system has evolved from the Sol Genomics Network (SGN) and Cassavabase and related sites (see RTBbase.org). There are a number of instances running for diverse crops, including Cassava (https://cassavabase.org, sweet potato (https://sweetpotatobase.org), banana (https://musabase.org), rice (https://ricebase.org), tomato and other Solanaceae (https://solgenomics.net/) and many others. The Breedbase manual provides detailed information about available features. Resources in this dataset:Resource Title: Website Pointer for Breedbase. File Name: Web Page, url: https://breedbase.org/ Search utilities: Wizard; Accessions and Plots; Organisms; Progenies and Crosses; Field Trials; Genotyping Plates; Genotyping Data Projects; Genotyping Protocols; Traits; Images; People; FAQ. Manage utilities: User Roles; Breeding Programs; Locations; Accessions; Seed Lots; Crosses; Field Trials; Genotyping Trials; Field Book App; Phenotyping; Barcodes; Label Designer; Download; Upload; ODK Data Collection. Analyze utilities: Breeder Tools -- Selection Index; Genomic Selection; Population Structure; Accession Usage; Compare Trials; Graphical Filtering Sequence Analysis -- BLAST; VIGS Tool; HapMap Jbrowse Other -- Ontology Browser; Compose a New Trait.

Basic Info:

The Dataset represents the County Health Ranking of all states taking into account the various factors The County Health Rankings can be used to highlight regional variations in health, increase public understanding of the various factors that affect health, and inspire actions to improve community health. The Rankings capitalizes on our innate desire to compete by enabling comparisons across adjacent or comparable counties within states.

Dataset Information:

The CSV file contains the rankings and data details for the measures used in the 2022/23 County Health Rankings.
1) Outcomes and Factors Rankings --Ranks are all calculated and reported WITHIN states
2)**Outcomes and Factors SubRankings** --Ranks are all calculated and reported WITHIN states
3) Ranked Measure Data --The measures themselves are listed in bold.
4) Ranked Measure Sources & Years
5) Additional Measure Data --These are supplemental measures reported on the Rankings web site but not used in calculating the rankings.
6) Additional Measure Sources & Years

The Data Types of all Columns are automatically set to "Object" To change it just use data.apply(pd.to_numeric)