About

Data from the State of California. From website:

Access raw State data files, databases, geographic data, and other data sources. Raw State data files can be reused by citizens and organizations for their own web applications and mashups.

Openness

Open. Effectively in the public domain. Terms of use page says:

In general, information presented on this web site, unless otherwise indicated, is considered in the public domain. It may be distributed or copied as permitted by law. However, the State does make use of copyrighted data (e.g., photographs) which may require additional permissions prior to your use. In order to use any information on this web site not owned or created by the State, you must seek permission directly from the owning (or holding) sources. The State shall have the unlimited right to use for any purpose, free of any charge, all information submitted via this site except those submissions made under separate legal contract. The State shall be free to use, for any purpose, any ideas, concepts, or techniques contained in information provided through this site.

About this dataset

Context

The dataset tabulates the population of Gratis by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Gratis across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.0% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Gratis is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Gratis 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 Gratis Population by Race & Ethnicity. You can refer the same here

Data are the foundation of science, and there is an increasing focus on how data can be reused and enhanced to drive scientific discoveries. However, most seemingly “open data” do not provide legal permissions for reuse and redistribution. The inability to integrate and redistribute our collective data resources blocks innovation and stymies the creation of life-improving diagnostic and drug selection tools. To help the biomedical research and research support communities (e.g. libraries, funders, repositories, etc.) understand and navigate the data licensing landscape, the (Re)usable Data Project (RDP) (http://reusabledata.org) assesses the licensing characteristics of data resources and how licensing behaviors impact reuse. We have created a ruleset to determine the reusability of data resources and have applied it to 56 scientific data resources (e.g. databases) to date. The results show significant reuse and interoperability barriers. Inspired by game-changing projects like Creative Commons, the Wikipedia Foundation, and the Free Software movement, we hope to engage the scientific community in the discussion regarding the legal use and reuse of scientific data, including the balance of openness and how to create sustainable data resources in an increasingly competitive environment.

AI Generated Summary: Data.gov.ro is Romania's national open data portal, established in 2013 to centralize open data published by Romanian institutions according to international standards. It serves as a central access point for open datasets from public authorities and institutions in Romania and acts as a liaison with the European Commission's European Data Portal, promoting the free use, reuse, and redistribution of data under the Open Government License - OGL ROU 1.0. About: The data.gov.ro portal was created in 2013 within the framework of open data efforts at the international level, with the aim of centralizing open data published by Romanian institutions in accordance with the principles and standards in the field. Currently, the General Secretariat of the Government ensures the coordination of the process of opening public data in Romania and manages the national portal data.gov.ro, the central access point for open datasets published by the authorities and institutions of the public administration in Romania and the point of contact in relation to the European Commission (europeandataportal.eu). Open data is data that can be freely used, reused, and redistributed by anyone, freely, without imposing restrictions such as copyright, patents, or other control mechanisms. In this sense, the portal provides users with the Open Government License - OGL ROU 1.0, issued in 2014 by the General Secretariat of the Government as an open license model. For data to be considered open, at least two conditions must be met: technical: the data is published online in file formats that can be automatically processed using computer programs (machine-readable), which are, as far as possible, available to anyone, free of charge (free and open source software). legal: at the time of publication, the data is attached to a license by which the data owner and publisher establishes the conditions for its reuse. In Romania, the legal framework for publishing open data was established by Law no. 109/2007 regarding the reuse of information from public institutions, amended and supplemented by Law no. 299/2015. More details can be found in the Methodology for publishing open data, developed by the General Secretariat of the Government. Open data visualization tool List of datasets assumed by public institutions through the OGP National Action Plan List 2016 Status 2017 Other useful resources DCAT application profile for data portals in Europe DCAT-AP: Information on the DCAT application profile for data portals in Europe Translated from Romanian Original Text: Portalul data.gov.ro a fost realizat în anul 2013 în marja demersurilor open data la nivel internațional, în scopul centralizării datelor deschise publicate de instituțiile din România conform principiilor și standardelor în domeniu. În prezent, Secretariatul General al Guvernului asigură coordonarea procesului de deschidere a datelor publice în România și administrează portalul național data.gov.ro, punctul central de acces pentru seturile de date deschise publicate de autoritățile și instituțiile administrației publice din România și punctul de legătură în relația cu Comisia Europeană (europeandataportal.eu). Datele deschise sunt date ce pot fi utilizate în mod liber, reutilizate și redistribuite de către oricine, în mod liber, fără a impune restricții de tipul drepturi de autor (copyright), patente sau alte mecanisme de control. În acest sens, portalul pune la dispoziția utilizatorilor Licența pentru o Guvernare Deschisă - OGL ROU 1.0, emisă în 2014 de Secretariatul General al Guvernului ca model de licență deschisă. Pentru ca datele să fie considerate deschise, trebuie îndeplinite minim două condiții: tehnic: datele sunt publicate online în formate de fișiere ce pot fi procesate în mod automat folosind programe de calculator (machine-readable), care sunt, pe cât posibil, disponibile oricui, în mod gratuit (free and open source software). legal: în momentul publicării, datelor li se atașează o licență prin care cel care deține și publică datele stabilește condițiile de reutilizare a acestora. În România, cadrul legal pentru publicarea datelor deschise a fost stabilit de Legea nr. 109/2007 privind reutilizarea informațiilor din instituții publice, modificată și completată de Legea nr. 299/ 2015. Mai multe detalii găsiți în Metodologia pentru publicarea datelor deschise, elaborată de Secretariatul General al Guvernului. Instrument de vizualizare a datelor deschise Lista seturilor de date asumate de instituțiile publice prin Planul Național de Acțiune OGP Listă 2016 Stadiu 2017 Alte resurse utile DCAT application profile for data portals in Europe DCAT-AP: Information on the DCAT application profile for data portals in Europe

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive—the largest publicly available archive of FOSS source code with accompanying development history—all versions of files whose names are commonly used to convey licensing terms to software users and developers. The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing. Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared. The dataset is released as open data as an archive file containing all deduplicated license blobs, plus several portable CSV files for metadata, referencing blobs via cryptographic checksums.

For more details see the included README file and companion paper:

Stefano Zacchiroli. A Large-scale Dataset of (Open Source) License Text Variants. In proceedings of the 2022 Mining Software Repositories Conference (MSR 2022). 23-24 May 2022 Pittsburgh, Pennsylvania, United States. ACM 2022.

If you use this dataset for research purposes, please acknowledge its use by citing the above paper.

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

Adapted from Wikipedia: OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources. We've made available a number of tables (explained in detail below): history_* tables: full history of OSM objects planet_* tables: snapshot of current OSM objects as of Nov 2019 The history_* and planet_* table groups are composed of node, way, relation, and changeset tables. These contain the primary OSM data types and an additional changeset corresponding to OSM edits for convenient access. These objects are encoded using the BigQuery GEOGRAPHY data type so that they can be operated upon with the built-in geography functions to perform geometry and feature selection, additional processing. Example analyses are given below. This dataset is part of a larger effort to make data available in BigQuery through the Google Cloud Public Datasets program . OSM itself is produced as a public good by volunteers, and there are no guarantees about data quality. Interested in learning more about how these data were brought into BigQuery and how you can use them? Check out the sample queries below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

The USDA-Agricultural Research Service carried out a water productivity field trial for irrigated maize (Zea mays L.) at the Limited Irrigation Research Farm (LIRF) facility in northeastern Colorado in 2008 through 2011. The dataset includes daily measurements of irrigation, precipitation, soil water storage, and plant growth; daily estimates of crop evapotranspiration; and seasonal measurement of crop water use and crop yield. Soil parameters and hourly and daily weather data are also provided. The dataset can be useful to validate and refine maize crop models. The data are presented in spreadsheet format. The primary data files are the four annual LIRF Maize 20xx.xlsx files that include the daily water balance and phenology, final yield and biomass data, and crop management logs. Annual LIRF Weather 20xx.xlsx files provide hourly and daily weather parameters including reference evapotranspiration. The LIRF Soils.xlsx file gives soil parameters. Each spreadsheet contains a Data Descriptions worksheet that provides worksheet or column specific information. Comments are embedded in cells with specific information. A LIRF photos.pdf file provides images of the experimental area, measurement processes and crop conditions. Photo credit Peggy Greb, ARS; copyright-free, public domain copyright policy. Resources in this dataset:Resource Title: LIRF Weather 2008. File Name: LIRF Weather 2008.xlsxResource Description: LIRF hourly and daily weather data for 2008Resource Title: LIRF Weather 2009. File Name: LIRF Weather 2009.xlsxResource Description: LIRF hourly and daily weather data for 2009Resource Title: LIRF Weather 2010. File Name: LIRF Weather 2010.xlsxResource Description: LIRF hourly and daily weather data for 2010Resource Title: LIRF Weather 2011. File Name: LIRF Weather 2011.xlsxResource Description: LIRF hourly and daily weather data for 2011Resource Title: LIRF Soils. File Name: LIRF Soils.xlsxResource Description: LIRF soil maps, soil texture, moisture retention, and chemical constituentsResource Title: LIRF Photo Log. File Name: LIRF Photo Log.pdfResource Description: Photos of the LIRF Water Productivity field trials and instrumentation.Resource Title: Data Dictionaries. File Name: DataDictionary r1.xlsxResource Description: Data descriptions of all the data resources (also included in their respective data files).Resource Title: LIRF Methodology. File Name: LIRF Methodology r1.pdfResource Description: Description of data files, data, and data collection methodology for the LIRF 2008-2011 Water Productivity field trials.Resource Title: LIRF Maize 2008. File Name: LIRF Maize 2008 r1.xlsxResource Description: Water balance and yield data for 2008 LIRF field trialResource Title: LIRF Maize 2009. File Name: LIRF Maize 2009 r1.xlsxResource Description: Water balance and yield data for 2009 LIRF field trialResource Title: LIRF Maize 2010. File Name: LIRF Maize 2010 r1.xlsxResource Description: Water balance and yield data for 2010 LIRF field trialResource Title: LIRF Maize 2011. File Name: LIRF Maize 2011 r1.xlsxResource Description: Water balance and yield data for 2011 LIRF field trial

🗃️ Dataset

The dataset released is anonymized and not representative of the production characteristics.

The Multilingual Shopping Session Dataset is a collection of anonymized customer sessions containing products from six different locales: English, German, Japanese, French, Italian, and Spanish. It consists of two main components: user sessions and product attributes. User sessions are a list of products that a user has engaged with in chronological order, while product attributes include various details like product title, price in local currency, brand, colour, and description.

The dataset has been divided into three splits: train, phase-1 test, and phase-2 test. For Task 1 and Task 2, the proportions for each language are roughly 10:1:1. For Task 3, the number of samples in the phase-1 test and phase-2 test is fixed at 10,000. All three tasks share the same train set, while their test sets have been constructed according to their specific objectives. Task 1 uses English, German, and Japanese data, while Task 2 uses French, Italian, and Spanish data. Participants in Task 2 are encouraged to use transfer learning to improve their system's performance on the test set. For Task 3, the test set includes products that do not appear in the training set, and participants are asked to generate the title of the next product based on the user session.

Table 1 summarizes the dataset statistics, including the number of sessions, interactions, products, and average session length. The dataset will be made publicly available as part of the KDD Cup competition. Each product will be identified by a unique Amazon Standard Identification Number (ASIN), making extracting more information from the web easy. Participants are free to use external sources of information to train their systems, such as public datasets and pre-trained language models, but must declare them when describing their systems beyond the provided dataset.

Table 1: Dataset statistics

In addition, we list the column names and their meanings for product attribute data: - locale: the locale code of the product (e.g., DE) - id: a unique for the product. Also known as Amazon Standard Item Number (ASIN) (e.g., B07WSY3MG8) - title: title of the item (e.g., “Japanese Aesthetic Sakura Flowers Vaporwave Soft Grunge Gift T-Shirt”) - price: price of the item in local currency (e.g., 24.99) - brand: item brand name (e.g., “Japanese Aesthetic Flowers & Vaporwave Clothing”) - color: color of the item (e.g., “Black”) - size: size of the item (e.g., “xxl”) - model: model of the item (e.g., “iphone 13”) - material: material of the item (e.g., “cotton”) - author: author of the item (e.g., “J. K. Rowling”) - desc: description about a item’s key features and benefits called out via bullet points (e.g., “Solid colors: 100% Cotton; Heather Grey: 90% Cotton, 10% Polyester; All Other Heathers …”)

This dataset provides locations of open spaces in London identified by research and data analysis as Privately Owned Public Spaces (POPS), based on the definition below and available data in 2017. This is not a fully comprehensive dataset and is based on multiple sources of information. Subsequent versions will provide updates as more information becomes available. Read more here. The dataset has been created by Greenspace Information for Greater London CIC (GiGL). GiGL mobilises, curates and shares data that underpin our knowledge of London’s natural environment. We provide impartial evidence to enable informed discussion and decision-making in policy and practice. GiGL maps under licence from the Greater London Authority. Research for this dataset has been assisted by The Guardian Cities team. Data sources Boundaries and attributes are based on GiGL’s Open Space dataset, which is a collated dataset of spatial and attribute information from various sources, including: habitat and open space survey information provided to GiGL by the GLA and London boroughs, borough open space survey information where provided to GiGL or available under open licence, other attribute information inferred from field visits or research. Available open space information has been analysed by GiGL to identify POPS included in this dataset. Future updates to the GiGL Open Space dataset will inform future, improved releases of the POPS dataset. Definition For the purposes of creating the dataset, POPS have been carefully defined as below. The definition is based on review of similar definitions internationally and appropriateness for application to available London data. Privately Owned Public Spaces (POPS): publicly accessible spaces which are provided and maintained by private developers, offices or residential building owners. They include city squares, atriums and small parks. The spaces provide several functional amenities for the public. They are free to enter and may be open 24 hours or have restricted access arrangements. Whilst the spaces look public, there are often constraints to use. For the Greater London dataset no consideration is taken as to a site’s formal status in planning considerations, and only unenclosed POPS are included. POPS may be destination spaces, which attract visitors from outside of the space’s immediate area and are designed for use by a broad audience, or neighbourhood spaces, which draw residents and employees from the immediate locale and are usually strongly linked with the adjacent street or host building. These spaces are of high quality and include a range of amenities. The POPS may also be a hiatus space, accommodating the passing user for a brief stop only – for example it may include seating but few other amenities, a circulation space, designed to improve a pedestrian’s journey from A to B, or a marginal space, which whilst a public space is not very accommodating and experiences low levels of usage. (Ref: Privately Owned Public Space: The New York City Experience, by Jerold S. Kayden, The New York City Department of City Planning, and the Municipal Art Society of New York, published by John Wiley & Sons, 2000). NOTE: The boundaries are based on Ordnance Survey mapping and the data is published under Ordnance Survey's 'presumption to publish'.Contains OS data © Crown copyright and database rights 2017.

About this dataset

Context

The dataset tabulates the Free Soil population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Free Soil.

Content

The dataset constitues the following two datasets across these two themes

• Free Soil, MI Population Breakdown by Gender
• Free Soil, MI Population Breakdown by Gender and Age

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/.

This dataset focuses on chamber-based methane (CH4) flux measurements in tidal wetlands across the Contiguous United States (CONUS)and is intended to serve as a community resource for Earth and environmental science research, climate change synthesis studies, and model evaluation. The database contains 35 contributed datasets with a total of 10,445 chamber-based CH4 flux observations across 41 years and 120 sites distributed across CONUS Atlantic and Pacific coasts and the Gulf of Mexico. Contributed datasets are converted to a standard format and units and organized hierarchically (site, chamber, chamber time series, porewater chemistry, and plant species) with metadata on contributors, geographic location, measurement conditions, and ancillary environmental variables. While focused on CH4 flux measurements, the database accommodates other greenhouse gas flux data (CO2 and N2O) as well as porewater profiles of various analytes, experimental treatments (e.g., fertilization, elevated CO2), and ecosystem disturbance classes (e.g., salinization, tidal restrictions, restoration). This database results from the Coastal Carbon Network’s (CCN) tidal wetland CH4 flux data synthesis. A description and analysis of the dataset are available in Arias-Ortiz et al. 2024, co-authored by members of the CCN Data Methane Working Group and data contributors.

This dataset was originally collected for a data science and machine learning project that aimed at investigating the potential correlation between the amount of time an individual spends on social media and the impact it has on their mental health.

The project involves conducting a survey to collect data, organizing the data, and using machine learning techniques to create a predictive model that can determine whether a person should seek professional help based on their answers to the survey questions.

This project was completed as part of a Statistics course at a university, and the team is currently in the process of writing a report and completing a paper that summarizes and discusses the findings in relation to other research on the topic.

The following is the Google Colab link to the project, done on Jupyter Notebook -

https://colab.research.google.com/drive/1p7P6lL1QUw1TtyUD1odNR4M6TVJK7IYN

The following is the GitHub Repository of the project -

https://github.com/daerkns/social-media-and-mental-health

Libraries used for the Project -

Pandas
Numpy
Matplotlib
Seaborn
Sci-kit Learn

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

The USGS Governmental Unit Boundaries dataset from The National Map (TNM) represents major civil areas for the Nation, including States or Territories, counties (or equivalents), Federal and Native American areas, congressional districts, minor civil divisions, incorporated places (such as cities and towns), and unincorporated places. Boundaries data are useful for understanding the extent of jurisdictional or administrative areas for a wide range of applications, including mapping or managing resources, and responding to natural disasters. Boundaries data also include extents of forest, grassland, park, wilderness, wildlife, and other reserve areas useful for recreational activities, such as hiking and backpacking. Boundaries data are acquired from a variety of government sources. The data represents the source data with minimal editing or review by USGS. Please refer to the feature-level metadata for information on the data source. The National Map boundaries data is commonly combined with other data themes, such as elevation, hydrography, structures, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain boundaries data in either ESRI File Geodatabase or Shapefile formats. For additional information on the boundaries data model, go to https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map.

This feature layer contains the most up-to-date COVID-19 cases and latest trend plot. It covers China, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals)and the US at county-level. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. . The China data is automatically updating at least once per hour, and non-China data is updating hourly. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact us.

This .zip file contains pre-configured files for members of the public to interact with Kendall County's public GIS layers in a desktop environment. Included are:An ArcGIS Pro PackageA QGIS Project FIleArcGIS Pro requires an ESRI license to use. See the ArcGIS Pro product page for more information.QGIS is free, open-source software that is available for a variety of computing environments. See the QGIS Downloads page to select the appropriate installation method.With the appropriate software installed, users can simply open the corresponding file. It may take a minute or two to load, due to the number of layers that need to load. Once loaded, users will have read-only access to all of the major public layers, and can adjust how they are displayed. In a desktop environment, users can also create and interact with other data sources, such as private site plans, annotations, and other public data layers from non-County entities.Please note that the layers included in these packages are the same live data sources found in the web maps. An internet connection is required for these files to function properly.

This dataset contains the name and geography ID of all the groundwater basins in Arizona. The purpose of this feature class is to provide service area boundaries for community water systems regulated by the Arizona Department of Water Resources.Use this spatial data to join data tables downloaded from the Arizona EPHT Explorer that has the "Geography" filter set to "PWS Service Areas (Public Water System)". his feature class contains service area polygons for each Community Water System (CWS). To determine the service area, ADWR utilized primary data provided directly from the water system (i.e. PDF, shapefile, verbal definition). If primary data is unavailable, secondary data was utilized to determine service area boundaries (i.e. Certificate of Convenience and Necessity (CCN), Census Designated Place shapefile from U.S Census Bureau.) New water systems are added, and contact information is updated for existing water systems on an annual basis.Environmental Public Health Tracking (EPHT) is a tool to help Arizonans learn about environmental hazards in the state that could impact their health. EPHT was built on the idea that health and environmental problems are not always separate issues with unrelated solutions. EPHT has gathered data from national and local sources in order to view both environmental and health outcome data in one easily accessible place. For example, Arizonans can review air quality information and compare the information with respiratory issues such as asthma. Dataset and web based maps display a variety of topics in the Environmental Public Health Tracking network. For more information about where to download the data tables and how, feel free to visit the Arizona EPHT Explorer or the Environmental Public Health Tracking webpage. Last Update: March 2022Update Frequency: Annually

About this dataset

Context

The dataset tabulates the population of Parks by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Parks across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 52.97% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Parks is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Parks 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 Parks Population by Gender. You can refer the same here