As of early 2025, LinkedIn had an audience reach of *** million users in the *************. The country was by far the leading market of the professional job networking service, with runner-up India having an audience of *** million. LinkedIn: the company Launched in 2003, LinkedIn is a professional networking service where jobseekers can post their CVs, and employers or recruiters can post job ads and search for prospective candidates. In December 2016, Microsoft acquired LinkedIn, making it a wholly owned subsidiary. In 2020, the platform generated over ***** billion U.S. dollars in revenue. Despite its great success, the company has not always seen positive numbers only, and in 2018, LinkedIn reported an operating loss of *** million U.S. dollars. LinkedIn marketing Greater exposure, lead generation and increased thought leadership are all key benefits of social media marketing, and LinkedIn is a popular marketing tool in the B2B segment. Whereas the company predominantly generates revenue by selling access to member information to professional parties, LinkedIn is the second-most popular social media platform used by B2B marketers, ranking only behind Facebook.

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Harness Success.ai's robust LinkedIn and User Profiles Data, featuring extensive insights from 700M+ profiles and 70M+ companies for ideal customer profiling and competitive intelligence. Ensure data-driven decisions with our GDPR-compliant, accurately validated datasets - At Unbeatable Prices.

This statistic shows a ranking of the estimated number of LinkedIn users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Success.ai’s User Profiles Data for Nonprofit and NGO Leaders provides businesses, organizations, and researchers with comprehensive access to global leaders in the nonprofit and NGO sectors. With data sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and contact details for executives, program managers, administrators, and decision-makers. Whether your goal is to partner with nonprofits, support global causes, or conduct research into social impact, Success.ai ensures your outreach is backed by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s User Profiles Data for Nonprofit and NGO Leaders? Comprehensive Professional Profiles

Access verified LinkedIn profiles of nonprofit leaders, NGO managers, program directors, grant writers, and administrative executives. AI-driven validation ensures 99% accuracy for efficient communication and minimized bounce rates. Global Coverage Across Nonprofit Sectors

Includes profiles from nonprofits, humanitarian organizations, environmental groups, social enterprises, and advocacy organizations. Covers key markets across North America, Europe, APAC, South America, and Africa for global reach. Continuously Updated Dataset

Reflects real-time professional updates, organizational changes, and emerging trends in the nonprofit landscape to keep your targeting relevant and effective. Tailored for Nonprofit Insights

Enriched profiles include work histories, organizational affiliations, areas of expertise, and social impact projects for deeper engagement opportunities. Data Highlights: 700M+ Verified LinkedIn Profiles: Access a vast network of nonprofit and NGO professionals worldwide. 100M+ Work Emails: Direct communication with executives, managers, and decision-makers in the nonprofit sector. Enriched Organizational Data: Gain insights into leadership structures, mission focuses, and operational scales. Industry-Specific Segmentation: Target nonprofits focused on healthcare, education, environmental sustainability, human rights, and more. Key Features of the Dataset: Nonprofit and NGO Leader Profiles

Identify and connect with executives, program managers, fundraisers, and policy directors in global nonprofit and NGO sectors. Engage with individuals who drive decision-making and operational strategies for impactful organizations. Detailed Organizational Insights

Leverage firmographic data, including organizational size, mission, regional activity, and funding sources, to align with specific nonprofit goals. Advanced Filters for Precision Targeting

Refine searches by region, mission type, role, or organizational focus for tailored outreach. Customize campaigns based on social impact priorities, such as climate action, gender equality, or economic development. AI-Driven Enrichment

Enhanced datasets provide actionable insights into professional accomplishments, partnerships, and leadership achievements for targeted engagement. Strategic Use Cases: Partnership Development and Outreach

Identify nonprofits and NGOs for collaboration on social impact projects, sponsorships, or grant distribution. Build relationships with decision-makers driving advocacy, fundraising, and community initiatives. Donor Engagement and Fundraising

Target nonprofit leaders responsible for managing fundraising campaigns and donor relationships. Tailor outreach efforts to align with specific causes and funding priorities. Research and Analysis

Analyze leadership trends, mission focuses, and regional nonprofit activities to inform program design and funding strategies. Use insights to evaluate the effectiveness of social impact initiatives and partnerships. Recruitment and Talent Acquisition

Target HR professionals and administrators seeking qualified staff, consultants, or volunteers for nonprofits and NGOs. Offer talent solutions for specialized roles in program management, advocacy, and administration. Why Choose Success.ai? Best Price Guarantee

Access industry-leading, verified User Profiles Data at unmatched pricing to ensure your campaigns are cost-effective and impactful. Seamless Integration

Easily integrate verified nonprofit data into your CRM or marketing platforms with APIs or downloadable formats. AI-Validated Accuracy

Rely on 99% accuracy to minimize wasted outreach efforts and maximize engagement outcomes. Customizable Solutions

Tailor datasets to focus on specific nonprofit types, geographical regions, or areas of social impact to meet your strategic objectives. Strategic APIs for Enhanced Campaigns: Data Enrichment API

Update your internal records with verified nonprofit leader profiles to enhance targeting and engagement. Lead Generation API

Automate lead generation for a consistent pipeline of nonprofit and NGO professionals, scaling your outreach efforts efficiently. Success.ai’s User Profiles Data for Nonprofit and NGO Leader...

The dataset contains information on 30,000+ job postings collected from LinkedIn till the year 2023 which provides a rich source of information on job postings on LinkedIn, with concise information on the job title, company, location, and other key attributes of each posting. This data can be used to gain insights into employment trends and dynamics, identify key skills and experiences that are in high demand, and optimize job postings to attract the right candidates.

Taxonomy of the Dataset https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13623947%2F85fde0e9bcd9e6532b63e65ca1e5b58a%2FWhatsApp%20Image%202024-02-27%20at%2012.12.59.jpeg?generation=1709016197299811&alt=media" alt="">

📊 Job Postings Data for Talent Acquisition, HR Strategy & Market Research Canaria’s Job Postings Data product is a structured, AI-enriched dataset that captures and organizes millions of job listings from leading sources such as Indeed, LinkedIn, and other recruiting platforms. Designed for decision-makers in HR, strategy, and research, this data reveals workforce demand trends, employer activity, and hiring signals across the U.S. labor market and enhanced with advanced enrichment models.

The dataset enables clients to track who is hiring, what roles are being posted, which skills are in demand, where talent is needed geographically, and how compensation and employment structures evolve over time. With field-level normalization and deep enrichment, it transforms noisy job listings into high-resolution labor intelligence—optimized for strategic planning, analytics, and recruiting effectiveness.

🧠 Use Cases: What This Job Postings Data Solves This enriched dataset empowers users to analyze workforce activity, employer behavior, and hiring trends across sectors, geographies, and job categories.

🔍 Talent Acquisition & HR Strategy • Identify hiring trends by industry, company, function, and geography • Optimize job listings and outreach with enriched skill, title, and seniority data • Detect companies expanding or shifting their workforce focus • Monitor new roles and emerging skills in real time

📈 Labor Market Research & Workforce Planning • Visualize job market activity across cities, states, and ZIP codes • Analyze hiring velocity and job volume changes as macroeconomic signals • Correlate job demand with company size, sector, or compensation structure • Study occupational dynamics using AI-normalized job titles • Use directional signals (job increases/declines) to anticipate market shifts

📊 HR Analytics & Compensation Intelligence • Map salary ranges and benefits offerings by role, location, and level • Track high-demand or hard-to-fill positions for strategic workforce planning • Support compensation planning and headcount forecasting • Feed job title normalization and metadata into internal HRIS systems • Identify talent clusters and location-based hiring inefficiencies

🌐 What Makes This Job Postings Data Unique

🧠 AI-Based Enrichment at Scale • Extracted attributes include hard skills, soft skills, certifications, and education requirements • Modeled predictions for seniority level, employment type, and remote/on-site classification • Normalized job titles using an internal taxonomy of over 50,000 unique roles • Field-level tagging ensures structured, filterable, and clean outputs

💰 Salary Parsing & Compensation Insights • Parsed salary ranges directly from job descriptions • AI-based salary predictions for postings without explicit compensation • Compensation patterns available by job title, company, and location

🔁 Deduplication & Normalization • Achieves approximately 60% deduplication rate through semantic and metadata matching • Normalizes company names, job titles, location formats, and employment attributes • Ready-to-use, analysis-grade dataset—fully structured and cleansed

🔗 Company Matching & Metadata • Each job post is linked to a structured company profile, including metadata • Records are cross-referenced with LinkedIn and Google Maps to validate company identity and geography • Enables aggregation at employer or location level for deeper insights

🕒 Freshness & Scalability • Updated hourly to reflect real-time hiring behavior and job market shifts • Delivered in flexible formats (CSV, JSON, or data feed) and customizable filters • Supports segmentation by geography, company, seniority, salary, title, and more

🎯 Who Uses Canaria’s Job Postings Data • HR & Talent Teams – to benchmark roles, optimize pipelines, and compete for talent • Consultants & Strategy Teams – to guide clients with labor-driven insights • Market Researchers – to understand employment dynamics and job creation trends • HR Tech & SaaS Platforms – to power salary tools, job market dashboards, or recruiting features • Economic Analysts & Think Tanks – to model labor activity and hiring-based economic trends • BI & Analytics Teams – to build dashboards that track demand, skill shifts, and geographic patterns

📌 Summary Canaria’s Job Postings Data provides an AI-enriched, clean, and analysis-ready view of the U.S. job market. Covering millions of listings from Indeed, LinkedIn, other job boards, and ATS sources, it includes detailed job attributes, inferred compensation, normalized titles, skill extraction, and employer metadata—all updated hourly and fully structured.

With deep enrichment, reliable deduplication, and company matchability, this dataset is purpose-built for users needing workforce insights, market trends, and strategic talent intelligence. Whether you're modeling skill gaps, benchmarking compensation, or visualizing hiring momentum, this dataset provides a complete toolkit for HR and labor intellig...

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting, employee data / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contact ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contact to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Overview

This dataset is a minimal example of Data Subject Access Request Packages (SARPs), as they can be retrieved under data protection laws, specifically the GDPR. It includes data from two data subjects, each with accounts for five major sevices, namely Amazon, Apple, Facebook, Google, and Linkedin.

Purpose and Usage

This dataset is meant to be an initial dataset that allows for manual exploration of structures and contents found in SARPs. Hence, the number of controllers and user profiles should be minimal but sufficient to allow cross-subject and cross-controller analysis. This dataset can be used to explore structures, formats and data types found in real-world SARPs. Thereby, the planning of future SARP-based research projects and studies shall be facilitated.

We invite other researchers to use this dataset to explore the structure of SARPs. The envisioned primary usage includes the development of user-centric privacy interfaces and other technical contributions in the area of data access rights. Moreover, these packages can also be used for examplified data analyses, although no substantive research questions can be answered using this data. In particular, this data does not reflect how data subjects behave in real world. However, it is representative enough to give a first impression on the types of data analysis possible when using real world data.

Data Generation

In order to allow cross-subject analysis, while keeping the re-identification risk minimal, we used research-only accounts for the data generation. A detailed explanation of the data generation method can be found in the paper corresponding to the dataset, accepted for the Annual Privacy Forum 2024.

In short, two user profiles were designed and corresponding accounts were created for each of the five services. Then, those accounts were used for two to four month. During the usage period, we minimized the amount of identifying data and also avoided interactions with data subjects not part of this research. Afterwards, we performed a data access request via the controller's web interface. Finally, the data was cleansed as described in detail in the acconpanying paper and in brief within the following section.

Data Cleansing

Before publication, both possibly identifying information and security relevant attributes need to be obfuscated or deleted. Moreover, multi-party data (especially messages with external entities) must be deleted. If data is obfuscated, we made sure to substitute multiple occurances of the same information with the same replacement.
We provide a list of deleted and obfuscated items, the obfuscation scheme and, if applicable, the replacement.

The list of obfuscated items looks like the following example:

Data Characterization

To give you an overview of the dataset, we publicly provide some meta-data about the usage time and SARP characteristics of exports from subject A/ subject B.

Authors

This data collection was performed by Daniela Pöhn (Universität der Bundeswehr München, Germany), Frank Pallas and Nicola Leschke (Paris Lodron Universität Salzburg, Austria). For questions, please contact nicola.leschke@plus.ac.at.

Accompanying Paper

The dataset was collected according to the method presented in:
Leschke, Pöhn, and Pallas (2024). "How to Drill Into Silos: Creating a Free-to-Use Dataset of Data Subject Access Packages". Accepted for Annual Privacy Forum 2024.

Author: Daniel Vázquez Pombo, ORCID: https://orcid.org/0000-0001-5664-9421The best way to contact me is through LinkedIn: https://www.linkedin.com/in/dvp/-------------------------------------------------------------------------------This item includes the SOLETE dataset which is disclosed in [1] to increase the transparency and replicability of [2, 3, 4].SOLETE includes 15 months measurements with different resolutions (from second to hourly) from the 1st June 2018 to 1st September 2019 covering: Timestamp, air temperature, relative humidity, pressure, wind speed, wind direction, global horizontal irradiance, plane of array irradiance, and active power recorded from an 11 kW Gaia wind turbine and a 10 kW PV inverter.The origin of the data is SYSLAB, part of DTU Wind and Energy Systems. If you want to learn more about the dataset check out [1].You can use the SOLETE dataset with the codes available in GitHub: https://github.com/DVPombo/SOLETE/tree/main The different scripts have various functions. One allows to import SOLETE and show some plots. Another is a platform where you can play with different Machine Learning models for time series forecasting. The application focuses on predicting PV power, but it can be easily edited by the user.The publications related to this item are:[1] Pombo, D. V., Gehrke, O., & Bindner, H. W. (2022). SOLETE, a 15-month long holistic dataset including: Meteorology, co-located wind and solar PV power from Denmark with various resolutions. Data in Brief, 42, 108046.[2] Pombo, D. V., Bindner, H. W., Spataru, S. V., Sørensen, P. E., & Bacher, P. (2022). Increasing the accuracy of hourly multi-output solar power forecast with physics-informed machine learning. Sensors, 22(3), 749.[3] Pombo, D. V., Bacher, P., Ziras, C., Bindner, H. W., Spataru, S. V., & Sørensen, P. E. (2022). Benchmarking physics-informed machine learning-based short term PV-power forecasting tools. Energy Reports, 8, 6512-6520.[4] Pombo, D. V., Rincón, M. J., Bacher, P., Bindner, H. W., Spataru, S. V., & Sørensen, P. E. (2022). Assessing stacked physics-informed machine learning models for co-located wind–solar power forecasting. Sustainable Energy, Grids and Networks, 32, 100943.To cite this item, I would appreciate if you use [1]. Alternatively, you can also use the following (but note that I won't get credit for it):@misc{Pombo2022SOLETE, author = "Daniel Vazquez Pombo", title = "{The SOLETE dataset}", year = "2023", month = "Apr", url = "https://data.dtu.dk/articles/dataset/The_SOLETE_dataset/17040767", doi = "10.11583/DTU.17040767", note = {Retrieved from {DTU-Data}, url{https://data.dtu.dk/articles/dataset/The_SOLETE_dataset/17040767}, {DOI}: {10.11583/DTU.17040767}},}

Analysis of ‘Deep-NLP’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/samdeeplearning/deepnlp on 28 January 2022.

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

What's In The Deep-NLP Dataset?

Sheet_1.csv contains 80 user responses, in the response_text column, to a therapy chatbot. Bot said: 'Describe a time when you have acted as a resource for someone else'. User responded. If a response is 'not flagged', the user can continue talking to the bot. If it is 'flagged', the user is referred to help.

Sheet_2.csv contains 125 resumes, in the resume_text column. Resumes were queried from Indeed.com with keyword 'data scientist', location 'Vermont'. If a resume is 'not flagged', the applicant can submit a modified resume version at a later date. If it is 'flagged', the applicant is invited to interview.

What Do I Do With This?

Classify new resumes/responses as flagged or not flagged.

There are two sets of data here - resumes and responses. Split the data into a train set and a test set to test the accuracy of your classifier. Bonus points for using the same classifier for both problems.

Good luck.

Acknowledgements

Thank you to Parsa Ghaffari (Aylien), without whom these visuals (cover photo is in Parsa Ghaffari's excellent LinkedIn article on English, Spanish and German postive v. negative sentiment analysis) would not exist.

There Is A 'deep natural language processing' Kernel. I will update it. I Hope You Find It Useful.

You can use any of the code in that kernel anywhere, on or off Kaggle. Ping me at @_samputnam for questions.

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

Job Opportunities in Ecuador

Analyzing Employment Characteristics and Trends (2013-2020)

By [source]

About this dataset

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For more datasets, click here.

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How to use the dataset

In order to explore this dataset, users should start by examining the different columns in the table: cargo (Job Title), modo (Mode of Job Offering), plazas (Number of available positions), experiencia (Required Experience) , capacitacion (Required Training), jornadas (Working Hours) ,remuneracion(Remuneration ), nivelInstruccion()Level of Education Required , areaEstudios(Area of Studies ) ciudad() Equipped City parroquia(Parishes) sector(Private/Public Sector Jobs).

Users can then filter the data according to their needs using specific column values. For example, one can create a list with all job openings that require 5 years experience and above or they could search for all jobs related to accounting that are offered through an agency or advertised on social media platforms like LinkedIn or Twitter. Alternatively, users can look at trends by analyzing changes in the number of available positions over time or differences between private and public sector opportunities

Research Ideas

• Analyzing the correlation between amount of job opportunities and education level required to fill those positions, to evaluate the skills gap in Ecuador and improve the educational system.
• Conducting a temporal analysis to identify trends in type of jobs available in different parts of Ecuador, remuneration offered and cities that are most competitive for job seekers.
• Using demographic data from private/public sector jobs to generate insights about diversity within each sector as well as overall opportunities available for each group - i.e., women in STEM fields or under-represented minorities with regard to accessibility among certain industries nationwide

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: oferta_laboral_ecuador.csv | Column name | Description | |:---------------------|:------------------------------------------------| | cargo | Job title. (String) | | modo | Mode of offering. (String) | | fechaPublicado | Date published. (Date) | | fechaFin | End date. (Date) | | plazas | Number of openings. (Integer) | | experiencia | Experience required. (String) | | capacitacion | Training needed. (String) | | jornadas | Working hours. (String) | | remuneracion | Remuneration involved. (String) | | nivelInstruccion | Level of education required. (String) | | areaEstudios | Areas of studies demanded. (String) | | ciudad | City hosting the position. (String) | | parroquia | Parishes known to have jobs available. (String) | | sector | Private/public sector jobs. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

This dataset was created by Harry Field and contains the labelled images for capturing the game state of a draughts/checkers 8x8 board.

This was a fun project to develop a mobile draughts applciation enabling users to interact with draughts-based software via their mobile device's camera.

The data captured consists of: * White Pieces * White Kings * Black Pieces * Black Kings * Bottom left corner square * Top left corner square * Top right corner square * Bottom right corner square

Corner squares are captured so the board locations of the detected pieces can be estimated.

https://github.com/ShippingTycoon/roboflow-draughts/blob/main/PXL_20210603_093949805_jpg.rf.30e2a64a0a646e8ea8e121727cf0f1ee.jpg?raw=true" alt="Results of Yolov5 model after training with this dataset">

From this data, the locations of other squares can be estimated and game state can be captured. The image below shows the data of a different board configuration being captured. Blue circles refer to squares, numbers refer to square index and the coloured circles refer to pieces. https://github.com/ShippingTycoon/roboflow-draughts/blob/main/pieces.png?raw=true" alt="">

Once game state is captured, integration with other software becomes possible. In this example, I created a simple move suggestion mobile applciation seen working here.

The developed application is a proof of concept and is not available to the public. Further development is required in training the model accross multiple draughts boards and implementing features to add vlaue to the physical draughts game.

The dataset consists of 759 images and was trained using Yolov5 with a 70/20/10 split.

The output of Yolov5 was parsed and filtered to correct for duplicated/overlapping detections before game state could be determined.

I hope you find this dataset useful and if you have any questions feel free to drop me a message on LinkedIn as per the link above.

This dataset contains information about rental properties in São Paulo, Brazil. The data was extracted from the QuintoAndar platform using web scraping techniques on May 1st, 2023. The dataset includes several useful pieces of information, such as the property's address, district, area, number of bedrooms, garage availability, monthly rent, type of property, and total cost.

The dataset can be used for various analyses, such as understanding the average rental prices in different districts or identifying the most common types of properties in certain areas. Additionally, the data can be used to train machine learning models that predict rental prices based on property characteristics.

It's important to note that since the data was obtained through web scraping techniques, there may be errors or incomplete information. Therefore, it's recommended that users of the dataset verify the information before using it for analysis or model training. Nevertheless, this dataset is a valuable source of information for anyone interested in analyzing the real estate market in São Paulo.

Link of the webscrapping project: QuintoAndar-WebScrapping

GitHub LinkedIn

Este conjunto de dados contém informações sobre aluguel de imóveis em São Paulo, Brasil. Os dados foram extraídos da plataforma QuintoAndar usando técnicas de web scraping em 1º de maio de 2023. O conjunto de dados inclui várias informações úteis, como o endereço do imóvel, o bairro, a área, o número de quartos, a disponibilidade de garagem, o preço mensal do aluguel, o tipo de imóvel e o custo total.

O conjunto de dados pode ser usado para diversas análises, como entender os preços médios de aluguel em diferentes bairros ou identificar os tipos de imóveis mais comuns em determinadas áreas. Além disso, os dados podem ser usados para treinar modelos de aprendizado de máquina que prevejam os preços de aluguel com base nas características do imóvel.

É importante observar que, como os dados foram obtidos por meio de técnicas de web scraping, pode haver erros ou informações incompletas. Portanto, é recomendável que os usuários do conjunto de dados verifiquem as informações antes de usá-las para análises ou treinamento de modelos. No entanto, este conjunto de dados é uma fonte valiosa de informações para quem está interessado em analisar o mercado imobiliário em São Paulo.

Link do projeto de WebScrapping: QuintoAndar-WebScrapping

GitHub LinkedIn

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

Face mask segmentation mask dataset for more efficient detection and localization.

• 222 images, 222 masks.
• Original images are in "images" folder.
• Segmentation masks with the same name as the images are in "masks" images.
• If more masks are present on the image, each has a unique color in order to instance each of them (max 3 masks per image):
  • Mask 1: #ffffff
  • Mask 2: #fdeded
  • Mask 3: #fcdbdb

Contact: https://www.linkedin.com/in/pericnikola/

• Big thanks to all users on Pexels and Unsplash - find their user names in the names of the images.

• Why I made this? I was bored.

• No animals were hurt during the creation of this dataset (dataset was presented to them and they had absolutely no idea what to do with it).

This dataset provides comprehensive real-time job listing data aggregated from multiple job boards and company websites. It includes detailed job information such as titles, descriptions, requirements, salaries, locations, and company details. The data is continuously updated to provide the most current job opportunities. Users can leverage this dataset for job search applications, market research, salary analysis, and career development tools. Whether you're building a job search platform, conducting employment market analysis, or developing career guidance tools, this dataset provides current and reliable job market data. The dataset is delivered in a JSON format via REST API.

Longitudinal study with a representative sample of adult Dutch online users on the effects of social media use on various indicators of social capital and well-being. Social media use covers the use of Facebook or other platforms mainly used for private purposes, LinkedIn or other platforms used for professional purposes and Twitter or other microblogging services.

Context

The dataset was created by my team during the NASA Space Apps Challenge in 2018, the goal was using the dataset to develop a model that can recognize the images with fire. If you seek more info about the Context or the challenge, then you can visit Our team page.

Content

Data was collected to train a model to distinguish between the images that contain fire (fire images) and regular images (non-fire images), so the whole problem was binary classification.

Data is divided into 2 folders, fire_images folder contains 755 outdoor-fire images some of them contains heavy smoke, the other one is non-fire_images which contain 244 nature images (eg: forest, tree, grass, river, people, foggy forest, lake, animal, road, and waterfall).

Hint: Data is skewed, which means the 2 classes(folders) doesn't have an equal number of samples, so make sure that you have a validation set with an equally-sized number of images per class (eg: 40 images of both fire and non-fire classes).

Acknowledgements

Team Members: 1-Ahmed Gamaleldin: https://www.linkedin.com/in/ahmedgamal1496/ 2-Ahmed Atef: https://www.linkedin.com/in/ahmed-atef-a081aa141/ 3-Heba Saker: https://www.linkedin.com/in/heba-sakr/ 4-Ahmed Shaheen: https://www.linkedin.com/in/ahmed-a-shaheen/