Employee-Reviews Description Context Over 67k employee reviews for Google, Amazon, Facebook, Apple, and Microsoft

Content This dataset contains employee reviews separated into the following categories:

Index: index Company: Company name Location : This dataset is global, as such it may include the country's name in parenthesis [i.e "Toronto, ON(Canada)"]. However, if the location is in the USA then it will only include the city and state[i.e "Los Angeles, CA" ] Date Posted: in the following format MM DD, YYYY Job-Title: This string will also include whether the reviewer is a 'Current' or 'Former' Employee at the time of the review Summary: Short summary of employee review Pros: Pros Cons: Cons Overall Rating: 1-5 Work/Life Balance Rating: 1-5 Culture and Values Rating: 1-5 Career Opportunities Rating: 1-5 Comp & Benefits Rating: 1-5 Senior Management Rating: 1-5 Helpful Review Count: A count of how many people found the review to be helpful Link to Review : This will provide you with a direct link to the page that contains the review. However it is likely that this link will be outdated NOTE: 'none' is placed in all cells where no data value was found.

Acknowledgements This data was scraped from Glassdoor

3 Inspiration To inspire people to create ML models to search for meaningful trends within this dataset

This dataset contains current and historical demographic data on Google's workforce since the company began publishing diversity data in 2014. It includes data collected for government reporting and voluntary employee self-identification globally relating to hiring, retention, and representation categorized by race, gender, sexual orientation, gender identity, disability status, and military status. In some instances, the data is limited due to various government policies around the world and the desire to protect Googler confidentiality. All data in this dataset will be updated yearly upon publication of Google’s Diversity Annual Report . Google uses this data to inform its diversity, equity, and inclusion work. More information on our methodology can be found in the Diversity Annual Report. 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 .

Introduction

This case study is my capstone project for the Google Data Analytics Certification. This is my first notebook, so should I incorrectly credit or display this data, please add a comment and I will make adjustments. I have enjoyed this challenge, and I look forward to growing and developing as a data analyst.

Thank you to Cedric Aubin for introducing me to this dataset here.

Scenario

I am a junior data analyst in an HR department of a multi-national company. I have been given data on AI/ML salaries from countries around the world (over a three year period from 2020 to 2022). The company is having difficulty balancing the high cost of tech talent with the highly competitive market for talent. The business questions are, what is the going rate for ML/AI employees in the US where our highest salaries are paid, and which countries might provide the same workforce at a reduced cost to help manage the growing salary overhead?

I will analyze the data to answer the questions with a particular focus on the United States where salaries are amongst the highest and there is a highly competitive market for tech talent. I will also analyze the data to provide answers to the question of which countries might be worth investing in to establish a network of employees at a potentially cheaper rate.

Ask

Study the data on salaries and provide answers to the following more detailed questions:

1) What are the average salaries per job title in the US?

Reason: With current salaries (per job title) we can target our candidate packages to be enticing and help beat the competition in the war for talent in the US, our country source for most employees.

2) What countries pay the lowest salaries on average so that we can consider establishing a presence in those countries, or hiring there to reduce global salary overhead?

Reason: While considering our global hiring strategy, we would like to consider setting up hubs around the world that will offer tech talent at a reduced cost.

Prepare

The original data was collected and made available at ai-jobs.net..

Ai-jobs collects salary information from professionals around the world in the AI/ML and Big Data space and makes it publicly available for anyone to use, share and play with. The data is being updated regularly with new data coming in, usually on a weekly basis. The primary goal is to have data that can provide better guidance in regards to what's being paid globally.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10824815%2F856a5811338d692669b8c86d4ef2ccad%2FThis%20one%20too.png?generation=1671140523190325&alt=media" alt="">

Process

I decided to use SQL for my analysis and Tableau for visualization of the key findings.

In total, the complete data is held in one table "salaries" and has 1,332 rows. For my analysis, I removed a number of fields that weren't pertinent e.g. employment_type and experience_level. Instead, I focused on employee_residence, salary_in_usd, work_year and job_title.

Data was cleaned and checks were made to ensure there were no duplicates or other errors:

SELECT DISTINCT job_title FROM salaries

• I discovered that I have 64 job titles to work with and no duplicates or errors. Each job title was listed from this query.

SELECT DISTINCT work_year FROM salaries

• This query checked that the data only spans 2020, 2021, and 2022

SELECT DISTINCT salary_in_usd
FROM salaries 
ORDER BY salary_in_usd

• This yielded 574 results for all salaries from the minimum to the maximum in all job titles. The lowest salary in US dollars per year is 2,324 USD, and the highest is 600,000 USD.

SELECT DISTINCT employee_residence FROM salaries

• There are 64 distinct employee countries of residence in the data. The abbreviations are from the ISO 3166 standard country codes. Here's an example of some of the results:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10824815%2F9ce5e3fa93709fa4e72257ea93264f57%2FScreen%20Shot%202022-12-01%20at%208.28.53%20AM.png?generation=1669904953100886&alt=media" alt="">

When considering averages per country in all job categories, I decided to remove any employee residence results with ****less than 10**** entries. This removes outliers or countries with incomplete amounts of data. This is the query I used:

select employee_residence, COUNT (*)
FROM 
salaries 
GROUP BY employee_residence
having count(*) > 10

Analyze & Share

As expected, the country with the highest salaries for 2020, 2021 and 2022 is the United States, by a large degree. The closest 3 countries are ...

US Covid-19 Cases, Deaths and Mobility by State/County

Analyzing the Impact of the Pandemic on Low-Income Populations

By Liz Friedman [source]

About this dataset

Welcome to the Opportunity Insights Economic Tracker! Our goal is to provide a comprehensive, real-time look into how COVID-19 and stabilization policies are affecting the US economy. To do this, we have compiled a wide array of data points on spending and employment, gathered from several sources.

This dataset includes daily/weekly/monthly information at the state/county/city level for eight types of data: Google Mobility; Low-Income Employment and Earnings; UI Claims; Womply Merchants and Revenue; as well as weekly Math Learning from Zearn. Additionally, three files- Accounting for Geoids-State/County/City provide crosswalks between geographic areas that can be merged with other files having shared geographical levels.

Our goal here is to enable data users around the world to follow economic conditions in the US during this tumultuous period with maximum clarity and precision. We make all our datasets freely available so if you use them we kindly ask you attribute our work by linking or citing both our accompanying paper as well as this Economic Tracker at https://tracktherecoveryorg By doing so you are also agreeing to uphold our privacy & integrity standards which commit us both to individual & business confidentiality without compromising on independent nonpartisan research & policy analysis!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides US COVID-19 case and death data, as well as Google Community Mobility Reports, on the state/county level. Here is how to use this dataset:

• Understand the file structure: This dataset consists of three main files: 1) US Cases & Deaths by State/County, 2) Google Community Mobility Reports, and 3) Data from third-parties providing small business openings & revenue information and unemployment insurance claim data (Low Inc Earnings & Employment, UI Claims and Womply Merchants & Revenue).
• Select your Subset: If you are interested in particular types of data (e.g., mobility or employment), select the corresponding files from within each section based on your geographic area of interest – national, state or county level – as indicated in each filename.
• Review metadata variables: Become familiar with the provided variables so that you can select which ones you need to explore further in your analysis. For example, if analyzing mobility trends at a city level look for columns such as ‘Retailer_and_recreation_percent_change’ or ‘Transit Stations Percent Change’; if focusing on employment decline look for columns such pay or emp figures that align with industries of interest to you such as low-income earners (emp_{inclow},pay_{inclow}).
• Unify dateformatting across row values : Convert date formats into one common unit so that all entries have consistent formatting if necessary; for exampe some entries may display dates using YYYY/MM/DD notation while others may use MM//DD//YY format depending on their source datasets; make sure to review column labels carefully before converting units where needed..

• Merge datasets where applicable : Utilize GeoID crosswalks to combine multiple sets with same geographical coverageregionally covering ; example might be combining low income earnings figures with specific county settings by reference geo codes found in related documents like GeoIDs-County .
  6 . Visualise Data : Now that all the different measures have been reviewed can begin generating charts visualize findings . This process may include cleaning up raw figures normalizing across currency formats , mapping geospatial locations others ; once ready create bar graphs line charts maps other visual according aggregate output desired Insightful representations at this stage will help inform concrete policy decisions during outbreak recovery period..

  Remember to cite

Research Ideas

• Estimating the Impact of the COVID-19 Pandemic on Small Businesses - By comparing county-level Womply revenue and employment data with pre-COVID data, policymakers can gain an understanding of the economic impact that COVID has had on local small businesses.
• Analyzing Effects of Mobility Restrictions - The Google Mobility data provides insight into geographic areas where...

This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 23-Jun-2023 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman Institute.

The dataset forms part of three outputs from a project titled ‘Fostering cultures of open qualitative research’ which ran from January 2023 to June 2023:

· Fostering cultures of open qualitative research: Dataset 1 – Survey Responses · Fostering cultures of open qualitative research: Dataset 2 – Interview Transcripts · Fostering cultures of open qualitative research: Dataset 3 – Coding Book

The project was funded with £13,913.85 Research England monies held internally by the University of Sheffield - as part of their ‘Enhancing Research Cultures’ scheme 2022-2023.

The dataset aligns with ethical approval granted by the University of Sheffield School of Sociological Studies Research Ethics Committee (ref: 051118) on 23-Jan-2021.This includes due concern for participant anonymity and data management.

ORDA has full permission to store this dataset and to make it open access for public re-use on the basis that no commercial gain will be made form reuse. It has been deposited under a CC-BY-NC license.

This dataset comprises one spreadsheet with N=91 anonymised survey responses .xslx format. It includes all responses to the project survey which used Google Forms between 06-Feb-2023 and 30-May-2023. The spreadsheet can be opened with Microsoft Excel, Google Sheet, or open-source equivalents.

The survey responses include a random sample of researchers worldwide undertaking qualitative, mixed-methods, or multi-modal research.

The recruitment of respondents was initially purposive, aiming to gather responses from qualitative researchers at research-intensive (targetted Russell Group) Universities. This involved speculative emails and a call for participant on the University of Sheffield ‘Qualitative Open Research Network’ mailing list. As result, the responses include a snowball sample of scholars from elsewhere.

The spreadsheet has two tabs/sheets: one labelled ‘SurveyResponses’ contains the anonymised and tidied set of survey responses; the other, labelled ‘VariableMapping’, sets out each field/column in the ‘SurveyResponses’ tab/sheet against the original survey questions and responses it relates to.

The survey responses tab/sheet includes a field/column labelled ‘RespondentID’ (using randomly generated 16-digit alphanumeric keys) which can be used to connect survey responses to interview participants in the accompanying ‘Fostering cultures of open qualitative research: Dataset 2 – Interview transcripts’ files.

A set of survey questions gathering eligibility criteria detail and consent are not listed with in this dataset, as below. All responses provide in the dataset gained a ‘Yes’ response to all the below questions (with the exception of one question, marked with an asterisk (*) below):

· I am aged 18 or over · I have read the information and consent statement and above. · I understand how to ask questions and/or raise a query or concern about the survey. · I agree to take part in the research and for my responses to be part of an open access dataset. These will be anonymised unless I specifically ask to be named. · I understand that my participation does not create a legally binding agreement or employment relationship with the University of Sheffield · I understand that I can withdraw from the research at any time. · I assign the copyright I hold in materials generated as part of this project to The University of Sheffield. · * I am happy to be contacted after the survey to take part in an interview.

The project was undertaken by two staff: Co-investigator: Dr. Itzel San Roman Pineda ORCiD ID: 0000-0002-3785-8057 i.sanromanpineda@sheffield.ac.uk

Postdoctoral Research Assistant Principal Investigator (corresponding dataset author): Dr. Matthew Hanchard ORCiD ID: 0000-0003-2460-8638 m.s.hanchard@sheffield.ac.uk Research Associate iHuman Institute, Social Research Institutes, Faculty of Social Science

Technology companies worldwide saw a significant reduction in their workforce in 2025. One of the most recent tech layoffs was by Amazon on October 27, 2025, with ****** employees being laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of ******* employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of ******* laid-off employees in the global tech sector by the end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks, leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

Global Economic, Environmental, Health, and Social indicators Ready for Analysis

📝 Description

This comprehensive dataset merges global economic, environmental, technological, and human development indicators from 2000 to 2020. Sourced and transformed from multiple public datasets via Google BigQuery, it is designed for advanced exploratory data analysis, machine learning, policy modeling, and sustainability research.

Curated by combining and transforming data from the Google BigQuery Public Data program, this dataset offers a harmonized view of global development across more than 40 key indicators spanning over two decades (2000–2020). It supports research across multiple domains such as:

• Economic Growth
• Climate Sustainability
• Digital Transformation
• Public Health
• Human Development
• Resilience and Governance

for formulas and more details check: https://github.com/Michael-Matta1/datasets-collection/tree/main/Global%20Development

📅 Temporal Coverage

• Years: 2000–2020

• Includes calculated features:

  • years_since_2000
  • years_since_century
  • is_pandemic_period (binary indicator for pandemic periods)

🌍 Geographic Scope

• Countries: Global (identified by ISO country codes)
• Regions and Income Groups included for aggregated analysis

📊 Key Feature Groups

• Economic Indicators:

  • GDP (USD), GDP per capita
  • FDI, inflation, unemployment, economic growth index

• Environmental Indicators:

  • CO₂ emissions, renewable energy use
  • Forest area, green transition score, CO₂ intensity

• Technology & Connectivity:

  • Internet usage, mobile subscriptions
  • Digital readiness score, digital connectivity index

• Health & Education:

  • Life expectancy, child mortality
  • School enrollment, healthcare capacity, health development ratio

• Governance & Resilience:

  • Governance quality, global resilience
  • Human development composite, ecological preservation

🔍 Use Cases

• Trend analysis over time
• Country-level comparisons
• Modeling development outcomes
• Predictive analytics on sustainability or human development
• Correlation and clustering across multiple indicators

⚠️ Note on Missing Region and Income Group Data

Approximately 18% of the entries in the region and income_group columns are null. This is primarily due to the inclusion of aggregate regions (e.g., Arab World, East Asia & Pacific, Africa Eastern and Southern) and non-country classifications (e.g., Early-demographic dividend, Central Europe and the Baltics). These entries represent groups of countries with diverse income levels and geographic characteristics, making it inappropriate or misleading to assign a single region or income classification. In some cases, the data source may have intentionally left these fields blank to avoid oversimplification or due to a lack of standardized classification.

📋 Column Descriptions

• year: Year of the recorded data, representing a time series for each country.
• country_code: Unique code assigned to each country (ISO-3166 standard).
• country_name: Name of the country corresponding to the data.
• region: Geographical region of the country (e.g., Africa, Asia, Europe).
• income_group: Income classification based on Gross National Income (GNI) per capita (low, lower-middle, upper-middle, high income).
• currency_unit: Currency used in the country (e.g., USD, EUR).
• gdp_usd: Gross Domestic Product (GDP) in USD (millions or billions).
• population: Total population of the country for the given year.
• gdp_per_capita: GDP divided by population (economic output per person).
• inflation_rate: Annual rate of inflation (price level rise).
• unemployment_rate: Percentage of the labor force unemployed but seeking employment.
• fdi_pct_gdp: Foreign Direct Investment (FDI) as a percentage of GDP.
• co2_emissions_kt: Total CO₂ emissions in kilotons (kt).
• energy_use_per_capita: Energy consumption per person (kWh).
• renewable_energy_pct: Percentage of energy consumption from renewable sources.
• forest_area_pct: Percentage of total land area covered by forests.
• electricity_access_pct: Percentage of the population with access to electricity.
• life_expectancy: Average life expectancy at birth.
• child_mortality: Deaths of children under 5 per 1,000 live births.
• school_enrollment_secondary: Percentage of population enrolled in secondary education.
• health_expenditure_pct_gdp: Percentage of GDP spent on healthcare.
• hospital_beds_per_1000...

This version of the CivilComments Dataset provides access to the primary seven labels that were annotated by crowd workers, the toxicity and other tags are a value between 0 and 1 indicating the fraction of annotators that assigned these attributes to the comment text.

The other tags are only available for a fraction of the input examples. They are currently ignored for the main dataset; the CivilCommentsIdentities set includes those labels, but only consists of the subset of the data with them. The other attributes that were part of the original CivilComments release are included only in the raw data. See the Kaggle documentation for more details about the available features.

The comments in this dataset come from an archive of the Civil Comments platform, a commenting plugin for independent news sites. These public comments were created from 2015 - 2017 and appeared on approximately 50 English-language news sites across the world. When Civil Comments shut down in 2017, they chose to make the public comments available in a lasting open archive to enable future research. The original data, published on figshare, includes the public comment text, some associated metadata such as article IDs, publication IDs, timestamps and commenter-generated "civility" labels, but does not include user ids. Jigsaw extended this dataset by adding additional labels for toxicity, identity mentions, as well as covert offensiveness. This data set is an exact replica of the data released for the Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge. This dataset is released under CC0, as is the underlying comment text.

For comments that have a parent_id also in the civil comments data, the text of the previous comment is provided as the "parent_text" feature. Note that the splits were made without regard to this information, so using previous comments may leak some information. The annotators did not have access to the parent text when making the labels.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('civil_comments', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

OpenPack is an open access logistics-dataset for human activity recognition, which contains human movement and package information from 10 experienced subjects in two scenarios. The package information includes the size and number of items included in each packaging job. Human movement information is subdivided into three types of data, acceleration, physiological, and depth-sensing.

In the "Humanware laboratory" at IST Osaka University, with the supervision of industrial engineers, an experiment to mimic logistic center labor was designed. Workers with previous packaging experience performed a set of packaging tasks according to an instruction manual from a real-life logistics center. During the two experiments, subjects were recorded while performing packing operations using Lidar, Kinect, and Realsense depth sensors while also wearing 4 IMU devices and 2 Empatica E4 wearable sensors. Besides sensor data, this dataset contains timestamp information collected from the handy terminal used to register product, packet, and address label codes.

Each of the subjects performed 20 packing jobs in 5 separate sessions for a total of 100 packing jobs. Approximately 50 hours of packaging operations have been labeled into 10 global operation classes and 16 action classes for this dataset. Action classes are not unique to each operation but may only appear in one or two operations.

We are hosting an activity recognition competition, using this dataset (OpenPack v0.3.x) at a PerCom 2023 Workshop! The task is very simple: Recognize 10 work operations from the OpenPack dataset. Please visit our website and check the details. https://open-pack.github.io/challenge2022

Tutorial Dataset (Updated: 2023-03-29)

In this repository (Full Dataset), the data and label files are contained in separate files, we have received many comments that it was difficult to combine them. Therefore, for tutorial purposes, we have created a number of CSV files containing the four IMU's sensor data and the operation labels. Before downloading the "Full Dataset", please check the contents of the data in this CSV file.

Please access Google Drive from the following URL and download the files. Please be aware some operation labels have been slightly changed from those on version (v0.3.1) to correct annotation errors. We plan to integrate the data distribution location into zenodo for the next release.

Tutorial (ATR & Operation Label)

• Google Drive - Sample - Tutorial CSV.

Work is continuously being done to update and improve this dataset. When downloading and using this dataset please verify that the version is up to date with the latest release. The latest release [0.3.1] was uploaded on 17/10/2022. You can find information on how to use this dataset at https://github.com/open-pack/openpack-toolkit.

OnPoint Weather is a global weather dataset for business available for any lat/lon point and geographic area such as ZIP codes. OnPoint Weather provides a continuum of hourly and daily weather from the year 2000 to current time and a forward forecast of 45 days. OnPoint Climatology provides hourly and daily weather statistics which can be used to determine ‘departures from normal’ and to provide climatological guidance of expected weather for any location at any point in time. The OnPoint Climatology provides weather statistics such as means, standard deviations and frequency of occurrence. Weather has a significant impact on businesses and accounts for hundreds of billions in lost revenue annually. OnPoint Weather allows businesses to quantify weather impacts and develop strategies to optimize for weather to improve business performance. Examples of Usage Quantify the impact of weather on sales across diverse locations and times of the year Understand how supply chains are impacted by weather Understand how employee’s attendance and performance are impacted by weather Understand how weather influences foot traffic at malls, stores and restaurants OnPoint Weather is available through Google Cloud Platform’s Commercial Dataset Program and can be easily integrated with other Google Cloud Platform Services to quickly reveal and quantify weather impacts on business. Weather Source provides a full range of support services from answering quick questions to consulting and building custom solutions. 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 瞭解詳情

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

Cash-and-Equivalents Time Series for Salesforce.com Inc. Salesforce, Inc. provides customer relationship management (CRM) technology that connects companies and customers together worldwide. The company offers Agentforce, an agentic layer of the salesforce platform; Data Cloud, a data engine; Industries AI for creating industry-specific AI agents with Agentforce ; Salesforce Starter, a suite of solution for small and medium-size business; Slack, a workplace communication and productivity platform; Tableau, an end-to-end analytics solution for range of enterprise use cases and intelligent analytics with AI models, spot trends, predict outcomes, timely recommendations, and take action from any device; and integration and analytics solutions, as well as Agentforce Command Center, an observability solution to manage, track, and scale AI agent activity. It also provides marketing platform; commerce services, which empowers shopping experience across various customer touchpoint; and field service solution that enables companies to connect service agents, dispatchers, and mobile employees through one centralized platform to schedule and dispatch work, as well as track and manage jobs. The company has a strategic partnership with Google to integrate Agentforce 360 with Google Workspace for sales and IT service, which expands the Salesforce Gemini integration. Salesforce, Inc. was incorporated in 1999 and is headquartered in San Francisco, California.

Retained-Earnings Time Series for Salesforce.com Inc. Salesforce, Inc. provides customer relationship management (CRM) technology that connects companies and customers together worldwide. The company offers Agentforce, an agentic layer of the salesforce platform; Data Cloud, a data engine; Industries AI for creating industry-specific AI agents with Agentforce ; Salesforce Starter, a suite of solution for small and medium-size business; Slack, a workplace communication and productivity platform; Tableau, an end-to-end analytics solution for range of enterprise use cases and intelligent analytics with AI models, spot trends, predict outcomes, timely recommendations, and take action from any device; and integration and analytics solutions, as well as Agentforce Command Center, an observability solution to manage, track, and scale AI agent activity. It also provides marketing platform; commerce services, which empowers shopping experience across various customer touchpoint; and field service solution that enables companies to connect service agents, dispatchers, and mobile employees through one centralized platform to schedule and dispatch work, as well as track and manage jobs. The company has a strategic partnership with Google to integrate Agentforce 360 with Google Workspace for sales and IT service, which expands the Salesforce Gemini integration. Salesforce, Inc. was incorporated in 1999 and is headquartered in San Francisco, California.

Geospatial Services Land management within the US Forest Service and on the 900,000+ acre Monongahela National Forest (NF) is driven by a wide mix of resource and societal demands that prove a challenge in fulfilling the Forest Service’s mission of “Caring for the Land and Serving the People.” Programmatically, the 2006 Land and Resource Management Plan guide natural resource management activities on lands administered by the Monongahela National Forest. The Forest Plan describes management direction and practices, resource protection methods and monitoring, desired resource conditions, and the availability and suitability of lands for resource management. Technology enables staff to address these land management issues and Forest Plan direction by using a science-based approach to facilitate effective decisions. Monongahela NF geospatial services, using enabling-technologies, incorporate key tools such as Environmental Systems Research Institute’s ArcGIS desktop suite and Trimble’s global positioning system (GPS) units to meet program and Forest needs. Geospatial Datasets The Forest has a broad set of geospatial datasets that capture geographic features across the eastern West Virginia landscape. Many of these datasets are available to the public through our download site. Selected geospatial data that encompass the Monongahela National Forest are available for download from this page. A link to the FGDC-compliant metadata is provided for each dataset. All data are in zipped format (or available from the specified source), in one of two spatial data formats, and in the following coordinate system: Coordinate System: Universal Transverse Mercator Zone: 17 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Map files – All map files are in pdf format. These maps illustrate the correlated geospatial data. All maps are under 1 MB unless otherwise noted. Metadata file – This FGDC-compliant metadata file contains information pertaining to the specific geospatial dataset. Shapefile – This downloadable zipped file is in ESRI’s shapefile format. KML file – This downloadable zipped file is in Google Earth’s KML format. Resources in this dataset:Resource Title: Monongahela National Forest Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/mnf/landmanagement/gis/?cid=stelprdb5108081 Selected geospatial data that encompass the Monongahela National Forest are available for download from this page.

Free-Cash-Flow-To-The-Firm Time Series for Salesforce.com Inc. Salesforce, Inc. provides customer relationship management (CRM) technology that connects companies and customers together worldwide. The company offers Agentforce, an agentic layer of the salesforce platform; Data Cloud, a data engine; Industries AI for creating industry-specific AI agents with Agentforce ; Salesforce Starter, a suite of solution for small and medium-size business; Slack, a workplace communication and productivity platform; Tableau, an end-to-end analytics solution for range of enterprise use cases and intelligent analytics with AI models, spot trends, predict outcomes, timely recommendations, and take action from any device; and integration and analytics solutions, as well as Agentforce Command Center, an observability solution to manage, track, and scale AI agent activity. It also provides marketing platform; commerce services, which empowers shopping experience across various customer touchpoint; and field service solution that enables companies to connect service agents, dispatchers, and mobile employees through one centralized platform to schedule and dispatch work, as well as track and manage jobs. The company has a strategic partnership with Google to integrate Agentforce 360 with Google Workspace for sales and IT service, which expands the Salesforce Gemini integration. Salesforce, Inc. was incorporated in 1999 and is headquartered in San Francisco, California.

As of April 2024, it was found that men between the ages of 25 and 34 years made up Facebook largest audience, accounting for 18.4 percent of global users. Additionally, Facebook's second largest audience base could be found with men aged 18 to 24 years.

              Facebook connects the world

              Founded in 2004 and going public in 2012, Facebook is one of the biggest internet companies in the world with influence that goes beyond social media. It is widely considered as one of the Big Four tech companies, along with Google, Apple, and Amazon (all together known under the acronym GAFA). Facebook is the most popular social network worldwide and the company also owns three other billion-user properties: mobile messaging apps WhatsApp and Facebook Messenger,
              as well as photo-sharing app Instagram. Facebook usersThe vast majority of Facebook users connect to the social network via mobile devices. This is unsurprising, as Facebook has many users in mobile-first online markets. Currently, India ranks first in terms of Facebook audience size with 378 million users. The United States, Brazil, and Indonesia also all have more than 100 million Facebook users each.

Total-Revenue Time Series for Salesforce.com Inc. Salesforce, Inc. provides customer relationship management (CRM) technology that connects companies and customers together worldwide. The company offers Agentforce, an agentic layer of the salesforce platform; Data Cloud, a data engine; Industries AI for creating industry-specific AI agents with Agentforce ; Salesforce Starter, a suite of solution for small and medium-size business; Slack, a workplace communication and productivity platform; Tableau, an end-to-end analytics solution for range of enterprise use cases and intelligent analytics with AI models, spot trends, predict outcomes, timely recommendations, and take action from any device; and integration and analytics solutions, as well as Agentforce Command Center, an observability solution to manage, track, and scale AI agent activity. It also provides marketing platform; commerce services, which empowers shopping experience across various customer touchpoint; and field service solution that enables companies to connect service agents, dispatchers, and mobile employees through one centralized platform to schedule and dispatch work, as well as track and manage jobs. The company has a strategic partnership with Google to integrate Agentforce 360 with Google Workspace for sales and IT service, which expands the Salesforce Gemini integration. Salesforce, Inc. was incorporated in 1999 and is headquartered in San Francisco, California.

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

LinkedIn Company Data for Company Analysis, Valuation & Portfolio Strategy LinkedIn company data is one of the most powerful forms of alternative data for understanding company behavior, firmographics, business dynamics, and real-time hiring signals. Canaria’s enriched LinkedIn company data provides detailed company profiles, including hiring activity, job postings, employee trends, headquarters and branch locations, and verified metadata from Google Maps. This LinkedIn corporate data is updated weekly and optimized for use in company analysis, startup scouting, private company valuation, and investment monitoring. It supports BI dashboards, risk models, CRM enrichment, and portfolio strategy.

Use Cases: What Problems This LinkedIn Data Solves Our LinkedIn company insights transform opaque business landscapes into structured, analyzable data. Whether you’re conducting M&A due diligence, tracking high-growth companies, or benchmarking performance, this dataset empowers fast, confident decisions.

Company Analysis • Identify a company’s size, industry classification, and headcount signals using LinkedIn firmographic data • Analyze social presence through LinkedIn follower metrics and employee engagement • Understand geographic expansion through branch locations and hiring distribution • Benchmark companies using LinkedIn profile activity and job posting history • Monitor business changes with real-time LinkedIn updates

Company Valuation & Financial Benchmarking • Feed LinkedIn-based firmographics into comps and financial models • Use hiring velocity from LinkedIn job data as a proxy for business growth • Strengthen private market intelligence with verified non-financial signals • Validate scale, structure, and presence via LinkedIn and Google Maps footprint

Company Risk Analysis • Detect red flags using hiring freezes or drop in profile activity • Spot market shifts through location downsizing or organizational changes • Identify distressed companies with decreased LinkedIn job posting frequency • Compare stated presence vs. active behavior to identify risk anomalies

Business Intelligence (BI) & Strategic Planning • Segment companies by industry, headcount, growth behavior, and hiring activity • Build BI dashboards integrating LinkedIn job trends and firmographic segmentation • Identify geographic hiring hotspots using Maps and LinkedIn signal overlays • Track job creation, title distribution, and skill demand in near real-time • Export filtered LinkedIn corporate data into CRMs, analytics tools, and lead scoring systems

Portfolio Management & Investment Monitoring • Enhance portfolio tracking with LinkedIn hiring data and firmographic enrichment • Spot hiring surges, geographic expansions, or restructuring in real-time • Correlate LinkedIn growth indicators with strategic outcomes • Analyze competitors and targets using historical and real-time LinkedIn data • Generate alerts for high-impact company changes in your portfolio universe

What Makes This LinkedIn Company Data Unique

Includes Real-Time Hiring Signals • Gain visibility into which companies are hiring, at what scale, and for which roles using enriched LinkedIn job data

Verified Location Intelligence • Confirm branch and HQ locations with Google Maps coordinates and public company metadata

Weekly Updates • Stay ahead of the market with fresh, continuously updated LinkedIn company insights

Clean & Analysis-Ready Format • Structured, deduplicated, and taxonomy-mapped data that integrates with CRMs, BI platforms, and investment models

Who Benefits from LinkedIn Company Data • Hedge funds, VCs, and PE firms analyzing startup and private company activity • Portfolio managers and financial analysts tracking operational shifts • Market research firms modeling sector momentum and firmographics • Strategy teams calculating market size using LinkedIn company footprints • BI and analytics teams building company-level dashboards • Compliance and KYC teams enriching company identity records • Corp dev teams scouting LinkedIn acquisition targets and expansion signals

Summary Canaria’s LinkedIn company data delivers high-frequency, high-quality insights into U.S. companies, combining job posting trends, location data, and firmographic intelligence. With real-time updates and structured delivery formats, this alternative dataset enables powerful workflows across company analysis, financial modeling, investment research, market segmentation, and business strategy.

About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, and Glassdoor salary analytics. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our AI-powered pipeline is developed by a seasoned team of machine learning experts from Google, Meta, and Amazon, and by alumni of Stanford, Caltech, and Columbia ...

Net-Income-From-Continuing-Operations Time Series for Salesforce.com Inc. Salesforce, Inc. provides customer relationship management (CRM) technology that connects companies and customers together worldwide. The company offers Agentforce, an agentic layer of the salesforce platform; Data Cloud, a data engine; Industries AI for creating industry-specific AI agents with Agentforce ; Salesforce Starter, a suite of solution for small and medium-size business; Slack, a workplace communication and productivity platform; Tableau, an end-to-end analytics solution for range of enterprise use cases and intelligent analytics with AI models, spot trends, predict outcomes, timely recommendations, and take action from any device; and integration and analytics solutions, as well as Agentforce Command Center, an observability solution to manage, track, and scale AI agent activity. It also provides marketing platform; commerce services, which empowers shopping experience across various customer touchpoint; and field service solution that enables companies to connect service agents, dispatchers, and mobile employees through one centralized platform to schedule and dispatch work, as well as track and manage jobs. The company has a strategic partnership with Google to integrate Agentforce 360 with Google Workspace for sales and IT service, which expands the Salesforce Gemini integration. Salesforce, Inc. was incorporated in 1999 and is headquartered in San Francisco, California.