In the U.S. public companies, certain insiders and broker-dealers are required to regularly file with the SEC. The SEC makes this data available online for anybody to view and use via their Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database. The SEC updates this data every quarter going back to January, 2009. To aid analysis a quick summary view of the data has been created that is not available in the original dataset. The quick summary view pulls together signals into a single table that otherwise would have to be joined from multiple tables and enables a more streamlined user experience. 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.Learn more

This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address. Contact: Lynn Flaaen-Hanna, Development Services Specialist Contact E-mail Link: Map that Lets You Explore and Export Address Data Data Source: The initial dataset was created by combining several datasets and then reviewing the information to remove duplicates and identify errors. This published dataset is the system of record for Tempe addresses going forward, with the address information being created and maintained by The Building Safety Division of Community Development.Data Source Type: ESRI ArcGIS Enterprise GeodatabasePreparation Method: N/APublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

A computer program for accessing and visualization of thermodynamic and transport property data for chemical compounds and mixtures available at the TRC/NIST ThermoML archive https://data.nist.gov/od/id/mds2-2422. The data collection contains 2.2 million distinct property values (the whole archive can also be downloaded from that link, stored, and accessed from a local storage). The program has been compiled for Windows OS and tested under Windows 10. The operation procedures are described in the embedded Help.

Envestnet®| Yodlee®'s Consumer Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

About

Data from the State of California. From website:

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

Openness

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

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

Comprehensive dataset of 6 Public libraries in Free municipal consortium of Enna, Italy as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Graph and download economic data for Federal Debt: Total Public Debt (GFDEBTN) from Q1 1966 to Q1 2025 about public, debt, federal, government, and USA.

Chicago sites that offer free or affordable technology resources and services, like computers with Internet access, Wi-Fi hotspots and technology training. Call or visit the organization's website before going to the location. For more information, visit http://locations.weconnectchicago.org/.

Download complete UK quango datasets including budgets, officials, historical data 1900-2025. Free access to government data in multiple formats.

These dataset, dashboard and app tools identify what city-owned sites have free public Wi-Fi available. Between 2025 and 2026, all of these sites are slated to receive fiber install and Meraki Wi-Fi routers that greatly increase the speed and quality of available Wi-Fi. The dataset and dashboard notes what sites have had this install completed, among other amenities available at each site.

Replication Data for: Public Opinion, Free Markets, and Business Involvement in Politics

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

This data was collected by the Geological Survey Ireland, the Department of Culture, Heritage and the Gaeltacht, the Discovery Programme, the Heritage Council, Transport Infrastructure Ireland, New York University, the Office of Public Works and Westmeath County Council. All data formats are provided as GeoTIFF rasters but are at different resolutions. Data resolution varies depending on survey requirements. Resolutions for each organisation are as follows: GSI – 1m DCHG/DP/HC - 0.13m, 0.14m, 1m NY – 1m TII – 2m OPW – 2m WMCC - 0.25m Both a DTM and DSM are raster data. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns. The grid cell size varies depending on the organisation that collected it. GSI data has a grid cell size of 1 meter by 1 meter. This means that each cell (pixel) represents an area of 1 meter squared.

About this dataset

Context

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

Key observations

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

Content

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

Scope of gender :

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

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Gratis is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Gratis total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gratis Population by Race & Ethnicity. You can refer the same here

UniCourt’s PACER API provides you with a real-time interface and bulk access to the entire PACER database of civil and criminal federal court data from U.S. District Courts, Bankruptcy Courts, Courts of Appeal, and more.

Our PACER API fully integrates with PACER data so you can streamline pulling the court data you need to automate your internal workflows while saving money on outrageous fees.

Leave behind PACER’s outdated search tools for a modern case search with the precision you need.

Search Smarter and Curb Costs

• With UniCourt’s PACER API you can download the court data you need and lower your PACER costs by pulling data smarter. • When you search for court cases using our API for PACER, your search results show (1) which cases are already available in UniCourt, (2) when they were added to our database and last updated, and (3) the UniCourt Case IDs for each case so you can easily pull any additional data you need. • Don’t pay for PACER data when you don’t have to and stop wasting time logging into PACER everyday when there’s a smarter way to search.

Bulk Access to PACER Data and Documents

• Get the complete historical data set you need for criminal and civil PACER data seamlessly integrated with all your internal applications and client facing solutions. • Leverage UniCourt's extensive free repository of case metadata, docket entries, and court documents to get bulk API access to PACER data without breaking your budget. • Get bulk court data from PACER that has been normalized with our artificial intelligence and enriched with other public data sets like attorney bar data, Secretary of State data, and judicial data.

Track PACER Litigation at Scale

• Combine the power of UniCourt’s PACER API with our Court Data API to track your litigation at scale. • Automatically track PACER cases with ease and receive alerts when new docket updates are available so you never miss a federal court filing. • Save money on outrageous PACER fees by leveraging the sophisticated algorithms we’ve developed to intelligently track court cases in bulk without incurring over-the-top fees.

Data, lately, has received a lot of attention from various circles such as government officials, the community, businesses, law enforcement, and also civil society. The reason is actually very simple, because credible data is the key to the quality of development and good governance. Public policy, public services, law enforcement, government performance monitoring, and business opportunities all require credible data. Unfortunately, in practice, data is still often not managed seriously. There are still many cases where there are data that not only have various versions, but often also contradict each other. The One Data Initiative, or commonly called One Data Indonesia, is one of the Indonesian government's initiatives that tries to fix problems in the implementation and management of government data. The development of this initiative is also overseen by the Open Government Indonesia Action Plan. Along with welcoming International Open Data Day which falls on March 4, 2017, As an initiative that is being promoted by the central government regarding data governance reform within the Indonesian government, One Data Indonesia is an initiative that is expected to help the integration of planning, implementation, monitoring, evaluation, and control of development between the central government and the regions, and at the next level is the disclosure of government data that can be used by the community. In addition, the implementation of One Data is also expected to accelerate the implementation of the Electronic-Based Government System (SPBE/E-government) which is being prepared, both in terms of regulations and operational stages, by a number of related agencies involving the Presidential Staff Office, the Ministry of National Development Planning/Bappenas, the Ministry of PAN & RB, the Ministry of Communication and Informatics, and the State Administration Institution. In principle, Satu Data Indonesia strives to encourage better government data governance. Good data governance is highly dependent on quality and consistency in data management. For this reason, Satu Data Indonesia defines good data governance into three main principles. The three main principles that will be encouraged through the One Data Indonesia policy are: (i) a single standard data standard, (ii) one standard metadata, and (iii) data interoperability. With the application of these principles, it is hoped that the One Data policy will be able to realize an accountable, accurate, integrated, up-to-date, and open data management system. These three principles will be implemented through the Presidential Regulation on One Data that we are currently compiling. Currently, the initiation of One Data has reached the finalization stage of the preparation of the Presidential Regulation on One Data Indonesia. If it has been passed, the Presidential Regulation on One Data Indonesia is expected to stimulate efforts to improve government data governance in Indonesia. This means that the use of data will be more structured and will improve the quality of policies and public services in Indonesia as well. In addition, to ensure the smooth implementation later, pilot activities are also being carried out in seven ministries, institutions and also seven local governments. At the central government level, the One Data Initiative is currently being pilotedn in several Ministries, including the Ministry of National Development Planning/Bappenas and the Ministry of Maritime Affairs and Fisheries. At the local government level, the implementation of One Data Indonesia has been piloted in several pilot areas, including DKI Jakarta Province, Demak Regency, Bojonegoro Regency, Semarang City, Banda Aceh City, Mojokerto City, and Pontianak City. There are actually several priorities that can be resolved in order to facilitate the implementation of One Data Indonesia. First, harmonization between the role of Presidential Regulations (Perpres) and Regional Regulations (Perda). Second, finding a clear business process for the implementation of One Data Indonesia both in the Ministries/Institutions of a region. Third, the integration of other Ministries/Institutions data portals in one portal, to facilitate access and use of data by the public. There are several things that are still challenges for the implementation of One Data in Indonesia. One of them is the process of ratifying the Presidential Regulation on One Data Indonesia which requires coordination with various stakeholders which takes a long time. In addition, there are several policies related to statistics, which need to be adjusted to the context of data governance reform within the current government. One of them is Law No. 16 of 1997 concerning Statistics which still defines data only in the form of numbers, so that other forms of data such as spatial data are not included in it. In addition, the reluctance of many parties to integrate cross-sectoral data management within the government is also an obstacle. Where there are various types of data in each sector, but without any integration with other sectors. Finally, one of the things that hinders the implementation of this initiative according to the One Data Indonesia team is the existence of non-tax state revenue (PNPB) collected from data requests. This severely limits access to open data that should be easily and free of charge accessible to the public.

Retail sales of specific packaged goods (coffee, laundry detergent, shampoo) broken out by U.S. region, brand, size, packaging material, UPC, and price.

Comprehensive dataset containing 1 verified Department of Public Safety businesses in Free municipal consortium of Syracuse, Italy with complete contact information, ratings, reviews, and location data.

The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about our nation and its people by contacting over 3.5 million households across the country. The resulting data provides incredibly detailed demographic information across the US aggregated at various geographic levels which helps determine how more than $675 billion in federal and state funding are distributed each year. Businesses use ACS data to inform strategic decision-making. ACS data can be used as a component of market research, provide information about concentrations of potential employees with a specific education or occupation, and which communities could be good places to build offices or facilities. For example, someone scouting a new location for an assisted-living center might look for an area with a large proportion of seniors and a large proportion of people employed in nursing occupations. Through the ACS, we know more about jobs and occupations, educational attainment, veterans, whether people own or rent their homes, and other topics. Public officials, planners, and entrepreneurs use this information to assess the past and plan the future. For more information, see the Census Bureau's ACS Information Guide . This public dataset is hosted in Google BigQuery as part of the Google Cloud Public Datasets Program , with Carto providing cleaning and onboarding support. It 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 .

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D