Context

The Open Data 500, funded by the John S. and James L. Knight Foundation (http://www.knightfoundation.org/) and conducted by the GovLab, is the first comprehensive study of U.S. companies that use open government data to generate new business and develop new products and services.

Study Goals

• Provide a basis for assessing the economic value of government open data

• Encourage the development of new open data companies

• Foster a dialogue between government and business on how government data can be made more useful

The Govlab's Approach

The Open Data 500 study is conducted by the GovLab at New York University with funding from the John S. and James L. Knight Foundation. The GovLab works to improve people’s lives by changing how we govern, using technology-enabled solutions and a collaborative, networked approach. As part of its mission, the GovLab studies how institutions can publish the data they collect as open data so that businesses, organizations, and citizens can analyze and use this information.

Company Identification

The Open Data 500 team has compiled our list of companies through (1) outreach campaigns, (2) advice from experts and professional organizations, and (3) additional research.

Outreach Campaign

• Mass email to over 3,000 contacts in the GovLab network

• Mass email to over 2,000 contacts OpenDataNow.com

• Blog posts on TheGovLab.org and OpenDataNow.com

• Social media recommendations

• Media coverage of the Open Data 500

• Attending presentations and conferences

Expert Advice

• Recommendations from government and non-governmental organizations

• Guidance and feedback from Open Data 500 advisors

Research

• Companies identified for the book, Open Data Now

• Companies using datasets from Data.gov

• Directory of open data companies developed by Deloitte

• Online Open Data Userbase created by Socrata

• General research from publicly available sources

What The Study Is Not

The Open Data 500 is not a rating or ranking of companies. It covers companies of different sizes and categories, using various kinds of data.

The Open Data 500 is not a competition, but an attempt to give a broad, inclusive view of the field.

The Open Data 500 study also does not provide a random sample for definitive statistical analysis. Since this is the first thorough scan of companies in the field, it is not yet possible to determine the exact landscape of open data companies.

Content

This is a slightly modified version of the openly available data set 'Open Data 500 Companies - Full List' provided by the OD500 Global Network ('http://www.opendata500.com/').

I am using this dataset for a kernel project series which will be investigating the value and worth of a company's choice of logo design. Therefore, have removed columns such as "description" and "short description", as well as a few others. If you'd like the entire original dataset please download from the original source here --> 'http://www.opendata500.com/us/download/us_companies.csv'

Acknowledgements

I take no credit for the collection, production, or presentation of this data set. I am simply using it for a person research study. The creators are: http://www.opendata500.com/us/list/

The Open Data 500 Australia is part of the Australian Government's Open Data initiative, and is the first comprehensive study of Australian organisations that use open government data to generate new business, develop new products and services or create social value. The Minister for Communications, the Hon. Malcolm Turnbull MP, launched the Open Data 500 on 10 March 2015. This dataset includes responses received as of 6 July 2015.

This city boundary shapefile was extracted from Esri Data and Maps for ArcGIS 2014 - U.S. Populated Place Areas. This shapefile can be joined to 500 Cities city-level Data (GIS Friendly Format) in a geographic information system (GIS) to make city-level maps.

When the Chief Minister’s Department conducted a review of the States of Jersey’s policies and processes for claiming expenses, the Chief Minister announced that spending on travel costing more than £500 would be published regularly from the first quarter of 2017. This dataset is updated annually. The resources below that cover 6 month periods are based on travel date, not booking date. Download the 'All data' resources for a complete record of all items.

Homeland Infrastructure Foundation-Level Data (HIFLD) geospatial data sets containing information on Fortune 500 Corporate Headquarters.

Context

There's a need for information on large companies in this age of big data.

Content

In this dataset you'll find info on 500 large American corporations and their business models.

Acknowledgements

This data comes from https://data.world/bgadoci/open-data-500-companies.

We present a dataset of open source software developed mainly by enterprises rather than volunteers. This can be used to address known generalizability concerns, and, also, to perform research on open source business software development. Based on the premise that an enterprise's employees are likely to contribute to a project developed by their organization using the email account provided by it, we mine domain names associated with enterprises from open data sources as well as through white- and blacklisting, and use them through three heuristics to identify 17,252 enterprise GitHub projects. We provide these as a dataset detailing their provenance and properties. A manual evaluation of a dataset sample shows an identification accuracy of 89%. Through an exploratory data analysis we found that projects are staffed by a plurality of enterprise insiders, who appear to be pulling more than their weight, and that in a small percentage of relatively large projects development happens exclusively through enterprise insiders.

The main dataset is provided as a 17,252 record tab-separated file named enterprise_projects.txt with the following 27 fields.

• url: the project's GitHub URL
• project_id: the project's GHTorrent identifier
• sdtc: true if selected using the same domain top committers heuristic (9,006 records)
• mcpc: true if selected using the multiple committers from a valid enterprise heuristic (8,289 records)
• mcve: true if selected using the multiple committers from a probable company heuristic (7,990 records),
• star_number: number of GitHub watchers
• commit_count: number of commits
• files: number of files in current main branch
• lines: corresponding number of lines in text files
• pull_requests: number of pull requests
• most_recent_commit: date of the most recent commit
• committer_count: number of different committers
• author_count: number of different authors
• dominant_domain: the projects dominant email domain
• dominant_domain_committer_commits: number of commits made by committers whose email matches the project's dominant domain
• dominant_domain_author_commits: corresponding number for commit authors
• dominant_domain_committers: number of committers whose email matches the project's dominant domain
• dominant_domain_authors: corresponding number of commit authors
• cik: SEC's EDGAR "central index key"
• fg500: true if this is a Fortune Global 500 company (2,232 records)
• sec10k: true if the company files SEC 10-K forms (4,178 records)
• sec20f: true if the company files SEC 20-F forms (429 records)
• project_name: GitHub project name
• owner_login: GitHub project's owner login
• company_name: company name as derived from the SEC and Fortune 500 data
• owner_company: GitHub project's owner company name
• license: SPDX license identifier

The file cohost_project_details.txt provides the full set of 309,531 cohort projects that are not part of the enterprise data set, but have comparable quality attributes.

• url: the project's GitHub URL
• project_id: the project's GHTorrent identifier
• stars: number of GitHub watchers
• commit_count: number of commits

This is the complete dataset for the 500 Cities project 2016 release. This dataset includes 2013, 2014 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2013, 2014), Census Bureau 2010 census population data, and American Community Survey (ACS) 2009-2013, 2010-2014 estimates. More information about the methodology can be found at www.cdc.gov/500cities. Note: During the process of uploading the 2015 estimates, CDC found a data discrepancy in the published 500 Cities data for the 2014 city-level obesity crude prevalence estimates caused when reformatting the SAS data file to the open data format. . The small area estimation model and code were correct. This data discrepancy only affected the 2014 city-level obesity crude prevalence estimates on the Socrata open data file, the GIS-friendly data file, and the 500 Cities online application. The other obesity estimates (city-level age-adjusted and tract-level) and the Mapbooks were not affected. No other measures were affected. The correct estimates are update in this dataset on October 25, 2017.

2017, 2016. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census tract-level data in GIS-friendly format can be joined with census tract spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-Census-Tract-Boundaries/x7zy-2xmx) in a geographic information system (GIS) to produce maps of 27 measures at the census tract level. There are 7 measures (all teeth lost, dental visits, mammograms, Pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) in this 2019 release from the 2016 BRFSS that were the same as the 2018 release.

The Herschel infrared Galactic Plane Survey (Hi-GAL) covers the Galactic plane at five wavelengths from 70 to 500 microns. Hi-GAL DR1 is limited to the inner Milky Way in the longitude range +68d > l > -70d and latitude range 1d > b > -1d. The generation of the Hi-GAL photometric catalogs is discussed in detail in Molinari et al. (2016).

1) Data Introduction • The S&P 500 stock data is a tabular stock market dataset of daily stock price information (market, high price, low price, closing price, trading volume, etc.) for the last five years (the latest data is until February 2018) of all companies in the S&P 500 index.

2) Data Utilization (1) S&P 500 stock data has characteristics that: • Each row contains key stock metrics such as date, open, high, low, close, volume, and stock ticker name. • Data is provided as individual stock files and all stock integrated files, so it can be used for various analysis purposes. (2) S&P 500 stock data can be used to: • Stock Price Forecasting and Investment Strategy Development: Using historical stock price data, a variety of investment strategies and forecasting models can be developed, including time series forecasting, volatility analysis, and moving averages. • Market Trends and Corporate Comparison Analysis: It can be used to visualize stock price fluctuations across stocks, compare performance between stocks, analyze market trends, optimize portfolios, and more.

SaaS Companies Dataset

This dataset contains a list of 500 Software as a Service (SaaS) companies, providing a valuable resource for those interested in the SaaS industry. The dataset includes essential information such as the company's name, website, type of service, industry category, relevant keywords, and a brief description.

  Dataset Overview

Number of Companies: 500 Data Format: CSV Fields Included: Name: The name of the company. URL: The website URL of the company.… See the full description on the dataset page: https://huggingface.co/datasets/company-enrich/saas-companies.

2017, 2016. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level. There are 7 measures (all teeth lost, dental visits, mammograms, Pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) in this 2019 release from the 2016 BRFSS that were the same as the 2018 release.

The Herschel Multi-tiered Extragalactic Survey (HerMES) is a legacy programme (KPGT_soliver1) designed to map a set of nested fields totalling 380 sq. deg. Fields range in size from 0.01 to 20 sq. deg., using SPIRE at 250, 350 and 500 microns. These bands cover the peak of the redshifted thermal spectral energy distribution from interstellar dust and thus capture the reprocessed optical and ultraviolet radiation from star formation that has been absorbed by dust, and are critical for forming a complete multiwavelength understanding of galaxy formation and evolution.

This census tract shapefile for the 500 Cities project was extracted from the Census 2010 Tiger/Line database and modified to remove portions of census tracts that were outside of city boundaries. This shapefile can be joined with 500 Cities census tract-level Data (GIS Friendly Format) in a geographic information system (GIS) to make maps at the census tract level.

Mole Valley District Council (MVDC). Expenditure exceeding £500 - MVDC and MOVA. Published under Transparency Code 2015 and Open Government Licence (OGL). Excel format. Quarterly Publication. In accordance with LGA guidance http://schemas.opendata.esd.org.uk/Spend. Surrey, UK.

The PACS Evolutionary Probe (PEP, Lutz et al. 2011) is a Herschel guaranteed time deep extragalactic survey (KPGT_dlutz_1) targeting six among the most popular "blank fields", ten lensing clusters of galaxies, and two z ~1 clusters at wavelengths of 100, and 160 microns. PEP includes SPIRE observations of the two z ~1 clusters at wavelengths of 250, 350, and 500 microns. SPIRE coverage of all other fields is available from the HerMES survey (Oliver et al. 2010). In addition, deep SPIRE GOODS-N data are provided by the GOODS-Herschel program (Elbaz et al. 2011).PEP used the Starfinder IDL code (Diolaiti et al. 2000a,b) to blindly extract the PACS catalogs, by means of PSF-fitting. PEP adopted the "direct" noise maps and extracted PSFs directly from the observed maps (see documentation). The released catalogs include all sources above a S/N threshold of 3 sigma, derived directly from the measured fluxes and flux uncertainties. Users should keep in mind that the error estimate does not take into account confusion noise. PEP recommends to use any flux below 0.6 mJy in the green band and below 2.0 mJy in the red band with care.

The dataset contains the stock prices of all the assets comprising the S&P 500 index. Features such as Date, Open, Close, High , Low, Volume for all companies are included in the csv file. Data from 2010 to current date is present. This dataset can be effectively used for Exploratory Data Analysis, Time Series Analysis, Predictive Modelling, comparing growth of different companies and visualization.

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