This dataset lists out all software in use by NASA

This dataset lists various data sources used within the Department of Community Resources & Services for various internal and external reports. This dataset allows individuals and organizations to identify the type of data they are looking for and to which geographical level they are trying to get the data for (i.e. National, State, County, etc.). This dataset will be updated every quarter and should be utilized for research purposes

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

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

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

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

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

Classification of Mars Terrain Using Multiple Data Sources Alan Kraut1, David Wettergreen1 ABSTRACT. Images of Mars are being collected faster than they can be analyzed by planetary scientists. Automatic analysis of images would enable more rapid and more consistent image interpretation and could draft geologic maps where none yet exist. In this work we develop a method for incorporating images from multiple instruments to classify Martian terrain into multiple types. Each image is segmented into contiguous groups of similar pixels, called superpixels, with an associated vector of discriminative features. We have developed and tested several classification algorithms to associate a best class to each superpixel. These classifiers are trained using three different manual classifications with between 2 and 6 classes. Automatic classification accuracies of 50 to 80% are achieved in leave-one-out cross-validation across 20 scenes using a multi-class boosting classifier.

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?

This dataset represents the total number of Open Data Portal assets and the frequency of how often the asset is accessed. This data is collected by using Socrata Analytics. This dataset supports measure GTW.G.4 of SD23. Data Source: Socrata. Calculations: (GTW.G.4) Percentage of datasets published in the Open Data portal that are being accessed frequently (such as through a website views, API interactions, embeds or mobile views). Measure Time Period: Fiscal Year Annually Automated: No Date of Last description update: 4/1/2020 For questions please contact CTMCollaborationServices@austintexas.gov

The General Offense Crime Report Dataset includes criminal and city code violation offenses which document the scope and nature of each offense or information gathering activity. It is used to computate the Uniform Crime Report Index as reported to the Federal Bureau of Investigation and for local crime reporting purposes.Contact E-mailLink: N/AData Source: Versaterm Informix RMS \Data Source Type: Informix and/or SQL ServerPreparation Method: Preparation Method: Automated View pulled from SQL Server and published as hosted resource onto ArcGIS OnlinePublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

This is a PDF document created by the Department of Information Technology (DoIT) and the Governor's Office of Performance Improvement to assist training Maryland state employees on use of the Open Data Portal, https://opendata.maryland.gov. This document covers direct data entry, uploading Excel spreadsheets, connecting source databases, and transposing data. Please note that this tutorial is intended for use by state employees, as non-state users cannot upload datasets to the Open Data Portal.

Refined DataCo Smart Supply Chain Geospatial Dataset

Optimized for Geospatial and Big Data Analysis

This dataset is a refined and enhanced version of the original DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS dataset, specifically designed for advanced geospatial and big data analysis. It incorporates geocoded information, language translations, and cleaned data to enable applications in logistics optimization, supply chain visualization, and performance analytics.

Key Features

1. Geocoded Source and Destination Data

• Accurate latitude and longitude coordinates for both source and destination locations.
• Facilitates geospatial mapping, route analysis, and distance calculations.

2. Supplementary GeoJSON Files

• src_points.geojson: Source point geometries.
• dest_points.geojson: Destination point geometries.
• routes.geojson: Line geometries representing source-destination routes.
• These files are compatible with GIS software and geospatial libraries such as GeoPandas, Folium, and QGIS.

3. Language Translation

• Key location fields (countries, states, and cities) are translated into English for consistency and global accessibility.

4. Cleaned and Consolidated Data

• Addressed missing values, removed duplicates, and corrected erroneous entries.
• Ready-to-use dataset for analysis without additional preprocessing.

5. Routes and Points Geometry

• Enables the creation of spatial visualizations, hotspot identification, and route efficiency analyses.

Applications

1. Logistics Optimization

• Analyze transportation routes and delivery performance to improve efficiency and reduce costs.

2. Supply Chain Visualization

• Create detailed maps to visualize the global flow of goods.

3. Geospatial Modeling

• Perform proximity analysis, clustering, and geospatial regression to uncover patterns in supply chain operations.

4. Business Intelligence

• Use the dataset for KPI tracking, decision-making, and operational insights.

Dataset Content

Files Included

1. DataCoSupplyChainDatasetRefined.csv

   • The main dataset containing cleaned fields, geospatial coordinates, and English translations.

2. src_points.geojson

   • GeoJSON file containing the source points for easy visualization and analysis.

3. dest_points.geojson

   • GeoJSON file containing the destination points.

4. routes.geojson

   • GeoJSON file with LineStrings representing routes between source and destination points.

Attribution

This dataset is based on the original dataset published by Fabian Constante, Fernando Silva, and António Pereira:
Constante, Fabian; Silva, Fernando; Pereira, António (2019), “DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS”, Mendeley Data, V5, doi: 10.17632/8gx2fvg2k6.5.

Refinements include geospatial processing, translation, and additional cleaning by the uploader to enhance usability and analytical potential.

Tips for Using the Dataset

• For geospatial analysis, leverage tools like GeoPandas, QGIS, or Folium to visualize routes and points.
• Use the GeoJSON files for interactive mapping and spatial queries.
• Combine this dataset with external datasets (e.g., road networks) for enriched analytics.

This dataset is designed to empower data scientists, researchers, and business professionals to explore the intersection of geospatial intelligence and supply chain optimization.

Market research dataset covering growth of the global open-source software market, including benefits, adoption, and enterprise usage in 2025.

Dataset describes measurements of host-associated qPCR genetic markers along with other water quality parameters and precipitation from samples collected at marine, estuary, and freshwater recreational sites. Additional details provided in attached Dataset Description document. “This research dataset has been reviewed in accordance with U.S. Environmental Protection Agency (U.S. EPA), Office of Research and Development, and approved for release. Mention of brand names or vendors does not constitute an endorsement of products or services by the U.S. EPA.”

This database table consists of a preliminary source list for the Einstein Observatory's High Resolution Imager (HRI). The source list, obtained from EINLINE, the Einstein On-line Service at the Smithsonian Astrophysical Observatory (SAO), contains basic information about the sources detected with the HRI. This is a service provided by NASA HEASARC .

This is the accompanying dataset to the following paper https://www.nature.com/articles/s41597-023-01975-w

Caravan is an open community dataset of meteorological forcing data, catchment attributes, and discharge daat for catchments around the world. Additionally, Caravan provides code to derive meteorological forcing data and catchment attributes from the same data sources in the cloud, making it easy for anyone to extend Caravan to new catchments. The vision of Caravan is to provide the foundation for a truly global open source community resource that will grow over time.

If you use Caravan in your research, it would be appreciated to not only cite Caravan itself, but also the source datasets, to pay respect to the amount of work that was put into the creation of these datasets and that made Caravan possible in the first place.

All current development and additional community extensions can be found at https://github.com/kratzert/Caravan

Channel Log:

23 May 2022: Version 0.2 - Resolved a bug when renaming the LamaH gauge ids from the LamaH ids to the official gauge ids provided as "govnr" in the LamaH dataset attribute files.

24 May 2022: Version 0.3 - Fixed gaps in forcing data in some "camels" (US) basins.

15 June 2022: Version 0.4 - Fixed replacing negative CAMELS US values with NaN (-999 in CAMELS indicates missing observation).

1 December 2022: Version 0.4 - Added 4298 basins in the US, Canada and Mexico (part of HYSETS), now totalling to 6830 basins. Fixed a bug in the computation of catchment attributes that are defined as pour point properties, where sometimes the wrong HydroATLAS polygon was picked. Restructured the attribute files and added some more meta data (station name and country).

16 January 2023: Version 1.0 - Version of the official paper release. No changes in the data but added a static copy of the accompanying code of the paper. For the most up to date version, please check https://github.com/kratzert/Caravan

10 May 2023: Version 1.1 - No data change, just update data description.

17 May 2023: Version 1.2 - Updated a handful of attribute values that were affected by a bug in their derivation. See https://github.com/kratzert/Caravan/issues/22 for details.

16 April 2024: Version 1.4 - Added 9130 gauges from the original source dataset that were initially not included because of the area thresholds (i.e. basins smaller than 100sqkm or larger than 2000sqkm). Also extended the forcing period for all gauges (including the original ones) to 1950-2023. Added two different download options that include timeseries data only as either csv files (Caravan-csv.tar.xz) or netcdf files (Caravan-nc.tar.xz). Including the large basins also required an update in the earth engine code

16 Jan 2025: Version 1.5 - Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climated indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND").

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.

This table is a representation of part of the Hamburg/ROSAT All-Sky Survey (RASS) Catalog (HRC) of optical identifications of X-ray sources at high-galactic latitude, namely the list of X-ray sources. (The list of proposed and possible optical counterparts is given in the linked Browse table HRASSOPTID). The HRC includes all X-ray sources from the ROSAT Bright Source Catalog (RASS-BSC) with galactic latitude |b| >= 30 degrees and declination Dec >= 0 degrees. In this part of the sky covering ~10,000 square degrees, the RASS-BSC contains 5341 X-ray sources. For the optical identification, the HRC authors used blue Schmidt prism and direct plates taken for the northern hemisphere Hamburg Quasar Survey (HQS) which are now available in digitized form. The limiting magnitudes are 18.5 and 20, respectively. For 82% of the selected RASS-BSC, an identification could be given. For the rest, either no counterpart was visible in the error circle, or a plausible identification was not possible. With ~42%, AGN represent the largest group of X-ray emitters, ~31% have a stellar counterpart, whereas galaxies and cluster of galaxies comprise only ~4% and ~5%, respectively. In ~3% of the RASS-BSC sources, no object was visible on the blue direct plates within 40" around the X-ray source position. The catalog has been used as a source for the selection of (nearly) complete samples of the various classes of X-ray emitters. This table was produced by the HEASARC in February 2005 based on the CDS Catalog table J/A+A/406/353/x-ray.dat. This is a service provided by NASA HEASARC .

The Planck list of high-redshift source candidates (PHZ) is a list of 2151 sources located in the cleanest 26% of the sky and identified as point sources exhibiting an excess in the submillimeter compared to their environment. It has been built using the 48 months Planck data at 857, 545, 353 and 217 GHz combined with the 3 THz IRAS data, as it is described in Planck-2015-XXXIX. These sources are considered as high-z source candidates (z>1.5-2), given the very low contamination by Galactic cirrus, and their typical colour-colour ratio. A subsample of the PHZ list has already been followed-up with Herschel, and chararcterized as overdensities of red galaxies for more than 93% of the population, and as strongly lensed galaxies in 3% of the cases, as detailed in Planck-2014-XXVIII.

On October 15, 2013, Louisville Mayor Greg Fischer announced the signing of an open data policy executive order in conjunction with his compelling talk at the 2013 Code for America Summit. In nonchalant cadence, the mayor announced his support for complete information disclosure by declaring, "It's data, man."Sunlight Foundation - New Louisville Open Data Policy Insists Open By Default is the Future Open Data Annual ReportsSection 5.A. Within one year of the effective Data of this Executive Order, and thereafter no later than September 1 of each year, the Open Data Management Team shall submit to the Mayor an annual Open Data Report.The Open Data Management team (also known as the Data Governance Team is currently led by the city's Data Officer Andrew McKinney in the Office of Civic Innovation and Technology. Previously (2014-16) it was led by the Director of IT.Full Executive OrderEXECUTIVE ORDER NO. 1, SERIES 2013AN EXECUTIVE ORDERCREATING AN OPEN DATA PLAN. WHEREAS, Metro Government is the catalyst for creating a world-class city that provides its citizens with safe and vibrant neighborhoods, great jobs, a strong system of education and innovation, and a high quality of life; andWHEREAS, it should be easy to do business with Metro Government. Online government interactions mean more convenient services for citizens and businesses and online government interactions improve the cost effectiveness and accuracy of government operations; andWHEREAS, an open government also makes certain that every aspect of the built environment also has reliable digital descriptions available to citizens and entrepreneurs for deep engagement mediated by smart devices; andWHEREAS, every citizen has the right to prompt, efficient service from Metro Government; andWHEREAS, the adoption of open standards improves transparency, access to public information and improved coordination and efficiencies among Departments and partner organizations across the public, nonprofit and private sectors; andWHEREAS, by publishing structured standardized data in machine readable formats the Louisville Metro Government seeks to encourage the local software community to develop software applications and tools to collect, organize, and share public record data in new and innovative ways; andWHEREAS, in commitment to the spirit of Open Government, Louisville Metro Government will consider public information to be open by default and will proactively publish data and data containing information, consistent with the Kentucky Open Meetings and Open Records Act; andNOW, THEREFORE, BE IT PROMULGATED BY EXECUTIVE ORDER OF THE HONORABLE GREG FISCHER, MAYOR OF LOUISVILLE/JEFFERSON COUNTY METRO GOVERNMENT AS FOLLOWS:Section 1. Definitions. As used in this Executive Order, the terms below shall have the following definitions:(A) “Open Data” means any public record as defined by the Kentucky Open Records Act, which could be made available online using Open Format data, as well as best practice Open Data structures and formats when possible. Open Data is not information that is treated exempt under KRS 61.878 by Metro Government.(B) “Open Data Report” is the annual report of the Open Data Management Team, which shall (i) summarize and comment on the state of Open Data availability in Metro Government Departments from the previous year; (ii) provide a plan for the next year to improve online public access to Open Data and maintain data quality. The Open Data Management Team shall present an initial Open Data Report to the Mayor within 180 days of this Executive Order.(C) “Open Format” is any widely accepted, nonproprietary, platform-independent, machine-readable method for formatting data, which permits automated processing of such data and is accessible to external search capabilities.(D) “Open Data Portal” means the Internet site established and maintained by or on behalf of Metro Government, located at portal.louisvilleky.gov/service/data or its successor website.(E) “Open Data Management Team” means a group consisting of representatives from each Department within Metro Government and chaired by the Chief Information Officer (CIO) that is responsible for coordinating implementation of an Open Data Policy and creating the Open Data Report.(F) “Department” means any Metro Government department, office, administrative unit, commission, board, advisory committee, or other division of Metro Government within the official jurisdiction of the executive branch.Section 2. Open Data Portal.(A) The Open Data Portal shall serve as the authoritative source for Open Data provided by Metro Government(B) Any Open Data made accessible on Metro Government’s Open Data Portal shall use an Open Format.Section 3. Open Data Management Team.(A) The Chief Information Officer (CIO) of Louisville Metro Government will work with the head of each Department to identify a Data Coordinator in each Department. Data Coordinators will serve as members of an Open Data Management Team facilitated by the CIO and Metro Technology Services. The Open Data Management Team will work to establish a robust, nationally recognized, platform that addresses digital infrastructure and Open Data.(B) The Open Data Management Team will develop an Open Data management policy that will adopt prevailing Open Format standards for Open Data, and develop agreements with regional partners to publish and maintain Open Data that is open and freely available while respecting exemptions allowed by the Kentucky Open Records Act or other federal or state law.Section 4. Department Open Data Catalogue.(A) Each Department shall be responsible for creating an Open Data catalogue, which will include comprehensive inventories of information possessed and/or managed by the Department.(B) Each Department’s Open Data catalogue will classify information holdings as currently “public” or “not yet public”; Departments will work with Metro Technology Services to develop strategies and timelines for publishing open data containing information in a way that is complete, reliable, and has a high level of detail.Section 5. Open Data Report and Policy Review.(A) Within one year of the effective date of this Executive Order, and thereafter no later than September 1 of each year, the Open Data Management Team shall submit to the Mayor an annual Open Data Report.(B) In acknowledgment that technology changes rapidly, in the future, the Open Data Policy should be reviewed and considered for revisions or additions that will continue to position Metro Government as a leader on issues of openness, efficiency, and technical best practices.Section 6. This Executive Order shall take effect as of October 11, 2013.Signed this 11th day of October, 2013, by Greg Fischer, Mayor of Louisville/Jefferson County Metro Government.GREG FISCHER, MAYOR

Open-Source Database Software Market size was valued at USD 10.00 Billion in 2024 and is projected to reach USD 35.83 Billion by 2032, growing at a CAGR of 20% during the forecast period 2026-2032.

Global Open-Source Database Software Market Drivers

The market drivers for the Open-Source Database Software Market can be influenced by various factors. These may include:

Cost-Effectiveness: Compared to proprietary systems, open-source databases frequently have lower initial expenses, which attracts organizations—especially startups and small to medium-sized enterprises (SMEs) with tight budgets. Flexibility and Customisation: Open-source databases provide more possibilities for customization and flexibility, enabling businesses to modify the database to suit their unique needs and grow as necessary. Collaboration and Community Support: Active developer communities that share best practices, support, and contribute to the continued development of open-source databases are beneficial. This cooperative setting can promote quicker problem solving and innovation. Performance and Scalability: A lot of open-source databases are made to scale horizontally across several nodes, which helps businesses manage expanding data volumes and keep up performance levels as their requirements change. Data Security and Sovereignty: Open-source databases provide businesses more control over their data and allow them to decide where to store and use it, which helps to allay worries about compliance and data sovereignty. Furthermore, open-source code openness can improve security by making it simpler to find and fix problems. Compatibility with Contemporary Technologies: Open-source databases are well-suited for contemporary application development and deployment techniques like microservices, containers, and cloud-native architectures since they frequently support a broad range of programming languages, frameworks, and platforms. Growing Cloud Computing Adoption: Open-source databases offer a flexible and affordable solution for managing data in cloud environments, whether through self-managed deployments or via managed database services provided by cloud providers. This is because more and more organizations are moving their workloads to the cloud. Escalating Need for Real-Time Insights and Analytics: Organizations are increasingly adopting open-source databases with integrated analytics capabilities, like NoSQL and NewSQL databases, as a means of instantly obtaining actionable insights from their data.