A catalog of high-value public science and research data sets from across the Federal Government.

Catalog of high value data inventories produced by Connecticut executive branch agencies and compiled by the Office of Policy and Management. This catalog contains information on high value GIS data only. A catalog of high value non-GIS data may be found at the following link: https://data.ct.gov/Government/CT-Data-Catalog-Non-GIS-/ghmx-93jn

As required by Public Act 18-175, executive branch agencies must annually conduct a high value data inventory to capture information about the high value data that they collect.

High value data is defined as any data that the department head determines (A) is critical to the operation of an executive branch agency; (B) can increase executive branch agency accountability and responsiveness; (C) can improve public knowledge of the executive branch agency and its operations; (D) can further the core mission of the executive branch agency; (E) can create economic opportunity; (F) is frequently requested by the public; (G) responds to a need and demand as identified by the agency through public consultation; or (H) is used to satisfy any legislative or other reporting requirements.

This dataset was last updated 1/2/2019 and will continue to be updated as high value data inventories are submitted to OPM.

The World Bank’s ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories. In order to shift financial flows so that they are better aligned with global goals, the World Bank Group (WBG) is working to provide financial markets with improved data and analytics that shed light on countries’ sustainability performance. Along with new information and tools, the World Bank will also develop research on the correlation between countries’ sustainability performance and the risk and return profiles of relevant investments. | This dataset contains important information and resources. For comprehensive details, documentation, and inquiries, please contact data@worldbank.org. Additional metadata and related resources are available on this page.

The GovTech Maturity Index (GTMI) provides a snapshot of the public sector digital transformation worldwide, presenting the good practices together with gaps that represent opportunities for improvement. The GTMI is a composite index that captures the maturity of four GovTech focus areas through 48 indicators covering: (i) core government systems and shared digital infrastructure, (ii) online public service delivery and open data, (iii) digital citizen engagement, and (iv) GovTech enablers including dimensions such as strategy, institutions, laws, digital skills, innovation, and startup policies. Based on the maturity of GovTech focus areas, economies are grouped into four categories, A to D. The GTMI is not intended to create a ranking or assess a country’s readiness for or performance of GovTech; rather, it complements existing tools and diagnostics by providing an overview of global GovTech practices to assist practitioners in the design of digital transformation interventions. The 2020 GovTech dataset contained data/evidence collected from the government websites of 198 economies (due to the COVID-19 pandemic) using remotely measurable indicators mostly reflecting de jure practices. The 2022 and 2025 updates are mainly based on the responses provided by the government officials directly through an online survey. The GovTech 2022 update (covering 198 economies) is based on the Central Government (CG) GTMI survey responses submitted by 135 countries directly, as well as the remotely collected data from the government websites of 63 non-participating economies. Additionally, the 2022 version includes the Sub-National Government (SNG) GTMI data submitted by 113 subnational government entities (states, municipalities) from 16 countries as a pilot implementation. The 2025 GTMI online survey includes 43 updated/expanded GovTech indicators measuring the maturity of four GovTech focus areas. Additionally, 5 highly relevant external indicators measured by other relevant indexes, including all three components of the United Nations (UN) e-Government Development Index (EGDI), the UN e-Participation Index (EPI), and the ITU’s Global Cybersecurity Index (GCI) are used in the calculation of GTMI groups. The GTMI 2025 update (covering 197 economies) is based on the self-reported survey responses from 158 participating economies and publicly available data from 39 non-participating economies. The construction of the GTMI is primarily based on the World Bank’s GovTech dataset which is an Excel file posted on the WBG Data Catalog (since 2020). The GTMI 2025 update maintains the 2022 methodology (presented in the 2022 GTMI Report) with minor adjustments made to accommodate the three new key indicators (replacing three ID4D external indicators used in 2022). Other knowledge products generated from the dataset include: (i) the GTMI Briefs presenting key findings, trends, good practices, and conclusions; and (ii) the GTMI Dashboard for data visualization through maps and graphs aimed at helping the end-user digest and explore the findings of the GovTech Dataset, linked with the GovTech Projects Database (presenting the details of 1500+ digital government investments funded by the WBG in 147 countries since 1995). Historically, the GovTech Dataset is a substantially expanded version of the Digital Government Systems and Services (DGSS) global dataset, originally developed in 2014 to support the preparation of several WBG studies and flagship reports (e.g., 2014 FMIS and Open Budget Data Study; WDR 2016: Digital Dividends; 2018 WBG Digital Adoption Index; WDR 2021: Data for Better Lives; and 2020 GovTech Maturity Index). The GTMI 2025 Update, catalogued under the World Bank Reproducible Research Repository, is fully reproducible.

TCGA Acute Myeloid Leukemia. Source data from GDAC Firehose. Previously known as TCGA Provisional. This dataset contains summary data visualizations and clinical data from a broad sampling of 200 carcinomas from 200 patients. The data was gathered as part of the Broad Institute of MIT and Harvard Firehose initiative, a cancer analysis pipeline. The clinical data includes mutation count, information about mutated genes, patient demographics, sample type, disease code, Abnormal Lymphocyte Percent, Atra Exposure, Basophils Cell Count, Blast Count, Cytogenetic abnormality type, and FAB. The dataset includes Next-Generation Clustered Heat Maps (NG-CHM) viewable via an embedded NG-CHM Heat Map Viewer, provided my MD Anderson Cancer Center, which provides a graphical environment for exploration of clustered or non-clustered heat map data. The data set also includes copy-number segment data downloadable as .seg files and viewable via the Integrative Genomics Viewer.

Based on the NDC waterway link model. Limited to DVRPC facilities and modified to reflect local conditions.

Data used to link profiles with data in the data center.

The data files are provided for use in replication package located at https://reproducibility.worldbank.org | This dataset contains important information and resources. For comprehensive details, documentation, and inquiries, please contact data@worldbank.org. Additional metadata and related resources are available on this page.

Purification of RecJ3/4-RNase J complex from Haloferax volcanii

Packet headers (upto transport layer, inclusive) for Anonymized Internet Traces 2016 Dataset. Derived from OC192 traces on Equinix San Jose and Chicago monitors.

The Water Quality Portal is the nation's largest source for water quality monitoring data. The Water Quality Portal (WQP) uses the Water Quality Exchange (WQX) data format to share over 340 million water quality data records data from 400 federal, state, tribal and other partners.

This dataset includes combined and standardized Gini data from eight original sources: Luxembourg Income Study (LIS), Socio-Economic Database for Latin America (SEDLAC), Survey of Living Conditions (SILC) by Eurostat, World Income Distribution (WYD; the full data set is available here), World Bank Europe and Central Asia dataset, World Institute for Development Research (WIDER), World Bank Povcal, and Ginis from individual long-term inequality studies (just introduced in this version).

The U.S. Census Bureau releases annual estimates of population by counties and municipalities as part of the Population Estimates Program (PEP). This is an estimate of population on July 1 of each year. Adjustments to previous estimate years are made with each release, dating back to the year of the last decennial census. Decennial figures for April 1 of the most recent decennial year will not get updated, but the July 1 estimate for that same year can adjust with each PEP release. The U.S. Census Bureau produces these estimates based on administrative records. At the municipal level, the PEP reports only population totals. At the county level, PEP data gives estimates for age, sex, race, and ethnicity. PEP releases come out in the spring following the latest estimate year. The demographic estimates of the PEP are used as control totals for the American Community Survey results released later that year.

This shapefile contains municipalities head offices in Guatemala. | This dataset contains important information and resources. For comprehensive details, documentation, and inquiries, please contact data@worldbank.org. Additional metadata and related resources are available on this page.

Packet headers (upto transport layer, inclusive) for Anonymized Internet Traces 2015 Dataset. Derived from OC192 traces on Equinix San Jose and Chicago monitors. Contains months 02,05,09,12.

Description from EGA:

"Exome capture sequencing of SCLC tumor/normal pairs and cell lines"

Part of the Genentech Small Cell Lung Cancer (SCLC) Screen Study, EGA accession number EGAS00001000334

Description from EGA:

"The dataset for Genome-wide cell-free DNA fragmentation in patients with cancer includes 538 bam files from whole genome next-generation sequencing on the Illumina HiSeq2500. The samples analyzed include plasma samples from healthy individuals and patients with cancer."

Population information, and demographics for selected years, for the North Slope Borough.

This database is not owned by me, I uploaded this merely to make importing it to kaggle kernels more convenient. I don't have any responsibility for maintaining this dataset, and all rights are reserved for the original author(s)

All data is downloaded from https://datacatalog.worldbank.org/dataset/human-capital-index

For documentation, please see https://datacatalog.worldbank.org/dataset/human-capital-index

This dataset contains a collection of JSON files used to configure map catalogs in TerriaJS, an interactive geospatial data visualization platform. The files include detailed configurations for services such as WMS, WFS, and other geospatial resources, enabling the integration and visualization of diverse datasets in a user-friendly web interface. This resource is ideal for developers, researchers, and professionals who wish to customize or implement interactive map catalogs in their own applications using TerriaJS.