People data provides complete people information and gives the ability to link individual information to organizations and roles.

The Human Exposure Database System (HEDS) provides public access to data sets, documents, and metadata from EPA on human exposure. It is primarily intended for scientists involved in human exposure studies or work requiring such data.

The Human Mitochondrial Protein Database (HMPDb) provides comprehensive data on mitochondrial and human nuclear encoded proteins involved in mitochondrial biogenesis and function. This database consolidates information from SwissProt, LocusLink, Protein Data Bank (PDB), GenBank, Genome Database (GDB), Online Mendelian Inheritance in Man (OMIM), Human Mitochondrial Genome Database (mtDB), MITOMAP, Neuromuscular Disease Center and Human 2-D PAGE Databases. This database is intended as a tool not only to aid in studying the mitochondrion but in studying the associated diseases.

The Consolidated Human Activity Database (CHAD) is a resource for learning about human exposure and health studies and predictive models.

A database providing detailed mortality and population data to those interested in the history of human longevity. For each country, the database includes calculated death rates and life tables by age, time, and sex, along with all of the raw data (vital statistics, census counts, population estimates) used in computing these quantities. Data are presented in a variety of formats with regard to age groups and time periods. The main goal of the database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. New data series is continually added to this collection. However, the database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included are relatively wealthy and for the most part highly industrialized. The database replaces an earlier NIA-funded project, known as the Berkeley Mortality Database. * Dates of Study: 1751-present * Study Features: Longitudinal, International * Sample Size: 37 countries or areas

EOG

Full profile of 10,000 people in the US - download here, data schema here, with more than 40 data points including - Full Name - Education - Location - Work Experience History and many more!

There are additionally 258+ Million US people profiles available, visit the LinkDB product page here.

Our LinkDB database is an exhaustive database of publicly accessible LinkedIn people and companies profiles. It contains close to 500 Million people and companies profiles globally.

A collection of population life tables covering a multitude of countries and many years. Most of the HLD life tables are life tables for national populations, which have been officially published by national statistical offices. Some of the HLD life tables refer to certain regional or ethnic sub-populations within countries. Parts of the HLD life tables are non-official life tables produced by researchers. Life tables describe the extent to which a generation of people (i.e. life table cohort) dies off with age. Life tables are the most ancient and important tool in demography. They are widely used for descriptive and analytical purposes in demography, public health, epidemiology, population geography, biology and many other branches of science. HLD includes the following types of data: * complete life tables in text format; * abridged life tables in text format; * references to statistical publications and other data sources; * scanned copies of the original life tables as they were published. Three scientific institutions are jointly developing the HLD: the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, the Department of Demography at the University of California at Berkeley, USA and the Institut national d''��tudes d��mographiques (INED) in Paris, France. The MPIDR is responsible for maintaining the database.

This dataset is a selection of The Therapeutic Target Database (release 4.3.02, 18th Oct 2013) protein IDs for successful targets. The web page states 388 but these reduced to 345 human Swiss-Prot accessions.

Database of Bacterial ExoToxins for Human is a database of sequences, structures, interaction networks and analytical results for 229 exotoxins, from 26 different human pathogenic bacterial genus. All toxins are classified into 24 different Toxin classes. The aim of DBETH is to provide a comprehensive database for human pathogenic bacterial exotoxins. DBETH also provides a platform to its users to identify potential exotoxin like sequences through Homology based as well as Non-homology based methods. In homology based approach the users can identify potential exotoxin like sequences either running BLASTp against the toxin sequences or by running HMMER against toxin domains identified by DBETH from human pathogenic bacterial exotoxins. In Non-homology based part DBETH uses a machine learning approach to identify potential exotoxins (Toxin Prediction by Support Vector Machine based approach).

This dataset, which is updated daily, covers organisations at all stages of their Investors in People Journey (Lead, Working with, Accredited). It provides details of customers since Investors in People's conception in 1991. Information is categorised in a number of ways, including economic sector, geographic region and organisational size.

overall health

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed. More information on the National Walkability index: https://www.epa.gov/smartgrowth/smart-location-mapping More information on the Smart Location Calculator: https://www.slc.gsa.gov/slc/

[1] The Progress by Population Group analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included subsets of the 1,111 measurable HP2020 objectives that have data available for any of six broad population characteristics: sex, race and ethnicity, educational attainment, family income, disability status, and geographic location. Progress toward meeting HP2020 targets is presented for up to 24 population groups within these characteristics, based on objective data aggregated across HP2020 topic areas. The Progress by Population Group data are also available at the individual objective level in the downloadable data set. [2] The final value was generally based on data available on the HP2020 website as of January 2020. For objectives that are continuing into HP2030, more recent data will be included on the HP2030 website as it becomes available: https://health.gov/healthypeople. [3] For more information on the HP2020 methodology for measuring progress toward target attainment and the elimination of health disparities, see: Healthy People Statistical Notes, no 27; available from: https://www.cdc.gov/nchs/data/statnt/statnt27.pdf. [4] Status for objectives included in the HP2020 Progress by Population Group analysis was determined using the baseline, final, and target value. The progress status categories used in HP2020 were: a. Target met or exceeded—One of the following applies: (i) At baseline, the target was not met or exceeded, and the most recent value was equal to or exceeded the target (the percentage of targeted change achieved was equal to or greater than 100%); (ii) The baseline and most recent values were equal to or exceeded the target (the percentage of targeted change achieved was not assessed). b. Improved—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved 10% or more of the targeted change. c. Little or no detectable change—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was not statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved less than 10% of the targeted change; (iii) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was not statistically significant; (iv) Movement was away from the baseline and target, standard errors were not available, and the objective had moved less than 10% relative to the baseline; (v) No change was observed between the baseline and the final data point. d. Got worse—One of the following applies: (i) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was statistically significant; (ii) Movement was away from the baseline and target, standard errors were not available, and the objective had moved 10% or more relative to the baseline. NOTE: Measurable objectives had baseline data. SOURCE: National Center for Health Statistics, Healthy People 2020 Progress by Population Group database.

The Genetic Association Database is an archive of human genetic association studies of complex diseases and disorders. The goal of this database is to allow the user to rapidly identify medically relevant polymorphism from the large volume of polymorphism and mutational data, in the context of standardized nomenclature. The data is from published scientific papers. Study data is recorded in the context of official human gene nomenclature with additional molecular reference numbers and links. It is gene centered. That is, each record is a record of a gene or marker. If a study investigated 6 genes for a particular disorder, there will be 6 records. Anyone may view this database and anyone may submit records. You do not have to be an author on the original study to submit a record. All submitted records will be reviewed before inclusion in the archive. Both genetic and environmental factors contribute to human diseases. Most common diseases are influenced by a large number of genetic and environmental factors, most of which individually have only a modest effect on the disease. Though genetic contributions are relatively well characterized for some monogenetic diseases, there has been no effort at curating the extensive list of environmental etiological factors. From a comprehensive search of the MeSH annotation of MEDLINE articles, they identified 3,342 environmental etiological factors associated with 3,159 diseases. They also identified 1,100 genes associated with 1,034 complex diseases from the NIH Genetic Association Database (GAD), a database of genetic association studies. 863 diseases have both genetic and environmental etiological factors available. Integrating genetic and environmental factors results in the etiome, which they define as the comprehensive compendium of disease etiology.

Data Records The database is deposited on the Dryad Digital Repository as a series of Microsoft Excel files prepared to be used in coding. It is presented as individual files for each layer (epidermis, stratum corneum, dermis) and chemical type (fragrance related, non-volatile, hydrocortisone). Additional spreadsheets containing all information, the chemical descriptors, and time course data are also included along with a notated and color-coded file which is condensed and not recommended for coding. Data Validation The collection of experimental data was collected from its corresponding publication and the additional features were collected from the EPA CompTox Chemicals Dashboard (version 2.2.0), Padel-descriptor, as well as additional literature. The database was curated by a team of two and reviewed by an additional team member in order to ensure that the data were accurately reported with correct units. The dermal absorption coefficients were collected from peer-reviewed publications and included in the database, taking into account any additional supplementary materials and corrections. This dataset is associated with the following publication: Stevens, J., A. Prockter, H. Fisher, H. Tran, and M. Evans. A database of chemical absorption in human skin with mechanistic modeling applications. Scientific Data. Springer Nature, New York, NY, USA, 11: 755, (2024).

Dataset

This dataset was created by Amir Baniasadi

Contents

HbVar is a relational database of information about hemoglobin variants and mutations that cause thalassemia. The initial data came from Syllabi authored by Prof. Titus H.J. Huisman, Mrs. Marianne F.H. Carver, Dr. Erol Baysal, and Prof. Georgi D. Efremov. This information was converted to a database, and now new entries are added and old entries are corrected by curators. HbVar results from a collaboration among several investigators at Penn State University (USA), INSERM Creteil (France), and Boston University Medical Center (USA). Visit our query page or summary page to see the types of information available.

This is the first version of the IHA database, which is being created in the project HAPPAA C1 funded by the Deutsche Forschungsgemeinschaft (DFG) – Projektnummer 352015383 - SFB 1330 C1. (https://uol.de/en/sfb-1330-hearing-acoustics)

The database includes a subsample of 10 human geometries comprising the torso, head and the entire outer ear including the ear canal and eardrum. The data are available in two different 3D object formats: ply binary file, stl binary file.

The Population Database of Mexico contains geographically referenced population data for Mexican states, municipalities and localities from the 1990 Mexican population and housing census. The data include population by gender and age group for approximately 83.7% of the Mexican population. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).