R-universe search: cdc

brry

rdwd:Select and Download Climate Data from 'DWD' (German Weather Service)

Handle climate data from the 'DWD' ('Deutscher Wetterdienst', see <https://www.dwd.de/EN/climate_environment/cdc/cdc_node_en.html> for more information). Choose observational time series from meteorological stations with 'selectDWD()'. Find raster data from radar and interpolation according to <https://bookdown.org/brry/rdwd/raster-data.html>. Download (multiple) data sets with progress bars and no re-downloads through 'dataDWD()'. Read both tabular observational data and binary gridded datasets with 'readDWD()'.

Maintained by Berry Boessenkool. Last updated 6 days ago.

22.9 match 73 stars 7.77 score 79 scripts

openintrostat

openintro:Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

Supplemental functions and data for 'OpenIntro' resources, which includes open-source textbooks and resources for introductory statistics (<https://www.openintro.org/>). The package contains datasets used in our open-source textbooks along with custom plotting functions for reproducing book figures. Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system.

Maintained by Mine Çetinkaya-Rundel. Last updated 3 months ago.

data openintro

11.1 match 240 stars 11.39 score 6.0k scripts

katilingban

paleta:Collection of Palettes, Themes, and Theme Components

A collection of palettes, themes, and theme components based on publicly available branding guidelines of various non-governmental organisations, government agencies, and United Nations units.

Maintained by Ernest Guevarra. Last updated 2 months ago.

24.6 match 2 stars 4.48 score 8 scripts

kjhealy

covdata:COVID-19 Data

COVID-19 related data from the ECDC, the COVID-19 Tracking Project, the New York Times, the Human Mortality Database, and Apple. Packaged for R.

Maintained by Kieran Healy. Last updated 2 years ago.

17.8 match 83 stars 4.73 score 129 scripts

stefvanbuuren

AGD:Analysis of Growth Data

Tools for the analysis of growth data: to extract an LMS table from a gamlss object, to calculate the standard deviation scores and its inverse, and to superpose two wormplots from different models. The package contains a some varieties of reference tables, especially for The Netherlands.

Maintained by Stef van Buuren. Last updated 11 months ago.

anthropometry cdc dutch growth growth-charts lms who z-score

18.5 match 1 stars 4.38 score 48 scripts

carriedaymont

growthcleanr:Data Cleaner for Anthropometric Measurements

Identifies implausible anthropometric (e.g., height, weight) measurements in irregularly spaced longitudinal datasets, such as those from electronic health records.

Maintained by Carrie Daymont. Last updated 16 days ago.

ehr ehr-data

9.3 match 14 stars 6.68 score 41 scripts 1 dependents

cmu-delphi

epidatr:Client for Delphi's 'Epidata' API

The Delphi 'Epidata' API provides real-time access to epidemiological surveillance data for influenza, 'COVID-19', and other diseases for the USA at various geographical resolutions, both from official government sources such as the Center for Disease Control (CDC) and Google Trends and private partners such as Facebook and Change 'Healthcare'. It is built and maintained by the Carnegie Mellon University Delphi research group. To cite this API: David C. Farrow, Logan C. Brooks, Aaron 'Rumack', Ryan J. 'Tibshirani', 'Roni' 'Rosenfeld' (2015). Delphi 'Epidata' API. <https://github.com/cmu-delphi/delphi-epidata>.

Maintained by David Weber. Last updated 4 months ago.

9.6 match 5 stars 6.14 score 114 scripts

r-forge

surveillance:Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.

Maintained by Sebastian Meyer. Last updated 2 days ago.

cpp

4.5 match 2 stars 10.68 score 446 scripts 3 dependents

statist7

sitar:Super Imposition by Translation and Rotation Growth Curve Analysis

Functions for fitting and plotting SITAR (Super Imposition by Translation And Rotation) growth curve models. SITAR is a shape-invariant model with a regression B-spline mean curve and subject-specific random effects on both the measurement and age scales. The model was first described by Lindstrom (1995) <doi:10.1002/sim.4780141807> and developed as the SITAR method by Cole et al (2010) <doi:10.1093/ije/dyq115>.

Maintained by Tim Cole. Last updated 2 months ago.

5.4 match 13 stars 8.69 score 58 scripts 3 dependents

usepa

httk:High-Throughput Toxicokinetics

Pre-made models that can be rapidly tailored to various chemicals and species using chemical-specific in vitro data and physiological information. These tools allow incorporation of chemical toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE") into bioinformatics, as described by Pearce et al. (2017) (<doi:10.18637/jss.v079.i04>). Chemical-specific in vitro data characterizing toxicokinetics have been obtained from relatively high-throughput experiments. The chemical-independent ("generic") physiologically-based ("PBTK") and empirical (for example, one compartment) "TK" models included here can be parameterized with in vitro data or in silico predictions which are provided for thousands of chemicals, multiple exposure routes, and various species. High throughput toxicokinetics ("HTTK") is the combination of in vitro data and generic models. We establish the expected accuracy of HTTK for chemicals without in vivo data through statistical evaluation of HTTK predictions for chemicals where in vivo data do exist. The models are systems of ordinary differential equations that are developed in MCSim and solved using compiled (C-based) code for speed. A Monte Carlo sampler is included for simulating human biological variability (Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>) and propagating parameter uncertainty (Wambaugh et al., 2019 <doi:10.1093/toxsci/kfz205>). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>). These functions and data provide a set of tools for using IVIVE to convert concentrations from high-throughput screening experiments (for example, Tox21, ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK") (Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).

Maintained by John Wambaugh. Last updated 1 months ago.

comptox ord

3.8 match 27 stars 10.22 score 307 scripts 1 dependents

brendensm

CDCPLACES:Access the 'CDC PLACES' API

Allows users to seamlessly query several 'CDC PLACES' APIs (<https://data.cdc.gov/browse?q=PLACES%20&sortBy=relevance>) by geography, state, measure, and release year. This package also contains a function to explore the available measures for each release year.

Maintained by Brenden Smith. Last updated 1 months ago.

7.5 match 14 stars 4.91 score 13 scripts

smouksassi

coveffectsplot:Produce Forest Plots to Visualize Covariate Effects

Produce forest plots to visualize covariate effects using either the command line or an interactive 'Shiny' application.

Maintained by Samer Mouksassi. Last updated 1 months ago.

3.5 match 32 stars 7.86 score 40 scripts

silentspringinstitute

RNHANES:Facilitates Analysis of CDC NHANES Data

Tools for downloading and analyzing CDC NHANES data, with a focus on analytical laboratory data.

Maintained by Herb Susmann. Last updated 2 days ago.

nhanes publichealth

3.5 match 77 stars 7.58 score 83 scripts

jazznbass

scan:Single-Case Data Analyses for Single and Multiple Baseline Designs

A collection of procedures for analysing, visualising, and managing single-case data. These include piecewise linear regression models, multilevel models, overlap indices ('PND', 'PEM', 'PAND', 'PET', 'tau-u', 'baseline corrected tau', 'CDC'), and randomization tests. Data preparation functions support outlier detection, handling missing values, scaling, and custom transformations. An export function helps to generate html, word, and latex tables in a publication friendly style. More details can be found in the online book 'Analyzing single-case data with R and scan', Juergen Wilbert (2025) <https://jazznbass.github.io/scan-Book/>.

Maintained by Juergen Wilbert. Last updated 15 days ago.

3.8 match 4 stars 6.42 score 62 scripts 1 dependents

ropensci

quadkeyr:Generate Raster Images from QuadKey-Identified Datasets

A set of functions of increasing complexity allows users to (1) convert QuadKey-identified datasets, based on Microsoft's Bing Maps Tile System, into Simple Features data frames, (2) transform Simple Features data frames into rasters, and (3) process multiple Meta (Facebook) QuadKey-identified human mobility files directly into raster files. For more details, see D’Andrea et al. (2024) <doi:10.21105/joss.06500>.

Maintained by Florencia DAndrea. Last updated 4 days ago.

geospatial quadkey raster tilemap

2.7 match 12 stars 5.64 score 7 scripts

reichlab

zoltr:Interface to the 'Zoltar' Forecast Repository API

'Zoltar' <https://www.zoltardata.com/> is a website that provides a repository of model forecast results in a standardized format and a central location. It supports storing, retrieving, comparing, and analyzing time series forecasts for prediction challenges of interest to the modeling community. This package provides functions for working with the 'Zoltar' API, including connecting and authenticating, getting meta information (projects, models, and forecasts, and truth), and uploading, downloading, and deleting forecast and truth data.

Maintained by Matthew Cornell. Last updated 10 days ago.

1.7 match 2 stars 7.58 score 175 scripts 3 dependents

nutriverse

zscorer:Child Anthropometry z-Score Calculator

A tool for calculating z-scores and centiles for weight-for-age, length/height-for-age, weight-for-length/height, BMI-for-age, head circumference-for-age, age circumference-for-age, subscapular skinfold-for-age, triceps skinfold-for-age based on the WHO Child Growth Standards.

Maintained by Ernest Guevarra. Last updated 4 years ago.

anthropometric-indices anthropometry growth-charts growth-standards height-for-age nutrition weight-for-age weight-for-height z-score

1.7 match 14 stars 7.30 score 47 scripts 1 dependents

cran

SSDforR:Functions to Analyze Single System Data

Functions to visually and statistically analyze single system data.

Maintained by Charles Auerbach. Last updated 3 months ago.

6.7 match 1.48 score

mvogel78

childsds:Data and Methods Around Reference Values in Pediatrics

Calculation of standard deviation scores and percentiles adduced from different standards (WHO, UK, Germany, Italy, China, etc). Also, references for laboratory values in children and adults are available, e.g., serum lipids, iron-related blood parameters, IGF, liver enzymes. See package documentation for full list.

Maintained by Mandy Vogel. Last updated 2 months ago.

3.4 match 2.83 score 51 scripts

shihao-yang

argo:Accurate Estimation of Influenza Epidemics using Google Search Data

Augmented Regression with General Online data (ARGO) for accurate estimation of influenza epidemics in United States on national level, regional level and state level. It replicates the method introduced in paper Yang, S., Santillana, M. and Kou, S.C. (2015) <doi:10.1073/pnas.1515373112>; Ning, S., Yang, S. and Kou, S.C. (2019) <doi:10.1038/s41598-019-41559-6>; Yang, S., Ning, S. and Kou, S.C. (2021) <doi:10.1038/s41598-021-83084-5>.

Maintained by Shihao Yang. Last updated 2 years ago.

5.1 match 2 stars 1.30 score 7 scripts

cdcgov

surveytable:Formatted Survey Estimates

Short and understandable commands that generate tabulated, formatted, and rounded survey estimates. Mostly a wrapper for the 'survey' package (Lumley (2004) <doi:10.18637/jss.v009.i08> <https://CRAN.R-project.org/package=survey>) that identifies low-precision estimates using the National Center for Health Statistics (NCHS) presentation standards (Parker et al. (2017) <https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf>, Parker et al. (2023) <doi:10.15620/cdc:124368>).

Maintained by Alex Strashny. Last updated 4 days ago.

estimates formatted-output pretty-print survey tables

0.8 match 6 stars 6.71 score 19 scripts

cjendres1

nhanesA:NHANES Data Retrieval

Utility to retrieve data from the National Health and Nutrition Examination Survey (NHANES) website <https://www.cdc.gov/nchs/nhanes/>.

Maintained by Christopher Endres. Last updated 2 months ago.

nhanes

0.5 match 59 stars 9.37 score 239 scripts

wencanhong

cdcsis:Conditional Distance Correlation Based Feature Screening and Conditional Independence Inference

Conditional distance correlation <doi:10.1080/01621459.2014.993081> is a novel conditional dependence measurement of two multivariate random variables given a confounding variable. This package provides conditional distance correlation, performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data <https://www3.stat.sinica.edu.tw/statistica/J28N1/J28N114/J28N114.html>, and conducts conditional distance covariance test for conditional independence assumption of two multivariate variable.

Maintained by Canhong Wen. Last updated 7 months ago.

cpp

1.7 match 1 stars 2.88 score 25 scripts 1 dependents

heli-xu

findSVI:Calculate Social Vulnerability Index for Communities

Developed by CDC/ATSDR (Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry), Social Vulnerability Index (SVI) serves as a tool to assess the resilience of communities by taking into account socioeconomic and demographic factors. Provided with year(s), region(s) and a geographic level of interest, 'findSVI' retrieves required variables from US census data and calculates SVI for communities in the specified area based on CDC/ATSDR SVI documentation. Reference for the calculation methods: Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B (2011) <doi:10.2202/1547-7355.1792>.

Maintained by Heli Xu. Last updated 1 months ago.

0.8 match 12 stars 5.68 score 16 scripts

rsparapa

nftbart:Nonparametric Failure Time Bayesian Additive Regression Trees

Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a complete description of the model at <doi:10.1111/biom.13857>.

Maintained by Rodney Sparapani. Last updated 1 years ago.

cpp openmp

3.6 match 1.15 score 14 scripts

biostats-dev

ggsurveillance:Tools for Outbreak Investigation/Infectious Disease Surveillance

Create epicurves or epigantt charts in 'ggplot2'. Prepare data for visualisation or other reporting for infectious disease surveillance and outbreak investigation. Includes tidy functions to solve date based transformations for common reporting tasks, like (A) seasonal date alignment for respiratory disease surveillance, (B) date-based case binning based on specified time intervals like isoweek, epiweek, month and more, (C) automated detection and marking of the new year based on the date/datetime axis of the 'ggplot2'. An introduction on how to use epicurves can be found on the US CDC website (2012, <https://www.cdc.gov/training/quicklearns/epimode/index.html>).

Maintained by Alexander Bartel. Last updated 14 days ago.

epidemiology infectious-disease-surveillance infectious-diseases outbreaks

0.8 match 2 stars 5.31 score

cran

MSigSeg:Multiple SIGnal SEGmentation

Traditional methods typically detect breakpoints from individual signals, which means that when applied separately to multiple signals, the breakpoints are not aligned. However, this package implements a common breakpoint detection approach for multiple piecewise constant signals, resulting in increased detection sensitivity and specificity. By employing various techniques, optimal performance is ensured, and computation is accelerated. We hope that this package will be beneficial for researchers in signal processing, bioinformatics, economy, and other related fields. The segmentation(), lambda_estimator() functions are the main functions of this package.

Maintained by Xuanyu Liu. Last updated 1 years ago.

3.5 match 1.00 score

dewittpe

pedbp:Pediatric Blood Pressure

Data and utilities for estimating pediatric blood pressure percentiles by sex, age, and optionally height (stature) as described in Martin et.al. (2022) <doi:10.1001/jamanetworkopen.2022.36918>. Blood pressure percentiles for children under one year of age come from Gemelli et.al. (1990) <doi:10.1007/BF02171556>. Estimates of blood pressure percentiles for children at least one year of age are informed by data from the National Heart, Lung, and Blood Institute (NHLBI) and the Centers for Disease Control and Prevention (CDC) <doi:10.1542/peds.2009-2107C> or from Lo et.al. (2013) <doi:10.1542/peds.2012-1292>. The flowchart for selecting the informing data source comes from Martin et.al. (2022) <doi:10.1542/hpeds.2021-005998>.

Maintained by Peter DeWitt. Last updated 2 months ago.

blood-pressure growth-standards pediatric cpp

0.5 match 6 stars 6.43 score 45 scripts

jhchou

peditools:Pediatric Clinical Data Science Tools

A collection of tools for newborn and pediatric anthropometric calculations and data abstraction from Vermont Oxford Network registry exports. Includes charts based on Lambda, Mu, Sigma (LMS) parameters, including: Fenton 2003, Olsen 2010, Olsen BMI, CDC infant, CDC pediatric, CDC BMI, CDC (Addo) skin, WHO infant, WHO skin, Abdel-Rahman 2017, Mramba 2017, Zemel Down Syndrome, Brooks cerebral palsy, WHO expanded, Cappa 2024 (except BMI). Includes functions to take a Vermont Oxford Network XML or CSV data file export read into a data frame, converting the coded variables into human readable factors.

Maintained by Joseph Chou. Last updated 2 months ago.

peditools

1.0 match 5 stars 3.00 score 2 scripts

rpruim

NHANES:Data from the US National Health and Nutrition Examination Study

Body Shape and related measurements from the US National Health and Nutrition Examination Survey (NHANES, 1999-2004). See http://www.cdc.gov/nchs/nhanes.htm for details.

Maintained by Randall Pruim. Last updated 10 years ago.

0.5 match 5.20 score 880 scripts

gshs-ornl

revengc:Reverse Engineering Summarized Data

Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations (e.g. World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), World Bank, and various national censuses). The challenge therefore is to infer what the original data could have been given summarized information. We present an R package that reverse engineers decoupled and/or censored count data with two main functions. The cnbinom.pars() function estimates the average and dispersion parameter of a censored univariate frequency table. The rec() function reverse engineers summarized data into an uncensored bivariate table of probabilities.

Maintained by Samantha Duchscherer. Last updated 6 years ago.

0.5 match 5 stars 3.44 score 11 scripts

cran

CMHSU:Mental Health Status, Substance Use Status and their Concurrent Status in North American Healthcare Administrative Databases

Patients' Mental Health (MH) status, Substance Use (SU) status, and concurrent MH/SU status in the American/Canadian Healthcare Administrative Databases can be identified. The detection is based on given parameters of interest by clinicians including the list of plausible ICD MH/SU codes (3/4/5 characters), the required number of visits of hospital for MH/SU , the required number of visits of service physicians for MH/SU, and the maximum time span within MH visits, within SU visits, and, between MH and SU visits. Methods are described in: Khan S <https://pubmed.ncbi.nlm.nih.gov/29044442/>, Keen C, et al. (2021) <doi:10.1111/add.15580>, Lavergne MR, et al. (2022) <doi:10.1186/s12913-022-07759-z>, Casillas, S M, et al. (2022) <doi:10.1016/j.abrep.2022.100464>, CIHI (2022) <https://www.cihi.ca/en>, CDC (2024) <https://www.cdc.gov>, WHO (2019) <https://icd.who.int/en>.

Maintained by Chel Hee Lee. Last updated 2 months ago.

0.8 match 2.00 score

iembry

vaersvax:US Vaccine Adverse Event Reporting System (VAERS) Vaccine Data for Present

US VAERS vaccine data for 01/01/2018 - 06/14/2018. If you want to explore the full VAERS data for 1990 - Present (data, symptoms, and vaccines), then check out the 'vaers' package from the URL below. The URL and BugReports below correspond to the 'vaers' package, of which 'vaersvax' is a small subset (2018 only). 'vaers' is not hosted on CRAN due to the large size of the data set. To install the Suggested 'vaers' and 'vaersND' packages, use the following R code: 'devtools::install_git("<https://gitlab.com/iembry/vaers.git>", build_vignettes = TRUE)' and 'devtools::install_git("<https://gitlab.com/iembry/vaersND.git>", build_vignettes = TRUE)'. "The Vaccine Adverse Event Reporting System (VAERS) is a national early warning system to detect possible safety problems in U.S.-licensed vaccines. VAERS is co-managed by the Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA)." For more information about the data, visit <https://vaers.hhs.gov/>. For information about vaccination/immunization hazards, visit <http://www.questionuniverse.com/rethink.html#vaccine>.

Maintained by Irucka Embry. Last updated 7 years ago.

0.5 match 1 stars 1.70 score 1 scripts

iembry

vaersNDvax:Non-Domestic Vaccine Adverse Event Reporting System (VAERS) Vaccine Data for Present

Non-Domestic VAERS vaccine data for 01/01/2016 - 06/14/2016. If you want to explore the full VAERS data for 1990 - Present (data, symptoms, and vaccines), then check out the 'vaersND' package from the URL below. The URL and BugReports below correspond to the 'vaersND' package, of which 'vaersNDvax' is a small subset (2016 only). 'vaersND' is not hosted on CRAN due to the large size of the data set. To install the Suggested 'vaers' and 'vaersND' packages, use the following R code: 'devtools::install_git("https://gitlab.com/iembry/vaers.git", build_vignettes = TRUE)' and 'devtools::install_git("https://gitlab.com/iembry/vaersND.git", build_vignettes = TRUE)'. "VAERS is a national vaccine safety surveillance program co-sponsored by the US Centers for Disease Control and Prevention (CDC) and the US Food and Drug Administration (FDA). VAERS is a post-marketing safety surveillance program, collecting information about adverse events (possible side effects) that occur after the administration of vaccines licensed for use in the United States." For more information about the data, visit <https://vaers.hhs.gov/index>. For information about vaccination/immunization hazards, visit <http://www.questionuniverse.com/rethink.html/#vaccine>.

Maintained by Irucka Embry. Last updated 9 years ago.

0.5 match 1.70 score 1 scripts

iembry

chem.databases:Collection of 3 Chemical Databases from Public Sources

Contains the Multi-Species Acute Toxicity Database (CAS & SMILES columns only) [United States (US) Department of Health and Human Services (DHHS) National Institutes of Health (NIH) National Cancer Institute (NCI), "Multi-Species Acute Toxicity Database", <https://cactus.nci.nih.gov/download/acute-toxicity-db/>] combined with the Toxic Substances Control Act (TSCA) Inventory [United States Environmental Protection Agency (US EPA), "Toxic Substances Control Act (TSCA) Chemical Substance Inventory", <https://www.epa.gov/tsca-inventory/how-access-tsca-inventory} and <https://cdxapps.epa.gov/oms-substance-registry-services/substance-list-details/169>] and the Agency for Toxic Substances and Disease Registry (ATSDR) Database [United States (US) Department of Health and Human Services (DHHS) Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR), "Agency for Toxic Substances and Disease Registry (ATSDR) Database", <https://cdxapps.epa.gov/oms-substance-registry-services/substance-list-details/105>] in 2 data sets. One data set has a focus on the latter 2 databases and one data set focuses on the former database. Also contains the collection of chemical data from Wikipedia compiled in the US EPA CompTox Chemicals Dashboard [United States Environmental Protection Agency (US EPA) / Wikimedia Foundation, Inc. "CompTox Chemicals Dashboard v2.2.1", <https://comptox.epa.gov/dashboard/chemical-lists/WIKIPEDIA>].

Maintained by Irucka Embry. Last updated 1 years ago.

0.5 match 1.70 score