This dataset tracks the updates made on the dataset "SDOH Measures for Census Tract, ACS 2017-2021" as a repository for previous versions of the data and metadata.

This dataset tracks the updates made on the dataset "American Community Survey (ACS) – Vision and Eye Health Surveillance" as a repository for previous versions of the data and metadata.

This dataset tracks the updates made on the dataset "SDOH Measures for County, ACS 2017-2021" as a repository for previous versions of the data and metadata.

The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein–ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (Tm) and relative melt shifts from proteomics abundance data. Here, we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impact the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and have made the R based program Inflect available for research community use through the CRAN repository [McCracken, N. Inflect: Melt Curve Fitting and Melt Shift Analysis. R package version 1.0.3, 2021]. The Inflect outputs include melt curves for each protein which passes filtering criteria in addition to a data matrix which is directly compatible with downstream packages such as UpsetR for replicate comparisons and identification of biologically relevant changes. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared toward specific applications.

This dataset tracks the updates made on the dataset "SDOH Measures for Place, ACS 2017-2021" as a repository for previous versions of the data and metadata.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Proteomic investigations of Alzheimer’s and Parkinson’s disease have provided valuable insights into neurodegenerative disorders. Thus far, these investigations have largely been restricted to bottom-up approaches, hindering the degree to which one can characterize a protein’s “intact” state. Top-down proteomics (TDP) overcomes this limitation; however, it is typically limited to observing only the most abundant proteoforms and of a relatively small size. Therefore, fractionation techniques are commonly used to reduce sample complexity. Here, we investigate gas-phase fractionation through high-field asymmetric waveform ion mobility spectrometry (FAIMS) within TDP. Utilizing a high complexity sample derived from Alzheimer’s disease (AD) brain tissue, we describe how the addition of FAIMS to TDP can robustly improve the depth of proteome coverage. For example, implementation of FAIMS with external compensation voltage (CV) stepping at −50, −40, and −30 CV could more than double the mean number of non-redundant proteoforms, genes, and proteome sequence coverage compared to without FAIMS. We also found that FAIMS can influence the transmission of proteoforms and their charge envelopes based on their size. Importantly, FAIMS enabled the identification of intact amyloid beta (Aβ) proteoforms, including the aggregation-prone Aβ1–42 variant which is strongly linked to AD. Raw data and associated files have been deposited to the ProteomeXchange Consortium via the MassIVE data repository with data set identifier PXD023607.

This repository contains the raw data used in Patterns of Settlement Following Forced Migration: The Case of Bosnians in the United States. These data were downloaded in February 2018 from the U.S. Census Bureau’s American Community Survey website at https://www.census.gov/programs-surveys/acs. The code used to analyze these data and intermediate datasets derived from them is available in GitHub at https://github.com/JohnPalmer/bosnian_settlement.

This dataset contains the experimental data for the paper "XUV Absorption Spectroscopy and Photoconversion of a Tin-Oxo Cage Photoresist", by Najmeh Sadegh, Quentin Evrard, Peter M. Kraus and Albert M. Brouwer, The Journal of Physical Chemistry C, 2024, https://doi.org/10.1021/acs.jpcc.3c07480Files in the archivesdata_processing.zip:data_processing.pdf describes the processing of the data in more detail than in the Supporting Informationdata_high_energy.zip:data_high_energy.xlsx excel file with raw data and processed data for the energy range 42 - 70 eVreadme_data_high_energy.txt description of the content of data_high_energy.xlsxdata_low_energy.zip:readme_data_low_energy.txt explanation of the content of the filedata_low_energy.xlsx excel file with raw data for energy range 22 - 42 eVabsorbance vs. dose and vs. photons absorbed for 13 HHG peaksconversion_low_E.xslx fit parameters of the fits of the absorbance vs. dose on sample and vs. photons absorbed in the range 22 - 42 eVQuantum Yields vs. photon energy and number of photons absorbed; average quantum yield and number of butyl groups lostfigures.zip folder with the data that reproduce figures in the main text, as well as the cross section data retrieved from the CXRO database (https://henke.lbl.gov/optical_constants/)mol2.zip folder with the structures of relevant optimized structures (B3LYP/Def2SVP) in mol2 format

Abstract (en): This repository contains all of the inputs, outputs, and validation for "Understanding America's Neighborhoods Using Uncertain Data From the American Community Survey" by Spielman and Singleton (in press). See the paper at URL for detailed descriptions of the files. On 2016-08-20, the openICPSR web site was moved to new software. In the migration process, some projects were not published in the new system because the decisions made in the old site did not map easily to the new setup. An ICPSR staff member manually published these projects, taking care to preserve the original wishes of the depositor. Funding insitution(s): National Science Foundation (1132008).

This dataset contains Iowa median age by sex for State of Iowa, individual Iowa counties, Iowa places and census tracts within Iowa. Data is from the American Community Survey, Five Year Estimates, Table B01002.

Sex includes the following: Male, Female and Both

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset tracks the updates made on the dataset "SDOH Measures for ZCTA, ACS 2017-2021" as a repository for previous versions of the data and metadata.

This dataset tracks the updates made on the dataset "Nutrition, Physical Activity, and Obesity - American Community Survey" as a repository for previous versions of the data and metadata.

The measurement data accompanying the journal article titled: "Collinear Photothermal Gas Sensing with a Custom-Made Dichroic and Fiber-Coupled Fabry-Pérot Etalon"

The article was published in the journal ACS Omega.

The data is sorted for each individual figure and should enable readers to reproduce the presented results.

Spectral libraries play a central role in the analysis of data-independent-acquisition (DIA) proteomics experiments. A main assumption in current spectral library tools is that a single characteristic intensity pattern (CIP) suffices to describe the fragmentation of a peptide in a particular charge state (peptide charge pair). However, we find that this is often not the case. We carry out a systematic evaluation of spectral variability over public repositories and in-house data sets. We show that spectral variability is widespread and partly occurs under fixed experimental conditions. Using clustering of preprocessed spectra, we derive a limited number of multiple characteristic intensity patterns (MCIPs) for each peptide charge pair, which allow almost complete coverage of our heterogeneous data set without affecting the false discovery rate. We show that a MCIP library derived from public repositories performs in most cases similar to a ”custom-made” spectral library, which has been acquired under identical experimental conditions as the query spectra. We apply the MCIP approach to a DIA data set and observe a significant increase in peptide recognition. We propose the MCIP approach as an easy-to-implement addition to current spectral library search engines and as a new way to utilize the data stored in spectral repositories.

On 7/18/2025, we will be pausing COVID-19 hospitalization admission data to assess data quality and completeness. A. SUMMARY This dataset includes information on COVID+ hospital admissions for San Francisco residents into San Francisco hospitals. Specifically, the dataset includes the count and rate of COVID+ hospital admissions per 100,000. The data are reported by week. B. HOW THE DATASET IS CREATED Hospital admission data is reported to the San Francisco Department of Public Health (SFDPH) via the COVID Hospital Data Repository (CHDR), a system created via health officer order C19-16. The data includes all San Francisco hospitals except for the San Francisco VA Medical Center. San Francisco population estimates are pulled from a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2018-2022 5-year American Community Survey (ACS). C. UPDATE PROCESS Data updates weekly on Wednesday with data for the past Wednesday-Tuesday (one week lag). Data may change as more current information becomes available. D. HOW TO USE THIS DATASET New admissions are the count of COVID+ hospital admissions among San Francisco residents to San Francisco hospitals by week. The admission rate per 100,000 is calculated by multiplying the count of admissions each week by 100,000 and dividing by the population estimate. E. CHANGE LOG 7/18/2025 - Dataset update is paused to assess data quality and completeness. 9/12/2024 - We updated the data source for our COVID-19 hospitalization data to a San Francisco specific dataset. These new data differ slightly from previous hospitalization data sources but the overall patterns and trends in hospitalizations remain consistent. You can access the previous data here.

Context

This is the US Coronavirus data repository from The New York Times . This data includes COVID-19 cases and deaths reported by state and county. The New York Times compiled this data based on reports from state and local health agencies. More information on the data repository is available here . For additional reporting and data visualizations, see The New York Times’ U.S. coronavirus interactive site

Sample Queries

Query 1

Which US counties have the most confirmed cases per capita? This query determines which counties have the most cases per 100,000 residents. Note that this may differ from similar queries of other datasets because of differences in reporting lag, methodologies, or other dataset differences.

SELECT covid19.county, covid19.state_name, total_pop AS county_population, confirmed_cases, ROUND(confirmed_cases/total_pop *100000,2) AS confirmed_cases_per_100000, deaths, ROUND(deaths/total_pop *100000,2) AS deaths_per_100000 FROM bigquery-public-data.covid19_nyt.us_counties covid19 JOIN bigquery-public-data.census_bureau_acs.county_2017_5yr acs ON covid19.county_fips_code = acs.geo_id WHERE date = DATE_SUB(CURRENT_DATE(),INTERVAL 1 day) AND covid19.county_fips_code != "00000" ORDER BY confirmed_cases_per_100000 desc

Query 2

How do I calculate the number of new COVID-19 cases per day? This query determines the total number of new cases in each state for each day available in the dataset SELECT b.state_name, b.date, MAX(b.confirmed_cases - a.confirmed_cases) AS daily_confirmed_cases FROM (SELECT state_name AS state, state_fips_code , confirmed_cases, DATE_ADD(date, INTERVAL 1 day) AS date_shift FROM bigquery-public-data.covid19_nyt.us_states WHERE confirmed_cases + deaths > 0) a JOIN bigquery-public-data.covid19_nyt.us_states b ON a.state_fips_code = b.state_fips_code AND a.date_shift = b.date GROUP BY b.state_name, date ORDER BY date desc

Herein are the data files that support a simple python package to facilitate profiling of chemicals through the ECHA ARN groupings using the Random Forest model that was developed in Karamertzanis, P, Patlewicz G, Sannicola M, Paul-Friedman K, Shah (2024) Systematic Approaches for the Encoding of Chemical Groups: A Case study. Chem. Res. Toxicol. 37, 600-619. https://doi.org/10.1021/acs.chemrestox.3c00411

The original repository supporting this publication can be found here https://github.com/pkaramertzanis/regulatory_grouping/tree/master. In https://github.com/patlewig/arn_cats/ we provide a simpler means of applying the best random forest model developed in the original study to make grouping assignments for new chemicals.

Although the arn_cats package and associated repository provide notebooks to recreate the random forest models and necessary input files, these are provided for convenience. The dataset of REACH chemicals that was supplied in the SI of the original publication is provided here as it is useful set to verify the RF model predictions.

The Synthetic Biology Knowledge System (SBKS) is an instance of the SynBioHub repository that includes text and data information that has been mined from papers published in ACS Synthetic Biology. This paper describes the SBKS curation framework that is being developed to construct the knowledge stored in this repository. The text mining pipeline performs automatic annotation of the articles using natural language processing techniques to identify salient content such as key terms, relationships between terms, and main topics. The data mining pipeline performs automatic annotation of the sequences extracted from the supplemental documents with the genetic parts used in them. Together these two pipelines link genetic parts to papers describing the context in which they are used. Ultimately, SBKS will reduce the time necessary for synthetic biologists to find the information necessary to complete their designs.

Data Source: Open Data DC and American Community Survey (ACS) 1-Year Estimates.

Why This Matters

Living further from full-service grocery stores can force residents to shop for food at locations that are more expensive or have fewer healthy options, leading to worse health outcomes for conditions such as obesity or diabetes.

Beyond basic nutrition, food is an integral part of culture. Having access to a wide array of culturally relevant foods has been shown to improve well-being among Black, Indigenous, and people of color communities.

Across the United States, predominantly-Black communities have fewer supermarkets than predominantly white and Hispanic communities. A pattern of disinvestment limits the availability of fresh and healthy foods.

The District Response

The Food Access Fund (FAF) Grant increases equitable access to fresh, healthy, and affordable food by supporting the opening of new grocery stores in areas with low food access, with priority given to locations in Ward 7 or Ward 8. The Produce Plus Program provides financial support for residents with low access to fresh foods to spend at local farmers markets.

The SUN Bucks program provides additional grocery-buying benefits to income-eligible families when schools are closed for the summer and children no longer have access to free or reduced-cost meals at school.

The DC Food Policy Council convenes six working groups, including the Food Access & Equity working group that aims to communicate and collaborate with residents to increase awareness of District food benefit programs and healthy food retail.