Raw data tables and the statistical analysis applied to the data. Files are labeled by figure number. Within each file, each table and linked graph and analysis is annotated by figure number and panel letter. All files are generated in graphpad prism.

Raw data describing the samples distributions with different methodologies.

No description is available. Visit https://dataone.org/datasets/d7a76cd4b62f683e960b1d65351a9f04 for complete metadata about this dataset.

This metadata record provides details of the raw data produced from the SHARE-seq experiments for the HDMA study (Liu et al. bioRxiv 2025).

Summary

De-identified tissue samples were collected at Stanford University School of Medicine from elective termination of pregnancy procedures with informed consent for the research use of tissues in observance of relevant legal and institutional ethical regulations. SHARE-seq was performed on isolated nuclei (Methods, Note S1).

In total, N=76 samples were profiled, from across 10-23 post-conception weeks, and covering a total of 12 tissues. The full list of samples, along with experimental batch, age, and sex, is provided in Supplementary Table 1.

SHARE-seq library raw data files

All DNA libraries were sequenced on a NovaSeq 6000 using 300-cycle S4 v1.5 reagent kits with XP workflow. Paired-end sequencing was run with a 96-99-8-96 configuration (Read1-Index1-Index2-Read2). Sequencing was performed at the Stanford Genome Technology Center.

We developed a highly parallelized, rapid, and storage-efficient pre-processing pipeline to convert BCL files from sequencers to ATAC fragment files and RNA sparse matrices (Fig. S1, Methods, and available in full at https://github.com/GreenleafLab/shareseq-pipeline (stable release v1.0.0).

The raw data is in the form of FASTQs pairs per sample per data modality. Please contact Dr. William Greenleaf (wjg@stanford.edu) regarding access to raw data.

SHARE-seq library processed data

Processed data in the form of fragments per sample (ATAC modality) and gene expression count matrices (RNA modality) are provided. The full list of datasets deposited is provided in Supplementary Table 14.

Sample data for exercises in Further Adventures in Data Cleaning.

The intention is to collect data for the calendar year 2009 (or the nearest year for which each business keeps its accounts. The survey is considered a one-off survey, although for accurate NAs, such a survey should be conducted at least every five years to enable regular updating of the ratios, etc., needed to adjust the ongoing indicator data (mainly VAGST) to NA concepts. The questionnaire will be drafted by FSD, largely following the previous BAS, updated to current accounting terminology where necessary. The questionnaire will be pilot tested, using some accountants who are likely to complete a number of the forms on behalf of their business clients, and a small sample of businesses. Consultations will also include Ministry of Finance, Ministry of Commerce, Industry and Labour, Central Bank of Samoa (CBS), Samoa Tourism Authority, Chamber of Commerce, and other business associations (hotels, retail, etc.).

The questionnaire will collect a number of items of information about the business ownership, locations at which it operates and each establishment for which detailed data can be provided (in the case of complex businesses), contact information, and other general information needed to clearly identify each unique business. The main body of the questionnaire will collect data on income and expenses, to enable value added to be derived accurately. The questionnaire will also collect data on capital formation, and will contain supplementary pages for relevant industries to collect volume of production data for selected commodities and to collect information to enable an estimate of value added generated by key tourism activities.

The principal user of the data will be FSD which will incorporate the survey data into benchmarks for the NA, mainly on the current published production measure of GDP. The information on capital formation and other relevant data will also be incorporated into the experimental estimates of expenditure on GDP. The supplementary data on volumes of production will be used by FSD to redevelop the industrial production index which has recently been transferred under the SBS from the CBS. The general information about the business ownership, etc., will be used to update the Business Register.

Outputs will be produced in a number of formats, including a printed report containing descriptive information of the survey design, data tables, and analysis of the results. The report will also be made available on the SBS website in “.pdf” format, and the tables will be available on the SBS website in excel tables. Data by region may also be produced, although at a higher level of aggregation than the national data. All data will be fully confidentialised, to protect the anonymity of all respondents. Consideration may also be made to provide, for selected analytical users, confidentialised unit record files (CURFs).

A high level of accuracy is needed because the principal purpose of the survey is to develop revised benchmarks for the NA. The initial plan was that the survey will be conducted as a stratified sample survey, with full enumeration of large establishments and a sample of the remainder.

v01: This is the first version of the documentation. Basic raw data, obtained from data entry.

The scope of the 2009 BAS is all employing businesses in the private sector other than those involved in agricultural activities.

Included are:
· Non-governmental organizations (NGOs, not-for profit organizations, etc.);
· Government Public Bodies

Excluded are:
· Non-employing units (e.g., market sellers);
· Government ministries, constitutional offices and those public bodies involved in public administration and included in the Central Government Budget Sector;
· Agricultural units (unless large scale/commercial - if the Agriculture census only covers household activities);
· “Non-resident” bodies such as international agencies, diplomatic missions (e.g., high commissions and embassies, UNDP, FAO, WHO);

The survey coverage is of all businesses in scope as defined above. Statistical units relevant to the survey are the enterprise and the establishment. The enterprise is an institutional unit and generally corresponds to legal entities such as a company, cooperative, partnership or sole proprietorship. The establishment is an institutional unit or part of an institutional unit, which engages in one, or predominantly one, type of economic activity. Sufficient data must be available to derive or meaningfully estimate value added in order to recognize an establishment. The main statistical unit from which data will be collected in the survey is the establishment. For most businesses there will be a one-to-one relationship between the enterprise and the establishment, i.e., simple enterprises will comprise only one establishment. The purpose of collecting data from establishments (rather than from enterprises) is to enable the most accurate industry estimates of value added possible.

• Collection start: 2009
• Collection end: 2009

Source: Kaggle users Data analysis: Student's placement scores with their years to join the club. The file includes some worksheets such as raw data, summary data, pivot table and a chart, SMART questions and SOW. In the chart: You can see the connection between student's placement scores and their time to join the club. The table involves 5 columns, and then it was added more two columns (Year and Time_Join_Club) so that you can calculate the number of years in which the students have joined the club.

Math_Score| Reading_Score| Writing_Score |Placement_Score |Club_Join_Date|Year | Time _Join_Club|

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows fertility in past 12 months by age of mother. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. The calculated percentages are slightly different from traditional age-specific fertility rates in that the total number of live births (due to twins or higher-order multiple births) is not available in this table. Note: Data are not available for all geographies within this layer due to data suppression done by the American Community Survey. Since there was a data collection error in the 2011 and 2012 surveys, this impacts the 2010-2014 5-year estimates. To learn more and to see a list of the affected geographies, visit this page about Errata 119.This layer is symbolized to show the percent of women age 15 to 50 who had a birth in the past 12 months. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B13016 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 11, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This table contains the data on identity, pathology, and dental wear used in the study. All dental wear measurements and the individuals they are associated with are included. (XLSX)

The State Contract and Procurement Registration System (SCPRS) was established in 2003, as a centralized database of information on State contracts and purchases over $5000. eSCPRS represents the data captured in the State's eProcurement (eP) system, Bidsync, as of March 16, 2009. The data provided is an extract from that system for fiscal years 2012-2013, 2013-2014, and 2014-2015

Data Limitations:
Some purchase orders have multiple UNSPSC numbers, however only first was used to identify the purchase order. Multiple UNSPSC numbers were included to provide additional data for a DGS special event however this affects the formatting of the file. The source system Bidsync is being deprecated and these issues will be resolved in the future as state systems transition to Fi$cal.

Data Collection Methodology:

The data collection process starts with a data file from eSCPRS that is scrubbed and standardized prior to being uploaded into a SQL Server database. There are four primary tables. The Supplier, Department and United Nations Standard Products and Services Code (UNSPSC) tables are reference tables. The Supplier and Department tables are updated and mapped to the appropriate numbering schema and naming conventions. The UNSPSC table is used to categorize line item information and requires no further manipulation. The Purchase Order table contains raw data that requires conversion to the correct data format and mapping to the corresponding data fields. A stacking method is applied to the table to eliminate blanks where needed. Extraneous characters are removed from fields. The four tables are joined together and queries are executed to update the final Purchase Order Dataset table. Once the scrubbing and standardization process is complete the data is then uploaded into the SQL Server database.

Secondary/Related Resources:

• State Contract Manual (SCM) vol. 2 http://www.dgs.ca.gov/pd/Resources/publications/SCM2.aspx
• State Contract Manual (SCM) vol. 3 http://www.dgs.ca.gov/pd/Resources/publications/SCM3.aspx
• Buying Green http://www.dgs.ca.gov/buyinggreen/Home.aspx
• United Nations Standard Products and Services Code, http://www.unspsc.org/

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

Plasmodium vivax serological exposure markers (SEMs) have emerged as promising tools for the actionable surveillance and implementation of targeted interventions to accelerate malaria elimination. To determine the dynamic profiles of SEMs in current and past P. vivax infections, we screened and selected 11 P. vivax proteins from 210 putative proteins using protein arrays, with a set of serum samples obtained from patients with acute P. vivax and documented past P. vivax infections. Then we used a murine protein immune model to initially investigate the humoral and memory B cell response involved in the generation of long-lived antibodies. We show that of the 11 proteins, especially C-terminal 42-kDa region of P. vivax merozoite surface protein 1 (PvMSP1-42) induced longer-lasting long-lived antibodies, as these antibodies were detected in individuals infected with P. vivax in the 1960-1970s who were not re-infected until 2012. In addition, we provide a potential mechanism for the maintenance of long-lived antibodies after the induction of PvMSP1-42. The results indicate that PvMSP1-42 induces more CD73+CD80+ memory B cells (MBCs) compared to P. vivax GPI-anchored micronemal antigen (PvGAMA), allowing IgG anti-PvMSP1-42 antibodies to be maintained for a long time.

The information in this dataset is from "Feasibility of Infiltration Galleries for Managed Aquifer Recharge in the Northeast Arkansas Delta" by Godwin et al., 2020. Included in the dataset are the following raw data: Table of well log point -Coordinates and other characteristic data for each groundwater well log point used in confining unit mapping survey. These points can be used for various spatial analyses of the Mississippi River Valley Alluvial Aquifer (MRVAA) and its upper confining unit. Check the Arkansas Water Well Construction Commission database for updated spatial information, updated and improved logs, and newly added well logs. Raw geophysical data files -Electrical resistivity survey files from the selected reservoir sites collected in partnership with the United States Geological Survey. These are the raw files from Inverse-Schlumberger method survey lines at five reservoir sites, which measure differences in soil electrical properties. These differences correspond to changes in soil texture. Soil sample textural analysis data -Data includes sand/silt/clay analysis result sheet and sand fractionization result sheets for samples from the reservoir sites selected after geophysical surveys were conducted. Data should be used only with considerations of the sampling and analysis methods described in the publication. Soil sample chemical analysis data -Includes major and minor metals/nutrients, pH, and other chemical properties for samples from selected sites. Data should be used only with considerations of the sampling and analysis methods described in the publication. Resources in this dataset:Resource Title: Table of Well Points . File Name: WellLogPointsTable.xlsxResource Description: This table includes the spatial coordinates, web links, and confining unit thickness data for all of the irrigation wells used in the mapping survey. Well data are from the Arkansas Water Well Construction Commission Database.Resource Title: Sand-Silt-Clay Soil Sample Data. File Name: All Samples Sand-Silt-Clay.xlsxResource Description: Soil boring sample analysis results from University of Missouri Soil Lab for sand-silt-clay fractionalization. Resource Title: Select Soil Sample Sand Fractionalization . File Name: Select Samples Sand Fractionation.xlsxResource Description: The results of sand grain-size fractionalization analysis conducted on select samples at the University of Missouri Soil LabResource Title: Soil Sample Sieve Analysis. File Name: Soil Sample Sieve Analysis DWMRU Lab.xlsxResource Description: Results of in-house sieve analysis (USDA Delta Water Management Research Unit) on selected soil boring samples. Resource Title: Soil Sample Chemical Analyses. File Name: MissouriSoilTestingLaboratory_Results Sheet.xlsxResource Description: Results of various soil chemical analyses conducted at the University of Missouri Soil LabResource Title: Raw Geophysical Data Files. File Name: Electrical Resistivity Profile Raw Files.zipResource Description: Raw geophysical data files -Electrical resistivity survey files from the selected reservoir sites collected in partnership with the United States Geological Survey. These are the raw files from Inverse-Schlumberger method survey lines at five reservoir sites, which measure differences in soil electrical properties. These differences correspond to changes in soil texture.

This data release contains numerous comma-separated text files with data summarizing observations in the within and adjacent to the Woodbury Fire, which burned from 8 June to 15 July 2019. In particular, this monitoring data was focused on debris flows in burned and unburned areas. Rainfall data (Wdby_Rainfall.zip) are contained in csv files called Wdby_Rainfall for 3 rain gages named: B2, B6, and Reavis. This is time-series data where the total rainfall is recorded at each timestamp. The location of each rain gage is listed as a latitude/longitude in each file. Data from absolute (i.e. not vented) pressure transducers (Wdby_Pressure.zip), which can be used to constrain the time of passage of a flood or debris flow, are available in csv files called Wdby_Pressure for four drainages (B1, B6, Reavis 1, and Reavis 2). This is time-series data where the measured pressure in kilopascals is recorded at each timestamp. The location of each pressure transducer is listed as a latitude/longitude in each file. Infiltration data are located in the csv file called WoodburyInfiltration.csv. The location of the measurement is listed as a latitude/longitude. Three measurement values are reported at each location: Saturated Hydraulic Conductivity (Ks) [mm/hr], Sorptivity (S) [mm/h^(1/2)], and pressure head (hf) [m]. The date of each measurement and soil burn severity class are also reported at each location, as well as a table explaining the burn-severity numerical class conversion. Particle size analyses using laser diffraction (WoodburyLaserDiffractionSummary.zip) are located in the files called WoodburyLaserDiffractionSummary for the fine fraction (< 2 mm) of hillslope and debris flow Deposits. The diameter of each particle size class is listed in the first column. All subsequent columns begin with the sample name. The value in each row is the percentage of the grain sizes in the size class. Location data for each of these samples is listed in the accompanying data table titled: WoodburyParticleSizeSummary.csv. The particle size data are summarized in the csv files (WoodburyParticleSizeSummary.zip) called WoodburyParticleSizeSummary by debris flow deposits and hillslope samples. These files group the raw data into more useable information. The sample name (Lab ID) is used to identify the Laser Diffraction data. The data columns (Lat) and (Lon) show the latitude and longitude of the sample locations. The total fraction of all the grain sizes, determined by sieving, are listed in three classes (Fraction < 16 mm, Fraction < 4 mm, Fraction < 2 mm). The fine fractions (< 2 mm) are also summarized in the columns (%Sand, %Silt, %Clay), as determined by laser diffraction. The data are identfied as in the burn area using entries of Yes, whereas unburned areas are shown as No, indicating no burn. The median particle size (D50) is listed if the sample collected in the field was representative of the deposit. In some cases, large cobbles and boulders had to be removed from the sample because were much too large to be included in sample bags that were brought back to the lab for analysis. The last column label (Description) contains notes about each sample. Pebble count data (WoodburyPebbleCountsSummary.zip) are available in csv files called WoodburyPebbleCountsSummary for six drainages (U10 Fan, U10 Channel, U22 Channel, B1 Channel, B7 Fan, and U42 Fan). Here U represents unburned, and B represents burned. The data name indicates whether the data come from a deposit located in a channel or a fan. In each file the particle is numbered (Num) and the B-axis measurement of the particle is reported in centimeters. The location of each pebble count is listed as a latitude/longitude in each file. Channel width measurements for 23 channels are saved in unique shapefiles within the file called Channel_Width_Transects.zip. These width measurements were made using Digital Globe imagery from 19 October 2019. The study basins used for the entire study can be found in the shapefile: Woodbury_StudyBasins.shp. The attribute table along with many morphometric and fire related statistics for each basin is also available in the file Woodbury_StudyBasins_Table.csv. A description of each column name in the table is available in the file Woodbury_StudyBasins_Table_descriptions.csv. Debris flow volumes were available in eleven drainage basins. The volume data is contained in the file Wdby_FlowVolume.csv in a column named (Volume). The volume units are cubic meters. The other column is the Basin ID, which can be found in the shapefile: Woodbury_StudyBasins.shp.

Contents in C:\Bachelorproject\Data_Thesis_FleurVerdonkschot_12228125

In this folder the raw data of the mass spectrometer and root weight of Cucumis sativus are stored

In Data_MS_Thesis_FleurVerdonkschot_12228125 the extracted peak areas of themass spectrometer are stored, for the freeze drying and the SPE methods.

In Raw_Data_Thesis_FleurVerdonkschot_12228125 the varying sheets contain the following data: -Sample identification (the treatment of each sample, the label and its randomization number) -The table lay-out (where each plant was placed according to the randomization) -The rootweight of each sample -Freeze drying: the peak areas of each sample before and after normalization with the root weight -SPE: the peak areas of each sample before and after normalization with the root weight -The signal to noise ratio of each relevant sample and their means

Data format: [Sample ID]_[VALUE]

Sea surface salinity (SSS) is the least constrained major variable of the past (paleo) ocean but is fundamental in controlling the density of seawater and thus large-scale ocean circulation. The hydrogen isotopic composition (δD) of non-exchangeable hydrogen of algal lipids, specifically alkenones, has been proposed as a promising new proxy for paleo SSS. The δD of surface seawater is correlated with SSS, and laboratory culture studies have shown the δD of algal growth water to be reflected in the δD of alkenones. However, a large-scale field study testing the validity of this proxy is still lacking. Here we present the δD of open-ocean Atlantic and Pacific surface waters and coincident δD of alkenones sampled by underway filtration. Two transects of approximately 100° latitude in the Atlantic Ocean and more than 50° latitude in the Western Pacific sample much of the range of open ocean salinities and seawater δD, and thus allow probing the relationship between δD of seawater and alkenones. Overall, the open ocean δD alkenone data correlate significantly with SSS, and also agree remarkably well with δD water vs δD alkenone regressions developed from culture studies. Subtle deviations from these regressions are discussed in the context of physiological factors as recorded in the carbon isotopic composition of alkenones. In a best-case scenario, the data presented here suggest that SSS variations as low as 1.2 can be reconstructed from alkenone δD.

This layer shows housing costs as a percentage of household income by age. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. This layer is symbolized to show the predominant housing type for householders where the householder is age 65+ and spending at least 30% of their income on housing. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25072, B25093 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Raw data repositories for PLOS One journal manuscript titled "Evaluating continuous nanosecond pulsed electric field (nsPEF) treatment as a nonthermal alternative for human milk pasteurisation" The excel sheet include raw data for : Figure 2 (Instantaneous voltage waveforms comparing MilliQ water, three different salts (NaCl) concentrations of saline (0.05%, 0.10%, and 0.20% w/v) and human milk in PEF treatment) Figure 3 (Temperature monitoring over time for 0.1% saline and human milk samples during nsPEF treatment using an FO sensor) Table 2 (Specific conductance measurements of all samples before PEF treatment) Table 3 (Effectiveness of nsPEF treatment in E. coli inactivation (total bacterial count, log CFU/mL and log reduction) in inoculated 0.05% saline and 0.1% saline at various initial bacterial concentrations) Table 4 (Effect of nsPEF treatment in E. coli inactivation (total bacterial count, log CFU/mL and log reduction) in inoculated donor human milk samples at various initial bacterial concentrations).

The Crude Oil Analysis (COA) database contains the digital data compilation of 9,076 crude oil analyses from samples collected from 1920 through 1983 from the United States and around the world and analyzed by the United States Bureau of Mines (National Institute for Petroleum and Energy Research, 1995). Two laboratories (Bartlesville, Oklahoma, and Laramie, Wyoming) performed routine crude oil analyses by a standardized method, and the data were originally reported in more than 50 reports by the Bureau of Mines. Analyses include specific gravity, API gravity, pour point, viscosity, sulfur content, nitrogen content, and color of the crude oil, as well as the bulk properties of the distillation cuts. The data were digitized in the late 1970s and a database retrieval system was implemented in 1980 and made available to the public. The Department of Energy (DOE) updated this system in 1995-96 with public access through a dial-up bulletin board system. The database was operated by the National Institute for Petroleum and Energy Research (NIPER) in Bartlesville, Oklahoma. A stand-alone version of the database (COADB) was available in 1995 in the form of a series of tables in Foxpro (.dbf) format. In 1998, an updated version of COADB was available on the NIPER website that included a Microsoft Access 97 version of the database called "coadb.mdb". The file contains more tables than the original 1995 version but we believe the number of oil samples and the amount of raw data are the same. The additional tables contain text translations for codes used in other tables regarding color, county, laboratory, formation, geologic age, lithology, and state name. Sample location information is generally inadequate to identify the specific well in most cases. The sample location information lacks lease name and in many cases well number and section-township-range. In rare cases, the latitude and longitude are given. A 2002 version was provided by the National Energy Technology Laboratory.