The Delta Neighborhood Physical Activity Study was an observational study designed to assess characteristics of neighborhood built environments associated with physical activity. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns and neighborhoods in which Delta Healthy Sprouts participants resided. The 12 towns were located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys between August 2016 and September 2017 using the Rural Active Living Assessment (RALA) tools and the Community Park Audit Tool (CPAT). Scale scores for the RALA Programs and Policies Assessment and the Town-Wide Assessment were computed using the scoring algorithms provided for these tools via SAS software programming. The Street Segment Assessment and CPAT do not have associated scoring algorithms and therefore no scores are provided for them. Because the towns were not randomly selected and the sample size is small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one contains data collected with the RALA Programs and Policies Assessment (PPA) tool. Dataset two contains data collected with the RALA Town-Wide Assessment (TWA) tool. Dataset three contains data collected with the RALA Street Segment Assessment (SSA) tool. Dataset four contains data collected with the Community Park Audit Tool (CPAT). [Note : title changed 9/4/2020 to reflect study name] Resources in this dataset:Resource Title: Dataset One RALA PPA Data Dictionary. File Name: RALA PPA Data Dictionary.csvResource Description: Data dictionary for dataset one collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA Data Dictionary. File Name: RALA TWA Data Dictionary.csvResource Description: Data dictionary for dataset two collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA Data Dictionary. File Name: RALA SSA Data Dictionary.csvResource Description: Data dictionary for dataset three collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT Data Dictionary. File Name: CPAT Data Dictionary.csvResource Description: Data dictionary for dataset four collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One RALA PPA. File Name: RALA PPA Data.csvResource Description: Data collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA. File Name: RALA TWA Data.csvResource Description: Data collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA. File Name: RALA SSA Data.csvResource Description: Data collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT. File Name: CPAT Data.csvResource Description: Data collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary. File Name: DataDictionary_RALA_PPA_SSA_TWA_CPAT.csvResource Description: This is a combined data dictionary from each of the 4 dataset files in this set.

Data Dictionary template for Tempe Open Data.

Supplementary_Data_Dictionary_Sheet_v1.0.xls

The data dictionary Excel sheet is the main supporting document for the paper.

DD_-_Neonatal_Data.csv

The patient dataset is provided as a format for capturing data with respect to data dictionary.

The Delta Produce Sources Study was an observational study designed to measure and compare food environments of farmers markets (n=3) and grocery stores (n=12) in 5 rural towns located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys from June 2019 to March 2020 using a modified version of the Nutrition Environment Measures Survey (NEMS) Farmers Market Audit tool. The tool was modified to collect information pertaining to source of fresh produce and also for use with both farmers markets and grocery stores. Availability, source, quality, and price information were collected and compared between farmers markets and grocery stores for 13 fresh fruits and 32 fresh vegetables via SAS software programming. Because the towns were not randomly selected and the sample sizes are relatively small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Resources in this dataset:Resource Title: Delta Produce Sources Study dataset . File Name: DPS Data Public.csvResource Description: The dataset contains variables corresponding to availability, source (country, state and town if country is the United States), quality, and price (by weight or volume) of 13 fresh fruits and 32 fresh vegetables sold in farmers markets and grocery stores located in 5 Lower Mississippi Delta towns.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Delta Produce Sources Study data dictionary. File Name: DPS Data Dictionary Public.csvResource Description: This file is the data dictionary corresponding to the Delta Produce Sources Study dataset.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

The Delta Food Outlets Study was an observational study designed to assess the nutritional environments of 5 towns located in the Lower Mississippi Delta region of Mississippi. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns in which Delta Healthy Sprouts participants resided and that contained at least one convenience (corner) store, grocery store, or gas station. Data were collected via electronic surveys between March 2016 and September 2018 using the Nutrition Environment Measures Survey (NEMS) tools. Survey scores for the NEMS Corner Store, NEMS Grocery Store, and NEMS Restaurant were computed using modified scoring algorithms provided for these tools via SAS software programming. Because the towns were not randomly selected and the sample sizes are relatively small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one (NEMS-C) contains data collected with the NEMS Corner (convenience) Store tool. Dataset two (NEMS-G) contains data collected with the NEMS Grocery Store tool. Dataset three (NEMS-R) contains data collected with the NEMS Restaurant tool. Resources in this dataset:Resource Title: Delta Food Outlets Data Dictionary. File Name: DFO_DataDictionary_Public.csvResource Description: This file contains the data dictionary for all 3 datasets that are part of the Delta Food Outlets Study.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One NEMS-C. File Name: NEMS-C Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for convenience stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two NEMS-G. File Name: NEMS-G Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for grocery stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three NEMS-R. File Name: NEMS-R Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for restaurants.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

The Pesticide Data Program (PDP) is a national pesticide residue database program. Through cooperation with State agriculture departments and other Federal agencies, PDP manages the collection, analysis, data entry, and reporting of pesticide residues on agricultural commodities in the U.S. food supply, with an emphasis on those commodities highly consumed by infants and children.This dataset provides information on where each tested sample was collected, where the product originated from, what type of product it was, and what residues were found on the product, for calendar years 1992 through 2023. The data can measure residues of individual compounds and classes of compounds, as well as provide information about the geographic distribution of the origin of samples, from growers, packers and distributors. The dataset also includes information on where the samples were taken, what laboratory was used to test them, and all testing procedures (by sample, so can be linked to the compound that is identified). The dataset also contains a reference variable for each compound that denotes the limit of detection for a pesticide/commodity pair (LOD variable). The metadata also includes EPA tolerance levels or action levels for each pesticide/commodity pair. The dataset will be updated on a continual basis, with a new resource data file added annually after the PDP calendar-year survey data is released.Resources in this dataset:Resource Title: CSV Data Dictionary for PDP.File Name: PDP_DataDictionary.csv. Resource Description: Machine-readable Comma Separated Values (CSV) format data dictionary for PDP Database Zip files. Defines variables for the sample identity and analytical results data tables/files. The ## characters in the Table and Text Data File name refer to the 2-digit year for the PDP survey, like 97 for 1997 or 01 for 2001. For details on table linking, see PDF. Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excelResource Title: Data dictionary for Pesticide Data Program. File Name: PDP DataDictionary.pdf. Resource Description: Data dictionary for PDP Database Zip files. Resource Software Recommended: Adobe Acrobat, url: https://www.adobe.comResource Title: 2023 PDP Database Zip File. File Name: 2023PDPDatabase.zipResource Title: 2022 PDP Database Zip File. File Name: 2022PDPDatabase.zipResource Title: 2021 PDP Database Zip File. File Name: 2021PDPDatabase.zipResource Title: 2020 PDP Database Zip File. File Name: 2020PDPDatabase.zipResource Title: 2019 PDP Database Zip File. File Name: 2019PDPDatabase.zipResource Title: 2018 PDP Database Zip File. File Name: 2018PDPDatabase.zipResource Title: 2017 PDP Database Zip File. File Name: 2017PDPDatabase.zipResource Title: 2016 PDP Database Zip File. File Name: 2016PDPDatabase.zipResource Title: 2015 PDP Database Zip File. File Name: 2015PDPDatabase.zipResource Title: 2014 PDP Database Zip File. File Name: 2014PDPDatabase.zipResource Title: 2013 PDP Database Zip File. File Name: 2013PDPDatabase.zipResource Title: 2012 PDP Database Zip File. File Name: 2012PDPDatabase.zipResource Title: 2011 PDP Database Zip File. File Name: 2011PDPDatabase.zipResource Title: 2010 PDP Database Zip File. File Name: 2010PDPDatabase.zipResource Title: 2009 PDP Database Zip File. File Name: 2009PDPDatabase.zipResource Title: 2008 PDP Database Zip File. File Name: 2008PDPDatabase.zipResource Title: 2007 PDP Database Zip File. File Name: 2007PDPDatabase.zipResource Title: 2006 PDP Database Zip File. File Name: 2006PDPDatabase.zipResource Title: 2005 PDP Database Zip File. File Name: 2005PDPDatabase.zipResource Title: 2004 PDP Database Zip File. File Name: 2004PDPDatabase.zipResource Title: 2003 PDP Database Zip File. File Name: 2003PDPDatabase.zipResource Title: 2002 PDP Database Zip File. File Name: 2002PDPDatabase.zipResource Title: 2001 PDP Database Zip File. File Name: 2001PDPDatabase.zipResource Title: 2000 PDP Database Zip File. File Name: 2000PDPDatabase.zipResource Title: 1999 PDP Database Zip File. File Name: 1999PDPDatabase.zipResource Title: 1998 PDP Database Zip File. File Name: 1998PDPDatabase.zipResource Title: 1997 PDP Database Zip File. File Name: 1997PDPDatabase.zipResource Title: 1996 PDP Database Zip File. File Name: 1996PDPDatabase.zipResource Title: 1995 PDP Database Zip File. File Name: 1995PDPDatabase.zipResource Title: 1994 PDP Database Zip File. File Name: 1994PDPDatabase.zipResource Title: 1993 PDP Database Zip File. File Name: 1993PDPDatabase.zipResource Title: 1992 PDP Database Zip File. File Name: 1992PDPDatabase.zip

The Agricultural Research Service of the US Department of Agriculture (USDA) in collaboration with other government agencies has a program to track changes in the sodium content of commercially processed and restaurant foods. This monitoring program includes these activities: Tracking sodium levels of ~125 popular foods, called "Sentinel Foods," by periodically sampling them at stores and restaurants around the country, followed by laboratory analyses. Tracking levels of "related" nutrients that could change when manufacturers reformulate their foods to reduce sodium; these related nutrients are potassium, total and saturated fat, total dietary fiber, and total sugar. Sharing the results of these monitoring activities to the public periodically in the Sodium Monitoring Dataset and USDA National Nutrient Database for Standard Reference and once every two years in the Food and Nutrient Database for Dietary Studies. The Sodium Monitoring Dataset is downloadable in Excel spreadsheet format. Resources in this dataset:Resource Title: Data Dictionary. File Name: SodiumMonitoringDataset_datadictionary.csvResource Description: Defines variables, descriptions, data types, character length, etc. for each of the spreadsheets in this Excel data file: Sentinel Foods - Baseline; Priority-2 Foods - Baseline; Sentinel Foods - Monitoring; Priority-2 Foods - Monitoring.Resource Title: Sodium Monitoring Dataset (MS Excel download). File Name: SodiumMonitoringDatasetUpdatedJuly2616.xlsxResource Description: Microsoft Excel : Sentinel Foods - Baseline; Priority-2 Foods - Baseline; Sentinel Foods - Monitoring; Priority Foods - Monitoring.

The National Health and Nutrition Examination Survey (NHANES) provides data and have considerable potential to study the health and environmental exposure of the non-institutionalized US population. However, as NHANES data are plagued with multiple inconsistencies, processing these data is required before deriving new insights through large-scale analyses. Thus, we developed a set of curated and unified datasets by merging 614 separate files and harmonizing unrestricted data across NHANES III (1988-1994) and Continuous (1999-2018), totaling 135,310 participants and 5,078 variables. The variables conveydemographics (281 variables),dietary consumption (324 variables),physiological functions (1,040 variables),occupation (61 variables),questionnaires (1444 variables, e.g., physical activity, medical conditions, diabetes, reproductive health, blood pressure and cholesterol, early childhood),medications (29 variables),mortality information linked from the National Death Index (15 variables),survey weights (857 variables),environmental exposure biomarker measurements (598 variables), andchemical comments indicating which measurements are below or above the lower limit of detection (505 variables).csv Data Record: The curated NHANES datasets and the data dictionaries includes 23 .csv files and 1 excel file.The curated NHANES datasets involves 20 .csv formatted files, two for each module with one as the uncleaned version and the other as the cleaned version. The modules are labeled as the following: 1) mortality, 2) dietary, 3) demographics, 4) response, 5) medications, 6) questionnaire, 7) chemicals, 8) occupation, 9) weights, and 10) comments."dictionary_nhanes.csv" is a dictionary that lists the variable name, description, module, category, units, CAS Number, comment use, chemical family, chemical family shortened, number of measurements, and cycles available for all 5,078 variables in NHANES."dictionary_harmonized_categories.csv" contains the harmonized categories for the categorical variables.“dictionary_drug_codes.csv” contains the dictionary for descriptors on the drugs codes.“nhanes_inconsistencies_documentation.xlsx” is an excel file that contains the cleaning documentation, which records all the inconsistencies for all affected variables to help curate each of the NHANES modules.R Data Record: For researchers who want to conduct their analysis in the R programming language, only cleaned NHANES modules and the data dictionaries can be downloaded as a .zip file which include an .RData file and an .R file.“w - nhanes_1988_2018.RData” contains all the aforementioned datasets as R data objects. We make available all R scripts on customized functions that were written to curate the data.“m - nhanes_1988_2018.R” shows how we used the customized functions (i.e. our pipeline) to curate the original NHANES data.Example starter codes: The set of starter code to help users conduct exposome analysis consists of four R markdown files (.Rmd). We recommend going through the tutorials in order.“example_0 - merge_datasets_together.Rmd” demonstrates how to merge the curated NHANES datasets together.“example_1 - account_for_nhanes_design.Rmd” demonstrates how to conduct a linear regression model, a survey-weighted regression model, a Cox proportional hazard model, and a survey-weighted Cox proportional hazard model.“example_2 - calculate_summary_statistics.Rmd” demonstrates how to calculate summary statistics for one variable and multiple variables with and without accounting for the NHANES sampling design.“example_3 - run_multiple_regressions.Rmd” demonstrates how run multiple regression models with and without adjusting for the sampling design.

Processed FTIR spectral data that demonstrates performance of quality assurance procedures used for data validation. This data is presented in "QA Summary of Surrogate Injections" Excel spreadsheet and contains data dictionary of parameters measured.

Shark Tank India Data set.

Shark Tank India - Season 1 to season 4 information, with 80 fields/columns and 630+ records.

All seasons/episodes of 🦈 SHARKTANK INDIA 🇮🇳 were broadcasted on SonyLiv OTT/Sony TV.

Here is the data dictionary for (Indian) Shark Tank season's dataset.

• Season Number - Season number
• Startup Name - Company name or product name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Episode Title - Episode title in SonyLiv
• Anchor - Name of the episode presenter/host
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Company Website URL
• Started in - Year in which startup was started/incorporated
• Number of Presenters - Number of presenters
• Male Presenters - Number of male presenters
• Female Presenters - Number of female presenters
• Transgender Presenters - Number of transgender/LGBTQ presenters
• Couple Presenters - Are presenters wife/husband ? 1-yes, 0-no
• Pitchers Average Age - All pitchers average age, <30 young, 30-50 middle, >50 old
• Pitchers City - Presenter's town/city or place where company head office exists
• Pitchers State - Indian state pitcher hails from or state where company head office exists
• Yearly Revenue - Yearly revenue, in lakhs INR, -1 means negative revenue, 0 means pre-revenue
• Monthly Sales - Total monthly sales, in lakhs
• Gross Margin - Gross margin/profit of company, in percentages
• Net Margin - Net margin/profit of company, in percentages
• EBITDA - Earnings Before Interest, Taxes, Depreciation, and Amortization
• Cash Burn - In loss in current year; burning/paying money from their pocket (yes/no)
• SKUs - Stock Keeping Units or number of varieties, at the time of pitch
• Has Patents - Pitcher has Patents/Intellectual property (filed/granted), at the time of pitch
• Bootstrapped - Startup is bootstrapped or not (yes/no)
• Part of Match off - Competition between two similar brands, pitched at same time
• Original Ask Amount - Original Ask Amount, in lakhs INR
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in lakhs INR
• Received Offer - Received offer or not, 1-received, 0-not received
• Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
• Total Deal Amount - Total Deal Amount, in lakhs INR
• Total Deal Equity - Total Deal Equity, in percentages
• Total Deal Debt - Total Deal debt/loan amount, in lakhs INR
• Debt Interest - Debt interest rate, in percentages
• Deal Valuation - Deal Valuation, in lakhs INR
• Number of sharks in deal - Number of sharks involved in deal
• Deal has conditions - Deal has conditions or not? (yes or no)
• Royalty Percentage - Royalty percentage, if it's royalty deal
• Royalty Recouped Amount - Royalty recouped amount, if it's royalty deal, in lakhs
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Namita Investment Amount - Namita Investment Amount, in lakhs INR
• Namita Investment Equity - Namita Investment Equity, in percentages
• Namita Debt Amount - Namita Debt Amount, in lakhs INR
• Vineeta Investment Amount - Vineeta Investment Amount, in lakhs INR
• Vineeta Investment Equity - Vineeta Investment Equity, in percentages
• Vineeta Debt Amount - Vineeta Debt Amount, in lakhs INR
• Anupam Investment Amount - Anupam Investment Amount, in lakhs INR
• Anupam Investment Equity - Anupam Investment Equity, in percentages
• Anupam Debt Amount - Anupam Debt Amount, in lakhs INR
• Aman Investment Amount - Aman Investment Amount, in lakhs INR
• Aman Investment Equity - Aman Investment Equity, in percentages
• Aman Debt Amount - Aman Debt Amount, in lakhs INR
• Peyush Investment Amount - Peyush Investment Amount, in lakhs INR
• Peyush Investment Equity - Peyush Investment Equity, in percentages
• Peyush Debt Amount - Peyush Debt Amount, in lakhs INR
• Ritesh Investment Amount - Ritesh Investment Amount, in lakhs INR
• Ritesh Investment Equity - Ritesh Investment Equity, in percentages
• Ritesh Debt Amount - Ritesh Debt Amount, in lakhs INR
• Amit Investment Amount - Amit Investment Amount, in lakhs INR
• Amit Investment Equity - Amit Investment Equity, in percentages
• Amit Debt Amount - Amit Debt Amount, in lakhs INR
• Guest Investment Amount - Guest Investment Amount, in lakhs INR
• Guest Investment Equity - Guest Investment Equity, in percentages
• Guest Debt Amount - Guest Debt Amount, in lakhs INR
• Invested Guest Name - Name of the guest(s) who invested in deal
• All Guest Names - Name of all guests, who are present in episode
• Namita Present - Whether Namita present in episode or not
• Vineeta Present - Whether Vineeta present in episode or not
• Anupam ...

This dataset provides data for new prescription drugs introduced to market in California with a Wholesale Acquisition Cost (WAC) that exceeds the Medicare Part D specialty drug cost threshold. Prescription drug manufacturers submit information to HCAI within a specified time period after a drug is introduced to market. Key data elements include the National Drug Code (NDC) administered by the FDA, a narrative description of marketing and pricing plans, and WAC, among other information. Manufacturers may withhold information that is not in the public domain. Note that prescription drug manufacturers are able to submit new drug reports for a prior quarter at any time. Therefore, the data set may include additional new drug report(s) from previous quarter(s).

There are two types of New Drug data sets: Monthly and Annual. The Monthly data sets include the data in completed reports submitted by manufacturers for calendar year 2025, as of November 7, 2025. The Annual data sets include data in completed reports submitted by manufacturers for the specified year. The data sets may include reports that do not meet the specified minimum thresholds for reporting.

The program regulations are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/CTRx-Regulations-Text.pdf

The data format and file specifications are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/Format-and-File-Specifications-version-2.0-ada.pdf

DATA NOTES: Due to recent changes in Excel capabilities, it is not recommended that you save these files to .csv format. If you do, when importing back into Excel the leading zeros in the NDC number column will be dropped. If you need to save it into a different format other than .xlsx it must be .txt

Submitted reports that are still under review by HCAI are not included in these files.

DATA UPDATES: Drug manufacturers may submit New Drug reports to HCAI for prescription drugs which were not initially reported when they were introduced to market. CTRx staff update the posted datasets monthly for current year data and as needed for previous years. Please check the 'Data last updated' date on each dataset page to ensure you are viewing the most current data.

SDR 2.0 Cotton File: Cumulative List of Variables in the Surveys of the SDR Database is a comprehensive data dictionary, in Microsoft Excel format. Its main purpose is to facilitate the overview of 88118 variables (i.e. variable names, values, and labels) available in the original (source) data files that we retrieved automatically for harmonization purposes in the SDR Project. Information in the Cotton File comes from 215 source data files that comprise ca. 3500 national surveys administered between 1966 and 2017 in 169 countries or territories, as part of 23 international survey projects. The COTTON FILE SDR2 is a product of the project Survey Data Recycling: New Analytic Framework, Integrated Database, and Tools for Cross-national Social, Behavioral and Economic Research, financed by the US National Science Foundation (PTE Federal award 1738502). We thank the Ohio State University and the Institute of Philosophy and Sociology, Polish Academy of Sciences, for organizational support.

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a CDPH data system created to manage state licensing-related data and enforcement actions. The “Health Facilities’ State Enforcement Actions” dataset includes summary information for state enforcement actions (state citations or administrative penalties) issued to California healthcare facilities. This file, a sub-set of the ELMS system data, includes state enforcement actions that have been issued from July 1, 1997 through June 30, 2024. Data are presented for each citation/penalty, and include information about the type of enforcement action, violation category, penalty amount, violation date, appeal status, and facility. The “LTC Citation Narrative” dataset contains the full text of citations that were issued to long-term care (LTC) facilities between January 1, 2012 – December 31, 2017. DO NOT DOWNLOAD in Excel as this file has large blocks of text which may truncate. For example, Excel 2007 and later display, and allow up to, 32,767 characters in each cell, whereas earlier versions of Excel allow 32,767 characters, but only display the first 1,024 characters. Please refer to instructions in “E_Citation_Access_DB_How_To_Docs”, about how to download and view data. These files enable providers and the public to identify facility non-compliance and quality issues. By making this information available, quality issues can be identified and addressed. Please refer to the background paper, “About Health Facilities’ State Enforcement Actions” for information regarding California state enforcement actions before using these data. Data dictionaries and data summary charts are also available. Note: The Data Dictionary at the bottom of the dataset incorrectly lists the data column formats as all Text. For proper format labels, please go here.

Description and PurposeThese data include the individual responses for the City of Tempe Annual Community Survey conducted by ETC Institute. These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Community Survey results are used as indicators for several city performance measures. The summary data for each performance measure is provided as an open dataset for that measure (separate from this dataset). The performance measures with indicators from the survey include the following (as of 2022):1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Crime Reporting1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community SurveyMethodsThe survey is mailed to a random sample of households in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used. To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city. Additionally, demographic data were used to monitor the distribution of responses to ensure the responding population of each survey is representative of city population. Processing and LimitationsThe location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city. This data is the weighted data provided by the ETC Institute, which is used in the final published PDF report.The 2022 Annual Community Survey report is available on data.tempe.gov. The individual survey questions as well as the definition of the response scale (for example, 1 means “very dissatisfied” and 5 means “very satisfied”) are provided in the data dictionary.Additional InformationSource: Community Attitude SurveyContact (author): Wydale HolmesContact E-Mail (author): wydale_holmes@tempe.govContact (maintainer): Wydale HolmesContact E-Mail (maintainer): wydale_holmes@tempe.govData Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData Dictionary

Description: This dataset consists of field data (arthropods, nematodes and NDVI) collected over the course of 6 field excursions in 2015 and 2016 near TyTy, GA, in a field used for growing Miscanthus x giganteus. It also includes interpolated values of soil measurements collected in 2015 and meteorological data collected on an adjacent farm. Point-in-time measurements include all meteorological, NDVI, arthropod and nematode measurements and their derivatives. Fixed values were measurements that were held constant across all sampling dates, including location, terrain and soils measurements and their derivatives. Dawn Olson and Jason Schmidt collected and processed arthropod count data. Jason Schmidt collected and processed spider count data and computed spider diversity. Richard Davis collected and processed nematode count data. Alisa Coffin collected and processed NDVI data and positional locations. Tim Strickland collected and processed soils data and Alisa Coffin interpolated soils values using kriging to derive values at arthropod sample locations. David Bosch collected and processed meteorological data. Lynne Seymour provided statistical expertise in deriving any estimated values (phloem feeders, parasitoids, spiders, and natural enemies). Alisa Coffin derived terrain data (elevation, slope, aspect, and distances) from publicly available datasets, transformed values (SI, WI, etc), carried out the geographically weighted regression analysis and calculated C:SE values, harmonized the full dataset, and compiled it using Esri's ArcGIS Pro 2.5. Methods for most data are published in the accompanying paper and associated supplements. Questions about dataset development and management should be directed to Alisa Coffin (alisa.coffin@usda.gov). This work was accomplished as a joint USDA and University of Georgia project funded by a cooperative agreement (#6048-13000-026-21S). This research was a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. At request of the author, the data resources are under embargo. The embargo will expire on Fri, Jan 01, 2021. Resources in this dataset:Resource Title: Spreadsheet of data. File Name: GibbsMisFarm_Arthrop_Env_DepVar_201516_final.xlsxResource Description: This workbook contains all of the data used in this analysis. The first worksheet contains data dictionary information.Resource Software Recommended: Microsoft Excel, Office 365,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: GeoJSON. File Name: MiscanthusXGiganteusGeoJSON.json

An excel file where each row is a sample station and columns provide station descriptors, number and density of Dreissena veligers in the zooplankton samples, substrate type, and water quality values, A second sheet in the excel file provides the data dictionary.

This dataset is associated with the following publication: Trebitz, A., C. Hatzenbuhler, J. Hoffman, C. Meredith, G. Peterson, E. Pilgrim, J. Barge, A. Cotter, and M. Wick. Dreissena veligers in western Lake Superior -- inference from new low-density detection. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 45(3): 691-699, (2019).

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

For questions about this data please contact ITOpenData@minneapolismn.gov2014 Minneapolis Community Technology Survey Data

Thanks to the 3,015 residents for their participation, the third year's results are in on a survey the City of Minneapolis conducted to understand how Minneapolis residents use computers, mobile devices and the Internet. Access to computers and the Internet, along with the skills to use these tools is critical as technology becomes more and more a part of our daily lives and is integrated in our economic, educational, health, and workforce systems. The results will inform priorities for the City’s digital inclusion initiatives, and help engage businesses, neighborhood and community groups, public sector partners, and funders to more effectively address community technology and economic development needs. In addition, the survey provides data to measure changes in the community over time.

The City of Minneapolis Information Technology Department contracted with National Research Center, Inc. (NRC) to conduct a survey of residents to inform the City’s efforts to overcome the digital equity gap between individuals and groups in their access to and use and knowledge of information and communication technologies. This is the third iteration of the Minneapolis Community Technology Survey; the first was conducted in 2012 and the second in 2013.Summary of Data Fields:Field 1 – Overall percentage of respondents who have lived in Minneapolis for 5 years or less by community and user levelField 2 – Overall percentage of foreign-born respondents by community and user levelField 3 – Overall percentage of respondents who rent their homes by community and user levelField 4 – Overall percentage of respondents who live in attached homes by community and user levelField 5 – Overall percentage of respondents living in households with three or more people by community and user levelField 6 – Overall percentage of respondents living in households with children under the age of 18 by community and user levelField 7 – Overall percentage of female respondents by community and user levelField 8 – Overall percentage of respondents aged 55 years or older by community and user levelField 9 – Overall percentage of respondents who are hispanic and/or any race other than white by community and user levelField 10 – Overall percentage of respondents who prefer to speak a language other than English at home by community and user levelField 11 – Overall percentage of respondents having annual household incomes of less than $50,000 by community and user levelField 12 – Overall percentage of respondents who do not work full- or part-time by community and user level

Field 13 – Overall percentage of respondents who do not have a 4-year degree by community and user level

Full data set (Raw data and data dictionary in Excel format)

The workbook has two tabs, the first is the data dictionary that is needed to translate the data; the second is the raw data.

See data summarized in a variety of formats at: http://www.minneapolismn.gov/it/inclusion/WCMS1P-118865

For additional details about the survey, the survey questionnaire, methodology and more, see: http://www.minneapolismn.gov/it/inclusion/WCMS1P-118865 or contact: Elise Ebhardt, 612-673-2026, City of Minneapolis IT Department

See also: 2012 and 2013 survey results

The City's IT Vision includes a component for addressing the digital divide in Minneapolis: All City residents, institutions and businesses will have the tools, skills and motivation to gain value from the digital society. Our residents and businesses need to be equipped to effectively compete with others around the world—to be smarter, more creative, more knowledgeable, and more innovative. Leveraging technology is a necessary ingredient of success.

The GCEW herbicide data were collected from 1991-2010, and are documented at plot, field, and watershed scales. Atrazine concentrations in Goodwater Creek Experimental Watershed (GCEW) were shown to be among the highest of any watershed in the United States based on comparisons using the national Watershed Regressions for Pesticides (WARP) model and by direct comparison with the 112 watersheds used in the development of WARP. This 20-yr-long effort was augmented with a spatially broad effort within the Central Mississippi River Basin encompassing 12 related claypan watersheds in the Salt River Basin, two cave streams on the fringe of the Central Claypan Areas in the Bonne Femme watershed, and 95 streams in northern Missouri and southern Iowa. The research effort on herbicide transport has highlighted the importance of restrictive soil layers with smectitic mineralogy to the risk of transport vulnerability. Near-surface soil features, such as claypans and argillic horizons, result in greater herbicide transport than soils with high saturated hydraulic conductivities and low smectitic clay content. The data set contains concentration, load, and daily discharge data for Devils Icebox Cave and Hunters Cave from 1999 to 2002. The data are available in Microsoft Excel 2010 format. Sheet 1 (Cave Streams Metadata) contains supporting information regarding the length of record, site locations, parameters measured, parameter units, method detection limits, describes the meaning of zero and blank cells, and briefly describes unit area load computations. Sheet 2 (Devils Icebox Concentration Data) contains concentration data from all samples collected from 1999 to 2002 at the Devils Icebox site for 12 analytes and two computed nutrient parameters. Sheet 3 (Devils Icebox SS Conc Data) contains 15-minute suspended sediment (SS) concentrations estimated from turbidity sensor data for the Devils Icebox site. Sheet 4 (Devils Icebox Load & Discharge Data) contains daily data for discharge, load, and unit area loads for the Devils Icebox site. Sheet 5 (Hunters Cave Concentration Data) contains concentration data from all samples collected from 1999 to 2002 at the Hunters Cave site for 12 analytes and two computed nutrient parameters. Sheet 6 (Hunters Cave SS Conc Data) contains 15-minute SS concentrations estimated from turbidity sensor data for the Hunters Cave site. Sheet 7 (Hunters Cave Load & Discharge Data) contains daily data for discharge, load, and unit area loads for the Hunters Cave site. [Note: To support automated data access and processing, each worksheet has been extracted as a separate, machine-readable CSV file; see Data Dictionary for descriptions of variables and their concentration units.] Resources in this dataset:Resource Title: README - Metadata. File Name: LTAR_GCEW_herbicidewater_qual.xlsxResource Description: Defines Water Quality and Sediment Load/Discharge parameters, abbreviations, time-frames, and units as rendered in the Excel file. For additional information including site information, method detection limits, and methods citations, see Metadata tab. For Definitions used in machine-readable CSV files, see Data Dictionary.Resource Title: Excel data spreadsheet. File Name: c3.jeq2013.12.0516.ds1_.xlsxResource Description: Multi-page data spreadsheet containing data as well as metadata from this study. A direct download of the data spreadsheet can be found here: https://dl.sciencesocieties.org/publications/datasets/jeq/C3.JEQ2013.12.0516.ds1/downloadResource Title: Devils Icebox Concentration Data. File Name: DevilsIceboxConcData.csvResource Description: Concentrations of herbicides, metabolites, and nutrients (extracted from the Excel tab into machine-readable CSV data).Resource Title: Devils Icebox Load and Discharge Data. File Name: DevilsIceboxLoad&Discharge.csvResource Description: Discharge and Unit Area Loads for herbicides, metabolites, and suspended sediments (extracted from Excel tab as machine-readable CSV data)Resource Title: Devils Icebox Suspended Sediment Concentration Data. File Name: DevilsIceboxSSConcData.csvResource Description: Suspended Sediment Concentration Data (extracted from Excel tab as machine-readable CSV data)Resource Title: Hunters Cave Load and Discharge Data. File Name: HuntersCaveLoad&Discharge.csvResource Description: Discharge and Unit Area Loads for herbicides, metabolites, and suspended sediments (extracted from Excel tab as machine-readable CSV data)Resource Title: Hunters Cave Suspended Sediment Concentration Data. File Name: HuntersCaveSSConc.csvResource Description: Suspended Sediment Concentration Data (extracted from Excel tab as machine-readable CSV data)Resource Title: Data Dictionary for machine-readable CSV files. File Name: LTAR_GCEW_herbicidewater_qual.csvResource Description: Defines Water Quality and Sediment Load/Discharge parameters, abbreviations, time-frames, and units as implemented in the extracted machine-readable CSV files.Resource Title: Hunters Cave Concentration Data. File Name: HuntersCaveConcData.csvResource Description: Concentrations of herbicides, metabolites, and nutrients (extracted from the Excel tab into machine-readable CSV data)

This dataset consists of 1 excel data files (.xlsx format) that contains the results from the in situ deployment of instruments in Cleveland Bay to measure sediment deposition from a trailer suction hopper dredge working in Platypus channel. The aim of the study was to measure sediment deposition rates caused by a working Trailer Suction Hopper dredge in Platypus channel in Cleveland Bay. Methods: Two deposition sensors were deployed on the seabed 100 m (-19.226083° 146.843500°) away from the edge of the Platypus channel in Cleveland Bay near marker beacon P11 at a depth of 5–8 m. Single deposition sensors were also placed on a transect line running at right angles from the shipping channel at distance of 200 m (19.225639° 146.842889°), 400 m (-19.224528° 146.841111), and 800 m (-19.222806° 146.837889°). The deposition sensors were calibrated according to methods described in Ridd et al. (2001), Thomas et al. (2003), Whinney et al. (2017). The deposition sensor uses infrared optical backscatters techniques to estimate the mass of sediment per unit area that deposits on the sensor surface every 10– 20 minutes. After 1 h the surface is wiped clean and the process repeated. Additional instruments were deployed alongside the sensors including a nephelometer measuring turbidity by means of optical backscatter, a pressure sensor for estimating wave activity, and a tilt current meter for estimating current speed. For instrument descriptions and calibration details see Macdonald et al. (2013), Marchant et al. (2014), Whinney et al. (2017). Limitation of the data: All data went through a quality assurance process involving an algorithm to remove occasional data spikes. This algorithm compares each reading to the average of the readings directly before and after it, and if the reading is greater than twice the average it is replaced by the average. Fouling of sensors was examined by looking for drift in values over the deployment period and from observations of the condition of the sensors at the time of retrieval. Data that have been affected by fouling were removed. Format: This data is in a single Excel file Data Dictionary: Site 1A - 100m from Channel Site 1B - 100m from Channel Site 2 – 200 m from Channel Site 3 – 400 m from Channel Site 4 – 800 m from Channel Columns B – F : hourly sensor readings of sediment deposition as mg/cm2/day per 10 minute intervals: Column I: 2 – NTU (Nephelometric turbidity unit) at Site 2 – 200 m from Channel Column J: m (metres, water depth) at Site 4 - 800 m from the Channel Column K: m/s (metres per second) current sensor readings at Site 4 - 800 m from the Channel Graphs of the data can be found in the Report in figures 16 B, 16 C, and 16 D: Ross Jones, Rebecca Fisher, David Francis, Wojciech Klonowski, Heidi Luter, Andrew Negri, Mari-Carmen Pineda, Gerard Ricardo, Matt Slivkoff James Whinney. Risk Assessing Dredging activities. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (74 pp). References: Macdonald R, Ridd P, Whinney J, Larcombe P, Neil D (2013) Towards environmental management of water turbidity within open coastal waters of the Great Barrier Reef. Mar Pollut Bull 74:82-94 Marchant R, Stevens T, Choukroun S, Coombes G, Santarossa M, Whinney J, Ridd P (2014) A Buoyant Tethered Sphere for Marine Current Estimation. IEEE Journal of Oceanic Engineering 39:2-9 Ridd P, Day G, Thomas S, Harradence J, Fox D, Bunt J, Renagi O, Jago C (2001) Measurement of Sediment Deposition Rates using an Optical Backscatter Sensor. Estuar Coast Shelf Sci 52:155-163 Thomas S, Ridd PV, Day G (2003) Turbidity regimes over fringing coral reefs near a mining site at Lihir Island, Papua New Guinea. Mar Pollut Bull 46:1006-1014 Whinney J, Jones R, Duckworth A, Ridd P (2017) Continuous in situ monitoring of sediment deposition in shallow benthic environments. Coral Reefs 36:521–533 Ross Jones, Rebecca Fisher, David Francis, Wojciech Klonowski, Heidi Luter, Andrew Negri, Mari-Carmen Pineda, Gerard Ricardo, Matt Slivkoff James Whinney. Risk Assessing Dredging activities. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (74 pp). Data Location: This dataset is filed in the eAtlas enduring data repository at: data esp2\2.1.9-dredging-marine-response