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

What is IPGOD? The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has …Show full descriptionWhat is IPGOD? The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD. How do I use IPGOD? IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar. IP Data Platform IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform References The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset. Patents Trade Marks Designs Plant Breeder’s Rights Updates Tables and columns Due to the changes in our systems, some tables have been affected. We have added IPGOD 225 and IPGOD 325 to the dataset! The IPGOD 206 table is not available this year. Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use. Data quality improvements Data quality has been improved across all tables. Null values are simply empty rather than '31/12/9999'. All date columns are now in ISO format 'yyyy-mm-dd'. All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0. All tables are encoded in UTF-8. All tables use the backslash \ as the escape character. The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

Metadata for 3883 botanical specimen collected by William Colenso, now in Te Papa's collections. This data was exported from EMu on 13/02/2015 and supplied to VUW Wai-te-ata Press in response to a request for research data. Included minimal data dictionary. MS Excel format.

Hand transcribed content from the United States Bureau of Labour Statistics Dictionary of Titles (DoT). The DoT is a record of occupations and a description of the tasks performed. Five editions exist from 1939, 1949, 1965, 1977 and 1991. The DoT was replaced by O*NET structured data on jobs, workers and their characteristics. However, apart from the 1991 data, the data in the DoT is not easily ingestible, existing only in scalar PDF documents. Attempts at Optical Character Recognition led to low accuracy. For that reason we present here hand transcribed textual data from these documents. Various data are available for each occupation e.g. numerical codes, references to other occupations as well as the free text description. For that reason the data for each edition is presented in 'long' format with a variable number of lines, with a blank line between occupations. Consult the transcription instructions for more details. Structured meta-data (see here) on occupations is also available for the 1965, 1977 and 1991 editions. For the 1965, 1977 and 1991 editions, this data can be extracted from the numerical codes with the occupational entries, the key for these codes is found in the 1965 edition in separate tables exist which were transcribed. The instructions provided to transcribers for this edition are also added to the repository. The original documents are freely available in PDF format (e.g. here) This data accompanies the paper 'Longitudinal Complex Dynamics of Labour Markets Reveal Increasing Polarisation' by Althobaiti et al

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.

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.

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.

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.

These data include the individual responses for the City of Tempe Annual Community Survey conducted by ETC Institute. This dataset has two layers and includes both the weighted data and unweighted data. Weighting data is a statistical method in which datasets are adjusted through calculations in order to more accurately represent the population being studied. The weighted data are used in the final published PDF report.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 2023):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 SurveyMethods:The 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 Limitations:The 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. The weighted data are used by the ETC Institute, in the final published PDF report.The 2023 Annual Community Survey report is available on data.tempe.gov or by visiting https://www.tempe.gov/government/strategic-management-and-innovation/signature-surveys-research-and-dataThe 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): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData Dictionary

The National Health and Nutrition Examination Survey (NHANES) provides data on the health and environmental exposure of the non-institutionalized US population. Such data have considerable potential to understand how the environment and behaviors impact human health. These data are also currently leveraged to answer public health questions such as prevalence of disease. However, these data need to first be processed before new insights can be derived through large-scale analyses. NHANES data are stored across hundreds of files with multiple inconsistencies. Correcting such inconsistencies takes systematic cross examination and considerable efforts but is required for accurately and reproducibly characterizing the associations between the exposome and diseases (e.g., cancer mortality outcomes). Thus, we developed a set of curated and unified datasets and accompanied code by merging 614 separate files and harmonizing unrestricted data across NHANES III (1988-1994) and Continuous (1999-2018), totaling 134,310 participants and 4,740 variables. The variables convey 1) demographic information, 2) dietary consumption, 3) physical examination results, 4) occupation, 5) questionnaire items (e.g., physical activity, general health status, medical conditions), 6) medications, 7) mortality status linked from the National Death Index, 8) survey weights, 9) environmental exposure biomarker measurements, and 10) chemical comments that indicate which measurements are below or above the lower limit of detection. We also provide a data dictionary listing the variables and their descriptions to help researchers browse the data. We also provide R markdown files to show example codes on calculating summary statistics and running regression models to help accelerate high-throughput analysis of the exposome and secular trends on cancer mortality. csv Data Record: The curated NHANES datasets and the data dictionaries includes 13 .csv files and 1 excel file. The curated NHANES datasets involves 10 .csv formatted files, one for each module and 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. The eleventh file 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 4,740 variables in NHANES ("dictionary_nhanes.csv"). The 12th csv file contains the harmonized categories for the categorical variables ("dictionary_harmonized_categories.csv"). The 13th file contains the dictionary for descriptors on the drugs codes (“dictionary_drug_codes.csv”). The 14th file 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 datasets (“nhanes_inconsistencies_documentation.xlsx”). R Data Record: For researchers who want to conduct their analysis in the R programming language, the curated NHANES datasets and the data dictionaries can be downloaded as a .zip file which include an .RData file and an .R file. We provided an .RData file that contains all the aforementioned datasets as R data objects (“w - nhanes_1988_2018.RData”). Also in this .RData file, we make available all R scripts on customized functions that were written to curate the data. We also provide an .R file that shows how we used the customized functions (i.e. our pipeline) to curate the data (“m - nhanes_1988_2018.R”).

This dataset corresponds to a current availability of plant species in various containers being offered to clients of the Greenbelt Native Plant Center. Each line of data represents one instance of a particular plant species in a specific container size within the dataset. Data is generated using groware software application to export to an excel document. Data is updated ad hoc, depending on production and distribution needs (sometimes multiple times daily, other times 1xweekly or bi-weekly, for example). Data has not been geocoded, anonymized, etc to the best of my knowledge. The dataset changes to include new species that are grown and were not prevously distributed by GNPC. The dataset also has entries removed over time, as species are no longer grown. All fields are required.

Data dictionary: https://docs.google.com/spreadsheets/d/1TtrIKLJUStYKbVe6mdtRfqpKy1tNtXKJsiU1qmXWgF8/edit#gid=1281948611

This dataset comes from the Annual Community Survey question about satisfaction with the Value of Special Events. The Community Survey question relating to the Value of Special Events performance measure: "Please rate your level of satisfaction with each of the following: a) Value & benefits received by City from special events." Respondents are asked to rate their satisfaction level on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (without "don't know" as an option).The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.This page provides data for the Value of Special Events performance measure. The performance measure dashboard is available at 3.19 Value of Special Events.Additional InformationSource: Community Attitude Survey ( Vendor: ETC Institute)Contact: Wydale HolmesContact E-Mail: wydale_holmes@tempe.govData Source Type: Excel and PDFPreparation Method: Extracted from Annual Community Survey resultsPublish Frequency: AnnualPublish Method: Manual Data Dictionary

This dataset contains the empirical data that are analysed and discussed in the doctoral dissertation "Frasar til besvær? Studiar av norm og bruk i norsk fraseologi" [The trouble with phrases: Studies of Norwegian phraseology in norm and usage]. The dataset consists of two collections. The first is a phrase collection which forms the empirical basis for the discussion in chapter 5 of the dissertation, and the second collection is discussed in chapter 6. The dataset has been manually curated. The data in chapter 5 is a collection of 713 phrases and the different forms they are given in different dictionaries. The sources are two printed phraseological collections from 2011 ("Prikken over i-en og andre uttrykk" (Vannebo 2011) and "Ord og uttrykk på fire språk" (Erichsen 2011)) and the online dictionary Islex (islex.no) in 2014. Furthermore it is registered whether, and in what form, the phrases extracted from these sources are included in the online version of Bokmålsordboka and Nynorskordboka (ordbok.uib.no) from 2014. The selection criteria for the first collection are described on pages 203–211 in the dissertation and moreover attached as an appendix below. The data in chapter 6 is a collection of attestations of the phrase "kaste barnet ut med badevatnet" (‘throw out the baby with the bathwater’), with variations, creative modifications and divergent use, in 833 Norwegian texts. There are 460 unique examples in Bokmål and 373 examples in Nynorsk excerpted from 10 different text corpora and other digital text collections in 2015 and 2016. The data were entered in table format (Excel sheet) and ordered with one attestation (one corpus instance) per line. For each attestation (i.e. for each line), attribute values were added in columns to describe and document the attestation further. The collection and methodological considerations of the second collection are described on pages 302–306 in the dissertation. These pages are attached as an appendix below. The dataset is replication data for the following publication: Rauset, Margunn (2022). "Frasar til besvær? Studiar av norm og bruk i norsk fraseologi." ISBN 9788230854143 (print), 9788230866634 (PDF).

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset is an excel spreadsheet extract of all GAL coal deposits and resources was derived by the Bioregional Assessment Programme from the 2012 OZMIN database from Geoscience …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset is an excel spreadsheet extract of all GAL coal deposits and resources was derived by the Bioregional Assessment Programme from the 2012 OZMIN database from Geoscience Australia. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Dataset History Black coal deposits and resources from the 2012 OZMIN database, which lie within the GAL subregion were extracted in tabular form. http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_a05f7892-b68d-7506-e044-00144fdd4fa6/OZMIN+Mineral+Deposits+Database The data within this dataset is derived directly from the corporate ORACLE OZMIN Mineral Deposits database. An ASCII extraction of the Geoscience Australia ORACLE database is generated as ASCII comma-delimited files for each table that is part of or used by the OZMIN database. Only data that is part of the current release of OZMIN (Release 3 - October 2000) is included. An MS ACCESS database format is also replicated from the ORACLE database and uses the same table structure. Only data that is part of the current release of OZMIN (Release 3 - October 2000) is included. The spatial representation of this database in (ArcView and MapInfo format) is extracted and generated using ArcInfo GIS software to meet the published data standard within the Geoscience Australia "http://www.ga.gov.au/standards/datadict.html" data dictionary. The extraction of the spatial GIS datasets is done within ArcInfo using advanced AML code (ORACOV.AML). Dataset Citation Bioregional Assessment Programme (2014) OZMIN black coal deposits and resources 2012 GAL. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/8d304612-8415-40c9-9fac-86978115655c. Dataset Ancestors Derived From OZMIN Mineral Deposits Database

Endovascular therapy (EVT) has changed the landscape of acute stroke treatment in the context of large vessel occlusion (LVO). Still, procedural success is typically determined by the degree of large vessel recanalization, despite the fact that large vessel recanalization does not always result in microvascular reperfusion. To address this discrepancy, we performed bedside optical CBF monitoring (with diffuse correlation spectroscopy) during endovascular therapy. This allowed comparison of CBF pre vs post-recanalization.Note 3 files uploaded:The .dta file is a stata file which contains all variables labels which includes all necessary variables details.The .xlsx file database is in numerical format (i.e. without applying text labels).The .xlsx file data dictionary contains all variable names, variable labels, and code to translate the numerical values.

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.

A later version of this dataset exists published 2019-01-18, accessible through the data links on this page.

This dataset contains records of sting events and specimen samples of jellyfish (Irukandji) along the north Queensland coast from December 1998 to March 2017. This dataset contains an extract (265 records in CSV format) of the publicly available data contained in the Venomous Jellyfish Database. The full database contains approximately 3000 sting events from around Australia and includes records from sources that have not yet been cleared for release.

This extract was made for eAtlas as part of the 2.2.3 NESP Irukandji forecasting system project and used as part of the development of the Irukandji forecasting model. The data was compiled from numerous sources (noted in each record), including Lisa-ann Gershwin and media reports.

The sting data includes primary information such as date, time of day and locality of stings, as well as secondary details such as age and gender of the sting victim, where on the body they were stung, their activity at the time of the sting and their general medical condition.

Limitations:

This data shows the occurrence of reported jellyfish stings and specimens along the north Queensland coast. It does NOT provide a prediction of where jellyfish or jellyfish sting events may occur.

These records represent a fraction of known sting events and specimen collections, with more being added to the list of publicly available data as permissions are granted.

Historical data dates may be coarse, showing month and year that the sting occurred in. Some events have date only.

Methods:

This data set contains information on sting events and specimen collections that have occurred around Australia, which involved venomous jellyfish (Irukandji syndrome-producing species in the genera Carukia, Malo, Morbakka).

This data was collected over numerous years by Lisa-ann Gershwin from various sources, predominantly news reports. This data was entered into an Excel spreadsheet, which formed the basis of the Venomous Jellyfish Database. This database was developed as part of the 2.2.3 NESP Irukandji forecasting system project.

Some data have been standardised, e.g., location information and sting site on the body. Data available to the public have been approved by the data owners, or came from a public source (e.g. newspaper reports, media alerts).

Format:

Comma Separated Value (CSV) table. eAtlas Note: The original database extract was provided as an Excel spreadsheet table. This was converted to a CSV file.

Data Dictionary:

• CSIRO_ID: Unique id
• EVENT_TYPE: Type of event – sting or specimen
• STATE: State in which event occurred
• REGION: Broader region of State the event occurred in
• LOCAL_GOV_AREA: Local government area that the event occurred in – if known
• MAIN_LOCALITY: Main locality that the event occurred in
• SITE_INFO: Site details/comments
• YEAR: Year event occurred
• MONTH: Month event occurred
• DAY: Day of the month the event occurred
• EVENT_TIME: Time the event occurred HH24:MI If time is unknown then NULL
• EVENT_RECORDED: time/date event reported e.g. early afternoon, morning, on weekend
• EVENT_COMMENTS: Comments about the event
• LAT: Latitude in decimal degrees
• LON: Longitude in decimal degrees
• LOCATION_ACCURACY: How accurate the location is
• EVENT_OFFSHORE_ONSHORE: Where the event occurred (if known) – beach, island, reef
• LOCATION_COMMENTS: Comments relating to the location of the event
• WATER_DEPTH_M: Depth of water, in metres, that the event occurred in (if known)
• AGE: Age of patient if known
• SEX: Gender of patient if known
• HOME: Home state/county of patient
• HOSPITAL: Hospital the patient was treated at (if known)
• STING_SITE_REPORTED: Reported sting site on the body
• STING_SITE_BODY: Standardised area on body that sting was reported – upper limb, lower limb etc.
• NUMBER_STINGS: Number of stings recorded, if known
• VISIBLE_STING: The nature of visible sting marks, if reported
• PPE_WORN: Was Personal Protective Equipment (PPE) worn?
• PATIENT_COMMENTS: Comments about the patient
• TIME_TO_ONSET: Delay between sting and onset of symptoms, if reported
• PATIENT_CONDITION: State the patient was in, e.g. distressed, calm, stable
• BLOOD_PRESSURE: Comments relating to blood pressure of the patient
• NAUSEA_VOMITING: Did the patient experience nausea and/or vomiting?
• PAIN: Location and/or intensity of pain experienced by the patient
• SWEATING: Did the patient experience sweating?
• TREATMENT: What treatment the patient was given
• DISCHARGED: When the patient was discharged from hospital
• ONGOING_SYMPTOMS: What ongoing symptoms the patient is experiencing
• NEMATO_SAMPLES: Were nematocyst samples taken?
• SPECIES_NAME: Species name, if determined
• PATROL: Was the sting on a patrolled beach
• CURATOR: Where the data came from e.g. Gershwin = Lisa-ann Gershwin
• DATA_CODE: Access constraint on data
• REFERENCE: Source of the information for event
• ENTERED_BY: Who entered the data
• ENTERED_DATE: When the data was entered

References:

Gershwin, L. (2013). Stung! On Jellyfish Blooms and the Future of the Ocean. Chicago, University of Chicago Press.

Lisa-Ann Gershwin , Monica De Nardi , Kenneth D. Winkel & Peter J. Fenner (2010) Marine Stingers: Review of an Under-Recognized Global Coastal Management Issue, Coastal Management, 38:1, 22-41, http://dx.doi.org/10.1080/08920750903345031

Gershwin L, Condie SA, Mansbridge JV, Richardson AJ. 2014 Dangerous jellyfish blooms are predictable. J. R. Soc. Interface 11: 20131168. http://dx.doi.org/10.1098/rsif.2013.1168

Gershwin, L., A. J. Richardson, K. D. Winkel, P. J. Fenner, J. Lippmann, R. Hore, G. Avila-Soria, D. Brewer, R. J. Kloser, A. Steven and S. Condie (2013). Biology and ecology of Irukandji jellyfish (Cnidaria: Cubozoa). Advances in Marine Biology 66: 1-85.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2016-18-NESP-TWQ-2\2.2.3_Jellyfish-early-warning\AU_NESP-TWQ-2-2-3_CSIRO_Venomous-Jellyfish-DB

This dataset contains records of sting events and specimen samples of jellyfish (Irukanji, Halo irukanji, Box jellyfish and Morbakka) from the Venomous Jellyfish Database. This dataset contains an extract of 1081 sting events (in CSV format) from along the north Queensland coast between December 1883 to March 2017. The full database contains approximately 3000 sting events from around Australia and includes records from sources that have not yet been cleared for release.

This extract from the Venomous Jellyfish Database was made for eAtlas as part of the 2.2.3 NESP Irukandji forecasting system project. It contains jellyfish sting and specimen information. Data were compiled from numerous sources (noted in each record), including Lisa-ann Gershwin and media reports. These data will be used as part of the Irukandji forecasting model. The extract data file contains data that is publicly available.

The sting data includes primary information such as date, time of day and locality of stings, as well as secondary details such as age and gender of the sting victim, where on the body they were stung, their activity at the time of the sting and their general medical condition.

Limitations: This data shows the occurrence of reported jellyfish stings and specimens along the north Queensland coast. It does NOT provide a prediction of where jellyfish or jellyfish sting events may occur.

These records represent a fraction of known sting events and specimen collections, with more being added to the list of publicly available data as permissions are granted.

Historical data dates may be coarse, showing month and year that the sting occurred in. Some events have date only.

Methods: This data set contains information on sting events and specimen collections that have occurred around Australia, which involved venomous jellyfish (Irukandji syndrome-producing species in the genera Carukia, Malo, Morbakka).

These data were collected over numerous years by Lisa-ann Gershwin and others from various sources (primarily media). These data were entered into an excel spreadsheet, which formed the basis of the Venomous Jellyfish Database. This database was developed as part of the 2.2.3 NESP Irukandji forecasting system project.

Some data have been standardised, e.g., location information and sting site on the body. Data available to the public have been approved by the data owners, or came from a public source (e.g. newspaper reports, media alerts).

Format:

This dataset consists of one Comma Separated Value (CSV) table containing information on jellyfish events along the north Queensland coast. eAtlas Note: The original database extract was provided as an Excel spreadsheet table. This was converted to a CSV file.

Data Dictionary:

CSIRO_ID: unique id EVENT_TYPE: type of event – sting or specimen STATE: state in which event occurred REGION: broader region of State the event occurred in LOCAL_GOV_AREA: local government area that the event occurred in – if known MAIN_LOCALITY: main locality that the event occurred in SITE_INFO: site details/comments YEAR: year event occurred MONTH: month event occurred DAY: day of the month the event occurred DATE_RANGE: date range event may have occurred in EVENT_TIME: time the event occurred HH24:MI. If time is unknown then NULL EXACT_DATE: if exact date unknown then N. Use with DATE_RANGE EXACT_TIME: if exact time unknown then N. TIME_REPORTED: time event reported e.g. early afternoon, morning EVENT_RECORDED: date event reported e.g. on weekend, in February, Jan-March EVENT_COMMENTS: comments about the event LAT: latitude in decimal degrees LON: longitude in decimal degrees LOCATION_ACCURACY: How accurate the location is, 0=within a few hundred metres, 1=within a few kilometres, 2=more than a few kilometres EVENT_OFFSHORE_ONSHORE: where the event occurred (if known) – beach, island, reef LOCATION_COMMENTS: comments relating to the location of the event WATER_DEPTH_M: depth of water, in metres, that the event occurred in (if known) AGE: number: age of patient if known SEX: gender of patient if known HOME: home state/county of patient HOSPITAL: hospital the patient was treated at (if known) RETRIEVAL: method by which the patient was transported to hospital (if known) STING_SITE_REPORTED: reported sting site on the body STING_SITE_BODY: standardised area on body that sting was reported – upper limb, lower limb etc. NUMBER_STINGS: number of stings recorded, if known VISIBLE_STING: the nature of visible sting marks, if reported PPE_WORN: was Personal Protective Equipment (PPE) worn? PATIENT_COMMENTS: comments about the patient TIME_TO_ONSET: delay between sting and onset of symptoms, if reported PATIENT_CONDITION: state the patient was in, e.g. distressed, calm, stable BLOOD_PRESSUREL: comments relating to blood pressure of the patient NAUSEA: did the patient experience nausea and/or vomiting? PAIN: location and/or intensity of pain experienced by the patient SWEATING: did the patient experience sweating? TREATMENT: what treatment the patient was given DISCHARGED: when the patient was discharged from hospital ONGOING_SYMPTOMS: what ongoing symptoms the patient is experiencing NEMATO_SAMPLES: were nematocyst samples taken? SPECIES_NAME: species name, if determined PATROL: was the sting on a patrolled beach CURATOR: where the data came from e.g. Gershwin = Lisa-ann Gershwin DATA_CODE: access constraint on data PUBLIC_REFERENCE: source of the information for event ENTERED_BY: who entered the data ENTERED_DATE: when the data was entered

References:

Gershwin, L. (2013). Stung! On Jellyfish Blooms and the Future of the Ocean. Chicago, University of Chicago Press. Gershwin, L., De Nardi, M., Winkel, K.D., and Fenner, P.J. (2010) Marine Stingers: Review of an Under-Recognized Global Coastal Management Issue. Journal of Coastal Management, 38:1, 22-41, DOI: 10.1080/08920750903345031

Gershwin L, Condie SA, Mansbridge JV, Richardson AJ. 2014 Dangerous jellyfish blooms are predictable. Journal of the Royal Society. Interface 11: 20131168.http://dx.doi.org/10.1098/rsif.2013.1168

Gershwin, L., Richardson, A.J., Winkel, K.D., Fenner, P.J., Lippmann, J., Hore, R., Avila-Soria, G., Brewer, D., Kloser, R.J., Steven, A. and Condie, S. (2013). Biology and ecology of Irukandji jellyfish (Cnidaria: Cubozoa). Advances in Marine Biology 66: 1-85.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2016-18-NESP-TWQ-2\2.2.3_Jellyfish-early-warning\AU_NESP-TWQ-2-2-3_CSIRO_Venomous-Jellyfish-DB

This dataset contains information on Government of Canada tender information published according to the Financial Administration Act. It includes data for all Schedule I, Schedule II and Schedule III departments, agencies, Crown corporations, and other entities (unless specifically exempt) who must comply with the Government of Canada trade agreement obligations. CanadaBuys is the authoritative source of this information. Visit the How procurement works page on the CanadaBuys website to learn more. All data files in this collection share a common column structure, and the procurement category field (labelled as “*procurementCategory-categorieApprovisionnement*”) can be used to filter by the following four major categories of tenders: - Tenders for construction, which will have a value of “CNST” - Tenders for goods, which will have a value of “GD” - Tenders for services, which will have a value of “SRV” - Tenders for services related to goods, which will have a value of “SRVTGD” A tender may be associated with one or more of the above procurement categories. >Note: Some records contain long tender description values that may cause issues when viewed in certain spreadsheet programs, such as Microsoft Excel. When the information doesn’t fit within the cell’s character limit, the program will insert extra rows that don’t conform to the expected column formatting. (Though, all other records will still be displayed properly, in their own rows.) To quickly remove the “spill-over data” caused by this display error in Excel, select the publication date field (labelled as “*publicationDate-datePublication*”), then click the Filter button on the Data menu ribbon. You can then use the filter pull-down list to remove any blank or non-date values from this field, which will hide the rows that only contain “spill-over” description information. --- The following list describes the resources associated with this CanadaBuys tender notices dataset. Additional information on Government of Canada tenders can also be found on the Tender notices tab of the CanadaBuys tender opportunities page. >NOTE: While the CanadaBuys online portal includes tender opportunities from across multiple levels of government, the data files in this related dataset only include notices from federal government organizations. --- (1) CanadaBuys data dictionary: This XML file offers descriptions of each data field in the tender notices files linked below, as well as other procurement-related datasets CanadaBuys produces. Use this as a guide for understanding the data elements in these files. This dictionary is updated as needed to reflect changes to the data elements. (2) New tender notices: This file contains up to date information on all new tender notices that are published to CanadaBuys throughout a given day. The file is updated every two hours, from 6:15 am until 10:15 pm (UTC-0500) to include new tenders as they are published. All tenders in this file will have a publication date matching the current day (displayed in the field labelled “*publicationDate-datePublication*”), or the day prior for systems that feed into this file on a nightly basis. (3) Open tender notices: This file contains up to date information on all tender notices that are open for bidding on CanadaBuys, including any amendments made to these tender notices during their lifecycles. The file is refreshed each morning, between 7:00 am and 8:30 am (UTC-0500) to include newly published open tenders. All tenders in this file will have a status of open (displayed in the field labelled “*tenderStatus-tenderStatut-eng*”). (4) All CanadaBuys tender notices, 2022-08-08 onwards: This file contains up to date information on all tender notices published through CanadaBuys. This includes any tender notices that were open for bids on or after August 8, 2022, when CanadaBuys launched as the system of record for all Tender Notices for the Government of Canada. This file includes any amendments made to these tender notices during their lifecycles. It is refreshed each morning, between 7:00 am and 8:30 am (UTC-0500) to include any updates or amendments, as needed. Tender notices in this file can have any publication date on or after August 8, 2022 (displayed in the field labelled “*publicationDate-datePublication*”), and can have a status of open, cancelled or expired (displayed in the field labelled “*tenderStatus-tenderStatut-eng*”). (5) Legacy tender notices, 2009 to 2022-08 (prior to CanadaBuys): This file contains details of the tender notices that were launched prior to the implementation of CanadaBuys, which became the system of record for all tender notices for the Government of Canada on August 8, 2022. This datafile is refreshed monthly. The over 70,000 tenders in this file have publication dates from August 5, 2022 and before (displayed in the field labelled “*publicationDate-datePublication*”) and have a status of cancelled or expired (displayed in the field labelled “*tenderStatus-tenderStatut-eng*”). >Note: Procurement data was structured differently in the legacy applications previously used to administer Government of Canada tender notices. Efforts have been made to manipulate these historical records into the structure used by the CanadaBuys data files, to make them easier to analyse and compare with new records. This process is not perfect since simple one-to-one mappings can’t be made in many cases. You can access these historical records in their original format as part of the archived copy of the original tender notices dataset. You can also refer to the supporting documentation for understanding the new CanadaBuys tender and award notices datasets. (6) Tender notices, YYYY-YYYY: These files contain information on all tender notices published in the specified fiscal year that are no longer open to bidding. The current fiscal year's file is refreshed each morning, between 7:00 am and 8:30 am (UTC-0500) to include any updates or amendments, as needed. The files associated with past fiscal years are refreshed monthly. Tender notices in these files can have any publication date between April 1 of a given year and March 31 of the subsequent year (displayed in the field labelled “*publicationDate-datePublication*”) and can have a status of cancelled or expired (displayed in the field labelled “*tenderStatus-tenderStatut-eng*”). New records are added to these files once related tenders reach their close date, or are cancelled. >Note: New tender notice data files will be added on April 1 for each fiscal year.