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.

This dataset contains data collected during a study ("Towards High-Value Datasets determination for data-driven development: a systematic literature review") conducted by Anastasija Nikiforova (University of Tartu), Nina Rizun, Magdalena Ciesielska (Gdańsk University of Technology), Charalampos Alexopoulos (University of the Aegean) and Andrea Miletič (University of Zagreb) It being made public both to act as supplementary data for "Towards High-Value Datasets determination for data-driven development: a systematic literature review" paper (pre-print is available in Open Access here -> https://arxiv.org/abs/2305.10234) and in order for other researchers to use these data in their own work.

The protocol is intended for the Systematic Literature review on the topic of High-value Datasets with the aim to gather information on how the topic of High-value datasets (HVD) and their determination has been reflected in the literature over the years and what has been found by these studies to date, incl. the indicators used in them, involved stakeholders, data-related aspects, and frameworks. The data in this dataset were collected in the result of the SLR over Scopus, Web of Science, and Digital Government Research library (DGRL) in 2023.

Methodology

To understand how HVD determination has been reflected in the literature over the years and what has been found by these studies to date, all relevant literature covering this topic has been studied. To this end, the SLR was carried out to by searching digital libraries covered by Scopus, Web of Science (WoS), Digital Government Research library (DGRL).

These databases were queried for keywords ("open data" OR "open government data") AND ("high-value data*" OR "high value data*"), which were applied to the article title, keywords, and abstract to limit the number of papers to those, where these objects were primary research objects rather than mentioned in the body, e.g., as a future work. After deduplication, 11 articles were found unique and were further checked for relevance. As a result, a total of 9 articles were further examined. Each study was independently examined by at least two authors.

To attain the objective of our study, we developed the protocol, where the information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information.

Test procedure Each study was independently examined by at least two authors, where after the in-depth examination of the full-text of the article, the structured protocol has been filled for each study. The structure of the survey is available in the supplementary file available (see Protocol_HVD_SLR.odt, Protocol_HVD_SLR.docx) The data collected for each study by two researchers were then synthesized in one final version by the third researcher.

Description of the data in this data set

Protocol_HVD_SLR provides the structure of the protocol Spreadsheets #1 provides the filled protocol for relevant studies. Spreadsheet#2 provides the list of results after the search over three indexing databases, i.e. before filtering out irrelevant studies

The information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information

Descriptive information
1) Article number - a study number, corresponding to the study number assigned in an Excel worksheet 2) Complete reference - the complete source information to refer to the study 3) Year of publication - the year in which the study was published 4) Journal article / conference paper / book chapter - the type of the paper -{journal article, conference paper, book chapter} 5) DOI / Website- a link to the website where the study can be found 6) Number of citations - the number of citations of the article in Google Scholar, Scopus, Web of Science 7) Availability in OA - availability of an article in the Open Access 8) Keywords - keywords of the paper as indicated by the authors 9) Relevance for this study - what is the relevance level of the article for this study? {high / medium / low}

Approach- and research design-related information 10) Objective / RQ - the research objective / aim, established research questions 11) Research method (including unit of analysis) - the methods used to collect data, including the unit of analy-sis (country, organisation, specific unit that has been ana-lysed, e.g., the number of use-cases, scope of the SLR etc.) 12) Contributions - the contributions of the study 13) Method - whether the study uses a qualitative, quantitative, or mixed methods approach? 14) Availability of the underlying research data- whether there is a reference to the publicly available underly-ing research data e.g., transcriptions of interviews, collected data, or explanation why these data are not shared? 15) Period under investigation - period (or moment) in which the study was conducted 16) Use of theory / theoretical concepts / approaches - does the study mention any theory / theoretical concepts / approaches? If any theory is mentioned, how is theory used in the study?

Quality- and relevance- related information
17) Quality concerns - whether there are any quality concerns (e.g., limited infor-mation about the research methods used)? 18) Primary research object - is the HVD a primary research object in the study? (primary - the paper is focused around the HVD determination, sec-ondary - mentioned but not studied (e.g., as part of discus-sion, future work etc.))

HVD determination-related information
19) HVD definition and type of value - how is the HVD defined in the article and / or any other equivalent term? 20) HVD indicators - what are the indicators to identify HVD? How were they identified? (components & relationships, “input -> output") 21) A framework for HVD determination - is there a framework presented for HVD identification? What components does it consist of and what are the rela-tionships between these components? (detailed description) 22) Stakeholders and their roles - what stakeholders or actors does HVD determination in-volve? What are their roles? 23) Data - what data do HVD cover? 24) Level (if relevant) - what is the level of the HVD determination covered in the article? (e.g., city, regional, national, international)

Format of the file .xls, .csv (for the first spreadsheet only), .odt, .docx

Licenses or restrictions CC-BY

For more info, see README.txt

The Joint Army Navy NASA Air Force Modeling and Simulation Subcommittee's Integrated Health Management panel was started about 6 years ago to help foster communication and collaboration in health management related issues for liquid and solid rocket propulsion systems. The panel is co-chaired by Mr. Scott Hyde (ATK) and Ashok N. Srivastava, Ph.D. (NASA). In order to have a common langauge for health management, we need to have a common set of definitions. We have attached a MS Excel spreadsheet that covers the many terms that are of interest to us in the field. Please take a look at the definitions and provide comments and additional terms (with or without definitions) using the feedback box below. We will compile all the definitions into a master list for submittal to the Modeling and Simulation Subcommittee.

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://res1wwwd-o-tmicrosoftd-o-tcom.vcapture.xyz/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://res1wwwd-o-tmicrosoftd-o-tcom.vcapture.xyz/en-us/microsoft-365/excel

The dataset contains information from a cohort of 799 patients admitted in the hospital for COVID-19, characterized with sociodemographic and clinical data. Retrospectively, from November 2020 to January 2021, data was collected from the medical records of all hospital admissions that occurred from March 1st, 2020, to December 31st, 2020. The analysis of these data can contribute to the definition of the clinical and sociodemographic profile of patients with COVID-19. Understanding these data can contribute to elucidating the sociodemographic profile, clinical variables and health conditions of patients hospitalized by COVID-19. To this end, this database contains a wide range of variables, such as: Month of hospitalization Sex Age group Ethnicity Marital status Paid work Admission to clinical ward Hospitalization in the Intensive Care Unit (ICU) COVID-19 diagnosis Number of times hospitalized by COVID-19 Hospitalization time in days Risk Classification Protocol Data is presented as a single Excel XLSX file: dataset.xlsx of clinical and sociodemographic characteristics of hospital admissions by COVID-19: retrospective cohort of patients in two hospitals in the Southern of Brazil. Researchers interested in studying the data related to patients affected by COVID-19 can extensively explore the variables described here. Approved by the Research Ethics Committee (No. 4.323.917/2020) of the Federal University of Santa Catarina.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

These are the most recent Data Dictionary (pop-ups) and Panarctic Species List (PASL) zip files for all the vegetation plot data entered into Turboveg for the Alaska AVA. These files are necessary to correctly use the Turboveg data with regards to coded data. The Data Dictionary file will be updated when new datasets are entered into Turboveg which result in additions to coded data such as references, author code, habitat type, surficial geology, etc. Updates to the PASL will occur less frequently. Check the dates in the file names to be certain that you are using the most current files. Our data model is a set of tables that comprise our relational database. The Excel spreadsheet included in the resources below provides information about each field in our database, such as data type, description, if it is a required field, whether the information within the field is selected from a pop-up list, and whether the field is a standard within Turboveg or is specific to the AVA. Using Turboveg: 1) Download the installation file available through the link at Alaska Arctic Geoecological Atlas portal from the official Turboveg webpage (general installation file for worldwide users, however, some adjustments will be needed when using data from AAVA after installation of this program). 2) Open the Turboveg program and restore the most recent Data Dictionary and PASL zipped files into the Turboveg program by using the function 'Database-Backup/Restore-Restore.' All the previous versions of data dictionary files and PASL that are already in program will be overwritten. 3) Use the Alaska-AVA following the manual for Turboveg for Windows which is available at http://www.synbiosys.alterra.nl/turboveg/tvwin.pdf

Researchers sequenced 10,368 expressed sequence tags (EST) clones using a normalized cDNA library made from pooled samples of the trophont, tomont, and theront life-cycle stages, and generated 9,769 sequences (94.2% success rate). Post-sequencing processing led to 8,432 high quality sequences. Clustering analysis of these ESTs allowed identification of 4,706 unique sequences containing 976 contigs and 3,730 singletons. The ciliate protozoan Ichthyophthirius multifiliis (Ich) is an important parasite of freshwater fish that causes 'white spot disease' leading to significant losses. A genomic resource for large-scale studies of this parasite has been lacking. To study gene expression involved in Ich pathogenesis and virulence, our goal was to generate ESTs for the development of a powerful microarray platform for the analysis of global gene expression in this species. Here, we initiated a project to sequence and analyze over 10,000 ESTs. Resources in this dataset:Resource Title: Data Dictionary - Supplemental Tables 1, 2, and 3. File Name: IchthyophthiriusESTs_DataDictionary.csvResource Description: Machine-readable comma-separated values (CSV) definitions for data elements of Supplemental Tables 1-3 concerning I. multifiliis unique EST sequences, BLAST searches of the Ich ESTs against Tetrahymena thermophila and Plasmodium falciparum genomes, and gene ontology (GO) profile.Resource Title: Table 3. Table of gene ontology (GO) profiles.. File Name: 12864_2006_889_MOESM3_ESM.xlsResource Description: Supplemental Table 3, Excel spreadsheet; Table of gene ontology (GO) profiles; Provided information includes unique EST name, accession numbers, BLASTX top hit, GO identification numbers and enzyme commission (EC) numbers. Data resources found on the main article page under the "Electronic supplementary material" section: http://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-8-176 Direct download for this resource: https://static-content.springer.com/esm/art:10.1186/1471-2164-8-176/MediaObjects/12864_2006_889_MOESM3_ESM.xls Title: Table I. Multifiliis unique EST sequences. File Name: 12864_2006_889_MOESM1_ESM.xlsResource Description: Supplemental Table 1 for article, "Generation and analysis of expressed sequence tags from the ciliate protozoan parasite Ichthyophthirius multifiliis." Excel spreadsheet; Table of I. multifiliis unique EST sequences; Provided information includes I. multifiliis BLASTX top hits to the non-redundant database in GenBank with unique EST name and accession numbers. Also included are significant protein domain comparisons to the Swiss-Prot database. Putative secretory proteins are highlighted. Data resources found on the main article page under the "Electronic supplementary material" section: http://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-8-176 Direct download for this resource: https://static-content.springer.com/esm/art:10.1186/1471-2164-8-176/MediaObjects/12864_2006_889_MOESM1_ESM.xls Title: Table 2. Excel spreadsheet; Summary of BLAST searches of the Ich ESTs against Tetrahymena thermophila and Plasmodium falciparum genomes. File Name: 12864_2006_889_MOESM2_ESM.xlsResource Description: Table 2 from "Generation and analysis of expressed sequence tags from the ciliate protozoan parasite Ichthyophthirius multifiliis." Excel spreadsheet; Summary of BLAST searches of the Ich ESTs against Tetrahymena thermophila and Plasmodium falciparum genomes. Provided information includes I. multifiliis BLASTX top hits to the non-redundant database in GenBank with unique EST name, tBLASTx top hits to the T. thermophila genome, and BLASTX top hits to the P. falciparum genome sequences. This table correlates with the Venn diagram in figure 1. Data resources found on the main article page under the "Electronic supplementary material" section: http://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-8-176 Direct download link for this data resource: https://static-content.springer.com/esm/art:10.1186/1471-2164-8-176/MediaObjects/12864_2006_889_MOESM2_ESM.xls

Context

This file contains 5 years of daily time series data for several measures of traffic on a statistical forecasting teaching notes website whose alias is statforecasting.com. The variables have complex seasonality that is keyed to the day of the week and to the academic calendar. The patterns you you see here are similar in principle to what you would see in other daily data with day-of-week and time-of-year effects. Some good exercises are to develop a 1-day-ahead forecasting model, a 7-day ahead forecasting model, and an entire-next-week forecasting model (i.e., next 7 days) for unique visitors.

Content

The variables are daily counts of page loads, unique visitors, first-time visitors, and returning visitors to an academic teaching notes website. There are 2167 rows of data spanning the date range from September 14, 2014, to August 19, 2020. A visit is defined as a stream of hits on one or more pages on the site on a given day by the same user, as identified by IP address. Multiple individuals with a shared IP address (e.g., in a computer lab) are considered as a single user, so real users may be undercounted to some extent. A visit is classified as "unique" if a hit from the same IP address has not come within the last 6 hours. Returning visitors are identified by cookies if those are accepted. All others are classified as first-time visitors, so the count of unique visitors is the sum of the counts of returning and first-time visitors by definition. The data was collected through a traffic monitoring service known as StatCounter.

Inspiration

This file and a number of other sample datasets can also be found on the website of RegressIt, a free Excel add-in for linear and logistic regression which I originally developed for use in the course whose website generated the traffic data given here. If you use Excel to some extent as well as Python or R, you might want to try it out on this dataset.

There are six datasets in this collection. Common symbols and abbreviations used in the datasets are defined in the resource titled, "Symbols and Abbreviations for Bushland, TX, Weighing Lysimeter Datasets". Datasets consist of Excel (xlsx) files. Each xlsx file contains an Introductory tab that explains the other tabs, lists the authors, describes conventions and symbols used and lists any instruments used. The remaining tabs in a file consist of dictionary and data tabs. The six datasets are as follows: Agronomic Calendars for the Bushland, Texas Winter Wheat Datasets Growth and Yield Data for the Bushland, Texas Winter Wheat Datasets Weighing Lysimeter Data for The Bushland, Texas Winter Wheat Datasets Soil Water Content Data for The Bushland, Texas, Large Weighing Lysimeter Experiments Evapotranspiration, Irrigation, Dew/frost - Water Balance Data for The Bushland, Texas Winter Wheat Datasets Standard Quality Controlled Research Weather Data – USDA-ARS, Bushland, Texas See the README for descriptions of each dataset. The soil is a Pullman series fine, mixed, superactive, thermic Torrertic Paleustoll. Soil properties are given in the resource titled "Soil Properties for the Bushland, TX, Weighing Lysimeter Datasets". The land slope in the lysimeter fields is Resources in this dataset: Resource Title: Geographic Coordinates of Experimental Assets, Weighing Lysimeter Experiments, USDA, ARS, Bushland, Texas. File Name: Geographic Coordinates, USDA, ARS, Bushland, Texas.xlsx. Resource Description: The file gives the UTM latitude and longitude of important experimental assets of the Bushland, Texas, USDA, ARS, Conservation & Production Research Laboratory (CPRL). Locations include weather stations [Soil and Water Management Research Unit (SWMRU) and CPRL], large weighing lysimeters, and corners of fields within which each lysimeter was centered. There were four fields designated NE, SE, NW, and SW, and a weighing lysimeter was centered in each field. The SWMRU weather station was adjacent to and immediately east of the NE and SE lysimeter fields.

These are results of a series of laboratory experiments to determine if topical application of methoprene and 20-ecdysone can terminate reproductive diapause of the weevil, Ceratapion basicorne, which is a recently permitted biological control agent of yellow starthistle (Centaurea solstitialis). Adult weevils feed on leaves, creating pin holes, and lay eggs inside leaves. Diapausing weevils were treated with various doses of methoprene (0, 0.01, 0.1, 1.0 micrograms) dissolved in acetone in experiments 1 and 2. They were treated sequentially first with acetone or 20-ecdysone (1.0 microgram) and then with methoprene (1.0 microgram) in experiment 3 and were treated with 20-ecdysone followed by methoprene in experiment 4. Resources in this dataset:Resource Title: data dictionary. File Name: JH Data Dictionary.csvResource Description: description of data fieldsResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: experiment 1. File Name: JH expt1 data.csvResource Description: Methoprene dissolved in acetone was applied topically at doses of 0.0, 0.01 and 0.1 and 1.0 μg per female weevil, and the number of feeding holes and eggs were recorded daily on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: experiment 2. File Name: JH expt2 data.csvResource Description: Methoprene dissolved in acetone was applied topically at doses of 0.0 and 1.0 μg to female weevils that did not produce eggs in experiment 1. The number of feeding holes and eggs were recorded daily on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: experiment 3. File Name: JH expt3 data.csvResource Description: Three types of treatments were applied with sequential applications 2 days apart: 1) acetone + acetone [AA: control], 2) acetone + methoprene [AM], and 20-ecdysone + methoprene 174 [2M]. All doses were 1.0 μg. The number of feeding holes and eggs were recorded every 2 days on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: experiment 4. File Name: JH expt4 data.csvResource Description: Females from experiment 3 that did not oviposit consistently were treated with 1.0 μg of 20-ecdysone followed 2 days later by 1.0 μg of methoprene. The treatments AA, AM, 2M refer to experiment 3. The number of feeding holes and eggs were recorded every 2 days on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/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 2020. 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.csvResource 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/excel Resource Title: Data dictionary for Pesticide Data Program. File Name: PDP DataDictionary.pdfResource Description: Data dictionary for PDP Database Zip files.Resource Software Recommended: Adobe Acrobat,url: https://www.adobe.com Resource 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: 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.zipResource Title: 2006 PDP Database Zip File. File Name: 2006PDPDatabase.zipResource Title: 2020 PDP Database Zip File. File Name: 2020PDPDatabase.zipResource Description: Data and supporting files for PDP 2020 surveyResource Software Recommended: Microsoft Access,url: https://products.office.com/en-us/access

Democracy Timeseries Data Release 3.0, January 2009

This dataset is in a country-year case format, suitable for time-series analysis. It contains data on the social, economic and political characteristics of 191 nations with over 600 variables from 1971 to 2007. It merges the indicators of democracy by Freedom House, Vanhanen, Polity IV, and Cheibub and Gandhi, plus selected institutional classifications and also socio-economic indicators from the World Bank. New variables including the KOF Globalization Index and the new Norris-Inglehart Cosmopolitan Index. Note that you should check the original codebooks for the meaning and definition of each of the variables. The period for each series also varies. Note that the Excel version is for Office 2007 only. This is the dataset used in the book, Driving Democracy.

January 2009

Stored in Stata, SPSS, Excel and CSV.

Business licenses issued by the Department of Business Affairs and Consumer Protection in the City of Chicago from 2002 to the present. This dataset contains a large number of records/rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: ‘ISSUE’ is the record associated with the initial license application. ‘RENEW’ is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. ‘C_LOC’ is a change of location record. It means the business moved. ‘C_CAPA’ is a change of capacity record. Only a few license types may file this type of application. ‘C_EXPA’ only applies to businesses that have liquor licenses. It means the business location expanded. 'C_SBA' is a change of business activity record. It means that a new business activity was added or an existing business activity was marked as expired.

LICENSE STATUS: ‘AAI’ means the license was issued. ‘AAC’ means the license was cancelled during its term. ‘REV’ means the license was revoked. 'REA' means the license revocation has been appealed.

LICENSE STATUS CHANGE DATE: This date corresponds to the date a license was cancelled (AAC), revoked (REV) or appealed (REA).

Business License Owner information may be accessed at: https://data.cityofchicago.org/dataset/Business-Owners/ezma-pppn. To identify the owner of a business, you will need the account number or legal name, which may be obtained from this Business Licenses dataset.

Data Owner: Business Affairs and Consumer Protection. Time Period: January 1, 2002 to present. Frequency: Data is updated daily.

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.

Data Dictionary template for Tempe Open Data.

ReadMe File for PADDDtracker.org Data Release Version 2.1

Prepared by Conservation International, May 2021

Thank you for downloading the PADDDtracker.org Data Release Version 2.1. This dataset includes data on Protected Area Downgrading, Downsizing, and Degazettement (PADDD). Most data have been validated by peer-review, with the limited exception of newly added data from the United States and Brazil; see below for further details and links to publications.

Definitions for Protected Area Downgrading, Downsizing, and Degazettement (PADDD):

• Downgrading: A decrease in legal restrictions on the number, magnitude, or extent of human activities within a protected area by a relevant authority
• Downsizing: A decrease in the size of a protected area as a result of excision of land or sea area through a legal boundary change
• Degazettement: A loss of legal status of a protected area under government administration (national, state, provincial or local)

This data release contains data from the following peer-reviewed studies:

• M.B. Mascia, S. Pailler, Protected area downgrading, downsizing, and degazettement (PADDD) and its conservation implications. Conservation Letters. 4, 9–20 (2011). DOI: 10.1111/j.1755-263X.2010.00147.x .
• M.B. Mascia et al., Protected area downgrading, downsizing, and degazettement (PADDD) in Africa, Asia, and Latin America and the Caribbean, 1900–2010. Biological Conservation. 169, 355–361 (2014). DOI: 10.1016/j.biocon.2013.11.021 .
• J.L. Forrest et al., Tropical deforestation and carbon emissions from protected area downgrading, downsizing, and degazettement (PADDD). Conservation Letters. 8, 153–161 (2015). DOI: 10.1111/conl.12144 .
• S.M. Pack et al., Protected area downgrading, downsizing, and degazettement (PADDD) in the Amazon. Biological Conservation. 197, 32–39 (2016). DOI: 10.1016/j.biocon.2016.02.004.
• R.E. Golden Kroner, R. Krithivasan, M.B. Mascia, Effects of protected area downsizing on habitat fragmentation in Yosemite National Park (USA), 1864 - 2014. Ecology and Society. 21 (2016). DOI: 10.5751/ES-08679-210322.
• C.N. Cook, R.S. Valkan, M.B. Mascia, M.A. McGeoch, Quantifying the extent of protected-area downgrading, downsizing, and degazettement in Australia. Conservation Biology. 31, 1039–1052 (2017). DOI: 10.1111/cobi.12904.
• R.E. Golden Kroner et al., The uncertain future of protected lands and waters. Science. 31, 364 (6443), 881-886 (2019). DOI: 10.1126/science.aau5525.
• T. Dorji, S. Linke, F. Sheldon, Half century of protected area dynamism in the country of Gross National Happiness, Bhutan. Conservation Science and Practice. (2019). DOI: 10.1111/csp2.46.
• A. De Vos, H. Clements, D. Biggs, G.S. Cumming, The dynamics of proclaimed privately protected areas in South Africa over 83 years. Conservation Letters. 12 (6) (2019). DOI: 10.1111/conl.12644.
• R. Albrecht, C.N. Cook, O. Andrews, K.E. Roberts, M.F.J. Taylor, M.B. Mascia, R.E. Golden Kroner, Protected area downgrading, downsizing, and degazettement (PADDD) in marine protected areas. Marine Policy. 129 (2021). 104437, ISSN 0308-597X. DOI: 10.1016/j.marpol.2021.104437.

Please contact paddd.team@gmail.com to request full text versions of publications if not otherwise open access.

Please note that 353 (7% of) records in the database (new records from the United States and Brazil) have not yet been validated by peer review; see Olsson et al. 2021 for more information about these data:

• Olsson, E., Albrecht, R., & Golden Kroner, R.E. (2021). PADDDtracker Data Release Version 2.1: Technical Notes. Conservation International, Arlington, VA. DOI: 10.5281/zenodo.4749615.

Differences between Version 2.1 and previous data releases:

PADDDtracker data release Version 2.1 contains 21 new fields:

• Sys_Code
• Map_Details
• Map_Source
• Notes
• Last_Update
• AddedBy
• isLatestVe
• Data_Status
• Peer_Reviewed
• Study_Link
• Off_Type
• Off_Area
• Off_Details
• Off_Source
• Rev_Type
• Rev_Area
• Rev_Details
• Rev_Source
• Legal_Type
• Marine
• Marine_ZID

Two fields included in the previous data release have been archived; these are both out-of-date ID fields that are no longer necessary to retain.

• PADDDIDOld
• GID_String

Version history

• Version 1.0 was released in January 2014, and contains 601 PADDD events from Africa, Asia, Latin America and the Caribbean, spanning from 1900 to 2012.
• Version 1.1 was released in January 2017, and contains a total of 721 PADDD events, including updated data from version 1.0 as well as additional PADDD events in the Democratic Republic of the Congo, Peru and Brazil, spanning from 1900 to 2016.
• Version 2.0 was released in May 2019, and contains a total of 4597 PADDD events, including updated PADDD events from previous data releases (1.0 and 1.1), and new records from around the world (Africa, Asia, Australia, Europe, Latin America and the Caribbean, North America).
• Version 2.1 contains a total of 4962 PADDD events, including updated data from version 2.0 as well as additional PADDD events in Australia, Brazil, Bhutan, Palau, South Africa, and the United States. It also includes several new fields for supporting details data, including for offsets and reversals. Please consult the Field Definitions PDF and/or Olsson et al. 2021 in the data release package for information on these new field names, field name definitions, data values, and clarifications.

In the folder PADDDtracker_DataReleaseV2_1_2021, you will find:

PADDDtracker_DataReleaseV2_1_2021.xlsx: this Excel file contains data on all known PADDD events, including numerical and categorical data. This includes data on location, dates, areas, IUCN categories, proximate causes and other descriptive attributes associated with PADDD events. The file contains the following tabs:

• ReadMe: introduction to dataset.
• FieldDefinitions: contains definitions for all fields (attributes) in the database.
• PADDDEvents: Dataset of known enacted and proposed PADDD events, including attributes describing location, timing, proximate causes, and associated supporting information and sources. The Supporting Details and References are omitted from the shapefile, as the text exceeds the character limit.
• PADDDReversals: information about location, dates, etc. on PADDD events with full or partial reversals for which spatial data are available. The Supporting Details and References are omitted from the shapefile, as the text exceeds the character limit.
• PADDDOffsets: information about location, dates, etc. on PADDD events with known spatial or regulatory offsets for which spatial data are available. The Supporting Details and References are omitted from the shapefile, as the text exceeds the character limit.
• PADDDMPAZones: information about location, dates, zone name(s), and other descriptive information on PADDD events in marine protected areas (MPAs) in cases where MPAs were split into two or more use zones (see FieldDefinitions tab for zone domain values). The Supporting Details and References are omitted from the shapefile, as the text exceeds the character limit.

Primary GIS Datasets:

• PADDDtracker_DataReleaseV2_1_2021_Pts.shp: This shapefile contains point data for all validated PADDD events corresponding with the accompanying Excel sheet.

Please note that for an event for which the exact location is unknown, it is represented by a point placed either at the PA centroid or within the PA extent if a multipart polygon (for downgrades or downsizes), or on the capital city of the country. If using PADDD events data for spatially explicit analyses for which locations of event areas are necessary, please use the field “Location_K” as a filter to remove events with unknown locations.

• PADDDtracker_DataReleaseV2_1_2021_Poly.shp: This shapefile contains validated polygon data for PADDD events corresponding with the accompanying Excel sheet.

Supplemental GIS Datasets:

• PADDDtracker_DataReleaseV2_1_2021_Pts_Reversals.shp: Point shapefile of reversals to PADDD events.
• PADDDtracker_DataReleaseV2_1_2021_Poly_Reversals.shp: Polygon shapefile of reversals to PADDD events.
• PADDDtracker_DataReleaseV2_1_2021_Poly_Offsets.shp: Polygon shapefile of offsets to PADDD events.
• PADDDtracker_DataReleaseV2_1_2021_Poly_MPAZones.shp: Polygon shapefile of zoning changes that constitute PADDD events.

Additional Resources

• PADDDtracker Technical Guide V2, 2020: contains guidance for researchers to collect PADDD data.
• PADDDtracker Data Release 2.1 Technical Note, 2021: provides more details on PADDD Reversals,

This digital dataset was created as part of a U.S. Geological Survey study in cooperation with the Santa Barbara County Water Agency to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Cuyama 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 Cuyama Valley 3-D hydrogeologic framework models (3DHFM), that define the elevation, thickness, extent, and lithology-based texture variations of three hydrogeologic units in the Cuyama Valley, CA, groundwater basin. A USGS report that described the construction of 3-D geologic framework and textural models for Cuyama Valley groundwater basin was published in 2013 (Sweetkind and others, 2013). This data release formalizes the input geologic data and model outputs as a digital dataset. The Cuyama Valley 3DHFM incorporates as input data stratigraphic and lithologic information derived from water, monitoring, and oil and gas wells, as well as data from geologic maps and interpreted structure contour maps. Input surface and subsurface data have been reduced to points that define the top elevation and textural or grain-size characteristics of each hydrogeologic units at x,y locations; these point data sets serve as digital input to the framework models. The location of wells used sources of subsurface stratigraphic and lithologic information are provided as separate point feature classes in a geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also provided in a Microsoft Excel spreadsheet that includes separate TABs for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole. Two types of geologic frameworks were constructed: (1) a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the three basin-fill hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on kriging-based interpolation of classed downhole lithologic data. Each of the frameworks is stored within a second geospatial database as an array of polygonal cells or cell centroids: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a point feature class which contains a mesh of cell centroids that represent model cells that have multiple attributes including x,y location, elevation, and thickness of each hydrogeologic unit. Computed textural information for each of the three basin-fill hydrogeologic units are stored in separate feature classes of polygonal cells where a single textural variable “percent coarse grained” is an attribute at each x,y location. 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, and a Data Dictionary that duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles and borehole data are provided in Microsoft Excel spreadsheet. The elevation, thickness, and textural model of each hydrogeologic unit are also released as raster files.

These data include the individual responses for the City of Tempe Annual Business 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 Business Survey results are used as indicators for city performance measures. The performance measures with indicators from the Business Survey include the following (as of 2023):1. Financial Stability and Vitality5.01 Quality of Business ServicesThe 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.Additional InformationSource: Business 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 DictionaryMethods:The survey is mailed to a random sample of businesses 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.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 data are used by the ETC Institute in the final published PDF report.

This dataset comes from a study conducted in Poland with 44 participants. The goal of the study was to measure personality traits known as the Dark Triad. The Dark Triad consists of three key traits that influence how people think and behave towards others. These traits are Machiavellianism, Narcissism, and Psychopathy. Machiavellianism refers to a person's tendency to manipulate others and be strategic in their actions. People with high Machiavellianism scores often believe that deception is necessary to achieve their goals. Narcissism is related to self-importance and the need for admiration. Individuals with high narcissism scores may see themselves as special and expect others to recognize their greatness. Psychopathy is linked to impulsive behavior and a lack of empathy. People with high psychopathy scores tend to be less concerned about the feelings of others and may take risks without worrying about consequences. Each participant in the dataset answered 30 questions, divided into three sections, with 10 questions per trait. The answers were recorded using a Likert scale from 1 to 5, where: 1 means "Strongly Disagree" 2 means "Disagree" 3 means "Neutral" 4 means "Agree" 5 means "Strongly Agree" This scale helps measure how much a person agrees with statements related to each of the three traits. The dataset also includes basic demographic information. Each participant has a unique ID (such as P001, P002, etc.) to keep their identity anonymous. The dataset records their age, which ranges from 18 to 60 years old, and their gender, which is categorized as "Male," "Female," or "Other." The responses in the dataset are realistic, with small variations to reflect natural differences in personality. On average, participants scored around 3.2 for Machiavellianism, meaning most people showed a moderate tendency to be strategic or manipulative. The average Narcissism score was 3.5, indicating that some participants valued themselves highly and sought admiration. The average Psychopathy score was 2.8, showing that most participants did not strongly exhibit impulsive or reckless behaviors. This dataset can be useful for many purposes. Researchers can use it to analyze personality traits and see how they compare across different groups. The data can also be used for cross-cultural comparisons, allowing researchers to study how personality traits in Poland differ from those in other countries. Additionally, psychologists can use this data to understand how Dark Triad traits influence behavior in everyday life. The dataset is saved in a CSV format, which makes it easy to open in programs like Excel, SPSS, or Python for further analysis. Because the data is structured and anonymized, it can be used safely for research without revealing personal information. In summary, this dataset provides valuable insights into personality traits among people in Poland. It allows researchers to explore how Machiavellianism, Narcissism, and Psychopathy vary among individuals. By studying these traits, psychologists can better understand human behavior and how it affects relationships, decision-making, and personal success.