Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

Database of Genotype and Phenotype (dbGaP) was developed to archive and distribute the data and results from studies that have investigated the interaction of genotype and phenotype in Humans.

The NYS Department of Environmental Conservation (DEC) collects and maintains several datasets on the locations, distribution and status of species of plants and animals. Information on distribution by county from the following three databases was extracted and compiled into this dataset. First, the New York Natural Heritage Program biodiversity database: Rare animals, rare plants, and significant natural communities. Significant natural communities are rare or high-quality wetlands, forests, grasslands, ponds, streams, and other types of habitats. Next, the 2nd NYS Breeding Bird Atlas Project database: Birds documented as breeding during the atlas project from 2000-2005. And last, DEC’s NYS Reptile and Amphibian Database: Reptiles and amphibians; most records are from the NYS Amphibian & Reptile Atlas Project (Herp Atlas) from 1990-1999.

Dataset Overview

This dataset, titled "Anabolic Steroids", provides a meticulously curated compilation of nearly 50 steroids. It includes detailed information on their original names, common names, medicinal applications, abuse potential, side effects, historical context, and relative molecular mass (RMM). The dataset aims to serve as a resource for exploring the dual nature of anabolic steroids—both their therapeutic benefits and their misuse in sports and bodybuilding.

Anabolic steroids are synthetic derivatives of testosterone that have been used for decades in medicine to treat conditions like anemia, muscle-wasting diseases, and hormone deficiencies. However, they are also widely abused for performance enhancement and aesthetic purposes. This dataset captures a comprehensive view of these compounds, making it valuable for researchers, educators, and data enthusiasts.

Data Science Applications

While this dataset is relatively small (approx 50 entries), it offers rich opportunities for exploratory analysis and domain-specific insights. Potential applications include:

• Exploratory Data Analysis (EDA):

  • Analyze trends in medicinal vs. non-medicinal use.
  • Study correlations between molecular mass and reported side effects.
  • Visualize the historical development of anabolic steroids over time.

• Domain-Specific Insights:

  • Examine the evolution of steroid formulations from the 1930s to the present.
  • Investigate patterns in therapeutic uses versus abuse potential.

• Educational Use:

  • Serve as a teaching tool for understanding data cleaning, visualization, and analysis.
  • Provide insights into the pharmacological and chemical properties of anabolic steroids.

Column Descriptors

1. Original Name: The scientific or chemical name of the steroid compound (e.g., Testosterone).
2. Common Name: The popular or brand name under which the steroid is marketed (e.g., Testoviron).
3. Medicinal Use: Approved therapeutic applications of the steroid (e.g., treating anemia or hormone replacement therapy).
4. Abused For: Non-medical uses often associated with performance enhancement or bodybuilding (e.g., bulking cycles, lean muscle retention).
5. Side Effects: Documented adverse effects resulting from steroid use or abuse (e.g., liver toxicity, gynecomastia).
6. History: A brief historical context about the steroid's development or usage (e.g., year introduced, medical approval status).
7. Relative Molecular Mass (g/mol): The molar mass of the steroid compound, useful for chemical analysis.

Ethically Mined Data

This dataset has been ethically compiled from publicly available sources such as scientific journals, chemical databases, and educational websites. No proprietary or confidential information has been included. The data was aggregated to ensure accuracy and relevance while respecting intellectual property rights.

Acknowledgements

The following sources were instrumental in compiling this dataset: 1. PubChem Database – For verifying chemical properties and molecular mass values. 2. Wikipedia – For historical context and general information on anabolic steroids. 3. NIST Chemistry WebBook – For accurate molecular mass values and chemical details. 4. Scientific Journals – Referenced for medicinal uses, side effects documentation, and abuse patterns. 5. DALL·E 3 by OpenAI – Used to generate illustrative images related to anabolic steroids to complement dataset visualizations.

Discouraging Steroid Usage and Highlighting Harms

The misuse of anabolic steroids poses significant health risks and ethical concerns. While anabolic steroids have legitimate medical applications, their abuse for performance enhancement or aesthetic purposes can lead to severe physical and psychological side effects. Common adverse effects include liver damage, cardiovascular strain, hormonal imbalances, infertility, aggression, and mental health issues such as depression. Prolonged misuse can also result in irreversible damage to vital organs and an increased risk of life-threatening conditions like heart attacks or strokes. Beyond individual health risks, steroid abuse undermines the integrity of sports and creates unfair advantages in competitive environments. It is crucial to prioritize natural methods of achieving fitness goals and seek professional guidance for any medical conditions requiring treatment.

Notes for Kaggle Users

This dataset is not intended for machine learning due to its small size but serves as an excellent resource for exploratory data analysis (EDA), visualization projects, and domain-specific research into anabolic steroids' pharmacology and societal impact.

This dataset is a compilation of data obtained from the Idaho Department of Water Quality, the Idaho Department of Water Resources, and the Water Quality Portal. The 'SiteID' table catalogues organization-specific identification numbers assigned to each monitoring location.

The ARG Database is a huge collection of labeled and unlabeled graphs realized by the MIVIA Group. The aim of this collection is to provide the graph research community with a standard test ground for the benchmarking of graph matching algorithms.

Flexibility Overview This dataset contains information on what we will look to put forward in our upcoming trades, with the aim to provide more visibility ahead of the actual trade opportunities. For general Constraint Management Zone (CMZ) information and overall requirements, please go to the Flexibility – Forecasts page. National Grid facilitates its flexibility procurement activity through its online portal, Market Gateway. Flexibility Service Providers (FSPs) seeking an award to deliver flexibility services should register on the Market Gateway and complete the pre-qualification requirements to enable their eligibility to enter into flexibility Trades. Pre-qualification is always open and can be completed at any time. Further guidance on this process is available here. Any questions should be sent to nged.flexiblepower@nationalgrid.co.uk. Data Currently, this dataset only covers Long Term trade opportunities for HV and LV in detail. HV – Long Term Trade Parameters.csv includes information for Scheduled Availability Operational Utilisation - Day Ahead Notice and Operational Utilisation - 15min Instruction flexibility products within HV Zones, such as peak MW requirements (min, max), ceiling prices, and delivery windows (dates, times, days required). LV – Long Term Trade Parameters.csv contains information for all zones where Scheduled Utilisation is available. This information includes the capacity we need (minimum and maximum kW), the maximum price we can pay (ceiling price in £/kW/season and £/MWh), and service delivery windows (dates, times, days required). The trade results are presented in the Trade_Results.csv file in detail, and in Trade_Results_Summary.csv in a more simplified, aggregated view. The weekly trade auction results are presented in the Weekly_Trade_Results.csv and Weekly_Trade_Results_Summary.csv .

The Global Retail Sales Data provided here is a self-generated synthetic dataset created using Random Sampling techniques provided by the Numpy Package. The dataset emulates information regarding merchandise sales through a retail website set up by a popular fictional influencer based in the US between the '23-'24 period. The influencer would sell clothing, ornaments and other products at variable rates through the retail website to all of their followers across the world. Imagine that the influencer executes high levels of promotions for the materials they sell, prompting more ratings and reviews from their followers, pushing more user engagement.

This dataset is placed to help with practicing Sentiment Analysis or/and Time Series Analysis of sales, etc. as they are very important topics for Data Analyst prospects. The column description is given as follows:

Order ID: Serves as an identifier for each order made.

Order Date: The date when the order was made.

Product ID: Serves as an identifier for the product that was ordered.

Product Category: Category of Product sold(Clothing, Ornaments, Other).

Buyer Gender: Genders of people that have ordered from the website (Male, Female).

Buyer Age: Ages of the buyers.

Order Location: The city where the order was made from.

International Shipping: Whether the product was shipped internationally or not. (Yes/No)

Sales Price: Price tag for the product.

Shipping Charges: Extra charges for international shipments.

Sales per Unit: Sales cost while including international shipping charges.

Quantity: Quantity of the product bought.

Total Sales: Total sales made through the purchase.

Rating: User rating given for the order.

Review: User review given for the order.

Metadata for the OpenFEMA API data set fields. It contains descriptions, data types, and other attributes for each field.rnrnIf you have media inquiries about this dataset please email the FEMA News Desk FEMA-News-Desk@dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open government program please contact the OpenFEMA team via email OpenFEMA@fema.dhs.gov.

This database, compiled by Matthews and Fung (1987), provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. The original data consists of five global 1-degree latitude by 1-degree longitude arrays. This subset, for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America, retains all five arrays at the 1-degree resolution but only for the area of interest (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N). The arrays are (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type. The data subsets are in both ASCII GRID and binary image file formats.The data base is the result of the integration of three independent digital sources: (1) vegetation classified according to the United Nations Educational Scientific and Cultural Organization (UNESCO) system (Matthews, 1983), (2) soil properties from the Food and Agriculture Organization (FAO) soil maps (Zobler, 1986), and (3) fractional inundation in each 1-degree cell compiled from a global map survey of Operational Navigation Charts (ONC). With vegetation, soil, and inundation characteristics of each wetland site identified, the data base has been used for a coherent and systematic estimate of methane emissions from wetlands and for an analysis of the causes for uncertainties in the emission estimate.The complete global data base is available from NASA/GISS [http://www.giss.nasa.gov] and NCAR data set ds765.5 [http://www.ncar.ucar.edu]; the global vegetation types data are available from ORNL DAAC [http://www.daac.ornl.gov].

The ADBB is a meta-dataset from Comparative Area Studies that collects and categorizes datasets in the study of institutions and conflict in divided societies at a global level (from 1945 - 2012). For detailed information see GIGA Working Paper No. 234.

The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.

Abstract

The Namoi Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP). The Analysis Database brings together many of the data sets of the scientific disciplines of the Programme and includes modelling results from hydrogeology and hydrology, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Data Register for each subregion and can be found on the Bioregional Assessments web site (http://www.bioregionalassessments.gov.au/).

An Analysis Database of common design and schema was implemented for each individual subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Database in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of a different specification and origin.

The Analysis Database provided for this subregion is an exact replica of the original used for the assessment of the subregion with the exception that a few spatial data for individual Assets subject to restrictions have been removed before publication. The restrictions are typically for threatened species spatial data but occasionally, restrictive licencing conditions imposed by some custodians prevented publication of some data. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump.

A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c

Purpose

The Namoi Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of a Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer.

An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure.

Dataset History

This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2.

Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems.

During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology.

Dataset Citation

Bioregional Assessment Programme (2018) NAM Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/1549c88d-927b-4cb5-b531-1d584d59be58.

Dataset Ancestors

• Derived From River Styles Spatial Layer for New South Wales

• Derived From Geofabric Surface Network - V2.1

• Derived From Surface Geology of Australia, 1:1 000 000 scale, 2012 edition

• Derived From HUN SW footprint shapefiles v01

• Derived From HUN Groundwater footprint polygons v01

• Derived From Namoi Environmental Impact Statements - Mine footprints

• Derived From Namoi CMA Groundwater Dependent Ecosystems

• Derived From Landscape classification of the Namoi preliminary assessment extent

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From Soil and Landscape Grid National Soil Attribute Maps - Clay 3 resolution - Release 1

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014

• Derived From Interim Biogeographic Regionalisation for Australia (IBRA), Version 7 (Regions)

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From Bioregional Assessment areas v03

• Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013

• Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From Asset list for Namoi - CURRENT

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From Namoi bore locations, depth to water for June 2012

• Derived From Victoria - Seamless Geology 2014

• Derived From Murray-Darling Basin Aquatic Ecosystem Classification

• Derived From HUN SW GW Mine Footprints for IMIA 20170303 v03

• Derived From Climate model 0.05x0.05 cells and cell centroids

• Derived From Namoi hydraulic conductivity measurements

• Derived From Namoi groundwater uncertainty analysis

• Derived From Historical Mining footprints DTIRIS HUN 20150707

• Derived From Namoi NGIS Bore analysis for 2012

• Derived From Australian 0.05º gridded chloride deposition v2

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From Bioregional Assessment areas v06

• Derived From NAM Analysis Boundaries 20160908 v01

• Derived From Namoi groundwater drawdown grids

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From BOM, Australian Average Rainfall Data from 1961 to 1990

• Derived From Namoi Existing Mine Development Surface Water Footprints

• Derived From Surface water Preliminary Assessment Extent (PAE) for the Namoi (NAM) subregion - v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From [National Surface Water sites

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

This data set contains a number of variables from collected on children and their parents who took part in the SMILE trial at assessment and follow up. It does not include data on age and gender as we want to be certain that no child or parent can be identified through the data. Researchers can apply to access a fuller data set (https://data.bris.ac.uk/data/dataset/1myzti8qnv48g2sxtx6h5nice7) containing age and gender through application to the University of Bristol's Data Access Committee, please refer to the data access request form (http://bit.ly/data-bris-request) for details on how to apply for access. Complete download (zip, 1.5 MiB)

This study presents a dataset on global afforestation and reforestation efforts compiled from primary (meta-)information and augmented with time-series satellite imagery and other secondary data. Our dataset covers 1,289,068 planting sites from 45,628 projects spanning 33 years.

The North American Dataset contains sets of Maximum, Minimum and Average Temperature data and Precipitation data that are either (1) raw (non-adjusted though flagged for possible quality issues), (2) adjusted due to time of observation bias (TOB) or (3) put through the Pairwise Homogenization Algorithm (PHA). These files contain North American stations and its data are measured in hundredths of degrees Celsius (without decimal place) for temperature and tenths of millimeters (without decimal place) for Precipitation. Each file includes the entire available Period of Record.

TCGA Cancer Variant and Clinical Data

  Dataset Description

This dataset combines genetic variant information at the protein level with clinical data from The Cancer Genome Atlas (TCGA) project, curated by the International Cancer Genome Consortium (ICGC). It provides a comprehensive view of protein-altering mutations and clinical characteristics across various cancer types.

  Dataset Summary

The dataset includes:

Protein sequence data for both mutated and… See the full description on the dataset page: https://huggingface.co/datasets/seq-to-pheno/TCGA-Cancer-Variant-and-Clinical-Data.

The Forest Inventory and Analysis (FIA) research program has been in existence since mandated by Congress in 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and depletion of timber on the Nation's forest land. Before 1999, all inventories were conducted on a periodic basis. The passage of the 1998 Farm Bill requires FIA to collect data annually on plots within each State. This kind of up-to-date information is essential to frame realistic forest policies and programs. Summary reports for individual States are published but the Forest Service also provides data collected in each inventory to those interested in further analysis. Data is distributed via the FIA DataMart in a standard format. This standard format, referred to as the Forest Inventory and Analysis Database (FIADB) structure, was developed to provide users with as much data as possible in a consistent manner among States. A number of inventories conducted prior to the implementation of the annual inventory are available in the FIADB. However, various data attributes may be empty or the items may have been collected or computed differently. Annual inventories use a common plot design and common data collection procedures nationwide, resulting in greater consistency among FIA work units than earlier inventories. Links to field collection manuals and the FIADB user's manual are provided in the FIA DataMart.

Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 to a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.1.3 Codebook.pdf - This 15-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised February 2024 2. Coup Data v2.1.3.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1000 observations. Revised February 2024 3. Source Document v2.1.3.pdf - This 325-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised February 2024 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised February 2024
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2024. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2024. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7