This dataset provides detailed county-level returns for U.S. presidential general elections, compiled by Dave Leip’s Atlas of U.S. Presidential Elections. For each election year included, the dataset is distributed as an Excel workbook (.xlsx) with multiple worksheets and accompanied by machine-readable CSV files for additional administrative levels (county, congressional district, state). There are two codebooks for the this data collection describing variable names and meanings: one for the Congressional District level data and the other for County level data.The Excel workbook contains:Candidates – names and party ballot listings by state.Vote Data by State – statewide vote totals for each candidate, with boundary identifiers (FIPS codes).Vote Data by County – county-level vote totals for all states and the District of Columbia, with FIPS codes.Vote Data by Town – town-level results for New England states (ME, MA, CT, RI, VT, NH), with FIPS codes.Graphs – pie charts summarizing results by state and nationally.Party – statewide vote strength of major parties.Statistics – summary statistics including closest races, maxima, and other aggregate indicators.Data Sources – documentation of sources used to compile the dataset.For the 2016, 2020, and 2024 elections, additional Excel workbooks and CSV files are provided at the congressional district (CD) level, containing:Vote Data by Congressional District – vote totals by district for each candidate, with FIPS codes. Includes detailed allocations for counties that span multiple congressional districts.Data Sources – documentation of sources used to compile the dataset.Candidates – candidate names and national party ballot listings.Notes – state-level notes describing data compilation details.

This is the dataset I used to figure out which sociodemographic factor including the current pandemic status of each state has the most significan impace on the result of the US Presidential election last year. I also included sentiment scores of tweets created from 2020-10-15 to 2020-11-02 as well, in order to figure out the effect of positive/negative emotion for each candidate - Donald Trump and Joe Biden - on the result of the election.

Details for each variable are as below: - state: name of each state in the United States, including District of Columbia - elec16, elec20: dummy variable indicating whether Trump gained the electoral votes of each state or not. If the electors casted their votes for Trump, the value is 1; otherwise the value is 0 - elecchange: dummy variable indicating whether each party flipped the result in 2020 compared to that of the 2016 - demvote16: the rate of votes that the Democrats, i.e. Hillary Clinton earned in the 2016 Presidential election - repvote16: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2016 Presidential election - demvote20: the rate of votes that the Democrats, i.e. Joe Biden earned in the 2020 Presidential election - repvote20: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2020 Presidential election - demvotedif: the difference between demvote20 and demvote16 - repvotedif: the difference between repvote20 and repvote16 - pop: the population of each state - cumulcases: the cumulative COVID-19 cases on the Election day - caseMar ~ caseOct: the cumulative COVID-19 cases during each month - Marper10k ~ Octper10k: the cumulative COVID-19 cases during each month per 10 thousands - unemp20: the unemployment rate of each state this year before the election - unempdif: the difference between the unemployment rate of the last year and that of this year - jan20unemp ~ oct20unemp: the unemployment rate of each month - cumulper10k: the cumulative COVID-19 cases on the Election day per 10 thousands - b_str_poscount_total: the total number of positive tweets on Biden measured by the SentiStrength - b_str_negcount_total: the total number of negative tweets on Biden measured by the SentiStrength - t_str_poscount_total: the total number of positive tweets on Trump measured by the SentiStrength - t_str_poscount_total: the total number of negative tweets on Trump measured by the SentiStrength - b_str_posprop_total: the proportion of positive tweets on Biden measured by the SentiStrength - b_str_negprop_total: the proportion of negative tweets on Biden measured by the SentiStrength - t_str_posprop_total: the proportion of positive tweets on Trump measured by the SentiStrength - t_str_negprop_total: the proportion of negative tweets on Trump measured by the SentiStrength - white: the proportion of white people - colored: the proportion of colored people - secondary: the proportion of people who has attained the secondary education - tertiary: the proportion of people who has attained the tertiary education - q3gdp20: GDP of the 3rd quarter 2020 - q3gdprate: the growth rate of the 3rd quarter 2020, compared to that of the same quarter last year - 3qsgdp20: GDP of 3 quarters 2020 - 3qsrate20: the growth rate of GDP compared to that of the 3 quarters last year - q3gdpdif: the difference in the level of GDP of the 3rd quarter compared to the last quarter - q3rate: the growth rate of the 3rd quarter compared to the last quarter - access: the proportion of households having the Internet access

This data set consists of all Fulton County Election results from April 2012 to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.

The Voter Participation indicator presents voter turnout in Champaign County as a percentage, calculated using two different methods.

In the first method, the voter turnout percentage is calculated using the number of ballots cast compared to the total population in the county that is eligible to vote. In the second method, the voter turnout percentage is calculated using the number of ballots cast compared to the number of registered voters in the county.

Since both methods are in use by other agencies, and since there are real differences in the figures that both methods return, we have provided the voter participation rate for Champaign County using each method.

Voter participation is a solid illustration of a community’s engagement in the political process at the federal and state levels. One can infer a high level of political engagement from high voter participation rates.

The voter participation rate calculated using the total eligible population is consistently lower than the voter participation rate calculated using the number of registered voters, since the number of registered voters is smaller than the total eligible population.

There are consistent trends in both sets of data: the voter participation rate, no matter how it is calculated, shows large spikes in presidential election years (e.g., 2008, 2012, 2016, 2020, 2024) and smaller spikes in intermediary even years (e.g., 2010, 2014, 2018, 2022). The lowest levels of voter participation can be seen in odd years (e.g., 2015, 2017, 2019, 2021, 2023).

This data primarily comes from the election results resources on the Champaign County Clerk website. Election results resources from Champaign County include the number of ballots cast and the number of registered voters. The results are published frequently, following each election.

Data on the total eligible population for Champaign County was sourced from the U.S. Census Bureau, using American Community Survey (ACS) 1-Year Estimates for each year starting in 2005, when the American Community Survey was created. The estimates are released annually by the Census Bureau.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because this data is not available for Champaign County, the eligible voting population for 2020 is not included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes datasets on Population by Sex and Population Under 18 Years by Age.

Sources: Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2024 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (24 November 2025).; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (10 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (5 October 2023).; Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (7 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; Champaign County Clerk Election History; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (6 March 2017).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey 2012 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).

Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data Attributes Ward Data Overview:July 2020 municipal wards were collected by LTSB through the WISE-Decade system. Current statutes require each county clerk, or board of election commissioners, no later than January 15 and July 15 of each year, to transmit to the LTSB, in an electronic format (approved by LTSB), a report confirming the boundaries of each municipality, ward and supervisory district within the county as of the preceding “snapshot” date of January 1 or July 1 respectively. Population totals for 2011 wards are carried over to the 2020 dataset for existing wards. New wards created since 2011 due to annexations, detachments, and incorporation are allocated population from Census 2010 collection blocks. LTSB has topologically integrated the data, but there may still be errors.Election Data Overview:The 2012-2020 Wisconsin election data that is included in this file was collected by LTSB from the *Wisconsin Elections Commission (WEC) after each general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. Disaggregation of Election Data:Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward. The data then is distributed down to the block level, again based on total population. When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the WEC at ward level may not match the ward totals in the disaggregated election data file. Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2010 historical population limits.Other things of note…We use a static, official ward layer (in this case created in 2020) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if Cityville has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward 5 was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards 1 and 4 according to population percentage. Outline Ward-by-Ward Election ResultsThe process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards 2018 (Census 2010 totals used for disaggregation)Elections 2018: Wards 2018Elections 2020: Wards 2020Blocks 2011 -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined aboveIn the event that a ward exists now in which no block exists (occurred with spring 2020) due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks 2011This yields a block centroid layer that contains all elections from 1990 to 2018Blocks 2011 [with all election data] -> Wards 2020 (then MCD 2020, and County 2020) All election data (including later elections) is aggregated to the Wards 2020 assignment of the blocksNotes:Population of municipal wards 1991, 2001 and 2011 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though MCD and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same, so data should total within a county the same between wards 2011 and wards 2020.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2020. This is due to boundary corrections in the data from 2011 to 2020, and annexations, where a block may have been reassigned.*WEC replaced the previous Government Accountability Board (GAB) in 2016, which replaced the previous State Elections Board in 2008.

Our codes provide a tool for researchers using any part of the integrated datasets of the European Social Survey (European Social Survey Cumulative File, ESS 1-9, 2020) project to easily differentiate between respondents based on their political affiliation, dividing them into pro-government and pro-opposition groups. Individuals are coded as “government supporters”, “opposition supporters” and “non-identifiers” according to their survey response, while we excluded refusals. The database includes data for 422 985 respondents from eight data rounds between 2002 and 2020 from 33 European countries, organized all in all in 215 country-years.

There are two data files attached.

1. The “European Government-Opposition Voters (EGOV) Data Set” is a comma-separated values table (.csv format file) that includes three variables.

a. The variable “votedforwinner” differentiates between government voters (1), opposition voters (0) and non-voters (missing values); thus it defines the government-opposition status of European voters based on their last vote on the previous election.

b. The variable “closetowinner” differentiates between government partisans (1), opposition partisans (0) and non-partisans (missing values); thus it defines the government-opposition status of European party identifiers based on their partisan attachment.

c. The variable “cseqno” is a unique identification number for European Social Survey (ESS) respondents included in the integrated data sets of the ESS project.

1. The “EGOV – do file” is a do file that can be used to reproduce the content of the above table. These codes are annotated, that is, unusual changes in government composition and overlaps of elections and fieldwork periods are indicated.

The European Government-Opposition Voters Data Set has been produced by using the following pieces of information coming from the (European Social Survey Cumulative File, ESS 1-9, 2020), Comparative Political Data Sets (Armingeon, Isler, Knöpfel, Weisstanner, et al., 2016) and ParlGov (Döring and Manow, 2019) data sets.

•    partisan
  

  preferences, that is, respondents’ vote on the last general election (164 variables, ESS) and respondents’ partisan identity (167 variables, ESS)

•    date of
  

  the interview (year, month, day, ESS)

•    date of
  

  national elections and investitures in each country-case (CPDS and ParlGov)

•    cabinet
  

  composition (CPDS and ParlGov)

•    official
  

  sites on information on national elections for clarification, if necessary

This is a dataset of the Dail Constituencies in 2016, the number of seats, candidates, quota, total electorate and poll.

The dataset contains the resources with attendance and participation in the vote of the directors, divided according to the time periods illustrated below. The order used for displaying advisors is alphabetical. The datasets contain the cumulative totals which, for the years 2016/2017/2018, are grouped by entire years (a single resource contains all the data for one year), while for 2019/2020/2021 the resources are grouped in monthly instalments, albeit with some discontinuities. More in detail, the months present in recent years are: for 2019: * January * February * March * April * May * June * July * September * October * December For 2020: * February * March * April * May * June * July * September * October * November * December For 2021: * January * February * March * April * May * June * July * September Note: There are usually no city council meetings in August. This dataset has been issued by the Municipality of Milan.

The "https://electionstudies.org/data-center/2020-time-series-study/" Target="_blank">American National Election Studies (ANES) 2020 Time Series Study is a continuation of the series of election studies conducted since 1948 to support analysis of public opinion and voting behavior in U.S. presidential elections. This year's study features re-interviews with "https://electionstudies.org/data-center/2016-time-series-study/" Target="_blank">2016 ANES respondents, a freshly drawn cross-sectional sample, and post-election surveys with respondents from the "https://gss.norc.org/" Target="_blank">General Social Survey (GSS). All respondents were assigned to interview by one of three mode groups - by web, video or telephone. The study has a total of 8,280 pre-election interviews and 7,449 post-election re-interviews.

New content for the 2020 pre-election survey includes variables on sexual harassment and misconduct, health insurance, identity politics, immigration, media trust and misinformation, institutional legitimacy, campaigns, party images, trade tariffs and tax policy.

New content for the 2020 post-election survey includes voting experiences, attitudes toward public health officials and organizations, anti-elitism, faith in experts/science, climate change, gun control, opioids, rural-urban identity, international trade, sexual harassment and #MeToo, transgender military service, perception of foreign countries, group empathy, social media usage, misinformation and personal experiences.

(American National Election Studies. 2021. ANES 2020 Time Series Study Full Release [dataset and documentation]. July 19, 2021 version. "https://electionstudies.org/" Target="_blank">https://electionstudies.org/)

This file contains election results from Minnesota state general elections that took place following the 2012 redistricting. The results are at the voting precinct level, and include all federal races, all state races (except judicial races), and constitutional amendments. For US President, US Senator, and Minnesota constitutional office races, results are provided for all filed candidates. For other races, results are provided for all major party candidates. Voting precinct boundaries are specific to each election.

The dataset includes the party strength at the Thurgau Grand Council elections 2008, 2012, 2016 and 2020 by political municipalities (in %). At the municipal level, the party strength corresponds to the proportion of the party votes of that party in the total of all party votes. For information on the calculation of party strengths at cantonal level, see the document “Calculation Party strength” under “More information”. (Note: New district regulations from 2010).Note on the year 2020: Data as published in Official Journal No. 12/2020 of 20 March 2020 (Districts of Arbon, Kreuzlingen, Münchwilen and Weinfelden) and in Official Journal No 27/2020 of 3 July 2020 (Frauenfeld District)

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.

For a deep dive into the data model including every specific metric, see the ACS 2016-2020 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (statewide)

CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)

City (statewide)

City of Atlanta Council Districts (City of Atlanta)

City of Atlanta Neighborhood Planning Unit (City of Atlanta)

City of Atlanta Neighborhood Planning Unit STV (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (City of Atlanta)

County (statewide)

Georgia House (statewide)

Georgia Senate (statewide)

MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)

Regional Commissions (statewide)

State of Georgia (statewide)

Superdistrict (ARC region)

US Congress (statewide)

UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)

WFF = Westside Future Fund (subarea of City of Atlanta)

ZIP Code Tabulation Areas (statewide)

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2016-2020). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Source: U.S. Census Bureau, Atlanta Regional Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

Context

Legislative elections were held in Russia from 17 to 19 September 2021. At stake were 450 seats in the State Duma of the 8th convocation, the lower house of the Federal Assembly. Going into the elections, United Russia was the ruling party after winning the 2016 Russian legislative election with 54.2% of the vote, taking 343 seats, and retaining a supermajority. In March 2020, it was proposed to hold a snap election in September 2020 due to proposed constitutional reforms, but this idea was abandoned. On 18 June 2021, President Vladimir Putin signed a decree calling the election for 19 September the same year.Owing to the COVID-19 pandemic in Russia, voting in the election lasted for three days, from 17 to 19 September, per decision made by the Central Election Commission (CEC). Final turnout was reported to be 51.72%. Due to various semi-legal and unfair practices used by ruling party, currently incumbent president Vladimir Putin, and his administration various media argue that these legislative election in Russia were not free and fair. Some people claim the election was marred by nation's most prominent opposition leaders and figures being excluded from ballot, imprisoned or exiled in months coming before the election, duplicates and fake parties were utilized to disguise real opposition. Critical media and NGOs were demonized, silenced, and otherwise targeted by authorities.

Content

The controversial electronic voting (E-voting) introduced in various regions, including Moscow, was harshly criticized as a black-box and accused of being a tool in hands of ruling party to sway results in its favor. Opposition has denounced the election as flagrantly falsified, and sought to annul E-voting results by legal means. Some candidates have formed a coalition to pursue abolition of the E-voting.

About types of constituency: - "one" means an electoral district from which one deputy is elected. Voting must be organized by relative or absolute majority system. ...there is another types: single and multimondate constituencies, but they aren't presented in this dataset.

The data were downloaded from the observer.mos.ru database as of 19:55 on 19 September 2021 and decrypted by the utility from https://github.com/PeterZhizhin/moscow_deg_decode_votes. Transaction totals are shown without taking into account re-votes. Use constituencies as filters.

Acknowledgements

Thanks to Artur Khachuyan, who shared the dashboard and cleaned data on social media.

Context

In September 2020, I read llewellynjean's wonderful piece on the NBADiscussion subreddit on ranking 2020's NBA awards voters by how contrarian their ballots were. I'd also read his post last year, where he'd continued the work of TroyAtWork the year prior. It got me curious about what the most contrarian ballot has looked like and who the most contrarian voters have been since the publication of ballots began in 2014.

Content

This contains voting for all NBA player awards since NBA ballots first became public. Here are the original sources:

2013-2014: https://official.nba.com/2014-nba-year-annual-award-voting-results/ (links dead on original page, need to use wayback machine)

2014-2015: https://official.nba.com/2015-nba-year-annual-award-voting-results/

2015-2016: https://official.nba.com/2015-16-nba-annual-award-voting-results/

2016-2017: https://official.nba.com/2016-17-nba-annual-award-voting-results/

2017-2018: https://pr.nba.com/voting-results-2017-18-nba-regular-season-awards/

2018-2019: https://pr.nba.com/voting-results-2018-19-nba-regular-season-awards/

2019-2020: https://pr.nba.com/voting-results-2019-20-nba-regular-season-awards/

2020-2021: https://pr.nba.com/voting-results-2020-21-nba-regular-season-awards/

2021-2022: https://pr.nba.com/voting-results-2021-22-nba-regular-season-awards/

2022-2023: https://pr.nba.com/voting-results-2022-23-nba-regular-season-awards/

Awards: Most Improved Player, Most Valuable Player, Defensive Player of the Year, Sixth Man of the Year, Rookie of the Year, Clutch Player of the Year (began in 2023), All-NBA, All-Defense, All-Rookie

How much a vote is worth for each award: - MVP: 10 points for 1st, 7 for 2nd, 5 for 3rd, 3 for 4th, 1 for 5th - All-NBA: 5 points for 1st team, 3 points for 2nd team, 1 point for 3rd team - All-Defense and All-Rook: 2 points for 1st team, 1 point for 2nd team - all other awards: 5 points for 1st, 3 points for 2nd, 1 point for 3rd

The first five columns are from the ballots (voter name, affiliation, season, award and player). Voter and player names were cleaned to be standardized across seasons.

Acknowledgements

Again, thanks to llewellynjean and TroyAtWork for planting the seeds of curiosity!

1 Code smell datasetIn order to create a high quality code smell datasets, we merged five different datasets. These datasets are among the largest and most accurate in our paper “Predicting Code Quality Attributes Based on Code Smells ”. Various software projects were analyzed automatically and manually to collect these labels. Table 1 shows the dataset details.Table 1. Merged datasets and their characteristics.DatasetSamplesProjectsCode smellsPalomba (2018) [1]40888395 versions of 30 open-source projectsLarge class, complex class, class data should be private, inappropriate intimacy, lazy class, middle man, refused equest, spaghetti code, speculative generality, comments, long method, long parameter list, feature envy, message chainsMadeyski [2]3291523 open-source and industrial projectsBlob, data classKhomh [3]_54 versions of 4 open-source projectsAnti-singleton, swiss army knifePecorelli [4]3419 open-source projectsBlobPalomba (2017) [5]_6 open-source projectsDispersed coupling, shotgun surgeryCode smell datasets have been prepared at two levels: class and method. The class level is 15 different smells as labels and 81 software metrics as features. As well, there are five smells and 31 metrics on the method level. This dataset contains samples of Java classes and methods. A sample can be identified by its longname, which contains the project-name, package-name, JavaFile-name, class-name, and method-name. The quantity of each smell ranges from 40 to 11000. The total number of samples is 37517, while the number of non-smells is nearly 3 million. As a result, our dataset is the largest in the study. You can see the details in Table 2.Table 2. The number of smells and non-smells at class and method levelsLevelMetricsSmellSamplesTotalClass81Complex class126523438Class data should be private1839Inappropriate intimacy780Large class990Lazy class774Middle man193Refused bequest1985Spaghetti code3203Speculative generality2723Blob988Data class938Anti-singleton2993Swiss army knife4601Dispersed coupling41Shotgun surgery125Non-smell40506 [3] +8334 [5] +296854 [1]+43862 [2] +55214 [4]444770Method31Comments10714079Feature envy525Long method11366Long parameter list1983Message chains98Non-smell246917624691762 Quality datasetThis dataset contains over 1000 Java project instances where for each instance the relative frequency of 20 code smells has been extracted along with the value of eight software quality attributes. The code quality dataset contains 20 smells as features and 8 quality attributes as labels: Coverageability, extendability, effectiveness, flexibility, functionality, reusability, testability, and understandability. The samples are Java projects identified by their name and version. Features are the ratio of smelly and non-smelly classes or methods in a software project. The quality attributes are a normalized score calculated by QMOOD metrics [6] and models extracted by [7], [8]. 1014 samples of small and large open-source and industrial projects are included in this dataset.The data samples are used to train machine learning models predicting software quality attributes based on code smells.References[1] F. Palomba, G. Bavota, M. Di Penta, F. Fasano, R. Oliveto, and A. De Lucia, “A large-scale empirical study on the lifecycle of code smell co-occurrences,” Inf Softw Technol, vol. 99, pp. 1–10, Jul. 2018, doi: 10.1016/J.INFSOF.2018.02.004.[2] L. Madeyski and T. Lewowski, “MLCQ: Industry-Relevant Code Smell Data Set,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Apr. 2020, pp. 342–347. doi: 10.1145/3383219.3383264.[3] F. Khomh, M. Di Penta, Y. G. Guéhéneuc, and G. Antoniol, “An exploratory study of the impact of antipatterns on class change- and fault-proneness,” Empir Softw Eng, vol. 17, no. 3, pp. 243–275, Jun. 2012, doi: 10.1007/s10664-011-9171-y.[4] F. Pecorelli, F. Palomba, F. Khomh, and A. De Lucia, “Developer-Driven Code Smell Prioritization,” Proceedings - 2020 IEEE/ACM 17th International Conference on Mining Software Repositories, MSR 2020, pp. 220–231, 2020, doi: 10.1145/3379597.3387457.[5] F. Palomba, M. Zanoni, F. A. Fontana, A. De Lucia, and R. Oliveto, “Smells like teen spirit: Improving bug prediction performance using the intensity of code smells,” in Proceedings - 2016 IEEE International Conference on Software Maintenance and Evolution, ICSME 2016, Institute of Electrical and Electronics Engineers Inc., Jan. 2017, pp. 244–255. doi: 10.1109/ICSME.2016.27.[6] J. Bansiya and C. G. Davis, “A hierarchical model for object-oriented design quality assessment,” IEEE Transactions on Software Engineering, vol. 28, no. 1, pp. 4–17, Jan. 2002, doi: 10.1109/32.979986.[7] M. Zakeri-Nasrabadi and S. Parsa, “Learning to predict test effectiveness,” International Journal of Intelligent Systems, 2021, doi: 10.1002/INT.22722.[8] M. Zakeri-Nasrabadi and S. Parsa, “Testability Prediction Dataset,” Mar. 2021, doi: 10.5281/ZENODO.4650228.

The American Community Survey 5-year Data Profile (DP05) of Demographic and Housing Estimates was downloaded from the U.S. Census Bureau for state, county, place, reservation, house district, senate district and tract geographies in the state of Montana.Selected demographic and housing estimates in this data set include: SEX AND AGE, RACE, HISPANIC OR LATINO AND RACE, TOTAL HOUSING UNITS, CITIZEN, VOTING AGE POPULATION. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates. Downloaded April 2022.Please refer to the American Community Survey section of the U.S. Census Bureau website for detailed information about this data set.