Dossier on the Election in the Lab

Dossier on the election available in the Lab -a collection of election information from our site. All materials where this election is mentioned.

Election Cards

Russia, Smolensk region, Governor 2020

electoral card

Official title:

Election of the Governor of Smolensk Region

Winner/leader:

Alexei Ostrovsky

Wikipedia article:

Выборы губернатора Смоленской области (2020)

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Original data source:The CEC of Russia
URL of the source:www.smolensk.vybory.izbirkom.ru/region/region/smolensk?action=show&root=1&tvd=26720001150502&vrn=26720001150498®ion=67&global=&sub_region=0&prver=0&pronetvd=null&vibid=26720001150502&type=234
Data validation filescreenshot.zip
ParsingShpilkin
Dataset:Smolensk Governor 2020.zip
Date of verification10/3/2020
Verification of the dataset with the source:

Reconciliation result: Discrepancy of 175 registered voters, 80 ballots (no digital PECs).

Digital PECs didn't make it into Spilkin's parsings in 2020.

Credibility of captcha The CEC's creation of obstacles to data collection and data analysis by captcha.

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Articles on the Elections

Azat Gabdulvaleev "Analysis of election results in the city of Grozny using graphical visualization and turnout counting based on video recordings from polling stations"

Presentation at the II Round Table of Mathematicians

Azat Gabdulvaleev 0 16708

Azat Gabdulvaleev "Analysis of election results in the city of Grozny using graphic visualization and turnout counting based on video recordings from polling stations

The subject of the study is the capital of the Chechen Republic - the city of Grozny. It should be said that Chechnya consistently shows superloyal results in elections at any level.

Azat Gabdulvaleev "Graphical visualization of open data as a simple way to detect anomalies in voting results"

Presentation at the II Round Table of Mathematicians

Azat Gabdulvaleev 0 11937

One such method is graphical visualization of open data. There is little or no math involved, but the charts can immediately identify some anomalies and indicate areas that need more detailed investigation.

So you can visualize open data in a graphical way.

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