விலங்கு

Article "விலங்கு" in Tamil Wikipedia has 81.8959 points for quality and 0.3204 points for popularity. The article contains, among others metrics, 26 references and 19 sections. This article has the best quality in English Wikipedia. Also, this article is the most popular in that language version.

TA

471 place in the rating of Tamil Wikipedia.

655 place in the multilingual rating of all topics.

1112 place in the multilingual rating of Global Authors’ Interest.

விலங்கு - kingdom of multicellular eukaryotic organisms. There are 55 language versions for this article in WikiRank database (of the considered 55 Wikipedia language editions).

Data as of May 1, 2019. Query time 0.0232 sec.

The table below shows the language versions of the article with the highest quality.

#	Language	Quality grade	Quality score
1	English Animal		100
2	Indonesian Hewan		100
3	Basque Animalia		100
4	Malay Haiwan		98.5714
5	Japanese 動物		94.7937

The following table shows the most popular language versions of the article.

#	Language	Relative award	Relative popularity
1	English Animal		100
2	Spanish Animalia		37.6621
3	Russian Животные		21.8248
4	French Animal		13.3411
5	Ukrainian Тварини		7.167

The following table shows the language versions of the article with the highest Authors’ Interest.

The project is intended for automatic relative evaluation of the articles in the various language versions of Wikipedia. At the moment the service allows to compare over 38 million Wikipedia articles in 55 languages. Quality scores of articles are based on Wikipedia dumps from May, 2019. When calculating popularity, view statistics of articles for last month (April 2019) were taken into account.

You can also visit WikiRank.Live (previous version of the main tool), which can calculate quality and popularity based on current versions of Wikipedia articles in 7 languages.

It's planned to add different improvements to the project (such as adding to the analysis not only new quantitative characteristics, but also qualitative). In addition, it is planned to add estimates using machine learning algorithms and artificial intelligence, as well as based on the results of user comparison of specific multilingual information (for example, the WikiBest project). Please leave your comments and suggestions