Wikistats upgraded to new page view definition

tl;dr New and upgraded Wikistats data files, reports and charts, with cleaner metrics.

Recently Wikimedia Foundation has upgraded its data feeds for hourly page view counts, using a new definition which excludes crawler traffic. These new data are now available from May onwards (backfilled).  A big THANK YOU to the Analytics Team.SnippetAnalyticsTeam
16_Oliver_Keyes_-_Wikimedia_Foundation_016 and Oliver Keyes


As prominent consumer of these data feeds Wikistats couldn’t stay behind. Besides a major upgrade to existing files, charts and reports (see below) new charts were added as well.

New charts

There are now charts for overall totals per project, for 6 metrics: Total Editors, Total Edits, Total Articles, New Articles, Total Page views and Active Wikis.

Some examples:

Often in the news: the editor trend on Wikipedia. Here all Wikipedias taken together show a pretty slow overall drop since 2007 (a sharper decline on English Wikipedia is almost offset by increases on other Wikipedias). Trend for very active active editors is totally flat. 



This chart shows how editor activity spiked on Wikivoyage, early in 2013, just after the fork from Wikitravel.



Early in 2013 all interwikis were migrated by bots to Wikidata, which caused lots of bot activity. 



A new metric: Active Wikis, indicate per project how many wikis are actually being maintained. The threshold of 3+ active editors per wiki is of course arbitrary. Open for discussion.



BIG CHANGE: In new data feeds all crawler traffic is (finally) filtered out. Overall this results in a drop of around 20% page views (or actually bot requests). The drop is larger on small wikis and projects

Upgraded reports

Wikistats uses the upgraded data feeds to produce several sets of reports:

    • Monthly pageview reports for all wikis and projects, normalized/raw, mobile/non-mobile/combined. You’ll see a drop in page views after the upgrade in May 2015, but much of that is due to the new definition filtering crawler requests.SnippetMonthlyPageViews
      Transition to new page view definition is clearly marked.

  • Summaries per wikis, and now also per project.
    SnippetSummaryAllWIkis

 

Upgraded downloads

Daily/monthly per article page views

SnippetDownloads
Wikistats grabs the upgraded hourly files, both the page views per article and per wiki, and aggregates each into several larger entities. For per article page views daily and monthly aggregates now also used the new feed.

Page view totals per wiki (aka ‘project totals’)

SnippetDownloads2

Wikistats collects the hourly per-wiki ‘projectview’ files, packages them in to a yearly tar file, and produces a large set of csv files available for download as one zip file (both of these, input and output, are here). The csv files include totals per wikis per hour, day, day of week, month, and more, and contain separate counts for WMF’s mobile and non-mobile sites.

Upgraded process flow

This diagram shows the upgraded process flow, and all files involved. Monthly Pageview Reports
Thanks for your patience.

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Active editor trends as year-over-year changes

For many years I publish active editors trends for all Wikimedia wikis, see e.g. these summaries. Here I’d like to present these same editor trends in a slighty different way, which may help to show there is some cause for optimism at least for the largest Wikipedias.

In the conventional charts it may be a bit difficult to see if a growth or decline is speeding up or slowing down. This is easier to see when we plot year over year changes (YoY) rather than the absolute values.

We’ll start with a totally hypothetical idealized example without deeper significance (parameters manually tweaked), just to demonstrate how absolute and YoY values are connected.
Active_editors_YoY0
In the above diagram both ways of presenting the data have been combined. The red trend line shows absolute values (vertical scale at the left). The black line is the YoY trend (vertical scale at the right)
In the following real-world example the red trend line is the all too familiar explosive growth followed by a very slow but persistent decline in active editors on the English Wikipedia.The decline starts in November 2007, and is rather consistent in following years, with YoY mostly between 0.90 and 0.99 for next seven years.
Active_editors_YoY1
A bit more precise: average YoY for 2008 and following years is
0.906, 0.945, 0.921, 0.976, 0.932, 0.942, 0.998.
For the last nine months YoY for the English wikipedia is above flat trend (YoY > 1)!
Active_editors_YoY1b
How YoY value is derived from absolute values. With the scale of these first diagrams the subtle fluctuations in YoY after 2007 are too small to see. We need to change the vertical scale for that.
 The following three charts show the largest eight Wikipedias in terms of active editors. Each chart shows a different selection.
Active_editors_YoY2

Three Wikipedias with the largest number of editors.
Active_editors_YoY3

Largest Wikipedias with growing editor base in last 12 months  (avg YoY > 1).
Active_editors_YoY4

Largest Wikipedias with a still (barely) declining editor base in last 12 months (avg YoY < 1).

Files with active editors (5+ and 100+ edits per month, absolute and YoY, are available for download at http://dumps.wikimedia.org/other/pagecounts-ez/wikistats/ (see csv_wp_active_editors.zip, and similar)

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New Wikistats report, for once about Wikistats itself

There is a new Wikistats report, which as an exception (and one-off) reports about Wikistats itself. It shows which reports on stats.wikimedia.org are most popular, how many ‘unique’ (sort of) people requested those reports, and how often.

To this end all traffic to stats.wikimedia.org in April 2015 has been analyzed. A pretty rigorous filtering process removed most bot traffic (perhaps even erring on the side of low counts). First all explicit bot traffic was removed (based on the user agent string), then lots of implicit bot traffic was filtered as well (where request patterns showed bot-like behavior). In the end only 3.2% of all html requests to stats.wikimedia.org qualified for the analysis, and only 78% of the ip addresses (see footer notes).

Most table rows are about a functionally equivalent set of reports, with first three columns showing the overall total for the entire set. The right-most column lists the 10 most popular unique files within that set, with number of requests per file. For conciseness those top 10 files are only shown when you hover over the first link in that column.

The files have been distributed over two tables which reflects the most important dichotomy in Wikistats: reports are about
– database content and content creators, with counts distilled from xml dumps, or
– site traffic, with counts distilled from Kraken (either via 1:1000 sampled log, or hourly aggregations)

See for more this Wikistats Overview diagram (the new report cross-links to this diagram in column ‘srce’).

These numbers should not be taken too lightly as a measure of the relative importance of any report. Popularity of a report is just one factor in the weighing process.

Note: Unique visitors is by necessity an approximation. Some people may have accessed the site several times over the month, using a provider which hands out dynamic ip addresses. But on the assumption that few people will visits the site on more than one occasion and also have a dynamic address, that may not affect the overall counts that much, also relative popularity of different reports will be even less affected.

http://stats.wikimedia.org/wikistats-traffic-2015-04.html

 

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Wiki Loves Africa 2014 – Celebrating African Cuisine

The first Wiki Loves Africa media contest was held in 2014, October and November.

People could contribute with photos, videos and interviews. There was a great response in many countries all over Africa, with overall 873 unique contributors. Soon there will be winners and prizes. The organizers should feel proud of what they accomplished.

Food from Tunisia
Kaouther Bedoui CC BY-SA 4.0
Tajines_in_a_pottery_shop_in_Morocco
Jafri Ali CC BY-SA 4.0

Thanks to Romaine for supplying many country specific templates and categories on short notice. These can be counted with a script. Here are charts for contributions/uploads and contributors per country.

WLA uploads 2014
Click to show full size
WLA contributors 2014
Click to show full size

Images on this page are from the first jury selection. For all images browse Wiki Loves Africa categories.

By the way, did you know? “The chick which is always near its mother eats the best part of the grasshopper”. (Kenyan proverb)

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Wiki Loves Monuments 2014

Here are results of Wiki Loves Monument (WLM) 2014 contest.

Map

Some charts are about image uploads.
Some about image uploaders, also known as contributors.

Countries

With 41 participating countries, 11 less than in 2013, that is again an awesome achievement. (See first table). 8 countries participated for the first time: Albania, Iraq, Ireland, Kosovo, Lebanon, Macedonia, Pakistan, Palestinian Territory.

In those 5 years since WLM started 14 countries each participated 4 times: Austria, Belgium, Estonia, France, Germany, Luxembourg, The Netherlands, Norway,  Poland, Romania, Russia, Spain, Sweden and Switzerland.

The contest ran in different countries during different periods.

Participants

Uploads

The number of images uploaded was 268,667, which is 72% of last year’s record count (375,160).

WLM_2014_uploads_per_year



All contest taken together, Poland contributed by far the most images:
a whopping 161,250.

WLM_2014_uploads_by_country_cumulative



Same data as previous chart, with yearly results unstacked.

WLM_2014_uploads_by_country



Ukraine contributed most images in 2014: 46350.

WLM_2014_uploads_2014

Contributors

Italy ranked first in number of contributors in 2014: 1045.

WLM_2014_uploaders_per_year



The largest volunteer base of any year in any country is still India,
where in 2012 2089 volunteers contributed to the contest.

UploadsByCountry-2010-2014-Top25



A every year the peak in activity around WLM is easily detectable
in Wikistats charts for Commons, e.g.

PlotEditorsCOMMONS

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WikiProject Medicine Translation Task Force

JamesHeilman At Wikimania 2014 James Heilman – Canadian emergency room physician – gave a presentation on Wikipedia and Medicine HeilmanOnHealth.
Logos2He explained how leading non-profit health organizations like the Cochrane Collaboration, Cancer Research UK and the National Institutes of Health (NIH) help to improve Wikipedia’s content, and that of its sister projects. LogosSmallIn particular James talked about the WikiProject Medicine Translation task force where medical content on the English Wikipedia is improved and simplified by non-profit Wiki Project Med Foundation (100 people from 20 countries), then translated into many languages by volunteers from Translators without Borders , co-founded by CEO Lori Thicke.  LoriBanner
ProjectPagePartialIn good open project fashion the ambitions are stellar: bring 100 medical articles to good/featured article status (GA/FA), and translate those, plus 1000 abbreviated articles, into as many languages as there are Wikipedias. The project page shows their very impressive progress in languages as Hindi, Chinese, Persian, Indonesian, Turkish, Swahili. But many more language projects have been started, also for languages where overall Wikipedia coverage is very limited, like Quechua, Yoruba to name just a few. PageViewsTaskForce

After the conference I reached out to the project and made a script to parse the project page, extract the links to the published articles for all languages, look-up the monthly page views  in our monthly aggregated page view dump and regularly present the results in a (I hope) informative status page, with a (I’m certain) boring layout. As page views per article are currently only collected for WMF’s non-mobile site,  the extra mobile page views were by necessity estimated (we do know overall percentage mobile traffic per wiki).

For many languages the stats are clearly encouraging: on the Japanese Wikipedias the 5 articles get on average almost 25K views each per month, on Spanish 23K, Italian 15K. For some languages, particularly those where Wikipedia after many years are still in the start-up phase, numbers seem disappointing: and some may indeed be, but there is a technical artifact that also comes into play (*).



Aedes_aegypti_during_blood_meal Suppose the 23 monthly page views for the article on Dengue Fever on the Farsi Wikipedia (110M speakers) are indeed accurate (it may well be the technical artifact fools us here, but suppose), if someone prints 10 copies of the article and puts these up for display at 10 health posts. Wouldn’t that already make it worthwhile?

* Tech details: one issue with the stats is many of lowest scoring ‘page titles’ are actually redirects. I query the API to find the proper article to which the redirect resolves, but as there is no standard encoding for the page titles in the dump file (titles are counted and written in the encoding in which they are received) not all resolved redirects were actually found in the dump file. A good reason to apply standard encoding to page titles, if not before actual counting takes places (may be too costly for this real-time process), then in aggregation phase (post processing). 

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Setting the standard for a new unit of measurement can be tedious and even hazardous

A small essay for your amusement: I am reading a book about how some of today’s most fundamental units of measurement were defined and calibrated.

The more precision one requires, the harder a measurement becomes (this was an known adagium even before Heisenberg): in 1790 a new length unit was proposed: the meter. It would consist of 1/1000 of 1/10,000 of the distance from the equator to the poles. It only remained to measure that distance as precise as possible.

The French took the lead here, and thus the requirements for which meridian to use as a base line was stated as: “the one with the longest stretch over land, being well charted territory, which ends at both sides at sea level”. By sheer coincidence this happened to be in France.

France.



So what was needed was to measure the total distance between both ends (near Dunkirk and Barcelona) by triangulation, and measure the length of one side of one of those triangles  in terms of a provisional meter (using a platinum bar, compensating for temperature at each position, and for the curvature of the road).

Two expeditions, one headed by Delambre, the other by Méchain, were formed to do the triangulation first, then measure the exact latitude of the end-points. They were to report findings within a year. I would take six years to arrive at acceptable results.

The triangulation itself took several months, as many mountains lay on the path. One of numerous perils encountered was an angry mob in Paris. People discovered the esoteric instruments the team carried in their baggage, and suspicion arose these might be spying tools, to aid reactionary forces opposing the Revolution. An angry mob gathered and demanded an explanation. That explanation would better not meet deaf ears, because at that time a mob, when in doubt, tended to regard the guillotine the safer option. Fortunately the team leader was an experienced teacher, so he knew how to balance between saying too little, and saying too much (and thus lose most of the audience), but the ‘trial’ still lasted for many hours.

After the triangulation was done all that remained was getting the exact positioning of the end points. An new device, called the Borda Circle (with two telescopes) made it possible to determine the angle between two stars with twice as much precision as before. All that remained was to repeat the measurement 10,000 times to reduce the human reading error by averaging the outcomes.

Unfortunately the expedition leader suffered an almost fatal accident and recovery took months, in which the expedition could not travel, so the team settled down in a place just 2 (provisional) kilometers away from the end-point of the meridian: biding their time they took it upon themselves to repeat the initial measurements and do another batch of 10,000. To their dismay the two averages diverged noticeably. They knew of the imperfect curvature of the earth, (Earth radius to the equator is 6400 km, 64 km more than the radius at the poles) [3]. What they didn’t know yet (and which brought them to despair) was that every meridian has a different length, as the earth’s curvature isn’t even uniform for every place at the same latitude. It took years to get this sorted out.


Tien verdwenen dagen

This story came from an excellent book by Michiel van Straten, called “Tien verdwenen dagen” (“Ten lost days”, alas in Dutch only), about humanities’ struggle to define good units of measurement.

There is a story about the difficult transition from Julian to Gregorian calendar (which has still not been completed as this Wikipedia chart shows);

5548500808a3da878b2d71602a5cb5aa

a story about how before the invention of time zones not only every town had their own unique time, but with the emergence of railroads different railroad companies used a different time for the same town (and we think planning a trip with stop-overs today is time consuming), a story about Napoleons new metric calendar, and how the Catholic Church made him retract it after several years (10-day weeks with only one day off didn’t help either), stories about long debates to establish which meridian to make the Prime Meridian, and where to draw the international date line, and many more.

 

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Isotype diagrams are now easier to build on Wikipedia

Did you ever study a table with many large numbers, where the moment you put it away you realized nothing from what you just saw had stuck? I guess most of us suffer from this handicap that large numbers are difficult to absorb or evaluate.

In the 1930’s Gerd Arntz created a coherent set of 4000 pictograms and together with Otto Neurath built from these ‘words’ a ‘language’ called Isotype.
Isotype-statistiek-bron-Wikimedia

Wouldn’t it be nice if we could use a similar method to convert numbers into symbols on Wikipedia? Now we can:

I created a small Lua script (my first) called ‘Repeat_symbols’ to translate numbers into icons. Within 20 minutes the entire script had been replaced by a better version,. Thanks again, Jackmcbarn.

India

Legend

 

Using fast and slow traffic to symbolize land size was a compromise. I tried many symbols but most didn’t work at this small size. Suggestions welcome.

The following image is a tiny section from a huge table, that compares all European countries (and then some), in terms of population, land area, GDP. You can see at a glance that Russia is much larger in land area than my country: the Netherlands. Who would have thought :-)

What did surprise me was that Russia’s GDP is not even 3 times as much a that of the Netherlands. I told several people and they hardly believed. (click to zoom)

Also, instead of presenting the raw numbers I show all metrics as percentages of the EU total, which makes it much easier to evaluate and even remember some of them, especially when you see the full table with European countries (and a few more).
RussiaNetherlands

 

 

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Traffic to Wikipedia’s mobile site is growing fast

Since 2008 WMF count monthly page views for the non-mobile site.
Since June 2010 also for the mobile site.

From the respective monthly totals we can calculate which share of the traffic goes to the mobile site. Evidently this share has grown dramatically over recent years.

The first chart shows the trend for the eight most read Wikipedias.

PercMobileTop8LanguagesByTotalPageviews2


The second chart shows the same trends, now for the nine ‘most mobile’ Wikipedias
(which also are above a threshold popularity of 1 million views a month).

PercMobileTop9LanguagesByPercMobile2

 

Caveats

Please don’t confuse traffic to the mobile site with traffic from mobile devices. One can choose to visit the non-mobile site from a phone or tablet. One can choose to visit the mobile site from a desktop computer.

These numbers have been collected with webstatscollector. There are a numbers of issues with that tool. My colleagues Christian Aistleitner and Andrew Otto are working on a new version of the tool, which will be more robust, more precise in which messages to count, and draw data from the new Kafka infrastructure instead of direct messages from each server (via udp2log). Later on with that new infrastructure we will also be able to do a more complete breakdown, by country, and hence by region.

Data files

The following data files are available for offline analysis:

Pageview reports

The Wikipedia pageview reports now also show % mobile for last 24 months. Example: pageviews for Wikipedia, all platforms, normalized.
PageViewsWikipediaAllPlatformsNormalized

Breakdown by region (sort of)

Here, for what it’s worth, a breakdown by region, but languages spoken in several regions are listed separately. So please use these regional results with a grain of salt.

region: Africa

languages:aa:Afar, af:Afrikaans, ak:Akan, am:Amharic, arz:Egyptian Arabic, bm:Bambara, ee:Ewe, ff:Fulfulde, ha:Hausa, hz:Herero, ig:Igbo, kab:Kabyle, kg:Kongo, ki:Kikuyu, kj:Kuanyama, kr:Kanuri, lg:Ganda, ln:Lingala, mg:Malagasy, ng:Ndonga, nso:Northern Sotho, ny:Chichewa, om:Oromo, rn:Kirundi, rw:Kinyarwanda, sg:Sangro, sn:Shona, so:Somali, ss:Siswati, st:Sesotho, sw:Swahili, ti:Tigrinya, tn:Setswana, ts:Tsonga, tum:Tumbuka, tw:Twi, ve:Venda, wo:Wolof, xh:Xhosa, yo:Yoruba, zu:Zulu
perc mobile: 22.5%

regions: Africa/Asia
languages:ar:Arabic
perc mobile: 37.8%

region: Artificial
languages:eo:Esperanto, ia:Interlingua, ie:Interlingue, io:Ido, jbo:Lojban, nov:Novial, vo:Volap&uuml;k
perc mobile: 13.1%

region: Asia
languages:ab:Abkhazian, ace:Acehnese, arc:Aramaic, as:Assamese, az:Azeri, ba:Bashkir, bcl:Central Bicolano, bh:Bihari, bjn:Banjar, bn:Bengali, bo:Tibetan, bpy:Bishnupriya Manipuri, bug:Buginese, bxr:Buryat, cbk-zam:Chavacano, cdo:Min Dong, ceb:Cebuano, ckb:Sorani, cv:Chuvash, diq:Zazaki, dv:Divehi, dz:Dzongkha, fa:Persian, gan:Gan, glk:Gilaki, gu:Gujarati, hak:Hakka, he:Hebrew, hi:Hindi, hy:Armenian, id:Indonesian, ii:Yi, ilo:Ilokano, ja:Japanese, jv:Javanese, kaa:Karakalpak, kbd:Karbadian, kk:Kazakh, km:Khmer, kn:Kannada, ko:Korean, krc:Karachay-Balkar, ks:Kashmiri, ku:Kurdish, ky:Kirghiz, lad:Ladino, lbe:Lak, lo:Laotian, map-bms:Banyumasan, min:Minangkabau, ml:Malayalam, mn:Mongolian, mr:Marathi, mrj:Western Mari, ms:Malay, my:Burmese, myv:Erzya, mzn:Mazandarani, ne:Nepali, new:Nepal Bhasa, or:Oriya, os:Ossetic, pa:Punjabi, pag:Pangasinan, pam:Kapampangan, pi:Pali, pnb:Western Panjabi, ps:Pashto, sa:Sanskrit, sah:Sakha, sd:Sindhi, si:Sinhala, su:Sundanese, ta:Tamil, te:Telugu, tet:Tetum, tg:Tajik, th:Thai, tk:Turkmen, tl:Tagalog, tpi:Tok Pisin, tt:Tatar, tyv:Tuvan, udm:Udmurt, ug:Uyghur, ur:Urdu, uz:Uzbek, vi:Vietnamese, war:Waray-Waray, wuu:Wu, za:Zhuang, zh:Chinese, zh-classical:Classical Chinese, zh-min-nan:Min Nan, zh-yue:Cantonese
perc mobile: 33.0%

region: Europe
languages:als:Alemannic, an:Aragonese, ang:Anglo-Saxon, ast:Asturian, av:Avar, bar:Bavarian, bat-smg:Samogitian, be:Belarusian, be-x-old:Belarusian (Taraškievica), bg:Bulgarian, br:Breton, bs:Bosnian, ca:Catalan, ce:Chechen, co:Corsican, cs:Czech, csb:Cassubian, cu:Old Church Slavonic, cy:Welsh, da:Danish, de:German, dsb:Lower Sorbian, el:Greek, eml:Emilian-Romagnol, et:Estonian, eu:Basque, ext:Extremaduran, fi:Finnish, fiu-vro:Voro, fo:Faroese, frp:Arpitan, frr:North Frisian, fur:Friulian, fy:Frisian, ga:Irish, gd:Scots Gaelic, gl:Galician, got:Gothic, gv:Manx, hr:Croatian, hsb:Upper Sorbian, hu:Hungarian, is:Icelandic, it:Italian, ka:Georgian, koi:Komi-Permyak, ksh:Ripuarian, kv:Komi, kw:Cornish, lb:Luxembourgish, lez:Lezgian, li:Limburgish, lij:Ligurian, lmo:Lombard, lt:Lithuanian, ltg:Latgalian, lv:Latvian, mdf:Moksha, mhr:Eastern Mari, mk:Macedonian, mo:Moldavian, mt:Maltese, mwl:Mirandese, nap:Neapolitan, nds:Low Saxon, nds-nl:Dutch Low Saxon, nn:Nynorsk, no:Norwegian, nrm:Norman, oc:Occitan, pcd:Picard, pl:Polish, pms:Piedmontese, pnt:Pontic, rm:Romansh, rmy:Romani, ro:Romanian, roa-rup:Aromanian, roa-tara:Tarantino, rue:Rusyn, sc:Sardinian, scn:Sicilian, sco:Scots, se:Northern Sami, sh:Serbo-Croatian, sk:Slovak, sl:Slovene, sq:Albanian, sr:Serbian, stq:Saterland Frisian, sv:Swedish, szl:Silesian, uk:Ukrainian, vec:Venetian, vep:Vepsian, vls:West Flemish, wa:Walloon, xal:Kalmyk, zea:Zealandic
perc mobile: 25.9%

regions: Europe/Asia
languages:crh:Crimean Tatar, ru:Russian, tr:Turkish
perc mobile: 20.6%

regions: Europe/North-America/Oceania/Asia/Africa
languages:en:English, simple:Simple English
perc mobile: 31.5%

regions: Europe/North-America/South-America/Asia/Africa
languages:es:Spanish
perc mobile: 31.9%

regions: Europe/North-America/South-America/Asia/Africa/Oceania
languages:fr:French
perc mobile: 28.0%

regions: Europe/South-America
languages:nl:Dutch
perc mobile: 27.4%

regions: Europe/South-America/Africa/Asia
languages:pt:Portuguese
perc mobile: 25.0%

region: North-America
languages:cho:Choctaw, chr:Cherokee, chy:Cheyenne, cr:Cree, ht:Haitian, ik:Inupiak, iu:Inuktitut, kl:Greenlandic, mus:Muskogee, nah:Nahuatl, nv:Navajo, pdc:Pennsylvania German
perc mobile: 14.6%

region: Oceania
languages:bi:Bislama, ch:Chamorro, fj:Fijian, haw:Hawai’ian, hif:Fiji Hindi, ho:Hiri Motu, mh:Marshallese, mi:Maori, na:Nauruan, pih:Norfolk, sm:Samoan, to:Tongan, ty:Tahitian
perc mobile: 16.6%

region: South-America
languages:ay:Aymara, gn:Guarani, pap:Papiamentu, qu:Quechua, srn:Sranan
perc mobile: 13.8%

region: World
languages:la:Latin, yi:Yiddish
perc mobile: 11.5%

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Wiki Loves Monuments 2013

This gallery contains 5 photos.

Here are some charts on the breakdown by country of contributions and contributors to Wiki Loves Monuments 2013. Better late than never. I meant to publish this together with retention stats, but those are still in the pipeline, and may … Continue reading

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