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:Volapük
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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