QCOR -5.29% -19.27% 42.78 -13.85% -8.98% -5.35% 10.52\nSAVE 16.00% -9.34% 61.32 -4.85% -11.01% 6.92% 3.72\nNUS 22.47% -1.68% 75.77 -1.47% -1.71% 0.24% 2.42\nKORS 5.49% -0.30% 60.49 1.56% 0.82% 0.74% 2.38\nHCI 14.11% -4.92% 60.94 -0.86% -0.64% -0.23% 1.3\nTIVO 9.33% -5.72% 61.97 -1.07% -1.72% 0.66% 1.25\nEAT 8.21% -1.98% 70.83 -0.11% -0.18% 0.07% 1.15\nWGO 20.03% -3.24% 70.24 -2.59% -2.40% -0.19% 1.01\nPRLB 12.89% -2.32% 63.26 0.01% -0.03% 0.05% 0.82\nCLR 8.68% -5.37% 55.53 -2.19% -2.43% 0.26% 0.81\nSBGI 5.35% -11.14% 46.59 -2.40% -1.89% -0.52% 0.8\nOAS 12.71% -7.06% 56.33 -2.18% -2.22% 0.04% 0.8\nYNDX 4.26% -9.83% 50.01 0.21% -0.58% 0.79% 0.77\nCMG 20.64% -0.72% 85.14 -0.35% -0.70% 0.35% 0.72\nHLF 0.32% -10.93% 53.82 -1.74% -2.41% 0.69% 0.71\nLL 9.06% -1.32% 66.34 -0.67% -0.73% 0.05% 0.66\nUBNT 8.87% -13.55% 52.5 -1.76% -2.16% 0.41% 0.63\nPCLN 7.13% -2.39% 61.63 0.27% -0.23% 0.51% 0.5\nIGTE 18.47% -4.80% 73.07 -0.82% -0.37% -0.46% 0.41\nCRME 33.24% -18.75% 57.66 -0.66% 0.22% -0.87% 0.36\nWe have a downgrade on QCOR which is dropping fast, and a report from SAVE, which ha had a very nasty reversal of a large gap up. NUS is down on high volume, KORS up. Overall the leaders seem to be struggling today, with only 3 new highs, and all to those pulling back - SAVE, NUS, LL.


Football. Atletico Madrid 2:2 Sevilla - result and match statistics, online - Live-Result - February 26, 2011\nTue, 01:32 New York Login\n15 Diego Godin\n3 Antonio Lopez\n17 Ujfalusi Tomas\n8 Raul Garcia\n5 Mendes Tiago\n32 Jorge Resurreccion Merodio\n19 Jose Antonio Reyes\n10 Sergio Leonel Aguero\n14 Filipe Kasmirski\nHead Coach: Diego Simeone\n2 Federico Fazio\n20 Mouhamadou Dabo\n25 Ivan Rakitić\n7 Gonzalez Jesus Navas\n9 Diego Perotti\n18 Alvaro Negredo\n10 Luis Fabiano\n29 Dani Gimenez\n23 Delgado Alexis\n11 Renato Dirnei\n8 Zokora Didier\n15 Alejandro Alfaro\n17 Sergio Sanchez\n24 Gary Soto\n16 Shots 10\n11 Shots on Goal 6\n1. Sergio Leonel Aguero 20 (1) 0.53\n2. Diego Forlan 8 0.21\n3. Diego Costa 6 0.16\n4. Jose Antonio Reyes 6 0.16\n5. Sabrosa Simao 4 (1) 0.11\n6. Tiago Fereira 3 0.08\n7. Mario Suárez 2 0.05\n8. Álvaro Domínguez 2 0.05\n9. Jorge Resurreccion Merodio 2 0.05\n10. Mendes Elias 2 0.05\n11. Raul Garcia 1 0.03\n12. Mendes Tiago 1 0.03\n13. Juanfran Torres 1 0.03\n14. Jose Manuel Jurado 1 0.03\n15. Juan Valera 1 0.03\n16. Filipe Kasmirski 1 0.03\n1. Alvaro Negredo 20 (4) 0.53\n2. Frederic Kanoute 12 (3) 0.32\n3. Luis Fabiano 10 (3) 0.26\n4. Ivan Rakitić 5 0.13\n5. Diego Perotti 3 0.08\n6. Alejandro Alfaro 2 0.05\n7. Abdoulay Konko 2 0.05\n8. Julien Escudé 2 0.05\n9. Renato Dirnei 2 0.05\n10. Federico Fazio 1 0.03\n11. Cáceres Martín 1 0.03\n12. Gonzalez Jesus Navas 1 0.03\n13. Ndri Romaric 1 0.03\n1. Ujfalusi Tomas 10 2\n2. Luis Perea 11 1\n3. Jose Antonio Reyes 9 1\n4. Mario Suárez 9 0\n5. Paulo Assuncao 8 0\n6. Raul Garcia 7 0\n7. Sergio Leonel Aguero 6 0\n8. Diego Costa 5 0\n9. Mendes Tiago 5 0\n10. Tiago Fereira 5 0\n11. Álvaro Domínguez 5 0\n12. Diego Godin 3 1\n13. Juan Valera 4 0\n14. Filipe Kasmirski 4 0\n15. Antonio Lopez 2 1\n16. Perez Merida Fran 3 0\n17. Diego Forlan 3 0\n18. Sabrosa Simao 3 0\n19. Juanfran Torres 2 0\n20. Jorge Pulido 2 0\n21. David De Gea 1 0\n22. Mendes Elias 1 0\n1. Fernando Navarro 8 2\n2. Zokora Didier 10 0\n3. Delgado Alexis 8 1\n4. Gary Soto 7 1\n5. Mouhamadou Dabo 6 1\n6. Frederic Kanoute 6 0\n7. Julien Escudé 6 0\n8. Cáceres Martín 6 0\n9. Ndri Romaric 5 0\n10. Abdoulay Konko 3 1\n11. Federico Fazio 4 0\n12. Alvaro Negredo 4 0\n13. Diego Perotti 4 0\n14. Andres Palop 2 1\n15. Sergio Sanchez 3 0\n16. Lautaro Acosta 3 0\n17. Gonzalez Jesus Navas 3 0\n18. Javi Varas 3 0\n19. Luis Fabiano 2 0\n20. Luca Cigarini 2 0\n21. Diego Capel 2 0\n22. Rodrigo Rios 2 0\n23. Ivan Rakitić 1 0\n24. Alejandro Alfaro 1 0\n25. Tiberio Guarente 1 0\n26. Renato Dirnei 1 0\n27. Antonio Luna 1 0\nAtletico Madrid 18.03% (11) 11.48% (7) 16.39% (10) 9.84% (6) 22.95% (14) 21.31% (13)\nSevilla 8.06% (5) 16.13% (10) 22.58% (14) 11.29% (7) 22.58% (14) 19.35% (12)\nAtletico Madrid Last 20 matches: +10 =5 -5\n22.05.2021 Valladolid 1:2 Atletico Madrid 16.05.2021 Atletico Madrid 2:1 CA Osasuna 12.05.2021 Atletico Madrid 2:1 Real Sociedad 08.05.2021 Barcelona 0:0 Atletico Madrid\n01.05.2021 Elche 0:1 Atletico Madrid 25.04.2021 Athletic Bilbao 2:1 Atletico Madrid 22.04.2021 Atletico Madrid 2:0 Huesca 18.04.2021 Atletico Madrid 5:0 SD Eibar 11.04.2021 Real Betis 1:1 Atletico Madrid 04.04.2021 Sevilla 1:0 Atletico Madrid 21.03.2021 Atletico Madrid 1:0 Alaves 17.03.2021 Chelsea 2:0 Atletico Madrid 13.03.2021 Getafe 0:0 Atletico Madrid 10.03.2021 Atletico Madrid 2:1 Athletic Bilbao 07.03.2021 Atletico Madrid 1:1 Real Madrid 28.02.2021 Villareal 0:2 Atletico Madrid 23.02.2021 Atletico Madrid 0:1 Chelsea 20.02.2021 Atletico Madrid 0:2 UD Levante 17.02.2021 UD Levante 1:1 Atletico Madrid 13.02.2021 Granada 1:2 Atletico Madrid\nSevilla Last 20 matches: +11 =3 -6\n23.05.2021 Sevilla 1:0 Alaves 16.05.2021 Villareal 4:0 Sevilla 12.05.2021 Sevilla 1:0 Valencia 09.05.2021 Real Madrid 2:2 Sevilla\n03.05.2021 Sevilla 0:1 Athletic Bilbao 25.04.2021 Sevilla 2:1 Granada 21.04.2021 UD Levante 0:1 Sevilla 18.04.2021 Real Sociedad 1:2 Sevilla 12.04.2021 Celta Vigo 3:4 Sevilla 04.04.2021 Sevilla 1:0 Atletico Madrid 20.03.2021 Valladolid 1:1 Sevilla 17.03.2021 Sevilla 2:0 Elche 14.03.2021 Sevilla 1:0 Real Betis 09.03.2021 Borussia Dortmund 2:2 Sevilla 06.03.2021 Elche 2:1 Sevilla 03.03.2021 Barcelona 3:0 Sevilla 27.02.2021 Sevilla 0:2 Barcelona 22.02.2021 CA Osasuna 0:2 Sevilla 17.02.2021 Sevilla 2:3 Borussia Dortmund 13.02.2021 Sevilla 1:0 Huesca\nAtletico Madrid: wins: 8, draws: 8, defeats: 4\nSevilla: wins: 4, draws: 8, defeats: 8\n04.04.2021 Sevilla 1:0 Atletico Madrid LaLiga 12.01.2021 Atletico Madrid 2:0 Sevilla LaLiga 07.03.2020 Atletico Madrid 2:2 Sevilla LaLiga 02.11.2019 Sevilla 1:1 Atletico Madrid LaLiga\n12.05.2019 Atletico Madrid 1:1 Sevilla Primera division 06.01.2019 Sevilla 1:1 Atletico Madrid Primera division 25.02.2018 Sevilla 2:5 Atletico Madrid Primera division 23.01.2018 Sevilla 3:1 Atletico Madrid Copa del Rey 17.01.2018 Atletico Madrid 1:2 Sevilla Copa del Rey 23.09.2017 Atletico Madrid 2:0 Sevilla Primera division 19.03.2017 Atletico Madrid 3:1 Sevilla Primera division 23.10.2016 Sevilla 1:0 Atletico Madrid Primera division 24.01.2016 Atletico Madrid 0:0 Sevilla Primera division 30.08.2015 Sevilla 0:3 Atletico Madrid Primera division 01.03.2015 Sevilla 0:0 Atletico Madrid Primera division 27.09.2014 Atletico Madrid 4:0 Sevilla Primera division 19.01.2014 Atletico Madrid 1:1 Sevilla Primera division 18.08.2013 Sevilla 1:3 Atletico Madrid Primera division 21.04.2013 Sevilla 0:1 Atletico Madrid Primera division 27.02.2013 Sevilla 2:2 Atletico Madrid Copa del Rey\nWhere to watch the match Atletico Madrid 2:2 Sevilla online\n20:00 FT 1 Argentina 0 Paraguay


Alarm-Net Maciej Pączkiewicz - wizytówka NAP\n44c U0 ż2f yf t3ae k3 o17d w8 n57 iac9 kf8c óc w0 d9 z8d a1d r35 e8f7 j99 e635 s0 t9 ra o081 w2a ab n3 y425 c40 h: 259\n9cb O479 dbd w23 ib3 ef d2 z1 iab n: 667124\n948 W2 i18 z1 yc tb58 ó4b w05a ee k: 538\n23 W307 pb6d id s1af óa w5 732 n4 a22 48c bd9f l70 o3 gc97 u: 39\n245 We4d s1e6 zc10 y006 s4 ta73 k33 i0 c35 h4e f Of5b na74 l69 i9ad n16e e: 9 (8 64 Mb ob b734 i2 la e, 1 812 Dae e66 s24 k676 t17 o9 p)\n0 G37b od ś6 c8 i: 6\nd4 U5 ż0b y4d t4 ke21 oe1 w4a nd3 i794 kef7 ófe7 w: 3\nInstalacje monitoringu, systemów alarmowych, Instalacje Inteligentnego Budynku, Instalacje klimatyzacji i telewizji przemysłowej. Jedna z bardziej wartościowych dostępnych ofert na rynku!\ne-mail: f bf3b ie u6d rd79 o1 099 a919 lc a4ce r32 m9 -4 n1 e92 t6f6 .67b p11 l\n907 D4 a4 nfa ee1 4 mf ab3 j7 ą8a 8 cabe h4 af1 r0 afa k8a8 t0f6 e7 r poglądowy, c0 ab k2 tc31 u40e ae lf n2 o5d1 śf ć2 c4 df a64 n510 y0 c1dd h816 a sd5 pa8 r8 a90 wa d1 ź w bazie CEIDG\n© All right reserved. 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Bread Crumb LinkHome > eBay Stores > PAYNESPARTSSHOP > Ford Parts > Ford Fiesta > FusionSave this seller|Sign up for Store newsletter PAYNESPARTSSHOPMaintained by:Member id paynespartsshop ( Feedback Score Of 38201) Welcome to the paynes parts shop, we sell all NEW GENUINE FORD,ROVER, ISUZU parts and aftermarket (UNIPART) parts for all makes of cars within the UK,EUROPE OR WORLDWIDE(EXC NORTH AMERICA). If there is anything you require and its not listed then email or ring our friendly staff for a quote!!! Store searchEnter your search keywordin titles & descriptionStore categoriesStore homeFord Parts (1)Ford Focus (1)MK2 2005-2008 (1)Hide all subcategoriesDisplayHide gallery viewView time leftStore pagesAbout the seller\nAn eBay Store maintained by:Member id paynespartsshop ( Feedback Score Of 38201) Seller, manage StoreTools:My eBay Learn more about feeds


Carlos Tevez – Page 2 – Carlos Tevez\nCarlos Tevez Wants Gianluigi Buffon Reunion At Boca Juniors\nPosted on June 10, 2018 by indexComments Off on Carlos Tevez Wants Gianluigi Buffon Reunion At Boca Juniors\nBoca Juniors striker Carlos Tevez has reportedly called former teammate Gianluigi Buffon to discuss a sensational move to Argentina. The Italy international has been with Juventus since the summer of 2001, but Tevez has urged the 40-year-old to make a switch to Boca before the conclusion of his professional career. According to The Sun, Boca…


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Darnell Edge Player Profile, Fairleigh Dickinson, NCAA Stats, International Stats, Game Logs, Bests, Awards - RealGM\nDarnell Edge G #1\nBorn: Aug 12, 1997 (24 years old)\nBirthplace/Hometown: Saugerties, New York\nHigh School: Saugerties High School [Saugerties, New York]\nJun 20, 2019 - Darnell Edge went undrafted in the 2019 NBA Draft making him an Unrestricted FA.\n2019-20 * All Teams - 3 0 5.7 3.00 1.00 1.67 .600 0.33 0.67 .500 0.67 0.67 1.000 0.00 0.67 0.67 0.00 0.33 0.00 0.67 0.00\n2019-20 * UTH Las Vegas 2 0 6.7 4.50 1.50 2.00 .750 0.50 1.00 .500 1.00 1.00 1.000 0.00 0.50 0.50 0.00 0.50 0.00 1.00 0.00\n2019-20 * UTH Salt Lake City 1 0 3.9 0.00 0.00 1.00 .000 0.00 0.00 .000 0.00 0.00 .000 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00\nCAREER 3 0 5.7 3.00 1.00 1.67 .600 0.33 0.67 .500 0.67 0.67 1.000 0.00 0.67 0.67 0.00 0.33 0.00 0.67 0.00\n2019-20 * All Teams - 3 0 17.2 9 3 5 .600 1 2 .500 2 2 1.000 0 2 2 0 1 0 2 0\n2019-20 * UTH Las Vegas 2 0 13.3 9 3 4 .750 1 2 .500 2 2 1.000 0 1 1 0 1 0 2 0\n2019-20 * UTH Salt Lake City 1 0 3.9 0 0 1 .000 0 0 .000 0 0 .000 0 1 1 0 0 0 0 0\nCAREER 3 0 17.2 9 3 5 .600 1 2 .500 2 2 1.000 0 2 2 0 1 0 2 0\n2019-20 * All Teams - 3 0 0 0 0 0 0 0 .036 0.00 0.50 0.40 1 2 .333 0.04 0.01 0.05\n2019-20 * UTH Las Vegas 2 0 0 0 0 0 0 0 .056 0.00 0.50 0.50 0 2 .000 0.04 - 0.04\n2019-20 * UTH Salt Lake City 1 0 0 0 0 0 0 0 .000 0.00 0.00 0.00 1 0 1.000 -0.02 - -0.02\nCAREER 3 0 0 0 0 0 0 0 .036 0.00 0.50 0.40 1 2 .333 0.04 0.01 0.05\n2019-20 * All Teams - 3 0 .765 .700 - 13.07 6.11 - 25.38 2.78 - 19.10 210.00 -12.19 1.80 107.8 104.1 19.97\n2019-20 * UTH Las Vegas 2 0 .922 .875 - 9.05 4.13 - 29.07 3.59 - 21.85 225.00 -15.86 2.25 111.9 105.5 26.91\n2019-20 * UTH Salt Lake City 1 0 .000 .000 - 25.00 12.20 - - - - 10.27 0.00 - 0.00 - 101.3 -4.66\nCAREER 3 0 .765 .700 - 13.07 6.11 - 25.38 2.78 - 19.10 210.00 -12.19 1.80 107.8 104.1 22.65\nMore Darnell Edge NBA Summer League Stats »\n2015-16 Fairleigh Dickinson Fr 33 1 13.2 4.36 1.61 3.61 .445 0.58 1.36 .422 0.58 0.61 .950 0.58 0.67 1.24 0.85 0.33 0.00 0.42 1.00\n2016-17 Fairleigh Dickinson So 30 16 28.1 8.97 3.20 6.37 .503 1.00 2.73 .366 1.57 1.73 .904 0.60 2.43 3.03 1.33 0.87 0.03 1.20 1.97\n2017-18 Fairleigh Dickinson Jr 30 30 34.0 14.53 4.87 10.83 .449 2.00 5.53 .361 2.80 2.97 .944 0.43 2.47 2.90 2.37 1.33 0.03 1.60 1.77\n2018-19 Fairleigh Dickinson Sr 35 35 37.0 16.63 5.43 11.49 .473 2.63 5.51 .477 3.14 3.54 .887 0.40 3.11 3.51 2.43 1.31 0.03 1.97 1.94\nCAREER NCAA DI 128 82 28.1 11.18 3.79 8.10 .468 1.57 3.80 .414 2.03 2.23 .912 0.50 2.17 2.67 1.75 0.96 0.02 1.30 1.66\n2015-16 Fairleigh Dickinson Fr 33 1 435 144 53 119 .445 19 45 .422 19 20 .950 19 22 41 28 11 0 14 33\n2016-17 Fairleigh Dickinson So 30 16 842 269 96 191 .503 30 82 .366 47 52 .904 18 73 91 40 26 1 36 59\n2017-18 Fairleigh Dickinson Jr 30 30 1,021 436 146 325 .449 60 166 .361 84 89 .944 13 74 87 71 40 1 48 53\n2018-19 Fairleigh Dickinson Sr 35 35 1,295 582 190 402 .473 92 193 .477 110 124 .887 14 109 123 85 46 1 69 68\nCAREER NCAA DI 128 82 3,593 1,431 485 1,037 .468 201 486 .414 260 285 .912 64 278 342 224 123 3 167 213\n2015-16 Fairleigh Dickinson Fr 33 0 0 0 0 0 0 .087 2.00 0.79 0.17 18 15 .545 0.88 0.15 1.03\n2016-17 Fairleigh Dickinson So 30 0 0 0 0 0 0 .180 1.11 0.72 0.27 11 19 .367 1.58 0.63 2.21\n2017-18 Fairleigh Dickinson Jr 30 0 0 0 0 0 0 .267 1.48 0.83 0.27 13 17 .433 2.53 0.82 3.35\n2018-19 Fairleigh Dickinson Sr 35 0 0 0 0 0 0 .299 1.23 0.67 0.31 21 14 .600 3.85 1.18 5.03\nCAREER NCAA DI 128 0 0 0 0 0 0 .207 1.34 0.74 0.27 63 65 .492 8.62 2.65 11.27\n2015-16 Fairleigh Dickinson Fr 33 .560 .525 4.80 5.73 5.26 11.21 9.82 1.40 0.00 15.81 181.76 1.03 1.21 118.1 112.8 12.93\n2016-17 Fairleigh Dickinson So 30 .624 .581 2.48 10.01 6.25 9.28 14.30 1.76 0.13 14.97 177.23 -1.10 1.41 117.9 108.6 13.31\n2017-18 Fairleigh Dickinson Jr 30 .594 .542 1.43 8.16 4.80 13.01 11.56 2.19 0.11 19.68 175.45 -0.06 1.34 119.1 108.5 16.27\n2018-19 Fairleigh Dickinson Sr 35 .631 .587 1.37 9.76 5.76 13.08 13.02 2.07 0.09 21.13 183.64 -0.97 1.45 121.6 106.4 18.12\nCAREER NCAA DI 128 .610 .565 2.07 8.85 5.49 11.82 12.47 1.94 0.09 18.49 179.36 -0.49 1.38 119.3 108.7 15.75\nNABC All-District (18) First Team - 2019\nAll-NEC First Team - 2019\nNEC Tournament MVP - 2019\nNEC Player of the Week - 11/26/2018\n2019-20 Polpharma Starogard Gdanski P-EBL 3 0 23.5 6.33 2.33 7.33 .318 0.00 1.67 .000 1.67 1.67 1.000 0.00 2.33 2.33 1.33 0.33 0.00 2.00 1.00\n2020-21 MBC Mykolaiv Uk Superleague 32 15 24.4 11.75 4.25 8.84 .481 1.56 3.97 .394 1.69 1.84 .915 0.34 2.25 2.59 2.09 0.97 0.00 1.38 1.50\n2021-22 Gemlik Basketbol Bursa T-TBL 30 30 36.6 22.53 7.63 16.03 .476 2.57 7.30 .352 4.70 5.10 .922 0.47 3.53 4.00 4.77 1.13 0.00 2.53 1.63\n2019-20 Polpharma Starogard Gdanski P-EBL 3 0 70.4 19 7 22 .318 0 5 .000 5 5 1.000 0 7 7 4 1 0 6 3\n2020-21 MBC Mykolaiv Uk Superleague 32 15 779.7 376 136 283 .481 50 127 .394 54 59 .915 11 72 83 67 31 0 44 48\n2021-22 Gemlik Basketbol Bursa T-TBL 30 30 1,096.6 676 229 481 .476 77 219 .352 141 153 .922 14 106 120 143 34 0 76 49\n2019-20 Polpharma Starogard Gdanski P-EBL 3 0 0 0 0 0 0 0 .129 0.67 0.17 0.23 0 3 .000 -0.27 -0.00 -0.27\n2020-21 MBC Mykolaiv Uk Superleague 32 15 0 0 0 0 0 0 .217 1.52 0.70 0.21 5 27 .156 1.82 -0.14 1.67\n2021-22 Gemlik Basketbol Bursa T-TBL 30 30 1 0 0 0 0 0 .441 1.88 0.45 0.32 12 18 .400 4.54 0.66 5.20\n2019-20 Polpharma Starogard Gdanski P-EBL 3 0 .393 .318 0.00 11.68 5.81 9.85 19.87 0.71 0.00 19.42 131.82 -4.61 0.86 71.0 116.4 1.99\n2020-21 MBC Mykolaiv Uk Superleague 32 15 .608 .569 1.62 10.82 6.18 15.48 12.47 2.13 0.00 21.20 178.95 0.09 1.33 114.9 117.6 17.45\n2021-22 Gemlik Basketbol Bursa T-TBL 30 30 .616 .556 1.51 11.73 6.54 26.83 12.17 1.72 0.00 26.89 174.93 1.77 1.41 122.1 113.0 21.29\nMore Darnell Edge International Stats »\nSep 1, 2019 - Darnell Edge has signed with Polpharma Starogard Gdanski.\nOct 19, 2019 - Darnell Edge has left Polpharma Starogard Gdanski.


Stadegoù Wikipedia - Tables - Greunlandeg\nStadegoù Wikipedia Greunlandeg\nZoum Greunlandeg Novial Pennsylvania German Haousa Kinyarwanda Inuktitut Pangasinan Kashmiri Cherokee Tok Pisin Kongo Romanieg Pali Goan Konkani Hawaieg Wolof Bambara Old Church Slavonic Norfolk Tuvan Bislama Moksha Ewe Xhosa Oromo Samoan Komi-Permyak Ganda Romani Gilaki Pontic Cheyenne Goteg Siswati Lak Fidjieg Chamorru Bugi Twi Tulu Chichewa Tumbuka Sranan Sangro Inupiak Tahitianeg Sotho ar Su Fulfulde Tswana Tigrina Venda Kirundi Cree Moldoveg Atikamekw An holl yezhoù An holl yezhoù Saozneg Rusianeg Alamaneg Spagnoleg Japaneg Galleg Sinaeg Italianeg Portugaleg Poloneg Nederlandeg Svedeg Arabeg Koreaneg Tchekeg Indonezeg Farsi Ukraineg Finneg Vietnameg Hungareg Hebraeg Thai Daneg Norvegeg Katalaneg Roumaneg Turkeg Gresianeg Bulgareg Serbeg Saozneg eeun Slovakeg Estoneg Kazak Kroateg Lituaneg Hindi Malayseg Sloveneg Jorjianeg Latveg Tagalog Azeri Serb-kroateg Armenianeg Bengaleg Euskareg Tamoul Albaneg Bosneg Makedoneg Cantonese Galizeg Esperanteg Marathi Belaruseg Malayalam Norvegeg (Nynorsk) Ouzbeg Ourdu Afrikaneg Mongoleg Islandeg Telougou Minnan Latin Egyptian Arabic Asturianeg Cebuano Kembraeg Kannada Scots Elzaseg Kirgiz Swahili Brezhoneg Luksembourgeg Waray-Waray Singhaleg Okitaneg Frizeg Tatar Birmaneg Bachkir Aragoneg Nepaleg Javaneg Sorani Iwerzhoneg Ido Izelsaksoneg Punjabeg Goudjarati Yiddish Venetian Bavarian Western Panjabi Classical Chinese Kurdeg Oriya Tadjik Sundaneg Faeroeg Sikilianeg Lombard Malgacheg Interlingua Kechuaeg Nepal Bhasa Kreoleg Haiti Belarusian (TaraÅ¡kievic) Somali Khmer Limbourgeg Amhareg Chechen Asameg Ilokano Chuvash Gouezeleg Skos Sanskriteg Yorouba Min Dong Bihari Wu Hakka Sindhi Upper Sorbian Sakha Minangkabau North Frisian Walloneg Zazaki Maithili Pachto Neapolitan Volapük Banyumasan Lower Sorbian Ossetic Eastern Mari Fiji Hindi Rusyn Piedmontese Dutch Low Saxon Samogitian Mazandarani West Flemish Emilian-Romagnol Anglosaksoneg Bishnupriya Manipuri Guarani Central Bicolano Laoseg Manaveg Silesian Voro Vepsian Gan Cassubian Ligurian Ladino Nahuatl Korseg Turkmen Sardeg Kapampangan Romañcheg Ouigour Tonga Sámi an norzh Erzya Akana Papiamentu Tibeteg Friulian Abkhazeg Udmurt Igbo Banjar Chavacano Picard Arpitan Saterland Frisian Malteg Maldiveg Lezgian Buryat Extremaduran Mirandese Ripuarian Crimean Tatar Nauru Lingala Komi Acehnese Avieneg Maori Cornish Navaho Norman Aramaic Shona Latgalian Tarantino Zealandic Aymara Western Mari Karakalpak Interlingue Northern Sotho Kabardian Zhuang Zouloueg Kikuyu Kalmyk Tetum Dzongkha Tsonga Kabyle Lojban\nMae 2018 0% 0% -75% 0%\nEbr 2018 +4% +1% +300% +10%\nGwe 2018 58 1 1,7 K 28,4 11 238\nEos 2018 58 1,7 K 28,5 12 237\nGoue 2018 58 1,7 K 28,5 6 235\nMez 2018 58 1,7 K 28,5 7 235\nMae 2018 58 1,7 K 28,5 21 235\nEbr 2018 58 2 5 1,7 K 28,5 84 234\nMeu 2018 56 2 1,7 K 28,6 21 212\nChwe 2018 56 1 1,7 K 28,7 13 212\nGen 2018 56 1 1,7 K 28,7 36 212\nKzu 2017 56 1 1 1,7 K 28,6 34 212\nDu 2017 55 1,7 K 28,6 16 212\nHer 2017 55 1,7 K 28,7 22 211\nGwe 2017 55 1,7 K 28,7 8 210\nEos 2017 55 1 1,7 K 28,7 12 210\nGoue 2017 55 1 1,7 K 28,7 49 210\nMez 2017 55 1 1,7 K 28,7 10 210\nMae 2017 54 1,7 K 28,7 12 210\nEbr 2017 54 1,7 K 28,7 10 210\nMeu 2017 54 1,7 K 28,7 9 210\nChwe 2017 54 1 1 1,7 K 28,7 19 210\nGen 2017 53 1,7 K 28,7 11 210\nKzu 2016 53 1,7 K 28,7 13 210\nDu 2016 53 1,7 K 28,7 11 210\nHer 2016 53 1,7 K 28,7 16 210\nGwe 2016 53 1,7 K 28,7 7 210\nEos 2016 53 2 2 1,7 K 28,7 40 210\nGoue 2016 51 1 1,7 K 28,8 22 205\nMez 2016 51 1,7 K 28,8 4 205\nMae 2016 51 1,7 K 28,8 55 205\nEbr 2016 51 2 3 1,7 K 28,8 112 204\nMeu 2016 49 1 1,7 K 28,9 14 200\nChwe 2016 49 1,7 K 28,9 8 200\nGen 2016 49 1,7 K 28,9 21 200\nKzu 2015 49 1 1 1,7 K 28,9 32 200\nDu 2015 48 1,7 K 28,9 30 198\nHer 2015 48 1,6 K 28,9 17 198\nGwe 2015 48 1,6 K 28,9 19 198\nEos 2015 48 1 1 1,6 K 29 34 198\nGoue 2015 47 1,6 K 28,9 13 198\nMez 2015 47 1 1,6 K 29 27 198\nMae 2015 47 1,6 K 29 18 198\nEbr 2015 47 1,6 K 29 8 198\nMeu 2015 47 1 1,6 K 29 54 198\nChwe 2015 47 1 1 1,6 K 29 34 198\nGen 2015 46 1,6 K 29,1 11 198\nKzu 2014 46 1 1,6 K 29,1 23 198\nDu 2014 46 1,6 K 29,1 10 198\nHer 2014 46 1,6 K 29,1 19 198\nGwe 2014 46 2 1 1,6 K 29,2 28 198\nEos 2014 44 2 1,6 K 29,2 22 198\nGoue 2014 44 1,6 K 29,2 5 198\nMez 2014 44 1,6 K 29,2 12 198\nMae 2014 44 1 1,6 K 29,2 30 198\nEbr 2014 44 1 1,6 K 29,2 28 197\nMeu 2014 44 1,6 K 29,2 20 194\nChwe 2014 44 1,6 K 1,2 K 29,3 580 38% 2% 15 1,2 Mb 101 K 9,2 K 169 97 856 194\nGen 2014 44 1,6 K 1,2 K 29,3 579 38% 2% 21 1,2 Mb 101 K 9,2 K 169 96 856 194\nKzu 2013 44 1,6 K 1,1 K 29,3 579 38% 2% 9 1,2 Mb 101 K 9,2 K 175 96 855 194\nDu 2013 44 1 1,6 K 1,1 K 29,3 579 38% 2% 16 1,2 Mb 101 K 9,2 K 175 96 855 194\nHer 2013 44 1 1 1,6 K 1,1 K 29,3 579 38% 2% 35 1,2 Mb 101 K 9,2 K 174 96 855 193\nGwe 2013 43 1,6 K 1,1 K 29,3 579 38% 2% 6 1,2 Mb 101 K 9,2 K 172 96 852 193\nEos 2013 43 1 1,6 K 1,1 K 29,3 579 38% 2% 16 1,2 Mb 101 K 9,2 K 172 96 852 193\nGoue 2013 43 1,6 K 1,1 K 29,3 579 38% 2% 21 1,2 Mb 101 K 9,2 K 181 96 852 193\nMez 2013 43 1 1 1,6 K 1,1 K 29,3 579 38% 2% 39 1,2 Mb 100 K 9,1 K 187 93 851 193\nMae 2013 42 1 1,6 K 1,1 K 29,3 579 38% 2% 36 1,2 Mb 100 K 9,2 K 318 93 852 189\nEbr 2013 42 1 1,6 K 1,1 K 29,3 575 38% 2% 104 1,2 Mb 100 K 9,1 K 350 94 843 189\nMeu 2013 42 1,6 K 1,1 K 29,2 575 38% 2% 1,9 K 1,3 Mb 100 K 9,1 K 2,7 K 93 843 189\nChwe 2013 42 1 1,6 K 1,1 K 28,1 575 38% 2% 714 4,3 Mb 100 K 9,1 K 127 K 92 843 188\nGen 2013 42 1 2 1,6 K 1,1 K 27,7 574 38% 2% 857 4,3 Mb 100 K 9,1 K 127 K 92 843 187\nKzu 2012 41 1 1 1,6 K 1,1 K 27,3 574 38% 2% 937 4,3 Mb 99 K 9,0 K 126 K 84 843 186\nDu 2012 40 1 1,6 K 1,1 K 26,8 571 38% 2% 790 4,2 Mb 99 K 9,0 K 125 K 82 815 186\nHer 2012 40 1,6 K 1,1 K 26,3 569 38% 2% 952 4,2 Mb 98 K 8,9 K 125 K 81 813 186\nGwe 2012 40 1 1,6 K 1,1 K 25,8 569 38% 2% 823 4,2 Mb 98 K 8,9 K 125 K 80 813 186\nEos 2012 40 1,6 K 1,1 K 25,4 570 37% 2% 934 4,2 Mb 98 K 8,9 K 124 K 82 811 186\nGoue 2012 40 1,6 K 1,1 K 24,8 570 37% 2% 1,0 K 4,2 Mb 98 K 8,9 K 123 K 87 811 186\nMez 2012 40 1 1 1,6 K 1,1 K 24,1 570 37% 2% 1,2 K 4,2 Mb 98 K 8,9 K 123 K 89 811 186\nMae 2012 39 1 2 1,6 K 1,1 K 23,4 570 37% 2% 839 4,2 Mb 98 K 8,9 K 123 K 91 810 186\nEbr 2012 38 1,6 K 1,1 K 22,8 570 37% 2% 997 4,1 Mb 98 K 8,9 K 123 K 100 810 186\nMeu 2012 38 1 1 1,6 K 1,1 K 2 22,3 570 37% 2% 1,2 K 4,1 Mb 98 K 8,9 K 122 K 100 809 185\nChwe 2012 38 1 1,5 K 1,1 K 1 22,3 560 35% 2% 1,0 K 4,0 Mb 92 K 8,3 K 118 K 100 765 185\nGen 2012 38 2 2 1,5 K 1,1 K 1 21,9 558 35% 2% 1,1 K 3,9 Mb 90 K 8,2 K 116 K 99 741 185\nKzu 2011 36 3 1,5 K 1,0 K 1 21,5 552 34% 2% 1,2 K 3,8 Mb 87 K 7,8 K 115 K 82 721 182\nDu 2011 36 3 1,4 K 1,0 K 2 21,3 554 34% 2% 1,4 K 3,7 Mb 85 K 7,5 K 111 K 69 698 181\nHer 2011 36 1 2 1 1,4 K 961 2 21 555 34% 2% 1,1 K 3,6 Mb 82 K 7,2 K 108 K 64 675 176\nGwe 2011 35 1 1,3 K 906 1 21,1 558 34% 2% 1,5 K 3,5 Mb 78 K 7,0 K 105 K 60 661 175\nEos 2011 35 1 1,3 K 873 20,6 557 32% 2% 1,1 K 3,4 Mb 76 K 6,7 K 103 K 60 626 175\nGoue 2011 35 1 2 1 1,3 K 859 2 20 554 32% 2% 1,1 K 3,3 Mb 74 K 6,6 K 102 K 60 608 174\nMez 2011 34 2 1,2 K 796 1 20,1 555 31% 3% 982 3,2 Mb 70 K 6,2 K 100 K 61 555 169\nMae 2011 34 1 3 1,2 K 769 1 19,7 540 30% 2% 1,8 K 3,1 Mb 67 K 6,0 K 98 K 69 530 169\nEbr 2011 33 1 4 1,1 K 742 1 18,9 537 30% 2% 837 3,0 Mb 64 K 5,8 K 95 K 35 505 164\nMeu 2011 32 1 4 1,1 K 701 2 18,9 534 30% 3% 1,4 K 2,9 Mb 61 K 5,5 K 92 K 35 443 156\nChwe 2011 31 2 1,0 K 646 18,5 536 30% 3% 944 2,8 Mb 59 K 5,2 K 89 K 34 397 145\nGen 2011 31 1 3 1,0 K 644 1 17,7 534 30% 3% 1,3 K 2,8 Mb 58 K 5,1 K 89 K 33 397 143\nKzu 2010 30 1 3 1 1,0 K 628 8 16,7 534 30% 3% 1,2 K 2,7 Mb 57 K 5,1 K 88 K 33 383 140\nDu 2010 29 2 785 598 1 20,3 633 38% 4% 751 2,0 Mb 51 K 4,4 K 59 K 36 354 136\nHer 2010 29 4 755 569 1 20,1 642 38% 4% 868 1,9 Mb 49 K 4,2 K 57 K 37 332 133\nGwe 2010 29 2 718 533 1 19,9 651 39% 4% 554 1,9 Mb 47 K 3,9 K 56 K 39 287 130\nEos 2010 29 2 4 1 696 513 1 19,8 658 40% 4% 1,1 K 1,8 Mb 46 K 3,8 K 55 K 40 270 130\nGoue 2010 27 3 1 672 493 1 18,9 658 38% 4% 1,4 K 1,8 Mb 45 K 3,8 K 54 K 49 258 117\nMez 2010 27 3 6 1 629 432 2 18 648 39% 4% 969 1,7 Mb 41 K 3,3 K 51 K 69 228 113\nMae 2010 24 2 4 1 560 382 3 18,5 658 40% 5% 872 1,5 Mb 37 K 2,9 K 45 K 72 199 113\nEbr 2010 22 2 1 463 305 2 20,5 647 41% 4% 752 1,3 Mb 30 K 2,4 K 38 K 62 117 106\nMeu 2010 22 2 4 397 247 2 22 667 40% 5% 765 1,1 Mb 26 K 2,1 K 33 K 60 93 106\nChwe 2010 20 1 1 341 214 2 23,4 712 43% 5% 448 979 Kb 24 K 1,9 K 28 K 58 71 105\nGen 2010 20 1 296 198 1 25,5 743 46% 6% 360 818 Kb 21 K 1,7 K 22 K 57 62 101\nKzu 2009 20 1 267 184 26,9 767 48% 6% 330 769 Kb 20 K 1,6 K 21 K 59 35 98\nDu 2009 20 1 265 183 25,8 766 48% 6% 318 759 Kb 20 K 1,6 K 21 K 59 35 98\nHer 2009 20 2 2 261 178 1 25 771 48% 7% 473 735 Kb 19 K 1,5 K 20 K 59 35 97\nGwe 2009 18 1 215 156 28,2 836 48% 8% 393 612 Kb 18 K 1,3 K 16 K 64 35 84\nEos 2009 18 1 203 145 27,9 829 47% 8% 423 574 Kb 17 K 1,3 K 15 K 60 34 81\nGoue 2009 18 1 2 196 138 1 26,7 833 46% 8% 534 551 Kb 16 K 1,3 K 15 K 61 31 80\nMez 2009 17 2 174 113 1 27,1 869 45% 9% 344 489 Kb 15 K 1,2 K 12 K 121 29 75\nMae 2009 17 2 156 98 28 939 48% 10% 228 443 Kb 14 K 1,2 K 11 K 156 29 70\nEbr 2009 17 1 3 146 84 28,3 948 48% 10% 196 414 Kb 14 K 1,1 K 9,9 K 175 22 66\nMeu 2009 16 135 81 29,2 974 51% 10% 242 401 Kb 13 K 1,1 K 9,6 K 197 20 66\nChwe 2009 16 130 80 28,4 998 53% 10% 121 353 Kb 13 K 1,1 K 7,5 K 197 16 66\nGen 2009 16 128 78 27,9 1010 52% 10% 151 348 Kb 13 K 1,1 K 7,4 K 194 13 65\nKzu 2008 16 1 2 127 78 1 27 1017 53% 10% 228 342 Kb 13 K 1,1 K 7,3 K 193 13 64\nDu 2008 15 1 1 1 99 77 32,3 1276 67% 13% 243 330 Kb 13 K 1,1 K 7,1 K 169 13 20\nHer 2008 14 98 73 30,1 1259 65% 13% 127 322 Kb 12 K 1,0 K 7,1 K 146 12 20\nGwe 2008 14 98 73 28,8 1259 65% 13% 223 318 Kb 12 K 1,0 K 6,9 K 147 12 20\nEos 2008 14 96 72 27,1 1285 66% 14% 160 306 Kb 12 K 1,0 K 6,5 K 148 12 19\nGoue 2008 14 96 72 25,5 1296 66% 12% 111 304 Kb 12 K 1,0 K 6,5 K 147 12 19\nMez 2008 14 1 94 71 24,8 1320 67% 13% 174 299 Kb 12 K 1,0 K 6,3 K 144 11 19\nMae 2008 14 1 1 90 70 24 1351 70% 13% 163 287 Kb 12 K 1,0 K 6,0 K 137 10 19\nEbr 2008 13 1 90 69 22,2 1346 69% 13% 171 283 Kb 12 K 1,0 K 6,0 K 137 10 19\nMeu 2008 13 1 87 67 21 1339 69% 13% 108 267 Kb 11 K 982 5,7 K 133 10 16\nChwe 2008 13 86 67 20 1339 70% 13% 124 266 Kb 11 K 982 5,7 K 134 10 16\nGen 2008 13 3 84 65 19 1336 69% 13% 149 252 Kb 11 K 916 5,2 K 125 10 16\nKzu 2007 13 2 78 60 18,5 1380 69% 14% 174 226 Kb 10 K 763 4,5 K 104 9 16\nDu 2007 13 1 1 69 38 18,4 962 46% 10% 116 153 Kb 5,4 K 395 3,1 K 67 8 9\nHer 2007 12 4 5 63 37 18,3 1034 51% 11% 185 137 Kb 5,3 K 385 2,7 K 60 6 9\nGwe 2007 8 60 35 16,1 1048 52% 12% 83 130 Kb 5,0 K 367 2,5 K 58 5 9\nEos 2007 8 60 34 14,8 1006 50% 10% 61 122 Kb 4,6 K 321 2,4 K 49 4 9\nGoue 2007 8 1 60 34 13,8 1006 50% 10% 41 118 Kb 4,6 K 321 2,3 K 49 4 9\nMez 2007 8 1 1 55 31 14,3 1006 51% 9% 39 99 Kb 4,3 K 311 1,9 K 47 4 9\nMae 2007 7 2 54 30 13,8 930 50% 6% 61 89 Kb 3,8 K 257 1,8 K 38 3 9\nEbr 2007 7 1 54 30 12,7 930 50% 6% 14 81 Kb 3,8 K 253 1,5 K 37 3 9\nMeu 2007 7 1 1 54 30 12,4 931 50% 6% 40 81 Kb 3,8 K 253 1,5 K 34 5 9\nChwe 2007 6 50 30 12,6 950 54% 6% 13 82 Kb 3,8 K 253 1,5 K 34 5 9\nGen 2007 6 1 50 30 12,3 949 54% 6% 37 82 Kb 3,8 K 249 1,5 K 34 5 9\nKzu 2006 6 1 50 30 11,6 922 54% 6% 23 76 Kb 3,7 K 249 1,2 K 34 5 9\nDu 2006 6 1 48 29 11,6 949 54% 6% 21 70 Kb 3,6 K 249 1,0 K 32 5 5\nHer 2006 6 1 1 48 28 11,2 897 52% 4% 63 63 Kb 3,4 K 221 837 32 3 4\nGwe 2006 5 1 35 15 13,5 638 37% 6% 31 45 Kb 1,9 K 187 821 22 4 2\nEos 2006 5 1 2 27 10 16,4 478 30% 7% 25 31 Kb 1,2 K 120 705 20 4 2\nGoue 2006 4 1 1 15 8 27,8 788 47% 13% 32 23 Kb 993 84 518 6 2 2\nMez 2006 3 1 12 5 32,1 742 33% 17% 11 15 Kb 728 61 310 2 2 2\nMae 2006 3 11 5 34 741 36% 18% 7 15 Kb 726 61 309 2 2 2\nEbr 2006 3 1 10 4 36,7 762 40% 20% 26 14 Kb 676 53 282 3 2 2\nMeu 2006 3 1 1 10 3 34,1 699 30% 20% 25 12 Kb 613 36 239 3 1 2\nChwe 2006 2 9 3 35,1 786 33% 22% 17 12 Kb 615 32 205 3 1 1\nGen 2006 2 8 2 37,4 595 25% 12% 24 8,6 Kb 373 32 176 2 1\nKzu 2005 2 1 1 8 2 34,4 686 25% 12% 47 8,3 Kb 368 32 159 2 1\nDu 2005 1 1 7 2 32,6 686 29% 14% 27 8,2 Kb 368 32 159 2\nHer 2005 1 2 7 3 28,7 715 43% 14% 12 9,6 Kb 446 35 169 2 7\nGwe 2005 1 7 3 27 715 43% 14% 26 9,6 Kb 446 35 169 2 7\nEos 2005 1 5 1 32,6 265 20% 11 3,4 Kb 113 6 68 1 7\nGoue 2005 1 4 1 38 301 25% 8 3,2 Kb 102 5 62 1 6\nMez 2005 1 4 1 36 309 25% 3 3,2 Kb 104 4 62 1 7\nMae 2005 1 4 1 35,2 334 25% 8 3,6 Kb 115 11 66 1 7\nEbr 2005 1 4 1 33,2 366 25% 17 2,9 Kb 92 7 46 7\nMeu 2005 1 2 1 58 493 50% 15 1,7 Kb 82 4 16 7\nChwe 2005 1 2 1 50,5 442 50% 7 1,6 Kb 69 4 16 7\nGen 2005 1 2 1 47 398 50% 17 1,4 Kb 60 4 8 7\nKzu 2004 1 1 1 77 2622 100% 100% 32 6,0 Kb 262 5 5 115\nDu 2004 1 1 1 45 2368 100% 100% 7 5,5 Kb 246 2 3 117\nHer 2004 1 1 1 38 2368 100% 100% 1 5,5 Kb 246 2 3 117\nGwe 2004 1 1 1 37 2286 100% 100% 8 5,5 Kb 242 1 2 117\nEos 2004 1 1 1 29 2286 100% 100% 12 5,5 Kb 242 1 2 117\nGoue 2004 1 1 1 17 2012 100% 6 5,2 Kb 202 1 117\nMez 2004 1 1 1 11 2204 100% 100% 1 5,4 Kb 223 1 118\nMae 2004 1 1 1 10 2201 100% 100% 5,4 Kb 221 1 118\nEbr 2004 1 1 1 10 2201 100% 100% 3 5,4 Kb 221 1 118\nMeu 2004 1 1 1 7 2123 100% 100% 1 5,2 Kb 214 1 117\nChwe 2004 1 1 1 6 2110 100% 100% 5,2 Kb 213 1 117\nGen 2004 1 1 1 6 2110 100% 100% 5 5,2 Kb 213 1 117\nKzu 2003 1 1 1 1 1 376 1 607 61 1 3\nGoue 2018 199 223 54 248 278\nEbr 2018 199 106 128 224 54 193 278\nGen 2018 199 230 224 53 223 278\nHer 2017 198 223 55 230 278\nGoue 2017 196 229 223 54 194 278\nEbr 2017 195 222 54 243 278\nHer 2016 192 221 55 234 278\nGoue 2016 194 215 219 55 217 278\nEbr 2016 192 104 151 217 55 164 278\nHer 2015 194 216 58 231 278\nGoue 2015 191 213 60 234 278\nEbr 2015 189 211 62 255 278\nHer 2014 186 208 65 217 278\nGoue 2014 186 209 66 260 278\nEbr 2014 184 218 208 68 219 278\nGen 2014 182 174 216 148 208 194 159 69 193 169 208 229 221 219 209 224 254 195 278\nHer 2013 179 159 206 129 205 194 150 70 195 168 208 200 222 220 207 228 254 195 278\nGoue 2013 176 145 211 138 203 194 160 70 194 165 209 218 221 220 206 230 253 193 278\nEbr 2013 178 152 211 152 200 190 161 71 193 163 209 202 220 218 204 212 252 190 277\nGen 2013 176 143 187 153 199 186 161 81 192 161 206 199 202 217 202 175 252 186 277\nHer 2012 177 150 212 140 198 184 152 92 191 162 205 187 201 216 201 172 254 185 277\nGoue 2012 175 147 214 146 198 184 155 102 189 159 203 180 200 214 199 170 253 185 277\nEbr 2012 175 143 218 150 196 183 152 113 189 156 201 186 199 213 198 164 245 182 277\nGen 2012 169 113 181 145 197 183 147 115 187 160 199 174 199 215 199 165 244 182 277\nHer 2011 170 146 186 147 199 183 120 121 184 158 199 184 200 217 207 167 256 185 277\nGoue 2011 168 152 179 141 197 182 137 129 182 161 195 177 201 218 206 168 256 183 277\nEbr 2011 170 140 143 136 198 187 159 134 178 161 190 186 200 220 206 168 261 185 277\nGen 2011 170 147 160 141 199 187 159 146 175 160 181 183 196 225 207 168 263 186 277\nHer 2010 170 156 137 135 202 188 150 193 192 191 189 179 205 224 209 183 256 188 277\nGoue 2010 170 169 166 138 201 190 154 189 188 187 185 150 205 222 212 181 252 189 277\nEbr 2010 186 156 188 139 207 200 118 186 185 184 183 188 211 227 219 192 244 213 277\nGen 2010 186 150 204 134 223 215 153 183 183 182 181 195 226 232 226 210 242 230 277\nHer 2009 182 105 180 137 221 213 158 181 181 180 179 191 222 229 224 208 238 238 277\nGoue 2009 184 185 172 137 223 217 148 173 173 172 171 192 224 230 224 212 239 239 277\nEbr 2009 179 138 154 147 229 222 147 170 170 169 168 213 229 228 223 216 215 240 277\nHer 2008 182 143 210 137 235 219 147 159 159 158 157 216 225 225 216 218 209 245 274\nGoue 2008 178 156 215 134 233 215 150 153 153 152 150 224 225 223 210 215 201 239 273\nEbr 2008 176 153 208 132 227 209 154 147 147 145 143 200 217 218 204 207 197 238 273\nGen 2008 169 159 164 136 223 204 155 143 143 141 140 199 215 212 202 204 194 229 269\nHer 2007 160 69 111 136 223 204 151 141 141 139 139 197 219 221 212 210 202 227 267\nGoue 2007 180 151 197 129 212 200 138 134 134 133 131 191 211 216 207 203 203 231 262\nEbr 2007 174 144 181 128 198 193 143 126 126 126 124 214 206 208 198 197 193 227 257\nGen 2007 173 125 180 127 193 185 140 122 122 122 120 199 195 195 190 190 188 217 254\nHer 2006 167 129 177 125 183 179 139 114 114 114 113 170 193 191 185 191 183 220 251\nGoue 2006 179 121 162 111 194 186 120 105 105 105 105 169 197 198 187 181 203 209 238\nEbr 2006 182 130 165 111 189 192 122 92 92 92 91 163 191 192 184 177 188 207 233\nGen 2006 172 108 149 92 174 186 108 85 85 85 84 150 177 186 175 163 173 202 215\nHer 2005 191 101 105 87 162 162 99 81 81 81 80 143 165 165 161 157 155 161 213\nGoue 2005 178 91 125 83 158 181 90 70 70 70 70 142 173 184 171 159 144 157 202\nEbr 2005 168 89 113 77 142 172 84 59 59 59 59 116 166 178 153 149 133 147 195\nGen 2005 161 79 98 63 143 168 76 54 54 54 54 99 176 181 153 164 119 147 191\nHer 2004 156 70 92 56 152 153 70 51 51 51 51 141 134 136 141 152 99 81 189\nGoue 2004 138 76 82 51 136 132 59 42 42 42 42 92 117 119 128 126 84 75 160\nEbr 2004 124 59 68 45 117 110 56 37 37 37 37 94 99 99 108 92 73 57 143\nGen 2004 113 52 54 35 102 93 42 28 28 28 28 71 80 82 86 74 56 43 127\nGwe 2018 4 2 1 3 4\nEos 2018 5 1 1\nMez 2018 5 1 3 1\nMae 2018 5 1 2 2\nEbr 2018 7 5 5 3 1 1 1 4 1\nMeu 2018 6 2 2 1\nChwe 2018 4 1 1 2\nKzu 2017 7 2 1 1 1 2 2 1 1\nDu 2017 5 2 1 2\nHer 2017 9 3 1 3\nEos 2017 6 1 1 2\nGoue 2017 5 1 1 1 1 1 1 1 1 1\nMez 2017 3 2 3 1 1 1 1\nMae 2017 5 3 1\nEbr 2017 4 1 2 2\nChwe 2017 5 1 1 1 2\nKzu 2016 5 1 1\nDu 2016 6 1 1 3\nHer 2016 7 1 1\nGwe 2016 5 2 1 1 1\nEos 2016 8 4 2 2 4 2 5 2\nGoue 2016 7 3 1 2 1 1\nMae 2016 5 1 1 1 1 2\nEbr 2016 11 5 3 2 1 1 4 1 1 4 1\nMeu 2016 4 1 1 3 1 1 1 1\nChwe 2016 3 2\nHer 2015 7 1 3 1 4 1 1\nGoue 2015 6 1 2 4\nEbr 2015 3 1 2 2 1 1 1\nHer 2014 11 1 3\nGoue 2014 3 1\nEbr 2014 7 2 1 1 1 1 4 1\nHer 2013 12 1 1 1 1 3 1 1\nGoue 2013 8 1 6\nEbr 2013 11 2 1 1 2 2 1 10 1 5 3\nHer 2012 7 1 13 7 2 10 1 5 2 1\nGoue 2012 8 1 18 12 3 4 11 2 5 4\nEbr 2012 9 1 19 14 3 2 1 7 2 1 9 5\nHer 2011 11 2 2 1 1 1 20 14 4 3 5 10 9 1\nGoue 2011 9 2 2 2 1 1 17 13 3 3 1 1 1 6 3 6 1\nEbr 2011 6 5 4 4 2 13 9 4 3 3 2 7 3 3 10 7\nHer 2010 12 4 4 3 2 16 9 3 3 1 1 11 2 1 1 8 6\nGoue 2010 7 3 3 2 2 1 1 19 11 2 3 3 2 6 3 2 2 2 1 7 4\nEbr 2010 5 2 2 2 2 1 14 12 1 3 1 1 8 5 1 1 1 1 8 6\nHer 2009 3 2 2 2 2 13 10 3 2 1 1 9 1 1 1 1 1 4 2\nGoue 2009 6 4 2 2 1 11 6 1 5 4 3 1 11 5 4 2 1 5 1\nEbr 2009 7 4 3 1 11 6 4 2 2 8 5 5 1 1 1 6 3\nHer 2008 2 5 4 3 13 3 1 5 1\nGoue 2008 1 7 4 4 1 8 1\nEbr 2008 3 2 1 1 1 5 3 1 7 1\nHer 2007 8 6 5 4 2 3 3 7 6 1 1\nGoue 2007 5 2 1 2 1 4 8 3 1\nEbr 2007 3 2 1 2 2 2 1\nHer 2006 3 3 1 1 1 2 1\nGoue 2006 2 2 1 1 1 1 2 1 1 1 1\nEbr 2006 2 1 1 1 4\n1 496 100.0% 7,113 100.0%\n3 124 25.0% 6,593 92.7%\n10 57 11.5% 6,216 87.4%\n32 25 5.0% 5,663 79.6%\n100 10 2.0% 4,917 69.1%\n316 3 0.6% 3,662 51.5%\n1000 2 0.4% 2,966 41.7%\nBALMAINM UC 88 +396 6 5 4 4 Chwe 19, 2018 222 1 1 - -\nErcé UC 104 +391 5 4 1 - Eos 23, 2018 37 - - - -\nSorenhk UC 211 +172 2 1 - - Her 26, 2013 1799 1 - - -\nJackmarriott2697 UC 496 ... 1 1 - - Gwe 12, 2018 17 1 1 - -\nJhendin UC 1 1,623 Meu 19, 2010 3116 Meu 25, 2012 2379\nGerthn UC 2 1,343 Ebr 19, 2009 3450 Ebr 16, 2016 896\nWhere R U? UC 3 696 Mez 17, 2010 3026 Goue 16, 2010 2997\nClavardage UC 4 300 Eos 28, 2010 2954 Her 27, 2010 2894\nKaare UC 5 264 Kzu 04, 2006 4317 Goue 17, 2018 74\nSobreira UC 6 201 Du 27, 2008 3593 Ebr 20, 2009 3449\nLuka~klwiki UC 7 129 Gwe 12, 2007 4035 Her 07, 2008 3644\nDkc UC 8 122 Goue 09, 2006 4465 Ebr 20, 2008 3814\nWhat symbolize it? UC 9 121 Mae 28, 2010 3046 Mez 01, 2010 3042\nP'tit frappé UC 10 118 Du 20, 2010 2870 Ebr 18, 2013 1990\nDARIO SEVERI UC 11 98 Ebr 09, 2016 903 Chwe 26, 2018 215\nSpøka UC 12 70 Eos 06, 2010 2976 Eos 12, 2010 2970\nHelpgreenland UC 13 64 Her 15, 2009 3271 Du 05, 2009 3250\nאלקנה פיינר UC 14 60 Goue 30, 2015 1157 Mez 22, 2016 829\nSeti6908 UC 15 56 Mez 26, 2012 2286 Mae 06, 2014 1607\nIketsi UC 16 53 Mez 07, 2011 2671 Ebr 11, 2013 1997\nValeti~klwiki UC 17 49 Gen 07, 2012 2457 Mae 15, 2013 1963\nTrondtr UC 18 46 Her 02, 2006 4380 Ebr 13, 2009 3456\nBrendahmong~klwiki UC 19 42 Her 01, 2007 4016 Her 01, 2007 4016\nMakkalitta UC 20 38 Ebr 01, 2018 181 Ebr 02, 2018 180\nAn holl a-gevret e oa bet degaset 1.347 kemm gant implijerien dianav war un hollad a 48.509 kemm (3E+00US%)\nEmausBot UC 1 4,703 Goue 08, 2010 3005 Mae 24, 2018 128 - -\nXqbot UC 2 4,351 Gen 21, 2009 3538 Mae 21, 2016 861 - -\nLuckas-bot UC 3 3,496 Chwe 07, 2009 3521 Mae 20, 2012 2323 - -\nWikitanvirBot UC 4 3,118 Kzu 09, 2010 2851 Ebr 23, 2012 2350 - -\nMerlIwBot UC 5 2,056 Mae 10, 2011 2699 Eos 08, 2013 1878 - -\nZéroBot UC 6 1,680 Kzu 22, 2010 2838 Meu 06, 2013 2033 - -\nSieBot UC 7 1,553 Eos 19, 2007 4059 Gen 25, 2011 2804 - -\nTXiKiBoT UC 8 1,071 Mae 09, 2008 3795 Kzu 20, 2012 2109 - -\nEscarbot UC 9 983 Chwe 06, 2007 4253 Mae 13, 2016 869 - -\nLegobot UC 10 944 Meu 11, 2013 2028 Ebr 02, 2013 2006 - -\nVolkovBot UC 11 934 Mez 13, 2007 4126 Meu 04, 2013 2035 - -\nAddbot UC 12 821 Meu 07, 2013 2032 Eos 22, 2013 1864 - -\nFoxBot UC 13 813 Her 04, 2009 3282 Chwe 05, 2012 2428 - -\nRedBot UC 14 753 Gwe 10, 2010 2941 Eos 21, 2012 2230 - -\nArthurBot UC 15 668 Gen 10, 2009 3549 Gwe 09, 2012 2211 - -\nTjBot UC 16 598 Mez 01, 2010 3042 Meu 06, 2013 2033 - -\nKamikazeBot UC 17 552 Goue 04, 2010 3009 Gen 26, 2013 2072 - -\nRipchip Bot UC 18 545 Meu 06, 2011 2764 Chwe 18, 2012 2415 - -\nLaaknorBot UC 19 525 Eos 18, 2008 3694 Meu 07, 2013 2032 - -\nJAnDbot UC 20 493 Meu 28, 2007 4203 Goue 02, 2013 1915 - -\nMelancholieBot UC 21 493 Mae 21, 2008 3783 Du 28, 2009 3227 - -\nMGA73bot UC 22 491 Goue 12, 2009 3366 Goue 16, 2010 2997 - -\nThijs!bot UC 23 461 Gen 13, 2007 4277 Goue 30, 2012 2252 - -\nMjbmrbot UC 24 456 Her 15, 2010 2906 Meu 27, 2011 2743 - -\nHRoestBot UC 25 361 Mez 19, 2010 3024 Chwe 15, 2013 2052 - -\nJackieBot UC 26 355 Kzu 27, 2010 2833 Meu 06, 2013 2033 - -\nVagobot UC 27 333 Du 02, 2011 2523 Gwe 19, 2012 2201 - -\nAvicBot UC 28 321 Mez 20, 2011 2658 Kzu 09, 2012 2120 - -\nAvocatoBot UC 29 315 Her 20, 2011 2536 Chwe 27, 2013 2040 - -\nIdioma-bot UC 30 312 Gwe 08, 2008 3673 Meu 04, 2013 2035 - -\nDinamik-bot UC 31 310 Chwe 21, 2010 3142 Kzu 23, 2012 2106 - -\nMovses-bot UC 32 263 Gen 27, 2011 2802 Meu 16, 2012 2388 - -\nCarsracBot UC 33 250 Kzu 28, 2008 3562 Chwe 18, 2013 2049 - -\nAlexbot UC 34 244 Gen 30, 2008 3895 Eos 16, 2011 2601 - -\nCommonsDelinker UC 35 235 Kzu 15, 2007 3941 Eos 30, 2018 30 - -\nAlleborgoBot UC 36 222 Du 04, 2007 3982 Du 30, 2008 3590 - -\nJotterbot UC 37 220 Goue 27, 2009 3351 Gen 28, 2013 2070 - -\nPtbotgourou UC 38 218 Her 18, 2008 3633 Meu 04, 2013 2035 - -\nYFdyh-bot UC 39 210 Mez 27, 2012 2285 Chwe 14, 2013 2053 - -\nMystBot UC 40 198 Gen 07, 2010 3187 Meu 03, 2012 2401 - -\nGerakibot UC 41 176 Du 07, 2008 3613 Meu 05, 2013 2034 - -\nMastiBot UC 42 173 Kzu 31, 2009 3194 Chwe 11, 2013 2056 - -\nRubinbot UC 43 164 Ebr 10, 2009 3459 Meu 04, 2013 2035 - -\nAmirobot UC 44 161 Mez 29, 2010 3014 Du 19, 2011 2506 - -\nMakecat-bot UC 45 159 Her 24, 2012 2166 Meu 05, 2013 2034 - -\nLoveless UC 46 152 Mae 10, 2007 4160 Ebr 12, 2011 2727 - -\nHerculeBot UC 47 131 Gwe 04, 2008 3677 Goue 13, 2012 2269 - -\nSynthebot UC 48 126 Du 11, 2008 3609 Du 25, 2012 2134 - -\nCocuBot UC 49 126 Mez 05, 2011 2673 Kzu 15, 2012 2114 - -\nPipepBot UC 50 115 Eos 31, 2007 4047 Mez 09, 2008 3764 - -\nHer 2010 0.0% 0.7% 8.5% 30.6% 62.2% 84.2% 96.2% 98.7% 99.5% 100.0% 100.0% 100.0%\nGwe 2010 0.0% 0.7% 8.8% 32.1% 60.9% 83.6% 96.0% 98.6% 99.4% 100.0% 100.0% 100.0%\nEos 2010 0.0% 0.9% 9.1% 32.8% 60.3% 83.3% 95.9% 98.6% 99.5% 100.0% 100.0% 100.0%\nGoue 2010 0.0% 0.9% 9.2% 33.1% 62.2% 82.8% 95.7% 98.5% 99.4% 100.0% 100.0% 100.0%\nMez 2010 0.0% 0.9% 10.2% 36.8% 61.2% 83.4% 95.6% 98.6% 99.4% 100.0% 100.0% 100.0%\nMae 2010 0.0% 1.1% 11.7% 37.4% 60.4% 83.1% 95.0% 98.4% 99.3% 100.0% 100.0% 100.0%\nEbr 2010 0.0% 1.3% 14.3% 39.2% 59.2% 81.4% 95.9% 98.9% 99.5% 99.9% 99.9% 99.9%\nMeu 2010 0.0% 1.5% 17.1% 43.7% 60.8% 80.2% 95.3% 99.0% 99.7% 99.9% 99.9% 99.9%\nChwe 2010 0.0% 1.7% 19.9% 43.5% 57.0% 78.0% 94.7% 98.7% 99.6% 99.9% 99.9% 99.9%\nGen 2010 0.0% 2.0% 22.2% 40.1% 55.0% 77.2% 94.4% 99.0% 99.7% 100.0% 100.0% 100.0%\nKzu 2009 0.0% 2.2% 24.6% 36.0% 52.2% 77.6% 93.8% 98.9% 99.6% 100.0% 100.0% 100.0%\nDu 2009 0.0% 2.2% 24.8% 35.9% 52.6% 77.4% 93.7% 98.9% 99.6% 100.0% 100.0% 100.0%\nHer 2009 0.0% 2.3% 25.3% 36.2% 52.8% 77.0% 93.6% 98.9% 99.7% 100.0% 100.0% 100.0%\nGwe 2009 0.0% 2.2% 22.8% 34.5% 52.9% 74.0% 92.4% 98.7% 99.6% 100.0% 100.0% 100.0%\nEos 2009 0.0% 2.4% 24.1% 36.8% 54.3% 73.6% 92.5% 98.6% 99.5% 100.0% 100.0% 100.0%\nGoue 2009 0.0% 2.5% 25.2% 38.5% 55.7% 72.9% 92.1% 98.5% 99.5% 100.0% 100.0% 100.0%\nMez 2009 0.0% 12.3% 30.7% 41.3% 55.8% 69.8% 91.6% 98.3% 99.4% 100.0% 100.0% 100.0%\nMae 2009 0.6% 13.9% 32.9% 40.5% 52.5% 65.8% 90.5% 98.1% 99.4% 100.0% 100.0% 100.0%\nEbr 2009 3.4% 18.4% 38.1% 44.2% 52.4% 65.3% 89.8% 97.3% 99.3% 100.0% 100.0% 100.0%\nMeu 2009 0.7% 15.8% 36.7% 42.5% 50.4% 64.1% 90.7% 97.2% 99.4% 100.0% 100.0% 100.0%\nChwe 2009 0.7% 15.5% 35.5% 41.4% 48.8% 62.9% 90.3% 97.0% 99.2% 99.9% 99.9% 99.9%\nGen 2009 0.8% 16.0% 35.7% 41.8% 49.4% 62.3% 90.3% 97.1% 99.4% 100.0% 100.0% 100.0%\nKzu 2008 0.8% 15.3% 35.1% 41.2% 48.8% 61.8% 90.0% 96.9% 99.2% 100.0% 100.0% 100.0%\nDu 2008 2.9% 4.9% 18.6% 24.5% 35.3% 51.0% 87.3% 96.1% 99.0% 100.0% 100.0% 100.0%\nHer 2008 2.0% 9.1% 20.2% 26.3% 35.4% 50.6% 87.0% 96.1% 99.1% 100.0% 100.0% 100.0%\nGwe 2008 2.0% 9.1% 20.2% 26.3% 35.4% 50.6% 87.0% 96.1% 99.1% 100.0% 100.0% 100.0%\nEos 2008 2.1% 9.4% 19.8% 25.0% 34.4% 50.0% 86.5% 95.9% 99.0% 100.0% 100.0% 100.0%\nGoue 2008 2.1% 9.5% 20.0% 24.2% 33.7% 49.5% 87.4% 95.8% 99.0% 100.0% 100.0% 100.0%\nMez 2008 2.2% 8.7% 19.5% 23.8% 32.4% 48.5% 87.2% 95.8% 99.0% 100.0% 100.0% 100.0%\nMae 2008 1.1% 7.8% 17.8% 22.2% 30.0% 46.7% 86.7% 95.6% 98.9% 100.0% 100.0% 100.0%\nEbr 2008 1.1% 7.8% 17.8% 23.4% 31.2% 46.8% 86.8% 95.7% 99.0% 100.0% 100.0% 100.0%\nMeu 2008 1.1% 8.0% 17.2% 22.9% 30.9% 45.8% 87.2% 96.4% 98.7% 99.8% 99.8% 99.8%\nChwe 2008 1.1% 8.0% 17.2% 22.9% 30.9% 45.8% 87.2% 96.4% 98.7% 99.8% 99.8% 99.8%\nGen 2008 1.2% 9.4% 18.8% 23.5% 31.7% 47.0% 87.0% 96.4% 98.8% 100.0% 100.0% 100.0%\nKzu 2007 1.3% 9.0% 18.0% 23.1% 30.8% 47.5% 86.0% 96.3% 98.9% 100.0% 100.0% 100.0%\nDu 2007 9.8% 26.2% 34.4% 39.3% 47.5% 60.6% 88.5% 98.3% 99.9% 99.9% 99.9% 99.9%\nHer 2007 8.9% 25.0% 30.4% 35.8% 42.9% 57.2% 87.6% 98.3% 100.0% 100.0% 100.0% 100.0%\nGwe 2007 9.4% 26.4% 32.1% 34.0% 41.5% 56.6% 86.8% 98.1% 100.0% 100.0% 100.0% 100.0%\nEos 2007 9.6% 26.9% 32.7% 34.6% 42.3% 55.8% 88.5% 98.1% 100.0% 100.0% 100.0% 100.0%\nGoue 2007 9.6% 26.9% 32.7% 34.6% 42.3% 55.8% 88.5% 98.1% 100.0% 100.0% 100.0% 100.0%\nMez 2007 10.4% 27.1% 33.3% 35.4% 41.6% 56.2% 89.5% 97.8% 99.9% 99.9% 99.9% 99.9%\nMae 2007 10.6% 27.6% 34.0% 36.1% 42.5% 59.5% 93.5% 97.8% 99.9% 99.9% 99.9% 99.9%\nEbr 2007 10.6% 27.6% 34.0% 36.1% 42.5% 59.5% 93.5% 97.8% 99.9% 99.9% 99.9% 99.9%\nMeu 2007 10.6% 27.6% 34.0% 36.1% 42.5% 59.5% 93.5% 97.8% 99.9% 99.9% 99.9% 99.9%\nChwe 2007 13.0% 26.0% 32.5% 34.7% 41.2% 58.6% 93.4% 97.7% 99.9% 99.9% 99.9% 99.9%\nGen 2007 13.0% 26.0% 32.5% 34.7% 41.2% 58.6% 93.4% 97.7% 99.9% 99.9% 99.9% 99.9%\nKzu 2006 13.0% 26.0% 32.5% 34.7% 41.2% 60.8% 93.4% 97.7% 99.9% 99.9% 99.9% 99.9%\nDu 2006 11.4% 25.0% 31.8% 34.1% 40.9% 59.1% 93.2% 97.7% 100.0% 100.0% 100.0% 100.0%\nHer 2006 11.6% 25.6% 32.6% 34.9% 41.9% 60.5% 95.4% 97.7% 100.0% 100.0% 100.0% 100.0%\nGwe 2006 16.1% 35.5% 48.4% 54.9% 58.1% 74.2% 93.6% 100.0% 100.0% 100.0% 100.0% 100.0%\nEos 2006 23.1% 46.2% 57.7% 65.4% 69.2% 84.6% 92.3% 100.0% 100.0% 100.0% 100.0% 100.0%\nGoue 2006 0.0% 7.7% 30.8% 38.5% 46.2% 77.0% 84.7% 100.0% 100.0% 100.0% 100.0% 100.0%\nMez 2006 0.0% 10.0% 40.0% 50.0% 60.0% 80.0% 80.0% 100.0% 100.0% 100.0% 100.0% 100.0%\nMae 2006 0.0% 20.0% 40.0% 50.0% 60.0% 80.0% 80.0% 100.0% 100.0% 100.0% 100.0% 100.0%\nEbr 2006 11.1% 33.3% 44.4% 55.5% 55.5% 77.7% 77.7% 99.9% 99.9% 99.9% 99.9% 99.9%\nMeu 2006 11.1% 44.4% 55.5% 66.6% 66.6% 77.7% 77.7% 99.9% 99.9% 99.9% 99.9% 99.9%\nChwe 2006 12.5% 25.0% 50.0% 62.5% 62.5% 75.0% 75.0% 100.0% 100.0% 100.0% 100.0% 100.0%\nGen 2006 14.3% 28.6% 57.2% 71.5% 71.5% 85.8% 85.8% 100.0% 100.0% 100.0% 100.0% 100.0%\nKzu 2005 16.7% 16.7% 50.0% 66.7% 66.7% 83.4% 83.4% 100.0% 100.0% 100.0% 100.0% 100.0%\nDu 2005 16.7% 16.7% 50.0% 66.7% 66.7% 83.4% 83.4% 100.0% 100.0% 100.0% 100.0% 100.0%\nHer 2005 14.3% 14.3% 42.9% 57.2% 57.2% 85.8% 85.8% 100.0% 100.0% 100.0% 100.0% 100.0%\nGwe 2005 14.3% 14.3% 42.9% 57.2% 57.2% 85.8% 85.8% 100.0% 100.0% 100.0% 100.0% 100.0%\nEos 2005 20.0% 20.0% 60.0% 80.0% 80.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%\nGoue 2005 25.0% 25.0% 50.0% 75.0% 75.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%\nMez 2005 25.0% 25.0% 50.0% 75.0% 75.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%\nMae 2005 25.0% 25.0% 25.0% 75.0% 75.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%\nEbr 2005 33.3% 33.3% 66.6% 66.6% 66.6% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9%\nMeu 2005 50.0% 50.0% 50.0% 50.0% 50.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%\nChwe 2005 50.0% 50.0% 50.0% 50.0% 50.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%\nGen 2005 50.0% 50.0% 50.0% 50.0% 50.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%\nGwe 2018 1,9 K 889 40 28 328 16 340\nEos 2018 1,9 K 887 40 28 327 16 339\nGoue 2018 1,9 K 886 40 28 327 16 339\nMez 2018 1,9 K 886 40 28 327 16 339\nMae 2018 1,9 K 885 40 28 327 16 339\nEbr 2018 1,9 K 884 40 28 327 16 339\nMeu 2018 1,9 K 883 40 28 327 16 336\nChwe 2018 1,9 K 883 40 28 327 16 335\nGen 2018 1,9 K 881 40 28 327 16 335\nKzu 2017 1,9 K 881 40 28 327 16 335\nDu 2017 1,9 K 878 40 28 327 16 333\nHer 2017 1,9 K 878 40 28 327 16 333\nGwe 2017 1,9 K 876 40 28 327 16 333\nEos 2017 1,9 K 875 40 28 327 16 333\nGoue 2017 1,9 K 874 40 28 327 16 333\nMez 2017 1,9 K 874 40 28 327 16 312\nMae 2017 1,9 K 874 40 28 327 16 249\nEbr 2017 1,9 K 873 40 28 327 16 249\nMeu 2017 1,9 K 873 40 28 327 16 249\nChwe 2017 1,9 K 873 40 28 327 16 249\nGen 2017 1,9 K 871 40 28 327 16 249\nKzu 2016 1,9 K 871 40 28 327 16 249\nDu 2016 1,9 K 871 40 28 327 16 249\nHer 2016 1,9 K 870 40 28 327 16 248\nGwe 2016 1,9 K 870 40 28 327 16 248\nEos 2016 1,9 K 869 40 28 327 16 248\nGoue 2016 1,9 K 866 40 28 326 16 247\nMez 2016 1,9 K 865 40 28 326 16 242\nMae 2016 1,9 K 865 40 28 326 16 242\nEbr 2016 1,9 K 863 40 28 326 16 242\nMeu 2016 1,9 K 860 40 28 326 16 240\nChwe 2016 1,9 K 860 40 28 325 16 231\nGen 2016 1,9 K 860 40 28 325 16 231\nKzu 2015 1,9 K 860 40 28 325 16 231\nDu 2015 1,8 K 856 40 28 324 16 231\nHer 2015 1,8 K 855 40 28 324 16 230\nGwe 2015 1,8 K 855 40 27 317 16 229\nEos 2015 1,8 K 854 40 27 317 16 229\nGoue 2015 1,8 K 854 40 27 316 16 229\nMez 2015 1,8 K 852 40 27 316 16 229\nMae 2015 1,8 K 851 40 27 316 16 229\nEbr 2015 1,8 K 851 40 27 316 16 229\nMeu 2015 1,8 K 850 40 27 316 16 229\nChwe 2015 1,8 K 848 40 27 316 16 229\nGen 2015 1,8 K 842 40 27 316 16 229\nKzu 2014 1,8 K 840 40 27 316 16 229\nDu 2014 1,8 K 839 40 27 316 16 229\nHer 2014 1,8 K 836 40 27 316 16 229\nGwe 2014 1,8 K 835 40 27 316 16 229\nEos 2014 1,8 K 831 40 27 316 16 229\nGoue 2014 1,8 K 823 40 27 316 16 229\nMez 2014 1,8 K 822 40 27 316 16 229\nMae 2014 1,8 K 822 40 27 316 16 229\nEbr 2014 1,8 K 814 40 27 316 16 229\nMeu 2014 1,8 K 811 40 27 316 16 229\nChwe 2014 1,8 K 805 40 27 316 16 229 8\nGen 2014 1,8 K 802 40 27 316 16 229 13\nKzu 2013 1,8 K 799 40 27 316 16 229 4\nDu 2013 1,8 K 798 40 27 316 16 229 12\nHer 2013 1,8 K 794 39 27 316 16 229 25\nGwe 2013 1,8 K 790 39 27 316 16 229 5\nEos 2013 1,8 K 775 39 27 316 16 229 12\nGoue 2013 1,8 K 773 39 27 316 16 229 8\nMez 2013 1,8 K 769 39 27 316 16 229 27\nMae 2013 1,8 K 766 39 27 313 16 229 21\nEbr 2013 1,8 K 761 39 27 313 16 229 35\nMeu 2013 1,8 K 737 39 27 313 16 229 9\nChwe 2013 1,8 K 658 39 27 313 16 229 8\nGen 2013 1,8 K 649 39 27 313 16 229 31\nKzu 2012 1,8 K 646 39 27 312 16 229 44\nDu 2012 1,8 K 638 39 27 312 16 229 24\nHer 2012 1,8 K 636 39 27 312 16 229 9\nGwe 2012 1,8 K 629 39 27 312 16 228 15\nEos 2012 1,8 K 624 39 27 312 16 228 17\nGoue 2012 1,8 K 619 39 27 311 16 228 10\nMez 2012 1,8 K 611 39 27 309 16 228 20\nMae 2012 1,8 K 601 39 27 309 16 228 50\nEbr 2012 1,8 K 592 39 27 309 16 228 14\nMeu 2012 1,8 K 584 39 27 308 16 228 212\nChwe 2012 1,7 K 580 39 27 308 16 219 71\nGen 2012 1,7 K 572 39 27 308 16 216 89\nKzu 2011 1,7 K 562 39 27 303 16 212 109\nDu 2011 1,6 K 559 38 27 303 16 209 109\nHer 2011 1,6 K 545 38 27 303 16 203 153\nGwe 2011 1,5 K 539 38 27 303 16 203 69\nEos 2011 1,5 K 529 38 27 303 16 200 53\nGoue 2011 1,4 K 520 38 27 303 16 199 151\nMez 2011 1,4 K 508 38 27 303 16 195 70\nMae 2011 1,4 K 502 38 27 283 16 191 107\nEbr 2011 1,3 K 493 38 27 283 16 189 153\nMeu 2011 1,3 K 484 38 27 281 16 182 199\nChwe 2011 1,2 K 481 38 27 277 16 167 14\nGen 2011 1,2 K 468 38 27 277 16 167 42\nKzu 2010 1,2 K 464 38 27 277 16 167 319\nDu 2010 921 457 38 27 274 16 158 114\nHer 2010 888 454 38 26 273 16 158 118\nGwe 2010 848 444 38 25 272 16 158 96\nEos 2010 826 437 38 25 270 16 155 331\nGoue 2010 789 414 38 25 251 16 149 463\nMez 2010 742 412 38 24 216 16 135 426\nMae 2010 673 395 37 23 215 16 128 336\nEbr 2010 569 391 37 23 211 16 121 225\nMeu 2010 503 382 36 23 167 16 116 142\nChwe 2010 446 371 36 21 160 16 111 106\nKzu 2009 365 349 34 12 134 16 103 32\nDu 2009 363 344 30 11 132 16 100 21\nHer 2009 358 337 30 11 132 16 99 116\nGwe 2009 299 330 24 11 115 16 96 38\nEos 2009 284 318 23 10 106 15 95 28\nGoue 2009 276 315 20 10 104 15 93 71\nMez 2009 249 305 20 10 100 9 89 65\nMae 2009 226 291 16 8 89 6 85 78\nEbr 2009 212 285 14 8 83 1 82 38\nMeu 2009 201 275 14 6 82 82 12\nChwe 2009 196 263 14 6 82 82 8\nKzu 2008 191 241 14 6 80 82 115\nDu 2008 119 232 14 6 78 60 107\nHer 2008 118 210 14 6 77 42 2\nGwe 2008 118 191 14 6 75 41 6\nEos 2008 115 176 14 6 75 41 7\nGoue 2008 115 165 13 6 68 37 2\nMez 2008 113 156 13 6 68 37 15\nMae 2008 109 143 13 6 67 36 25\nEbr 2008 109 139 12 6 67 35 49\nMeu 2008 103 132 11 6 66 34 13\nChwe 2008 102 126 11 6 64 34 4\nKzu 2007 94 117 9 6 61 31 103\nDu 2007 78 113 9 6 58 27 22\nHer 2007 72 108 9 6 41 2 110\nGwe 2007 69 106 8 6 41 2 9\nEos 2007 69 101 8 6 36 2 5\nGoue 2007 69 92 8 6 36 2 13\nMez 2007 64 86 6 6 35 2 17\nMae 2007 63 79 6 6 31 2 16\nEbr 2007 63 75 6 6 31 2 10\nMeu 2007 63 67 6 6 27 2 16\nChwe 2007 59 60 6 6 27 2\nKzu 2006 59 50 6 6 27 2 15\nDu 2006 53 47 6 6 27 2 8\nHer 2006 52 45 6 6 27 2 42\nGwe 2006 37 44 6 6 26 2 24\nEos 2006 29 44 6 6 26 2 23\nGoue 2006 17 40 6 6 15 1 31\nMez 2006 14 40 6 6 11 1 5\nMae 2006 13 40 6 6 10\nEbr 2006 12 38 6 6 5\nMeu 2006 12 36 6 6 5\nChwe 2006 10 34 5 6 4\nKzu 2005 9 29 5 6 4\nDu 2005 7 27 5 6 4\nHer 2005 7 22 5 6 1\nGwe 2005 7 19 5 6 1\nEos 2005 5 18 4 6 1\nGoue 2005 4 16 4 6\nMez 2005 4 16 4 6\nMae 2005 4 12 4 6\nEbr 2005 4 11 4 6\nMeu 2005 2 8 4 6\nChwe 2005 2 7 3 6\nKzu 2004 1 7 3 6\nDu 2004 1 4 3 6\nHer 2004 1 4 3 6\nGwe 2004 1 4 3 6\nEos 2004 1 3 3 6\nGoue 2004 1 2 3 6\nMez 2004 1 2 3 6\nMae 2004 1 2 2 5\nEbr 2004 1 2 2\nMeu 2004 1 2 2\nChwe 2004 1 2 2\nMez 2004: 1 1 Saqqaa\nMae 2005: 1 1 Skizofrenii\nGoue 2005: 1 1 Saqqaa\nEos 2005: 1 2 Nattoraliusaq\nGwe 2005: 1 1 Ussuk\nHer 2005: 1 2 Ammasunik imalik\nDu 2005: 1 1 Saqqaa\nMeu 2006: 1 2 Saqqaa , 2 1 Saqqaanut\nEbr 2006: 1 1 København\nMae 2006: 1 1 Oqaatsit\nMez 2006: 1 1 Matematikki\nGoue 2006: 1 1 Poleni\nEos 2006: 1 1 Ukraine\nGwe 2006: 1 2 Danmarki , 2 1 Maniitsoq\nHer 2006: 1 2 Illoqqortoormiut , 2 2 Kangaatsiaq , 3 2 Teletårni Nuuk , 4 2 Sisimiut , 5 1 Illoqortoormuit\nDu 2006: 1 1 Skizofreni\nKzu 2006: 1 2 Ukraine , 2 1 Uumannaq\nMeu 2007: 1 3 Sisimiut , 2 2 Niisarnaq , 3 2 Kigutilissuaq\nEbr 2007: 1 3 Aasiaat\nMae 2007: 1 3 Jens Fink-Jensen\nMez 2007: 1 1 Schweizi\nGoue 2007: 1 2 Jesusip Ilumiortup Ilagiissai , 2 1 Taparneq\nEos 2007: 1 2 Tjekkia\nGwe 2007: 1 2 Praha\nHer 2007: 1 4 Kimmernaq Kjeldsen , 2 4 Jesus Kristus , 3 4 Nanortalik , 4 4 Kangaatsiaq , 5 4 Sisimiut , 6 4 Nuuk , 7 4 Ammasunik imalik , 8 2 Katuaq , 9 2 Praha , 10 1 Karlstad\nDu 2007: 1 2 Europa , 2 2 Wikipedia , 3 1 Ammassalik\nKzu 2007: 1 3 Almazán , 2 2 Karlstad , 3 2 Biibili , 4 2 Svenskit Nunaat , 5 2 Europa , 6 1 Canada\nChwe 2008: 1 1 Italia\nMeu 2008: 1 1 Qalasersuaq Kujalleq\nEbr 2008: 1 2 Belgien , 2 2 Poleni , 3 1 Teletårnet i Nuuk\nMae 2008: 1 1 Naalagaaffeqatigiit\nMez 2008: 1 1 Siissisoq\nGoue 2008: 1 1 Ágreda\nEos 2008: 1 2 Praha , 2 1 Ágreda\nGwe 2008: 1 2 Nepal , 2 1 Frankrig\nHer 2008: 1 1 Jesusip Ilumiortup Ilagiissai\nDu 2008: 1 2 Kalaallit Nunaat , 2 1 Kaniina\nKzu 2008: 1 2 Ikerasaarsuk , 2 2 Amerika Kujalleq , 3 2 Phoca harbata , 4 1 Uruguay\nChwe 2009: 1 1 Australien\nMeu 2009: 1 1 Jiisusi-Kristus\nEbr 2009: 1 3 Danmarki , 2 2 Politikki , 3 2 Timersorneq\nMae 2009: 1 3 Céline Dion , 2 2 Nuna eqqissisimatitaq , 3 2 Shania Twain , 4 2 Tina Turner , 5 2 Kommuneqarfik Sermersooq , 6 1 Encyklopædi\nMez 2009: 1 2 Ilusilersugaaneq , 2 2 Kuupik Kleist , 3 2 Naalakkersuisut siulittaasuat , 4 2 Hans Enoksen , 5 1 Qaammat\nGoue 2009: 1 2 Slovakimiusut , 2 2 Nunaateqarneq , 3 2 Kalaallisut , 4 1 Økologi\nEos 2009: 1 2 Katuaq , 2 1 Islam\nGwe 2009: 1 2 London , 2 2 Peqqinneq , 3 2 ABBA , 4 2 Roxette , 5 1 Spania\nHer 2009: 1 2 Qarsaaq , 2 1 Kissaviarsuk\nDu 2009: 1 2 Islandi , 2 1 Advent\nKzu 2009: 1 5 Korea Avannarleq , 2 1 2010\nChwe 2010: 1 1 Juuli\nMeu 2010: 1 2 Aarhus , 2 2 Mölnlycke , 3 2 Roskilde , 4 2 Sønderborg , 5 2 London , 6 1 Aalborg\nEbr 2010: 1 2 Kópavogur , 2 2 Eyjafjallajökull (innermik anitsisartoq) , 3 2 Akranes , 4 2 Klaksvík , 5 2 Eiði , 6 2 Keflavík , 7 2 Riga , 8 2 Reykjavík , 9 2 Jylland , 10 1 Tasiusaq (Upernavik)\nMae 2010: 1 3 Paris , 2 3 Air Greenland , 3 3 Frankrig , 4 3 Kalaallit Nunaat , 5 2 Halden , 6 2 Gray (Franche-Comté) , 7 2 Clermont-Ferrand , 8 2 Brest , 9 2 Aix-en-Provence , 10 2 Nîmes , 11 2 Le Mans , 12 2 Angers , 13 2 Dijon , 14 2 Grenoble , 15 2 Toulon , 16 2 Saint-Étienne , 17 2 Le Havre , 18 2 Lille , 19 2 Strasbourg , 20 1 Mo i Rana\nMez 2010: 1 3 Gray (Franche-Comté) , 2 3 Frankrig , 3 2 Inooqatigiinnik ilisimatusarneq , 4 2 Ittarnisarsiorneq , 5 2 Tallinn , 6 2 Sofia , 7 2 Steinkjer , 8 2 Innallangialerineq , 9 2 Svalbard , 10 2 Ilusilersuineq , 11 2 Kongsberg , 12 2 Bodø , 13 2 Ålesund , 14 2 Saint-Denis (Reunion) , 15 2 Clermont-Ferrand , 16 2 Brest , 17 2 Aix-en-Provence , 18 2 Nîmes , 19 2 Le Mans , 20 2 Angers , 21 1 Inuunerup isumalerineq\nGoue 2010: 1 2 Juuni , 2 2 Barack Obama , 3 2 Arfivik , 4 1 Borås\nEos 2010: 1 2 Émile Friant , 2 2 Drake , 3 2 Östersund , 4 2 Kalmar , 5 2 Albert Küchler , 6 2 Karlskrona , 7 1 Svalbardi\nGwe 2010: 1 3 Georges de la Tour , 2 2 Kungälv , 3 2 Landskrona , 4 1 Piteå\nHer 2010: 1 3 Puisi , 2 2 Nationalparki Sirmilik , 3 2 Pori , 4 2 Kouvola , 5 2 Kuopio , 6 2 Marstrand , 7 2 Falköping , 8 2 Värnamo , 9 1 Mariehamn\nDu 2010: 1 2 Québec , 2 2 Newfoundland aamma Labrador , 3 2 Canadami nunatat killeqarfiillu , 4 1 Prince Edward Island\nKzu 2010: 1 2 St. John's (Newfoundland aamma Labrador) , 2 2 Saskatchewan , 3 2 Alberta , 4 2 Oktoberi 18 , 5 1 Frankrigi\nChwe 2011: 1 1 Qeqertarsuup tunua\nMeu 2011: 1 3 Finlandimiutut , 2 3 Ruslandi , 3 2 Estlandimiutut , 4 2 Humaniora , 5 2 Zaporizhzhia , 6 2 Kharkiv , 7 2 Kazan , 8 2 Inuunerup isumalerineq , 9 2 Inuup ineriartorneranik ilisimatusarneq , 10 2 Silarsuarmi nunat , 11 2 Filosofi , 12 2 Kosovo , 13 2 Konfusiusi\nEbr 2011: 1 3 Spanskisut , 2 2 León (Guanajuato) , 3 2 Guadalajara (Jalisco) , 4 2 Puebla (Puebla) , 5 2 Mexico City , 6 2 Grækerit Nunaat , 7 1 Mongoliamiutut\nMae 2011: 1 2 Hebraimiutut , 2 2 Bethlehem , 3 2 Eurovision Song Contest , 4 2 Japanimiusut , 5 1 Coyhaique\nMez 2011: 1 2 Xi'an , 2 2 Kineserisut (oqaatsit) , 3 1 Thailandi\nGoue 2011: 1 2 Vinh , 2 2 Koreamiusut , 3 1 Vietnamimiusut\nEos 2011: 1 2 Malgrat de Mar , 2 1 Indonesiamiutut\nHer 2011: 1 2 Paltoga , 2 2 Danmarki\nDu 2011: 1 2 Kisumu , 2 2 Bangladesh , 3 1 Farsimiutut\nKzu 2011: 1 2 Tabriz , 2 2 Korea Avannaa , 3 2 Rogier van der Weyden , 4 1 San Pedro de la Paz\nChwe 2012: 1 2 Adana , 2 1 Zarqa\nMeu 2012: 1 2 Bayda , 2 2 Tripoli , 3 2 Paris , 4 1 Jericho\nEbr 2012: 1 1 Sverige\nMae 2012: 1 3 Libya , 2 2 Lebanon , 3 2 Lira (Uganda) , 4 2 Paul Gauguin , 5 2 Kisitsisinik ilinniarneq , 6 2 Trongisvágur , 7 2 Franskit Nunaat , 8 2 Politikki , 9 1 Lionel Messi\nMez 2012: 1 2 Wikipedia , 2 1 Rosamaria Murtinho\nGoue 2012: 1 2 Talcahuano , 2 2 Tyrkia , 3 1 Petah Tikva\nEos 2012: 1 2 Inuttut pisinnaatitaafiit pillugit silarsuarmioqatigiinut nalunaarut , 2 2 Air Greenland , 3 2 Hans Enoksen , 4 1 Copiapó\nGwe 2012: 1 1 Wien\nHer 2012: 1 1 Riyadh\nDu 2012: 1 1 Cascadia\nKzu 2012: 1 2 Slovenia , 2 1 Nina Kreutzmann Jørgensen\nChwe 2013: 1 1 Ittoqqortoormiit\nEbr 2013: 1 1 Bangladesh\nMae 2013: 1 2 Michelangelo , 2 1 Murilo Benício\nMez 2013: 1 1 Mittarfik Carriel Sur\nGoue 2013: 1 2 Zac Efron , 2 1 Paramore\nEos 2013: 1 2 Aappilattoq (Upernavik) , 2 1 Nacka\nGwe 2013: 1 1 Mombasa\nHer 2013: 1 2 Nuuk , 2 1 Bukan\nDu 2013: 1 2 Aleppo , 2 2 Kraków , 3 1 Kárášjohka\nKzu 2013: 1 1 Francisco Goya\nChwe 2014: 1 1 Świnoujście\nMeu 2014: 1 1 Dmitry Samodranov\nMez 2014: 1 1 Indonesiamiutut\nGoue 2014: 1 1 Safed\nEos 2014: 1 2 Qatar , 2 1 Caracas\nGwe 2014: 1 1 Siumup Inuusuttunut Suleqatigiiffia\nHer 2014: 1 3 Aasaanerani nalunaaqqutaq , 2 1 Benedicta Boccoli\nKzu 2014: 1 1 Barisal\nChwe 2015: 1 1 Peeqin\nMeu 2015: 1 1 Radio Studio 54 Network\nEbr 2015: 1 2 Aapakaaq , 2 1 Sabrina Ferilli\nMez 2015: 1 1 Reconquista\nGoue 2015: 1 1 Benjamin Netanyahu\nGwe 2015: 1 2 Savalimmiut , 2 1 2015\nHer 2015: 1 2 Ilulissat , 2 1 Drake\nDu 2015: 1 2 KNI A/S , 2 1 Duel of the Fates\nKzu 2015: 1 1 Kádár\nChwe 2016: 1 1 Flóra Kádár\nMeu 2016: 1 1 UNS\nEbr 2016: 1 3 Palæstinensiske Selvstyreområder , 2 2 Palæstina , 3 2 Darwin , 4 2 Asia , 5 1 Salvelinus alpinus\nMae 2016: 1 2 Danmarki , 2 1 Dnipropetrovsk\nMez 2016: 1 1 Madrid\nGoue 2016: 1 1 Milano\nEos 2016: 1 1 Obama\nHer 2016: 1 1 Roubaix\nDu 2016: 1 1 Zazakisut\nKzu 2016: 1 1 Vulkan\nChwe 2017: 1 1 Milano\nMeu 2017: 1 1 Palæstinensiske Selvstyreområder\nEbr 2017: 1 2 Mexico City , 2 2 Mexico\nMae 2017: 1 1 Bukan\nMez 2017: 1 1 Klaipėda universitetia\nEos 2017: 1 2 Fjalla-Eyvindur\nGwe 2017: 1 1 Flóra Kádár\nHer 2017: 1 6 Kalaallit Nunaat , 2 1 Papua New Guinea\nDu 2017: 1 2 Kathmandu , 2 1 Bien Hoa\nKzu 2017: 1 1 Nunatsinni naasut aqqi kalaallisut\nChwe 2018: 1 1 Buon Ma Thuot\nMeu 2018: 1 1 Tulugaq\nEbr 2018: 1 2 Nerlernaarsuk , 2 2 Nerlernarnaq , 3 2 Nerlernaq , 4 2 Nunatsinni timmissat aqqi kalaallisut , 5 1 Somateria mollissima\nMae 2018: 1 1 Arctostaphylos alpina\nMez 2018: 1 2 Hebraimiutut , 2 1 Benghazi\nEos 2018: 1 1 Joynagar\nGwe 2018: 1 1 Nunat Avannarliit Siunnersuisooqatigiit\nDump file klwiki-20181001-stub-meta-history.xml.gz (edits only), size 4.4 Mb as gz -> 29 Mb\nDump processed till Sep 30, 2018, on server stat1005, ready at Tue-02/10/2018-21:26 after 20 sec.


France 3 Bretagne - Bali Breizh - Sul 11 a viz Genver 2015 - Astrakan Project\nBali Breizh - Sul 11 a viz Genver 2015 - Astrakan Project\nE kompagnunezh ar ganerez Simone Alves e vo lidet abadenn gentañ ar bloaz gant skipailh Bali Breizh. Kanañ a ra div ganaouenn hengounel war-eeun gant Yann Gourvil, soner : “Ar c’hased arc’hant” ha “Sekrejoù ar c’hloareg”.\nPar Yowan Denis Publié le 11/01/2015 à 11:00\nBali Breizh - 11.01.2015 - VO\nPedet : Simone Alves. E kompagnunezh ar ganerez Simone Alves e vo lidet abadenn gentañ ar bloaz gant skipailh Bali Breizh. Eus Portugal eo Simone, bet savet e Strasburg hag eno deus desket galleg. Stummet eo bet war ar c’han gant Erik Marchand ha rummad kentañ Breizh Akademi. Meur a vloavezh he deus bevet e Turkia, bremañ e vez etre Breizh ha Gresia. Kanañ a ra div ganaouenn hengounel war-eeun gant Yann Gourvil, soner : “Ar c’hased arc’hant” ha “Sekrejoù ar c’hloareg”.\nHeuliadoù : “A-viskoazh” Hunvreoù ha c’hoantoù kuzh a zo sanket don pep hini ac’hanomp. Ha ma teufe an hunvreoù kaer-se da wir ? Un toullad tud yaouank a zo bet betek penn o c’hoant a drugarez da A-viskoazh. Hiziv : « Yann Ewen Laridon » E hunvre ? Seniñ gant Ibrahim Malouf war al leurenn. Seveniñ : Yann-Herle Gourves.\n“Henri Rannou, martolod e boutailh”. Abaoe 40 vloaz emañ Henri Rannou o sevel bagoù da lakaat en boutailh. Reiñ a ra korf da istorioù a vor o deus bet skoet e spered. Istorioù ha darvoudoù bet bevet gant ar voraerien. Hiziv : “Ur mous a zo marv”. Seveniñ : Caroline Rubens, Ronan Hirrien – kenbroduet gant Vivement Lundi ha France3 Breizh.\nBali Breizh - 11.01.2015 - VOST\nL’invitée : Simone Alves. C’est en compagnie de Simone Alves que l’équipe de Bali Breizh démarre la nouvelle année. Cette jeune Portugaise, élevée à Strasbourg a fait partie de la première promotion de la Breizh Akademi. Après quelques années en Turquie, elle vie actuellement entre la Bretagne et la Grèce. Elle adore les « gwerz » et interprète en direct avec Yann Gourvil, musicien, deux chants traditionnels : « Ar c’hased arc’hant » et « Sekrejoù ar c’hloareg ».\nSéries : « A-viskoazh » (Depuis toujours). Chacun d’entre nous renferme au fond de lui une envie folle, un rêve impossible. Et si ce rêve inaccessible devenait réalité ? C’est le challenge que propose A-viskoazh à de jeunes brittophones. Aujourd’hui : « Yann Ewen Laridon » Son rêve ? Jouer de la musique avec Ibrahim Malouf. Réalisation : Yann-Herle Gourves.\n« Henri Rannou, martolod e boutailh » (Henri Rannou, navigateur en bouteille). Passionné par les histoires maritimes, Henri Rannou construit et mets des bateaux en bouteille depuis plus de 40 ans. Conteur né, il raconte avec verve les aventures de bateaux et de marins. Aujourd’hui : « Ur mous a zo marv » (Un mousse est mort). Réalisation : Caroline Rubens, Ronan Hirrien – Coproduction : Vivement Lundi/France3 Bretagne.\nAn deiziataer : L’agenda de Thelo.


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Author anyAbdallah, TAbdel-Kader, M HAbdelhafiz, A AAbdellah, A MAbdoon, A SAdbel-Ghani, Nour TAdibi, AAfifi, M MAgarwal, AAhmad, TS Ahmad, Samera Ahmadi, Temer SAhmed, S MAioub, MAioub, Mena SAkhtari, K. Al-Abd, A MAl-Ashqar, Eal.Alabbady, A. Alamgir, Faisal MAlamgir, FAlamgir, F MAli, M.R. Ali, M R KAli, HR Ali, Moustafa RK Alizadeh, R.H. Alkilany, A MAllam, N KAllam, Nageh Allam, Nageh KAlQaradawi, S YAmbalavanan, Namasivayam Amen, K.M. Amer, A WAneja, R. Asahi, TAsiri, Abdullah MAttar, F. Austin, Lauren Austin, L AAustin, Lauren AAwad, Elias SAziz, F.M. Barry, B ABeeby, ABellamkonda, R VBiesso, ABiswas, Sushmita Blinn, KBlinn, Kevin SBoone, EBordley, Justin ABordley, JA Bottomley, Lawrence ABottomley, L ABoussac, ABräuchle, Christoph Braun, Markus Browner, Richard Bryant, G WBryant, JBunz, UBunz, U H FBurda, Clemens Burda, C. Burgin, JBurke, SCai, YCai, LCapel, Malcom Castro, DChakravarty, PChamanzar, MChang, HChávez, Jorge LChen, XChen, Dongchang Chen, JChen, DChen, K. Chen, PChen, KChen, P CChen, Georgia Cheng, SChu, L KChu, H HCohen, Marvin LCollard, D MCombs, Z ACombs, Zachary ACusanovich, M ADahmen, KDansby, K. Darugar, QDefo, B A TDel Fatti, NDevanathan, SDickerson, E BDing, YDong, BDong, JDonlan, R MDoshi, PDreaden, Erik CDreaden, Erik CDreaden, E CDrummy, Lawrence FDuan, J SEbong, AEdmonds, PEftekhar, A A ;Eisa, W HEl-Borady, Ola MEl-Nadi, L MEl-Sayed, M.A. El-Sayed, M A ;El-Sayed, MEL-Sayed, Mostafa AEl-Sayed, I. El-Sayed, I HEl-Sayed, Mostafa AEl-Sayed, M AEl-Sayed, I HEl-Sayed, Mostafa El-Shabrawy, O AEl-Shaer, M AEl-Shenawy, S MEl‐Sayed, M AEl‐Sayed, Mostafa AEremenko, AEustis, Susie Fagerquist, Clifton KFaiad, A AFalahati, M. Fang, N. Fang, NFang, Y CFarag, N MFarghali, H.A. Fathi, Shaghayegh FitzGerald, SFreitas, John EFujii, Tsuneo Fujii, TFurube, AGaddis, C SGan, D JGanzoury, M AGao, J PGao, R PGarczarek, FGarlapati, C. Garlyyev, BGarlyyev, Batyr Garmestani, HGeldmeier, J AGeldmeier, Jeffrey AGeng, JGerwert, KGetmanenko, Yulia AGhuman, K KGoodson, TGoodson III, TGreen, T CGriffiths, Jennifer AGriﬂiths, Jennifer Grobmyer, SR Gryder, B EGupta, Maneesh KHafez, AHamedani, H AHan, THasan, A. Hathcock, D JHayden, S CHayden, SHe, MHenglein, AHesabi, Z RHeyes, C DHira, Steven MHira, S MHoch, L BHolland, NHong, X YHong, X MHooshmand, Nasrin Hooshmand, NHosseinali, S.H. Hsu, H YHuang, X HHuang, Wenyu YHuang, XHuang, Xiaohua HHughes, Robert AHumphries, William HHumstoe, J OHun, MHunter, R EHwang, Hyun Jin Hwangl, Hyun Jin Ismail, YIsmail, K ZIsmail, Yehea Jain, Prashant KJames, Masheika Jang, S SJang, HJeon, JW Jeon, J -WJilling, Tamas Jradi, Fadi MJung, Kwang-Woo Kageshima, Y. Kamalov, Valey FKang, Bing Kang, KKang, Xiongwu Kang, Bin Kang, BKang, S. Kelley‐Loughnane, Nancy Kerszulis, Justin Khalifa, A MKherani, N PKhoury, J TKieffer, M JKim, H TKing, John Kodiyath, Rajesh Koenig, Tobias Kondo, H. Konig, Tobias A FKönig, Tobias Konig, Tobias König, Tobias AF Kostova, I. König, Tobias Kramer, RKrylova, GLandes, Christy Landes, Christy FLandman, ULangot, PLanyi, Janos KLaurent, S. Ledin, Petr ALedin, P ALee, Jung-Pil Lee, S MLee, K SLi, YLi, X NLi, Xiaxi Li, X XLink, Stephan Lisunova, MLittle, Reginald BLiu, M LLiu, MLiu, M FLiu, S YLiu, CLiu, Meilin Liu, A. Logunov, Stephan LLoh, J YLopez-Sanchez, J.A. Lyon, L AMacheret, FMackey, Megan AMackey, M AMackey, Megan Mahmoud, S SMahmoud, Mahmoud AMahmoud, M.A. Mahmoud, MMahmoud, Mahmoud. 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Panikkanvalappil, S R ;Panikkanvalappil, S RPanikkanvalappil, Sajanlal RPark, Soojin Parker, Timothy CPeek, Mary EPeng, Xianghong HPetek, HPetroski, J MPhillips, R LPi, MPiede, J APileni, Marie-Paule Poncheri, A JPonder, J FPrausnitz, M RPreston, John SPriecel, P. Pushpanketh, SQian, WQian, Wei Qu, C. Quarles, L DQuigley, ERahman, MA Raji, Idris OReynolds, K BReynolds, J RReynolds, John RRohatgi, ARommel, Kathryn RRoselli, Cecile Rotello, V MRubio, Angel Russell, Michael Saboury, A.A. Saha, KSaira, FSalah, NSalem, H GSalihi, A. Sandhage, KSanii, L SSAXTON, R ESaxton, RSchaaff, T GSchatz, GC Schill, A WSchmidt-Krey, ISelim, S.A. Sensharma, Dilip KSetiawan, Rudi Agus Setiawan, RSetiawan, R ASeung-Soon, Jang Shabaka, A AShaheen, B SSharifi, M. Shekha, M.S. Shevchenko, Valery Shin, Dong MSilva, C. Singh, C VSmirnova, NSmith, M.J. Snyder, BSodji, Q HSong, Li Song, J. Soule, L. Sulchek, T.A. Svirko, Y PSzymanski, Paul Szymanski, PTabor, C ETabor, CTaghizadeh, A. Takami, STalaat, M HTanaka, ITanaka, NTang, YTasumi, Mitsuo Teshima, K. Thackston, DTkachenko, Ihor MTollin, GTsukruk, V VTsukruk, Vladimir VTsukruk, V.V. Uesugi, YUesugi, Yohei Vaia, Richard AVallee, FValverde-Chávez, D.A. Van Haute, DVarnavski, O PVolkov, V VWaller, G.H. Wang, J.H. Wang, FWang, Z LWang, Jianping Wang, Yuxiang Wang, Yiqing Weng, GWhetten, R LWu, Y. Wu, Shuguang Wu, YXiong, XXiong, RYang, Difei Yassin, N AYen, C WYi, X BYoo, J WYoo, Seoung-Kyo Yoo, Seungmin Yu, S. Zewail, Ahmed Zhang, KZhang, J ZZhang, S. ZHANG, N Y IZhang, SZhang, NZhao, G XZhao, BZhelyaskov, VZhou, JZhou, J. Zou, Bingsuo S


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