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Telegram profile photo for 🇺🇿🇺🇿 Scopus Web of science

Channel

🇺🇿🇺🇿 Scopus Web of science

@scopus_wos_uz

On this record: Topic · Also posting the same content · Growth · Engagement · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry

8,542subscribers

+143 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001429544458
TypeChannel
Username@scopus_wos_uz
CreatedBetween 1 April 2019 and 30 September 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live16 September 2026
Measurements held14
Confirmed unchanged1 time, most recently 16 September 2026
On Telegramt.me/scopus_wos_uz

Topic

Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 11 September 2026 and assigned it the closest of 31 fixed categories, at 71% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.

Also posting the same content

This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.

Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.

Growth

8,3988,5428,4706 August 2026 — 8,399 subscribers7 August 2026 — 8,398 subscribers9 August 2026 — 8,405 subscribers12 August 2026 — 8,411 subscribers16 August 2026 — 8,422 subscribers19 August 2026 — 8,430 subscribers22 August 2026 — 8,435 subscribers25 August 2026 — 8,440 subscribers28 August 2026 — 8,447 subscribers31 August 2026 — 8,457 subscribers3 September 2026 — 8,466 subscribers9 September 2026 — 8,498 subscribers12 September 2026 — 8,523 subscribers16 September 2026 — 8,542 subscribers6 August 202616 September 2026
14 measurements spanning 40 days, net +143. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 8,376–8,564 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
16 Sept 2026, 09:378,542+19
12 Sept 2026, 20:408,523+25
9 Sept 2026, 08:168,498+32
3 Sept 2026, 14:008,466+9
31 Aug 2026, 07:148,457+10
28 Aug 2026, 15:468,447+7
25 Aug 2026, 11:458,440+5
22 Aug 2026, 14:348,435+5
19 Aug 2026, 01:078,430+8
16 Aug 2026, 04:158,422+11
12 Aug 2026, 19:388,411+6
9 Aug 2026, 23:338,405+7
7 Aug 2026, 05:118,398-1
6 Aug 2026, 22:028,399first reading

Engagement

32 posts held, back to 20 June 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 24 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
9.25%
avg views ÷ 8,542 subscribers
Avg views / post
790
2 posts measured
Reaction rate
0.443%
reactions ÷ views · ER floor
Posts in window
2
of 32 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.

What these figures were computed from
WindowRolling 30 days · latest post in window 28 August 2026
Posts held32 (20 June 202628 August 2026)
Views total1,580
Reactions total7
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken28 Aug 2026, 17:26 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

Reaction mix

202 reactions across 31 posts, in 7 distinct kinds. The most used accounts for 45.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
9145.0%
👍6230.7%
👏167.92%
🥰157.43%
🔥104.95%
🎉73.47%
😍10.495%

No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.

Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.

Coverage. Reactions were read on 31 of the 32 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 202 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 32 most recent posts we hold, published 20 June 2026 to 28 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Recent posts

28 Aug 2026, 12:11 UTC694 views3 reactionsread 28 August 2026
Forwarded from @authorship_infoPhoto

✅ Янги лойиҳа ( 28-август) ✅ Хорижий тадқиқотчилар билан ҳамкорлик мақоласи ✅ геометаллургия, фойдали қазилмаларни бойитиш ва кончиликда сунъий интеллект бўйича ✅ Scopus Q1/Q2 Калит сўзлар: геометаллургия; рудадан фойдали компонентни ажратиб олиш; руда ажратиб олиш даражасини прогнозлаш; тушунтириладиган машинали ўқитиш; сунъий интеллект; XGBoost; SHAP таҳлили; кўп мезонли оптималлаштириш; фойдали қазилмаларни бо

1👍1😍1

25 Aug 2026, 14:49 UTC886 views4 reactionsread 28 August 2026
Forwarded from @authorship_info

ДИҚҚАТ!, МУҲИМ 🧬 Тиббиёт / молекуляр генетика йўналишида илмий ҳамкорлик 📌 Мақола йўналиши: Нейродегенератив касалликлар, ген терапияси ва CRISPR технологиялари 🔑 Калит сўзлар: Huntington’s disease; CRISPR gene editing; allele-specific targeting; RNA-targeting CRISPR; gene therapy; Cas9; Cas12; Cas13; molecular genetics; neurodegenerative diseases 📄 Мақола тури: Narrative Review 📚 Журнал: Q2/Q3 | Scopus | Web of

4

21 Aug 2026, 13:43 UTC≈7,130 views4 reactionsread 28 August 2026
Photo

‼️ 5 ой ичида ташкилотлар нашр фаоллиги қандай 🔍 ўзгарган (27-мартда ҳам таҳлил қилган эканмиз) ✅ ТОП 15 ташкилот ( 21-август бўйича) ✅ Скопус маълумотларига кўра, Бугунги сана бўйича Ташкилотлар нашр ишлари сони бўйича ўзгариш 🔍 динамикаси ✅ нашр ишлари бўйича хусусий ОТМ ларнинг улуши ҳам салмоқли ошиб бормоқда Сизнинг ташкилот қайси ўринда эканини кўриш учун 🔍 ушбу линкни босинг (скопус доступ орқали) ✅ 5 ой

2👍1👏1

20 Aug 2026, 11:19 UTC≈1,990 views3 reactionsread 28 August 2026
Forwarded from @authorship_info

🧬 Тиббиёт / молекуляр генетика йўналишида илмий ҳамкорлик 📌 Мақола йўналиши: Нейродегенератив касалликлар, ген терапияси ва CRISPR технологиялари 🔑 Калит сўзлар: Huntington’s disease; CRISPR gene editing; allele-specific targeting; RNA-targeting CRISPR; gene therapy; Cas9; Cas12; Cas13; molecular genetics; neurodegenerative diseases 📄 Мақола тури: Narrative Review 📚 Журнал: Q2/Q3 | Scopus | Web of Science Publis

👏21

19 Aug 2026, 07:25 UTC≈2,250 views4 reactionsread 28 August 2026
Forwarded from @authorship_infoPhoto

Scopus Q2 ✅ ИНДЕКС КАФОЛАТГА ЭГА ‼️ Илмий иш ҳимоялари(PhD, DSC) + 1030 устама+ШИР ҳисоьоти+Ташкилотлар рейтинглари+муаллифлар илмий профиллари учун махсус таклиф Йўналиш: Биология / Экология / Таксономия Калит сўзлар: био хилма-хиллик, криптик турлар, интегратив биология, геномик таҳлил, морфометрика, филогенетика, систематика, эволюцион биология. ✅ Саволлар: @azamat_umirzokov ✅ Канал: @authorship_info

👍3👏1

18 Aug 2026, 15:04 UTC≈2,150 views15 reactionsread 28 August 2026
Photo

✅ Журнал Квартили қандай аниқланади? Q- Журнал квартили - «Quartile» ‼️ Scopus ва Web of science журналлари 4 та квартилга бўлинади Содда айтилганда, квартил-журналнинг даражаси, Q1-энг юқори, Q4-энг паст, қуйи даража ⁉️ Scopus да журналнинг квартили асосан 2 кўринишда аниқланади 1️⃣ SJR- Scimago даги квартил- https://www.scimagojr.com/ га кириб, журнал ISSN ёки журнал номини ёзганида топиладиган квартил нат

👍4👏43🎉2🥰2

17 Aug 2026, 07:44 UTC≈1,950 views4 reactionsread 28 August 2026
Forwarded from @authorship_infoPhoto

✅ Хорижий муаллифлар билан тадқиқот иши учун ҳамкорлик (17-август) Scopus Q1/Q2 ✅ Йўналиш: Кончилик + AI + очиқ кон + портлатиш ишлари + тоғ жинслари фрагментацияси + машинали ўқитиш + оптималлаштириш ✅ Очиқ кон ишлари, бурғилаш-портлатиш ишлари, тоғ жинсларининг парчаланиши, портлатиш параметрлари, машинали ўқитиш, изоҳланувчи сунъий интеллект, фрагментацияни прогнозлаш, портлатишни оптималлаштириш, тоғ жинслар

4

16 Aug 2026, 13:31 UTC≈2,160 views5 reactionsread 28 August 2026
Forwarded from @authorship_info

✅ Хорижий тадқиқотчилар ҳаммуаллифлик SCOPUS Q1 ✅ Мавзу йўналиши / калит сўзлар: Кредит карта фирибгарлигини аниқлаш • Номувозанат маълумотлар • Машинали ўқитиш • Муҳим белгиларни танлаш • Изоҳланувчи сунъий интеллект • Чуқур ўқитиш ‼️ Мақола кредит карта орқали амалга ошириладиган транзакцияларда фирибгарлик ҳолатларини сунъий интеллект ва машинали ўқитиш усуллари ёрдамида аниқлашга бағишланган. Асосий эъти бор

4👏1

15 Aug 2026, 08:34 UTC≈2,610 views1 reactionsread 28 August 2026
Forwarded from @authorship_info

SCOPUS Q4 Ҳаммуаллиф бўлиш учун ИНДЕКС КАФОЛАТГА ЭГА 1) 1-тоифа диабетда қондаги глюкоза миқдорини бошқариш учун башорат уфқига асосланган мослашувчан тезкор сирпанма режимли бошқарувчи 2) Паркинсон касаллигида треморни бошқариш учун тез яқинлашувчи янги робаст нейрон бошқарувчини лойиҳалаш 3) Саратон ўсмаларида кимётерапия дори дозасини бошқариш учун силлиқ Super-Twisting алгоритмига асосланган белгиланган вақт

1

14 Aug 2026, 13:08 UTC≈1,730 views2 reactionsread 18 August 2026
Forwarded from @authorship_info

‼️ ҲАММУАЛЛИФЛИК УЧУН ТАКЛИФ ( 14-август) Scopus Q3/Q4 1 тадан муаллифга жой бор Индекс кафолатга эга 1️⃣ ) Цитокинлар ўртасидаги ўзаро таъсир ва молекуляр мимикрия: хавфли ўсмалар билан боғлиқ нефропатия механизмлари ҳақида янги қарашлар (SCOPUS Q4) 2️⃣ ) Гликемиядан ташқари: eGFR (коптокчалар фильтрациясининг ҳисобланган тезлиги)нинг турли даражаларида SGLT2 ингибиторларининг буйраклар фаолиятига узоқ муддатл

2

13 Aug 2026, 13:09 UTC≈2,660 views5 reactionsread 28 August 2026
Forwarded from @authorship_infoPhoto

✅ Хорижий тадқиқотчилар билан ҳамкорликдаги тадқиқот ✅ Йўналишлар: Нефть ва газ муҳандислиги, нефть қазиб олиш, EOR, нефт кимёси, кимёвий технология, сирт-фаол моддалар кимёси. ✅ ИНДЕКС КАФОЛАТГА ЭГА СИФАТЛИ ИЛМИЙ ЖУРНАЛДА НАШР ҚИЛИШ РЕЖА ҚИЛИНГАН ✅ SCOPUS Q1/Q2 Мавзу номи: Молекуляр олигомеризация — оғир қатлам шароитларида нефть қазиб олишни ошириш учун термик барқарор ва тузга чидамли катион сирт-фаол моддалар

5

12 Aug 2026, 11:02 UTC≈1,560 views2 reactionsread 18 August 2026
Forwarded from @authorship_infoPhoto

✅ ДИККАТ SCOPUS Q4 2 та жой бор Хорижий хаммуаллифлар билан хамкорликда ИНДЕКС Кафолатга эга ✅ Шаҳар ҳавосидаги токсинлар остеопороз ривожланишига таъсир қилувчи янги омиллар сифатида: молекуляр механизмлар ва эпидемиологик далиллар — тавсифий шарҳ тадқиқоти ✅ Токсины городского воздуха как новые факторы, способствующие развитию остеопороза: молекулярные механизмы и эпидемиологические данные — нарративный обзор

2

Showing the 12 most recent of 32 posts we hold for @scopus_wos_uz. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.

Forward network

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Doktorantlar Official || PhD, DSc
@doktorantlarofficial · 46,954
Telegram ranks this channel #16 of 84 here — alongside 83 others — read 27 August 2026
DISSERTATSIYA
@OAK_jurnallari_N1 · 24,458
Telegram ranks this channel #17 of 81 here — alongside 80 others — read 16 September 2026
Doktorantlar va ilmiy izlanuvchilar | PhD, DSc
@ILM_NUR_2020 · 24,362
Telegram ranks this channel #20 of 73 here — alongside 72 others — read 16 September 2026
UzA | Ilm-fan kanali
@uzauzilmfan · 125,898
Telegram ranks this channel #21 of 78 here — alongside 77 others — read 14 August 2026
Worldly Knowledge
@iqro_jurnali · 38,238
Telegram ranks this channel #25 of 65 here — alongside 64 others — read 16 September 2026
Faculty Plus
@facultypluss · 28,614
Telegram ranks this channel #36 of 75 here — alongside 74 others — read 9 September 2026
Ilmiy darajali kadrlarni tayyorlash
@ilmiy_darajali_kadrlar · 33,266
Telegram ranks this channel #43 of 81 here — alongside 80 others — read 5 September 2026

This channel appears in 7 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cite this entry

A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 16 September 2026 — this entry's latest reading, not the date you are reading this.

“🇺🇿🇺🇿 Scopus Web of science” (@scopus_wos_uz), 8,542 subscribers as measured 16 September 2026. Telegram Register, tgregister.com/channel/scopus_wos_uz.

Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.