Stack Overflow data/ML question intelligence (weekly)
Every distinct Stack Overflow question surfaced by per-tag sweeps of a curated ~38-tag data/ML universe over the trailing 7 complete UTC days, deduplicated on question_id (first sweep wins as primary_tag; all observed tags unioned into tags). The official keyless Stack Exchange /questions API (sort=creation, fixed fromdate/todate window) supplies the raw material; the transform derives day-granular ages from the as-of stamp, scores each question with a documented 0-100 attention composite (60% min-max-normalized log10(1+views_per_day) + 40% min-max-normalized non-negative score_per_day), ranks it as attention_rank (ties: views desc, question_id asc), and flags unanswered_pain_flag when a question has zero answers with at least 2x the snapshot's median view count (visible community demand, no resolution). Titles + metadata only (CC BY-SA 4.0, attribution required) — no bodies, comments, or user data. Primary key: (week, question_id). Cadence: weekly. Nullability: scores and ranks are never null; age is at least 1 day. Caveats: counts tick intraday, so same-day re-runs refresh values; attention scores are within-snapshot relative (recalibrated every week). Sample use: order by attention_rank for the week's hottest data/ML questions, or filter unanswered_pain_flag for under-served demand.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 17
- Colonnes
- 20
- Cadence de la source
- Hebdomadaire
- Dernière actualisation
- 25 sept. 2026
- Thème
- technology
| Colonne | Type | Description |
|---|---|---|
| week | string | ISO week of the trailing complete UTC day (e.g. 2026-W39); the snapshot's as-of grain. (unit: ISO week) |
| fetched_at | string | Trailing complete UTC day the 7-day window ends on (YYYY-MM-DD). (unit: date) |
| question_id | integer | Stack Overflow question id (deduplicated across tag sweeps). |
| title | string | Question title (CC BY-SA 4.0); no bodies or comments are stored. |
| question_url | string | Canonical Stack Overflow question URL. |
| tags | string | Pipe-joined union of every tag observed for the question across sweeps (sorted). |
| primary_tag | string | The sweep tag that first surfaced the question (first page wins). |
| category | string | Curated 14-category taxonomy of the primary tag (shared with so_tag_signals). |
| creation_date | string | Question creation date (UTC). (unit: date) |
| age_days | integer | Age in days at fetched_at ((fetched_at - creation_date) + 1), at least 1. (unit: days) |
| view_count | integer | Question view count at fetch time (ticks intraday). (unit: views) |
| score | integer | Question score (upvotes minus downvotes) at fetch time. |
| answer_count | integer | Number of answers at fetch time. |
| is_answered | boolean | Whether upstream marks the question as answered. |
| views_per_day | float | view_count / age_days; the volume leg of attention_score. (unit: views/day) |
| score_per_day | float | max(score, 0) / age_days; the quality leg of attention_score. (unit: score/day) |
| attention_score | float | 0-100 attention composite: 60% min-max-normalized log10(1 + views_per_day) + 40% min-max-normalized score_per_day (within-snapshot relative). (unit: 0-100) |
| attention_rank | integer | Deterministic rank by attention_score desc (ties: view_count desc, question_id asc). |
| unanswered_pain_flag | boolean | True when answer_count == 0 and view_count >= 2x the snapshot's median view count (visible demand, no resolution). |
| row_hash | string | Deterministic sha256-based row hash over (week, question_id, view_count, score, answer_count). |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| week | fetched_at | question_id | title | question_url | tags | primary_tag | category | creation_date | age_days | view_count | score | answer_count | is_answered | views_per_day | score_per_day | attention_score | attention_rank | unanswered_pain_flag | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2026-W39 | 2026-09-24 | 80 005 283 | Spring Kafka: Topic <topicName> not present in metadata after 60000ms | https://stackoverflow.com/questions/80005283/spring-kafka-topic-topicname-not-present-in-metadata-after-60000ms | apache-kafka|spring-kafka | kafka | data | 2026-09-23 | 2 | 57 | 2 | 1 | true | 28,5 | 1 | 65,72 | 1 | false | ae28a449df647ba7 |
| 2026-W39 | 2026-09-24 | 80 004 970 | Replace duplicated DataFrame indices with a single Series | https://stackoverflow.com/questions/80004970/replace-duplicated-dataframe-indices-with-a-single-series | pandas|python | pandas | data | 2026-09-22 | 3 | 130 | 2 | 3 | true | 43,33 | 0,67 | 62,46 | 2 | false | d7c028dcbeb7c82c |
| 2026-W39 | 2026-09-24 | 80 005 401 | Any idea on LLM compression | https://stackoverflow.com/questions/80005401/any-idea-on-llm-compression | best-practices|large-language-model|microcontroller | llm | ai-ml | 2026-09-24 | 1 | 117 | -6 | 0 | false | 117 | 0 | 60 | 3 | false | 3ffbb8da2e7a87ce |
| 2026-W39 | 2026-09-24 | 80 004 581 | Finding a specific phrase in a text using Neuro-Linguistic Programming in Python | https://stackoverflow.com/questions/80004581/finding-a-specific-phrase-in-a-text-using-neuro-linguistic-programming-in-python | deep-learning|machine-learning|nlp|python | machine-learning | ai-ml | 2026-09-21 | 4 | 200 | -3 | 1 | true | 50 | 0 | 39,25 | 4 | false | cc948fa9e2d610ce |
| 2026-W39 | 2026-09-24 | 80 005 305 | Why does model collapse happen in knowledge distillation? | https://stackoverflow.com/questions/80005305/why-does-model-collapse-happen-in-knowledge-distillation | artificial-intelligence|machine-learning|transformer-model | machine-learning | ai-ml | 2026-09-23 | 2 | 85 | -4 | 0 | false | 42,5 | 0 | 35,32 | 5 | false | fd840b6023dd7455 |
| 2026-W39 | 2026-09-24 | 80 005 437 | Is it possible to completely remove column headers from a Power BI Matrix visual? | https://stackoverflow.com/questions/80005437/is-it-possible-to-completely-remove-column-headers-from-a-power-bi-matrix-visual | powerbi | powerbi | data | 2026-09-24 | 1 | 41 | -3 | 0 | false | 41 | 0 | 34,45 | 6 | false | c0b95f0fc84acc8b |
| 2026-W39 | 2026-09-24 | 80 004 336 | Adding columns in matplotlib graph breaks row layout | https://stackoverflow.com/questions/80004336/adding-columns-in-matplotlib-graph-breaks-row-layout | matplotlib|python | matplotlib | data | 2026-09-19 | 6 | 105 | 3 | 1 | true | 17,5 | 0,5 | 34,18 | 7 | false | 034a6ff6b931d057 |
| 2026-W39 | 2026-09-24 | 80 005 073 | How to make Android app discoverable by AI agents using AppFunctions? | https://stackoverflow.com/questions/80005073/how-to-make-android-app-discoverable-by-ai-agents-using-appfunctions | agent|android|appfunctions|artificial-intelligence | artificial-intelligence | ai-ml | 2026-09-22 | 3 | 78 | -4 | 0 | false | 26 | 0 | 23,53 | 8 | false | 505756bc8e3ebc3b |
| 2026-W39 | 2026-09-24 | 80 004 416 | Frequency/histogram of an array | https://stackoverflow.com/questions/80004416/frequency-histogram-of-an-array | snowflake-cloud-data-platform | snowflake | data | 2026-09-20 | 5 | 125 | 0 | 1 | true | 25 | 0 | 22,59 | 9 | false | fc57c28cb06359bb |
| 2026-W39 | 2026-09-24 | 80 004 721 | How to count messages in compact topic(Kafka)? | https://stackoverflow.com/questions/80004721/how-to-count-messages-in-compact-topickafka | apache-kafka|kafka-consumer-api|kafka-python | kafka | data | 2026-09-21 | 4 | 90 | -3 | 1 | true | 22,5 | 0 | 20,09 | 10 | false | 52ef79b2404f9bff |
Profilé le 25 sept. 2026 à partir de l’instantané 20260925T172330Z-3c1716acb7bb
Mesuré- Complétude
- 100 %
- Lignes
- 17
- Colonnes
- 20
- Colonnes incomplètes
- 0
| Colonne | Manquant | Distinctes | Plage | Distribution |
|---|---|---|---|---|
| weekvarchar | 0 % | 1 | — |
|
| fetched_atvarchar | 0 % | 1 | — |
|
| question_idbigint | 0 % | 19 | 80 004 065 → 80 005 437médiane 80 004 721 | 2 hors du 1er–99e centile |
| titlevarchar | 0 % | 16 | — |
|
| question_urlvarchar | 0 % | 17 | — |
|
| tagsvarchar | 0 % | 18 | — |
|
| primary_tagvarchar | 0 % | 12 | — |
|
| categoryvarchar | 0 % | 2 | — |
|
| creation_datevarchar | 0 % | 7 | — |
|
| age_daysbigint | 0 % | 7 | 1 → 7médiane 4 | |
| view_countbigint | 0 % | 18 | 34 → 200médiane 85 | 2 hors du 1er–99e centile |
| scorebigint | 0 % | 9 | -7 → 3médiane -3 | 2 hors du 1er–99e centile |
| answer_countbigint | 0 % | 4 | 0 → 3médiane 0 | 1 hors du 1er–99e centile |
| is_answeredboolean | 0 % | 2 | — |
|
| views_per_daydouble | 0 % | 18 | 9,43 → 117médiane 22,5 | 2 hors du 1er–99e centile |
| score_per_daydouble | 0 % | 4 | 0 → 1médiane 0 | 1 hors du 1er–99e centile |
| attention_scoredouble | 0 % | 20 | 0 → 65,72médiane 22,59 | 2 hors du 1er–99e centile |
| attention_rankbigint | 0 % | 18 | 1 → 17médiane 9 | 2 hors du 1er–99e centile |
| unanswered_pain_flagboolean | 0 % | 1 | — |
|
| row_hashvarchar | 0 % | 18 | — |
|
- Actuelle
20260925T172330Z-3c1716acb7bb · sha256 3c1716acb7bb…
17 lignes · premier instantané
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/so_question_signals/so_questions_weekly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/so_question_signals/so_questions_weekly").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)Point d’accès API : https://datazimuts.com/v1/datasets/so_question_signals/so_questions_weekly
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
Citer cet instantané
Épinglé à l’instantané 20260925T172330Z-3c1716acb7bb et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
Stack Overflow data/ML question intelligence (agent-curated). (2026). Stack Overflow data/ML question intelligence (weekly) [Data set, snapshot 20260925T172330Z-3c1716acb7bb, sha256 3c1716acb7bb]. Datazimuts. Retrieved 2026-09-25, from https://datazimuts.com/fr/datasets/so_question_signals/so_questions_weekly?snapshot=20260925T172330Z-3c1716acb7bb
@misc{dz_so_question_signals_so_questions_weekly_3c1716ac,
title = {{Stack Overflow data/ML question intelligence (weekly)}},
author = {{Stack Overflow data/ML question intelligence (agent-curated)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/so_question_signals/so_questions_weekly?snapshot=20260925T172330Z-3c1716acb7bb}},
note = {Snapshot 20260925T172330Z-3c1716acb7bb, sha256 3c1716acb7bbf05ea92fcbad2c9c7fbff88791214c27e7202edf3b346e53cbe7; accessed 2026-09-25}
}Intégrer un tableau ou un graphique
Collez ce code dans n’importe quelle page. L’intégration est épinglée au même instantané, suit le thème clair ou sombre du lecteur et affiche toujours la source, la licence et un lien de retour.
<iframe src="https://datazimuts.com/embed/chart?dataset=so_question_signals%2Fso_questions_weekly&lang=fr&theme=auto&snapshot=20260925T172330Z-3c1716acb7bb&x=creation_date&y=question_id&agg=avg" title="Stack Overflow data/ML question intelligence (weekly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
Posez une question sur ce jeu de données. Les réponses viennent uniquement de sa fiche, de son profil mesuré et de son historique, et citent les faits utilisés.