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.
- Rows
- 17
- Columns
- 20
- Source cadence
- Weekly
- Last refreshed
- Sep 25, 2026
- Theme
- technology
| Column | 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). |
First 10 sample rows — a preview, not the complete dataset.
| 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 |
Profiled Sep 25, 2026 from snapshot 20260925T172330Z-3c1716acb7bb
Measured- Completeness
- 100%
- Rows
- 17
- Columns
- 20
- Columns with gaps
- 0
| Column | Missing | Distinct | Range | Distribution |
|---|---|---|---|---|
| weekvarchar | 0% | 1 | — |
|
| fetched_atvarchar | 0% | 1 | — |
|
| question_idbigint | 0% | 19 | 80,004,065 → 80,005,437median 80,004,721 | 2 outside 1st–99th percentile |
| 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 → 7median 4 | |
| view_countbigint | 0% | 18 | 34 → 200median 85 | 2 outside 1st–99th percentile |
| scorebigint | 0% | 9 | -7 → 3median -3 | 2 outside 1st–99th percentile |
| answer_countbigint | 0% | 4 | 0 → 3median 0 | 1 outside 1st–99th percentile |
| is_answeredboolean | 0% | 2 | — |
|
| views_per_daydouble | 0% | 18 | 9.43 → 117median 22.5 | 2 outside 1st–99th percentile |
| score_per_daydouble | 0% | 4 | 0 → 1median 0 | 1 outside 1st–99th percentile |
| attention_scoredouble | 0% | 20 | 0 → 65.72median 22.59 | 2 outside 1st–99th percentile |
| attention_rankbigint | 0% | 18 | 1 → 17median 9 | 2 outside 1st–99th percentile |
| unanswered_pain_flagboolean | 0% | 1 | — |
|
| row_hashvarchar | 0% | 18 | — |
|
- Current
20260925T172330Z-3c1716acb7bb · sha256 3c1716acb7bb…
17 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (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)API endpoint: https://datazimuts.com/v1/datasets/so_question_signals/so_questions_weekly
Tip: fetch /llms.txt for the full machine-readable catalog.
Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.
Cite this snapshot
Pinned to snapshot 20260925T172330Z-3c1716acb7bb and its content hash, so readers get exactly the data you used.
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/en/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/en/datasets/so_question_signals/so_questions_weekly?snapshot=20260925T172330Z-3c1716acb7bb}},
note = {Snapshot 20260925T172330Z-3c1716acb7bb, sha256 3c1716acb7bbf05ea92fcbad2c9c7fbff88791214c27e7202edf3b346e53cbe7; accessed 2026-09-25}
}Embed a table or a chart
Paste this into any page. The embed is pinned to the same snapshot, follows the reader's light or dark setting, and always shows the source, license and a link back.
<iframe src="https://datazimuts.com/embed/chart?dataset=so_question_signals%2Fso_questions_weekly&lang=en&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>
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