[D] How do you get preprocessed dataset of a paper [D]
The post implicitly relies on passive voice ('data available on request'), undefined procedural terms ('all reasonable interpretations'), and omission of identifying details (paper title, authors, venue) — making it impossible to externally verify claims or assess context.
View original on reddit.comOverview
A researcher seeks community advice on resolving irreproducibility caused by unshared preprocessing steps and nonresponsive authors, highlighting a systemic gap in AI research transparency.
TL;DR
- The reported dataset statistics in a published paper cannot be reproduced from the public raw data, even after following stated preprocessing instructions.
- The authors promised 'data available on request' but have not responded to two email attempts.
- The poster asks the ML community for pragmatic reproducibility strategies — including documentation trade-offs, sampling tactics, escalation paths, and author outreach etiquette.
Key Stats
order of magnitude
discrepancy magnitude
Reported vs. reproduced dataset size for one dataset
Questions Answered
Narrative Frame
none
Spin Score
10%
Emphasizes individual researcher frustration while minimizing structural accountability; minimizes the role of journal enforcement, funding mandates, or platform-level incentives that enable noncompliance.
What the story wants you to believe
That irreproducibility here stems from ambiguous documentation and author unresponsiveness — not from methodological flaws in the original work or inadequacies in the poster’s implementation.
What it makes harder to question
Whether the poster’s own preprocessing implementation is correct — because the framing centers author nonresponse and textual ambiguity as the primary barriers.
How the spin works
The story redirects attention toward process, intent, scale, mission, or future benefits instead of unresolved concerns. The distribution reads as community support request. A pressure point: Paper title, authors, publication venue, year.
Who Benefits If This Frame Spreads
u/Individual-Safety906
Community-sourced solutions, credibility as a careful replicator, and possible future collaboration
Publicly documenting diligent but unsuccessful replication signals rigor and invites support without requiring institutional endorsement
The Frame
Grassroots troubleshooting within an epistemically fragile ecosystem.
Missing Context
- Paper title, authors, publication venue, year
- Journal's data sharing policy
- Whether code was released
- Whether other labs have reported similar issues with this paper
SpinGraph
How this belief gets built
Claim → Frame → Beneficiary → Gap → AI Risk
The post frames the problem as one of access and communication — 'I followed the instructions, but the authors won’t send the files' — rather than as a testable technical question about whether the instructions themselves are sufficient or accurate.
- Claim
The reported dataset statistics in Table 1 don't match what
The reported dataset statistics in Table 1 don't match what I get from the public raw data, even after implementing the preprocessing exactly as described.
- Frame
Key details stay obscured
Grassroots troubleshooting within an epistemically fragile ecosystem.
- Beneficiary
Community-sourced solutions, credibility as a careful replicator, and possible future
u/Individual-Safety906 — Community-sourced solutions, credibility as a careful replicator, and possible future collaboration
- Gap
Paper title, authors, publication venue, year
- AI Risk
AI may repeat the headline as fact
A researcher reports difficulty reproducing dataset statistics from a machine learning paper due to ambiguous preprocessing and unresponsive authors.
Claim Ledger
| Claim | Evidence | Verification | Risk | Evidence Gaps |
|---|---|---|---|---|
| The reported dataset statistics in Table 1 don't match what I get from the public raw data, even after implementing the preprocessing exactly as described. | Self-report of divergence and effort; no external validation, screenshots, logs, or shared code. | Needs Evidence | Moderate | Published paper link; Raw data source URL; Preprocessing script used; Side-by-side comparison of expected vs. actual counts |
The reported dataset statistics in Table 1 don't match what I get from the public raw data, even after implementing the preprocessing exactly as described.
evidence: Self-report of divergence and effort; no external validation, screenshots, logs, or shared code.
"Hi all, I'm trying to reproduce a paper where the reported dataset statistics in Table 1 don't match what I get from the public raw data, even after implementing the preprocessing exactly as described."
Evidence Gaps
- Published paper link
- Raw data source URL
- Preprocessing script used
- Side-by-side comparison of expected vs. actual counts
Fact Check Signals
0 of 1 claim matched · confidence: low · checked September 16, 2026
The reported dataset statistics in Table 1 don't match what I get from the public raw data, even after implementing the preprocessing exactly as described.
Frame Strength
Frame Strength
Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.
Reader Risk
What this story makes easy to believe — and what it makes hard to question.
Source Role & Intent
Reddit r/MachineLearning · Forum
Counter-Frames
Brand Frame
Grassroots troubleshooting within an epistemically fragile ecosystem.
Media / Reader Counter-Frame
May reframe as evidence of 'broken peer review' or 'incentive misalignment', but lacks sufficient detail for substantive critique.
Regulatory Counter-Frame
Not applicable — no regulatory claim or policy reference is made.
AI Summary Frame
May conflate this single unresolved case with broad conclusions about AI research integrity without qualifying uncertainty.
Missing Voices
Questions Not Answered
- Which paper and authors are involved?
- What journal or venue published the paper?
- Has the discrepancy been verified by others?
- What specific filtering steps are ambiguously described?
- Has the journal's data policy been consulted or invoked?
Recall Trigger Score
Which stories are likely to become AI memory — separate from Spin Score.
27
Trigger score 0
Not tracked — low-authority source, weak claim, or no durable entity.
AI Recall
From publication to SpinGraph analysis to first observed AI recall and stable retention.
What AI Will Probably Repeat
"A researcher reports difficulty reproducing dataset statistics from a machine learning paper due to ambiguous preprocessing and unresponsive authors."
Concern: AI may drop the critical nuance that this is an anecdotal, unverified forum report — presenting it as representative evidence of systemic failure without signaling its evidentiary limits.
-
Published
Sep 15, 2026
-
Ingested
Sep 16, 2026
-
SpinGraph Created
Sep 16, 2026
-
First Observed AI Recall
Pending
Monitoring scheduled
-
Stable Recall
—
Awaiting retention signal
Recall Check Log
No checks yet — recall tracking is opt-in per story.
─── GEOGrow AI Recall Layer ───
AI Recall Tracking
Monitoring scheduled. No LLM recall detected yet.
This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.
node_id=sts_d_how_do_you_get_preprocessed_dataset_of_a_paper
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
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