{ "dataset": { "archive": "dandi", "id": "000127", "label": "DANDI:000127" }, "page": "datasets/dandi/000127/", "generated": "2026-10-20T19:09:16+00:00", "caution": "A failure to reproduce is not a finding that the paper is wrong. It may be a failure of the attempt.", "outcome_counts": { "reproduced": 4, "partially_reproduced": 2, "not_reproduced": 0, "blocked": 4 }, "outcome_counts_with_outside_data": { "reproduced": 0, "partially_reproduced": 0, "not_reproduced": 0, "blocked": 0 }, "outcome_counts_elsewhere": { "reproduced": 0, "partially_reproduced": 0, "not_reproduced": 0, "blocked": 0 }, "outcome_counts_links": { "reproduced": 0, "partially_reproduced": 0, "not_reproduced": 0, "blocked": 0 }, "attempts": [ { "id": "000127-chowdhury-2020-01", "dataset": { "archive": "dandi", "id": "000127", "version": "0.220113.0359", "doi": "10.48324/dandi.000127/0.220113.0359" }, "key": "dandi:000127", "label": "DANDI:000127", "dataset_version": "0.220113.0359", "paper": "10.7554/elife.48198", "paper_title": "Area 2 of primary somatosensory cortex encodes kinematics of the whole arm", "date": "2026-10-13", "kind": "agent", "accountable": "CatalystNeuro", "agent": { "harness": "Claude Code", "model": "not recorded", "protocol": "pilot-0" }, "runner": { "platform": "local" }, "tier": "agent_verified", "deprecated": "", "deprecated_on": null, "entry": "000127-chowdhury-2020", "entry_title": "Active and passive arm movements in somatosensory area 2 (Chowdhury et al. 2020)", "description": "Tests the active/passive findings of Chowdhury, Glaser and Miller (2020) on dandiset 000127, which holds one of the paper's twelve sessions. It compares hand and arm kinematics between active reaches and passive bumps, measures how well single neurons tell the two apart, and fits hand-only and whole-arm models of each neuron's firing. The paper's two-workspace experiment cannot be tested because those sessions are not in the dandiset.", "notebooks": [ { "path": "reproduce_chowdhury_2020.ipynb", "title": "Reproducing Chowdhury, Glaser & Miller (2020) from dandiset 000127", "executed": "notebooks/000127-chowdhury-2020/reproduce_chowdhury_2020.html" } ], "outcomes": [ { "claim": "fig4-whole-arm-fits-better", "statement": "In the two-workspace experiment, the whole-arm model predicted neural activity better than the hand-only model for 238 of 288 neurons, with no significant difference for the other 50.", "source": "Figure 4", "quote": "Of the 288 neurons recorded across the 8 sessions, 238 were significantly better predicted by the whole-arm model than the hand-only model, and for the other 50, there was no significant difference", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "data_missing", "notebook": null, "notes": "The dandiset holds one session of the active/passive experiment. None of the eight two-workspace sessions is in it.", "deviations": [], "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig5-consistent-across-workspaces", "statement": "A whole-arm model trained on both workspaces explained as much neural variance as models trained within a single workspace.", "source": "Figure 5", "quote": "The symbols lying very close to the unity line in each panel of Figure 5 indicate that the full model explained just as much neural variance as did the individual models.", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "data_missing", "notebook": null, "notes": "The two-workspace sessions are not in the dandiset.", "deviations": [], "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig6-tuning-curves", "statement": "The whole-arm model predicted the shape of directional tuning curves in the two workspaces better than the hand-only model for 138 of 288 neurons, and worse for 2.", "source": "Figure 6", "quote": "Only two out of 288 neurons were significantly better predicted by the hand-only model (using p < 0.05), while 138 of 288 neurons were significantly better predicted by the whole-arm model.", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "data_missing", "notebook": null, "notes": "The two-workspace sessions are not in the dandiset.", "deviations": [], "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig7-pd-shift", "statement": "The whole-arm model predicted the shifts of preferred direction between workspaces better than the hand-only model, with a circular variance accounted for of 0.75 against 0.57.", "source": "Figure 7B", "quote": "Figure 7B shows that the whole-arm model once again out-performed the hand-only model, with an average cVAF over all neurons of 0.75 compared to 0.57.", "statistics": [ { "about": "whole-arm against hand-only cVAF, the sessions in which the whole-arm model out-performed", "test": "corrected resampled t-test on repeated 5-fold cross-validation", "p": 0.05, "p_relation": "<", "corrected": "Bonferroni (correction factor of 6)", "n": "7 of 8 sessions, 100 cross-validation samples of the cVAF per model and session" }, { "about": "whole-arm against hand-only cVAF, the remaining session", "test": "corrected resampled t-test on repeated 5-fold cross-validation", "p": 0.05, "p_relation": ">", "corrected": "Bonferroni (correction factor of 6)", "n": "1 session, 100 cross-validation samples of the cVAF per model" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "data_missing", "notebook": null, "notes": "The two-workspace sessions are not in the dandiset.", "deviations": [], "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig8-hand-speed-similar", "statement": "In the 120 ms after movement onset, average hand speed was similar in active reaches and passive bumps.", "source": "Figure 8B", "quote": "However, average movement speed was similar between active and passive trials in this 120 ms window, which we used for data analysis.", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_chowdhury_2020.ipynb", "notes": "The speed traces of active and passive trials overlap in the 120 ms window and separate afterwards. Passive movements are 13% faster on average (0.207 against 0.183 m/s), half a standard deviation, which is statistically clear; the paper's \"similar\" is qualitative.", "deviations": [ "One session from one monkey; the paper has four sessions from two." ], "disagreed": true, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig8-arm-kinematics-separable", "statement": "A linear discriminant classifier predicted whether a movement was active or passive 89% of the time from whole-arm kinematics in the analysis window.", "source": "Results, Area 2 represents passive movements differently from active reaches, paragraph 3", "quote": "Averaged over the sessions, a linear discriminant analysis (LDA) classifier could predict the movement type 89% of the time, using only the whole-arm kinematics in the analysis window", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_chowdhury_2020.ipynb", "notes": "A linear discriminant tells active from passive trials 90% of the time from joint angles and velocities, against 58% from hand kinematics and 53% by chance. The paper reports 89% averaged over four sessions.", "deviations": [ "One session from one monkey; the paper has four sessions from two.", "Joint angles and velocities stand in for the hand and elbow marker kinematics, which are not in the dandiset." ], "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig8-neurons-separable", "statement": "Many neurons had an above-chance separability index, the accuracy of a linear discriminant predicting the trial type from that neuron's firing.", "source": "Figure 8E", "quote": "we used LDA to classify movement type based on individual neurons, calling this prediction rate the neuron's 'separability index' (Figure 8E). We found that many neurons had an above chance separability index", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_chowdhury_2020.ipynb", "notes": "20 of 50 neurons have a separability index above chance at p < 0.05, with a median of 0.56 and a highest value of 0.80.", "deviations": [ "One session from one monkey; the paper has four sessions from two.", "Neurons firing below 1 Hz in the window (15 of 65) are left out; the paper states no criterion." ], "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig9-whole-arm-fits-better", "statement": "In the active/passive experiment the whole-arm model again tended to out-perform the hand-only model, but for many more neurons than in the two-workspace experiment the difference was not significant.", "source": "Figure 9", "quote": "the whole-arm model again tended to out-perform the hand-only model (Filled circles above the dashed unity line in Figure 9). However, there were many more neurons (open circles) for which the difference between models was insignificant...", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_chowdhury_2020.ipynb", "notes": "The whole-arm stand-in is significantly better than the hand-only model for 15 of 50 neurons and worse for none; 35 (70%) show no significant difference, against 17% in the paper's two-workspace experiment. That comparison uses the paper's number.", "deviations": [ "One session from one monkey; the paper has four sessions from two.", "The whole-arm model is hand plus joint kinematics; the marker positions the paper's model uses are not in the dandiset.", "The hand-only model is two-dimensional, the fits carry a small ridge penalty, neurons below 1 Hz are left out, and no Bonferroni correction is applied." ], "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig10-inconsistent-across-conditions", "statement": "Unlike in the two-workspace experiment, many neurons had no consistent relationship with arm kinematics across active and passive movements, shown by negative pseudo-R2 for the model trained on both conditions.", "source": "Figure 10", "quote": "Surprisingly however, unlike our results from the two-workspace experiment (see Figure 5), many neurons in the active/passive task did not have this consistent relationship, indicated by the many neurons with negative pR2 values for the model trained over both conditions.", "statistics": [], "kind": "finding", "inferred": "", "outcome": "partially_reproduced", "blocked_by": null, "notebook": "reproduce_chowdhury_2020.ipynb", "notes": "19 of 50 neurons scored on active trials and 23 of 50 on passive trials have negative pseudo-R2 for the model trained on both conditions, but about as many (17 and 25) are negative when trained within one condition. Only 5 and 3 neurons are positive within a condition and negative across both. Training on both costs a little on active trials and nothing on passive trials.", "deviations": [ "One session from one monkey; the paper has four sessions from two.", "The whole-arm model is hand plus joint kinematics, as for fig9-whole-arm-fits-better.", "Pseudo-R2 for one trial type is computed on a repeat's pooled held-out predictions, against that type's own mean firing; the paper does not say which mean it used." ], "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig11-separability-vs-consistency", "statement": "The consistency of the whole-arm model, its pseudo-R2 when trained on both conditions, correlated negatively with a neuron's separability index.", "source": "Figure 11B", "quote": "Interestingly, we found the opposite result\u2014model consistency actually correlated negatively with the separability index.", "statistics": [], "kind": "finding", "inferred": "", "outcome": "partially_reproduced", "blocked_by": null, "notebook": "reproduce_chowdhury_2020.ipynb", "notes": "Scored on active trials, model consistency correlates negatively with separability (Spearman -0.38, 95% interval -0.60 to -0.11). Scored on passive trials the correlation is -0.13 and on all trials together 0.14; neither can be told apart from zero.", "deviations": [ "One session from one monkey; the paper has four sessions from two.", "The whole-arm model is hand plus joint kinematics, as for fig9-whole-arm-fits-better." ], "disagreed": false, "data": "dataset", "where": null, "proposed": null } ], "findings": [ { "kind": "data_missing", "statement": "The dandiset holds one of the twelve sessions analysed in the paper, and none from the two-workspace experiment, so four of the paper's main findings cannot be tested from it." }, { "kind": "data_missing", "statement": "The positions of the arm markers, which the paper's whole-arm model is built on, are not included. Joint angles and muscle lengths derived from them are." }, { "kind": "metadata_error", "statement": "The text descriptions of hand position and hand velocity give centimetres while the stated unit is metres. The stated unit, with the file's conversion factor, is correct." }, { "kind": "selection_undocumented", "statement": "The dandiset's metadata names the paper without saying which part of it the data covers. It was packaged for the Neural Latents Benchmark, not as the paper's data release." } ], "verifications": [ { "date": "2026-10-13", "kind": "agent", "by": "Independent agent run by CatalystNeuro (same harness, separate context, did not write the notebook)", "reviewed": "First draft, with the author's conclusions withheld. Only the six claims that were tested.", "agreed": 5, "of": 6, "disagreements": [ { "claim": "fig8-hand-speed-similar", "outcome": "reproduced", "reason": "The author's draft said partially reproduced because passive movements are 13% faster. The verifier judged the paper's qualitative \"similar\" to hold, as the traces overlap in the window. The author adopted that." } ] }, { "date": "2026-10-13", "kind": "agent", "by": "The same independent agent, second round", "reviewed": "The revised notebook with the author's conclusions. For the four blocked claims the verifier checked the dandiset's file list and metadata on the archive itself and found two files, the training and test splits of one active/passive session of one monkey.", "agreed": 10, "of": 10, "disagreements": [] } ] } ], "entries": [ { "name": "000127-chowdhury-2020", "type": "reproduction", "title": "Active and passive arm movements in somatosensory area 2 (Chowdhury et al. 2020)", "description": "Tests the active/passive findings of Chowdhury, Glaser and Miller (2020) on dandiset 000127, which holds one of the paper's twelve sessions. It compares hand and arm kinematics between active reaches and passive bumps, measures how well single neurons tell the two apart, and fits hand-only and whole-arm models of each neuron's firing. The paper's two-workspace experiment cannot be tested because those sessions are not in the dandiset.", "datasets": [ "dandi:000127" ], "papers": [ "10.7554/elife.48198" ], "derived_from": [], "notebooks": [ { "path": "reproduce_chowdhury_2020.ipynb", "title": "Reproducing Chowdhury, Glaser & Miller (2020) from dandiset 000127", "executed": "notebooks/000127-chowdhury-2020/reproduce_chowdhury_2020.html" } ] } ] }