NeuralFetch

Goal

NeuralFetch connects to 12 public repositories through a pluggable backend system and returns the same tidy events DataFrame regardless of the source.

DANDI DataLad Donders Dryad EEGDash Figshare HuggingFace OpenNeuro OSF PhysioNet Synapse Zenodo

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NeuralFetch

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NeuralSet Events DataFrame

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Quick install

pip install neuralfetch

Installing NeuralFetch automatically registers all curated studies in NeuralSet’s catalog — no extra imports needed.

import neuralset as ns

study = ns.Study(name="Gwilliams2022Neural", path="/data")  # MEG + speech, from OSF
study.download()
events = study.run()
events[["type", "start", "duration", "subject", "text"]].head()

type   start  duration              subject          text
Meg      0.0     396.0  Gwilliams2022Neural/A0001           NaN
Audio    0.0      42.3  Gwilliams2022Neural/A0001           NaN
Word     1.52     0.22  Gwilliams2022Neural/A0001         there
Word     1.74     0.18  Gwilliams2022Neural/A0001           was
Word     1.92     0.08  Gwilliams2022Neural/A0001             a

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Explore Available Studies

Browse all studies from their declared StudyInfo metadata, filter by event type, and click any study for a ready-to-paste snippet and source link.

Devices

EegEmgFmriFnirsIeegMegMultimodal

Events

ActionArtifactAudioCategoricalEventEpileptiformActivityEventEyeStateImageMissedWordSeizureSentenceSleepArousalSleepStageSpikesStimulusTextVideoWord

138studies

27,895subjects

137,148timelines

52.0Kestimated hours

 Plot  Table

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Tutorials

Each tutorial walks through one building block of the NeuralFetch pipeline.

Fetch a curated study

Browse the catalog, download a sample dataset, and preview the events DataFrame.

Snippet Tutorial

study = ns.Study(name="Grootswagers2022HumanSample",
                    path="./data")
study.download()
events = study.run()
print(events[["type","start","duration"]].head())

Open full tutorial →

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Create or share a study

Wrap any local or remote dataset as a Study subclass and register it in the catalog.

Snippet Tutorial

class MyStudy(studies.Study):
 def iter_timelines(self):
     yield {"subject": "sub-01"}
 def _load_timeline_events(self, tl):
     return pd.DataFrame([...])

Open full tutorial →

Installation → Go to Tutorials →