ArticleeLife2023
Pynapple, a toolbox for data analysis in neuroscience.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Tracking the Fidelity of Internal Neural Representations with Error-In-Variables Regression.bioRxiv : the preprint server for biology · 2026Article
- Sleep reveals dynamics integrating and segregating movement and stimulus representations in V1.Nature communications · 2026Article
- PyNeon: A Python package for the analysis of Neon multimodal mobile eye-tracking data.Behavior research methods · 2026Article
- Large-scale electrophysiology at single-spike resolution.Nature reviews. Neuroscience · 2026Review
- Quantifying Correlogram Shape to Analyze Neuronal Firing Dynamics Recorded in TBI-on-a-Chip.Neuroinformatics · 2026Article
- Months-long stability of the head-direction system.Nature · 2026Article
- Neurodatascience: Past, Present, and Future.Data science in science · 2026Article
- Automated ROI detection allows rapid quantification of synaptic activity across tens of thousands of synapses in cell culture.Frontiers in synaptic neuroscience · 2026Article
- Layer 1 NDNF interneurons form distinct subpopulations with opposite activation patterns during sleep in freely behaving mice.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Spyglass: a framework for reproducible and shareable neuroscience research.bioRxiv : the preprint server for biology · 2025Article
- New information triggers prospective codes to adapt for flexible navigation.Nature communications · 2025Article
- Characterization of depth perception information inferred from neuronal activity in primary visual cortex.PloS one · 2025Article
- Multiscale Cloud-Based Pipeline for Neuronal Electrophysiology Analysis and Visualization.bioRxiv : the preprint server for biology · 2024Article
- ElecFeX is a user-friendly toolbox for efficient feature extraction from single-cell electrophysiological recordings.Cell reports methods · 2024Article
- Harmonizing AI governance regulations and neuroinformatics: perspectives on privacy and data sharing.Frontiers in neuroinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high-dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.