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examples/data_release/data_release_behavior.ipynb renamed to examples/data_release/2021_data_release_behavior.ipynb

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}
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},
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"source": [
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"# Data Release - Behavior\n",
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"# 2021 - Behavior\n",
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"\n",
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"The International Brain Laboratory is a team of systems and computational neuroscientists, working collaboratively to understand the computations that support decision-making in the brain. To achieve this aim, we have developed a standardized decision-making task in mice in order that probes decision-making. The task requires mice to perform decisions by combining incoming visual evidence with internal beliefs about the dynamic structure of the environment. Please read our accompanying [paper (The International Brain Laboratory et al. 2020)]( https://doi.org/10.7554/eLife.63711) for details on the decision-making task and the experiment.\n"
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]

‎examples/data_release/2022_data_release_spikesorting_benchmarks.md‎

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# 2022 - Spike sorting benchmark datasets
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Spike sorting is the process by which one extracts the spikes information (times, waveforms) from the raw electrophysiology signals. In the case of our Brainwide map dataset, such electrophysiology signals are heterogeneous, changing from one brain region to another.
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By looking at recordings in widespread anatomical regions we discovered that the current spike sorting method has a limited range of applicability. Specifically, we uncovered two major issues. Firstly, there is no one-size-fit-all: an algorithm that is well tailored to extract signals for a given region or electrode type may perform poorly for another. Secondly, and most importantly, we had very limited means to conclude on the performance of such algorithms beyond eyes on the data and qualitative judgements.
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In order to facilitate the development of spike sorting algorithms, we aim to provide benchmarks datasets (for a full explanation, see our proposal [Spike Net](https://docs.google.com/document/d/1OA69Ptg58AQnGdmGi6UvZFrngwZDMixil1V7hJX6bNI/edit)). Here, we explain how to download these datasets, taken to represent various anatomical regions from our Brainwide map.
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## Overview of the Data
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We have selected 13 recordings for our benchmarks.
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The insertion IDs are :
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```python
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pids = [
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'1a276285-8b0e-4cc9-9f0a-a3a002978724',
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'1e104bf4-7a24-4624-a5b2-c2c8289c0de7',
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'5d570bf6-a4c6-4bf1-a14b-2c878c84ef0e',
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'5f7766ce-8e2e-410c-9195-6bf089fea4fd',
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'6638cfb3-3831-4fc2-9327-194b76cf22e1',
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'749cb2b7-e57e-4453-a794-f6230e4d0226',
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'd7ec0892-0a6c-4f4f-9d8f-72083692af5c',
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'da8dfec1-d265-44e8-84ce-6ae9c109b8bd',
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'dab512bd-a02d-4c1f-8dbc-9155a163efc0',
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'dc7e9403-19f7-409f-9240-05ee57cb7aea',
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'e8f9fba4-d151-4b00-bee7-447f0f3e752c',
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'eebcaf65-7fa4-4118-869d-a084e84530e2',
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'fe380793-8035-414e-b000-09bfe5ece92a',
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]
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```
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(Coming soon) In 2023, we will provide 20-30min chunks of raw electrophysiology data processed in a standardised fashion, with manual annotations of spikes and quality metrics for spike detection recall. These will serve as benchmarks for spike sorting algorithm development. In the meantime, you can familiarise yourself with the data heterogeneity by looking at the whole recordings.
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## View the data
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You can view the whole electrophysiology data:
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* [Through a specific AWS website](http://reveal.internationalbrainlab.org.s3-website-us-east-1.amazonaws.com/benchmarks.html)
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* [Through our main visualisation website](https://viz.internationalbrainlab.org/app) , inputting a PID in the search bar at the top
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* By downloading the electrophysiology data (see below), and using the GUI [viewephys](https://github.com/int-brain-lab/viewephys) to navigate through it
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## Data structure and download
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The organisation of the data follows the standard IBL data structure.
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Please see
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* [These instructions](https://int-brain-lab.github.io/iblenv/notebooks_external/data_structure.html) to download an example dataset for one session, and get familiarised with the data structure
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* Note that probe insertion ids `pid` are provided here, not session ids `eid`.
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* Note that you will be most interested in the folders [raw_ephys_data](https://int-brain-lab.github.io/iblenv/notebooks_external/data_structure.html#Datasets-in-raw_ephys_data) / [probeXX](https://int-brain-lab.github.io/iblenv/notebooks_external/data_structure.html#Datasets-in-raw_ephys_data/probeXX) for the raw ephys data, and [alf/probeXX/pykilosort](https://int-brain-lab.github.io/iblenv/notebooks_external/data_structure.html#Datasets-in-alf/probeXX/pykilosort) if using the pyKilosort spike sorting output.
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* [These instructions](https://int-brain-lab.github.io/iblenv/notebooks_external/data_download.html) to learn how to use the ONE-api to search and download the released datasets
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* [These instructions](https://int-brain-lab.github.io/iblenv/loading_examples.html) to get familiarised with specific data loading functions
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* You will want to load in particular the [raw ephys data](https://int-brain-lab.github.io/iblenv/notebooks_external/loading_raw_ephys_data.html) and the [spike sorting data](https://int-brain-lab.github.io/iblenv/notebooks_external/loading_spikesorting_data.html) for a given `pid`

examples/data_release/data_release_repro_ephys.ipynb renamed to examples/data_release/2024_data_release_repro_ephys.ipynb

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"id": "7ceb904d",
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"metadata": {},
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"source": [
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"# Data Release - Reproducible Ephys\n",
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"# 2024 - Reproducible Ephys\n",
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"\n",
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"Understanding whole-brain-scale electrophysiological recordings will rely on the collective work of multiple labs. Because two labs recording from the same brain area often reach different conclusions, it is critical to quantify and control for features that decrease reproducibility. To address these issues, we formed a multi-lab collaboration using a shared, open-source behavioral task and experimental apparatus. We repeatedly inserted Neuropixels multi-electrode probes targeting the same brain locations (called the repeated site, including posterior parietal cortex, hippocampus, and thalamus) in mice performing the behavioral task. For a full description of the study, please see the [associated article](https://doi.org/10.1101/2022.05.09.491042).\n",
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"<!--- TODO add ref to paper here once in press (Cell?) -->"

‎examples/data_release/2025_data_release_autism_noel.md‎

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# 2025 - Autism
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This is the dataset associated with the publication [A common computational and neural anomaly across mouse models of autism](https://www.nature.com/articles/s41593-025-01965-8).
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Electrophysiology, behavioral and video data were collected in mice performing the [IBL task](https://pubmed.ncbi.nlm.nih.gov/34011433/).
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Animals were either wildtype, or from one of 3 different autism models:
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- _B6.129P2-Fmr1tm1Cgr/J_ JAX Strain #003025
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- _B6.129(Cg)-Cntnap2tm2Pele/J_ JAX Strain #028635
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- _B6.129-Shank3tm2Gfng/J_ JAX Strain #017688
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The following data repository contains intermediate results and code to reproduce the analysis: https://osf.io/fap2s/ and https://osf.io/fap2s/wiki/home/.
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Please note that different quality standards were applied in the [article](https://doi.org/10.1038/s41593-025-01965-8) than what is presented in this page. For example, the IBL does not use a presence ratio to assess the quality of its good spike sorting units.
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## Notes
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The dataset has also been published via the [OSF platform](https://osf.io/fap2s/wiki/home/). However, downloading the dataset through OSF is not recommended.
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We recommend you download the data via ONE.
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## Overview of the Data
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We have released data from 198 Neuropixel experimental sessions, with single Neuropixel recordings, referred to as probe insertions. Those were obtained with 62 genetically modified subjects performing the IBL task.
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As output of spike-sorting, there are XXX units; of which XXX are considered to be of good quality. In total, XXX brain regions were recorded in sufficient numbers for comparison with IBL’s analyses [(IBL et al. 2023)](https://doi.org/10.1101/2023.07.04.547681).
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| Model | Subject Count | Neuropixel Count |
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|----------|---------------| --- |
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| Wildtype | 15 | 51|
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| Cntnap2 | 17 | 52|
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| Shank3b | 15 | 41|
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| Fmr1 | 15 | 54|
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## Data structure and download
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The organisation of the data follows the standard IBL data structure.
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Please see
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* [These instructions](https://int-brain-lab.github.io/iblenv/notebooks_external/data_structure.html) to download an example dataset for one session, and get familiarised with the data structure
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* [These instructions](https://int-brain-lab.github.io/iblenv/notebooks_external/data_download.html) to learn how to use the ONE-api to search and download the released datasets
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* [These instructions](https://int-brain-lab.github.io/iblenv/loading_examples.html) to get familiarised with specific data loading functions
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Note:
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* The tag associated to this release is `2025_Q3_Noel_et_al_Autism`
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## How to cite this dataset
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If you are using this dataset for your research please cite:
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- the paper [A common computational and neural anomaly across mouse models of autism](https://doi.org/10.1038/s41593-025-01965-8)
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- if you download the data via ONE (recommended), cite the DOI: 10.6084/m9.figshare.30024880
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- if you download the data via OSF (not recommended), please use the OSF DOI.
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## Data release notes and changelog
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### 2025-08-29: initial release
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Possible future evolutions
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- Spike sorting re-run with iblsorter or newer algorithm to have better yield and better pre-processing
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- Perform the missing alignments with an automatic tool to get
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#### Excluded sessions
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The following sessions were excluded as the synchronisation stream from the bpod couldn't be found in the FPGA. As such the behaviour information couldn't be extracted to a clock synced to the electrophysiology.
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| EID | Subject | Date | Number |
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| --- | --- | --- | --- |
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| 5d4e158b-7d6d-48fd-ad94-74ad4704e89f | CSP018 | 2021-01-20 | 001 |
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| 8507e9f6-4da3-454a-b553-3fc8f6299bbb | SH014 | 2020-07-13 | 001 |
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| e9620e9a-688a-45da-ba6e-33fce6753729 | SH008 | 2020-03-04 | 001 |
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| 429de7e8-1fc9-4aa2-87a1-0800268935d7 | CSP023 | 2020-11-17 | 001 |
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#### Video data: QC report
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We have only included video data where we could confidently align the frames timing to the main experiment clock.
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#### Electrophysiology: QC report
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##### Quality of uppermost channels
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What we believe to be a failing Neuropixel headstage translates in the loss of 10-15 of the uppermost channels of the probes. This affects many recordings.
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Make sure to perform an anomaly detection using [ibl-neuropixel](https://github.com/int-brain-lab/ibl-neuropixel) or [spike interface](https://spikeinterface.readthedocs.io/en/stable/)
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##### CRITICAL: Missing spike sorting
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The following insertions do not have any spike sorting available.
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| PID | EID | Subject | Date | Number | Probe |
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| --- | --- | --- | --- | --- | --- |
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| bcb1dac7-6d2b-47ad-bbbe-a4aaf9774481 | b33a5ab8-7b51-4fe4-9ee2-584fb430b41b | FMR025 | 2021-09-02 | 001 | probe01 |
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| a25d9370-a714-4662-88c5-ccf69b646bd5 | 1a4479ec-8511-48c9-9169-55a20f2626f9 | FMR031 | 2022-03-22 | 001 | probe01 |
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| 7bf6ff8d-6487-481e-a174-d1cbe31db2c4 | baab8aae-273e-4f83-80a9-81e9b7f99185 | FMR032 | 2022-03-15 | 001 | probe01 |
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| 65c6349d-6ec3-4a7d-aefc-7cfe328c1faf | 2fe8aa16-faab-49e3-8e13-aceb0b095a30 | SH006 | 2020-02-26 | 001 | probe00 |
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| 7b00f29f-f67f-4fa9-b4c9-e844c44b7b6b | ccffff9f-c432-4377-b228-e2710bc109b6 | SH011 | 2020-02-06 | 001 | probe01 |
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| 8ade2696-5a06-4e0e-8f73-674aaf60a6ca | 8658a5ad-58ce-4626-8d46-be68cd33581b | SH011 | 2020-02-07 | 001 | probe01 |
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| 1606f00b-e4a0-434e-a81b-a492016b42d9 | 792fe5db-dd66-4fc7-ab96-accfce28b7f4 | SH025 | 2022-02-01 | 002 | probe00 |
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| 2a32115c-bb3e-42ec-81a4-c24f11d1721f | 68c22775-1b40-44ff-8802-b95d0152d565 | SH025 | 2022-02-02 | 001 | probe01 |
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| 474965f4-5751-497b-9423-cb130fc30644 | 2d792481-75e5-4e51-a189-20e7cbbcd8ad | SH025 | 2022-02-04 | 002 | probe00 |
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| de7639c4-0f81-4370-84f2-70dde255eca7 | ae21bab0-65dc-40a2-bc6d-48a6a07e9c82 | SH027 | 2022-01-27 | 001 | probe01 |
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| 1034a9c5-85cd-49c1-bf6b-0fb2cbc4ee67 | 576de022-4a2b-4423-8f7f-53f83b1b896e | CSP003 | 2019-11-22 | 001 | probe01 |
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| ef8eb985-3731-48c5-ac62-38214f8d8ee4 | 95fa5278-3870-4087-9c7d-a306a068d334 | CSP031 | 2022-02-01 | 001 | probe01 |
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| eddf08b2-2b1f-489c-bceb-f28592518f61 | bf708a95-5980-463d-b54f-329b49754313 | CSP032 | 2022-01-27 | 001 | probe00 |
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| c31e3510-cc05-4992-ac30-808d3b3f0d81 | d133df68-dec9-4979-a500-a549719424d1 | CSP032 | 2022-01-28 | 001 | probe00 |
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| 131716c1-515e-4a45-9158-cf1af6da39c7 | 86bef629-a95d-4a68-890b-2f7cabb58504 | NYU-57 | 2021-11-18 | 001 | probe00 |
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##### CRITICAL: Missing Histology tracing
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| PID | EID | Subject | Date | Number | Probe |
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| --- | --- | --- | --- | --- | --- |
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| 7d475dc7-a60e-4418-837d-fa9c43a91cff | fb5831ac-d15b-437b-98fe-47d03d7edc15 | SH002 | 2019-11-25 | 001 | probe01 |
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| 2fe497f6-95e8-4349-9b92-47f26265b784 | 0c2f24d9-5184-43e6-97dc-17b31cb8cee9 | SH004 | 2020-02-10 | 002 | probe00 |
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| ada946d0-1195-4f04-9d45-8e7e6adf7f60 | cdfb4449-30fb-4dd9-85cd-c8dd385ddf75 | SH013 | 2020-07-23 | 001 | probe00 |
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| 2eeeee9a-678e-47c9-b2d9-22daec55ddbb | b73a16d7-555d-4c51-91a0-611d0ed0a975 | CSP003 | 2019-11-19 | 001 | probe00 |
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| 5831925c-4b27-425f-9b7a-ff8b0691e9f3 | c1f807b4-7538-4f82-a9a6-52d658eb0bd2 | CSP017 | 2020-11-17 | 001 | probe01 |
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| be8f5333-ea97-4a80-8574-ab937b2087cd | 5f1a76fa-a1b6-4140-83f2-e891db4e11a8 | CSP017 | 2020-11-18 | 001 | probe01 |
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| d8ed8bbe-c2fc-46ad-b769-872d326a8179 | 579881ec-e2f8-4079-b109-100e9ddbe8c0 | CSP017 | 2020-11-19 | 001 | probe01 |
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| 553b258e-e21d-48b2-8065-21246c82e51a | 9c257bc7-ac32-4255-bb03-8ff90dfc2547 | CSP017 | 2020-11-20 | 001 | probe00 |
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##### ERROR: Missing alignments
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Here we have spike sorting and histology tracing, but the channels haven´t been aligned. We are not releasing those datasets for the time being.
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Those would be good candidates for a future revision if the ephys atlas task force devises a tool to perform automatic alignments.
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| PID | EID | Subject | Date | Number | Probe |
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| --- | --- | --- | --- | --- | --- |
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| 66887465-3ace-43d6-b609-5e5e1878e8bd | 41eaf4a1-62d6-445f-9284-840b082b31da | FMR030 | 2022-03-29 | 001 | probe01 |
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| eacf11c1-47d5-4245-b715-0acc29ccec5c | 77d47a6a-6e05-4e3e-9c85-49a62feada2a | FMR030 | 2022-03-31 | 001 | probe01 |
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| 131e4225-f661-4d2a-9e82-ce5c476ca33c | 813e255f-9958-4faf-939d-4d1fdc6536de | FMR030 | 2022-04-01 | 001 | probe01 |
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| 58c392e4-1a2e-455e-89eb-65a3ceab8093 | 9c09d626-ef3f-4e51-b353-a578531a8a4b | FMR030 | 2022-04-04 | 001 | probe01 |
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| d805f64c-87dc-459c-b23d-8970483c3127 | badc1a08-030c-4144-91f4-d002a05b6d1f | SH020 | 2021-09-22 | 001 | probe00 |
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| f4fb1053-61a7-482a-9099-39ed436dd756 | 81adee43-8f38-459c-b9ec-f8fee29ddb60 | SH024 | 2021-09-28 | 001 | probe00 |
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| 6f74c301-8d73-49c3-b2fd-6ad1a74528ce | d6492634-aa84-4b51-ab8e-2434648f2d83 | SH024 | 2021-09-29 | 001 | probe00 |
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| af95a0e6-073e-416c-9209-7ab30da8ce02 | 66687fe4-c6f8-40db-96c1-9fc49e07d2b6 | SH024 | 2021-09-30 | 001 | probe00 |
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| 27271de8-f1ff-4299-a547-05f1be477417 | 1862cb56-2241-42c6-9298-a0861d8fb175 | SH024 | 2021-10-01 | 001 | probe00 |
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| 685fb0f1-ed10-4a8b-8e10-51b42a7d67eb | 54859a45-23eb-41ed-b0dc-aa6d6607f006 | SH026 | 2022-03-01 | 001 | probe01 |
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| fcdc2472-1025-41ef-bc0f-2c026c35dd6e | a152d17b-b1e3-4994-89a0-86edbf28661e | SH026 | 2022-03-02 | 001 | probe01 |
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| 94d1359a-3e87-40a5-b747-7e2b85a4330a | 340a0f2b-0201-4c8b-a491-57b8b6320498 | SH026 | 2022-03-03 | 001 | probe01 |
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| 2793cec9-4422-47b4-a58c-4dc5a71baa41 | 7b8d00bf-d7d9-43a4-8f69-50217cfb284c | SH026 | 2022-03-04 | 001 | probe01 |
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| 9dd7e4a7-391a-4f61-982f-0efa470ccf59 | fdd79794-88ea-4a9c-910b-524c150dec48 | CSP001 | 2019-11-20 | 001 | probe00 |
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| 326c22d1-dbce-41dd-92f7-26d6e4a8d9dc | 147d9be2-ab3a-4dd6-a9b8-fdf6fc129d84 | CSP003 | 2019-11-21 | 002 | probe00 |
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| a6b0d2db-c3fc-4967-bdbf-e3f338f3af5e | 9b304cf6-a878-4539-bfd0-aeebcf07f8d1 | CSP023 | 2020-11-18 | 001 | probe01 |
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| c9eb42ff-3d53-4044-9468-93e89d870368 | 9854d7b0-ab63-48d5-a7d6-6b5bf0cf5a30 | CSP026 | 2021-06-08 | 001 | probe00 |
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| 051a6c5f-4a75-42e2-aa82-b4e3cbe1cecf | 26ef4db9-d1b2-4df8-8de7-519accb9883c | CSP026 | 2021-06-09 | 002 | probe00 |
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| 8e5c51e1-690d-4b50-88c4-37242d5ccb65 | 8d3b57dd-4651-4c5f-a4c3-8de8865fdca3 | CSP026 | 2021-06-10 | 001 | probe00 |
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| fab06f3a-9ff5-405d-957c-6e628c25af3d | 7b3be2f3-e11c-4352-b73f-10e7813ccec9 | CSP026 | 2021-06-11 | 001 | probe00 |
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| 8d6c9ffc-6606-4f11-89bf-cc422ce5022a | 50911ee6-732b-4b07-afe0-8c48a287c803 | NYU-49 | 2021-07-23 | 001 | probe01 |

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