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Unexplained Repeated Pregnancy Loss is Associated with Altered Perceptual and Brain Responses to Men’s Body-Odor
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The Object Detection for Olfactory References (ODOR) Dataset
<p><strong>The Object Detection for Olfactory References (ODOR) Dataset</strong></p> <p>Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. </p> <p>Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. </p> <p>It has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. </p> <p>Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.</p> <p><strong>How to use</strong></p> <p>The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes or image descriptions, as well as formal annotations, such as artist, license, or creation year. </p> <p>In addition to a zip containing the dataset images, we provide links to their source collections in the metadata file and a Python script to conveniently download the artwork images (`download_imgs.py`).</p> <p>The mapping between the `images` array of the `annotations.json` and the `metadata.csv` file can be accomplished via the `file_name` attribute of the elements of the `images` array and the unique `File Name` column of the `metadata.csv` file, respectively.</p>
Calcium imaging of odor responses in the fruit fly mushroom body
<p><strong>Abstract</strong></p> <p>This dataset contains olfactory responses in the third stage of the olfactory circuit in fruit flies: the mushroom body. The responses are recorded with the GCaMP3 sensor. The methods used to collect the data and the procedures to process them are presented in detail in Campbell et al., 2013, Journal of Neuroscience. The dataset was also used in a recent manuscript by Srinivasan et al., 2023.</p> <p><strong>Methods</strong></p> <p>Please refer to Campbell et al., 2013, Journal of Neuroscience for details. Here, we present a description of how the data was collected, the odors presented, and the analysis, excerpted from Campbell et al., 2013.</p> <p><strong>Animal preparation</strong></p> <p>Flies carrying the genetically encoded calcium sensor UAS-GCaMP3 (Tian et al., 2009) were crossed with OK107-Gal4 flies (Connolly et al., 1996) to drive GCaMP3 expression in essentially all KCs (Lee and Luo, 1999; Aso et al., 2009). All experiments were conducted on female F1 heterozygotes from this cross, aged 2–5 d post-eclosion. Procedures for animal preparation were as described previously (Turner et al., 2008; Murthy and Turner, 2010; Honegger et al., 2011). Flies were anesthetized temporarily on ice and inserted into a small hole cut in the recording platform. The animal’s head was tilted forward, exposing the olfactory organs to the odor delivery nozzle located on the underside of the plat- form. The fly was fixed in place with fast-drying epoxy (Devcon 5 min epoxy). The top of the fly was bathed in oxygenated saline (Wilson et al., 2004) and the cuticle overlying the brain was dissected away. Air sacs overlying the MBs were pushed aside, but we did not attempt to remove the perineural sheath. To minimize movement of the brain inside the head capsule, we removed the pulsatile organ at the neck and the probos- cis retractor muscles that pass over the caudal aspect of the optic lobes.</p> <p> </p> <p><strong>Odor delivery </strong></p> <p>The following chemicals were used as stimuli: 2-heptanone (CAS #110-43- 0), 3-octanol (CAS #589-98-0), 6-methyl-5-hepten-2-one (CAS #110-93-0), ␣-humulene (CAS #6753-98-6), benzaldehyde (CAS #100-52-7), ethyl lactate (CAS #97-64-3), ethyl octanoate (CAS #106-32-1), hexanal (CAS #66-25-1), isoamyl acetate (CAS #123-92-2), 4-methylcyclo- hexanol (CAS #589-91-3), methyl octanoate (CAS #111-11-5), diethyl suc- cinate (CAS #123-25-1), pentanal (CAS #110-62-3), butyl acetate (CAS #123-86-4), 1-octen-3-ol (CAS #3391-86-4), 1-hepten-3-ol (CAS #4938-52- 7), and pentyl acetate (CAS #628-63-7). Odors were presented using a custom-built delivery system that uses serial air dilutions to control odor concentration while maintaining a constant total airflow of 1 L/min at the fly. Experiments were conducted at an odor dilution of 1:100 or, where appropriate, adjusted to match the concentrations used behaviorally. We used a photo-ionization detector (Aurora Scientific) to match concentrations between the imaging rig and the T-maze and to monitor odor delivery throughout each imaging ex- periment. Odor pulses were created by switching between clean and odorized air streams using a synchronous two-way valve (N-Research). This final valve was located 50 cm from the fly, leading to a delay of 300 ms between valve switching and the odor reaching the fly. The flow path was 1/8 inch in diameter throughout, which enabled the system to work near atmospheric pressure at these flow rates. The distance of the valve from the fly and the large tubing diameter virtually eliminated pressure transients caused by valve switching, as measured by the photo-ionization detector and a hot-wire anemometer.</p> <p><strong>Calcium imaging</strong></p> <p>Two-photon imaging was performed using a Prairie Ultima system (Prairie Technologies) and a Ti-Sapphire laser (Chameleon XR; Coher- ent) tuned to 920 nm delivering 8 –10 mW at the sample. All images were acquired with Olympus water-immersion objectives (LUMPlanFl/IR, 60x, numerical aperture 0.9; LUMPlanFl/IR, 40x, numerical aperture 0.8). Imaging planes were selected to maximize the number of visibleKCs. Typically imaging frames were 300 x 300 pixels, acquired with a pixel dwell time of 1.6 s, yielding frame rates near 3.8 Hz. On average, 120 KCs (range: 60 –170) were monitored in one plane. Custom MATLAB (MathWorks) routines were used to control odor presentation and synchronize stimulus delivery with data acquisition. Data were acquired in 20 s sweeps with a 1 s odor pulse triggered 8 s after sweep onset. The interstimulus interval was 25 s. Stimuli were presented in randomly interleaved fashion, adjusted so that the same odor was never presented twice in succession.</p> <p><strong>Imaging analysis</strong></p> <p>Data were analyzed using MATLAB and R (http://www.R-project.org). To correct for motion within the field of view, frames were aligned using 2D image registration approaches. In many cases, a Fourier-based sub-pixel translation correction was sufficient (Guizar-Sicairos et al., 2008). Some animals required an affine transform to cope with global distortions, such as rotational movement of the brain (Thirion, 1998). Where necessary a nonrigid transform was used to correct more localized dis- tortions (Klein et al., 2010). Fluorescent neural tissue was automatically segmented from the surrounding regions. Pixel intensity values from the area outside this boundary were considered to represent background (tissue autofluorescence plus shot noise) and the mean pixel intensity value from the back- ground was then subtracted from the overall image. To quantify the response of the KCs a small, circular region of interest 6 – 8 pixels in diameter was applied to each cell body. This allowed aver- aging of the pixel intensity values from each cell, treating individual KCs as separate units. Care was taken to ensure that each selected cell re- mained within its region of interest over the whole imaging session. Response amplitudes were calculated as the mean change in fluorescence (dF/F) in the 0.5– 4.5 s window after stimulus onset. A statistical test originally described in Honegger et al. (2011) was used to determine whether a KC responded significantly on a given trial. Briefly, the SD of the baseline activity was obtained 8 s before stimulus onset. The response time course was then smoothed using a five-point running average to control for outliers. The peak dF/F in the 0.5– 4.5 s window after stimulus onset was determined. The response was judged to be significant if this peak was 2.33 SDs greater than the baseline, which corresponds to a one-tailed significance test where alpha = 0.01.</p> <p><br> <strong>References</strong></p> <p>Aso Y, Grübel K, Busch S, Friedrich AB, Siwanowicz I, Tanimoto H (2009) The mushroom body of adult Drosophila characterized by GAL4 drivers. J Neurogenet 23:156 –172. </p> <p>Connolly JB, Roberts IJ, Armstrong JD, Kaiser K, Forte M, Tully T, O’Kane CJ (1996) Associative learning disrupted by impaired Gs signaling in Drosophila mushroom bodies. Science 274:2104 –2107.</p> <p>Honegger KS, Campbell RA, Turner GC (2011) Cellular-resolution population imaging reveals robust sparse coding in the Drosophila mushroom body. J Neurosci 31:11772–11785.</p> <p>Lee T, Luo L (1999) Mosaic analysis with a repressible cell marker for studies of gene function in neuronal morphogenesis. Neuron 22:451– 461.</p> <p>Murthy M, Turner GC (2010) In vivo whole-cell recordings in the Drosophila brain. In: Drosophila neurobiology methods: a laboratory manual (Zhang B, Waddell S, Freeman M, eds). Cold Spring Harbor, NY: Cold Spring Harbor Laboratory.</p> <p>Srinivasan, S., Daste, S., Modi, M., Turner, G., Fleischmann, A. & Navlakha, S (2023). Stochastic coding: a conserved feature of odor representations and its implications for odor discrimination. bioRxiv.</p> <p>Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2:243–260.</p> <p>Tian L, Hires SA, Mao T, Huber D, Chiappe ME, Chalasani SH, Petreanu L, Akerboom J, McKinney SA, Schreiter ER, Bargmann CI, Jayaraman V, Svoboda K, Looger LL (2009) Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat Methods 6:875–881.</p> <p>Turner GC, Bazhenov M, Laurent G (2008) Olfactory representations by Drosophila mushroom body neurons. J Neurophysiol 99:734 –746.</p> <p>Wilson RI, Turner GC, Laurent G (2004) Transformation of olfactory representations in the Drosophila antennal lobe. Science 303:366–370.</p> <p><strong>Usage notes</strong></p> <p>The files are all in csv format, and can be easily opened in R or Python or other programming languages.</p> <p>Please see the README.md file for directions on how to use the data.</p> <p>The dataset included here is broken into two parts. The main dataset was the one that was chiefly used in the Campbell and Srinivasan papers, with the second part containing 7 additional datasets that were used in some figures. A fuller description is available in the README.md file.</p>
The Object Detection for Olfactory References (ODOR) Dataset.
<p>Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. Existing datasets provide instance-level<br> annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The proposed ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. Conducting a statistical analysis, we showcase challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. Furthermore, we provide an extensive baseline analysis for object detection models and highlight the challenging properties of the dataset through a set of secondary studies. Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.</p> <p><strong>How to use</strong></p> <p>The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes [72 , 73]) or image<br> descriptions, as well as formal annotations, such as artist, license, or creation year. For the sake of license compliance, we do not publish the images directly (although most of the images are public domain). Instead, we provide links to their source collections in the metadata file (meta.csv) and a python script to download the artwork images (download_images.py).</p> <p> </p>
Example Datasets for Iliski - Neuronal calcium, RBC velocities and fUS responses to odorant stimuli in the mouse olfactory bulb
<p>Dataset containing 2 HDF5 files, one per mouse. It is intended to be used to test Iliski, a Transfer Function computation software. Iliski is available on GitLab (<a href="https://gitlab.com/AliK_A/iliski">https://gitlab.com/AliK_A/iliski</a>) along with the User Manual. Refer to the User Manual and to the ReadMe file for more details on Iliski. Data were already published on Zenodo (<a href="https://doi.org/10.5281/zenodo.3773863">https://doi.org/10.5281/zenodo.3773863</a>), but along an old version of the software. This upload is made for clarity purposes.</p> <p>Each file contains acquisitions of responses to odorant stimuli in the olfactory bulb made with :</p> <ul> <li>two-photon linescan microscopy (for Ca2+ and RBC velocity);</li> <li>functional ultrafast ultrasound, acquired from a coronal plane.</li> </ul> <p>HDF5 files tree is as follows :</p> <ul> <li>Data type <ul> <li>Raw : straight out of our extraction software, no specific treatment applied;</li> <li>Aligned : every acquisition has been aligned so that the odor delivery matches the 10 s mark. Acquisitions have also been interpolated to be meaned;</li> <li>Delta : aligned acquisitions are subtracted with the baseline value (between 5 and 10 s);</li> <li>DetaOverBSL : aligned acquisitions are subtracted and then divided with the baseline value.</li> </ul> </li> <li>Data source <ul> <li>Ca : calcium data from GCamP6f expressed in the mitral cells dendritic tufts;</li> <li>RBC : RBC velocities in a capillary near the calcium recording site, simultaneously acquired;</li> <li>FUS : fUS data, coronal plane. Only in FUS folder is two different folders then : High and Lowspeed, corresponding to different filter for fUS treatment, > 80Hz and 10-30Hz respectively.</li> </ul> </li> <li>Stimulation type : Odorant_Quantity_Duration <ul> <li>Odorant type, either Iso Amyl Acetate (AA) or Ethyl tiglate (ET);</li> <li>Odor quantity : measured and calibrated in volt with a photo-ionizator;</li> <li>Odor duration : from 5 s down to 120 ms, a single sniff for a mouse.</li> </ul> </li> </ul> <p>Ca2+ : Calcium</p> <p>fUS : functional ultrafast ultrasound</p> <p>RBC : Red Blood Cell</p>
Alternation emerges as a multi-modal strategy for turbulent odor navigation - Dataset
<p>Dataset from 3D direct numerical simulation of odor evolution in a turbulent channel flow.</p> <p>The dataset contains two .mat files with nose (z ~ 50 cm) and ground (z = 0) level 2D slices;</p> <p>coordinates.mat with the coordinates in X and Y directions;</p> <p>and a Jupyter Notebook file (Read_nose_ground_dataset) to read and plot the 2D fields.<br><br>Updated on May 20th 2025<br>3D velocity data have been added to the dataset.</p>
Fig. 2 in Comparison of natural and artificial odor lures for nilgai (Boselaphus tragocamelus) and white-tailed deer (Odocoileus virginianus) in South Texas: Developing treatment for cattle fever tick eradication
Fig. 2. Locations of nilgai lure transects (red bars) at the Santa Rosa Ranch near Riviera, TX. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 5 in Comparison of natural and artificial odor lures for nilgai (Boselaphus tragocamelus) and white-tailed deer (Odocoileus virginianus) in South Texas: Developing treatment for cattle fever tick eradication
Fig. 5. Nilgai cow visiting lure site (A) and (B) nilgai bull defecating at offal lure site at the East Foundation's Santa Rosa Ranch, near Riviera, TX.
High-resolution glomerular responses to a large variety of odorants in the mouse olfactory bulb
<p>Imaging of glomerular responses using intrinsic optical signal and synaptopHluorin. </p> <p>Find the software here: <a href="https://doi.org/10.5281/zenodo.3383874">https://doi.org/10.5281/zenodo.3383874</a></p> <p>The paper is here: </p> <p>Soelter, J., Schumacher, J., Spors, H., Schmuker, M.: Computational exploration of molecular receptive fields in the olfactory bulb reveals a glomerulus-centric chemical map. <em>Sci Rep</em> 10, 77 (2020). <a href="https://doi.org/10.1038/s41598-019-56863-4">https://doi.org/10.1038/s41598-019-56863-4</a></p>
Fig. 3 in Odorant-binding protein 2 is involved in the preference of Sogatella furcifera (Hemiptera: Delphacidae) for rice plants infected with the Southern rice black-streaked dwarf virus
Fig. 3. Effects of RNAi of SfurOBP-2 or -11 on the preferences of S. furcifera for healthy, virus-infected rice plants or air (empty pot with soil). The data of nonresponding insects were given on the right of the bar in the figure. Double asterisks indicate statistically significant difference (chi-square test: **P <0.01), NS indicates no significant difference. (A) S. furcifera choice tests between healthy or infected rice plants afer injection of dsRNA or water (Control); (B) S. furcifera choice tests between healthy rice plants or air afer injection of dsRNA or water (Control); (C) S. furcifera choice tests between virus-infected rice plants or air afer injection of dsRNA or water (control).
Fig. 2 in Odorant-binding protein 2 is involved in the preference of Sogatella furcifera (Hemiptera: Delphacidae) for rice plants infected with the Southern rice black-streaked dwarf virus
Fig. 2. Detection of the mRNA levels afer RNA interference. The histogram bars represent mean ± SE of 3 biological replicates. Different letters above bars indicate significant differences (1-way ANOVA: P <0.01). (A) mRNA levels of SfurOBP2 in S. furcifera injected with different dsRNA. RNase-free water injection (Control), EGFP-dsRNA (dsEGFP), SfurOBP2-dsRNA (dsOBP2). (B) mRNA levels of SfurOBP11 in S. furcifera injected with different dsRNA. RNase-free water injection (control), EGFP-dsRNA (dsEGFP), SfurOBP11-dsRNA (dsOBP11).
Fig. 1 in Odorant-binding protein 2 is involved in the preference of Sogatella furcifera (Hemiptera: Delphacidae) for rice plants infected with the Southern rice black-streaked dwarf virus
Fig. 1. Expression profiles of SfurOBP2 and SfurOBP11 in the antennae of virusfree and viruliferous S. furcifera. VFA: virus-free antennae; VIA: virus-infected antennae. The histogram bars represent mean ± SE of 3 biological replicates. Asterisk above bars indicate significant differences (t-test: *P <0.05, **P <0.01).
Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1- MD simulations
<p>Topology, parameter and coordinates files of the Molecular dynamics (MD) simulations of OR5K1 3D models from AlphaFold 2 (AF2) and Homology Modeling (HM). We used ACEMD3 (v3.5.1) as a molecular engine, CHARMM36 as force field. Three replicas of 100 ns (dcd files) for both systems are reported. Water molecules, ions, and membrane atoms (POPC: phosphatidylcholine) atoms were removed from the original trajectories before the upload.</p>
Figure 6 in Odorants Differentiate Australian Rattus with Increased Complexity in Sympatry
Figure 6. Variation in chemical complexity of preputial gland extracts among four subspecies of Rattus fuscipes and R. leucopus based on 80 quantitated compounds. (A) Box and whisker plots of the number of chemical compounds detected in each subspecies. (B) Box and whisker plots of the total abundance of chemical compounds detected in each subspecies. In both, asterisks above pairwise comparisons of conspecific subspecies indicate significantly higher values (p <0.01) with a one-way Mann-Whitney U Test for all sympatric to allopatric comparisons.
Figure 5 in Odorants Differentiate Australian Rattus with Increased Complexity in Sympatry
Figure 5. Variation in chemical composition of preputial glands among four subspecies of Rattus fuscipes and R. leucopus. (A) Twodimensional representation of chemical composition among individuals based on non-metric multidimensional scaling of all 80 quantitated compounds showing separation of species and subspecies (grey polygons represent grouping of samples using convex hulls). (B) Anosim plot of total compounds showing greater variation between than within species and among than within subspecies. (C) Two-dimensional representation of chemical composition among individuals based on non-metric multidimensional scaling using subset of thiazoline, carboxylic acid, and sesquiterpene compounds showing separation of species and subspecies (grey polygons represent grouping of samples using convex hulls). (D) Anosim plot of subset of compounds showing greater variation between than within species and among than within subspecies.
Figure 4. Representative ion m in Odorants Differentiate Australian Rattus with Increased Complexity in Sympatry
Figure 4. Representative ion m/z 60 trace with post-run selected ion chromatograms for thiazolines and carboxylic acids from preputial gland extracts of (A) Rattus fuscipes assimilis (QMJM 19152); (B) R. fuscipes coracius QMJM 19100; (C) R. leucopus cooktownensis QMJM 19131; and (D) R. leucopus leucopus QMJM 19060. Numbers above peaks identify specific compounds: (1) 2-methylthiazoline, 10.56 min; (2) 2-ethylthiazoline, 16.04 min; (3) 2-isopropylthiazoline, 19.59 min; (4) 2-propylthiazoline, 22.49 min; (5) 2-sec-butylthiazoline (SBT), 25.89 min; (6) 2-isobutylthiazoline, 26.10 min; (7) 2-butylthiazoline, 29.85 min; (8) dodecanoic acid, 56.28 min; (9) tetradecanoic acid, 67.25 min; (10) pentadecanoic acid, 72.34 min; and (11) hexadecanoic acid, 77.50 min.
Figure 3 in Odorants Differentiate Australian Rattus with Increased Complexity in Sympatry
Figure 3. Chemical structures of seven thiazoline compounds identified from preputial glands of Rattus
Figure 1 in Odorants Differentiate Australian Rattus with Increased Complexity in Sympatry
Figure 1. Map of sample localities across Queensland with select cities and towns indicated with stars. Preputial
Figure 2 in Odorants Differentiate Australian Rattus with Increased Complexity in Sympatry
Figure 2. STRUCTURE plot of allozyme variation among four subspecies of Australian Rattus. Each of the 166 samples analysed in this study (13 Rfa, 67 Rfc, 65 Rlc, 21 Rll) is represented by a vertical bar shaded based on the likelihood of assignment to one of four populations. Individual samples are not distinguishable where they share a high likelihood of assignment to the same population (e.g., all Rfa samples). Plot demonstrates lack of gene flow between species in sympatry (Rfc and Rlc) with no mixed likelihood between species. Limited gene flow (or shared polymorphism) among subspecies, Rlc and Rll, are evident in bars with mixed shading.
Data for: Chronic exposure to odors at naturally occurring concentrations triggers limited plasticity in early stages of Drosophila olfactory processing
<p>In insects and mammals, olfactory experience in early life alters olfactory behavior and function in later life. In the vinegar fly <em>Drosophila</em>, flies chronically exposed to a high concentration of a monomolecular odor exhibit reduced behavioral aversion to the familiar odor when it is re-encountered. This change in olfactory behavior has been attributed to selective decreases in the sensitivity of second-order olfactory projection neurons (PNs) in the antennal lobe that respond to the overrepresented odor. However, since odorant compounds do not occur at similarly high concentrations in natural sources, the role of odor experience-dependent plasticity in natural environments is unclear. Here, we investigated olfactory plasticity in the antennal lobe of flies chronically exposed to odors at concentrations that are typically encountered in natural odor sources. These stimuli were chosen to each strongly and selectively excite a single class of primary olfactory receptor neuron (ORN), thus facilitating a rigorous assessment of the selectivity of olfactory plasticity for PNs directly excited by overrepresented stimuli. Unexpectedly, we found that chronic exposure to three such odors did not result in decreased PN sensitivity but rather mildly increased responses to weak stimuli in most PN types. Odor-evoked PN activity in response to stronger stimuli was mostly unaffected by odor experience. When present, plasticity was observed broadly in multiple PN types and thus was not selective for PNs receiving direct input from the chronically active ORNs. We further investigated the DL5 olfactory coding channel and found that chronic odor-mediated excitation of its input ORNs did not affect PN intrinsic properties, local inhibitory innervation, ORN responses, or ORN-PN synaptic strength; however, broad-acting lateral excitation evoked by some odors was increased. These results show that PN odor coding is only mildly affected by strong persistent activation of a single olfactory input, highlighting the stability of early stages of insect olfactory processing to significant perturbations in the sensory environment.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.