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30,817 results for “type”
HT1080WT cells embedded in 3D collagen type I matrices - manual annotations for cell instance segmentation and tracking
<p>Human fibrosarcoma HT1080WT (ATCC) cells at low cell densities embedded in 3D collagen type I matrices [1]. The time-lapse videos were recorded every 2 minutes for 16.7 hours and covered a field of view of 1002 pixels × 1004 pixels with a pixel size of 0.802 μm/pixel The videos were pre-processed to correct frame-to-frame drift artifacts, resulting in a final size of 983 pixels × 985 pixels pixels.</p> <p><em>Hasini Jayatilaka, Anjil Giri, Michelle Karl, Ivie Aifuwa, Nicholaus J Trenton, Jude M Phillip, Shyam Khatau, and Denis Wirtz. EB1 and cytoplasmic dynein mediate protrusion dynamics for efficient 3-dimensional cell migration. FASEB J., 32(3):1207–1221, 2018. ISSN 0892-6638. doi: 10.1096/fj.201700444RR.</em></p> <p>Further information about how to use this data is given in <a href="http://github.com/esgomezm/microscopy-dl-suite-tf">https://github.com/esgomezm/microscopy-dl-suite-tf</a></p> <p><strong>This dataset is provided together with the following preprint and if you use it, we would like to kindly ask you to cite it properly:</strong></p> <p><a href="https://arxiv.org/abs/2112.08817">Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Çiçek, Thomas Brox, Denis Wirtz, Arrate Muñoz-Barrutia, *Search for temporal cell segmentation robustness in phase-contrast microscopy videos*, arXiv 2021 (arXiv:2112.08817)</a></p>
Data accompanying MetaChrom and "Annotating functional effects of non-coding variants in neuropsychiatric cell types by Deep Transfer Learning"
<p>This is the data accompanying the paper " Annotating functional effects of non-coding variants in neuropsychiatric cell types by Deep Transfer Learning" and the GitHub repository https://github.com/bl-2633/MetaChrom. </p> <p><strong>/data/bed_files/ </strong>contains the unprocessed bed file used in analysis</p> <p><strong>/data/seq_data/</strong> contains processed data from the bed files with corresponding partition and labels for each sequence segment.</p> <p><strong>/trained_model/MetaChrom_model/</strong> contains the pre-trained MetaChrom model on neural developmental context</p> <p><strong>/trained_models/MetaFeat_model/ </strong>contains the MetaFeat model used in training</p> <p><strong>/tool/</strong> contains files and software necessary for the processing pipeline.</p>
Raw Data for the article: A retrospective molecular epidemiological scenario of carbapenemase-producing Klebsiella pneumoniae clinical isolates in a Sicilian transplantation hospital shows a swift polyclonal divergence among sequence types, resistome and virulome
<p>In this work, we assessed and characterized the epidemiological scenario of carbapenem-resistant Klebsiella pneumoniae strains (CR-Kp) at IRCCS-ISMETT, a transplantation hospital in Palermo, Italy, from 2008 to 2017. A total of 288 K. pneumoniae clinical isolates were selected based on their resistance to carbapenems. Molecular characterization was also done in terms of the presence of virulence and resistance genes. All patients were inpatients from our facility and clinical isolates were collected from several sources, either from infection or colonization cases. We observed that, in agreement with the Italian epidemiological scenario, initially only ST258 and ST512 clade II (but not from clade I) were identified from 2008 to 2011. From 2012 onwards, other STs have been observed, including the clinically relevant ST101 and ST307, but also others not previously observed in other Italian health settings, such as ST220 and ST753. The presence of genes involved in resistance and virulence was confirmed, and a heterogeneous genetic resistance profile throughout the years was observed. Our work highlights that resistance genes are rapidly disseminating between different and novel K. pneumoniae clones which, combined with resistance to multiple antibiotics, can derive into more aggressive and pathogenic multidrug-resistant strains of clinical importance. Our results stress the importance of continuous surveillance of CR Enterobacterales in health facilities so that novel STs carrying resistance and virulence genes that may become increasingly pathogenic can be identified and adequate therapies to adopted to avoid their dissemination and derived pathologies.</p>
Data from: Partial mycoheterotrophy in green plants forming Paris-type arbuscular mycorrhiza requires a thorough investigation
<p>Recently, many green plants forming <em>Paris</em>-type arbuscular mycorrhiza (AM) have been suggested to receive fungal carbon (Giesemann <em>et al.</em> 2020b, 2021). Whereas the enrichment of natural abundance of heavy stable isotopes (particularly <sup>13</sup>C) has been considered as strong evidence for this 'partial mycoheterotrophy,' we show our own data on isotopic abundances and mycorrhizal colonization in Japanese plants and argue that the <sup>13</sup>C-enrichment may not always be the result of acquiring carbon from AM fungi.</p>
Data sets for 'Mechanical Compliance of Individual Fractures in a Heterogeneous Rock Mass from Production-type Full-waveform Sonic Data', submitted to JGR: Solid Earth
<p>Synthetic and field FWS data sets are supplied for validation of proposed methods for compliance estimation of individual fractures. Each zip contains ‘ReadMe.txt ‘, which illustrates the files and corresponding parameters. More details about the setup in the submitted paper ‘Mechanical Compliance of Individual Fractures in a Heterogeneous Rock Mass from Production-type Full-waveform Sonic Data’.</p>
Prediction and realisation of high mobility and degenerate p-type conductivity in CaCuP thin films Dataset
<p>Experimental and computational datasets for this publication, including README files. To unzip, in the command line paste the following command:</p> <blockquote> <p>tar -xzvf cacup_data_repository_v2.tar.gz</p> </blockquote> <p>And repeat the command:</p> <blockquote> <p>tar -xzvf <file.tar.gz></p> </blockquote> <p>for each necessary tar file.</p> <p>For any issues with accessibility, please email joe.willis.15@ucl.ac.uk.</p>
Dataset for "Oxidase-type C-H/C-H coupling using an isoquinoline-derived organic photocatalyst"
<p>We have uploaded the corresponding raw and processed data for each figure from main text and supporting information. We also uploaded the raw data of NMR and HR-MS for characterization of the organic products.</p>
Short-interval fires increasing in the Alaskan boreal forest as fire self-regulation decays across forest types: Code and data
<p>Code and data to reproduce results in the associated paper. Additional downloads of climate data and various R packages will be required for some analyses.</p> <p>Abstract: Climate drivers are increasingly creating conditions conducive to higher frequency fires. In the coniferous boreal forest, the world’s largest terrestrial biome, fires are historically common but relatively infrequent. Post-fire, regenerating forests are generally resistant to burning (strong fire self-regulation), favoring millennial coniferous resilience. However, short intervals between fires are associated with rapid, threshold-like losses of resilience and changes to broadleaf or shrub communities, impacting carbon content, habitat, and other ecosystem services.</p> <p>Fires burning the same location 2+ times comprise approximately 4% of all Alaskan boreal fire events since 1984, and the fraction of short-interval events (<20 years between fires) is increasing with time. While there is strong resistance to burning for the first decade after a fire, from 10-20 years post-fire resistance appears to decline. Reburning is biased towards coniferous forests and in areas with seasonally variable precipitation, and that proportion appears to be increasing with time, suggesting continued forest shifts as changing climatic drivers overwhelm the resistance of early postfire landscapes to reburning. As area burned in large fire years of ~15 years ago begin to mature, there is potential for more widespread shifts, which should be evaluated closely to understand finer grained patterns within this regional trend.</p>
Reduced genetic diversity associated with the northern expansion of an amphibian species with high habitat-specialization, Ascaphus truei, resolved using two types of genetic markers
<p>Reconstruction of historical relationships between geographic regions within a species' range can indicate dispersal patterns and help predict future responses to shifts in climate. <em>Ascaphus truei </em>(coastal tailed frog) is an indicator species of the health of forests and perennial streams in the Coastal and Cascade Mountains of the Pacific Northwest of North America. We used two genetic techniques — microsatellite and genotype-by-sequencing (GBS) — to compare the within region genetic diversity of populations near the northern extent of the species' range (British Columbia, Canada) to two geographic regions in British Columbia and two in Washington, USA, moving towards the core of the range. Allelic richness and heterozygosity declined substantially as latitude increased. The northernmost region had the lowest mean expected heterozygosities for both techniques (microsatellite, M = 0.20, SE = 0.080; GBS, M = 0.025, SE = 0.0010) and the southernmost region had the highest (microsatellite, M = 0.88, SE = 0.054; GBS, M = 0.20, SE = 0.0029). The northernmost regions (NC and MC) clustered together in population structure models for both genetic techniques. Our discovery of reduced diversity may have important conservation and management implications for population connectivity and the response of <em>A. truei</em> to climate change.</p>
Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency
<p class="MsoNormal">Alzheimer's disease (AD) is a progressive neurodegenerative disease observed with aging that represents the most common form of dementia. To date, therapies targeting end-stage disease plaques, tangles, or inflammation have limited efficacy. Therefore, we set out to identify an earlier targetable phenotype. Utilizing a mouse model of AD we found that cell intrinsic neural precursor cell (NPC) dysfunction precedes widespread inflammation and amyloid plaque pathology, making it one of the earlier defects in the evolution of the disease. We demonstrate that reversing impaired NPC self-renewal via genetic reduction of USP16, a histone modifier and critical physiological antagonist of the Polycomb Repressor Complex 1, can prevent downstream cognitive defects and decrease astrogliosis in vivo. To delineate potential self-renewal pathways that might contribute to the defect and rescue of Tg-SwDI NPCs and Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup></em> NPCs, respectively, we performed single-cell RNA-seq and gene set enrichment analysis (GSEA) on lineage depleted primary FACS-sorted CD31<sup><span>-</span></sup>CD45<sup><span>-</span></sup>Ter119<sup><span>-</span></sup>CD24<sup><span>-</span></sup> NPCs from Tg-SwDI, WT, and Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup></em> mice at 3-4 months and 1 year of age. Using the GSEA Hallmark gene sets, we found only three gene sets that were enriched in Tg-SwDI mice over WT mice and rescued in the Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup> </em>mice at both ages: TGF-ß pathway, oxidative phosphorylation, and Myc Targets. The TGF-ß pathway consistently had the highest normalized enrichment score in pairwise comparisons between Tg-SwDI vs WT and Tg-SwDI vs Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup> </em>of the three rescued pathways. These data suggest that USP16 may regulate neural precursor cell function in part through the BMP pathway.</p>
Identified journal descriptor (JD), semantic type (ST), and MAUI keywords for PubMed/MEDLINE articles
<p>The Journal descriptor (JD) and Semantic type (ST) of PubMed articles were identified using a tool, called Journal descriptor indexing (JDI).</p> <p>MAUI keywords are identified by the MAUI tool and they can be used as a complement to MESH terms, as MeSH keywords are not always available in all PubMed articles.</p> <p>The methods of building the datasets can be found in the two articles: "Author name disambiguation in MEDLINE based on journal descriptors and semantic types" and " Exploring author name disambiguation on PubMed-scale"</p> <p>The PubMed database used is the 2019 baseline version, the number of articles in the two datasets are as follows:</p> <p>$ wc -l pubmed-paper-jd-st.tsv <br> 29796281 pubmed-paper-jd-st.tsv</p> <p>$ wc -l pubmed-paper-maui-keywords.tsv <br> 26509210 pubmed-paper-maui-keywords.tsv<br> </p>
Figure 3. Living type males, Maratus candens. 6 in Maratus candens, a new peacock spider in the linnaei group from southwestern Australia (Araneae: Salticidae: Euophryini)
Figure 3. Living type males, Maratus candens. 6, Dorsal view of opisthosoma. 12, 14-15, Males engaged in courtship display. As in other Maratus, inflation and extension of a very flexible pedicel allows the opisthosoma to be rotated forward and then elevated in a vertical position (15).
Figure 5. Type males, Maratus candens. 1-7 in Maratus candens, a new peacock spider in the linnaei group from southwestern Australia (Araneae: Salticidae: Euophryini)
Figure 5. Type males, Maratus candens. 1-7, Detailed views of opisthosoma in alcohol. 8-9, Anterior views, in alcohol. 10-12, Ventral views of living type males.
Simulation for "Asymptotic behavior for a time-inhomogeneous Kolmogorov type diffusion"
<p>The code and the video are related to the article G. Mihai, and E. Luirard. « Asymptotic behavior for a time-inhomogeneous Kolmogorov type diffusion », available on http://arxiv.org/abs/2004.11576.</p>
Data sets for "Formation of an Al-rich niccolite-type silica in subducted oceanic crust: implications for water transport to the deep lower mantle"
<p>This is the XRD and IR datasets for the article "Formation of an Al-rich niccolite-type silica in subducted oceanic crust: implications for water transport to the deep lower mantle" by Liu and Yuan et al.</p>
Datasets for article "Transient behavior of three SU UMa-type dwarf novae; AR Pic, QW Ser and V521 Peg"
<p>Datasets for the article "Transient behavior of three SU UMa-type dwarf novae; AR Pic, QW Ser and V521 Peg"</p> <p> </p>
Comprehensive Kinetic and EMG Dataset of Daily Locomotion with 6 types of Sensors
<p> </p> <p><strong><a href="https://www.cambridge.org/core/journals/wearable-technologies/article/wearable-realtime-kinetic-measurement-sensor-setup-for-human-locomotion/488C21B7706FFDFA7FFAB387FD0A1A64?utm_campaign=shareaholic&utm_medium=copy_link&utm_source=bookmark">The paper</a> is now published in the recent Wearable Technology Journal, more detailed information on this dataset can be found there!</strong></p> <p>A human movement experiment with 12 young adults performing 13 daily movement trials (6 walking trials (speed: 0.9, 1.8, 2.7, 3.6, 4.5, 5.4 km/ h; 3 running trials (speed: 6.3, 8.8, 9.9 km/h); and four non-locomotion trials (vertical jump, squat, lunge, and single leg landing) was conducted with the ethic approved by the University of Twente (ET/A.21.19298, reference number 2021.57). </p> <p>Six types of measurement devices were used to capture different information of participants’ movements. They can be divided into two systems: the wearable system and the conventional non-wearable system. In the wearable measurement system, eight IMUs (Xsens Link, Enschede, The Netherlands) were used to measure the kinematic movements of lower limbs and trunk. A pair of pressure insoles (Moticon, Munich, Germany) was used to measure the vertical GRF and CoPs. In the conventional system, an optical motion capture system (OMC) containing 8 infrared light cameras (6+ series, Qualisys, Gothenburg, Sweden) was used to measure body kinematics data using reflective markers. A split-belt instrumented treadmill (Motek-Forcelink B.V, Culemborg, The Netherlands) was used to measure the GRFs under each foot. Two video cameras were also included inside the conventional system to capture the RGB images of participants’ body postures at the sagittal and frontal planes (<a href="https://doi.org/10.5281/zenodo.6644593">https://doi.org/10.5281/zenodo.6644593</a>). In addition, nine electromyography sensors (EMGs) (Delsys Trigno, Delsys, USA) were included to record the activations of nine major muscles in the dominant leg ("soleus", "medial gastrocnemius", "lateral gastrocnemius", "tibialis anterior", " semimembranosus", " biceps femoris long head", "vastus lateral", "rectus femoris", "vastus medial").</p> <p>In this shared data repository, both raw data (to be uploaded) and processed data (Processed_data.zip) are provided. The data processing pipeline can be found in this public GitHub repository: <a href="https://github.com/HuaweiWang/BioMechPro-WearableSystemVaildation">https://github.com/ET-BE/BioMechPro/tree/study/WearableSystemValidation</a>. Guidelines for creating the same wearable system in the corresponding comparison study are shared in this GitHub repo: <a href="https://github.com/HuaweiWang/WearableMeasurementSystem">https://github.com/HuaweiWang/WearableMeasurementSystem</a>.</p> <p><strong>[Note!]</strong> If you have unstable network that not able to download the huge data files, please check this version of dataset with small file sizes(2GB each)</p> <ul> <li>Raw data: <a href="https://doi.org/10.5281/zenodo.7422043">https://doi.org/10.5281/zenodo.7422043</a></li> <li>Processed data: <a href="https://doi.org/10.5281/zenodo.7422031">https://doi.org/10.5281/zenodo.7422031</a> </li> </ul> <p> </p> <p>Dataset structure descriptions:</p> <p><a href="https://zenodo.org/api/files/871df791-82f9-495a-ac56-eafd195c56ad/Raw_data.rar?versionId=41a0df36-b9b2-4e48-a50e-a6e81b23609b">Raw_data.rar</a>: Raw dataset</p> <ul> <li><em>Subjxx</em>: subject folder <ul> <li><em>Qualisys</em>: Qualisys project folder of the recordings</li> <li><em>Xsens</em>: Xsens project folder of the recordings</li> <li><em>Insoles</em>: Pressure insole project folder of the recordings</li> </ul> </li> </ul> <p><a href="https://zenodo.org/api/files/871df791-82f9-495a-ac56-eafd195c56ad/Processed_data.rar?versionId=d035b581-a334-48bb-88e1-3c655943b5cc">Processed_data.rar</a>: Processed dataset.</p> <ul> <li><em>allAverage.mat</em>: the overall summarization data of all subject at all movement trials. </li> <li><em>dataValidation.m</em>: the Matlab code to plot the summarization data by loading the allAverage.mat.</li> <li><em>subjs_info.txt</em>: general information of all participants. </li> <li><em>ComparisonPlots</em>: folder that contains the comparison plots between the laboratory-based system and the wearable measurement systems.</li> <li><strong><em>Subjxx</em></strong>: processed data for participants xx <ul> <li><em>dynMVCvalue.mat</em>: the dynamic Maximal Voluntary Contraction of measured muscles (highest value among all recorded movements)</li> <li><em>MVCvalue.mat</em>: the Maximal Voluntary Contraction of measured muscles (highest value in MVC recording trial only)</li> <li><em>Subjxx_xxxx_xx.mat</em>: the processed data (generated by the above mentioned processing pipeline) of current subject at specific movement trial.</li> <li><em>Qualisys</em>: The exported mat files from the Qualisys recordings, including markers, EMGs, GRFs.</li> <li><em>Xsens</em>: The exported .mvnx files from the Xsens MVN software reprocessing. This file can be directly loaded by Matlab without requiring the Xsens license.</li> <li><em>Insole</em>: Insole recorded data, parsed from the Moticon endpoint SDK output.</li> <li><em>OS</em>: The folder that contains the scaled OpenSim model and corresponding .xml and data files for IK and ID processing. Majority content in this folder is automatically generated by the processing pipeline</li> <li><em>Figures</em>: plots of the processed data, including joint angles, GRFs, joint torques, and EMGs. They are all generated in the last module of the processing pipeline.</li> </ul> </li> </ul> <p>Structure of the <strong>Subjxx_xxxx_xx.mat</strong>:</p> <p><em><strong>Subjxx_xxxx_xx</strong>.</em><em><strong>mat:</strong></em></p> <ul> <li><em>Info</em>: the general information of the participant and corresponding processing steps.</li> <li><em>Marker</em>: Marker data from Qualisys</li> <li><em>Force</em>: GRFs data from Qualisys</li> <li><em>EMG</em>: EMG data from Qualisys</li> <li><em>IMU</em>: Motion data from Xsens IMU system (from .mvnx)</li> <li><em>Insole</em>: The recorded pressure insole data</li> <li><strong>Resample</strong>: This data structure that contains the resampled data of the above mentioned sensor data <ul> <li><em>FrameRate</em>: the sampling rate for all resampled sensor data</li> <li><em>Marker</em>: Resampled marker data</li> <li><em>Force</em>: resampled force data</li> <li><em>EMG</em>: resampled EMG data</li> <li><em>IMU</em>: resampled IMU data</li> <li><em>Insole</em>: resampled Insole data</li> <li><em>CoM</em>: resampled center of mass data from Xsens IMU system</li> <li><strong>Sych</strong>: this data structure contains the IK & ID data of two measurement systems. They are also synchronized by calculate the highest correlation coefficient. <ul> <li><em>DeltaT</em>: the time differences between the laboratory-based system and the wearable measurement system.</li> <li><em>IKAngData</em>: the joint angle data from marker data inverse kinematics</li> <li><em>ForcePlateGFRData</em>: the ground reaction force data from instrumented treadmill</li> <li><em>IDTrqData</em>: the joint torque data from laboratory measurement system (optical + treadmill)</li> <li><em>IMUAngData</em>: the joint angle data from Xsens MVN software</li> <li><em>InsoleGRFData</em>: the ground reaction force data from pressure insoles</li> <li><em>IDTrqData_portable</em>: the joint torque data from the wearable system inverse dynamics</li> <li><em>EMG</em>: synchronized EMG data</li> <li><em>CoM</em>: synchronized CoM data</li> <li><em>xxxxxLabel</em>: the labels of each data column of corresponding data matrix</li> <li><strong>Average</strong>: this data structure contains the averaged gait/moment cycles <ul> <li><em>hsMatrix_right</em>: The heel strike data points of the right leg</li> <li><em>hsMatrix_left</em>: the heel strike data points of the left leg</li> <li><em>EMGAvedynNorFlag</em>: whether dynamic MVC normalization is applied on EMG.</li> <li><em>EMGAveNorFlag</em>: whether MVC normalization is applied on EMG.</li> <li><em>xxxx</em>: The averaged data of corresponding variables</li> <li><em>ForcePlateGRFDataInCalCn</em>: transferred treadmill GRF data from the treadmill global coordinate to the local Calcaneus coordinate of the scaled OpenSim model. </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Explaining a rapidly fading star as a type II ILOT in a triple star system
<p>Bear, Soker and Kashi 2022<br> ==========================</p> <p>The code follows 1M_pre_ms_to_wd example in test suite.<br> Prior to running this code we run 12Mo when we follow the example of 16_predictive_mix example for 12Mo.<br> We look when this star of 12Mo leaves the MS, the time taken is 1.46X10^7 years.</p> <p>The code is divided to 2 stages:<br> Stage A: up to 1.46X10^7 years when no other changes have been made to the example. We save the file produces at 1.46X10^7years.</p> <p>Stage B: We restart the file at 1.46X10^7years and insert mass loss by enabling mass_change.</p> <p>The mass parameter for stage A+B is: 0.3Mo and 0.5Mo.<br> The mass_change parameter is 0.3X10^-5, 1X10^-5, 3X10^-5 Mo/yr</p> <p>Each stage contains the inlist and the data files used in https://arxiv.org/abs/2202.08629</p> <p> </p>
Fermur lengths of Drosophila melanogaster at different developmental temperatures and Wolbachia-infection types
<p>To estimate the influence of developmental temperature and <em>Wolbachia</em> infections on body size in <em>Drosophila melanogaster,</em> we disected female flies and mounted left forelegs. Pictures were taken under a Leica DFC 490 microscope. Femur lengths, wich have proven to be a good proxy for bodysize, were measured in ImageJ. Our analyses revealed that temperature, but not <em>Wolbachia</em> infections strongly influenced female body size. See also the corresponing README.txt and the table "FullData.xlsx" within SupplementaryData.zip in the supplement of the corresponding manuscript "Complex effects of environment and Wolbachia infections on the life history of Drosophila melanogaster hosts" (DOI: <span>10.1111/jeb.14016) </span>for more details.</p>
Systematic review and meta-analysis: water type and temperature affect environmental DNA decay metadata
<p>Environmental DNA (eDNA) has been used in a variety of ecological studies and management applications. The rate at which eDNA decays has been widely studied but at present it is difficult to disentangle study-specific effects from factors that universally affect eDNA degradation. To address this, a systematic review and meta-analysis was conducted on aquatic eDNA studies. Analysis revealed eDNA decayed faster at higher temperatures and in marine environments (as opposed to freshwater). DNA type (mitochondrial or nuclear) and fragment length did not affect eDNA decay rate, although a preference for < 200 bp sequences in the available literature means this relationship was not assessed with longer sequences (<em>e.g.</em> > 800 bp). At present, factors such as ultraviolet light, pH, and microbial load lacked sufficient studies to feature in the meta-analysis. Moving forward, we advocate researching these factors to further refine our understanding of eDNA decay in aquatic environments. This dryad entry contains the metadata which formed the basis for the meta-analysis.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.