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30,334 results for “Responsiveness”
Serum response factor utilizes distinct promoter- and enhancer-based mechanisms to regulate cytoskeletal gene expression in macrophages.
<p>Cells of the monocyte/macrophage lineage play essential roles in tissue homeostasis and immune responses, but mechanisms underlying the coordinated expression of cytoskeletal genes required for specialized functions of these cells, such as directed migration and phagocytosis, remain unknown. Here, using genetic and genomic approaches, we provide evidence that serum response factor (SRF) regulates both general and cell type-restricted components of the cytoskeletal gene expression program in macrophages. Genome-wide location analysis of SRF in macrophages demonstrates enrichment of SRF binding at ubiquitously expressed target gene promoters, as expected, but also reveals that the majority of SRF binding sites associated with cell type-restricted target genes are at distal inter- and intragenic locations. Most of these distal SRF binding sites are established by the prior binding of the macrophage- and the B cell-specific transcription factor PU.1 and exhibit histone modifications characteristic of enhancers. Consistent with this, representative cytoskeletal target genes associated with these elements require both SRF and PU.1 for full expression. These findings suggest that SRF uses two distinct molecular strategies to regulate programs of cytoskeletal gene expression: a promoter-based strategy for ubiquitously expressed target genes and an enhancer-based strategy at target genes that exhibit cell type-restricted patterns of expression.</p>
The influence of social presence on facial affective responses to food images (FSC)
<p>Raw data from a study of social context and food liking. CSV files are generated by PsychoPy. ACQ files are generated by BIOPAC Acqknowledge software.</p> <p>Modified versions of Acqknowledge files have had markers for chocolate consumption readjusted by the researcher. These participants had failed to click the mouse in time with the start and finish of their eating episode. 188modified.acq file had the marker channel for the first trial edited because the fixation data for the first trial was missing.</p> <p> </p>
Responses to axonal current injection in cortical layer 5 pyramidal neurons
<p>Patch-clamp recordings in cortical layer 5 pyramidal neurons performed by Wenqin Hu. A current pulse is either injected in the soma or in an axonal bleb and recorded simultaneously in the axonal bleb or the soma, respectively.</p> <p>The data are related to figure 7 of the paper: Hu, W., & Bean, B. P. (2018). Differential control of axonal and somatic resting potential by voltage-dependent conductances in cortical layer 5 pyramidal neurons. <em>Neuron</em>, <em>97</em>(6), 1315-1326.</p> <p> </p>
Picophytoplankton lineages display clear niche partitioning but overall positive response to future ocean warming
<p>This repository contains data used in the Flombaum et al. (2019) Nature Geoscience paper entitled "<strong>Picophytoplankton lineages display clear niche partitioning but overall positive response to future ocean warming" </strong>for sensitivity tests.</p> <p>npp_91x180.mat is a 2D net primary production data from satellite ( SeaWiFS).</p> <p><a href="https://zenodo.org/api/files/ea47715e-18fe-42d4-8d36-848824829b53/omega2_ad1e-05_ai1000.mat">omega2_ad1e-05_ai1000.mat</a> contains advection and diffusion transport operator.</p> <p><a href="https://zenodo.org/api/files/ea47715e-18fe-42d4-8d36-848824829b53/po4obs_91x180x24.mat">po4obs_91x180x24.mat</a> is 3D dissolved inorganic phosphorus concentrations obtained from WOA2013 and interpolated into the inverse model gird (91x180x24);</p> <p><a href="https://zenodo.org/api/files/ea47715e-18fe-42d4-8d36-848824829b53/xhat_91x180_control.mat">xhat_91x180_control.mat</a> is optimal phosphorus model parameters; It has four parameters: <em>b - </em><em>Marine curve exponent; kappa_d - dissolved organic phosphorus remineralization rate constant; alpha and beta: parameters used to scale satellite NPP to model organic phosphorus production.</em></p>
The data for the article entitled "Soil carbon release responses to long-term versus short-term climatic warming in an arid ecosystem"
<p>The data for the article entitled "Soil carbon release responses to long-term versus short-term climatic warming in an arid ecosystem" Yu et al. </p>
Data and R code used in Baudson et al (2019) Developmental plasticity of Brachypodium distachyon in response to P deficiency: modulation by inoculation with phosphate-solubilizing bacteria
<p>This repository contains the raw data and R code used for the following paper: Baudson et al (2019) Developmental plasticity of <em>Brachypodium distachyon</em> in response to P deficiency: modulation by inoculation with phosphate-solubilizing bacteria</p>
Supplementary Information: The Late Lutetian Thermal Maximum: first record of the deep-sea benthic foraminiferal response in the Tasman Sea (IODP Site U1508)
<div> <div> <div> <div> <div> <div> <div> <div> <p>Here, we provide supplementary information including inorganic and organic geochemistry data, magnetostratigraphy data, foraminifera data, and the age model derived from sediment samples taken from IODP Hole U1508C.</p> </div> </div> </div> </div> <p>At this IODP Hole, from the Tasman Sea, we have documented the Late Lutetian Thermal Maximum (LLTM), a brief warming event in the middle Eocene (41.52 Ma). This is the first record of deep-sea benthic foraminifera changes during the LLTM in the SW Pacific. The event coincides with negative δ13C excursions in bulk sediment (0.47‰) and benthic foraminifera (0.36‰), as well as shifts in foraminiferal species and organic geochemistry. Reduced diversity in benthic foraminiferal assemblages during the event indicates environmental stress, likely due to low oxygen conditions, as suggested by dysoxic taxa. Despite the dominance of calcareous taxa, corrosion-resistant species point to slightly CaCO3-corrosive waters without evident dissolution. We infer a shallower thermocline and increased stratification during the LLTM.</p> <div> <div> <div> <div> </div> </div> </div> </div> <div> <div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <p> </p> </div> </div> </div>
Datas for Leveraging ecosystems responses to enhanced rock weathering in mitigation scenarios
<p>This dataset contains the jupyter notebook calibrating the P-cycle emulator based on the ORCHIDEE-CNP outputs produced by Daniel S. Goll. The zip file contains a set of results from the simulations. </p>
Testing of different methods to induce lime stress responses in grapevine rootstocks - supplementary figures
<p>Defined experiments are necessary to clarify the response to nutrient deficiency. The study tested different options to induce lime stress in pot experiments to select the most appropriate method with grapevine roostocks.We tested soil substrates, an hydroponic culture and an inert sand substrate by adding KHCO3. Grapevine rootstocks Teleki 5C, Couderc 3309 and Fercal were used. The figures represent supplementary material of the obtained phenotype with the different rootstocks and testing conditions.</p>
Fig. 1 in Laboratory feeding responses of Euthyrhynchus floridanus and Podisus maculiventris (Hemiptera: Pentatomidae) to the kudzu bug, Megacopta cribraria (Hemiptera: Plataspidae)
Fig. 1. Megacopta cribraria female.
Fig. 3 in Laboratory feeding responses of Euthyrhynchus floridanus and Podisus maculiventris (Hemiptera: Pentatomidae) to the kudzu bug, Megacopta cribraria (Hemiptera: Plataspidae)
Fig. 3. Euthyrhynchus floridanus feeding on Megacopta cribraria female adult.
Fig. 2 in Laboratory feeding responses of Euthyrhynchus floridanus and Podisus maculiventris (Hemiptera: Pentatomidae) to the kudzu bug, Megacopta cribraria (Hemiptera: Plataspidae)
Fig. 2. Podisus maculiventris female.
Data for: "Chromosome One QTLs Associated with Beta Vulgaris Response to Bacterial Leaf Spot"
<p>Listed here are data used in analysis for publication "Chromosome One QTLs Associated with Beta Vulgaris Response to Bacterial Leaf Spot".</p> <p>Data included: filtered VCF files of SNP marker data for full Wisconsin Beta Diversity Panel (WBDP, n=219) and table beet subset (n=152). Deposited also are the raw phenotypic values, the final weighted BLS score per pot, the BLUE value for each accession, and the leaf color covariate data.</p>
Fig. 4 in Predation functional response and life table parameters of Orius sauteri (Hemiptera: Anthocoridae) feeding on Megalurothrips usitatus (Thysanoptera: Thripidae)
Fig. 4. Age-stage survival rate (sxj) of Orius sauteri on Megalurothrips usitatus at 26 °C.
Fig. 2 in Response to enantiomers of (Z3Z9)-6,7-epoxy-octadecadiene, sex pheromone component of Ectropis obliqua Prout (Lepidoptera: Geometridae): electroantennagram test, field trapping, and in silico study
Fig. 2. Field trial of Ectropis obliqua Prout. (A) Water pan trap; (B) male moths captured.
AIDERv2 (Aerial Image Dataset for Emergency Response Applications)
<div>SUMMARY OF DATASET</div> <div> </div> <div>• This dataset consist of 167723 aerial images divided into 4 classes.</div> <div> </div> <div>• The dataset contains three commonly occurring natural disasters</div> <div>earthquake/collapsed buildings, flood, wildfire/fire, and a normal class; do not reflect any disaster</div> <div> </div> <div>• The images can be loaded as numpy arrays using Python programming language and then used to train a Convolutional Neural Network to detect natural disasters from aerial images.</div> <div> </div> <div>• The images are resized to 224x224x3 (heighty,width,channel number) when loaded as numpy arrays.</div> <div> </div> <div>• The dataset is an extension of the AIDER dataset (Aerial Image Dataset for Emergency Response Applications). </div> <div> </div> <div>• Additional images were collected by open source databases and extracted images as frames of videos downloaded from YouTube. </div> <div> </div> <div> </div> <div>The table below shows the number of images in each set.</div> <div> </div> <div> Train Validation Test Total</div> <div> Earthquakes 1927 239 239 2405</div> <div> Floods 4063 505 502 5070</div> <div> Fire 3509 439 436 4384</div> <div> Normal 3900 487 477 4864</div> <div> Total 13399 1670 1654 16723</div> <div> </div> <div> </div> <div>If you use this dataset please cite the following publications:</div> <div> </div> <div>[1] Shianios, D., Kyrkou, C., Kolios, P.S. (2023). A Benchmark and Investigation of Deep-Learning-Based Techniques for Detecting Natural Disasters in Aerial Images. In: Tsapatsoulis, N., <em>et al.</em> Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14185. Springer, Cham. https://doi.org/10.1007/978-3-031-44240-7_24</div> <div>Link: https://link.springer.com/chapter/10.1007/978-3-031-44240-7_24</div> <div> </div> <div>[2] D. Shianios, P. Kolios, C. Kyrkou, "DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition", SN Computer Science, 2024 (Accepted to Appear)</div> <div> </div> <div> </div> <div> </div> <div> </div> <div>DATASET FOLDERS FORMAT</div> <div> </div> <div>└───data</div> <div>│ │</div> <div>│ └───Dataset_Images</div> <div>│ │ └───Train</div> <div>│ │ │ | └───Earthquake</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Flood</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Normal</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Wildfire</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ └───Val</div> <div>│ │ │ | └───Earthquake</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Flood</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Normal</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Wildfire</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ └───Test</div> <div>│ │ │ | └───Earthquake</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Flood</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Normal</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Wildfire</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div> <div>DATA SOURCES AND DATA COLLECTION</div> <div> </div> <div>OPEN SOURCE DATABASES</div> <div> </div> <div>└───AIDER </div> <div>https://zenodo.org/record/3888300#.Yuu11nZBxD-</div> <div>Kyrkou, C. and Theocharides, T., 2020. EmergencyNet: Efficient aerial image classification for drone-based emergency monitoring using atrous convolutional feature fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp.1687-1699.</div> <div> </div> <div>└───ERA </div> <div>https://lcmou.github.io/ERA_Dataset/</div> <div>Mou, L., Hua, Y., Jin, P. and Zhu, X.X., 2020. Era: A data set and deep learning benchmark for event recognition in aerial videos [software and data sets]. IEEE Geoscience and Remote Sensing Magazine, 8(4), pp.125-133.</div> <div>@article{eradataset,</div> <div> title = {{ERA: A dataset and deep learning benchmark for event recognition in aerial videos}},</div> <div> author = {Mou, L. and Hua, Y. and Jin, P. and Zhu, X. X.},</div> <div> journal = {IEEE Geoscience and Remote Sensing Magazine},</div> <div> year = {in press}</div> <div>}</div> <div> </div> <div> </div> <div> </div> <div>└───ISBDA</div> <div>https://drive.google.com/file/d/1kEKJ8kr1aScXz_1El7Mn-Yi0ANducQIW/view</div> <div>Zhu, X., Liang, J. and Hauptmann, A., 2021. Msnet: A multilevel instance segmentation network for natural disaster damage assessment in aerial videos. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2023-2032).</div> <div>@misc{zhu2020msnet,</div> <div> title={MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos},</div> <div> author={Xiaoyu Zhu and Junwei Liang and Alexander Hauptmann},</div> <div> year={2020},</div> <div> eprint={2006.16479},</div> <div> archivePrefix={arXiv},</div> <div> primaryClass={cs.CV}</div> <div>}</div> <div> </div> <div> </div> <div>└───Floods 2013</div> <div>https://github.com/cvjena/eu-flood-dataset</div> <div>Barz, B., Schröter, K., Münch, M., Yang, B., Unger, A., Dransch, D. and Denzler, J., 2019. Enhancing flood impact analysis using interactive retrieval of social media images. arXiv preprint arXiv:1908.03361.</div> <div>@article{barz2019enhancing,</div> <div> title={Enhancing flood impact analysis using interactive retrieval of social media images},</div> <div> author={Barz, Bj{\"o}rn and Schr{\"o}ter, Kai and M{\"u}nch, Moritz and Yang, Bin and Unger, Andrea and Dransch, Doris and Denzler, Joachim},</div> <div> journal={arXiv preprint arXiv:1908.03361},</div> <div> year={2019}</div> <div>}</div> <div> </div> <div>└───Wildfire Research</div> <div>http://wildfire.fesb.hr/index.php?option=com_content&view=article&id=58&Itemid=54</div> <div> </div> <div> </div> <div>└───PyImages</div> <div>https://drive.google.com/file/d/1NvTyhUsrFbL91E10EPm38IjoCg6E2c6q/view</div> <div>The dataset was curated by PyImageSearch reader, Gautam Kumar.</div> <div> </div> <div> </div> <div> </div> <div> </div> <div>YOUTUBE VIDEOS</div> <div> </div> <div>└───Collapsed Buildings/Earthquakes</div> <div>• https://www.youtube.com/watch?v=TMow3WPcZrQ&t=133s&ab_channel=GORKHALYFOUNDATION</div> <div>• https://www.youtube.com/watch?v=_HT0tYKKjBI&t=47s&ab_channel=Effect.org</div> <div>• https://www.youtube.com/watch?v=rkb3y6K3waU</div> <div>• https://www.youtube.com/watch?v=yir6ArRZY4o&t=109s&ab_channel=UnicefUK</div> <div>• https://www.youtube.com/watch?v=CM9APmIR9Fk&ab_channel=ToonsZilla</div> <div>• https://www.youtube.com/watch?v=tmx2w6drAeU&ab_channel=AssociatedPress</div> <div>• https://www.youtube.com/watch?v=kuSEe8Emwrk&ab_channel=BloombergQuicktake%3ANow</div> <div>• https://www.youtube.com/watch?v=qoFHA3-m5ag&ab_channel=NBCNews</div> <div>• https://www.youtube.com/watch?v=MM3PToqEPhQ&ab_channel=GuardianNews</div> <div>• https://www.youtube.com/watch?v=zB_-TRnGuZE&ab_channel=DISASTERNEWS</div> <div>• https://www.youtube.com/watch?v=TqAMQQOEsBs&ab_channel=WHAS11</div> <div>• https://www.youtube.com/watch?v=0ixjTt-jmok&ab_channel=EveningStandard</div> <div>• https://www.youtube.com/watch?v=bNGA8Ms3d70&ab_channel=CatersClips</div> <div>• https://www.youtube.com/watch?v=wJ-2d5t23Lg&ab_channel=DailyDose</div> <div>• https://www.youtube.com/watch?v=ewUcI7I6Gf4&ab_channel=NBCNews</div> <div>• https://www.youtube.com/watch?v=Wx1cjOdlMZ4&ab_channel=ABCNews%28Australia%29</div> <div>• https://www.youtube.com/watch?v=jiMK_sVmbXk&t=12s&ab_channel=NewChinaTV </div> <div>• https://www.youtube.com/watch?v=M9au_9A2YRo&ab_channel=GuardianNews</div> <div>• https://www.youtube.com/watch?v=i6Lh8IXPjso&ab_channel=TheSun</div> <div>• https://www.youtube.com/watch?v=CKwxEr3I4Y8&ab_channel=GuardianNews</div> <div>• https://www.youtube.com/watch?v=hxqzcajBCNg&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=3&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=2GEeTDuf9mI&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=6&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=bDOuZWxIyNQ&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=9&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=vzoSADijLCQ&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=15&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=ZaL1fldTEAk&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=17&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=QSV81FdilZE&ab_channel=GlobalNews</div> <div>• https://www.youtube.com/watch?v=KgOk9otW1Bg&ab_channel=EricFeijten</div> <div> </div> <div> </div> <div> </div> <div>└───Floods</div> <div>• https://www.youtube.com/watch?v=DJqgv8Sa5bA&t=317s&ab_channel=7NEWSAustralia</div> <div>• https://www.youtube.com/watch?v=w5FintiCLJU&t=9s&ab_channel=GuardianNews</div> <div>• https://www.youtube.com/watch?v=HjMymNN6Ajc&t=143s&ab_channel=BioLogicTreeServices</div> <div>• https://www.youtube.com/watch?v=Tmba18C94C8&ab_channel=AL.com</div> <div>• https://www.youtube.com/watch?v=Dqvpv4Vg4lk&t=63s&ab_channel=ElevenEleven</div> <div>• https://www.youtube.com/watch?v=N7QGicNtN2A&ab_channel=PKSVideoProductions</div> <div>• https://www.youtube.com/watch?v=8CHagyQG16Q&ab_channel=Stolly-Sven</div> <div>• https://www.youtube.com/watch?v=vjH3zFqdzcE&ab_channel=BenChilders</div> <div>• https://www.youtube.com/watch?v=GFw89UB4fE8&ab_channel=BenChilders</div> <div>• https://www.youtube.com/watch?v=heP3LEJ_NkE&ab_channel=7NEWSAustralia</div> <div> </div> <div> </div> <div> </div> <div>└───Fires</div> <div>• https://www.youtube.com/watch?v=gbM_NPx2GPc&t=201s&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=M97sJdyeEM4&t=72s&ab_channel=Sanuck176</div> <div>• https://www.youtube.com/watch?v=1Z2K6lDt76M&t=557s&ab_channel=TheRelaxationChannel</div> <div> </div> <div>└───Normal</div> <div>• https://www.youtube.com/watch?v=SyxjsuNHWhM&t=328s&ab_channel=OneManWolfPack</div> <div>• https://www.youtube.com/watch?v=f1PTWsBtrtc&ab_channel=ChernobylPug</div> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div>
Energy-dependence of the response of X-ray multimeter for mammography- radiation qualities
<p>Supplementary data to the manuscript “Energy-dependence of the response of X-ray multimeter for mammography- radiation qualities” by Elisabeth Salomon and Paula Toroi</p>
FIGURE 5 in Pteropoda (Mollusca, Gastropoda, Thecosomata) from the Eocene/Oligocene boundary interval of three cored boreholes in southern coastal Tanzania and their response to the global cooling event
FIGURE 5. Limacina robusta (Eames, 195); RGM 777423b, apertural view. Bar equals 100 μm.
FrID: Frequency response based identification
<p>Measurements accompayning our article [1] and the repository https://github.com/tbreunung/FrID</p> <p>The folder 'MagMechOsci.zip' contains scripts and data for the magneto-mechanical oscillators studies in Section 3.1. These incldue</p> <ul> <li>Single degree-of-freedom configuration (subfolder: SDOF)</li> <li>Two and three degree-of-freedom configurations (subfolders: Three_Beams and Two_Beams>Separated Modes)</li> <li>Internally resonant configuration (subfolder: Two_Beams>Internal Resonance)</li> </ul> <p>The folder 'ClampedOsci.zip' contains scripts and data for the oscillator with campling nonlinearities studied in Section 3.2 of [1]. </p> <p>In <a href="https://www.youtube.com/watch?v=ggWzUJM4XPM">this tutorial</a> I walk you through the steps on how to use FrID. </p> <p>Consider also listening to <a href="https://www.youtube.com/watch?v=8cQhZ5BIbz4">my research talk</a> on this topic. </p> <p>[1] Breunung, T., & Balachandran, B. (2024). Frequency response based identification of nonlinear oscillators. <em>Journal of Sound and Vibration</em>, 118651. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jsv.2024.118651" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jsv.2024.118651</a></p>
Fig. 1 in Flight response of the barber surgeonfish, Acanthurus bahianus Castelnau, 1855 (Teleostei: Acanthuridae), to spearfisher presence
Fig. 1. Location of the study area at the Porto da Barra (black star), Salvador, Bahia, Brazil.
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.