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644 results for “data visualization”
Data for "CryoDRGN-ET: Deep reconstructing generative networks for visualizing dynamic biomolecules inside cells"
<p>Trained models weights, training parameters, sampled density maps, reconstructed density maps for featured classes, and plotting scripts are included for each of the following datasets and training runs:</p> <ul> <li><em>M. pneumoniae</em> ribosome, initial training run with all 18,466 particles, 1 tilt per particle</li> <li><em>M. pneumoniae</em> ribosome, training run with 16,655 filtered particles and 10 tilts per particle</li> <li><em>M. pneumoniae</em> ribosome, training run with 16,655 filtered particles and 41 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, initial training run with all 119,031 particles, 10 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, training run with 93,281 filtered particles, 10 tilts per particle</li> <li><em>S. cerevisiae</em> ribosome, training run with 30,657 particles in the non-rotated state, 10 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, training run with 62,624 particles in the rotated state, 10 tilts per particle</li> <li><em>S. cerevisiae </em>fatty acid synthase, initial training run with all 33,492 particles, 10 tilts per particle</li> <li><em>S. cerevisiae </em>fatty acid synthase, training run with all 5,239 filtered particles, 10 tilts per particle</li> </ul>
Data results of usability evaluation of a geo-temporal crowding visualization platform
<p>Data results of usability evaluation of a geo-temporal crowding visualization platform.</p> <p>NASA-TLX was used for assessing the cognitive load of performing one task with the platform.</p> <p>SUS and UEQ were used for asessing the usability of performing three tasks with the platform.</p>
Data: Optimal trans-saccadic integration relies on visual working memory
<p>Dataset for the published paper: </p> <p>Stewart, E. E. M., & Schütz, A. C. (2018). Optimal Trans-saccadic integration relies on visual working memory. <em>Vision research</em>.</p> <p>DOI: <a href="https://doi.org/10.1016/j.visres.2018.10.002">10.1016/j.visres.2018.10.002</a></p>
Supporting data for: "Steady state visual evoked potentials in reading aloud: Effects of lexicality, frequency and orthographic familiarity"
<p>Supporting data for the article "Steady state visual evoked potentials in reading aloud: Effects of lexicality, frequency and orthographic familiarity".</p> <p>The dataset consists of the 16 original .bdf files.</p>
Sample WebAssembly Data Files for Reproducible Analysis and Visualization of iEEG (RAVE)
<p>The data was derived from the following work and packaged into WebAssemply via Emscripten. The modification includes removing large data files and only keep up with the minimal requirements.</p> <blockquote> <p>Magnotti, J. F., Wang, Z., & Beauchamp, M. S. (2020). RAVE: Comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data. <em>NeuroImage</em>, <em>223</em>, 117341.</p> </blockquote> <p> </p>
Data and code for: Cellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons
<p>Data and accompanying analysis code to generate main figures from "Cellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons" in PNAS.</p> <p> </p> <p> </p>
Data from: Correlated evolution between colouration and ambush site in predators with visual prey lures
The evolution of a visual signal will be affected by signaller and receiver behaviour, and by the physical properties of the environment where the signal is displayed. Crab spiders are typical sit-and-wait predators found in diverse ambush sites, such as tree bark, foliage and flowers. Some of the flower-dweller species present a UV+-white visual lure that makes them conspicuous and attractive to their prey. We hypothesised that UV+-white colouration was associated with the evolution of a flower-dwelling habit. In addition, following up on results from a previous study we tested whether the UV+-white colouration evolved predominantly in flower-dwelling species occurring in Australia. We measured the reflectance of 1149 specimens from 66 species collected in Australia and Europe, reconstructed a crab spider phylogeny, and applied phylogenetic comparative methods to test our hypotheses. We found that the flower-dwelling habit evolved independently multiple times, and that this trait was correlated with the evolution of the UV+-white colouration. However, outside Australia non-flower-dwelling crab spiders also express a UV+-white colouration. Therefore, UV+-white reflectance is probably a recurring adaptation of some flower-dwellers for attracting pollinators, although it may have other functions in non-flower-dwellers, such as camouflage.
Raw data for: Stable Isotope Trajectory Analysis (SITA): A new approach to quantify and visualize dynamics in stable isotope studies. Sturbois et al., in revision in Ecological Monographs
<p>These data sets are used as ecological applications in Sturbois et al., in revision, Stable Isotope Trajectory Analysis (SITA): A new approach to quantify and visualize dynamics in stable isotope studies. submitted in Ecological Monographs.</p> <p>- DataS1_furseals.Rdata originates from: Kernaléguen, L., Cazelles, B., Arnould, J.P.Y., Richard, P., Guinet, C., Cherel, Y., 2012. Long-Term Species, Sexual and Individual Variations in Foraging Strategies of Fur Seals Revealed by Stable Isotopes in Whiskers. PLoS ONE 7, e32916. https://doi.org/10.1371/journal.pone.0032916</p> <p>- DataS2_Pike.Rdata originates from: Cucherousset, J., Paillisson, J.-M., Roussel, J.-M., 2013. Natal departure timing from spatially varying environments is dependent of individual ontogenetic status. Naturwissenschaften 100, 761–768. https://doi.org/10.1007/s00114-013-1073-y</p> <p>- DataS4_GT1.Rdata and DataS5_GT2.Rdata originate from: Quillien, N., Nordström, M.C., Schaal, G., Bonsdorff, E., Grall, J., 2016. Opportunistic basal resource simplifies food web structure and functioning of a highly dynamic marine environment. Journal of Experimental Marine Biology and Ecology 477, 92–102.</p> <p>- DataS6_Lakes.Rdata originates from: Zhao, T., Villéger, S., Cucherousset, J., 2019. Accounting for intraspecific diversity when examining relationships between non-native species and functional diversity. Oecologia 189, 171–183. https://doi.org/10.1007/s00442-018-4311-3</p> <p>Information about respective sampling strategies and sample preparation are available in these original articles. All use of this data sets must cite original article as well as the SITA article.</p>
Data for: Visual guidance of honeybees approaching a vertical landing surface
<p>Landing is a critical phase for flying animals, whereby many rely on visual cues to perform controlled touchdown. Foraging honeybees rely on regular landings on flowers to collect food, crucial for colony survival and reproduction. Here, we explore how honeybees utilize optical-expansion cues to regulate approach flight speed when landing on vertical surfaces. Three sensory-motor control models have been proposed for landings of natural flyers. Landing honeybees maintain a constant optical-expansion-rate set-point, resulting in a gradual decrease in approach velocity and gentile touchdown. Bumblebees exhibit a similar strategy, but they regularly switch to a new constant optic-expansion-rate set-point. Meanwhile, landing birds fly at a constant time-to-contact to achieve faster landings. Here, we re-examined the landing strategy of honeybee by fitting the three models to individual approach flights of honeybees landing on platforms with varying optic-expansion cues. Surprisingly, the landing model identified in bumblebees proves to be the most suitable for these honeybees. This reveals that honeybees adjust their optic-expansion-rate in a stepwise manner. Bees flying at low optic-expansion-rates tended to stepwise increase their set-point, while those flying at high optic-expansion-rates tend to stepwise decrease it. This modular landing control system enables honeybees to land rapidly and reliably under a wide range of initial flight conditions and visual landing platform patterns. The remarkable similarity between the landing strategies of honeybees and bumblebees suggests that this may also be prevalent among other flying insects. Furthermore, these findings hold promising potential for bioinspired guidance systems in flying robots.</p>
Data from: DNA metabarcoding improves the taxonomical resolution of visually determined diet composition of beaked redfish (Sebastes sp.)
<p class="pf0"><span>Beaked r</span><span>edfish, dominated by <em>Sebastes mentella</em>, have recently reached record abundance levels in the Gulf of St. Lawrence (GSL) and knowledge of their diet composition is essential to understand the trophic role that these groundfish play in the ecosystem. The objective of the present study was to compare the performance of the visual examination and DNA metabarcoding of stomach contents of the same individual redfish caught in the estuary and northern Gulf of St. Lawrence. Using a universal metazoan mitochondrial cytochrome c oxidase subunit I (COI) marker, a total of 27 taxonomic sequence matches, 16 at the species level considered as primary prey, were obtained from 185 stomachs with DNA metabarcoding and compared to </span><span>the</span> <span>26 prey types, 16 at genus or species level, obtained with stomach content analysis (SCA). While both techniques pointed to a similar definition of diet composition, our results</span><span> also revealed that the SCA and DNA metabarcoding perform differently among prey categories, both in terms of detectability and taxonomical resolution, as well as in estimated contribution to diet. </span><span>The use of DNA metabarcoding along with SCA improves the taxonomical resolution of visually determined prey,</span><span> which supports the concept that both techniques provide useful complementary information that is best used together to gain a maximum level of information on the predator's diet.</span></p>
tellingsounds/lama-data: LAMA Data - Capturing Entities (Persons, Topics, Music, etc.) in Austrian audio(-visual) archive material
<p>Data entered into LAMA (Linked Annotations for Media Analysis), a research software for capturing and visualizing the interaction of music and its contexts, developed by the Telling Sounds project.</p>
Data for: Inhibition drives habituation of a larval zebrafish visual response
<p>Habituation allows animals to learn to ignore persistent but inconsequential stimuli. Despite being the most basic form of learning, a consensus model on the underlying mechanisms has yet to emerge. To probe relevant mechanisms we took advantage of a visual habituation paradigm in larval zebrafish, where larvae reduce their reactions to abrupt global dimming (a dark flash). Using Ca<sup>2+</sup> imaging during repeated dark flashes, we identified 12 functional classes of neurons that differ based on their rate of adaptation, stimulus response shape, and anatomical location. While most classes of neurons depressed their responses to repeated stimuli, we identified populations that did not adapt, or that potentiated their response. To identify molecular players, we used a small molecule-screening approach to search for compounds that alter habituation learning. Among the pathways we identified were Melatonin and Estrogen signaling, as well as GABAergic inhibition. By analyzing which functional classes of neurons are GABAergic, and the result of pharmacological manipulations of the circuit, we propose that GABAergic inhibitory motifs drive habituation, perhaps through the potentiation of GABAergic synapses. Our results have identified multiple molecular pathways and cell types underlying a form of long-term plasticity in a vertebrate brain, and allow us to propose the first iteration of a model for how and where this learning process occurs.</p>
Data - "Suppressing feedback signals to visual cortex abolishes attentional modulation"
<p>Data associated with article "Suppressing feedback signals to visual cortex abolishes attentional modulation"</p>
HiCube: Interactive visualization of multiscale and multimodal Hi-C and 3D genome data
<p>Test dataset for HiCube.</p> <p>HiCube is a lightweight web application for interactive visualization and exploration of diverse types of genomics data at multiscale resolutions. Especially, HiCube displays synchronized views of Hi-C contact maps and three-dimensional (3D) genome structures with user-friendly annotation and configuration tools, thereby facilitating the study of 3D genome organization and function.</p> <p>HiCube is implemented in Javascript and can be installed via NPM. The source code is freely available at GitHub (https://github.com/wmalab/HiCube).</p>
Data from: Navigating the scales of diversity in subtropical and coastal fish assemblages ascertained by eDNA and visual surveys
<p>Environmental DNA (eDNA) metabarcoding emerges as a powerful method, allowing a more exhaustive investigation of fish fauna than any other methods. Yet, the general use of eDNA as a replacement of traditional methods such as visual surveys or physical sampling remains debatable. Therefore, a prior understanding of eDNA's spatial resolution is necessary. This study aimed to compare the assessments of fish diversity at three spatial scales by eDNA, underwater visual census (UVC), and diver-operated video (DOV) surveys across 21 reef sites in northern Taiwan. The specific objectives were to explore the regional species pool (γ-diversity), reveal spatial patterns of fish assemblages (β-diversity), and disentangle the relationships between fish assemblages and benthic composition (α-diversity). At the γ-diversity level, a total of 438 marine fish species were detected across methods. eDNA exhibits an extraordinary power to explore the regional species pool given sufficient replication, a power which is unachievable by DOV and UVC. At the β-diversity level, all the methods successfully revealed the same spatial patterns of beta diversity and the distance decay of similarity in fish assemblages. At the α-diversity level, none of the methods is capable of investigating the entire resident fish fauna, but visual surveys are more suitable for scrutinizing interactions between fish and benthos. Instead of undiscriminatingly recommending a combination of eDNA with traditional survey methods, we suggest implementing specific surveys in accordance with the ecological questions of interest.</p>
The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization
<h1>The SIGMA templates and atlases for the Wistar Rat Brain</h1> <p>The current document is a short description of the second version of the SIGMA resources for the Wistar rat brain. For a full description of the resources and the methodologies used to create them please consult the main publication [Barriere D.A. et al 2019].</p> <p>The SIGMA resources are a set of standardized MRI compatible templates and atlases meant to support the analysis of multimodal MRI data of the rat brain. They were developped as part of the SIGMA project, a collaborative project between French (CEA and INSERM) and Portuguese (ICVS) institutions (FCT-ANR/NEU-OSD/0258/2012). They provide a unified and standardized framework for the analysis of multimodal rat brain imaging data, allowing the reporting of results within the coordinate system of the Paxinos-Watson atlas.</p> <p>In this second version, standardized MRI compatible templates have been built from the original acquired data (11.7 Tesla Bruker Scanner at Neuropsin center <a href="https://www.cea.fr/drf/joliot/en/Pages/research_entities/NeuroSpin.aspx" rel="nofollow">https://www.cea.fr/drf/joliot/en/Pages/research_entities/NeuroSpin.aspx</a>) and emulated using the methods developed by Gabriel A. Devenyi (<a href="https://github.com/gdevenyi">https://github.com/gdevenyi</a>) and available here : <a href="https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction">https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction</a>. This pipeline is a re-implementation of the ANTs template construction pipeline requiring ANTs for the primary commands, and running on our cluster facilities using qbatch (<a href="https://islande.hub.inrae.fr/infrastructure" rel="nofollow">https://islande.hub.inrae.fr/infrastructure</a>).</p> <p>Using this methodology we firstly, updated the previous SIGMA spaces (T2sw, T2w, T1w) previously generated using the DARTEL Methods implemented in SPM8 and normalized the whole head images instead of brain.</p> <p>Secondly, we updated the probabilistic maps of the rat brain which are mandatory for the automatic segmentation of the rat brain and standardisation of morphometric analysis. Namely, we created new maps of Grey Matter, White Matter, CSF, Skull and outbrain. Those maps allow the use of SIGMA with the latter release of SPM12, a popular neuroimaging software dedicated to brain imaging analysis but also with ANTs, FSL and AFNI. Additionnally, we revised the Grey Matter/White Matter segmentations since the limits of which (particularly at the thalamic level) were a matter to debate with some users in the previous version of SIGMA.</p> <p>Thirdly, additionnal templates have been created using the optimized ANTs methodology to create from original unpublished data diffusion templates (B0, FA, etc.) at both ex-vivo and in-vivo resolutions.</p> <p>Eventually, using the same strategy, we created a CT/18FDG reference space from data obtained previously [Barrière D.A. et al 2018] which has been normalized with the MRI ex-vivo SIGMA template allowing to the SIGMA resource to propose a multimodal space for CT/TEP/MRI normalisation.</p> <h2><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#organisation-of-the-sigma-resources"></a></h2> <h2>Organisation of the SIGMA resources</h2> <p>The SIGMA resources have been organized as four sections : anatomical Imaging, functional imaging, atlases and TEP/CT imaging</p> <h3><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#anatomical-imaging"></a></h3> <h3>Anatomical Imaging</h3> <p>In this section a set of templates, priors and brain masks is available for ex-vivo and in-vivo data normalization</p> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#ex-vivo-t2-weighted"></a></h4> <h4>Ex-vivo T2*-weighted</h4> <p>T2*-weighted template + T2*-weighted map + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_ExVivo_Anatomical_Brain_csf.nii.gz SIGMA_ExVivo_Anatomical_Brain_gm.nii.gz SIGMA_ExVivo_Anatomical_Brain_mask.nii.gz SIGMA_ExVivo_Anatomical_Brain_out.nii.gz SIGMA_ExVivo_Anatomical_Brain_skull.nii.gz SIGMA_ExVivo_Anatomical_Brain_t2starmap.nii.gz SIGMA_ExVivo_Anatomical_Brain_template.nii.gz SIGMA_ExVivo_Anatomical_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#ex-vivo-diffusion"></a></h4> <h4>Ex-vivo diffusion</h4> <p>B0 template + FA template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.25x0.25x0.25mm.</p> <div> <pre><code> SIGMA_ExVivo_Diffusion_Brain_b0.nii.gz SIGMA_ExVivo_Diffusion_Brain_csf.nii.gz SIGMA_ExVivo_Diffusion_Brain_fa.nii.gz SIGMA_ExVivo_Diffusion_Brain_gm.nii.gz SIGMA_ExVivo_Diffusion_Brain_mask.nii.gz SIGMA_ExVivo_Diffusion_Brain_out.nii.gz SIGMA_ExVivo_Diffusion_Brain_skull.nii.gz SIGMA_ExVivo_Diffusion_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#in-vivo-t2-weighted"></a></h4> <h4>In-vivo T2-weighted</h4> <p>T2-weighted template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.15x0.15x0.15mm.</p> <div> <pre><code> SIGMA_InVivo_Anatomical_Brain_csf.nii.gz SIGMA_InVivo_Anatomical_Brain_gm.nii.gz SIGMA_InVivo_Anatomical_Brain_mask.nii.gz SIGMA_InVivo_Anatomical_Brain_out.nii.gz SIGMA_InVivo_Anatomical_Brain_skull.nii.gz SIGMA_InVivo_Anatomical_Brain_template.nii.gz SIGMA_InVivo_Anatomical_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#in-vivo-diffusion"></a></h4> <h4>In-vivo diffusion</h4> <p>T2-weighted template + B0 template + FA template + ADC template + brain mask.</p> <p>Spatial resolution 0.375x0.375x0.375mm.</p> <div> <pre><code> SIGMA_InVivo_Diffusion_Brain_adc.nii.gz SIGMA_InVivo_Diffusion_Brain_b0.nii.gz SIGMA_InVivo_Diffusion_Brain_fa.nii.gz SIGMA_InVivo_Diffusion_Brain_mask.nii.gz SIGMA_InVivo_Diffusion_Brain_t2.nii.gz </code></pre> <div> </div> </div> <h3><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#functional-imaging"></a></h3> <h3>Functional Imaging</h3> <p>T2-weighted template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.375x1x0.375mm.</p> <div> <pre><code> SIGMA_InVivo_Functional_Brain_csf.nii.gz SIGMA_InVivo_Functional_Brain_epi.nii.gz SIGMA_InVivo_Functional_Brain_gm.nii.gz SIGMA_InVivo_Functional_Brain_mask.nii.gz SIGMA_InVivo_Functional_Brain_t2.nii.gz SIGMA_InVivo_Functional_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h3><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-rat-brain-atlas-version-20--waxholm-atlas-feat-sigma"></a></h3> <h3>SIGMA Rat Brain Atlas Version 2.0 : Waxholm atlas Feat. SIGMA</h3> <p>In this second version of the SIGMA resources we deliver a new SIGMA brain atlas obtained by the normalization of the Waxholm space published by Kleven, H. et al. Nat Methods (2023, <a href="https://doi.org/10.1038/s41592-023-02034-3" rel="nofollow">https://doi.org/10.1038/s41592-023-02034-3</a><a title="La ressource a été trouvée dans UNPAYWALL" href="https://www.nature.com/articles/s41592-023-02034-3.pdf" target="_blank" rel="noopener"></a>). The Waxholm rat brain atlas is currently the best numerical 3D atlas of the rat brain. In accordance with authors of this paper we are authorized to modify and embed the WHS atlas within the SIGMA environement to standardize the identification of brain territories. We provide a normalized version the WHS for both ex-vivo and in-vivo of the anatomical SIGMA templates. Finally, we offer linear and non-linear transformations to enable your data to commute between the SIGMA and WHS ex-vivo environments using ANTs commands.</p> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#ex-vivo-atlas"></a></h4> <h4>Ex-vivo atlas</h4> <p>WHS rat brain atlas normalized in ex-vivo T2*-weighted SIGMA template + List of 222 labels created in ITKSnap Format + linear and non-linear transformations for SIGMA-WHS journeys (WHS-to-SIGMA_Transformations folder).</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_ExVivo_Anatomical_Brain_Atlas.nii.gz SIGMA_ExVivo_Anatomical_Brain_Atlas.txt ./WHS-to-SIGMA_Transformations/reference_SIGMA.nii.gz ./WHS-to-SIGMA_Transformations/reference_WHS.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_01_InverseWarp.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_01_Warp.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_02_GenericAffine.mat ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_03_GenericAffine.mat ./WHS-to-SIGMA_Transformations/WHS_SD_rat_atlas_v4.nii.gz ./WHS-to-SIGMA_Transformations/WHS-to-SIGMA_byANTS.txt </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#in-vivo-atlas"></a></h4> <h4>In-vivo atlas</h4> <p>WHS rat brain atlas normalized in in-vivo T2 SIGMA anatomical template + List of 222 labels created in ITKSnap Format.</p> <p>Spatial resolution 0.15x0.15x0.15mm.</p> <div> <pre><code> SIGMA_InVivo_Anatomical_Brain_Atlas.nii.gz SIGMA_InVivo_Anatomical_Brain_Atlas.txt </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-brain-meshes"></a></h4> <h4>SIGMA brain meshes</h4> <p>Rat brain mesh created using BrainNet viewers commands in matlab (<a href="https://www.nitrc.org/projects/bnv/" rel="nofollow">https://www.nitrc.org/projects/bnv/</a>).</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_Anatomical_Brain_Atlas_mesh.nv </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-functional-atlas"></a></h4> <h4>SIGMA functional atlas</h4> <p>In the original publication of the SIGMA resources, we developed a functional atlas for the rat brain, using a group ICA analysis validated through a RAICAR approach. From this analysis, we identified 59 bilateral ROIs covering cortical, sub-cortical and brainstem structures that are functionally distinct. Despite having been derived from purely functional data, this atlas broadly, if not precisely, correlates with the general anatomical boundaries and many are associated with specific anatomical structures. A primary motivation for the creation of this atlas is derived from the need to perform brain segmentations which is optimized for functional MRI analysis, since the signal sources do not necessarily match typical anatomical boundaries. A similar requirement has been identified by those performing human studies, resulting in efforts to generate more diverse, multi-modal atlases.</p> <p>SIGMA rat brain functional atlas normalized in in-vivo T2 SIGMA functional template + List of 59 labels created in ITKSnap Format.</p> <p>Spatial resolution 0.375x1x0.375mm.</p> <div> <pre><code> SIGMA_Functional_Brain_Atlas_Labels.txt SIGMA_Functional_Brain_Atlas_ListOfStructures.csv SIGMA_InVivo_Functional_Brain_Atlas.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-cttep-template"></a></h4> <h4>SIGMA CT/TEP template</h4> <p>In this version of the SIGMA resources we included a CT/TEP template built from the data that previously published (Barriere D.A. et al 2018 , Sci Rep. 2018 Jan 11;8(1):424. doi: 10.1038/s41598-017-18896-5) and acquired on a Triumph™ PET/CT dual modality imaging platform (Gamma Medica, Inc., Northridge, CA, USA), consisting of a LabPET™ avalanche photodiode-based digital PET scanner with a 7.5 cm axial field-of-view capable of achieving an isotropic spatial resolution. A caudal injection of approximately 30 MBq of [18F]-FDG was applied followed by a static acquisition to evaluate [18F]-FDG uptake within brain. CT images were acquired from the high-resolution X-ray computed tomography (CT) modality. Images were reconstructed using the Triumph™ PET/CT software. using the following parameters: 20 iterations, span of 63, field of view of 80 mm with a final matrix resolution of 160 × 160 × 128 and a voxel size of 0.5 × 0.5 × 0.597 mm. Brain dynamic [18F]-FDG images were reconstructed using the same protocol but we generated 32 frames (10 for 5 s, 7 for 10 s, 6 for 30 sec, 6 for 120 s, 2 for 240 s and 1 for 300 s). [18F]-FDG images were reconstructed using 3-D MLEM algorithm providing 0.5 × 0.5 × 0.597 mm images. CT scans were reconstructed using the standard FBP kernel analytical reconstruction algorithms, providing an isotropic image of 512 slices with a final resolution of 0.165 µm isotropic. Both [18F]-FDG and CT data were spatially normalized to the SIGMA ex-vivo template using the previously described methods.</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_InVivo_18FDG_Brain_template.nii.gz SIGMA_InVivo_CT_Brain_template.nii.gz </code></pre> <div> </div> </div> <h2><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#important-note"></a></h2> <h2>Important Note</h2> <p>SIGMA ressources are provided at the scanner resolution and are oriented in anterior commisure/posterior commisure axis. Center of the images have been set at the anterior commisure level (Bregma 0 mm). Nevertheless, users are invited to increase the resolution of the current images for using in SPM or FSL for accurate coregistration and normalization steps (we recommand x10 increasing). No manipulation of image resolution are required with ANTs. Not tested with AFNI.</p> <p>For any questions regarding the SIGMA ressource, please email the SIGMA Team (<a href="mailto:sigma.preclinical.resources@gmail.com">sigma.preclinical.resources@gmail.com</a>) or Email directly David A. Barrière (<a href="mailto:david.barriere@cnrs.fr">david.barriere@cnrs.fr</a>).</p> <h1><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#references"></a></h1> <h1>REFERENCES</h1> <p>Barrière, D.A. et al. The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization. Nat Commun 10, 5699 (2019). <a href="https://doi.org/10.1038/s41467-019-13575-7" rel="nofollow">https://doi.org/10.1038/s41467-019-13575-7</a><a title="La ressource a été trouvée dans UNPAYWALL" href="https://www.nature.com/articles/s41467-019-13575-7.pdf" target="_blank" rel="noopener"></a></p> <p>Kleven, H. et al. Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration. Nat Methods 20, 1822–1829 (2023). <a href="https://doi.org/10.1038/s41592-023-02034-3" rel="nofollow">https://doi.org/10.1038/s41592-023-02034-3</a></p> <p>Barrière, D.A. et al. Combination of high-fat/high-fructose diet and low-dose streptozotocin to model long-term type-2 diabetes complications. Sci Rep. 2018 Jan 11;8(1):424. doi: 10.1038/s41598-017-18896-5. PMID: 29323186; PMCID: PMC5765114.</p> <h1><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#related-works-using-the-sigma-ressources"></a></h1> <h1>RELATED WORKS USING THE SIGMA RESSOURCES</h1> <p>Grandjean J. et al. A consensus protocol for functional connectivity analysis in the rat brain. Nat Neurosci. 2023 Apr;26(4):673-681. doi: 10.1038/s41593-023-01286-8. Epub 2023 Mar 27. Erratum in: Nat Neurosci. 2023 Jun;26(6):1127-1128. PMID: 36973511; PMCID: PMC10493189.</p> <p>Vidal B. et al. Inter-subject registration and application of the SIGMA rat brain atlas for regional labeling in functional ultrasound imaging. J Neurosci Methods. 2021 May 1;355:109139. doi: 10.1016/j.jneumeth.2021.109139. Epub 2021 Mar 16. PMID: 33741345.</p> <p>Barrière D.A. et al. Paracetamol is a centrally acting analgesic using mechanisms located in the periaqueductal grey. Br J Pharmacol. 2020 Apr;177(8):1773-1792. doi: 10.1111/bph.14934. Epub 2020 Jan 22. PMID: 31734950; PMCID: PMC7070177</p> <p>Barrière D.A. et al. Structural and functional alterations in the retrosplenial cortex following neuropathic pain. Pain. 2019 Oct;160(10):2241-2254. doi: 10.1097/j.pain.0000000000001610. PMID: 31145220.</p> <p>Magalhães, R. et al Resting-State Functional MR Imaging and Spectroscopy Study of the Dorsal Hippocampus in the Chronic Unpredictable Stress Rat Model. J Neurosci. 2019 May 8;39(19):3640-3650. doi: 10.1523/JNEUROSCI.2192-18.2019. Epub 2019 Feb 25. PMID: 30804096; PMCID: PMC6510342.</p> <p>Magalhães, R. et al The dynamics of stress: a longitudinal MRI study of rat brain structure and connectome. Mol Psychiatry. 2018 Oct;23(10):1998-2006. doi: 10.1038/mp.2017.244. Epub 2017 Dec 5. PMID: 29203852.</p>
Data from: Fast visual adaptation to dim light in a cavity-nesting bird
<p class="MsoNormal"><span>Many birds move fast into dark nest cavities forcing the visual system to adapt to low light intensities. Their visual system takes between 15 and 60 minutes for complete dark adaptation, but little is known about the visual performance of birds during the first seconds in low light intensities. </span></p> <p class="MsoNormal"><span>In a forced two-choice behavioural experiment we studied how well budgerigars can discriminate stimuli of different luminance directly after entering a darker environment. The birds made their choices within about one second and did not wait to adapt their visual system to the low light intensities. When moving from a bright facility into an environment with 0.5 log unit lower illuminance, the budgerigars detected targets with a luminance of 0.825 cd/m<sup>2</sup> on a black background. When moving into an environment with 1.7 or 3.5 log units lower illuminance, they detected targets with luminances between 0.106 and 0.136 cd/m<sup>2</sup>. In tests with two simultaneously displayed targets, the birds discriminated similar luminance differences between the targets (Weber fraction of 0.41-0.54) in all light levels. Our results support the notion that partial adaptation of bird eyes to the lower illumination occurring within one second allows them to safely detect and feed their chicks. </span></p>
Data produced in the context of RCLN particpation to the Visual WSD task at SemEval 2023
<p>The data contain:</p> <p>- generated captions from train, trial and test images</p> <p>- generated images from the diffusion model</p> <p>refer to https://github.com/dbuscaldi/VisualWSD23 for code</p>
AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments - Supplementary Data
<p><strong>Introduction</strong></p> <p>In this zip, we release the auxiliary data that is beneficial to execute the implementation of AVLEN described in our paper AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments by Sudipta Paul, Amit K Roy-Chowdhury, and Anoop Cherian, NeurIPS, 2022.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped data is 4.6G</li> <li>The unzipped folder contains: (i) a README.md file and (ii) ./AVLEN-data folder. The latter contains the following zip files. Please see the AVLEN code to see how to unzip these files into their respective folders. <ul> <li>ckpt.119.pth -- 61M </li> <li>connectivity.zip -- 1.4M </li> <li>pretrained_weights.zip -- 1.7G</li> <li>ResNet-152-imagenet.zip -- 2.9G</li> <li>semantic_audionav_dialog_approx.zip -- 2.7M</li> <li>soundspaces.zip -- 479K</li> <li>speaker_model_weights.zip -- 51M</li> </ul> </li> </ul> <p><strong>Other Resources</strong></p> <p>For the implementation of AVLEN that uses the data shared here, please visit <a href="https://www.merl.com/publications/TR2022-131">MERL TR2022-131</a>.</p> <p><strong>Citation</strong></p> <p>If you use AVLEN in your research, please cite our paper:</p> <pre><code>@InProceedings{paul2022avlen, title={AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments}, booktitle={Advances in Neural Information Processing Systems}, author={Paul, Sudipta and Roy-Chowdhury, Amit and Cherian, Anoop}, volume={35}, pages={6236--6249}, year={2022} }</code></pre> <p><strong>Copyright and License</strong></p> <p>The AVLEN dataset is released under CC-BY-SA-4.0 license.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <p> </p>
Data From: Rapid expansion and visual specialisation of learning and memory centers in the brains of Heliconiini butterflies
<p class="MsoNormal">Changes in the abundance and diversity of neural cell types, and their connectivity, shape brain composition and provide the substrate for behavioral evolution. Although investment in sensory brain regions is understood to be largely driven by the relative ecological importance of particular sensory modalities, how selective pressures impact the elaboration of integrative brain centers has been more difficult to pinpoint. Here, we provide evidence of extensive, mosaic expansion of an integration brain center among closely related species, which is not explained by changes in sites of primary sensory input. By building new datasets of neural traits among a tribe of diverse Neotropical butterflies, the Heliconiini, we detected several major evolutionary expansions of the mushroom bodies, central brain structures pivotal for insect learning and memory. The genus <em>Heliconius</em>, which exhibits a unique dietary innovation, pollen-feeding, and derived foraging behaviors reliant on spatial memory, shows the most extreme enlargement. This expansion is primarily associated with increased visual processing areas and coincides with increased precision of visual processing, and enhanced long-term memory. These results demonstrate that selection for behavioral innovation and enhanced cognitive ability occurred through expansion and localized specialization in integrative brain centers.</p>
ScienceDex guides
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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.