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Figure 2 in Population structure and spatial distribution of the tiger (Panthera tigris, Felidae, Carnivora) in Southwestern Primorye (Russian Far East)
Figure 2. Distribution of tiger tracks in Southwestern Primorye.
Figure 2 in Assessing population size and survival rate of Pelophylax bedriagae caralitanus, in a well-protected Nature Park in Türkiye
Figure 2. The view of the habitat surrounding Gölcük Lake.
Figure 3 in Distribution and population density of Halyomorpha halys (Stål, 1855) (Hemiptera: Pentatomidae) in Black Sea Region of Türkiye
Figure 3. Average change in H. halys population in Rize in the years 2019 and 2021.
Figure 2 in Distribution and population density of Halyomorpha halys (Stål, 1855) (Hemiptera: Pentatomidae) in Black Sea Region of Türkiye
Figure 2. Average change in H. halys population in Artvin in the years 2019, 2020, and 2021.
Figure 1 in Distribution and population density of Halyomorpha halys (Stål, 1855) (Hemiptera: Pentatomidae) in Black Sea Region of Türkiye
Figure 1. Infested area in Middle and Eastern Black Sea Region.
Fig. 1 in Unpublished population data of Dendrobates azureus Hoogmoed 1969 obtained in 1968 and 1970, and its historical and current taxonomic status
Fig. 1. Dendrobates "azureus" (= tinctorius).
Figure3 in Population Status and Nesting Behavior of Red-Headed Vultures (Sarcogyps Calvus) at Dhorfirdi, Tanahun District, Nepal
Figure3. Nesting behavior of S. calvus
Figure1 in Population Status and Nesting Behavior of Red-Headed Vultures (Sarcogyps Calvus) at Dhorfirdi, Tanahun District, Nepal
Figure1. Location map showing vulture sighted areas in MTR
Figure2 in Population Status and Nesting Behavior of Red-Headed Vultures (Sarcogyps Calvus) at Dhorfirdi, Tanahun District, Nepal
Figure2. Summer and winter vulture population
Human head models and populational framework for simulating brain stimulations: part 6
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 6. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 4
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 4. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 3
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 3. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 5
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 5. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 2
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 2. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 1
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 1. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p> <p> </p>
Figure 2 in Effect of environmental change after the 2011 tsunami on the population dynamics of Japanese tubesnout Aulichthys japonicus (Gasterosteiformes)
Figure 2. – Schematic illustration of the framed seine net used in the present study.
Fig. 4. Metastrongylus spp. eggs from a in Lungworms (Metastrongylus spp.) and intestinal parasitic stages of two separated Swiss wild boar populations north and south of the Alps: Similar parasite spectrum with regional idiosyncrasies
Fig. 4. Metastrongylus spp. eggs from a wild boar faecal sample.
Fig. 1 in Influence of biotic and abiotic factors on flea species population dynamics on Lasiopodomys brandtii
Fig. 1. Map of the study area.
FIGURE 1 in Population genetics of the endangered catfish Pseudoplatystoma magdaleniatum (Siluriformes: Pimelodidae) based on species-specific microsatellite loci
FIGURE 1 | Sampling sites of Pseudoplatystoma magdaleniatum in the Magdalena-Cauca basin.
CoCs with 2023 NonDV PIT and HIC with 2022 Population Totals and Shelter Utilization Rates
<p>Continuum of Care level data with 2023 Non Domestic Violence HUD Point in Time and Housing Inventory Counts with 2022 Population Totals and Shelter Utilization Rates.</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.