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491 results for “population modelling”
Data availability: Random encounter model is a reliable method for estimating population density of multiple species using camera traps
<p>Data of the paper entitled "Random encounter model is a reliable method for estimating population density of multiple species using camera traps" published on Remote Sensing in Ecology and Conservation</p>
Revisiting the number of self‐incompatibility alleles in finite populations: From old models to new results
<p>Under gametophytic self-incompatibility (GSI), plants are heterozygous at the self-incompatibility locus (S-locus) and can only be fertilized by pollen with a different allele at that locus. The last century has seen a heated debate about the correct way of modeling the allele diversity in a GSI population that was never formally resolved. Starting from an individual-based model, we derive the deterministic dynamics as proposed by Fisher (1958), and compute the stationary S-allele frequency distribution. We find that the stationary distribution proposed by Wright (1964) is close to our theoretical prediction, in line with earlier numerical confirmation. Additionally, we approximate the invasion probability of a new S-allele, which scales inversely with the number of resident S-alleles. Lastly, we use the stationary allele frequency distribution to estimate the population size of a plant population from an empirically obtained allele frequency spectrum, which complements the existing estimator of the number of S-alleles. Our expression of the stationary distribution resolves the long-standing debate about the correct approximation of the number of S-alleles and paves the way to new statistical developments for the estimation of the plant population size based on S-allele frequencies.</p>
Figure 2 in A population growth model of Tetranychus urticae Koch (Acari: Tetranychidae)
Figure 2. Growth of total population of T. urticae on two bean fitted to logistic curve.
Figure 1 in A population growth model of Tetranychus urticae Koch (Acari: Tetranychidae)
Figure 1. Population fluctuation of total population of T. urticae on two bean fields in 2016.
Dataset belonging to SNF project: Use of physiologically based pharmacokinetic modelling to simulate dosing requirements of long-acting intramuscular antiretroviral drugs in special populations and to manage drug-drug interactions
Open the record for dataset details and reuse information.
DirtyGrid: 3D dust radiative transfer modeling of spectral energy distributions of dusty stellar populations
<p>Output global SEDs of a large grid of 3D stellar+dust radiative transfer models spanning the range of star formation and dust contents of regions of galaxies.</p> <p>Paper describing the DirtyGrid is Law, Gordo, & Misset (2018, ApJ, submitted)</p> <p>Code to make to access this data at: https://github.com/karllark/pydirtygrid</p>
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>
Vortex input files -- Linking habitat and population viability analysis models of a metapopulation of Florida scrub-jays
<p>Vortex input files for manuscript "<span>Linking </span><span><span>habitat and population viability analysis models to account for <span>vegetation dynamics, habitat fragmentation, and social behavior of a metapopulation of Florida scrub-jays</span></span></span>" by R. C. Lacy, D. B. Breininger, et al. </p>
A Modeling Framework for Near-Road Population Exposure to Traffic-Related PM2.5 and Environmental Equity Analysis: A Case Study in Atlanta, Georgia
<p>This is the dataset for the NCST project <em>"A Modeling Framework for Near-Road Population Exposure to Traffic-Related PM2.5 and Environmental Equity Analysis: A Case Study in Atlanta, Georgia"</em> by the Georgia Tech research team.</p> <p> </p> <p>Here is the abstract of the research: </p> <p>In this study, a modeling framework for population exposure to traffic-related PM2.5 with high spatiotemporal resolution is proposed and applied to the I-575/I-75 Northwest Corridor (NWC) in Atlanta, GA, for environmental equity analysis. The analyses retrieved trip data from the Atlanta Regional Commission’s (ARC) Activity-Based Model 2020 (ABM2020), after implementing path retention algorithms (Zhao, et al., 2019) to generate individual travel paths for more than 20 million predicted vehicle trips. Emission rates for each link were retrieved from MOVES-Matrix given the ABM link speed and facility type, the ARC’s county-level fleet composition data, and regional fuel properties and I&M program parameters. High-resolution downwind concentration profiles were predicted using EPA’s AERMOD microscale dispersion model with AERMET meteorology profiles for a huge array of receptors. Trip-end locations were derived from the ABM trip data, and the on-road trajectories for each person-trip (vehicle trace data) were derived from the travel paths through network. ABM synthetic household and person data were used in demographic assessment, and linked to representative household latitude and longitude locations in the Epsilon 2019 household demographic dataset. Individual exposure to traffic-related PM2.5 in time and space (average hourly concentration) was assessed by overlaying the second-by-second person location profiles (for 24 hours) against the hourly predicted PM2.5 concentration profiles. The analyses summarize the results across 16 demographic groups and the aggregate population exposure are compared to assess potential impact differences across demographics. High-income households in the corridor were exposed to less traffic-related air pollution as they tended to live further from the freeways. The analyses did not reveal large disproportionate negative impacts on low income groups along this specific corridor, but lager disproportionate negative impacts are expected elsewhere in the metro area due to the spatial clustering of income groups along other corridors. Overall, the research demonstrates the applicability of the modeling framework and describes how the various elements (e.g., link screening, dispersion modeling, path tracing, etc.) are optimized on the supercomputing cluster.</p>
Model results and data associated with "Antecedent effect models as an exploratory tool to link climate drivers to herbaceous perennial population dynamics data"
<p>Model results and data (including Bayesian posteriors) associated with "Antecedent effect models as an exploratory tool to link climate drivers to 3 herbaceous perennial population dynamics data".</p> <p>This is a repository created to store the posteriors of the models fit within this project. Because these occupy so much space, it makes sense to store them in a separate repository.</p> <p>There are two directories:</p> <ul> <li><em>model_results/</em> contains all of the posteriors (files with character pattern <em>main_posterior_#.csv</em>). The three types of files contained in this directory are described in <em>metadata_model_results.xlsx</em>. The number # corresponds to column "index" in file <em>raw_data/design_insample.csv</em>.</li> <li><em>raw_data/</em> is mostly not essential: it contains the raw data to fit models, and it replicates folder <em>data/</em> in repository https://dx.doi.org/10.5281/zenodo.13909628.</li> </ul>
Multiple-model stock assessment frameworks for precautionary management and conservation on fishery-targeted coastal dolphin populations off Japan
<p>1. Stock assessment approaches are often oversimplified due to lack of biological knowledge and insufficient data. In spite of worldwide attention, fishery-targeted coastal dolphin species in Japan have lacked in-depth quantitative stock assessments because of limited time-series of population size and an absence of associated biological information. We consequently developed integrated population models that analyzed multiple sources of data simultaneously with published biological information within a Bayesian framework.<br> 2. We estimate population status and trends for three main species targeted by fisheries, bottlenose dolphins <em>Tursiops truncatus</em>, Risso's dolphins <em>Grampus griseus</em>, and short-finned pilot whales <em>Globicephala macrorhynchus</em>, using single-species age-aggregated and age-structured population dynamics models. Models were fit to absolute abundance estimates from systematic line-transect surveys, four series of abundance indices calculated from fisher's logbooks, and historical catch records. Published biological information was used to develop prior distributions for the biological parameters. We assessed the possible effects of ecological disturbance and competition using state-space and multispecies models.<br> 3. The multispecies model was selected by the model selection both for the age-aggregated and the age-structured approaches.<br> 4. Single-species assessments found that population size declined 4.2% (Risso's dolphin) to 8.0% (bottlenose dolphin) for three species since the late 1800s based on median posterior values, while the state-space and multispecies models found that environmental disturbance and an interaction among species could have reduced population size more substantially.<br> 5. '<em>Policy implications</em>' Simple single-species models are often used to provide conservation and management advice for wild animals but, in this case, results from such models are overly optimistic because they overlook important ecological process. In contrast, current population status could be less favorable if environmental disturbance and interspecific competition actually control population dynamics. Even if that is not the case, considering ecological process in the model will provide more precautionary ways. Our approach of combining multiple modelling frameworks is applicable to many other management systems, and offers increased confidence in estimated status and trends over assessments that consider only a single model.</p>
Reproduction package for the paper "Multi-dimensional population modelling using frbpoppy: Magnetars can produce the observed fast radio burst sky"
<p>This is a basic reproduction package for the paper "Multi-dimensional population modelling using frbpoppy: Magnetars can produce the observed fast radio burst sky" by David Gardenier & Joeri van Leeuwen (2021).</p> <p>* arXiv: <a href="https://arxiv.org/abs/2012.06396">arXiv:2012.06396</a><br> * DOI: <a href="https://doi.org/10.1051/0004-6361/202040119">10.1051/0004-6361/202040119</a></p> <p> </p>
Biophysical larval dispersal models of observed bonefish (Albula vulpes) spawning events in Abaco, The Bahamas: An assessment of population connectivity and ocean dynamics
<p>Biophysical models are a powerful tool for assessing population connectivity of marine organisms that broadcast spawn. <em>Albula</em> <em>vulpes</em> is a species of bonefish that is an economically and culturally important sportfish found throughout the Caribbean and that exhibits genetic connectivity among geographically distant populations. We created ontogenetically relevant biophysical models for bonefish larval dispersal based upon multiple observed spawning events in Abaco, The Bahamas in 2013, 2018, and 2019. Biological parameterizations were informed through active acoustic telemetry, CTD casts, captive larval rearing, and field collections of related albulids and anguillids. Ocean conditions were derived from the Regional Navy Coastal Ocean Model American Seas dataset. Each spawning event was simulated 100 times using the program Ichthyop. Ten thousand particles were released at observed and putative spawning locations and were allowed to disperse for the full 71-day pelagic larval duration for <em>A</em>. <em>vulpes</em>. Settlement densities in defined settlement zones were assessed along with interactions with oceanographic features. The prevailing Northern dispersal paradigm exhibited strong connectivity with Grand Bahama, the Berry Islands, Andros, and self-recruitment to lower and upper Abaco. Ephemeral gyres and flow direction within Northwest and Northeast Providence Channels were shown to have important roles in larval retention to the Bahamian Archipelago. Larval development environments for larvae settling upon different islands showed few differences and dispersal was closely associated with the thermocline. Settlement patterns informed the suggestion for expansion of conservation parks in Grand Bahama, Abaco, and Andros, and the creation of a park in Eleuthera and the Berry Islands to protect fisheries. Further observation of spawning events and the creation of biophysical models will help to maximize protection for bonefish spawning locations and nursery habitat, and may help to predict year-class strength for bonefish stocks throughout the Greater Caribbean.</p>
Grey model analysis of vehicle population, road transport energy consumption, and vehicular emissions
<p>The files provide additional information to the paper “Grey model analysis of vehicle population, road transport energy consumption, and vehicular emissions". The supporting data file contains excel sheets of data used in the analysis, and the supporting information file provides some assumptions, background information and other results not included in the paper</p>
single-cell RNAseq data (data set 20) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset20) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from breast cancer samples downloaded from the GEO website (GSE180286)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <p> </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.