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2,721 results for “CONNECTIVITY”
Dataset related to "Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes"
<p>This is an accompanying dataset to the article with the title "Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes" (DOI: <a href="https://doi.org/10.1101/2024.03.15.585196">10.1101/2024.03.15.585196</a>). It contains structural and activity data related to the morphologically detailed model of the rat somatosensory cortex (<a href="https://doi.org/10.1016/j.cell.2015.09.029" target="_blank" rel="noopener">Markram et al., 2015</a>), refered to as "BBP" data in the article. Specificaly, following data items are included:</p> <p><strong>Simulation data:</strong> <code>simulation.xz</code></p> <p><em>"Reliability" protocol:</em></p> <p>Separate folders <code>BlobStimReliability_O1v5-SONATA_<circuit-name></code> with simulation data using the baseline and all manipulated connectomes respectively (see Technical info below), each of which containing:</p> <ul> <li><code>working_dir/connectome.h5</code>: Connectivity matrix in <code>ConnectivityMatrix</code> format, which can be loaded using <a href="https://github.com/BlueBrain/ConnectomeUtilities" target="_blank" rel="noopener">ConnectomeUtilities</a>.</li> <li><code>working_dir/raw_spikes_exc_<sim>.npy</code>: Raw (excitatory) spikes in numpy .npy format, containing an array of spike times (first column) and corresponding neuron GIDs (second column). One file for each of the 10 simulations with different simulator seeds, i.e., <sim> is 0..9.</li> <li><code>working_dir/stim_stream.npy</code>: Stimulus train in numpy .npy format, containing the sequence of stimulus identities.</li> <li><code>working_dir/time_windows.npy</code>: Time windows in ms in numpy .npy format, corresponding to the stimulus train.</li> <li><code>working_dir/processed_data_store.h5</code>: Data store in HDF5 format with preprocessed spike signals (e.g., required for <em>Gaussian kernel reliability</em> computations), which contains... <ul> <li><code>spike_signals_exc</code>: Group of simulations datasets "sim_0" to "sim_9", each of which is an array of size <#gids x #t_bins> and contains binned spike signals filtered with a Gaussian kernel.</li> <li><code>sigma</code>: Sigma in ms of Gaussian kernel used for smoothing.</li> <li><code>gids</code>: List of excitatory neuron GIDs.</li> <li><code>t_bins</code>: List of time bins in ms.</li> <li><code>firing_rates</code>: Average firing rates per simulation (0..9; rows) and (excitatory) neuron GID (columns); average firing rates were computed as the inverse of the mean inter-spike interval per neuron.</li> </ul> </li> </ul> <p><em>"Classification" protocol:</em></p> <p>Single folder <code>Toposample_O1v5-SONATA</code> with simulation data using the baseline connectome, stored in a format compatible with the <a href="https://github.com/JasonPSmith/TriDy" target="_blank" rel="noopener">TriDy</a> (<a href="https://doi.org/10.1162/netn_a_00228">Conceição et al., 2022</a>) and <a href="https://github.com/BlueBrain/topological_sampling/tree/merge_samples" target="_blank" rel="noopener">TopoSampling</a> (<a href="https://doi.org/10.1371/journal.pone.0261702">Reimann et al., 2022</a>) pipelines, containing:</p> <ul> <li><code>toposample_input/connectivity.npz</code>: Sparse connectivity matrix in Compressed Sparse Column format , which can be loaded using <code>scipy.sparse.load_npz</code>.</li> <li><code>toposample_input/neuron_info.pickle</code>: Pandas dataframe in pickle format, which can be loaded using <code>pandas.read_pickle</code>, containing additional information about each neuron.</li> <li><code>toposample_input/raw_spikes_exc.npy</code>: Raw (excitatory) spikes in numpy .npy format, containing an array of spike times (first column) and corresponding neuron GIDs (second column).</li> <li><code>toposample_input/stim_stream.npy</code>: Stimulus train in numpy .npy format, containing the sequence of stimulus identities.</li> <li><code>toposample_input/time_windows.npy</code>: Time windows in ms in numpy .npy format, corresponding to the stimulus train.</li> </ul> <p> </p> <p><strong>Classification data:</strong> <code>classification.xz</code></p> <p>Selected neighborhoods and classification results for the "PCA" method (<a href="https://github.com/BlueBrain/topological_sampling/tree/merge_samples" target="_blank" rel="noopener">TopoSampling</a> pipeline) as well as the "network_based" method using active subnetworks (<a href="https://github.com/JasonPSmith/TriDy" target="_blank" rel="noopener">TriDy</a> pipeline, using <a href="https://github.com/jlazovskis/TriDy-tools" target="_blank" rel="noopener">TriDy-tools</a> wrapper), stored as:</p> <p><em>"PCA" method:</em></p> <ul> <li><code>PCA/community_database_PCA.pkl</code>: Pandas dataframe in pickle format, containing the binary selection of 50 neighborhood centers (neuron GIDs; rows) for the different selection parameters (columns).</li> <li><code>PCA/classification_results_PCA.pkl</code>: Pandas dataframe in pickle format, containing the classification accuracies for all selection parameters (rows) and 6 cross-validation folds plus mean (columns).</li> </ul> <p><em>"network_based" method:</em></p> <ul> <li><code>network_based/selections_reliability.pkl</code>: Pandas dataframe in pickle format, containing different combinations of first/second selection parameters for the <em>double selection procedure</em> (rows) together with neuron indices (w.r.t. the EXC subcircuit) of the corresponding 50 neighborhood centers (chief0..49; columns).</li> <li><code>network_based/partition_reliability.npy</code>: Partition indices in numpy .npy format required to launch the pipeline using <a href="https://github.com/jlazovskis/TriDy-tools" target="_blank" rel="noopener">TriDy-tools</a>, which is an array of 50 neuron indices (w.r.t. the full circuit!) of the neighborhood centers (columns) for each combination of selections as in <code>selections_reliability.pkl</code> (rows).</li> <li><code>network_based/results/...</code>: Subfolder containing a list of pickle files with the classification results using different featurization parameters, as indicated by the filename. Each file contains a Pandas dataframe with the classification accuracies and numbers of (non-zero) features (columns) for each combination of selections as in <code>selections_reliability.pkl</code> (rows).</li> </ul> <p> </p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Functional connectivity and graph theory of self networks in toodlers with ASD
<p>The study collected fMRI data from children with autism and typical developmental disorders at 18-24 months and 25-48 months, and analyzed the brain's self network using functional connectivity and graph theory methods</p>
Competitive Metaheuristic Algorithms for Building a Performance Database of a Dual-Band Combline Bandpass Filter with Microstrip Connection (Version 17)
<p>To run the files, remove the prefixes (e.g. figA- or fig5A-)</p>
Competitive Metaheuristic Algorithms for Building a Performance Database of a Dual-Band Combline Bandpass Filter with Microstrip Connection (Version 17) [Dataset]. Zenodo.
<p>To run the files, remove the prefixes (e.g. figA- or fig5A-)</p>
Resources for Anatomical Connectivity and Single-Neuron Typing/Subtyping
<p>The dataset consists of local morphology reconstructions and necessary computational material for the paper: <a title="https://www.researchsquare.com/article/rs-2960606/latest" href="https://www.researchsquare.com/article/rs-2960606/latest" target="_blank" rel="noopener">Anatomical Connectivity of Single Neurons Helps Classify Cell Subtypes in Mouse Brains</a></p> <p>File Inventory:</p> <ul> <li>SEU_10860_dendrite.zip </li> <li>Materials.zip</li> </ul> <p>The Material file includes tables for the following:</p> <ul> <li>Cell type (single-neuron)</li> <li>Connectivity matrix (single-neuron)</li> <li>Morphological feature (single-neuron)</li> <li>Projection matrix (single-neuron)</li> <li>Soma coordinates (single-neuron)</li> <li>Gene expression profile (MOs_FRP cells)</li> </ul>
Intermittent, Heterogeneous, and Transient River-Groundwater Transitions from Disconnection to Connection
<p>This repository includes the data for model setup and HYDRUS-2D input/output files.</p> <p>The first version was released on 05/24/2024. The second version was released on 12/17/2024. </p>
Reducing transmission expansion by co-optimizing sizing of wind, solar, storage, and grid connection capacity: Raw Data
<p>This dataset contains all GenX model input and results data relevant to the working paper ‘Reducing transmission expansion by co-optimizing sizing of wind, solar, storage, and grid connection capacity.’ Data for each modeled scenario is contained within a folder in the main directory ('Final_Outputs'), using the naming convention p1_2030_case[number]. Scenarios correspond to the following table:</p> <table> <tbody> <tr> <td><strong>Case Number</strong></td> <td><strong>Scenario</strong></td> <td><strong>VRE Cost</strong></td> <td><strong>Forced Battery Capacity (GW)</strong></td> </tr> <tr> <td>1</td> <td>Fixed Interconnection</td> <td>Low</td> <td>3.75</td> </tr> <tr> <td>2</td> <td>Fixed Interconnection</td> <td>Low</td> <td>5</td> </tr> <tr> <td>3</td> <td>Fixed Interconnection</td> <td>Low</td> <td>7.5</td> </tr> <tr> <td>4</td> <td>Fixed Interconnection</td> <td>Low</td> <td>15</td> </tr> <tr> <td>5</td> <td>Optimized Interconnection</td> <td>Low</td> <td>3.75</td> </tr> <tr> <td>6</td> <td>Optimized Interconnection</td> <td>Low</td> <td>5</td> </tr> <tr> <td>7</td> <td>Optimized Interconnection</td> <td>Low</td> <td>7.5</td> </tr> <tr> <td>8</td> <td>Optimized Interconnection</td> <td>Low</td> <td>15</td> </tr> <tr> <td>9</td> <td>Co-Located Storage</td> <td>Low</td> <td>3.75</td> </tr> <tr> <td>10</td> <td>Co-Located Storage</td> <td>Low</td> <td>5</td> </tr> <tr> <td>11</td> <td>Co-Located Storage</td> <td>Low</td> <td>7.5</td> </tr> <tr> <td>12</td> <td>Co-Located Storage</td> <td>Low</td> <td>15</td> </tr> <tr> <td>13</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>3.75</td> </tr> <tr> <td>14</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>5</td> </tr> <tr> <td>15</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>7.5</td> </tr> <tr> <td>16</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>15</td> </tr> <tr> <td>17</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>3.75</td> </tr> <tr> <td>18</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>5</td> </tr> <tr> <td>19</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>7.5</td> </tr> <tr> <td>20</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>15</td> </tr> <tr> <td>21</td> <td>Co-Located Storage</td> <td>Mid</td> <td>3.75</td> </tr> <tr> <td>22</td> <td>Co-Located Storage</td> <td>Mid</td> <td>5</td> </tr> <tr> <td>23</td> <td>Co-Located Storage</td> <td>Mid</td> <td>7.5</td> </tr> <tr> <td>24</td> <td>Co-Located Storage</td> <td>Mid</td> <td>15</td> </tr> </tbody> </table> <p>The 'fixed interconnection' scenario describes the scenario where the capacity of interconnection for each solar photovoltaic (PV) or wind site is fixed to assumed values. The 'optimized interconnection' scenario enables the model to independently size the renewable energy to interconnection and grid connection capacity. The 'co-located storage' scenario enables any solar PV or wind resource and storage resource to be sited behind a grid connection point while optimizing the interconnection buildout for each site. These scenarios are further explained in the respective working paper. Renewable energy cost sensitivity tags include ‘low’ for assumed low projected VRE and battery costs in 2030 and ‘mid’ for assumed mid projected VRE and battery costs in 2030. Various storage discharge capacities are forced into the system as a percentage of peak demand and range from 3.75-15 GW. Within each case folder, all of the input files (.csv), result files directly outputted by the model (in the 'Results' folder), and setting files (GenX and solver settings in the 'Settings' folder) can be found. All model outputs are described in detail in the GenX documentation. The code can be found on the GenX GitHub repository: https://github.com/GenXProject/GenX.jl. This work has not yet been peer-reviewed.</p>
WildfireDB: An Open-Source Dataset Connecting Wildfire Spread with Relevant Determinants
<p>Modeling fire spread is critical in fire risk management. Creating data-driven models to forecast spread remains challenging due to the lack of comprehensive data sources that relate fires with relevant covariates. We present the first comprehensive and open-source dataset that relates historical fire data with relevant covariates such as weather, vegetation, and topography. Our dataset, named <em>WildfireDB</em>, contains over 17 million data points that capture how fires spread in the continental USA in the last decade. The paper accompanying this dataset is part of the 2021 Neural Information Processing Systems (NeurIPS) Dataset and Benchmark Track. The paper describes the algorithmic approach used to create and integrate the data, describe the dataset, and present benchmark results regarding data-driven models that can be learned to forecast the spread of wildfires.</p> <p> </p> <p>Please see https://colab.research.google.com/drive/1cm2Z4E0HzXMAcuUrE26wHXL2FS_pIj3t?usp=sharing for an introduction about how to load the database using python (pandas).</p>
Connectivity Matrices from biophysical modelling studies for A. millepora coral larvae in the Great Barrier Reef (Australia); present day and future scenarios
<p>These data contain connectivity matrices from biophysical modelling simulations of the dispersal of <em>Acropora millepora</em> coral larvae in the southern Great Barrier Reef (Australia), under present-day and future climate scenarios. The connectivity matrices represent modelled strength of larval transfer from one reef to another, and were obtained using a coupled reef-scale, high-resolution, depth-integrated finite element hydrodynamic model (SLIM) of water currents in the Great Barrier Reef, and Individual-Based particle tracking module. Biological parameters to model larval acquisition and loss of competency and mortality were based on the results of experiments detailed in the related journal article. We include connectivity matrices for 2 different water temperature scenarios representing current and future climates, for three different recent spawning seasons (2008, 2009, 2010), and also for scenarios where low-frequency currents through the domain are modulated to mimic the likely effects from future changes to large-scale circulation extracted from CMIP5 global climate models.</p> <p>We also include files summarising the relative changes, per reef, to certain key connectivity metrics between the 2 temperature scenarios (dispersal distance, local retention, number of incoming connections, "source index", and present day "source index" - all metrics are defined in the related journal article and Methods section of this metadata), averaged over all 3 spawning seasons modelled, as plotted in Figs 1a-e in the related journal article. Additionally, we include a separate file summarising changes to reef recovery times following a disturbance to coral cover between the 2 temperature scenarios, per reef, as modelled using the meta-population model described in the related journal article, and as shown in Fig 2 of the article.</p>
FIGURE 6 in Molecular phylogeny of Aplosporella abexaminans: a novel species revealing the second report of sexual-asexual connection in Aplosporellaceae (Botryosphaeriales) from India
FIGURE 6. Phylogram generated from Maximum likelihood analysis based on combined genes of ITS and LSU of botryosphaerialean taxa. ML and MP bootstrap support values above 50% and BPP support above 0.50 are indicated above the branches. The newly sequenced collection is in red.
FIGURE 5 in Molecular phylogeny of Aplosporella abexaminans: a novel species revealing the second report of sexual-asexual connection in Aplosporellaceae (Botryosphaeriales) from India
FIGURE 5. Phylogram generated from Maximum likelihood analysis based on ITS region and LSU gene of strains belong to Aplosporellaceae. Kellermania dasylirionicola (CBS 131720) was chosen as outgroup. Bootstrap values for ML and MP above 50% and BPP support above 0.50 are indicated. Red names indicate Aplosporella abexaminans.
FIGURE 4 in Molecular phylogeny of Aplosporella abexaminans: a novel species revealing the second report of sexual-asexual connection in Aplosporellaceae (Botryosphaeriales) from India
FIGURE 4. Asexual morph of Aplosporella abexaminans on PDA (NFCCI 5010, ex-type culture). a. Colony on PDA from above. b. Colony on PDA from below. c, d. Initial stage of germinated ascospores on PDA. e, f. Development of hyphae from germinated ascospores on PDA. g. Verruculose hyphae. h. Smooth hyphae. i–m. Conidia. n, o. Chlamydospores (yellow arrows). Scale bars a, b = 20 mm, c–o = 10 µm.
FIGURE 2 in Molecular phylogeny of Aplosporella abexaminans: a novel species revealing the second report of sexual-asexual connection in Aplosporellaceae (Botryosphaeriales) from India
FIGURE 2. Sexual morph of Aplosporella abexaminans (AMH 10212, holotype). a–e Vertical sections through ascostromata. f. Released asci from ascostromata. g, h. Ascus released from an additional sac like structure (blue arrows). Scale bars a, b = 200 µm, c = 100 µm, d–h = 50 µm.
FIGURE 1 in Molecular phylogeny of Aplosporella abexaminans: a novel species revealing the second report of sexual-asexual connection in Aplosporellaceae (Botryosphaeriales) from India
FIGURE 1. Stem of Murraya koenigii. a, b. Habit of ascostromata on the bark of host stem. Scale bars a, b = 5 mm.
Distribution. Midto high elevations in N & C Mozambique and neighboring Malawi, Zambia, and Zimbabwe; recently found in S Kenya (Taita Hills), and it is possible that these known populations are actually connected through still unknown populations in Tanzania. in Miniopteridae
Distribution. Midto high elevations in N & C Mozambique and neighboring Malawi, Zambia, and Zimbabwe; recently found in S Kenya (Taita Hills), and it is possible that these known populations are actually connected through still unknown populations in Tanzania.
Predictive utility of task-related functional connectivity vs. voxel activation - Data and code archive
<p>Functional connectivity, both in resting state and task performance, has steadily increased its share of neuroimaging research effort in the last 1.5 decades. In the current study, we investigated the predictive utility regarding behavioral performance and task information for 240 participants, aged 20-77, for both voxel activation and functional connectivity in 12 cognitive tasks, belonging to 4 cognitive reference domains (Episodic Memory, Fluid Reasoning, Perceptual Speed, and Vocabulary). We also added a model only comprising brain-structure information not specifically acquired during performance of a cognitive task. We used a simple brain-behavioral prediction technique based on Principal Component Analysis (PCA) and regression and studied the utility of both modalities in quasi out-of-sample predictions, using split-sample simulations (=5-fold Monte Carlo cross validation) with 1,000 iterations for which a regression model predicting a cognitive outcome was estimated in a training sample, with a subsequent assessment of prediction success in a non-overlapping test sample. The sample assignments were identical for functional connectivity, voxel activation, and brain structure, enabling apples-to-apples comparisons of predictive utility. All 3 models that were investigated included the demographic covariates age, gender, and years of education. A minimal reference model using simple linear regression with just these 3 covariates was included for comparison as well and was evaluated with the same resampling scheme as described above. Results of the comparison between voxel activation and functional connectivity were mixed and showed some dependency on cognitive outcome; however, mean differences in predictive utility between voxel activation and functional connectivity were rather small in terms of within-modality variability or predictive success. More notably, only in the case of Fluid Reasoning did concurrent functional neuroimaging provided compelling about cognitive performance beyond structural brain imaging or the minimal reference model.</p>
Impact of water resources development on connectivity and primary productivity across a tropical river floodplain
<p>1. Floodplain wetlands provide an important subsidy for riverine food webs as sites of high algal production. However, this subsidy depends on the degree of landscape connectivity during flood pulses, which provides the opportunity for movement of higher order consumers between rivers and floodplains to access these productive habitats. Changes in floodplain inundation extent and duration, due to variable wet season flows or water resources development (WRD), can impact landscape connectivity and ultimately the magnitude of the food web subsidy.</p> <p>2. We explored landscape connectivity using graph theory and derived four new metrics to measure how different flow scenarios can affect connectivity and algal production. We considered a historic scenario with the present level of water resource development in our study area, the Mitchell River, and a WRD scenario with the inclusion of three new dams in the catchment. We generated 240 unique daily spatial graphs, using surface water inundation maps across 40-day flood events to compare a dry year (2006), an average flow year (2001), and a wet year (2009) with and without a WRD scenario.</p> <p>3. Drier years and WRD scenario resulted in floodplain fragmentation, potentially constraining the movement of higher order consumers. Changes in connectivity due to WRD resulted in predicted reductions of up to 26% of algal production on the floodplain that was otherwise connected to the main river channels.</p> <p>4. Synthesis and applications. The approach developed in this study provides new metrics to identify how changes in floodplain surface water extent due to water resource development and climatic variation may impact ecosystem function such as connectivity that facilitates access of higher order consumers to primary production in floodplain wetlands. With a direct link to river flow alteration, these metrics can inform catchment planning and management to ensure that the conservation of floodplain ecosystem functions is adequately considered in water resource management decisions.</p>
Data linked to publication entitled "Biochar derived from Acai agroindustry waste enhances nutritional status and biomass in young Eucalyptus urophylla plants: Evidence connected to root development and leaf performance"
<p>Data linked to publication entitled "Biochar derived from Acai agroindustry waste enhances nutritional status and biomass in young Eucalyptus urophylla plants: Evidence connected to root development and leaf performance"</p>
Restoration priorities for Caatinga dry forests: landscape resilience, connectivity and biodiversity value
<p>1. Restoration actions can halt biodiversity loss and rescue its services. However, in order to be effective, priority areas for restoration should be chosen based on objective large-scale restoration planning. Here, a multi-criteria graph theory (GT) framework is proposed to indicate priority areas for active restoration, based on landscape resilience, landscape connectivity, and biodiversity conservation value, focusing on threatened endemic plant species.</p> <p>2. We applied this GT framework to 10,406 catchment basins of the Brazilian Caatinga, the largest seasonally dry tropical forest of the New World. Vegetation cover and within-catchment connectivity were used to identify catchments of intermediate landscape resilience, which in principle offer more effective opportunities for restoration. Then, such catchments were independently classified into (i) three classes according their value for between-catchment connectivity and (ii) three classes of biodiversity conservation value, based on richness of threatened, endemic plant species. By the integration of landscape resilience, landscape connectivity and biodiversity conservation values, three priority classes for restoration were generated.</p> <p>3. The multi-criteria framework generated several restoration priority cut-offs. Prioritization based on landscape resilience selected 36% of the Caatinga catchments as high priority for restoration. By independently adding landscape connectivity and biodiversity conservation value, only 12% and 3% of the catchments, respectively, were considered high priority. By combining all three criteria, 9% of the catchments were selected as high priority and less than 1% as top priority for restoration.</p> <p>4. Synthesis and applications: The multicriteria GT framework for restoration prioritization, which maximizes the effectiveness of restoration actions, landscape connectivity for climate change adaptation and conservation of threatened species, can be applied worldwide under different budged limitations and spatial scales, being useful for private, state, and federal initiatives.</p>
Subtle limits to connectivity revealed by outlier loci within two divergent metapopulations of the deep-sea hydrothermal gastropod Ifremeria nautilei
<p class="MsoNormal"><span>Hydrothermal vents form archipelagos of ephemeral deep-sea habitats that raise interesting questions about the evolution and dynamics of the associated endemic fauna, constantly subject to extinction-recolonization processes. These metal-rich environments are coveted for the mineral resources they harbor, thus raising recent conservation concerns. The evolutionary fate and demographic resilience of hydrothermal species strongly depend on the degree of connectivity among and within their fragmented metapopulations. In the deep sea, however, assessing connectivity is difficult and usually requires indirect genetic approaches. Improved detection of fine-scale genetic connectivity is now possible based on </span><span>genome-wide </span><span>screening for</span><span> </span><span>genetic differentiation.</span></p> <p class="MsoNormal"><span>Here, we explored population connectivity in the hydrothermal vent snail <em>Ifremeria nautilei </em>across its species range encompassing five distinct back-arc basins in the Southwest Pacific. The global analysis, based on 10 570 single nucleotide polymorphism (SNP) markers derived from double digest restriction-site associated DNA sequencing (ddRAD-seq), depicted two semi-isolated and homogeneous genetic clusters. Demo-genetic modeling suggests that these two groups began to diverge about 70 000 generations ago, but continue to exhibit weak and slightly asymmetrical gene flow. Furthermore, a careful analysis of outlier loci showed subtle limitations to connectivity between neighboring basins within both groups. This finding indicates that migration is not strong enough to totally counterbalance drift or local selection, hence questioning the potential for demographic resilience at this latter geographical scale. </span><span>These results illustrate the </span><span>potential</span><span> of large genomic datasets to understand </span><span>fine-scale</span><span> connectivity </span><span>patterns in</span><span> </span><span>hydrothermal vents and the deep sea</span><span>.</span></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.