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1,721 results for “network data”
FIGURE 3. Statistical parsimony haplotype network constructed from 621 in Description of two new species of Rhamphus related to R. oxyacanthae (Curculionidae, Curculioninae, Rhamphini) from Italy based on a morphological study supported by molecular data
FIGURE 3. Statistical parsimony haplotype network constructed from 621 bp of the mtCOI gene of Rhamphus bavierai n. sp. (GenBank accession number MW879286- MW879303). Circle sizes are proportional to haplotype frequency (for details see supplementary Table S1). Numbers in brackets above/beside the solid broken line represent the number of mutations connecting mitochondrial lineages.
FIGURE 2. Statistical parsimony haplotype network constructed from 621 in Description of two new species of Rhamphus related to R. oxyacanthae (Curculionidae, Curculioninae, Rhamphini) from Italy based on a morphological study supported by molecular data
FIGURE 2. Statistical parsimony haplotype network constructed from 621 bp of the mtCOI gene of Rhamphus oxyacanthae in Italy (GenBank accession number MW879276- MW879285). Circles sizes are proportional to haplotype frequency (for details see supplementary S1).
Brazilian academic network monitoring data
<p>The <a href="https://rnp.br/en/ipe-network">Ipê Network</a> is an academic network connecting several education, research, and health institutes across Brazil. This dataset is composed of a collection of JSON files made available by the monitoring tool <a href="https://viaipe.rnp.br">Via Ipê</a>. Each file contains information related to a specific minute. The period covers November 2020, with 55 minutes missing from the dataset. The folder hierarchy defines each file's corresponding time. For instance, the file 11/10/8/20/1d.json.gz corresponds to the JSON file for the monitoring information at 8:20 am on November 10th, 2020.<br> More details and useful code can be found on <a href="https://github.com/VitorSpa/ViaIpe-Tools">GitHub</a>.</p>
Data from: Performance of social network sensors during Hurricane Sandy
Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the "friendship paradox", is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users' network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple "sentiment sensing" technique that can detect and locate disasters.
Data from: Severance of arbuscular mycorrhizal networks in restoration grasslands enhances seedling biomass
<p>1. Establishment and growth of grassland plant species is generally promoted by arbuscular mycorrhizal fungi (AMF) when grown in isolation. However, in grassland communities AMF form networks that may connect individual plants of different ages within and between species. Here, we use an in-growth core approach to examine how mycorrhizal networks influences performance of seedlings in grasslands.</p> <p>2. We selected 4 grass and 4 forb species with known negative or neutral-positive plant-soil feedback and grew them individually in steel mesh cores filled with living field soil. Cores were placed in six restored grasslands, three grasslands were of relatively young and three were of older successional age.</p> <p>3. Ingrowing mycorrhizal fungal hyphae were severed twice a week in half of all cores, which resulted into reduced AMF colonisation and increased seedling biomass, irrespective of the fields' succession stage, and the plants' grass/forb group, or plant-soil feedback type. In the control cores, root colonization by AMF was negatively correlated to seedling biomass, whereas there was no such relationships in the cores that had been lifted.</p> <p>4. We conclude that connections to arbuscular mycorrhizal networks of surrounding plants had a negative impact on biomass of establishing forb and grass seedlings.</p>
All raw data for the transport part of 'In-plane selective area InSb-Al nanowire quantum networks'
<p>This folder contains all the raw data gathered on the devices used in the transport part of the paper "In-plane selective area InSb–Al nanowire quantum networks" https://doi.org/10.1038/s42005-020-0324-4.</p>
FIGURE 2. A phylogenetic network for a hypothetical data set. This network represents the relationships between four taxa, A-D in Exploring character conflict in molecular data*
FIGURE 2. A phylogenetic network for a hypothetical data set. This network represents the relationships between four taxa, A-D. The length of branch (a) is proportional to the strength of support for the relationship (A,C)(B,D). The length of branch (b) is proportional to the strength of support for the relationship (A,B)(C,D). In this example there is conflicting support for both of these arrangements, but more weight is given to (A,C)(B,D) than to (A,B)(C,D).
Plots source data for the Communications Earth & Environment research article titled: Diurnal expansion and contraction of englacial fracture networks revealed by seismic shear wave splitting
<p>The source data for plotting figures presented in the main text of article titled: Diurnal expansion and contraction of englacial fracture networks revealed by seismic shear wave splitting.</p>
Data from: Ongoing habenular activity is driven by forebrain networks and modulated by olfactory stimuli
<p>The data was collected using volumetric two-photon calcium imaging of the forebrain and habenula of juvenile zebrafish expressing a transgenic calcium indicator. The data provided is organized according to the figures of our manuscript and contains the necessary codes and data to replicate the figures presented. In short, included repository contains 6 datasets of experiments. Data structure includes neuronal data as normalised to the baseline fluorescence for each neuron over time (DF/F), 2 or 3 dimensional coordinates of each cell and information about duration and samplings rate of experiments, and appropriate information about stimulation either microstimulation or odor stimulation (if any was performed within the experiment).</p>
Data for "A Comparison of Linear Solvers for Resolving Flow in Three-Dimensional Discrete Fracture Networks"
<p>This data is related to the manuscript "A Comparison of Linear Solvers for Resolving Flow in Three-Dimensional Discrete Fracture Networks". It contains meshes, boundary conditions, and medium properties for discrete fracture networks used to test fluid flow solvers.</p>
Data from: Parallel cognitive processing streams in human prefrontal cortex: parsing areal-level brain network for response inhibition
<p>Multiple cognitive processes are recruited to achieve adaptive behavior. However, it is poorly understood how such cognitive processes are implemented in temporal cascades of human cerebral cortical areas as processing streams to achieve behavior. In the present study, we identify cortical processing streams for response inhibition and examine relationships among the processing streams. Functional magnetic resonance imaging (MRI) and time-resolved single-pulse transcranial magnetic stimulation (TMS) reveal three distinct critical timings of transient disruption in the functionally essential cortical areas that belong to two distinct cerebrocortical networks. Furthermore, single-pulse TMS following suppression of the ventral posterior inferior frontal cortex (vpIFC) with repetitive TMS reveals information flow from the vpIFC to the presupplementary motor area (preSMA) within the same network but not to the dorsal posterior inferior frontal cortex (dpIFC) across different networks. These causal behavioral effects suggest two parallel processing streams (vpIFC-preSMA versus dpIFC-intraparietal sulcus) that act concurrently during response inhibition.</p>
Data set for: Research trends of Academic Performance and Social Networking Sites
<p>Data set for: Research trends of Academic Performance and Social Networking Sites.</p>
Additional data "Client binding shifts the populations of dynamic Hsp90 conformations through an allosteric network"
<p>Additional data containing chemical shift perturbations and intensity changes of Hsp90 upon client binding, intermolecular PREs, and raw scattering data of Hsp90-client complexes.</p>
FIGURE. Median network analyses (MNA) of a subset of the C. trilobus aggregate (i.e. those in the clade A from Fig. 11) based on concatenated DNA sequence data from ITS, trnL-trnF and psbJ-petA. Stars and arrow indicate accessions discussed in the text. NI: North Island, SI: South Island. in Five new species of Corybas (Diurideae, Orchidaceae) endemic to New Zealand and phylogeny of the Nematoceras clade
FIGURE. Median network analyses (MNA) of a subset of the C. trilobus aggregate (i.e. those in the clade A from Fig. 11) based on concatenated DNA sequence data from ITS, trnL-trnF and psbJ-petA. Stars and arrow indicate accessions discussed in the text. NI: North Island, SI: South Island.
Fish abundance data in forest steppe and grassland river networks in Mongolia
<p>Fish abundance data (fish per m) collected during the MACRO project in Mongolia. We collected fish assemblages in river networks of two different ecoregions, the Forest Steppe (FS) and Grassland (G), in 2017 and 2019.</p>
Associated code and data for "A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer (doi: 10.1016/j.csbj.2022.11.037)"
<p>This deposit contains the latest data, code, and analysis to recreate the results in the manuscript "A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer". Computational and Structural Biotechnology Journal 2023. <a href="https://www.researchgate.net/publication/365864472_A_network_medicine_approach_for_identifying_diagnostic_and_prognostic_biomarkers_and_exploring_drug_repurposing_in_human_cancer">DOI: 10.1016/j.csbj.2022.11.037</a>. </p> <p>If you use the code or data from this repository, please cite our publication.</p> <p> </p>
Replication data for: Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty
<p>Simulated host-parasite communities in Llopis‐Belenguer, C., J. A. Balbuena, I. Blasco‐Costa, A. Karvonen, V. Sarabeev, and J. Jokela. 2022. Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty. Ecology</p> <ul> <li>Full communities</li> </ul> <p>01_full_communities.RDS</p> <ul> <li>Resampled communities affected by host sampling completeness. From 90% to 10% of host sampling completeness every 10% steps</li> </ul> <p>02_resampled_sampling_completeness_90.RDS, </p> <p>03_resampled_sampling_completeness_80.RDS, </p> <p>04_resampled_sampling_completeness_70.RDS, </p> <p>05_resampled_sampling_completeness_60.RDS, </p> <p>06_resampled_sampling_completeness_50.RDS, </p> <p>07_resampled_sampling_completeness_40.RDS, </p> <p>08_resampled_sampling_completeness_30.RDS, </p> <p>09_resampled_sampling_completeness_20.RDS, </p> <p>10_resampled_sampling_completeness_10.RDS</p> <ul> <li>Resampled communities affected by parasite taxonomic resolution at all levels of host sampling completeness. From 90% to 10% of parasite taxonomic resolution and from 100% to 10% of host sampling completeness every 10% steps</li> </ul> <p>11_resampled_taxonomic_resolution_90_sampling_completeness_100-10.RDS, </p> <p>12_resampled_taxonomic_resolution_80_sampling_completeness_100-10.RDS, </p> <p>13_resampled_taxonomic_resolution_70_sampling_completeness_100-10.RDS, </p> <p>14_resampled_taxonomic_resolution_60_sampling_completeness_100-10.RDS, </p> <p>15_resampled_taxonomic_resolution_50_sampling_completeness_100-10.RDS, </p> <p>16_resampled_taxonomic_resolution_40_sampling_completeness_100-10.RDS, </p> <p>17_resampled_taxonomic_resolution_30_sampling_completeness_100-10.RDS, </p> <p>18_resampled_taxonomic_resolution_20_sampling_completeness_100-10.RDS, </p> <p>19_resampled_taxonomic_resolution_10_sampling_completeness_100-10.RDS</p> <p> </p>
Temporal Network Benchmark Data
<p>We release a benchmark dataset for the evaluation and development of temporal network embedding methods that leverage such fundamental graph time-series representations. The benchmark consists of 26 temporal networks from a variety of different application domains. We also release the graphs in each of the different time-series representations as separate files that can be used for benchmarking purposes. This includes the $\tau$-graph time-series that decomposes the edge stream into a time-series of graphs based on an application time-scale such as 1 day, as well as the $\epsilon$-graph time-series that decomposes the edge stream into a time-series of graphs such that each graph has a constant number of edges. </p> <p>For a direct comparison between the $\tau$-graph and $\epsilon$-graph time series approaches, we require an equal number of graphs for each, that is, $|\mathcal{G}_{\tau}| = |\mathcal{G}_{\epsilon}|$. For this, we first fix $\tau$ to be an application time-scale (such as 1 hour, 1 day, and so on) and use this $\tau$ to derive a $\tau$-graph time-series $\mathcal{G}_{\tau}$. We then set the number of edges per snapshot in the $\epsilon$-graph time-series as $\epsilon = \ceil{\tfrac{|E|}{|\mathcal{G}_{\tau}|}}$, which results in an equal number of snapshots as desired.</p> <p>For each of the 26 temporal networks, we release a variety of temporal granulaties. For instance, for email-EU-core network, we have a directory called email-EU-core, inside that directory consists of the raw temporal network (edge stream) called email-EU-core.edges, which is a simple comma-delimited edge list where the first and second column are node ids, whereas the third is the timestamp of the edge. Furthermore, there are two other directories inside email-EU-core, namely, 1day and 1week, which indicates the temporal granulatiy, and inside either of those directories are two more directories called tau and epsilon, which contain the edge list files for each of the different graph time-series representations.</p>
Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data
<p>Numerical codes and results for the article: Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data</p>
Social network matrices data from sociology university classrooms
<p>Hypothetically, a student could attend a class, listen to lectures, and pass the class without knowing or interacting with other students. What happens to the network when the classroom expectations change? For example, there is a coursework expectation that students exchange contact information, or the instructor uses collaborative learning practices. Or what if the principal investigator (PI) of a scientific team goes on a sabbatical? This study uses the framework of classrooms because of their relatability across science. We asked how do different instructor coursework expectations change network structures within a classroom or other learning environments. A social network survey was administered at the start and end of the semester (pre- and post-test) in six university sociology classrooms to explore how expectations impacted the communication and learning networks. We found practical changes in course expectations impact the communication and learning networks, suggesting that instructors, facilitators, and others could be the archintorÔ (<strong>arch</strong>itect+<strong>in</strong>structor+facilita<strong>tor</strong>) of the network. Understanding that expectations can impact a network's structure marks a paradigm shift in educational assessment approaches. If the archintorÔ has identified the "optimal" network structure, then their task is to design expectations that result in specific interactions that ultimately improve student achievement and success. This work provides recommendations for classroom archintorsÔ to create the most impactful classroom networks. Future research should extend beyond education and classroom networks and identify the best or desired networks in other areas like public policy, urban planning, and more. If these "optimal" networks were identified, an archintorÔ could design a social network to solve wicked problems, manage a crisis, and create social change.</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.