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9,152 results for “Networking”
Trained tesseract networks for low-resolution optical character recognition
<p>Tesseract eng.traineddata files for low-resolution optical character recognition (English).</p>
Integrated Performance Evaluation of Extended Queueing Network Models with Line
<p>Numerical examples in the paper "Integrated Performance Evaluation of Extended Queueing Network Models with Line" by G. Casale accepted for publication in the Winter Simulation Conference (WSC) 2020.</p>
Dataset for: Contingent evolution of alternative metabolic network topologies determines whether cross-feeding evolves
<p>Data generated with VirtualMicrobes (<a href="https://bitbucket.org/thocu/virtual-microbes">https://bitbucket.org/thocu/virtual-microbes</a>) supporting: </p> <p>J.Meijer, B. van Dijk, P. Hogeweg. Contingent evolution of alternative metabolic network topologies determines whether cross-feeding evolves. Nature Communication Biology (2020)</p>
Synthetic histology images of colorectal cancer, generated by conditional generative adversarial networks
<p>These are generated (synthetic) histology images of colorectal cancer. These images were generated by conditional GANs and are in two classes: MSIH (microsatellite instable high) and nonMSIH. There are two sets: one set with 10K images per class and another one with 75K images per class. All images are RGB, 512x512 px at a resolution of 0.5 micrometers per pixel. For more information, please stay tuned for our upcoming manuscript on www.kather.ai. </p>
Stable carbon, oxygen and nitrogen isotopes in tree rings from temperate forests within long-term monitoring network in the UK
<p>Supporting dataset for the paper "Climate and atmospheric deposition effects on forest water‑use efficiency and nitrogen availability across Britain" by Guerrieri et al. 2020 Scientific Reports DOI is 10.1038/s41598-020-67562-w (<a href="https://www.nature.com/articles/s41598-020-67562-w">https://www.nature.com/articles/s41598-020-67562-w</a>)</p> <p>The dataset includes carbon, oxygen and nitrogen stable isotope composition measured in tree rings for four species at twelve forests across climate and atmospheric deposition gradients in Britain. Data were used to i) investigate spatial and temporal (over 30‑years) changes in tree intrinsic water use efficiency (iWUE, i.e., the ratio between CO<sub>2</sub> assimilation - A, and stomatal conductance- g<sub>s</sub>); ii) assess the possible species-specific physiological mechanisms (changes in A and/or g<sub>s</sub>) evaluate whether sites receiving high Ndep experience increase in ecosystem N availability and N saturation (using tree-ring nitrogen isotope composition as a proxy); iv) elucidate drivers of spatial and temporal changes in the isotope-derived physiological and ecological processes (i.e., relative contribution of climate vs. changes in atmospheric CO<sub>2</sub> and nitrogen and sulfur deposition). </p> <p>The following files are available:</p> <ul> <li>CSV file including: a) Site names; b) Species; c) Latitude; d) Longitude; e) Investigated years (1980-2010); f) Measured isotope-related parameters and tree ring nitrogen %. Isotope-related parameters included in the dataset are: 1) delta13C, 2) carbon isotope discrimination (Delta13C), 3) intercellular CO2 concentration (ci) and the ratio of intercellular to atmospheric CO2 concentrations (ci/ca), delta18O, estimated delta18O in precipitation (delta18OP) according to Barbour et al. 2001 (see Method in Guerrieri et al. 2020 Scientific Reports), oxygen isotope discrimination above the source water (Delta18O), delta15N. </li> <li>PDF file reporting the equations used to calculate the isotope-derived parameters, which can also be found in the paper by Guerrieri et al. 2020 Scientific Reports (accepted)</li> </ul>
Network origin-destination data
<p>The first sheet has the tonnage data for each of the 440 routes. Each of these routes is connecting some of the 72 segments, which are entered in Column B and C.</p> <p>Name of the routes/segments in the original format are provided in the second sheet. Column C has the names of the segments.</p>
Modified Normalized Difference Water Index (MNDWI) in one of EDIAs's water distribution network areas during 2019
<p>Modified Normalized Difference Water Index (MNDWI) in one of EDIAs's water distribution network areas during 2019. The MNDWI was computed from Sentinel 2 images.</p>
Data-driven identification of reliable sensor species to predict regime shifts in ecological networks
<p>Signals of critical slowing down are useful for predicting impending transitions in ecosystems. However, in a system with complex interacting components not all components provide the same quality of information to detect system-wide transitions. Identifying the best indicator species in complex ecosystems is a challenging task when a model of the system is not available. In this paper, we propose a data-driven approach to rank the elements of a spatially-distributed ecosystem based on their reliability in providing early-warning signals of critical transitions. The proposed method is rooted in experimental modal analysis techniques traditionally used to identify structural dynamical systems. We show that one could use natural system fluctuations and the system responses to small perturbations to reveal the slowest direction of the system dynamics and identify indicator regions that are best suited for detecting abrupt transitions in a network of interacting components. The approach is applied to several ecosystems to demonstrate how it successfully ranks regions based on their reliability to provide early-warning signals of regime shifts. The significance of identifying the indicator species and the challenges associated with ranking nodes in networks of interacting components are also discussed.</p>
Codes and datasets associated with the paper "Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network"
<p>The jupyter notebooks and datasets associated with the EEM20 forecasts are available here. More details will be provided shortly. </p> <p>Please check the EEM20 website (<a href="https://eem20.eu/forecasting-competition/">https://eem20.eu/forecasting-competition/</a>) for the details of the forecasting competition. </p>
The performance of permutations and exponential random graph models when analysing animal networks (R code and data)
<p>Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyse animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analysing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analysing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g. sex) on the number and combined strength of an individual's network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In grouping-event based networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analysing and interpreting their own network data using these methods.</p>
Molecular ecological network analyses: An effective conservation tool for the assessment of biodiversity, trophic interactions, and community structure
<p>Global biodiversity is threatened by the anthropogenic restructuring of animal communities, which rewires species interaction networks in real-time as individuals are extirpated or introduced. Conservation science and adaptive ecosystem management demands more rapid, quantitative, and non-invasive technologies for robustly capturing changing biodiversity and quantifying species interactions. Here we develop molecular ecological network analyses (MENA) as an ecosystem assessment tool to address these needs. To construct the ecological network, we used environmental DNA from feces to identify the plant and mammal diet of two carnivores: puma (<i>Puma concolor</i>) and bobcat (<i>Lynx rufus</i>); two omnivores: coyote (<i>Canis latrans</i>) and gray fox (<i>Urocyon cinereoargenteus</i>); and two herbivores: black-tailed deer (<i>Odocoileus hemionus</i>) and black-tailed jackrabbit (<i>Lepus californicus)</i> in a well-studied Californian reserve<i>. </i>To evaluate MENA as a comprehensive biodiversity tool, we applied our framework to identify the structure of the network, patterns of trophic interactions, key species, and to assess its utility in capturing the biodiversity of the area. The high dietary taxonomic resolution enabled the assessment of species diversity, niche breadth and overlap. The network analysis revealed a dense ecological network with a high diversity of weakly connected species and a community that is highly modular and non-nested. The significant prevalence of tri-trophic chain and exploitative competition patterns indicates (i) the removal or reintroduction of a top predator would trigger a trophic cascade within this community, directly affecting their prey and indirectly the plant communities, and (ii) the potential impact of indirect effects between two predators that consume the same prey. These results suggest that the recent resurgence of puma in the study area may impact the herbaceous and woody vegetation and the population size of other predators. This effect of fluctuating predator populations and plant communities could be predicted through MENA's fine-scale assessment of the diet selection and the identified keystone species. Although just using a subset of species, MENA more rapidly, accurately, and effectively captured the broader biodiversity of the area in comparison to other methodologies. MENA reconstructed and unveiled the hidden complexity in trophic structure and interaction networks within the community, providing a promising toolkit for biodiversity and ecosystem management.</p>
Test set - 4023 PubMed abstracts (for manuscript: Extracting knowledge networks from plant scientific literature: Potato tuber flesh color as an exemplary trait )
<p>A .zip archive containing the set of abstracts used in the test set (4023 abstracts from PubMed) in .txt format.</p> <p>This archive contains supplementary files for the manuscript Extracting knowledge networks from plant scientific literature: Potato tuber flesh color as an exemplary trait.</p>
[Videos] Design of resilient smart highway systems with data-driven monitoring from networked cameras
<p>Traditional high-way transportation systems are monitored based on traffic counters. Such sensors provide much less information compared to traffic cameras and make the system less secure/resilient to attacks/disasters. Thanks to the success of deep learning for object detection/segmentation on images and the publicly available large-scale image datasets with object labels, fusing the information from both traffic counters and traffic cameras has the potential to improve the security and resilience of existing high- way transportation systems. The purpose of the project is to investigate such a potential by developing a deep-learning-based highway video monitoring method that can reliably estimate the fine-grained (car/truck/motorcycle) traffic flow of a high-way network. First, we need to collect a large- scale traffic video dataset with traffic flow estimations from corresponding traffic counters. Then, we need to find efficient deep learning methods for extracting fine-grained local traffic information from individual traffic videos. At last, we need to correlate this information with traffic counters for sensor fusion and detection of defective counters.</p>
Modeling protoplanetary disk SEDs with artificial neural networks: Revisiting the viscous disk model and updated disk masses
<p>This repository contains the cornerplots of relevant parameters for the 23 protoplanetary disks modeled in the manuscript "Modeling protoplanetary disk SEDs with artificial neural networks: Revisiting the viscous disk model and updated disk masses" (Ribas et al. 2020).</p>
Research data supporting "Expanding and Optimizing 3D Bioprinting Capabilities Using Complementary Network Bioinks"
<p>Raw research data supporting:</p> <p>Ouyang L. et al., 2020, Science Advances, 6:eabc5529.</p>
Data from: Dynamic up- and down-regulation of the default (DMN) and extrinsic (EMN) mode networks during alternating task-on and task-off periods
Using fMRI, Hugdahl et al. (2015) reported the existence of a general-domain cortical network during active task-processing which was non-specific to the cognitive task being processed. They labelled this network the extrinsic mode network (EMN). The EMN would be predicted to be negatively, or anti-correlated with the classic default mode network (DMN), typically observed during periods of rest, such that while the EMN should be down-regulated and the DMN up-regulated in the absence of demands for task-processing, the reverse should occur when demands change from resting to task-processing. This would require alternating periods of task-processing and resting, and analyzing data continuously when demands change from active to passive periods and vice versa. We were particularly interested in how the networks interact in the critical transition points between conditions. For this purpose we used an auditory task with multiple cognitive demands in a standard fMRI block-design. Task-present (ON) blocks were alternated with an equal number of task-absent, or rest (OFF) blocks to capture network dynamics across time and changing environmental demands. To achieve this, we specified the onset of each block, and used a finite-impulse response function (FIR) as basis function for estimation of the fMRI-BOLD response. During active (ON) blocks, the results showed an initial rapid onset of activity in the EMN network, which remained throughout the period, and faded away during the first scan of the OFF-block. During OFF blocks, activity in the DMN network showed an initial time-lag where neither the EMN nor the DMN was active, after which the DMN was up-regulated. Studying network dynamics in alternating passive and active periods may provide new insights into brain network interaction and regulation.
Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: training datasets
<p>We provide here the datasets used for the development of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.3390%2Frs12193151?_sg%5B0%5D=0xE-347r7Hvb80klJcEo811AhUiXq-twG_E6l4yB-BfIKkVtW-lVLGcO02mTFkUczvozYYI0WCPyUBFR3kzWNGGZKg.ftvLheFrzHIJriO4qW2bdxalvR_TWt3MpwUfvto3EemhRgvDRGwJ9Mdy4Xr0IcGCfICivf4j-VqTgKxVvXRogA">10.3390/rs12193151</a>). </p> <p>The training/test dataset presented here includes different sets of co-located temperature and salinity vertical profiles and corresponding satellite surface data: </p> <ul> <li>in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);</li> <li>climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;</li> <li>co-located Sea Surface Temperature taken from the level 4 (L4, i.e. interpolated) multi-year reprocessed Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) developed by U.K. Met Office and distributed (upon free registration) through the Copernicus Marine Environment Monitoring Service (CMEMS, <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011);</li> <li>co-located Sea Surface Salinity taken from dataset developed within ESA-WOC project (https://doi.org/10.5281/zenodo.3943813);</li> <li>co-located Absolute Dynamic Topography (ADT) data distributed by CMEMS as reprocessed data (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: SEALEVEL_GLO_PHY_L4_REP_OBSERVATIONS_008_047), upsized here to the ESA-WOC 1/10°x1/10° grid through a cubic spline and adjusted to insitu steric heights by regressing steric heights and co-located ADT data in the neighbourhood of each grid point, considering matchups within a temporal window of 10 days (as in Buongiorno Nardelli et al., 2017).</li> </ul>
Exploring human splenic red pulp vasculature in virtual reality. Details of sheathed capillaries and the open capillary network. Supplementary material
<p>Supplementary files demonstrate our data in an interactive application. Please download und unpack the file, then launch file "start.bat". Please note that loading the original images takes some time. If you have a Steam VR headset, it is detected and used automatically.</p> <ol> <li><strong>Supplementary file 1 </strong><br> Localization of arteries and arterioles in ROI 4.<br> ROI 4 with all endothelia and some periarterial fibroblasts colored blue (CD34), smooth muscle alpha-actin (SMA) colored white and capillary sheaths depicted in green (CD271). Most arteries and arterioles can be localized by white color. White appears in a vertex of the blue reconstruction of CD34 staining, if a surface reconstructed for SMA labelling is closer than 8.75 µm to this vertex. Capillary sheaths connected to the selected arterioles (marked red in Suppl. file 2) are green and non-connected sheaths fed by other arterioles are dark green (CD271).</li> <li><strong>Supplementary file 2</strong><br> Relation of capillary sheaths to terminal arterioles in ROI4.<br> ROI 4 with all endothelia and some periarterial fibroblasts colored blue (CD34), selected terminal artery and terminal arterioles manually labelled red and capillary sheaths connected to the labelled arterioles in green (CD271). Non-connected sheaths fed by other arterioles are dark green (CD271). Follicular dendritic cells (FDCs) in follicles are light green (CD271). This file corresponds to Fig. 3 and to Suppl. Video 1-3.</li> <li><strong>Supplementary file 3</strong><br> Relation of capillary sheaths to terminal arterioles in ROI 2.<br> ROI 2 with all endothelia and some periarterial fibroblasts colored blue (CD34), selected terminal artery and terminal arterioles manually labelled red and capillary sheaths connected to the labelled arterioles in green (CD271). Non-connected sheaths fed by other arterioles are dark green (CD271). FDCs in follicles are light green (CD271). This file corresponds to Fig. 4a,b.</li> <li><strong>Supplementary file 4</strong><br> Relation of capillary sheaths to terminal arterioles in ROI 1.<br> ROI 1 with all endothelia and some periarterial fibroblasts colored blue (CD34), selected terminal artery and terminal arterioles manually labelled red and capillary sheaths connected to the labelled arterioles in green (CD271). Non-connected sheaths fed by other arterioles are dark green (CD271). FDCs in follicles are light green (CD271)</li> <li><strong>Supplementary file 5</strong><br> White pulp in 3D (ROI 3).<br> ROI 3 with all endothelia and some periarterial fibroblasts colored blue (CD34) and capillary sheaths in green (CD271). FDCs in follicles are light green (CD271). Arteries and terminal arterioles were not labelled.</li> <li><strong>Supplementary file 6 </strong><br> Sheathed capillaries exhibit short open-ended side branches.<br> ROI 2 with all endothelia and some periarterial fibroblasts colored blue (CD34) and with selected terminal artery and terminal arterioles labelled light blue. Capillary sheaths connected to the selected arterioles are green (CD271) and unconnected sheaths are dark green (CD271). The open-ended side branches of the selected sheathed capillaries were manually labelled red and are visualized in their entire length. This file corresponds to Fig. 4c.</li> <li><strong>Supplementary file 7</strong><br> Terminal arterioles may have a few unsheathed connections to the red pulp capillary network.<br> ROI 2 with all endothelia and some periarterial fibroblasts colored blue (CD34) and with selected terminal artery and terminal arterioles labelled light blue. Capillary sheaths connected to the selected arterioles are green (CD271) and unconnected sheaths are dark green (CD271). Connections from a terminal arteriole to the red pulp capillary network lacking sheaths were manually labelled red. A part of the capillary network after the connection is colored yellow. This file corresponds to Figs. 4d,e.</li> <li><strong>Supplementary file 8</strong><br> B-lymphocytes accumulate around terminal arteries and in/around capillary sheaths.<br> Lower part of ROI 2 with selected terminal artery and terminal arterioles colored blue and connected capillary sheaths in green (CD271). B-lymphocytes were digitally reduced in number and automatically labelled red (CD20). The B-cells accumulate around a terminal artery and around (and inside) capillary sheaths. This file corresponds to Fig. 6 and to Suppl. Video 4.</li> <li><strong>Supplementary file 9</strong><br> Reduction of B-lymphocytes is inevitable for visualization.<br> ROI 2 showing Suppl. file 8 integrated into the entire ROI 2. A large number of B-lymphocytes is present in the red pulp of ROI 2 (CD20 visualized in white). These cells are self-occluding and necessitate reducing B-lymphocytes as shown in Suppl. Fig. 8 to highlight accumulations.</li> <li><strong>Supplementary file 10</strong><br> Red pulp venule with potential connections to capillary network.<br> A red pulp venule with capillaries in ROI 1. Capillary and venous endothelium is shown in light blue (CD34). A part of a red pulp venule with potential contributing capillaries (CD34, blue) was manually selected in addition to a terminal arteriole (light red). The capillary sheaths connected with this arteriole are green (CD271). Non-connected sheaths are dark green (CD 271) and FDCs in follicles are light green (CD271). Capillaries from the red pulp network, which appear to be connected to the venule were manually colored red. Two of the red capillaries seem to originate from a sheathed capillary. This file corresponds to Fig. 5a-d and to Suppl. Video 5.</li> </ol> <p>Bounding boxes for all files have a side length of 1 mm with exception of Suppl. File 8 which is 800 (b) x 620 (h) x 614 (d) µm.</p> <p>As for videos, each video is in three quality settings:</p> <ul> <li>..._h264_8bit.mov is the most generic H.264 HQ setting. Should play everywhere, also with QuickTime.</li> <li>..._h265_10bit.mov is the 10 bit video version, encoded with HEVC encoder. Should be the same or higher quality with less size. Might not play everywhere.</li> <li>..._h264_8bit_small.mov is the H.264 LQ setting with much lower resolution, use this if your download bandwidth is limited.</li> </ul> <p>Now, there are 5 videos to supplement the submission:</p> <ol> <li><a href="https://zenodo.org/record/3993324/files/suppl%20video%201%20-%20sheath%20video%20r4%20full%20h264%208bit.mov">suppl video 1 - sheath r4 full .</a>.. shows the region ROI4 with full vasculature, similar to Fig. 3 of the manuscript,</li> <li><a href="https://zenodo.org/record/3993324/files/suppl%20video%202%20-%20sheath%20video%20r4%20select%20h264%208bit.mov">suppl video 2 - sheath r4 select .</a>... shows only the selection of the vasculature from ROI4, similar to Fig. 3 of the manuscript,</li> <li><a href="https://zenodo.org/record/3993324/files/suppl%20video%203%20-%20sheath%20video%20r4%20clipping%20h264%208bit.mov">suppl video 3 - sheath r4 clipping .</a>... showcases the front plane clipping technique we used for diagnostics in VR. This demonstrates the contents of Fig. 8 in dynamics.</li> <li><a href="https://zenodo.org/record/3993324/files/suppl%20video%204%20-%20sheath%20video%20r2%20bcell%20h264%208bit.mov">suppl video 4 - sheath bcell .</a>.. shows the selection of the B-cells to supplement Fig. 6 in the manuscript.</li> <li><a href="https://zenodo.org/record/3993324/files/suppl%20video%205%20-%20sheath%20video%20r1%20vene%20h264%208bit.mov">suppl video 5 - sheath r1 vene .</a>.. shows a red pulp venule and its apparent connections, this supplements Fig. 5 of the manuscript.</li> </ol>
Raw and Intermediate data associated with "Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multiomics integration"
<p>Data and intermediate structures for "Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multiomics integration"</p> <p>The project involves the comparison of gene expression data and estimated flux across over 44 million models of HMO biosynthesis. Here we provide intermediate objects to simplify the task of reproducing our calculations.</p> <ul> <li>Raw/Input data (raw.zip); data_HMO.xlsx is the central dataset including HMO concentrations and gene expression data. Other files are .csv exports from data_HMO.xlsx to circumvent a temporary bug in the xlsx reading package. They should not differ from the original file and are provided to facilitate code readability.</li> <li>(models_split.zip) HMO biosynthesis models generated through network generation ( /code/1.flux/A) and enumeration (/code/1.flux/B)</li> <li>(scores_spllit.zip) Correlation (R) and correlation p-values (P) between estimated flux and observed gene expression for corresponding genes (/code/1.flux/C)</li> <li>(big_mat.<bin/desc>.zip) Intermediate matrixes containing gene-linkage scores, models score, model classifications and other information generated/used by the Flux-Expression Comparision code (/code/3.flux_expression)</li> </ul>
Data from: Maximum parsimony inference of phylogenetic networks in the presence of polyploid complexes
<p>Phylogenetic networks provide a powerful framework for modeling and analyzing reticulate evolutionary histories. While polyploidy has been shown to be prevalent not only in plants but also in other groups of eukaryotic species, most work done thus far on phylogenetic network inference assumes diploid hybridization. These inference methods have been applied, with varying degrees of success, to data sets with polyploid species, even though polyploidy violates the mathematical assumptions underlying these methods. Statistical methods were developed recently for handling specific types of polyploids and so were parsimony methods that could handle polyploidy more generally yet while excluding processes such as incomplete lineage sorting.</p> <p>In this paper, we introduce a new method for inferring most parsimonious phylogenetic networks on data that include polyploid species. Taking gene trees as input, the method seeks a phylogenetic network that minimizes deep coalescences while accounting for polyploidy. The method could also infer trees, thus potentially distinguishing between auto- and allo-polyploidy. We demonstrate the performance of the method on both simulated and biological data. The inference method as well as a method for evaluating given phylogenetic networks are implemented and publicly available in the PhyloNet software package.</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.