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9,152 results for “Networking”
Trained deep neural networks for MSI/dMMR detection in colorectal cancer histology
<p>These are trained neural network models in PyTorch format to process tessellated images of colorectal cancer histology samples. The input is expected to be 224x224 px RGB image tiles normalized with the Macenko method. The output is a probability of the image tile for being MSI/dMMR or MSS/pMMR. </p> <p>The models have been trained on eight cohorts but not on the validation cohort. The validation cohorts are:</p> <p>Ex_0 : DACHS</p> <p>Ex_1 : DUSSEL</p> <p>Ex_2 : MECC</p> <p>Ex_3 : QUASAR</p> <p>Ex_4 : RAINBOW</p> <p>Ex_5 : TCGA</p> <p>Ex_6 : UMM</p> <p>Ex_7 : YORKSHIRE</p> <p>Ex_8 : MUNICH</p> <p>The models can be loaded in Python with </p> <p>>>> model = torch.load(path, map_location=torch.device('cpu'))</p> <p>Further details are given in the manuscript.</p>
Data from: Elements of metacommunity structure of diatoms and macroinvertebrates within stream networks differing in environmental heterogeneity
<p><strong>Aim:</strong> Idealized metacommunity structures (i.e. checkerboard, random, quasi-structures, nested, Clementsian, Gleasonian, and evenly spaced) have recently gained increasing attention, but their relationships with environmental heterogeneity and how they vary with organism groups remain poorly understood. Here we tested two main hypotheses: (1) gradient-driven patterns (Clementsian and Gleasonian) occur frequently in heterogeneous environments, and (2) small organisms (here, diatoms) are more likely to exhibit gradient-driven patterns than large organisms (here, macroinvertebrates).</p> <p><strong>Location:</strong> Streams in three regions in China.</p> <p><strong>Taxon:</strong> Diatoms and macroinvertebrates.</p> <p><strong>Methods:</strong> The stream diatom and macroinvertebrate data, as well as the environmental data collected from the same set of sites were used to examine the idealized metacommunity structures via the elements of the metacommunity structure (EMS; coherence, turnover, and boundary clumping) analysis in three regions. We extended the traditional EMS approach by ordering sites along known environmental gradients.</p> <p><strong>Results: </strong>We found that Clementsian structure with high degrees of coherence and turnover, and significantly positive clumping was typically observed in the high-heterogeneity regions, whereas randomness was prevalent in the low-heterogeneity region. Macroinvertebrates exhibited clearer Clementsian structures compared with diatoms, while diatoms showed more randomness compared with macroinvertebrates, indicating a stronger role of environmental filtering for macroinvertebrates than diatoms. In most cases, the results of the more novel EMS approach differed from the results of the traditional EMS technique.</p> <p><strong>Main Conclusions:</strong> Our results suggested that the occurrence of different metacommunity structures may be related with the degree of regional environmental heterogeneity. However, diatom metacommunities were more random than those of macroinvertebrate, and such an unexpected result may result from different dispersal abilities between the two organism groups. In addition, we found that the novel EMS approach increased power in discerning metacommunity structure in comparison to the traditional EMS technique.</p>
MuGNN: API Misuse Detection using Graph Neural Networks and Clustering
<div> <div>This artifact presents `MuGNN`, a novel framework for efficiently detecting API misuse in Java code. The approach leverages a `Graph Neural Network (GNN)` model to generate embeddings of Java API usage code using a custom `API Flow Graph (AFG)` representation. This representation captures execution sequences, data flow, and control flow, enabling better understanding of API usage patterns. MuGNN employs self-supervised pre-training and clustering to analyze API usage and identify potential misuse.</div> </div>
FIGURE 1 in On some sea cucumbers (Echinodermata: Holothuroidea) from off the south and west coasts of South Africa collected by the South African Environmental and Observation Network (SAEON)
FIGURE 1. Zygothuria lactea. (Theel,1886) A32143. A. Tentacle rods; B. Body wall ossicles.
Figure 1 from: Chen R, Yu W (2016) Matrix Proteins and Proteinases Network in Human Cardiovascular Diseases Explored By Cytoscape. Research Ideas and Outcomes 2: e9617. https://doi.org/10.3897/rio.2.e9617
Figure 1 - Cytoscape network, derived from MMP7, ADAM17, SDC, COL1, and VCAN (see Abstract for full spellings of abbreviations).
Figure 8 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 8 - The patchiness of survey coverage in Europe illustrated by the distribution map of Plantago lanceolata taken from GBIF in 2016. This species is one of the commonest and most widespread in Europe, it should occur in almost all areas of this map, but in fact the data traces out the borders of countries and area who have published data on GBIF.
Figure 9 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 9 - Information flows between EU BON and LTER Europe, as envisaged on the 3rd EU BON Stakeholder Roundtable in Granada on 9-11 December 2015.
Figure 6 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 6 - Individual Metacat instances can be connected to DataOne which replicates public files. Thus the data is still available if a single instance goes offline. https://search.dataone.org/#data/page/0
Figure 5 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 5 - PPBio has installed a Metacat instance for their researchers to upload and make publicly available the results of work related to biodiversity in the Western Amazon. https://ppbiodata.inpa.gov.br/metacatui/
Figure 3 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 3 - The implementation of Darwin Core Archive in Plazi to transfer treatment data. Observation data described with Darwin Core terms.
Figure 4 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 4 - The public data repository provided by the Knowledge Network for Biocomplexity (KNB). https://knb.ecoinformatics.org/#data/page/0
Figure 1 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 1 - ARPHA consists of two integrated workflows: in ARPHA-XML, the manuscript is written and processed via the ARPHA Writing Tool, and in ARPHA-DOC, the manuscript is submitted and processed as document file(s).
Figure 3 from: Vohland K, Häuser C, Regan E, Hoffmann A, Wetzel F (2016) 2nd EU BON Stakeholder Roundtable (Berlin, Germany): How can a European biodiversity network support citizen science? Research Ideas and Outcomes 2: e8616. https://doi.org/10.3897/rio.2.e8616
Figure 3 - Examples of citizen science projects and initiatives/tools from the EU BON consortium (Christoph Häuser and Florian Wetzel, MfN, 2014)
Figure 1 from: Vohland K, Häuser C, Regan E, Hoffmann A, Wetzel F (2016) 2nd EU BON Stakeholder Roundtable (Berlin, Germany): How can a European biodiversity network support citizen science? Research Ideas and Outcomes 2: e8616. https://doi.org/10.3897/rio.2.e8616
Figure 1 - EU BON Work Packages (WP) with the three sections (a) Data Sources and Infrastructure, (b) Science and Application and (c) Policy and Dialogue. The Stakeholder Roundtables are a specific task in the WP 6 that targets the stakeholder engagement and science-policy dialogue (credits: Pensoft).
Figure 4 from: Vohland K, Häuser C, Regan E, Hoffmann A, Wetzel F (2016) 2nd EU BON Stakeholder Roundtable (Berlin, Germany): How can a European biodiversity network support citizen science? Research Ideas and Outcomes 2: e8616. https://doi.org/10.3897/rio.2.e8616
Figure 4 - Species occurrence data records (in millions, x-axis) from CS sources, counted and freely available in Europe (Nils Valland, NBIC, updated in March 2016).
pypsa-earth_tutorial_networks_zip
<p>unsolved and solved networks for pypsa-earth tutorial config</p>
Drones, automatic counting tools and artificial neural networks in wildlife population censusing
<p>1. The use of a drone to count the flock sizes of 33 species of waterbirds during the breeding and non-breeding periods was investigated.</p> <p>2. In 96% of 343 cases, drone counting was successful. 18.8% of non-breeding birds and 3.6% of breeding birds exhibited adverse reactions: the former birds were flushed, whereas the latter attempted to attack the drone.</p> <p>3. The automatic counting of birds was best done with ImageJ/Fiji microbiology software – the average counting rate was 100 birds in 64 seconds.</p> <p>4. Machine learning using neural network algorithms proved to be an effective and quick way of counting birds – 100 birds in 7 seconds. However, the preparation of images and machine learning time is time-consuming, so this method is recommended only for large data sets and large bird assemblages.</p> <p>5. The responsible study of wildlife using a drone should only be carried out by persons experienced in the biology and behaviour of the target animals.</p>
Supplementary material 1 from: Indradi RB, Pitaloka DAE, Suryani (2022) Network pharmacology to uncover potential anti-inflammatory and immunomodulatory constituents in Curcuma longa rhizome as complementary treatment in COVID-19. Pharmacia 69(4): 995-1003. https://doi.org/10.3897/pharmacia.69.e89799
Datasets of Network Pharmacology Process
Smartphone-Based Incentive Framework for Dynamic Network-Level Traffic Congestion Management Project H3
<p>Task 2 numerical experiment results</p>
Dataset of "Super-resolution generative adversarial networks of randomly seeded fields"
<p>Dataset of the article "Super-resolution generative adversarial networks of randomly seeded fields" (https://doi.org/10.1038/s42256-022-00572-7)</p> <p>The codes processing data here are on https://github.com/eaplab/RaSeedGAN</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</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.