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1,944 results for “Class I”
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v2.1.0 - August 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>August 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/August-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v3.0.2 - October 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>October 18, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/October-18%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.1.0 - August 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>August 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/August-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v2.1.0 - July 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>July 06, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/July-06%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.1.0 - July 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>July 06, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/July-06%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v2.1.0 - July 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>July 06, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/July-06%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.1.0 - June 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>June 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/June-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.1.0 - July 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>July 06, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/July-06%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v2.1.0 - June 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>June 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/June-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.1.0 - September 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>September 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/September-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v2.1.0 - September 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>September 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/September-01%2C-2021">here</a>.</li></ul>
Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation
<p>Summary statistics for "Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation" </p><p>Files are organized in the following directory structure:</p><p><strong>G2G/</strong> - Summary statistics of G2G associations (SNPs, HLA, and gene-level analysis). </p><p><strong>HLA/ </strong>- Peptide binding prediction results</p><p><strong>preS1_haplotypes/ - </strong>Resolved intra-host haplotypes of the preS1 binding region. </p><p><strong>DnDs/</strong> - Calculation of intra-host positive selection, within the preS1 binding region. </p><p> </p>
2. Acipes atlanticus Attems, 1937 in Atlas of European millipedes 2: Order Julida (Class Diplopoda)
2. Acipes atlanticus Attems, 1937
Value of water level class observations for parameter set selection in hydrological modelling
<p>Data corresponding to the study "<span>Value of water level class observations for parameter set selection in hydrological modelling</span>", published in the Hydrological Sciences Journal.</p>
Experimental validation of simplicial complexes in multivariable coupled oscillators. Coupling: Lineal (Class III) vs NoLineal (Class III)
<p>The data sets correspond to the experimental implementation of synchronization phenomenon in <strong>simplicial complexes</strong>. In this particular case, the simplicial complex consists of 3-node network, where each node is an electronic Rössler-like oscillator whose parameters were fixed to operate in <strong>chaotic regime</strong>. In simplicial complexes, it is possible to model two types of interactions among nodes: <strong>pair-wise interactions (linear interactions)</strong> and <strong>high-order interactions (non-linear interactions)</strong>.</p> <p>The complete experiment consists of coupling simultaneously by means of linear and non-linear interactions the simplicial complex, the coupling occurs in state variables x (class III) and y (class II). The full data sets are organized in four Dataset, whose name explicitly indicates in which state variable occurs each coupling. In these case, Linearx-nonlinearx means that the linear coupling occurs in variable <em><strong>x</strong></em> (class III) whereas the non-linear coupling occurs in variable<em><strong> x</strong></em> (class III).</p> <p>Now, each folder contains 10000 files which come from varying the linear coupling and the nonlinear coupling 100 times each one. The file name is composed as follows: rootname XX YY, where XX corresponds to the variation number in linear coupling, whereas YY corresponds to the variation number in non-linear coupling.</p> <p>Internally in each file we can find 6 columns and 30,000 rows. Each pair of columns corresponds to the<strong><em> x</em></strong> and <strong><em>y</em></strong> variables of each oscillator and the rows correspond to time-varying samples.</p>
A dataset of 1600 images extracted from 5 cm RGB orthophotos for the classification of 12 classes of roofing materials
<p>This dataset contains a collection of 1601 image tiles of 64x64 pixels (3.2x3.2m²) annotated for 12 roofing materials. These tiles were extracted from 5 cm RGB orthophotos acquired by the city of Namur (Belgium) in 2017. The additional data used to create this dataset are (a) a Namur roof section mask, and (b) a set of 1601 material samples acquired using stratified random sampling. The tiles were obtained as follows: the centroid of each roof section containing a sample is used to extract tiles. A size of 64x64 pixels has been chosen so that a tile contains information for only one roof section, in order to learn only the colour and texture of the roof materials. This also avoids adding information outside the given roof section. The tiles are thus extracted for each orthophoto spectral band and labelled with the identifier of the class of roofing materials to which they belong. Here are the 12 material classes considered, preceded by their labels:</p> <p>0- Solar panels<br>1- Brown tiles<br>2- Orange tiles<br>3- Black tiles<br>4- Dark membranes<br>5- White membranes<br>6- Slates containing asbestos<br>7- Slates without asbestos<br>8- Corrugated asbestos-cement sheets<br>9- Gravel<br>10- Vegetation<br>12- Metals</p> <p>There are approximately 140 tiles by material class except for the vegetated roof sections (class 10) which contains only 47 samples due to its rarety.</p> <p>The dataset contains 1 folder for each spectral band. Each folder contains 1601 thumbnails in tif format named as follows:</p> <p><strong>img[tile id]_[class label].tif</strong></p> <p>It is suggested to apply pre-processing to these images as done by<a href="https://doi.org/10.1109/jurse57346.2023.10144142"> Wyard et al. (2023).</a></p>
Data from: Combined fluorescent and electron microscopic imaging unveils the specific properties of two classes of meiotic crossovers
<p>Crossovers (COs) shuffle genetic information and allow balanced segregation of homologous chromosomes during the first division of meiosis. Recombination nodules (RNs) are closely correlated with crossing over, and, because they are observed by electron microscopy of synaptonemal complexes (SCs) in extended pachytene chromosomes, RNs provide the highest-resolution cytological marker currently available for defining the frequency and distribution of crossovers along the length of chromosomes. In several organisms, mutants demonstrate that two molecularly distinct pathways produce COs. One pathway produces class I COs that exhibit interference (lowered probability of nearby COs), and the other pathway produces class II COs with little or no interference. However, the relative contributions, genomic distributions, and interactions of these two pathways are essentially unknown in nonmutant organisms because marker segregation only indicates that a CO has occurred, not its class type. Here, we combine the efficiency of light microscopy for revealing cellular functions using a fluorescent probe to MLH1 proteins to mark class I COs with the high resolution of electron microscopy to localize and characterize all COs (RNs) in the same sample of meiotic pachytene chromosomes from wild-type tomato. To our knowledge, for the first time, every CO along each chromosome can be identified by class to unveil specific characteristics of each pathway. We find that class I (MLH1-positive) and class II (MLH1-negative) COs have different recombination profiles along chromosomes. In particular, class II COs, which represent about 18% of all COs, exhibit no interference and are disproportionately represented in pericentric heterochromatin, a feature potentially exploitable in plant breeding. Finally, our results demonstrate that the two pathways are not independent because there is interference between class I and II COs.</p>
Experimental validation of simplicial complexes in multivariable coupled oscillators. Coupling: Lineal (Class III) vs NoLineal (Class II)
<p>The data sets correspond to the experimental implementation of synchronization phenomenon in<strong> simplicial complexe</strong>s. In this particular case, the simplicial complex consists of 3-node network, where each node is an electronic Rössler-like oscillator whose parameters were fixed to operate in <strong>chaotic regime</strong>. In simplicial complexes, it is possible to model two types of interactions among nodes: <strong>pair-wise interactions</strong> (linear interactions) and <strong>high-order interactions</strong> (non-linear interactions).</p> <p>The complete experiment consists of coupling simultaneously by means of linear and non-linear interactions the simplicial complex, the coupling occurs in state variables x (class III) and y (class II). The full data sets are organized in four Dataset (second), whose name explicitly indicates in which state variable occurs each coupling. In these case, Linearx-nonlineary means that the linear coupling occurs in variable <em><strong>x</strong></em> (class III) whereas the non-linear coupling occurs in variable <em><strong>y</strong></em> (class II).</p> <p>Now, each folder contains 10000 files which come from varying the linear coupling and the nonlinear coupling 100 times each one. The file name is composed as follows: rootname XX YY, where XX corresponds to the variation number in linear coupling, whereas YY corresponds to the variation number in non-linear coupling.</p> <p>Internally in each file we can find 6 columns and 30,000 rows. Each pair of columns corresponds to the <em><strong>x</strong></em> and <em><strong>y</strong></em> variables of each oscillator and the rows correspond to time-varying samples.</p>
Experimental validation of simplicial complexes in multivariable coupled oscillators. Coupling: Lineal (Class II) vs NoLineal (Class III)
<p>The data sets correspond to the experimental implementation of synchronization phenomenon in <strong>simplicial complexes</strong>. In this particular case, the simplicial complex consists of 3-node network, where each node is an electronic Rössler-like oscillator whose parameters were fixed to operate in <strong>chaotic regime</strong>. In simplicial complexes, it is possible to model two types of interactions among nodes: <strong>pair-wise interactions</strong> (linear interactions) and <strong>high-order interactions</strong> (non-linear interactions).</p> <p>The complete experiment consists of coupling simultaneously by means of linear and non-linear interactions the simplicial complex, the coupling occurs in state variables x (class III) and y (class II). The full data sets are organized in four Dataset (third), whose name explicitly indicates in which state variable occurs each coupling. In these case, Lineary-nonlinearx means that the linear coupling occurs in <strong>variable y</strong> (class II) whereas the non-linear coupling occurs in <strong>variable x</strong> (class III).</p> <p>Now, each folder contains 10000 files which come from varying the linear coupling and the nonlinear coupling 100 times each one. The file name is composed as follows: rootname XX YY, where XX corresponds to the variation number in linear coupling, whereas YY corresponds to the variation number in non-linear coupling.</p> <p>Internally in each file we can find 6 columns and 30,000 rows. Each pair of columns corresponds to the x and y variables of each oscillator and the rows correspond to time-varying samples.</p>
Experimental validation of simplicial complexes in multivariable coupled oscillators. Coupling: Lineal (Class II) vs NoLineal (Class II)
<p>The data sets correspond to the experimental implementation of synchronization phenomenon in <strong>simplicial complexes</strong>. In this particular case, the simplicial complex consists of 3-node network, where each node is an electronic Rössler-like oscillator whose parameters were fixed to operate in <strong>chaotic regime</strong>. In simplicial complexes, it is possible to model two types of interactions among nodes: <strong>pair-wise interactions</strong> (linear interactions) and<strong> high-order interaction</strong>s (non-linear interactions).</p> <p>The complete experiment consists of coupling simultaneously by means of linear and non-linear interactions the simplicial complex, the coupling occurs in state variables x (class III) and y (class II). The full data sets are organized in four Dataset (fourth), whose name explicitly indicates in which state variable occurs each coupling. In these case, Linearx-nonlinearx means that the linear coupling occurs in variable <strong>y</strong> (class II) whereas the non-linear coupling occurs in variable <strong>y</strong> (class II).</p> <p>Now, each folder contains 10000 files which come from varying the linear coupling and the nonlinear coupling 100 times each one. The file name is composed as follows: rootname XX YY, where XX corresponds to the variation number in linear coupling, whereas YY corresponds to the variation number in non-linear coupling.</p> <p>Internally in each file we can find 6 columns and 30,000 rows. Each pair of columns corresponds to the x and y variables of each oscillator and the rows correspond to time-varying samples.</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.