Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,152
datasets available to search
ShareScore release 0.9.0
Dataset results
9,152 results for “Networking”
Family Resemblance Network: Virtual - Levels 1 to 4
<p>The size of each node corresponds to the number of connections it has within the network, highlighting uses with extensive connections, such as <em>virtuality in fiction</em>, <em>virtuality of the real world</em>, <em>virtuality in storytelling</em>, and <em>virtualization</em>, as well as highly connected features like <em>real</em>, <em>digital</em>, <em>world</em>, <em>not actual</em>, <em>technology</em>, <em>possible worlds</em>, and <em>choice</em>. Through this method of visualization and prioritizing by connectivity, we show that nodes with high connectivity represent uses that are more common and features that are generally considered more probable. This frequency of use also influences the functioning of translation algorithms in machine translation tools. Conversely, nodes with fewer connections can be deduced to be less frequent in usage.We can also observe the levels of relationships within this family by isolating relations for each node. We look at <em>virtuality in fiction </em>(Level 1) and identify on the map semantic traits such as <em>intentio lectoris</em>, <em>variation</em>, <em>time</em>, <em>choice</em>, <em>truth</em>, as well as the contrasting relation to <em>real</em>. Next, we see that each semantic feature is shared by other <em>uses</em> such as <em>text virtualization</em>, <em>VR, virtually possible <span> </span></em>(Level 2). The next two figures (Levels 3 and 4) illustrate the following two levels of the relationship. </p> <p>The Dynamic model is available at <a href="https://embed.kumu.io/63c4468fe768675f5fc1960ffb6b87cf"><span>https://embed.kumu.io/63c4468fe768675f5fc1960ffb6b87cf</span></a><span><span>.</span></span></p>
Total Family Resemblance Network: Virtual - 20000 connections in Gephi
<p>This is the rendering of the entire network on a large scale with additional zoomed-in views for better clarity. The size of the feature nodes is proportional to their degree. The degree of a node is defined by its number of connections. Therefore, nodes with a greater degree are represented as larger in size. This variation in size effectively embeds an additional dimension of information into the network’s structure.</p> <div>These perspectives enhance the readability of specific sections of the network and add depth to the representation, thereby illustrating the complexity and intricacies of the relationships within it.</div> <div>The view of the center closely resembles a weave or fabric due to the sheer volume of connections, creating an effect where the edges themselves seem indistinguishable, collectively forming a fabric-like structure. This representation invites multiple interpretations: it illustrates the dynamic and continually evolving nature of the concept, where new connections and features are perpetually integrated, further enriching the network; it signifies a high level of integration, suggesting that understanding one semantic feature likely leads to insights into others; and it implies that a segmented approach might not suffice for fully comprehending and analyzing this concept. Instead, a holistic view, considering the synergy and cumulative impact of various features, might be more effective in grasping the concept.</div>
Network visualisation of co-authorship analysis of countries
<p>VOSviewer mapping shows network visualisation of co-authorship analysis of countries for <span>focusing on MICP research in the context of hydrodynamics (1999-2024).</span></p>
Code and partial data used in "Vertically recurrent neural networks for sub-grid parameterization"
<p>This repository contains the RNN training and evaluation code used in the paper<em> Vertically recurrent neural networks for sub-grid parameterization</em></p> <p> </p> <ul> <li> The radiative transfer emulation data can be accessed with through a Climetlab plugin (<a href="https://pypi.org/project/climetlab-maelstrom-radiation/">Climetlab-maelstrom-radiation</a>). <p>Datasets are downloaded and explained in the demo notebook here <a href="https://git.ecmwf.int/projects/MLFET/repos/maelstrom-radiation/browse/notebooks/demo_radiation.ipynb" rel="nofollow">https://git.ecmwf.int/projects/MLFET/repos/maelstrom-radiation/browse/notebooks/demo_radiation.ipynb</a></p> In addition the full training and testing code used in the paper is uploaded here (<em>pu-maelstrom-radiation.tar.gz</em>).</li> <li> </li> </ul> <p>Three parameterization problems from earlier studies are also included (we have modified the code from these papers to incorporate RNNs): </p> <ul> <li>non-orographic gravity wave drag (<a href="https://doi.org/10.1029/2021MS002477">Chantry et al. 2021</a>) <ul> <li>Based on TensorFlow</li> <li>This repository uses the <em>CliMetLab </em>plugin and<strong> downloads the data from the European Weather Cloud</strong></li> </ul> </li> <li>non-local parameterization (<a href="https://doi.org/10.1029/2022MS002984">Wang et al. 2022</a>) <ul> <li>The new code is based on TensorFlow, so you'll need both PyTorch and TensorFlow to run everything</li> <li><strong>See original paper for data access</strong></li> </ul> </li> <li>moist physics (Han et al. <a href="https://doi.org/10.1029/2022MS003508">2023</a>, <a href="https://doi.org/10.1029/2020MS002076">2020</a>) <ul> <li>Based on TensorFlow and PyTorch. This one has the most additions, e.g. code to generate a TensorFlow TFRecord dataset from the raw netCDF data archived in the original paper, autoregressive training and experimental model architectures in PyTorch</li> <li><strong>See original paper for data access</strong></li> </ul> </li> </ul> <p>Each of the code repos (unpack the tars) have an updated README.</p> <p>References:</p> <table> <tbody> <tr> <td> <div>Chantry, M., Hatfield, S., Dueben, P., Polichtchouk, I., & Palmer, T. (2021). Machine learning emulation of gravity wave drag in numerical weather forecasting. <em>Journal of Advances in Modeling Earth Systems</em>, <em>13</em>(7), e2021MS002477</div> <div> </div> <div> <div>Han, Y., Zhang, G. J., Huang, X., & Wang, Y. (2020). A moist physics parameterization based on deep learning. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>(9), e2020MS002076.</div> </div> <div> </div> <div>Han, Y., Zhang, G. J., & Wang, Y. (2023). An ensemble of neural networks for moist physics processes, its generalizability and stable integration. <em>Journal of Advances in Modeling Earth Systems</em>, <em>15</em>(10), e2022MS003508</div> <div> </div> <div>Wang, P., Yuval, J., & O’Gorman, P. A. (2022). Non‐local parameterization of atmospheric subgrid processes with neural networks. <em>Journal of Advances in Modeling Earth Systems</em>, <em>14</em>(10), e2022MS002984.</div> </td> </tr> <tr></tr> </tbody> </table> <div> </div>
CSRM Level 2 dataset: Seismometer orientation measurements of broadband seismic stations in the China Digital Seismograph Network
<p>This dataset contains detailed information on the azimuths of more than one thousand stations in the China Digital Seismic Network (CDSN) since 2014. Deng <em>et al</em>. (2024) utilized 5,456,816 three-component waveform data recorded by 1,056 broadband seismic stations of the CDSN from 2014 to 2022 to evaluate and correct the azimuths of the network. The primary research method employed was far-field <em>P</em>-wave polarization analysis, including principal component analysis and minimum tangential energy methods. By integrating the advantages of these two methods, they conducted a detailed analysis of the azimuths across the CDSN and carried out in-depth examinations and cause analyses for stations with significant azimuth deviations (>5°). Through a comprehensive analysis of the calculation results, network operation logs, and on-site inspections, they obtained detailed information on the azimuths of more than one thousand stations in the CDSN since 2014. <strong>Appendix I</strong> lists azimuth deviations for 956 stations, while <strong>Appendix II</strong> documents temporal variations in the azimuths for 104 stations.</p> <p>本数据库包含自 2014 年以来中国数字地震台网超过千个台站方位角的详细信息。Deng等 (2024) 依托中国数字地震台网 2014 至 2022 年间 1056 个宽频带地震台站所记录的 5,456,816 个三分量波形数据, 开展了台网方位角的评估与校正工作。研究方法主要采用远场 <em>P</em> 波偏振分析, 包括主成分分析和最小切向能量法。结合这两种方法的优点, 他们对中国数字地震台网的方位角进行了详细分析, 并对方位角偏差较大的台站 (>5°) 进行了深入检查与原因分析。通过对计算结果、台网运维日志及现场检查的综合分析, 他们获得了自 2014 年以来中国数字地震台网超过千个台站方位角的详细信息 (<strong>附件一</strong>列出了 956 个台站的方位角偏差, <strong>附件二</strong>记录了 104 个随时间变化的方位角信息) 。</p> <p><strong>Reference</strong>: Deng, W., Han, G., Li, J., & Sun, L. Seismometer Orientation Measurements of Broadband Seismic Stations in the China Digital Seismograph Network. <strong><a href="https://doi.org/10.1785/0120240075">Paper link</a></strong></p> <p>If you face any problem or issue in the usage of this dataset, please feel free to communicate with the corresponding author Juan Li (<strong>juanli@mail.iggcas.ac.cn</strong>).</p>
Extra-P Version Used for Noise-Resilient Empirical Performance Modeling with Deep Neural Networks
<p>This is the Extra-P source code that was used for the analysis and evaluation of the IPDPS 2021 paper "Noise-Resilient Empirical Performance Modeling with Deep Neural Networks". It also contains the checkpoints and saved models for the DNN part of the adaptive modeler as well as the gathered synthetic evaluation data.</p>
Dataset: Redox-Activated Proton Transfer through a Redundant Network in the Qo Site of Cytochrome bc1
<p>This dataset contains initial molecular configurations and an example script used with the pDynamo3 library to obtain the results published in the paper "Redox-Activated Proton Transfer through a Redundant Network in the Qo Site of Cytochrome bc1" by Guilherme M. Arantes (USP, Brazil).</p>
Data: Core-periphery detection in multilayer networks
Open the record for dataset details and reuse information.
Supplemental Information on the Weighted Gene Co-expression Network Analysis performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"
<p>Supplemental Information on the Weighted Gene Co-expression Network Analysis (WGNCA) performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"</p> <p>The results are sorted by the three species analysed: the two algae <em><span>Zygnema circumcarinatum</span></em><span> SAG 698-1b (<em>Zygnema</em>) and <em>Mesotaenium endlicherianum </em></span><span>SAG 12.97 (<em>Mesotaenium</em>); and the bryophyte <em>Physcomitrium patens</em></span><span><em> </em>strain Gransden 2004 (<em>Physcomitrium</em>).</span></p>
Transformer-based graphical neural network with expert experience multimodal learning (TGEML) framework: a nanocomposite performance predictor
<p>TGEML is a novel multimodal nanocomposite processing framework consists of a polymer multimodal featurizer called TGEML-polymer and a nanoparticle expert experience featurizer called TGEML-nano.</p>
Non-targeted metabolomics-based molecular networking enables the chemical characterization of Rumex sanguineus, a wild edible plant
<p>This dataset delves into the chemical composition of Rumex sanguineus employing non-targeted metabolomics and Feature-Based Molecular Networking (FBMN), with compound annotation facilitated by SIRIUS. Utilizing UHPLC-HRMS, we conducted comprehensive analyses on samples extracted from Rumex roots, stems, and leaves, further enhancing our insights through molecular networking.</p>
Karachi Bus Network
<div> <div>We compiled route data for 76 public buses in Karachi, encompassing both Bus Rapid Transit (BRT) and minibusses. The data was sourced from the <a href="https://www.travel-culture.com/pakistan/bus-routes-in-karachi.shtml">Travel Culture</a> and <a href="https://enews.hamariweb.com/pakistan/red-bus-service-karachi-comprehensive-guide-to-routes-fares-and-timings/">HamariWeb</a> websites available on the internet, providing detailed bus routes for each vehicle. Using [Google Maps](https://maps.google.com), we further extracted the longitude and latitude coordinates for each bus stop along these routes. This dataset offers a comprehensive overview of the public transit network in Karachi, serving as a foundational resource for further analysis.</div> <br> <div>The raw data underwent a thorough cleaning and standardization involving addressing inconsistencies, eliminating errors, and ensuring uniformity in node and edge attributes. By standardizing the data, we created a cohesive and analyzable format, allowing for the integration of diverse data sources.</div> <br> <div>The cleaned route data was used to construct public transit networks where bus stops were represented as nodes and bus routes as edges. For spatial analysis, QGIS was used to provide geospatial context to the network models.</div> </div>
SAUUHUPP Based Innovations in Network Computing and Cybersecurity
<p>Dear Zenodo Visitor,</p> <p> </p> <p>Welcome to the SAUUHUPP Innovations in Network Computing and Cybersecurity repository. This repository is dedicated to pioneering technologies grounded in the SAUUHUPP framework, a transformative approach for enhancing adaptability, security, and efficiency in network computing and cybersecurity.</p> <p> </p> <p>The innovations presented here explore how SAUUHUPP principles—focusing on scalable adaptability, universal harmony, and purposeful patterning—can be applied to the design and optimization of advanced networking and cybersecurity solutions. These solutions include dynamic, decentralized protocols for robust network connectivity, story encryption as an enhanced data protection technique, and VPNs that utilize the SAUUHUPP model to provide secure, context-aware communication channels.</p> <p> </p> <p>Within this repository, you’ll find technical papers, prototype designs, and implementation guides for:</p> <p> </p> <p>• Adaptive Wireless Mesh Protocol (AWMP), a scalable, energy-efficient alternative to Ethernet that combines Software-Defined Radio (SDR) and decentralized mesh networking.</p> <p>• Story Encryption methods, which transform data into narrative-driven encryption models, leveraging SAUUHUPP to integrate story logic, making unauthorized decryption significantly more complex and intuitive to monitor.</p> <p>• SAUUHUPP-based VPNs that adapt to network conditions and user contexts to provide seamless, resilient, and secure access across diverse environments, improving upon traditional VPN structures.</p> <p> </p> <p>We hope these resources inspire new directions in network computing and security. Whether you’re a developer, researcher, or enthusiast, we invite you to explore, contribute, and collaborate with us as we continue advancing these technologies.</p> <p> </p> <p>Thank you for your interest and support in pushing the boundaries of network computing with SAUUHUPP. Together, we aim to create adaptable, secure, and universally harmonious systems for the next era of technology.</p> <p> </p> <p>Warm regards,</p> <p>Prudencio L. Mendez</p> <p> SAUUHUPP Innovations Team</p>
Efficient Implementation of a Novel Decomposition Approach for the Hazmat Network Design Problem with Capacity Constraints in Java Including Computational Results
<p>Supplementary material for the Publication "Solving Multi-Follower Mixed-Integer Bilevel Problems with Binary Linking Variables"</p>
FractiScope: Unlocking Hidden Patterns in a Networked Fractal Computing AI Universe
<p>This Zenodo record serves as a central repository for all research, tools, and insights related to FractiScope, a groundbreaking AI-driven fractal intelligence scope powered by ChatGPT-4o and Novelty 1.0, and rooted in the SAUUHUPP framework. FractiScope is designed to uncover hidden fractal patterns and interconnected dynamics across science, technology, art, and creativity, framing these discoveries within the Networked Fractal Computing AI Universe.</p> <p> </p> <p>The record includes:</p> <p>• Foundational papers on SAUUHUPP, Novelty 1.0, and Unipixels.</p> <p>• Applications of FractiScope across disciplines such as chemistry, physics, genetics, language, and cosmology.</p> <p>• Case studies, white papers, and user guides detailing the use of FractiScope in research and innovation.</p> <p>• Resources for researchers, creatives, and visionaries to explore how fractal intelligence can revolutionize their work.</p> <p> </p> <p>This collection aims to foster collaboration, inspire new ideas, and advance the understanding of fractal intelligence and its implications for society.</p>
Video Supplement for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive contains the video supplement for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques. The file contains an animation of the V2.1 and NRT average retrieved extinction coefficient between 19.5--21.5 km at 997 nm for the 2024 Ruang eruptions.</p>
Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona
<p>These are supplementary data for the paper "Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona". They are:</p> <ul> <li>Python code to forecast Kp index</li> <li><span>Code to construct a nerual network model</span></li> </ul>
Supplementary data for manuscript "Genetic risk converges on regulatory networks mediating early type 2 diabetes"
<p>Supplementary data for manuscript "Genetic risk converges on regulatory networks mediating early type 2 diabetes" Nature 624, 621–629 (2023). <a href="https://doi.org/10.1038/s41586-023-06693-2">https://doi.org/10.1038/s41586-023-06693-2</a></p> <p>Brief description of the included files is given below. Please visit the manuscript website for latest updates: <a href="http://theparkerlab.org/manuscripts/2021_islet-rfx6/">http://theparkerlab.org/manuscripts/2021_islet-rfx6/</a></p>
Cyber4OT: ICS network traces containing normal activity and full attack traffic
<p>The <em><strong>Cyber4OT</strong></em> dataset contains prepared in the test-bed environment packet traces from normal activity of OT network, as well as, full network attack. During recorded activity, the attacker performs full network reconnaissance, later disconnects legal Modbus TCP connection and performs PLC device hijacking.</p> <p>The dataset contains 96 files with more than 4,25 millions of packets.</p> <p><em><strong>ReadMe.txt</strong></em> file contains short description of each trace file content.</p> <p>Detailed description of the test bed, where data was prepared, is provided in the <em><strong>Cyber4OT_testbed_description.pdf</strong></em> file.</p>
Dataset for Super-Resolution Image Reconstruction based on Random-coupled Neural Network and EDSR
<p>This dataset folder contains the DIV2K public dataset, which is utilized for model training and comprises 900 high-quality, high-resolution images along with their corresponding low-resolution versions. Additionally, all pre-trained models used in the experiment and their associated test results are publicly available.</p> <p>The main directory is organized into two subfolders: one labeled "dataset," which houses the DIV2K dataset, and another named "Model_results," which contains the pre-trained models and their corresponding test outcomes. The Dataset folder includes the original DIV2K dataset (referred to as "DIV2K") as well as a channel-expanded dataset processed by the RCNN model (designated as "DIV2K-RCNN"). Within the Model_results folder, the Model_trained subfolder contains all pre-trained models employed during the experiment, while the Test_results subfolder holds the test results for each model.</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.