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”
Compartment and Hub Definitions Tune Metabolic Networks for Metabolomic Interpretations
<p>This archive contains data for a report by the same title.<br> Data relate to software projects MetaboNet and DyMetaboNet.<br> MetaboNet: https://github.com/tcameronwaller/metabonet<br> DyMetaboNet: https://github.com/tcameronwaller/dymetabonet</p> <p>File descriptions</p> <p>dymetabonet_2019-08-29.mp4 ... raw screen capture video of DyMetaboNet<br> dock_metabonet_2019-08-18.zip ... complete MetaboNet export<br> model_* ... curation of human metabolic model by MetaboNet<br> model_dymetabonet.zip ... format for DyMetaboNet<br> model_compartments* ... compartments<br> model_processes* ... processes<br> model_reactions* ... reactions<br> model_metabolites* ... metabolites<br> measurement_* ... curation of metabolomic measurements by MetaboNet<br> measurement_study_*_report.tsv ... summary of match measurements to metabolites<br> measurement_study_*.tsv ... metabolites' fold changes and probabilities between groups<br> measurement_study_*_metaboanalyst.txt ... format for MetaboAnalyst<br> measurement_study_*_metaboanalyst_pair.txt ... format for MetaboAnalyst with sample pairs<br> network_* ... multiple definitions of metabolic networks<br> network_compartments-true_hubs-true.zip ... compartmental network with hubs<br> network_compartments-true_hubs-false.zip ... compartmental network without hubs<br> network_compartments-false_hubs-true.zip ... noncompartmental network with hubs<br> network_compartments-false_hubs-false.zip ... noncompartmental network without hubs<br> network_compartments-*_hubs_*/network_cytoscape.json ... format for Cytoscape<br> network_compartments-*_hubs_*/network_networkx.pickle ... format for NetworkX<br> network_compartments-*_hubs_*/nodes_reactions.pickle ... network's nodes for reactions<br> network_compartments-*_hubs_*/nodes_metabolites.pickle ... network's nodes for metabolites<br> network_compartments-*_hubs_*/links.pickle ... network's links<br> network_compartments-*_hubs_*/analysis/nodes_reactions.tsv ... nodes' metrics relative to reactions<br> network_compartments-*_hubs_*/analysis/nodes_metabolites.tsv ... nodes' metrics relative to metabolites<br> network_compartments-*_hubs_*/analysis/network_reactions.tsv ... network's metrics relative to reactions<br> network_compartments-*_hubs_*/analysis/network_metabolites.tsv ... network's metrics relative to metabolites<br> network_compartments-*_hubs_*/measurement/metabolites.tsv ... measurements on nodes for metabolites</p>
Time-resolved compound repositioning predictions on a text-mined knowledge network
<p><strong>gs_positives.csv</strong>: The re-processed version of DrugCentral indications, utilized as training and testing positives in the analysis.</p> <p><strong>top_5000_predictions.csv</strong>: The top 5000 drug-disease pairs, by probability, produced by this analysis pipeline.</p> <p><strong>file_info.txt</strong>: Information about the column headings in of the two files.</p> <p> </p>
Fed4Fire/CDN-X-ALL network metrics dataset for time series analysis in Media content delivery for 4G/5G networks
<p>The following dataset was generated at VICOMTECH (https://www.vicomtech.org) under project/experiment CDN-X-ALL: "CDN edge-cloud computing for efficient cache and reliable streaming aCROSS Aggregated unicast-multicast LinkS".</p> <p>Project funded by Fed4FIRE+ OC5 (<a href="https://www.fed4fire.eu/">https://www.fed4fire.eu</a>) under grant 732638.</p> <p>The dataset provides network metrics captures across several days employing a GStreamer-based MPEG-DASH player running on an UE connected to a LTE network.</p> <p>Nitos LTE/OpenAirInterface (OAI) testbed (<a href="https://nitlab.inf.uth.gr/NITlab/nitos/lte">https://nitlab.inf.uth.gr/NITlab/nitos/lte</a>) was used to deploy the LTE network.</p> <p><strong>CDN-like server/DASH Dataset -> Internet -> EPC/OAI -> eNodeB/OAI -> UE/DASH player</strong></p> <p>The player downloads MPEG-DASH video files provided by Distributed DASH dataset (<a href="https://dash.itec.aau.at/distributed-dash-datset/">https://dash.itec.aau.at/distributed-dash-datset/</a>), a dataset for CDN-like experiments, and captures the following data:</p> <ol> <li>Date: date when the data is collected</li> <li>Player: type of the player (in this case it is always "GStreamer")</li> <li>Num: identifier of the player</li> <li>URLVid: URL of the MPD file</li> <li>Latency: latency experienced by the player</li> <li>BW: bandwidth experienced by the player</li> <li>Quality: chosen DASH video representation</li> </ol> <p>During the experiments, other players run in order to generate realistic media streaming traffic at the CDN-like servers. These players start playing by following Poisson or Pareto distribution.</p> <p>The dataset was used to train Machine Learning Time Series predictor in order to forecast network capabilities and can be used for further experimentation concerning time series analysis.</p>
Results of the DYNAMO (Dynamic MEC Orchestration of Cellular Networks) experiment in the Fed4FIRE+ testbeds
<p>The main objective of the DYNAMO Fed4FIRE+ experiment was to perform Network Function Virtualization (NFV) Management and Network Orchestration (MANO) of a cellular network on top of cloud infrastructures, exploring one of the key enabling technologies for 5G systems and beyond. DYNAMO used cloud and radio access facilities at the IRIS testbed and cloud facilities at the University of Vigo (UVIGO) to deploy an end-to-end (E2E) cellular network and perform elastic changes on it if needed. The geographic distance in between facilitated the setup of a realistic Multi-Access Edge Computing (MEC) use case, where the virtual Evolved Packet Core (vEPC) was deployed at UVIGO (Spain) and the access network, i.e., the User Equipment (UE), the e-Node-B (eNB) and edge cloud, were implemented on IRIS testbed (Ireland).<br> <br> While the initial deployment of the E2E cellular network may be considered as static, DYNAMO showcases the elasticity that an E2E cellular network may need in runtime. Hence, we presented a use case consisting of a latency sensitive E2E cellular network (network slice), where the endpoint of the UE connection was initially located in the core (UVIGO) but then migrated to the edge (IRIS), in case the UE's latency ranges were unacceptable.<br> <br> In this regard, the UE reported the experienced latency to Open Network Automation Platform (ONAP), which is responsible to trigger specific policy-driven control actions if a predefined Service-Level Agreement (SLA) is violated.<strong> <em>This datased includes the reports provided by the UE to ONAP.</em></strong><br> <br> As a result, the endpoint of the data plane of the UE is automatically moved to the access network (IRIS) thus reducing significantly the latency for the UE. For the access part of the network, we implemented one srsLTE e-Node-B (eNB), one srsLTE User Equipment (UE) and a Devstack (Edge Cloud) in virtual machines on IRIS testbed. In addition, we also implemented an SDN switch controlled by an ONOS SDN controller. For the core part of the network, we considered a disaggregated vEPC from Open Air Interface (OAI) on a Devstack (Core Cloud) at UVIGO.<br> <br> DYNAMO has succeeded in the integration of a broad set of network elements and technologies between the two different domains (UVIGO and IRIS testbed) and fulfilled all initial objectives: (i) establishing communication between ONAP and IRIS testbed to deploy generic VNFs on core and edge clouds, (ii), deployment of an E2E cellular network with UE and eNB in IRIS and the vEPC at UVIGO, (iii), sending telemetry of the UE to ONAP and (iv) designing and testing closed-loop control actions in ONAP to migrate the data plane of the UE to the edge in case of unsatisfactorily SLA. DYNAMO paves the way to a broad set of future 5G experiments that will require resource orchestration, such as the deployment of network slices or the automatic scheduling of services in the limited resources of Edge Clouds.</p> <p>This repository contains the information sent from the UE to ONAP, in order to decide if the latency between the UE and the PGW is OK or if an action has to be considered to reduce such latency.<br> </p> <p> </p>
Experimental datasets of networks of nonlinear oscillators: Structure and dynamics during the path to synchronization
<p>The analysis of the interplay between structural and functional networks require experiments where both the specific structure of the connections between nodes and the time series of the underlying dynamical units are known at the same time. However, real datasets typically contain only one of the two ways (structural or functional) a network can be observed. Here, we provide experimental recordings of the dynamics of 28 nonlinear electronic circuits coupled in 20 different network configurations. For each network, we modify the coupling strength between circuits, going from an incoherent state of the system to a complete synchronization scenario. Time series containing 30000 points are recorded using a data-acquisition card capturing the analogic output of each circuit. The experiment is repeated three times for each network structure allowing to track the path to the synchronized state both at the level of the nodes (with its direct neighbors) and at the whole network. These datasets can be useful to test new metrics to evaluate the coordination between dynamical systems and to investigate to what extent the coupling strength is related to the correlation between functional and structural networks.</p> <p>We provide the times series of N=28 Rössler electronic oscillators for 20 different network configurations (compressed file with tag R1 to R20). For each network structure, we recorded the times series for 101 different coupling strengths between oscillators. Each one of the 101 corresponding files is labeled as ST_X_Y.dat where X is a value between X=0 and X=100 that corresponds, respectively, to the minimum and maximum coupling strength. The value of Y corresponds to the repetition number, which can be 1, 2 of 3 (i.e., we repeated the same experiment three times). Data files contain the second variable of the 28 nodes arranged in columns with a length of 30000 points. In a second file named Structure.zip, all the network structures are given, each file having a name Net_R.dat, where R=1, 2… 20. The degree of each node (i.e., number of output connections) is the same for all network configurations, where the specific neighbors of each node are re-arranged randomly.</p>
Temperature and rainfall datasets for the paper "STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting"
<p>This page includes spatiotemporal datasets used in the paper <a href="https://doi.org/10.1016/j.neucom.2020.09.060">STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting.</a></p> <p>ARIMA and deep learning models use datasets, as follow:</p> <ul> <li>ARIMA models</li> </ul> <p>baseline-chirps-1981-2019.nc (rainfall data)<br> baseline-ucar-1979-2015.nc (temperature data)</p> <ul> <li>Deep learning models:</li> </ul> <p><em>5-step ahead:</em></p> <p>dataset-chirps-1981-2019-seq5-ystep5.nc (rainfall data)<br> dataset-ucar-1979-2015-seq5-ystep5.nc (temperature data)</p> <p><em>15-step ahead:</em></p> <p>dataset-chirps-1981-2019-seq5-ystep15.nc (rainfall data)<br> dataset-ucar-1979-2015-seq5-ystep15.nc (temperature data)</p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon: Implication for identifying trends in dry season rainfall
<h1>Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon.</h1>
Data From: Harnessing Deep Belief Networks for Selective HDAC6 Inhibitors Identification
<p>This dataset contains the results from virtual screening of SPECS library after predicting the selectivity of molecules against HDAC6 over using a Deep Belief Network model that was trained and tested by the authors.</p> <p>The docking poses of 10 molecules with best docking scores were given along with their MMGBSA scores. </p> <p>The MD trajectory files along with the analysis were also provided for the selected three molecules as well as the reference molecule (co-crystalized ligand).</p>
Coping with Collapse: Functional Robustness of Coral-Reef Fish Network to Simulated Cascade Extinction
<p>Data set, codes and results related to the article "Coping with Collapse: Functional Robustness of Coral-Reef Fish Network to Simulated Cascade Extinction", accepted in the periodic Global Change Biology. Stored are the full results of site occupancy models fitted to fish data, with coral and turf algae cover as predictor variables (results published in Luza et al. 2022, Scientific Reports), and the results of the present article. The RData also contains site coordinates, and the fish traits used in trait-based analyzes.</p>
Data for publication "The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zürich"
<p>Please see README.md for a description of this package. </p> <p>This work was funded by the European Union's Horizon 2020 research and innovation programme, grant agreement number 101037319, named Pilot Applications in Urban Landscapes - towards integrated city observatories for greenhouse gases (PAUL) and is known as ICOS Cities.</p> <p> </p>
Data and code related to "Difficult control is related to instability in biologically inspired Boolean networks"
<p>This repository contains data and code related to the publication "Difficult control is related to instability in biologically inspired Boolean networks" by Bryan C. Daniels and Enrico Borriello.</p> <p>The python code in the `isolated_fixed_points_code` directory can be used to recreate all results in the paper. See the README.md file in the `isolated_fixed_points_code` directory for more information about how to run the code.</p> <p>The files `240916_cell_collective_ck_and_isolated_fp_data.csv`, `240916_iowa_database_ck_and_isolated_fp_data.csv`, and `240916_random_ck_and_isolated_fp_data.csv` contain data about the networks analyzed in the paper, including the number of attractors and mean control kernel size of each network.</p>
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting
<h2><strong>CESNET-TimeSeries24: The dataset for network traffic forecasting and anomaly detection</strong></h2> <p>The dataset called CESNET-TimeSeries24 was collected by long-term monitoring of selected statistical metrics for 40 weeks for each IP address on the ISP network CESNET3 (Czech Education and Science Network). The dataset encompasses network traffic from more than 275,000 active IP addresses, assigned to a wide variety of devices, including office computers, NATs, servers, WiFi routers, honeypots, and video-game consoles found in dormitories. Moreover, the dataset is also rich in network anomaly types since it contains all types of anomalies, ensuring a comprehensive evaluation of anomaly detection methods.<br><br>Last but not least, the CESNET-TimeSeries24 dataset provides traffic time series on institutional and IP subnet levels to cover all possible anomaly detection or forecasting scopes. Overall, the time series dataset was created from the 66 billion IP flows that contain 4 trillion packets that carry approximately 3.7 petabytes of data. The CESNET-TimeSeries24 dataset is a complex real-world dataset that will finally bring insights into the evaluation of forecasting models in real-world environments.<br><br></p> <p>Please cite the usage of our dataset as:</p> <blockquote> <p>Koumar, J., Hynek, K., Čejka, T. <em>et al.</em> CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting. <em>Sci Data</em> <strong>12</strong>, 338 (2025). https://doi.org/10.1038/s41597-025-04603-x<br><br>@Article{cesnettimeseries24,<br> author={Koumar, Josef and Hynek, Karel and {\v{C}}ejka, Tom{\'a}{\v{s}} and {\v{S}}i{\v{s}}ka, Pavel},<br> title={CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting},<br> journal={Scientific Data},<br> year={2025},<br> month={Feb},<br> day={26},<br> volume={12},<br> number={1},<br> pages={338},<br> issn={2052-4463},<br> doi={10.1038/s41597-025-04603-x},<br> url={https://doi.org/10.1038/s41597-025-04603-x}<br>}<br><br></p> </blockquote> <p> </p> <h3>Time series</h3> <p>We create evenly spaced time series for each IP address by aggregating IP flow records into time series datapoints. The created datapoints represent the behavior of IP addresses within a defined time window of 10 minutes. The vector of time-series metrics v_{ip, i} describes the IP address ip in the i-th time window. Thus, IP flows for vector v_{ip, i} are captured in time windows starting at t_i and ending at t_{i+1}. The time series are built from these datapoints. </p> <p>Datapoints created by the aggregation of IP flows contain the following time-series metrics:</p> <ul> <li><strong><em>Simple volumetric metrics:</em></strong> the number of IP flows, the number of packets, and the transmitted data size (i.e. number of bytes)</li> <li><strong><em>Unique volumetric metrics:</em></strong> the number of unique destination IP addresses, the number of unique destination Autonomous System Numbers (ASNs), and the number of unique destination transport layer ports. The aggregation of \textit{Unique volumetric metrics} is memory intensive since all unique values must be stored in an array. We used a server with 41 GB of RAM, which was enough for 10-minute aggregation on the ISP network. </li> <li><strong><em>Ratios metrics:</em></strong> the ratio of UDP/TCP packets, the ratio of UDP/TCP transmitted data size, the direction ratio of packets, and the direction ratio of transmitted data size</li> <li><em><strong>Average metrics:</strong></em> the average flow duration, and the average Time To Live (TTL)</li> </ul> <p> </p> <p><strong>Multiple time aggregation: </strong> The original datapoints in the dataset are aggregated by 10 minutes of network traffic. The size of the aggregation interval influences anomaly detection procedures, mainly the training speed of the detection model. However, the 10-minute intervals can be too short for longitudinal anomaly detection methods. Therefore, we added two more aggregation intervals to the datasets--1 hour and 1 day.</p> <p><strong>Time series of institutions:</strong> We identify 283 institutions inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution's data. </p> <p><strong>Time series of institutional subnets:</strong> We identify 548 institution subnets inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution subnet's data. </p> <p> </p> <h3>Data Records</h3> <p>The file hierarchy is described below:</p> <blockquote> <p>cesnet-timeseries24/</p> <p> |- institution_subnets/</p> <p> | |- agg_10_minutes/<id_institution>.csv</p> <p> | |- agg_1_hour/<id_institution>.csv</p> <p> | |- agg_1_day/<id_institution>.csv</p> <p> | |- identifiers.csv</p> <p> |- institutions/</p> <p> | |- agg_10_minutes/<id_institution_subnet>.csv</p> <p> | |- agg_1_hour/<id_institution_subnet>.csv</p> <p> | |- agg_1_day/<id_institution_subnet>.csv</p> <p> | |- identifiers.csv</p> <p> |- ip_addresses_full/</p> <p> | |- agg_10_minutes/<id_ip_folder>/<id_ip>.csv</p> <p> | |- agg_1_hour/<id_ip_folder>/<id_ip>.csv</p> <p> | |- agg_1_day/<id_ip_folder>/<id_ip>.csv</p> <p> | |- identifiers.csv</p> <p> |- ip_addresses_sample/</p> <p> | |- agg_10_minutes/<id_ip>.csv</p> <p> | |- agg_1_hour/<id_ip>.csv</p> <p> | |- agg_1_day/<id_ip>.csv</p> <p> | |- identifiers.csv</p> <p> |- times/</p> <p> | |- times_10_minutes.csv</p> <p> | |- times_1_hour.csv</p> <p> | |- times_1_day.csv</p> <p> |- ids_relationship.csv<br> |- weekends_and_holidays.csv</p> </blockquote> <p>The following list describes time series data fields in CSV files:</p> <ul> <li><strong>id_time: </strong>Unique identifier for each aggregation interval within the time series, used to segment the dataset into specific time periods for analysis.</li> <li><strong>n_flows: </strong>Total number of flows observed in the aggregation interval, indicating the volume of distinct sessions or connections for the IP address.</li> <li><strong>n_packets: </strong>Total number of packets transmitted during the aggregation interval, reflecting the packet-level traffic volume for the IP address.</li> <li><strong>n_bytes: </strong>Total number of bytes transmitted during the aggregation interval, representing the data volume for the IP address.</li> <li><strong>n_dest_ip: </strong>Number of unique destination IP addresses contacted by the IP address during the aggregation interval, showing the diversity of endpoints reached.</li> <li><strong>n_dest_asn: </strong>Number of unique destination Autonomous System Numbers (ASNs) contacted by the IP address during the aggregation interval, indicating the diversity of networks reached.</li> <li><strong>n_dest_port: </strong>Number of unique destination transport layer ports contacted by the IP address during the aggregation interval, representing the variety of services accessed.</li> <li><strong>tcp_udp_ratio_packets: </strong>Ratio of packets sent using TCP versus UDP by the IP address during the aggregation interval, providing insight into the transport protocol usage pattern. This metric belongs to the interval <0, 1> where 1 is when all packets are sent over TCP, and 0 is when all packets are sent over UDP.</li> <li><strong>tcp_udp_ratio_bytes:</strong> Ratio of bytes sent using TCP versus UDP by the IP address during the aggregation interval, highlighting the data volume distribution between protocols. This metric belongs to the interval <0, 1> with same rule as <em>tcp_udp_ratio_packets</em>.</li> <li><strong>dir_ratio_packets: </strong>Ratio of packet directions (inbound versus outbound) for the IP address during the aggregation interval, indicating the balance of traffic flow directions. This metric belongs to the interval <0, 1>, where 1 is when all packets are sent in the outgoing direction from the monitored IP address, and 0 is when all packets are sent in the incoming direction to the monitored IP address.</li> <li><strong>dir_ratio_bytes: </strong>Ratio of byte directions (inbound versus outbound) for the IP address during the aggregation interval, showing the data volume distribution in traffic flows. This metric belongs to the interval <0, 1> with the same rule as <em>dir_ratio_packets</em>.</li> <li><strong>avg_duration: </strong>Average duration of IP flows for the IP address during the aggregation interval, measuring the typical session length.</li> <li><strong>avg_ttl: </strong>Average Time To Live (TTL) of IP flows for the IP address during the aggregation interval, providing insight into the lifespan of packets.</li> </ul> <p>Moreover, the time series created by re-aggregation contains following time series metrics instead of <strong>n_dest_ip</strong>, <strong>n_dest_asn</strong>, and <strong>n_dest_port</strong>:</p> <ul> <li><strong>sum_n_dest_ip: </strong>Sum of numbers of unique destination IP addresses.</li> <li><strong>avg_n_dest_ip: </strong>The average number of unique destination IP addresses.</li> <li><strong>std_n_dest_ip: </strong>Standard deviation of numbers of unique destination IP addresses.</li> <li><strong>sum_n_dest_asn: </strong>Sum of numbers of unique destination ASNs.</li> <li><strong>avg_n_dest_asn: </strong>The average number of unique destination ASNs.</li> <li><strong>std_n_dest_asn: </strong>Standard deviation of numbers of unique destination ASNs)</li> <li><strong>sum_n_dest_port: </strong>Sum of numbers of unique destination transport layer ports.</li> <li><strong>avg_n_dest_port: </strong> The average number of unique destination transport layer ports.</li> <li><strong>std_n_dest_port: </strong>Standard deviation of numbers of unique destination transport layer ports.</li> </ul> <p> </p> <p>Moreover, files <em>identifiers.csv</em> in each dataset type contain IDs of time series that are present in the dataset. Furthermore, the <em>ids_relationship.csv</em> file contains a relationship between IP addresses, Institutions, and institution subnets. The <em>weekends_and_holidays.csv</em> contains information about the non-working days in the Czech Republic.</p>
Processed Data for "Improving Gene Regulatory Network Inference using Dropout Augmentation"
<p>Here are the processed dataset that are used in the manuscript "Improving Gene Regulatory Network Inference using Dropout Augmentation"</p>
Dataset for Advanced Persistent Threat (APT) Attacks on Power Substation Networks via GOOSE Protocol Exploitation
<p>This dataset captures network traffic from a simulated Advanced Persistent Threat (APT) campaign targeting a power substation's communication network. The attacker maintains a prolonged presence within the network, conducting low-profile scans using Nmap to stealthily discover the network configuration. The focus is on the communication between the Remote Terminal Unit (RTU), the Programmable Logic Controller (PLC), and the Bay Protection Unit, all of which utilize the Generic Object Oriented Substation Event (GOOSE) protocol for critical operations.</p>
Data for: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models"
<p>This repository contains the raw data to reproduce the paper: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models". Within the .tar.gz file, you will find the directory structure described above.</p> <h2>Directory Structure</h2> <h3>`data`</h3> <p>Contains the data to reproduce all figures in the manuscript. Used primarily by the Jupyter Notebooks that plot the data from the paper.</p> <h3>`eval`</h3> <p>Contains the predicted energies according to a MACE model for the following systems and facets:<br>- covsplit (100, 111, 211, 331, 410, 711): The NN model is trained on low-coverage structures and tested on high-coverage structures for a single facet<br>- evencov (100, 111, 211, 331, 410, 711): The NN is trained on even coverages and tested on odd coverages for a single facet<br>- facet (100, 111, 211, 331, 410, 711): the NN is trained on the facet indicated by the folder name (e.g., facet-100 means that the model was trained on Cu(100)) and tested on all of the other facets.<br>- full: the model was trained on all facets and all coverages<br>- slopes (various versions and configurations): the models were trained with different body-order correlation (v) for the Cu(711) facet and tested only on the Cu(711) facet<br>- Rh111: Energies for the Rh(111) + CHOH + CO systems.</p> <h3>`mcmc`</h3> <p>Contains the data for MCMC (Markov Chain Monte Carlo) evaluations for two systems: Cu and Rh<br>- copper-mcmc-public.tar.gz<br>- rhodium-mcmc-public.tar.gz</p> <h3>`models`</h3> <p>Contains the weights and parameters of the best-performing MACE models trained in this work, as selected by the validation loss:</p> <p>File formats: `.model` and `_swa.model` relate to the first-stage of training and the second-stage of training.</p> <h3>`pyscripts`</h3> <p>Python scripts to perform the MCMC sampling given the custom configuration file `sample_cfg.json`.</p> <h3>`scripts`</h3> <p>Shell scripts for evaluation and training the MACE models, along with the hyperparameters used in doing so.</p> <p>- Evaluation scripts (eval-*.sh)<br>- Training scripts (train-*.sh)</p> <h3>`train`</h3> <p>Training, validation, and testing data for all Cu and Rh facets in this work, according to the naming scheme described above.</p> <p>- Rh111<br>- covsplit<br>- evencov<br>- facet<br>- full<br>- slopes</p>
Social networks and transformative behaviors in a grassland social-ecological system
<p>Dataframe for analysis presented in Nesbitt et al.'s <span>Social networks and transformative behaviors in a grassland social-ecological system published in People and Nature. Dataframe includes responses from an ego network survey administered to Nebraska (USA) ranchers in 2021. </span></p> <p><span>Metadata describes each variable in further detail including the question number from the survey.</span></p> <p> </p>
Daily observed, modelled, and infilled river flows for an Irish hydrometric reference network of river flow stations
<p>Here we present a dataset of observed, modelled, and infilled daily river flow data relating to the newly updated Irish Hydrometric Reference Network (IHRN) of high-quality gauging stations located across the Republic of Ireland. Internationally applied selection criteria, analysis of historical observations and flow gauge metadata, stakeholder feedback, and trend assessments aided in the identification of the network’s 51 stations. A combination of the GR4J conceptual hydrological model and a backpropagation neural network driven by catchment specific precipitation and temperature extracted from gridded datasets was used to model flows that subsequently infilled gaps in the observational record for each of the series (from commencement of each station’s record till the end of 2022). Also included are the 2.5 and 97.5 quantile values for each station’s modelled data, which represent the upper and lower uncertainty bounds of the respective ensemble flows derived during the flow generation process. As well as providing a useful means for evaluating the impact of changing climatic conditions on Irish catchments, the IHRN data offers utility for assessing catchment based impacts for flow extremes, and the generation of both historical reconstructions and future climate projections for a range of flow regimes across the island of Ireland.</p>
Neural Network and objective analysis reconstruction of 3D Mediterranean physical fields from surface satellite and in situ observations at 1/24 deg
<p>Daily Mediterranean 3D fields of temperature, salinity and geostrophic current at 1/24° of resolution, up to 150m-depth and from 2016 to mid 2022, obtained through a 3 steps approach: (1) Temperature and salinity 3D fields have been first estimated by a machine learning approach by using mediterranean reanalysis outputs (https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R) together with satellite observations, (2) a combination of this first step with in situ observations through an Optimal interpolation to remove part of large scale biases, (3) the computation of geostrophic currents using the thermal wind equation. This work has been funded by the European Space Agency through the 4DMED-SEA project [ESA contract No. 4000141547/23/I-DT].</p>
Dataset - A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks
<p>The present dataset is open-sourced with the paper "A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks".</p> <p>The paper is authored by Giuseppe Caso, Mohammad Rajiullah, Anna Brunstrom, Luca De Nardis, Ozgu Alay, and Marco Neri. </p> <p>If you use the dataset for your own research activities and publications, please consider citing the paper as follows: </p> <p><strong>G. Caso et al., "A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks," in Proceedings of the IEEE Conference on Standards for Communications and Networking (IEEE CSCN'24), pp. 1-7, 2024.</strong></p> <p><strong>@inproceedings{caso2024standard,</strong><br><strong> title={{A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks}},</strong><br><strong> author={Caso, Giuseppe and Rajiullah, Mohammad and Brunstrom, Anna, and De Nardis, Luca and Alay, Ozgu and Neri, Marco},</strong><br><strong> booktitle={Proceedings of the IEEE Conference on Standards for Communications and Networking (IEEE CSCN'24)},</strong><br><strong> pages={1--7},</strong><br><strong> year={2024}</strong><br><strong>}</strong></p> <p>A detailed description of the dataset is provided in the paper, and the README file provides additional details.</p> <p>Contact Giuseppe Caso (giuseppe.caso@kau.se) for more information on the dataset and potential access to additional data. </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.