Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Dataset: Utilization of Novel (KNbO3)1-x(Ba2FeNbO6)x (x = 0.1, 0.2, 0.3) Solid Solutions for Efficient Photo-assisted Fenton Degradation of Methylene Blue Dye

Supplemental information containing the inputs and outputs of all DFT calculations performed as part of this work. This archive contains the following scripts: <ul> <li>defects_workup.py: a Python script for processing all calculations in a given folder. It extracts the total energy, estimated SCF accuracy (for non-converged results), and convergence status (true/false) for all cases found in each subfolder. For converged calculations, the mean Ba-Ba distance and its standard deviation as well as the mean Ba-Fe distance and its standard deviation is calculated. </li> <li>bands_plotter.ipynb: a Jupyter notebook for band structure analysis.</li> <li>ase_rdf.ipynb: a Jupyter notebook for bond distance vs energy analysis</li> </ul> Furthermore, the following data is included: <ul> <li>3x2x2.json: the output json file generated for the 3x2x2 dataset using defect_workup.py</li> <li>3x2x2.7z: a compressed folder containing the 3x2x2 dataset with QE input and output files.</li> <li>3x2x2-v2.7z: a compressed dataset containing some supplementary calculations used in band plotting.</li> </ul>

opencc-by-4.0Jul 2021View details →

7T MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry

MRI 7 T raw data for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset only contains additional raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively.

opencc-by-4.0Aug 2021View details →

Molecular characterization and genetic diversity of four undescribed novel oleaginous Mortierella alpina strains from Libya

A large number of undiscovered fungal species still exist on earth, which can be useful for bioprospecting, particularly for single cell oil (SCO) production. Mortierella is one of the significant genera in this field and contains about hundred species. Moreover, M. alpina is the main single cell oil producer / arachidonic acid producer at commercial scale under this genus.

opencc-by-4.0Dec 2020View details →

Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Supplementary Tables

This repository contains the Supplementary Tables for  Suriyalaksh et al.  Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive  of novel ageing genes. The list of table files can be found in <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/Supplementary%20table%20guide.pdf">Supplementary Tables guide.pdf</a> Tables S1, S2 and S3 corresponding to physical gene-gene interaction data are in a separate repository doi:10.5281/zenodo.4382337 Details about some of the Supplementary tables: TableS4_inferred_networks.csv - list of inferred GRNs for specified input combinations (set of input regulators, length of the time sequence, NI tool and prior used). <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS5_consensus_network_member.xlsx">TableS5_consensus_network_member.xlsx</a> - list of groups of topologically similar GRNs (from Table S4) Table S6: edge lists (source,target) for each one of the three consensus networks selected according to the GS validation metrics: middle PFE/AUFE, max AUFE, max PFE. TableS6a_max_AUFE_GRN.txt - max AUFE; largest network - this is the one we used in the main analysis and discussion TableS6b_max_PFE_GRN.xt - max PFE TableS6c_middle_AUFE_PFE_GRN.txt - middle PFE/AUFE <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS7_qRTPCR_ddCt_network_accuracy.csv">TableS7_qRTPCR_ddCt_network_accuracy.csv</a> - gene expression count differences for RNAi knockdown GRN validation experiments.  Table S8: Group membership for each one of the nodes in each one of the selected networks according to the SBM that best describes the observed network topology. Each column shows the group membership for each level in a SBM block hierarchy. Our analysis is in the second most coarse-grained level (level 1). TableS8a_max_AUFE_SBM.csv TableS8b_max_PFE_SBM.csv TableS8c_middle_AUFE_PFE_SBM.csv <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS9_glp_gs_datasets.pdf">TableS9_glp_gs_datasets.pdf</a> - list of datasets used for defining functional clusters. TableS14a_glp_l1_vs_fem_l1_lifespan_assay.xlsx - Day13 survival of fem-3(q20)ts vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1 TableS14b_glp_l1_vs_glp_l4_lifespan_assay.xlsx - Day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L4 vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1 TableS15a_glp1_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of glp-1(e2144)ts;rrf-3(pk1426) TableS15b_fem3_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of fem-3(q20)ts TableS17_input_regulators_annotated.csv - list input regulators used as input for Network Inference Tools annotated by source type (2nd column): GenAge, known transcription factors (TF) and gene with high variability in the gene expression time series (HV). The third column lists whether that regulator has an orthologue in human (y) according to WormBase (v 278). TableS20_epistasis_lifespan_data.xlsx - Epistasis lifespan data of glp-1(e2144)ts All the image (TIF) files represent representative images in the following genetic backgrounds (below) that have been treated  with empty vector (EV) or RNAi against the gene highlighted in the title of the image. See methods section for details.  femliu1:  fem-3(q20)ts.; dhs-3p::dhs-3::gfp femsod3: fem-3(q20)ts.; sod-3p::gfp glp1lgg1: glp-1(e2144); lgg-1p:lgg-1:gfp

opencc-by-4.0Nov 2022View details →

Dataset supporting publication: "A novel ROM methodology to support the estimation of the energy savings under the Measurement and Verification protocol."

DATASET suporting: "A novel ROM methodology to support the estimation of the energy savings under the Measurement and Verification protocol." Piccinini, Alessandro; Hajdukiewicz, Magdalena; D'Angelo, Letizia; Blanes, Luis Miguel; Keane, Marcus M. This paper presents a novel Reduced Order grey box Model (ROM) methodology, based on a ResistorCapacitor (RC) network, which supports the creation of the baseline energy consumption and the estimation of energy savings due to Energy Conservation Measures (ECMs) under the Measurement and Verification protocol. Within this scope, a description of the RC network, including a calculation of the parameters’ needed to execute the ROM, are presented. This ROM methodology is demonstrated on an educational building located in Sant Cugat, Spain as part of the H2020 GEOFIT project. The results presented in this paper demonstrate that the ROM is sufficiently accurate for the creation of the baseline energy consumption and for estimating the energy savings of different ECMs.

opencc-by-4.0Jan 2023View details →

Dataset and codebook for the article by Gaume J, Bertholet N, McCambridge J, et al. Effect of a Novel Brief Motivational Intervention for Alcohol-Intoxicated Young Adults in the Emergency Department: A Randomized Clinical Trial. JAMA Netw Open. 2022;5(10):e2237563. doi: 10.1001/jamanetworkopen.2022.37563

Dataset and codebook for the article Gaume J, Bertholet N, McCambridge J, et al. Effect of a Novel Brief Motivational Intervention for Alcohol-Intoxicated Young Adults in the Emergency Department: A Randomized Clinical Trial. JAMA Netw Open. 2022;5(10):e2237563. doi: <a href="http://jamanetwork.com/article.aspx?doi=10.1001/jamanetworkopen.2022.37563">10.1001/jamanetworkopen.2022.37563</a> The dataset contains all data needed to reproduce the results in the above cited article. Variable description and labels can be found in the codebook. Please refer to the published article and supplemental online content for further information about the data and the study procedures.

opencc-by-4.0Oct 2020View details →

CABra: a novel large-sample dataset for Brazilian catchments

Hydrometeorological time series and catchment attributes from the CABra dataset. The manuscript of "CABra: a novel large-sample dataset for Brazilian catchments" is under review in Hydrology and Earth System Sciences (HESS) journal. Here we present the Catchments Attributes for Brazil (CABra), which is a large-sample dataset for Brazilian catchments that includes long-term data (30 years) for 735 catchments in eight main catchment attribute classes (climate, streamflow, groundwater, geology, soil, topography, land-use and land-cover, and hydrologic disturbance). We have collected and synthesized data from multiple sources (ground stations, remote sensing, and gridded datasets). To prepare the dataset, we delineated all the catchments using the Multi-Error-Removed Improved-Terrain Digital Elevation Model and the coordinates of the streamflow stations provided by the Brazilian Water Agency (ANA), where only the stations with 30 years (1980-2010) of data and less than 10% of missing records were included. Catchment areas range from 9 to 4,800,000 km² and the mean daily streamflow varies from 0.02 to 9 mm day-1. Several signatures and indices were calculated based on the climate and streamflow data. Additionally, our dataset includes boundary shapefiles, geographic coordinates, and drainage areas for each catchment, aside from more than 100 attributes within the attribute classes. Data can also be accessed at: thecabradataset.shinyapps.io/CABra    * This version includes water demand in CABra catchments for 2020 and 2040 (projection).

openapache2.0Dec 2022View details →

fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions - supplementary data.

fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions - supplementary data.Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.

opencc-by-4.0Mar 2023View details →

Venkataraman et al. Two novel, tightly linked, and rapidly evolving genes underlie Aedes aegypti mosquito reproductive resilience during drought

VERSION 1: These supplementary files accompany the manuscript by Venkataraman et al. entitled "Rapidly evolving genes underlie Aedes aegypti mosquito reproductive resilience during drought." This includes all raw data in the paper, supplementary data, and instructions for the blood puck feeder. VERSION 2: Supplemental Data Files 16-20 were added on 12/19/2022 to accompany a revision of the original bioRxiv pre-print after peer-review at eLife. VERSION 3: New versions of all files were added on 3/21/2023 to accompany the version of record published in eLife: Krithika Venkataraman , Nadav Shai, Priyanka Lakhiani, Sarah Zylka, Jieqing Zhao, Margaret Herre, Joshua Zeng, Lauren A Neal, Henrik Molina, Li Zhao, Leslie B Vosshall. Two novel, tightly linked, and rapidly evolving genes underlie Aedes aegypti mosquito reproductive resilience during drought. Elife. 2023 Feb 6;12:e80489. PMID: 36744865 DOI: 10.7554/eLife.80489

opencc-by-4.0Dec 2018View details →

Data for: Bivariate Genome-Wide Association Scan Identifies 6 Novel Loci Associated With Lipid Levels and Coronary Artery Disease.

Summary of Bivariate GWAS scan results reported in: <a href="https://pubmed.ncbi.nlm.nih.gov/30525989/">Bivariate Genome-Wide Association Scan Identifies 6 Novel Loci Associated With Lipid Levels and Coronary Artery Disease. </a>Siewert KM, Voight BF. Circ Genom Precis Med. 2018 Dec;11(12):e002239. doi: 10.1161/CIRCGEN.118.002239. PMID: 30525989

opencc-by-4.0May 2023View details →

Dataset to: Novel aerosol diluter – Size dependent characterization down to 1 nm particle size

Dataset to: Lampimäki et al. Novel aerosol diluter – Size dependent characterization down to 1 nm particle size. Journal of Aerosol Science 172 (2023) 106180, doi: <a href="https://doi.org/10.1016/j.jaerosci.2023.106180">https://doi.org/10.1016/j.jaerosci.2023.106180</a>

opencc-by-4.0May 2023View details →

The First Transcriptomic Atlas of the Adult Lacrimal Gland Reveals Epithelial Complexity and Identifies Novel Progenitor Cells in Mice

This project contains the R objects and code to reproduce the analyses and figures presented in the research article: 'The First Transcriptomic Atlas of the Adult Lacrimal Gland Reveals Epithelial Complexity and Identifies Novel Progenitor Cells in Mice.' Cells 2023, 12, 1435. https://doi.org/10.3390/cells12101435 Raw data (FASTQ files and CellRanger output files used for the preprocessing of individual datasets) can be found on Gene Expression Omnibus database (www.ncbi.nlm.nih.gov/geo/) under accession # GSE232146.

opencc-by-4.0Jun 2023View details →

A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model

A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model

opencc-by-4.0Jul 2023View details →

Dataset for A Novel Spectroscopic Approach for Vaseline Quality Discrimination

This dataset contains spectroscopic measurement data and Orange project files used in the INDIN 2023 paper "A Novel Spectroscopic Approach for Vaseline Quality Discrimination". The paper only discusses the analysis using 2 principal components.

opencc-by-4.0Aug 2023View details →

Network traffic datasets with novel extended IP flow called NetTiSA flow

Network traffic datasets with novel extended IP flow called NetTiSA flow Datasets were created for the paper: NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classification -- Josef Koumar, Karel Hynek, Jaroslav Pe&scaron;ek, Tomá&scaron; Čejka -- which is published in The International Journal of Computer and Telecommunications Networking <a href="https://doi.org/10.1016/j.comnet.2023.110147" rel="nofollow">https://doi.org/10.1016/j.comnet.2023.110147</a> Please cite the usage of our datasets as: <blockquote> Josef Koumar, Karel Hynek, Jaroslav Pe&scaron;ek, Tomá&scaron; Čejka, "NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification", Computer Networks, Volume 240, 2024, 110147, ISSN 1389-1286 <pre><code>@article{KOUMAR2024110147, title = {NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification}, journal = {Computer Networks}, volume = {240}, pages = {110147}, year = {2024}, issn = {1389-1286}, doi = {https://doi.org/10.1016/j.comnet.2023.110147}, url = {https://www.sciencedirect.com/science/article/pii/S1389128623005923}, author = {Josef Koumar and Karel Hynek and Jaroslav Pe&scaron;ek and Tomá&scaron; Čejka} } </code></pre> </blockquote> This Zenodo repository contains 23 datasets created from 15 well-known published datasets, which are cited in the table below. Each dataset contains the NetTiSA flow feature vector.   NetTiSA flow feature vector The novel extended IP flow called NetTiSA (Network Time Series Analysed) flow contains a universal bandwidth-constrained feature vector consisting of 20 features. We divide the NetTiSA flow classification features into three groups by computation. The first group of features is based on classical bidirectional flow information---a number of transferred bytes, and packets.  The second group contains statistical and time-based features calculated using the time-series analysis of the packet sequences. The third type of features can be computed from the previous groups (i.e., on the flow collector) and improve the classification performance without any impact on the telemetry bandwidth.   Flow features The flow features are: <ul> <li>Packets is the number of packets in the direction from the source to the destination IP address.</li> <li>Packets in reverse order is the number of packets in the direction from the destination to the source IP address.</li> <li>Bytes is the size of the payload in bytes transferred in the direction from the source to the destination IP address.</li> <li>Bytes in reverse order is the size of the payload in bytes transferred in the direction from the destination to the source IP address.</li> </ul>   Statistical and Time-based features The features that are exported in the extended part of the flow. All of them can be computed (exactly or in approximative) by stream-wise computation, which is necessary for keeping memory requirements low. The second type of feature set contains the following features: <ul> <li>Mean represents mean of the payload lengths of packets</li> <li>Min is the minimal value from payload lengths of all packets in a flow</li> <li>Max is the maximum value from payload lengths of all packets in a flow</li> <li>Standard deviation is a measure of the variation of payload lengths from the mean payload length</li> <li>Root mean square is the measure of the magnitude of payload lengths of packets</li> <li>Average dispersion is the average absolute difference between each payload length of the packet and the mean value</li> <li>Kurtosis is the measure describing the extent to which the tails of a distribution differ from the tails of a normal distribution</li> <li>Mean of relative times is the mean of the relative times which is a sequence defined as \(st = \{t_1 - t_1, t_2 - t_1, ..., t_n - t_1\} \)</li> <li>Mean of time differences is the mean of the time differences which is a sequence defined as \(dt = \{ t_j - t_i | j = i + 1, i \in \{1, 2, \dots, n - 1\} \}.\)</li> <li>Min from time differences is the minimal value from all time differences, i.e., min space between packets.</li> <li>Max from time differences is the maximum value from all time differences, i.e., max space between packets.</li> <li>Time distribution describes the deviation of time differences between individual packets within the time series. The feature is computed by the following equation: \(tdist = \frac{ \frac{1}{n-1} \sum_{i=1}^{n-1} \left| \mu_{\{dt_{n-1}\}} - dt_i \right| }{ \frac{1}{2} \left(max\left(\{dt_{n-1}\}\right) - min\left(\{dt_{n-1}\}\right) \right) }\)</li> <li>Switching ratio represents a value change ratio (switching) between payload lengths. The switching ratio is computed by equation: \(sr = \frac{s_n}{\frac{1}{2} (n - 1)}\)</li> </ul>            where \(s_n\) is number of switches.    Features computed at the collector The third set contains features that are computed from the previous two groups prior to classification. Therefore, they do not influence the network telemetry size and their computation does not put additional load to resource-constrained flow monitoring probes. The NetTiSA flow combined with this feature set is called the Enhanced NetTiSA flow and contains the following features: <ul> <li>Max minus min  is the difference between minimum and maximum payload lengths</li> <li>Percent deviation is the dispersion of the average absolute difference to the mean value</li> <li>Variance is the spread measure of the data from its mean</li> <li>Burstiness is the degree of peakedness in the central part of the distribution</li> <li>Coefficient of variation is a dimensionless quantity that compares the dispersion of a time series to its mean value and is often used to compare the variability of different time series that have different units of measurement</li> <li>Directions describe a percentage ratio of packet direction computed as \(\frac{d_1}{ d_1 + d_0}\), where \(d_1\) is a number of packets in a direction from source to destination IP address and \(d_0\) the opposite direction. Both  \(d_1\) and \(d_0\) are inside the classical bidirectional flow.</li> <li>Duration is the duration of the flow</li> </ul>   The NetTiSA flow is implemented into IP flow exporter <a href="https://github.com/CESNET/ipfixprobe">ipfixprobe</a>.   Description of dataset files In the following table is a description of each dataset file: <table> <tbody> <tr> <td> File name </td> <td> Detection problem </td> <td> Citation of the original raw dataset </td> </tr> <tr> <td>botnet_binary.csv </td> <td>Binary detection of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>botnet_multiclass.csv </td> <td>Multi-class classification of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>cryptomining_design.csv </td> <td>Binary detection of cryptomining; the design part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>cryptomining_evaluation.csv </td> <td>Binary detection of cryptomining; the evaluation part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>dns_malware.csv </td> <td>Binary detection of malware DNS </td> <td>Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. </td> </tr> <tr> <td>doh_cic.csv </td> <td>Binary detection of DoH </td> <td>Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020 </td> </tr> <tr> <td>doh_real_world.csv </td> <td>Binary detection of DoH </td> <td>Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 </td> </tr> <tr> <td>dos.csv </td> <td>Binary detection of DoS </td> <td>Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019. </td> </tr> <tr> <td>edge_iiot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. </td> </tr> <tr> <td>edge_iiot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. </td> </tr> <tr> <td>https_brute_force.csv </td> <td>Binary detection of HTTPS Brute Force </td> <td>Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020 </td> </tr> <tr> <td>ids_cic_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>ids_cic_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>unsw_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. </td> </tr> <tr> <td>unsw_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. </td> </tr> <tr> <td>iot_23.csv </td> <td>Binary detection of IoT malware </td> <td>Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23 </td> </tr> <tr> <td>ton_iot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 </td> </tr> <tr> <td>ton_iot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 </td> </tr> <tr> <td>tor_binary.csv </td> <td>Binary detection of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>tor_multiclass.csv </td> <td>Multi-class classification of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>vpn_iscx_binary.csv </td> <td>Binary detection of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_iscx_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_vnat_binary.csv </td> <td>Binary detection of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 </td> </tr> <tr> <td>vpn_vnat_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 </td> </tr> </tbody> </table>

Fig. 3 in A novel species of Heterophoxus Shoemaker, 1925 (Crustacea, Amphipoda, Phoxocephalidae) from southeast and southern Brazil, with an identification key to world species of the genus

Fig. 3. Heterophoxus shoemakeri sp. nov., holotype, ♀ (UERJ 433). A. Gnathopod 1. B. Gnathopod 2. C. Pereopod 3. D. Pereopod 4. Scale bars = 0.2 mm.

Fig. 2 in A novel species of Heterophoxus Shoemaker, 1925 (Crustacea, Amphipoda, Phoxocephalidae) from southeast and southern Brazil, with an identification key to world species of the genus

Fig. 2. Heterophoxus shoemakeri sp. nov., holotype, ♀ (UERJ 433). A. Head. B. Antenna 1. C. Antenna 2. D. Left mandible. E. Right mandible. F. Maxilliped. G. Maxilla 1. H. Maxilla 2. Scale bars: A = 0.5 mm; B–F = 0.2 mm; G–H = 0.1 mm.

Fig. 1 in A novel species of Heterophoxus Shoemaker, 1925 (Crustacea, Amphipoda, Phoxocephalidae) from southeast and southern Brazil, with an identification key to world species of the genus

Fig. 1. Heterophoxus shoemakeri sp. nov., habitus. A. Holotype, ♀ (UERJ 433). B. Paratype, ♂ (UERJ 434). Scale bars = 1.0 mm.

Fig. 1 in Streptomyces aquilus sp. nov., a novel actinomycete isolated from a Chinese medicinal plant

Fig. 1. Optical micrograph (a) and scanning electron micrograph (b) of GGCR-6T grown on Gause's synthetic medium at 28 °C after incubation for 14 days.