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9,302 results for “DETECT”
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
<p>Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction.</p> <p>The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes.</p>
Supplementary Material from: Suppression of Pituitary Hormone Genes in Subjects Who Died From COVID-19 Independently of Virus Detection in the Gland
<p>Supplementary Table S1A. List of target genes analysed by the Human Host Response.</p> <p>Supplementary Table S1B. List of target genes analysed by the Coronavirus Panel Plus.</p> <p>Supplementary Table S1C. List of target genes analysed by the custom panel.</p> <p>Supplementary Table S2A. Differential gene expression analysis. Virus-positive vs control adenohypophyses.</p> <p>Supplementary Table S2B. Differential gene expression analysis. Virus-negative vs control adenohypophyses.</p>
deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques
<p>In this paper, we present datasets that can be utilised for synthetic near infrared (NIR) image and bounding box level fruit detection system. It is undeniable fact that high-caliber machine learning software frameworks such as Tensorflow or Pytorch and large scale dataset such as ImageNet and COCO, and accelerated GPU hardware support have pushed the limit of machine learning for more than decades.</p> <p>Among these breakthroughs quality dataset is one of important key building blocks that can lead to success in model generalisation and deployment for data-driven deep neural networks. Particularly, synthetic data generation such as generative adversarial networks often requires relatively larger scale data than other supervised approaches. In addition, posing constrains such as geometrical facial constrains in fake face generation or consistent and radiometrically calibrated reflectances from satellite imagery commonly yield better results. We share NIR+RGB dataset that are re-processed from other two public datasets (nirscene and SEN12MS) and our own novel sweetpepper dataset to be able to timely adopt to other following studies.</p> <p>We oversampled from original nirscene dataset at 10, 100, 200, and 400 ratios and total of 127k pair of images. For SEN12MS satellite multispectral dataset, we selected one largest subset; Summer (45k) and All seasons (180k). Our sweetpeppr dataset consists of 1,615 pairs of NIR+RGB images. We demonstrate these NIR+RGB datasets are sufficient to be used for synthetic NIR generation quantitatively and qualitatively. We achieved Frechet Inception Distance (FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweetpepper dataset respectively.</p> <p>We also release <em>11</em> fruits' bounding box annotations that can be exported as various formats using cloud service. 4 newly added fruits [blueberry, cherry, kiwi, and wheat] compounds 11 novel bounding box dastaset together with our previous work in deepFruits project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The total number of bounding box instances is 162k and all bounding box dataset is ready for use from cloud service. For evaluation of these dataset, Yolov5 single stage detector is exploited and reported impressive mean-average-precision, mAP[0.5:0.95] results of [min:0.49, max:0.812]. We hope these dataset is useful and serves as one of baseline for the following up studies.</p>
Denaturing and dNTPs reagents improve SARS-CoV-2 detection via single and multiplex RT-qPCR
<p>The datas correspond to article entitled: "Denaturing and dNTPs reagents improve SARS-CoV-2 detection via single and multiplex RT-qPCR". </p> <p>The file entitle GISAID have the fasta formats for 107259 genomes from the SARS-CoV-2 GISAID database from January to December 2020. Three documents in plane tex correspond:<br> sequences.fasta contain the original data.<br> sequences_clean.fasta. Corresponds to genomes sequences without nucleotides undeterminateds indicates with "N" in previous document.<br> alignment_clean.fasta. Contain the genomes sequences cleaned alingment. </p> <p>The file entitle GenBank have the data set from 19317 genomes from the SARS-CoV-2 GenBank database from January to October 2020 and the documets have the prrevious order.</p>
Data for a CML blackout gap detection example
<p><em>blackout_example_cmls.nc</em> consists of three years of data from two commercial microwave links. It includes transmitted and recieved signal levels with a temporal resolution of one minute.</p> <p><em>blackout_example_radar_reference.nc</em> consists of three years of rainfall data from RADKLIM-YW, a radar product from the German Weather Service, path-averaged along the two example CMLs.</p>
Properties of leak detection sensor, Arduino and application code
<p>The dataset contains properties of a leak detection sensor; absorbance spectra and reflectance in different pH environments, ionic strength, reversibility, FT-IR (Fourier-transform infrared spectroscopy), stability, TGA (Thermogravimetric analysis), and photoisomerization data. The microprocessor and application codes are also included. The information included herein will be helpful for users of the sensor.</p>
Fig. 1. A in New Trematode Collyriclum Faba (Plagiochiida, Collyriclidae) Detected In The Birds Of Uzbekistan
Fig. 1. A mountain whitethroat (Sylvia althaea) with cysts infected with the trematode Collyriclum faba (Bremser in Schmalz, 1831). Surkhan State Reserve, Surkhandarya Province, south of Uzbekistan, June 2017 (photo by N. N. Azimov).
Multi-Domain Outlier Detection Dataset
<p>The Multi-Domain Outlier Detection Dataset contains datasets for conducting outlier detection experiments for four different application domains:</p> <ol> <li>Astrophysics - detecting anomalous observations in the Dark Energy Survey (DES) catalog (data type: feature vectors)</li> <li>Planetary science - selecting novel geologic targets for follow-up observation onboard the Mars Science Laboratory (MSL) rover (data type: grayscale images)</li> <li>Earth science: detecting anomalous samples in satellite time series corresponding to ground-truth observations of maize crops (data type: time series/feature vectors)</li> <li>Fashion-MNIST/MNIST: benchmark task to detect anomalous MNIST images among Fashion-MNIST images (data type: grayscale images)</li> </ol> <p>Each dataset contains a "fit" dataset (used for fitting or training outlier detection models), a "score" dataset (used for scoring samples used to evaluate model performance, analogous to test set), and a label dataset (indicates whether samples in the score dataset are considered outliers or not in the domain of each dataset). </p> <p>To read more about the datasets and how they are used for outlier detection, or to cite this dataset in your own work, please see the following citation:</p> <p>Kerner, H. R., Rebbapragada, U., Wagstaff, K. L., Lu, S., Dubayah, B., Huff, E., Lee, J., Raman, V., and Kulshrestha, S. (2022). Domain-agnostic Outlier Ranking Algorithms (DORA)-A Configurable Pipeline for Facilitating Outlier Detection in Scientific Datasets. Under review for <em>Frontiers in Astronomy and Space Sciences</em>. </p>
Supplementary Information for "Comment on UV-Spectroscopic Detection of (Pyro-)Phosphate with the PUB Module"
<p>This is the external Supplementary Information for our publication "Comment on UV-Spectroscopic Detection of (Pyro-)Phosphate with the PUB module".</p> <p>Unlike most of our publications, this short paper does not come with an official Supplementary Information. However, this zenodo entry contains the raw data and metadata for all items in the main text as well as all raw NMR spectra.</p> <p>For more details on this work, please see the origin publication detailing PUB (10.1021/acs.analchem.1c05356) as well as its external Supplementary Information on this platform (10.5281/zenodo.5760392).</p>
Supporting information for the paper: Terrestrial Gamma-ray Flashes with Accompanying Elves Detected by ASIM
<p>Supporting data to the paper "Terrestrial Gamma-ray Flashes with Accompanying Elves Detected by ASIM"</p>
R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge
<p>This is the first R&D dataset for the LHC Olympics 2020 Anomaly Detection Challenge. It consists of 1M QCD dijet events and 100k W'->XY events, with X->qq and Y->qq. The W', X, and Y masses are 3.5 TeV, 500 GeV and 100 GeV respectively. The events are produced using Pythia8 and Delphes 3.4.1, with no pileup or MPI included. They are selected using a single fat-jet (R=1) trigger with pT threshold of 1.2 TeV. </p> <p>The events are randomly shuffled together, but for the purposes of testing and development, we provide the user with a signal/background truth bit for each event. Obviously, the truth bit will not be included in the actual challenge.</p> <p>These events are stored as pandas dataframes saved to compressed h5 format. For each event, all Delphes reconstructed particles in the event are assumed to be massless and are recorded in detector coordinates (pT, eta, phi). More detailed information such as particle charge is not included. Events are zero padded to constant size arrays of 700 particles, with the truth bit appended at the end. The array format is therefore (Nevents=1.1M, 2101).</p> <p>For more information, including an example Jupyter notebook illustrating how to read and process the events, see the official LHC Olympics 2020 webpage.</p> <p><a href="https://lhco2020.github.io/homepage/">https://lhco2020.github.io/homepage/</a></p> <p><strong>UPDATE May 18 2020</strong></p> <p>We have uploaded a second signal dataset for R&D, consisting of 100k W'->XY with X,Y->qqq (i.e. 3-prong substructure). Everything else about this signal dataset (particle masses, trigger, Pythia configuration, detector simulation) is the same as the previous one described above. </p> <p><strong>UPDATE November 23 2020</strong></p> <p>We now include high-level feature files for the background and 2-prong signal (events_anomalydetection_v2.features.h5) and for the 3-prong signal (events_anomalydetection_Z_XY_qqq.features.h5). To produce the features, we have clustered every event into R=1 jets using the anti-kT algorithm. The features (calculated using fastjet plugins) are the 3-momenta, invariant masses, and n-jettiness variables tau1, tau2 and tau3 for the highest pT jet (j1) and the second highest pT jet (j2):</p> <p>'pxj1', 'pyj1', 'pzj1', 'mj1', 'tau1j1', 'tau2j1', 'tau3j1', 'pxj2', 'pyj2', 'pzj2', 'mj2', 'tau1j2', 'tau2j2', 'tau3j2'</p> <p>The rows (events) in each feature file should be ordered exactly the same as in their corresponding raw event file. For convenience, we have also included the label (1 for signal and 0 for background) as an additional column in the first feature file (events_anomalydetection_v2.features.h5).</p> <p><strong>UPDATE February 11 2021</strong></p> <p>We have included the Delphes detector card and the Pythia8 command files used to produce the R&D datasets.</p> <p><strong>UPDATE April 17 2022</strong></p> <p>It was brought to our attention that somehow the raw events file events_anomalydetection.h5 was never updated to v2, which had a lower generator-level pT threshold (PhaseSpace:pTHatMin = 500) for QCD events to minimize artificial trigger sculpting. This v2 is the version that the features file (events_anomalydetection_v2.features.h5) corresponds to, as well as the Pythia cmnd file (pythia_RnD_qcd.cmnd). Now the raw events file has been brought up to date as well. </p>
DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description:</strong></p> <p>The development set for task 5 of DCASE 2022 "Few-shot Bioacoustic Event Detection" consists of 192 audio files acquired from different bioacoustic sources. The dataset is split into training and validation sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p><strong>this version (3):</strong><br> * fixes issues with annotations from HB set</p> <p> </p> <p><strong>Folder Structure:</strong></p> <p><em>Development_Set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___WMW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p> |__Validation_Set/</p> <p> |___HB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_Set_Annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>## Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br> TRAINING SET<br> -----------------------------------------------------<br> Number of audio recordings | 174<br> Total duration | 21 hours<br> Total classes | 47<br> Total events | 14229<br> -----------------------------------------------------<br> TRAINING SET/BV<br> -----------------------------------------------------<br> Number of audio recordings | 5<br> Total duration | 10 hours<br> Total classes | 11<br> Total events | 9026<br> Ratio event/duration | 0.04<br> Sampling rate | 24000 Hz<br> -----------------------------------------------------<br> TRAINING SET/HT<br> -----------------------------------------------------<br> Number of audio recordings | 5<br> Total duration | 5 hours<br> Total classes | 5<br> Total events | 611<br> Ratio event/duration | 0.05<br> Sampling rate | 6000 Hz<br> -----------------------------------------------------<br> TRAINING SET/JD<br> -----------------------------------------------------<br> Number of audio recordings | 1<br> Total duration | 10 mins<br> Total classes | 1<br> Total events | 357<br> Ratio event/duration | 0.06<br> Sampling rate | 22050 Hz<br> -----------------------------------------------------<br> TRAINING SET/MT<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 1 hour and 10 mins<br> Total classes | 4<br> Total events | 1294<br> Ratio event/duration | 0.04<br> Sampling rate | 8000 Hz<br> -----------------------------------------------------<br> TRAINING SET/WMW<br> -----------------------------------------------------<br> Number of audio recordings | 161<br> Total duration | 4 hours and 40 mins<br> Total classes | 26<br> Total events | 2941<br> Ratio event/duration | 0.24<br> Sampling rate | various sampling rates<br> -----------------------------------------------------</p> <p>-----------------------------------------------------<br> VALIDATION SET<br> -----------------------------------------------------<br> Number of audio recordings | 18<br> Total duration | 5 hours and 57 minutes<br> Total classes | 5<br> Total events | 1077<br> -----------------------------------------------------<br> VALIDATION SET/HB<br> -----------------------------------------------------<br> Number of audio recordings | 10<br> Total duration | 2 hours and 38 minutes<br> Total classes | 1<br> Total events | 712<br> Ratio event/duration | 0.7<br> Sampling rate | 44100 Hz<br> -----------------------------------------------------<br> VALIDATION SET/PB<br> -----------------------------------------------------<br> Number of audio recordings | 6<br> Total duration | 3 hours<br> Total classes | 2<br> Total events | 292<br> Ratio event/duration | 0.003<br> Sampling rate | 44100 Hz<br> -----------------------------------------------------<br> VALIDATION SET/ME<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 20 minutes<br> Total classes | 2<br> Total events | 73<br> Ratio event/duration | 0.01<br> Sampling rate | 44100 Hz<br> -----------------------------------------------------</p> <p> </p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2022_task5_training_set_classes.csv and DCASE2022_task5_validation_set_classes.csv provide a table with class code correspondence to class name for all classes in the Development set.</p> <p>DCASE2022_task5_training_set_classes.csv<br> ---------------------<br> dataset, class_code, class_name</p> <p>DCASE2022_task5_validation_set_classes.csv<br> ---------------------<br> dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for this task will be released on the 1st of June 2022</p> <p><strong>Open Access:</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info:</strong></p> <p>Please send any feedback or questions to:</p> <p>Ines Nolasco - i.dealmeidanolasco@qmul.ac.uk</p>
GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles (presentation video)
<p>Video of the presentation for the publication M. Kamal, A. Barua, C. Vitale, C. Laoudias and G. Ellinas, "GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles," 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 2021, pp. 1-7, doi: 10.1109/VTC2021-Fall52928.2021.9625567.</p>
Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection
<p>Fauna monitoring often relies on visual monitoring techniques such as camera trappings, which have biases leading to underestimates of vertebrate species diversity. Environmental DNA (eDNA) has emerged as a new source of biodiversity data that may improve biomonitoring; however, eDNA based assessments of species richness remain relatively untested in terrestrial environments. We investigated the suitability of fallen log hollow sediment as a source of vertebrate eDNA, across two sites in south-western Australia - one with a Mediterranean climate and the other semi-arid. We compared two different approaches (camera trapping and eDNA metabarcoding) for monitoring of vertebrate species, and investigated the effect of other factors (frequency of species, timing of visits, frequency of sampling, body size) on vertebrate species detectability. Metabarcoding of hollow sediments resulted in the detection of higher species richness in comparison Hollow sediment detected higher species richness (29 taxa: six birds, three reptiles and 20 mammals) to metabarcoding of soil at the entrance of the hollow (13 taxa: three birds, two reptiles and eight mammals). We detected 31 taxa in total with eDNA metabarcoding and 47 with camera traps, with 14 taxa detected by both (12 mammals and two birds). By comparing camera trap data with eDNA read abundance, we were able to detect vertebrates through eDNA metabarcoding that had visited the area up to two months prior to sample collection. Larger animals were more likely to be detected, and so were vertebrates that were identified multiple times in the camera traps. These findings demonstrate the importance of substrate selection, frequency of sampling, and animal size, on eDNA based monitoring. Future eDNA experimental design should consider all these factors as they affect detection of target taxa. </p>
Quantifying phenology and migratory behaviours of hummingbirds using single-site dynamics and mark-detection analyses
<p>Nuanced understanding of seasonal movements of partially migratory birds is paramount to species and habitat conservation. Using nascent statistical methods, we identified migratory strategies of birds outfitted with radio-frequency identification (RFID) tags detected at RFID feeders in two sites in California, USA. We quantified proportions of migrants and residents and the seasonal phenology for each movement strategy in Allen's and Anna's hummingbirds; we also validated our methodology by fitting our model to obligate migratory black-chinned hummingbirds. Allen's and Anna's hummingbirds exhibited characteristics of facultative migratory behaviour. We also quantified apparent annual survival for each migratory strategy and found that residents had significantly higher probabilities of apparent survival. Low survival estimates for migrants suggest that a high proportion of birds in the migrant group permanently emigrated from our study sites. Considered together, our analyses suggest that hummingbirds in both northern and southern California sites partake in diverse and highly plastic migratory behaviours. Our assessment elucidates the dynamics underlying idiosyncratic migratory behaviours of two species of hummingbirds, in addition to describing a framework for similar assessments of migratory behaviours using the multi-state open robust design with state uncertainty (MSORD-SU) model and single-site dynamics.</p>
A Database for Reasearch on Detection and Enhancement of Speech transmitted over HF links
<p>We present an open database for the development of detection and enhancement algorithms of speech transmitted over HF radio channels.<br> It consists of audio samples recorded by various receivers at different locations across Europe, all monitoring the same single-sideband modulated transmission from a base station in Paderborn, Germany. Transmitted and received speech signals are precisely time aligned to offer parallel data for supervised training of deep learning based detection and enhancement algorithms.</p>
PAN Arabic Intrinsic Plagiarism Detection Shared Task Corpus
<p>Evaluation corpus for ARAbic INtrinsic plagiarism detection (InAra Corpus) </p> <p> </p> <p>This corpus has been used in AraPlagDet 2015 shared task </p> <p>More details could be found in : <a href="http://araplagdet.misc-lab.org">https://araplagdet.misc-lab.org/</a> or <a href="http://pan.webis.de/fire15/pan15-web/index.html">https://pan.webis.de/fire15/pan15-web/index.html</a> </p> <p> </p> <p><strong>I. SYNOPSIS </strong></p> <p>InAra corpus comprises 2048 documents; 80% of them contain passages borrowed from other documents to simulate documents that contain plagiarized fragments. The corpus involves 2 parts: Training and test.</p> <p> </p> <p><strong>II. DESCRIPTION </strong></p> <p>Each part of the corpus (training and test) consists mainly of 2 datasets: textual files and XML files. The textual files represent the suspicious documents i.e., the documents that contain artificial plagiarism; and the XML files are the plagiarism annotation i.e. they provide for each plagiarized passage its starting offset in the suspicious document and its length (offset and length are both expressed in characters). A suspicious document file and its plagiarism annotation file share the same name.</p> <p> </p> <p><strong>III. PURPOSE </strong></p> <p>The purpose of InAra corpus is to evaluate automatic plagiarism detection methods, notably methods of the intrinsic approach. This approach consists in uncovering the plagiarized passages on the basis of the writing style inconsistency in a given suspicious document. As opposed to the external approach, the intrinsic approach does not necessitate any comparison of the suspicious document against the potential sources of plagiarism. Hence, InAra corpus is not appropriate for the evaluation of the external plagiarism detection because the source of plagiarism are not provided.</p> <p>It should be noted that some documents in InAra corpus contain religious quotations (e.g., Quran and Hadith). These quotations have a peculiar writing style and then a simple intrinsic plagiarism detection software can consider them as plagiarism. However, quotations are not plagiarism, and they are not annotated in the XML files in InAra. Hence, it is an important feature for the plagiarism detection systems evaluated on InAra to not consider religious quotations as plagiarism cases unless they appear as part of a larger plagiarism case.</p> <p> </p> <p><strong>IV. BUILDING METHODS </strong></p> <p>The documents that compose InAra corpus do not contain actual plagiarism cases. They are rather artificial suspicious documents in which plagiarism was created automatically by a software that takes fragments of text from one or more sources documents and inserts them in another one according to a set of parameters, namely the percentage of plagiarism and the plagiarized passages lengths. This building method is the same used to construct PAN 2009-2011 corpora of plagiarism detection (see <a href="http://pan.webis.de">http://pan.webis.de</a> for more information on PAN competition and its corpora). </p> <p> </p> <p><strong>V. LANGUAGE AND ENCODING </strong></p> <p>All the textual documents of this corpus are written in Arabic language and encoded in UTF-8 without BOM.</p> <p> </p> <p><strong>VI. SOURCES OF TEXTS </strong></p> <p>Texts used to build this corpus, either suspicious documents or the inserted passages, are taken mainly from the open library Arabic Wikisource (http://ar.wikisource.org), one of Wikimedia Foundation projects. A few numbers of documents were taken from other websites, namely: </p> <ul> <li>Create your own country blog: http://diycountry.blogspot.com </li> <li>Corpus of Classical Arabic (KSUCCA): http://ksucorpus.ksu.edu.sa </li> <li>Islamic book web site: http://www.islamicbook.ws </li> </ul> <p> </p> <p><strong>VII. COPYRIGHT AND AVAILABILITY </strong></p> <p>We were very careful to build the corpus with copyright-free texts only, to be able to make it publicly available without any sort of problems with texts owners. </p> <p> </p> <p><strong>VIII. HOW TO CITE THE CORPUS ?</strong></p> <p>If you publish a paper about your experimentations using InAra corpus, please cite the following paper:</p> <ul> <li>Bensalem, I., Boukhalfa, I., Rosso, P., Abouenour, L., Darwish, K., & Chikhi, S.: Overview of the AraPlagDet PAN@FIRE2015 Shared Task on Arabic Plagiarism Detection. In P. Majumder, M. Mitra, M. Agrawal, & P. Mehta (Eds.), Post Proceedings of the Workshops at the 7th Forum for Information Retrieval Evaluation (FIRE 2015), Gandhinagar, India, December 4-6, CEUR proceedings vol. 1587 (pp. 111–122). CEUR-WS.org (2015).</li> </ul> <p>We encourage you to compare your method tested on InAra with the methods of AraPlagDet competition described in the paper above.</p> <p>Additional information on the corpus building are in the papers:</p> <ul> <li>Bensalem, I., Rosso, P., Chikhi, S.: A New Corpus for the Evaluation of Arabic Intrinsic Plagiarism Detection. In: Forner, P., Müller, H., Paredes, R., Rosso, P., and Stein, B. (eds.) CLEF 2013, LNCS, vol. 8138. pp. 53–58. Springer, Heidelberg (2013).</li> <li>Bensalem, I., Rosso, P., Chikhi, S.: Building Arabic Corpora from Wikisource. 10th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA’13),May 27-30 Fes/Ifran, Morocco (2013).IEEE. </li> </ul> <p> </p> <p>You may wish to compare the results of your experiments with the result of the following papers that used InAra corpus:</p> <ul> <li>Bensalem I, Rosso P, Chikhi S (2019) On the use of character n-grams as the only intrinsic evidence of plagiarism. Language Resources and Evaluation 53:363–396. doi: 10.1007/s10579-019-09444-w</li> <li>Mahgoub AY, Magooda A, Rashwan M, et al (2015) RDI System for Intrinsic Plagiarism Detection (RDI_RID), Working Notes for PAN-AraPlagDet at FIRE 2015. In: Majumder P, Mitra M, Agrawal M, Mehta P (eds) Post Proceedings of the Workshops at the 7th Forum for Information Retrieval Evaluation (FIRE 2015), Gandhinagar, India, December 4-6, CEUR proceedings vol. 1587. CEUR-WS.org, pp 129–130</li> </ul> <p> </p> <p><strong>IX. WARNING </strong></p> <p>It should be noted that the Arabic texts may contain quotations from the Quran and the Hadith; and due to the fact that text insertion is automatic and in random positions, it is possible that the plagiarized text is inserted unintentionally between Quranic verses or sentences of a Hadith cited in a document. Hence, the inserted passages may alter the meaning of the original text. For these reasons, this corpus must not be used outside the purpose for which it was built. Examples of the inappropriate use include using the corpus documents as a source of knowledge or distributing them without mentioning that they contain borrowed texts. If you are not interested in plagiarism detection and you are retaining the corpus because it contains books you want to read, then this corpus is not the right source. Please, you should refer to the </p> <p>sources mentioned in Section VI where you can find the original content of the books you are looking for. We emphasize that we are not responsible for the results of any use of this corpus other than the evaluation of the intrinsic plagiarism detection methods. </p> <p> </p> <p><strong>X. CONTACT US</strong></p> <p>We will be happy to hear from you about your experience in using InAra corpus. Please do not hesitate to contact us with the following email address: bens.imene@gmail.com</p> <p> </p> <p>Imene Bensalem¹, Paolo Rosso², Salim Chikhi¹</p> <p>¹MISC Lab. Constantine 2 university, Algeria</p> <p>²PRHLT, Universitat Politècnica de València, Spain </p>
PAN Arabic External Plagiarism Detection Shared Task Corpus
<p>Evaluation Corpus for ARAbic EXternal plagiarism detection (ExAra Corpus) </p> <p> </p> <p>This corpus has been used in AraPlagDet 2015 shared task </p> <p>More details could be found in : <a href="http://araplagdet.misc-lab.org">https://araplagdet.misc-lab.org/</a> or <a href="http://pan.webis.de/fire15/pan15-web/index.html">https://pan.webis.de/fire15/pan15-web/index.html</a></p> <p>If you publish a paper about your experimentations using ExAra corpus, please cite the following paper:</p> <ul> <li>Bensalem, I., Boukhalfa, I., Rosso, P., Abouenour, L., Darwish, K., & Chikhi, S.: Overview of the AraPlagDet PAN@FIRE2015 Shared Task on Arabic Plagiarism Detection. In P. Majumder, M. Mitra, M. Agrawal, & P. Mehta (Eds.), Post Proceedings of the Workshops at the 7th Forum for Information Retrieval Evaluation (FIRE 2015), Gandhinagar, India, December 4-6, CEUR proceedings vol. 1587 (pp. 111–122). CEUR-WS.org (2015).</li> </ul> <p>We encourage you to compare your method tested on ExAra with the methods of AraPlagDet competition described in the paper above.</p> <p> </p> <p><strong>I. SYNOPSIS </strong></p> <p>ExAra corpus comprises 2345 documents; almost half of them (suspecious doucuments) contain passages borrowed from the other half (source docucments) to simulate documents that contain plagiarized fragments. The corpus involves 2 parts: Training and test.</p> <p> </p> <p><strong>II. DESCRIPTION </strong></p> <p>Each part of the corpus (training and test) consists mainly of 3 datasets: 2 sets of textual files and 1 set of XML files. The 2 sets of the textual files are the suspicious documents (i.e. the documents that contain artificial plagiarism) and the source documents (i.e., the documents from which the suspicious passages have been plagiarised). The 3rd set of documents contains XML files, which are the plagiarism annotation, i.e., they provide for each plagiarized passage its starting offset and its length in both the suspicious and source documents (offset and length were both expressed in characters). A suspicious document file (.txt) and its plagiarism annotation file (.xml) share the same name.</p> <p> </p> <p><strong>III. PURPOSE </strong></p> <p>The purpose of ExAra corpus is to evaluate automatic plagiarism detection methods, notably methods of the External approach. This approach consists in uncovering the plagiarized passages on the basis of their similarity with passages in the source documents.</p> <p>It should be noted that some suspicious documents in ExAra corpus contain religious quotations (e.g., Quran and Hadith) and common phrases. Some of them appear also in some source documents, and hence a simple plagiarism detection software can consider them as plagiarism. However, quotations and common phrases are legitimate text reuse cases and are not annotated in the XML files in ExAra. Therefore, it is an important feature for the plagiarism detection systems evaluated on ExAra to not consider religious quotations and common phrases as plagiarism cases unless they appear as part of a larger plagiarism case.</p> <p> </p> <p><strong>IV. BUILDING METHODS </strong></p> <p>The documents that compose ExAra corpus do not contain actual plagiarism cases, they are rather artificial suspicious documents in which plagiarism was created automatically by a software that takes fragments of text from one or more sources documents and inserts them in another one according to a set of parameters, namely the percentage of plagiarism and the lengths of the plagiarized passages. Some of the plagiarised fragments are obfuscated manually or automatically before inserting them in the suspicious documents.</p> <p>This building method is the same used to construct PAN 2009-2011 corpora of plagiarism detection (see http://pan.webis.de for more information on PAN competition and its corpora). </p> <p> </p> <p><strong>V. LANGUAGE AND ENCODING </strong></p> <p>All the textual documents of this corpus are written in Arabic language and encoded in UTF-8 without BOM.</p> <p> </p> <p><strong>VI. HOW TO CITE THE CORPUS ?</strong></p> <p>If you publish a paper about your experimentations using ExAra corpus, please cite the following paper:</p> <ul> <li>Bensalem, I., Boukhalfa, I., Rosso, P., Abouenour, L., Darwish, K., & Chikhi, S.: Overview of the AraPlagDet PAN@FIRE2015 Shared Task on Arabic Plagiarism Detection. In P. Majumder, M. Mitra, M. Agrawal, & P. Mehta (Eds.), Post Proceedings of the Workshops at the 7th Forum for Information Retrieval Evaluation (FIRE 2015), Gandhinagar, India, December 4-6, CEUR proceedings vol. 1587 (pp. 111–122). CEUR-WS.org (2015).</li> </ul> <p>We encourage you to compare your method tested on ExAra with the methods of AraPlagDet competition described in the paper above.</p> <p> </p> <p><strong>VII. WARNING </strong></p> <p>It should be noted that the Arabic texts may contain quotations from the Quran and the Hadith; and due to the fact that text insertion is automatic and in random positions, it is possible that the plagiarized text is inserted unintentionally between Quranic verses or sentences of a Hadith cited in a document. Hence, the inserted passages may alter the meaning of the original text. For these reasons, this corpus must not be used outside the purpose for which it was built. Examples of the inappropriate use include using the corpus documents as a source of knowledge or distributing them without mentioning that they contain borrowed texts. If you are not interested in plagiarism detection, and you are retaining the corpus because it contains articles you want to read, then this corpus is not the right source. Please, you should refer to the sources mentioned in (Bensalem et al. 2015) (i.e.,the paper above) where you can find the original content of the articles you are looking for.</p> <p>We emphasize that we are not responsible for the results of any use of this corpus other than the evaluation of the external plagiarism detection methods. </p> <p> </p> <p><strong>VIII. CONTACT US</strong></p> <p>We will be happy to hear from you about your experience in using ExAra corpus. Please do not hesitate to contact us with the following email address: bens.imene@gmail.com</p> <p> </p> <p>Imene Bensalem¹, Imene Boukhalfa¹, Paolo Rosso², Salim Chikhi¹</p> <p>¹MISC Lab. Constantine 2 university, Algeria</p> <p>²PRHLT, Universitat Politècnica de València, Spain </p>
Phage RBP detection data
<p>These datasets contain the filtered <em>RBP </em>and <em>Other</em> sequences utilized in our manuscript in submission for the special issue 'Virus Bioinformatics 2022' in <em>Viruses</em>: "Identification of phage receptor-binding protein sequences with hidden Markov models and an extreme gradient boosting classifier". These datasets enable to fully reproduce our analyses.</p>
Fire, smoke and gas leakage detection Dataset
<p>This is an open free dataset associated with fire, smoke and gas leakage detection captured from the Smart Building sensor system, namely SB112, with edge computing and Next Generation (NG) 112 emergency call capabilities. In case you use or process this dataset please refer to your work/study the following paper in which the SB112 is described in detail. </p> <p>Maltezos, E.; Petousakis, K.; Dadoukis, A.; Karagiannidis, L.; Ouzounoglou, E.; Krommyda, M.; Hadjipavlis, G.; Amditis, A. A Smart Building Fire and Gas Leakage Alert System with Edge Computing and NG112 Emergency Call Capabilities. <em>Information</em> 2022, <em>13</em>, 164. <a href="https://doi.org/10.3390/info13040164">https://doi.org/10.3390/info13040164</a></p> <p>The excel files contain data from 08/03/2022 to 11/03/2022. From 08/03 to 10/03/2022 the data is gathered from continuous operation without physical triggering. On 11/03/2022 a physical trigger of the sensors was performed by:</p> <ul> <li>Burning a piece of paper in a distance of 15 cm - 20cm to the Smoke and CO sensors.</li> <li>By exposing the LPG/CNG sensors to lighter gas in a distance of 5 cm - 10cm.</li> <li>By exposing the Flame/IR sensor to a moving flame from a lighter in a distance of 15 cm - 100cm.</li> </ul> <p>In each sheet of the excel files there are four columns: i) the datetime that a measurement was captured, ii) the type of the sensor, iii) the unit of the measurement, and iv) the data value.</p> <p><strong>Acknowledgments: </strong>This work is a part of the S4AllCities project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 883522. Content reflects only the authors’ views; the Research Executive Agency (REA)/European Commission is not responsible for any use that may be made of the information it contains.</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.