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.
1,078
datasets available to search
ShareScore release 0.9.0
Dataset results
1,078 results for “CAT”
Outdoor mesocosm study evaluating how mass, NaCl tolerance, and pesticide tolerance affect oxidative stress biomarkers (CAT, SOD, GR, GPx, TBARS) in larval wood frogs (Rana sylvatica) exposed to baseline and NaCl-contaminated conditions, 2019
Biomarkers of oxidative stress can aid in wildlife monitoring by allowing conservationists to detect sublethal environmental shifts. However, interpretation of stress responses can be complicated by multiple interacting factors (e.g., individual development, evolved physiological tolerance to stressors) which alter biomarker expression. Here, we investigated how individual ontogenetic traits and population-level tolerance traits influence oxidative stress responses under baseline and contaminated environmental conditions. For our model contaminant, we used NaCl (common freshwater contaminant due to factors such as coastal flooding, irrigation, airborne salt circulation, drought, runoff from road deicing salts). For our model wildlife populations, we used larval wood frogs (Rana sylvatica) from six noninteracting populations known to vary in two population-level tolerance traits: NaCl tolerance (calculated as average time to death from lethal NaCl exposure) and pesticide tolerance (determined by proxy of distance to agriculture - a consistent and highly repeatable relationship). At an outdoor research facility, R. sylvatica tadpoles were exposed to either baseline conditions (0 g/L NaCl added) or NaCl-contaminated conditions (1 g/L NaCl added for 21 days, then reduced to 0.5 g/L NaCl). Exposures were conducted in individual units with 40 replicates per population for each treatment. The experiment was terminated per individual to capture the full term of larval development (Developmental stage: Gosner stage 36), lasting between 33-48 days. For each individual, we measured mass, Snout-Vent-Length, and developmental stage before processing for biomarker expression. Individual homogenates were assayed for oxidative stress biomarkers superoxide dismutase (SOD; responsible for Reactive Oxygen Species capture and peroxide production), glutathione peroxidase (GPx; responsible for high-affinity peroxide reduction), catalase (CAT; responsible for low-affinity peroxide reducti
Dataset for algorithmic thinking skills assessment: Results from the virtual CAT large-scale study in Swiss compulsory education
<p><strong>Overview</strong><br>This dataset was collected during a main study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education.<br>As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention.</p> <p><strong>Study Context, Location and Participants</strong><br>To comprehensively investigate algorithmic competencies within compulsory education, exploring their variations and determining the factors influencing them, in Spring 2023 we conducted an experimental study with the virtual CAT's.<br>The sample comprises 129 students (65 girls and 64 boys), selected from nine classes across five public schools in Ticino and Solothurn cantons.</p> <p><strong>Data Collection</strong><br>During the data collection process, session and participant details were manually recorded by the administrator. <br>Each session has been assigned a unique identifier, and specific details, such as the date, canton, school name and type, and the students’ HarmoS grade (HG) level, have been recorded. <br>Student information are limited to sex and date of birth, with birth dates used to calculate ages, a significant factor in our demographic analysis. <br>To protect student privacy, unique identifiers have been assigned to each participant, keeping the data anonymous and secure. <br>The assessment tool automatically tracked all user interaction within the platform.<br>All data collected have been pseudonymised, aligning with prevailing open science practices in Switzerland (SNSF, 2021). <br>Data collection was integrated into a validation module of the app. </p> <p><strong>Data Features</strong><br>The dataset comprises the following files:</p> <ul> <li>STUDENTS_SESSIONS.csv</li> <li>RESULTS.csv</li> <li>LOGS.csv</li> <li>CANTONS.csv</li> <li>ALGORITHMS.csv</li> </ul> <p>These files collectively provide insights into the algorithmic actions of the students, demographic details, session logs, results, and more.</p> <p><strong>Usage & Ethics</strong><br>In the spirit of open science, this dataset is made available to the public after meticulous anonymisation to ensure all participants' privacy and ethical treatment. <br>Initial authorisations were secured from school administrators, teachers, and parents. <br>Detailed communication regarding the study's nature, data handling, and objectives was transparently shared with all stakeholders.</p> <p><strong>REFERENCES</strong></p> <p><strong>[1]</strong> A. Piatti, G. Adorni, L. El-Hamamsy, L. Negrini, D. Assaf, L. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166. <a href="https://doi.org/10.1016/j.chbr.2021.100166">https://doi.org/10.1016/j.chbr.2021.100166</a></p> <p><strong>[2]</strong> Adorni, G., & Piatti, S., & Karpenko, V. (2023). virtual CAT: An app for algorithmic thinking assessment within Swiss compulsory education. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10027851">https://doi.org/10.5281/zenodo.10027851</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/</a></p> <p><strong>[3]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT programming language interpreter. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10016535">https://doi.org/10.5281/zenodo.10016535</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/</a></p> <p><strong>[4]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT data infrastructure. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10015011">https://doi.org/10.5281/zenodo.10015011</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure</a></p> <p> </p>
The CATS dataset for 28Si1H2
<p>The dataset is an archive of ExoMol page, https://exomol.com/data/molecules/SiH2/28Si-1H2/CATS.<br>Please check the reference details according to the following description or directly from the website.<br> <strong>NB: The html description skips data which are not included in the current version for the purpose of simplicity. Please check SiH2_28Si1H2_CATS.md for detailed information.</strong> <br></p> <strong>Definitions file</strong> <blockquote> <p><strong>28Si-1H2__CATS.def</strong>[8.4 KB]<br></p> <p><strong>References:</strong><br> 1. Tennyson, J., Yurchenko, S. N., Al-Refaie, A. F., Clark, V. H. J., Chubb, K. L., Conway, E. K., Dewan, A., Gorman, M. N., Hill, C., Lynas-Gray, A. E., Mellor, T., McKemmish, L. K., Owens, A., Polyansky, O. L., Semenov, M., Somogyi, W., Tinetti, G., Upadhyay, A., Waldmann, I., Wang, Y., Wright, S., Yurchenko, O. P., "The 2020 release of the ExoMol database: molecular line lists for exoplanet and other hot atmospheres", J. Quant. Spectrosc. Rad. Transf., 255, 107228 (2020). [<a href="https://doi.org/10.1016/j.jqsrt.2020.107228">https://doi.org/10.1016/j.jqsrt.2020.107228</a>]</p> </blockquote> <strong>Spectroscopic Model</strong> <blockquote> <p><a href="https://exomol.com/models/SiH2/28Si-1H2/CATS/">https://exomol.com/models/SiH2/28Si-1H2/CATS/</a><br></p> </blockquote> <strong>CATS: partition function</strong> <p><em>SiH2 line list CATS obtained using a refined PES and ab initio DMS, computed with TROVE</em><br></p> <blockquote> <p><strong>28Si-1H2__CATS.pf</strong>[48.83 KB]<br>A CATS partition function, (28Si)(1H)2.</p> <p><strong>References:</strong><br> 1. Clark, V. H. J., Owens, A., Tennyson, J., Yurchenko, S. N., "The high-temperature rotation-vibration spectrum and rotational clustering of silylene (SiH2)", Journal of Quantitative Spectroscopy and Radiative Transfer 246, 106929 (2021). <a href="[https://doi.org/10.1016/j.jqsrt.2020.106929]">[https://doi.org/10.1016/j.jqsrt.2020.106929]</a>[21ClOwTe.SiH2]<br></p> </blockquote> <strong>CATS: line list</strong> <p><em>SiH2 line list CATS obtained using a refined PES and ab initio DMS, computed with TROVE</em><br></p> <blockquote> <p><strong>28Si-1H2__CATS.states.bz2</strong>[6.9 MB]<br>A CATS .states file. (28Si)(1H)2 hot line list.</p> <p><strong>28Si-1H2__CATS__00000-01000.trans.bz2</strong>[80.78 MB]<br>CATS hot line list transitions, (28Si)(1H)2: 0-1000 cm-1.</p> <p><strong>28Si-1H2__CATS__01000-02000.trans.bz2</strong>[105.67 MB]<br>CATS hot line list transitions, (28Si)(1H)2: 1000-2000 cm-1.</p> <p><strong>28Si-1H2__CATS__02000-03000.trans.bz2</strong>[134.01 MB]<br>CATS hot line list transitions, (28Si)(1H)2: 2000-3000 cm-1.</p> <p><strong>28Si-1H2__CATS__03000-04000.trans.bz2</strong>[167.82 MB]<br>CATS hot line list transitions, (28Si)(1H)2: 3000-4000 cm-1.</p> <p><strong>28Si-1H2__CATS__04000-05000.trans.bz2</strong>[205.19 MB]<br>CATS hot line list transitions, (28Si)(1H)2: 4000-5000 cm-1.</p> <p><strong>28Si-1H2__CATS__05000-06000.trans.bz2</strong>[249.32 MB]<br>CATS hot line list transitions, (28Si)(1H)2: 5000-6000 cm-1.</p> <p><strong>28Si-1H2__CATS__06000-07000.trans.bz2</strong>[298.96 MB]<br>CATS hot line list transitions, (28Si)(1H)2: 6000-7000 cm-1.</p> <p><strong>28Si-1H2__CATS__07000-08000.trans.bz2</strong>[354.93 MB]<br>CATS hot line list transitions, (28Si)(1H)2: 7000-8000 cm-1.</p> <p><strong>28Si-1H2__CATS__08000-09000.trans.bz2</strong>[415.19 MB]<br>CATS hot line list transitions, (28Si)(1H)2: 8000-9000 cm-1.</p> <p><strong>28Si-1H2__CATS__09000-10000.trans.bz2</strong>[476.85 MB]<br>CATS hot line list transitions, (28Si)(1H)2: 9000-10000 cm-1.</p> <p><strong>References:</strong><br> 1. Clark, V. H. J., Owens, A., Tennyson, J., Yurchenko, S. N., "The high-temperature rotation-vibration spectrum and rotational clustering of silylene (SiH2)", Journal of Quantitative Spectroscopy and Radiative Transfer 246, 106929 (2021). <a href="[https://doi.org/10.1016/j.jqsrt.2020.106929]">[https://doi.org/10.1016/j.jqsrt.2020.106929]</a>[21ClOwTe.SiH2]<br></p> </blockquote> <strong>CATS: opacity</strong> <p><em>SiH2 line list CATS obtained using a refined PES and ab initio DMS, computed with TROVE</em><br></p> <blockquote> <p><strong>28Si-1H2__CATS.R1000_0.3-50mu.ktable.ARCiS.fits.gz</strong>[339.68 MB]<br>ARCiS k-tables at R= 1000 (0.3-50mu) in fits format (gzipped): CATS (28Si)(1H)2 line list.</p> <p><strong>28Si-1H2__CATS.R1000_0.3-50mu.ktable.petitRADTRANS.h5</strong>[370.98 MB]<br>petitRADTRANS k-tables at R= 1000 (0.3-50mu) in HDF5 format: CATS (28Si)(1H)2 line list.</p> <p><strong>28Si-1H2__CATS.R1000_0.3-50mu.ktable.NEMESIS.kta</strong>[232.01 MB]<br>NEMESIS k-tables at R= 1000 (0.3-50mu) in NEMESIS-kta format: CATS (28Si)(1H)2 line list.</p> <p><strong>28Si-1H2__CATS.R15000_0.3-50mu.xsec.TauREx.h5</strong>[348.39 MB]<br>TauREx k-tables at R= 15000 (0.3-50mu) in HDF5 format: CATS (28Si)(1H)2 line list.</p> <p><strong>References:</strong><br> 1. Clark, V. H. J., Owens, A., Tennyson, J., Yurchenko, S. N., "The high-temperature rotation-vibration spectrum and rotational clustering of silylene (SiH2)", Journal of Quantitative Spectroscopy and Radiative Transfer 246, 106929 (2021). <a href="[https://doi.org/10.1016/j.jqsrt.2020.106929]">[https://doi.org/10.1016/j.jqsrt.2020.106929]</a>[21ClOwTe.SiH2]<br> 2. Chubb, K. L., Rocchetto, M., Yurchenko, S. N., Min, M., Waldmann, I., Barstow, J. K., Molliere, P., Al-Refaie, A. F, Phillips, M. W., Tennyson, J., "The ExoMolOP database: Cross sections and k-tables for molecules of interest in high-temperature exoplanet atmospheres", Astronomy and Astrophysics 646, A21 (2020). <a href="[http://dx.doi.org/10.1051/0004-6361/202038350]">[http://dx.doi.org/10.1051/0004-6361/202038350]</a>[20ChRoYu.]<br></p> </blockquote>
Dataset for algorithmic thinking skills assessment: Results from the virtual CAT pilot study in Swiss compulsory education
<p><strong>Overview</strong><br>This dataset was collected during a pilot study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education.<br>As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention.</p><p><strong>Study Context, Location and Participants</strong><br>To demonstrate the virtual CAT's effectiveness, we conducted a pilot study in March 2023.<br>The study was conducted in Switzerland, specifically within the Ticino canton.<br>The sample consisted of 31 students (21 girls and 10 boys) from a preschool class (ages 4-6) and two low secondary classes (1st grade, ages 11-12). </p><p><strong>Data Collection</strong><br>Data collection was integrated into a validation module of the app. <br>Sessions required manual input for details like date, canton, and school information. <br>Students' details, anonymised for privacy, encompassed their gender and date of birth. <br>Each interaction within the platform was meticulously logged, capturing operations like task confirmations, command updates, mode changes, and more.</p><p><strong>Data Features</strong><br>The dataset comprises the following files:</p><ul><li>ALGORITHMS.csv</li><li>CANTONS.csv</li><li>DF.csv</li><li>LOGS.csv</li><li>RESULTS.csv</li><li>SCHOOLS.csv</li><li>SESSIONS.csv</li><li>STUDENTS_SESSIONS.csv</li></ul><p>These files collectively provide insights into the algorithmic actions of the students, demographic details, session logs, results, and more.</p><p><strong>Usage & Ethics</strong><br>In the spirit of open science, this dataset is made available to the public after meticulous anonymisation to ensure all participants' privacy and ethical treatment. <br>Initial authorisations were secured from school administrators, teachers, and parents. <br>Detailed communication regarding the study's nature, data handling, and objectives was transparently shared with all stakeholders.</p><p> </p><p><strong>REFERENCES</strong></p><p><strong>[1]</strong> A. Piatti, G. Adorni, L. El-Hamamsy, L. Negrini, D. Assaf, L. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166. <a href="https://doi.org/10.1016/j.chbr.2021.100166">https://doi.org/10.1016/j.chbr.2021.100166</a></p><p><strong>[2]</strong> Adorni, G., & Piatti, S., & Karpenko, V. (2023). virtual CAT: An app for algorithmic thinking assessment within Swiss compulsory education. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10027851">https://doi.org/10.5281/zenodo.10027851</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/</a></p><p><strong>[3]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT programming language interpreter. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10016535">https://doi.org/10.5281/zenodo.10016535</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/</a></p><p><strong>[4]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT data infrastructure. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10015011">https://doi.org/10.5281/zenodo.10015011</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure</a></p>
syntact_cat
<p>Speech emotion recognition is in the focus of research since several decades and has many applications. One problem is sparse data for supervised learning. One way to tackle this problem is the synthesis of data with emotion-simulating speech synthesis approaches. We present a synthesized database of three basic emotions and neutral expression based on rule-based manipulation for a diphone synthesizer which we release to the public. The database has been validated in several machine learning experiments as a training set to detect emotional expression from natural speech data. The scripts to generate such a database have been made open source and could be used to aid speech emotion recognition for a low resourced language, as MBROLA supports 35 languages.<br> </p>
CAT snacks functional plasticity
Open the record for dataset details and reuse information.
CatMeows: A Publicly-Available Dataset of Cat Vocalizations
<p><strong>Abstract</strong></p> <p>This dataset, composed of 440 sounds, contains meows emitted by cats in different contexts. Specifically, 21 cats belonging to 2 breeds (Maine Coon and European Shorthair) have been repeatedly exposed to three different stimuli that were expected to induce the emission of meows:</p> <ol> <li><em>Brushing</em> - Cats were brushed by their owners in their home environment for a maximum of 5 minutes;</li> <li><em>Isolation in an unfamiliar environment</em> - Cats were transferred by their owners into an unfamiliar environment (e.g., a room in a different apartment or an office). Distance was minimized and the usual transportation routine was adopted so as to avoid discomfort to animals. The journey lasted less than 30 minutes and cats were allowed 30 minutes with their owners to recover from transportation, before being isolated in the unfamiliar environment, where they stayed alone for maximum 5 minutes;</li> <li><em>Waiting for food</em> - The owner started the routine operations that preceded food delivery in the usual environment the cat was familiar with. Food was given at most 5 minutes after the beginning of the experiment.</li> </ol> <p>The dataset has been produced and employed in the context of an interdepartmental project of the University of Milan (for further information, please refer to this <a href="https://doi.org/10.3390/ani9080543">doi</a>). The content of the dataset has been described in detail in a scientific work currently under review; the reference will be provided as soon as the paper is published.</p> <p><strong>File naming conventions </strong></p> <p>Files containing meows are in the <em>dataset.zip</em> archive. They are PCM streams (.wav).<br> Naming conventions follow the pattern C_NNNNN_BB_SS_OOOOO_RXX, which has to be exploded as follows:</p> <ul> <li>C = emission context (values: B = brushing; F = waiting for food; I: isolation in an unfamiliar environment);</li> <li>NNNNN = cat’s unique ID;</li> <li>BB = breed (values: MC = Maine Coon; EU: European Shorthair);</li> <li>SS = sex (values: FI = female, intact; FN: female, neutered; MI: male, intact; MN: male, neutered);</li> <li>OOOOO = cat owner’s unique ID;</li> <li>R = recording session (values: 1, 2 or 3)</li> <li>XX = vocalization counter (values: 01..99)</li> </ul> <p><strong>Extra content</strong></p> <p>The <em>extra.zip</em> archive contains excluded recordings (sounds other than meows emitted by cats) and uncut sequences of close vocalizations.</p> <p><strong>Terms of use</strong></p> <p>The dataset is open access for scientific research and non-commercial purposes.</p> <p>The authors require to acknowledge their work and, in case of scientific publication, to cite the most suitable reference among the following entries:</p> <p>Ntalampiras, S., Ludovico, L.A., Presti, G., Prato Previde, E., Battini, M., Cannas, S., Palestrini, C., Mattiello, S.: Automatic Classification of Cat Vocalizations Emitted in Different Contexts. Animals, vol. 9(8), pp. 543.1–543.14. MDPI (2019).<br> ISSN: 2076-2615</p> <p>Ludovico, L.A., Ntalampiras, S., Presti, G., Cannas, S., Battini, M., Mattiello, S.: CatMeows: A Publicly-Available Dataset of Cat Vocalizations. In: Li, X., Lokoč, J., Mezaris, V., Patras, I., Schoeffmann, K., Skopal, T., Vrochidis, S. (eds.) MultiMedia Modeling. 27th International Conference, MMM 2021, Prague, Czech Republic, June 22–24, 2021, Proceedings, Part II, LNCS, vol. 12573, pp. 230–243. Springer International Publishing, Cham (2021). <br> ISBN: 978-3-030-67834-0 (print), 978-3-030-67835-7 (online)<br> ISSN: 0302-9743 (print), 1611-3349 (online)</p>
Pembaharuan warna cat batas lingkungan maccini baji dan pintu masuk kelurahan tolo
<p>kami memulai pengecatan batas</p> <p>lingkungan maccini baji dan pintu masuk kelurahan Tolo 'dengan penuh semangat dan</p> <p>Kamimengerjakannya selama 4 hari berturut-turut mulai dari tanggal 28 september-01 oktober 2020</p>
Pembeharuan warna cat batas lingkungan dan pintu masuk kelurahan tolo'
<p>kami memulai pengecatan batas<br> lingkungan dan pintu masuk kelurahan<br> Tolo’ dengan penuh semangat dan.<br> Kami mengerjakannya selama 4 hari<br> berturut-turut.</p>
Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches (Replication Package Part 3: OpenSSL dataset)
<h1><strong>The Replication Package of</strong></h1> <h1><strong>"Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches"</strong></h1> <h3><strong>Part 3 (OPENSSL Dataset)</strong></h3> <div> <div>This repository includes:</div> <ol> <li><em><strong>Code.zip</strong></em> that contains the codes to replicate some parts of this study:<br>a. <em>1_generate_datasets</em> implements our methodology to generate the datasets.<br>b. <em>2_run_models</em> runs the ML models during the evaluation.<br>c. <em>3_result_replication </em>generates charts presented in the paper from the ML evaluation results.</li> <li><em><strong>Datasets.zip</strong></em> that contain 2 folders:<br>a. <em>original</em> datasets: 1 from <a href="https://github.com/CGCL-codes/VulDeePecker" target="_blank" rel="noopener">NVD Vuldeepecker</a> and 3 extracted from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a>.<br> <div> <div>b. <em>OPENSSL</em> datasets: train, validation, test sets for each time of observation extracted using our methodology from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a> dataset for project <em>openssl</em>.</div> </div> </li> <li><em><strong>Pretrained-models.zip</strong></em> that we generated during our evaluation (3 test results for each time point in the timeline [2013-2019]).</li> <li><em><strong>Results.zip</strong></em> of our evaluation, the folder <em>ALL</em> contains the overall results and other folders are results by model.</li> </ol> <p><strong>UPDATED version 5<br></strong>- added a GLOBAL_README.md which contains the 3 stages and how they are connected to each other<br>- updated LineVul.ipynb: import AdamW from torch.optim instead of transformers<br>- updated README.md in Code2Vec with the prerequisites of Java to run gradlew for astmine</p> <p><strong>UPDATED version 6<br></strong>- updated CodeBert.ipynb: import AdamW from torch.optim instead of transformers</p> <p>Documentations</p> <ol> <li><em><strong>INSTALL.pdf </strong></em>: how to install the codes</li> <li><em><strong>README.pdf</strong></em>: readme file</li> <li><em><strong>REQUIREMENTS.pdf</strong></em>: hardware and software requirements</li> <li><em><strong>STATUS.pdf</strong></em> : status for artifact submission</li> <li><em><strong>LICENSE.pdf</strong></em>: the license of this artifact</li> <li><em><strong>PAPER.pdf</strong></em>: the camera-ready version of the paper</li> </ol> </div> <div> <div>Please refer to the following repositories for the other datasets and pre-trained models:</div> <div>- Part 1 NVD Vuldeeepecker : <a href="https://doi.org/10.5281/zenodo.8207883" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8207883</a></div> - Part 2 LINUX : <a href="https://doi.org/10.5281/zenodo.10960662" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10960662</a><br> <div>- Part 4 POPPLER : <a href="https://doi.org/10.5281/zenodo.14713143">https://doi.org/10.5281/zenodo.14713143</a></div> <div> </div> <div>This work was partly funded by the EU under the H2020 Program AssureMOSS (Grant n. 952647) and the Horizon Europe Program Sec4AI4Sec (Grant n. 101120393), by the Italian Ministry of University and Research (MUR) under the P.N.R.R. – NextGenerationEU grant n.\ PE00000014 (SERICS subproject COVERT), and by the Dutch Research Council (NWO) under the grant NWA.1215.18.006 (Theseus) and grant KIC1.VE01.20.004 (HEWSTI). </div> </div>
PIOP_CAT_09/30/22
Documentation material from the Mastic pilot of the Mingei project
Dataset for: Owner-ascribed personality profiles distinguish domestic cats that capture and bring home wild animal prey
<p>Dataset allowing repetition of the analyses in the above paper, comprising personality scores and predation data, with details of cat characteristics. See readme.txt file.</p>
SARS-CoV-2 Infection and Clinical Signs in Cats and Dogs from Confirmed Positive Households in Germany
<p>Supplemental material and raw data referring to specified publication</p>
Data for ms. Do people really care less about their cats than about their dogs? A comparative study in three European countries
<p>The present dataset is based on a questionnaire which is also part of this package. The enclose questionnaire includes identifiable and relevant variables names (yellow highlighted).</p> <p>Participants were recruited by Norstat, a European-based survey company, with the aim of gaining a representative sample of Austrian, Danish and UK citizens, including pet owners. The survey company administers and hosts online panels comprising citizens from many European countries. We aimed for a sample that is representative in terms of age, gender, and region. Therefore, a stratified sampling principle was set up where individuals within each stratum were randomly invited to participate. The invitations were issued through e-mail that contained a link to the online questionnaire. Data was collected from 11-25<sup>th</sup> of March 2022 in Austria, from 11-24<sup>th</sup> of March 2022 in Denmark and from 8-23<sup>rd</sup> of March 2022 in the UK. The invitation provided information about the background of the study, the participating universities, ethical approval, estimated time for questionnaire completion and further, participants were informed that the completion of the questionnaire was voluntary and anonymous, and that they could exit the survey at any point. Before participants were directed to the survey, they ensured informed consent by confirming that they are over 17 years old, and consent to participate in this survey. </p> <p>Besides the questionnaire the dataset includes a csv and an Excel file consisting of the data that is used in the ms. and an rtf and a pdf file with data variable names/labels, and value labels.</p>
Controlled Anomalies Time Series (CATS) Dataset
<p>The Controlled Anomalies Time Series (CATS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system with 200 injected anomalies.</p> <p>The CATS Dataset exhibits a set of desirable properties that make it very suitable for benchmarking<strong> Anomaly Detection Algorithms in Multivariate Time Series </strong>[1]:</p> <ul> <li><strong>Multivariate (17 variables) </strong>including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including: <ul> <li><strong>4 Deliberate Actuations / Control Commands sent by a simulated operator / controller</strong>, for instance, commands of an operator to turn ON/OFF some equipment.</li> <li><strong>3 Environmental Stimuli / External Forces</strong> acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.</li> <li><strong>10 Telemetry Readings</strong> representing the observable states of the complex system by means of sensors, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.</li> </ul> </li> <li><strong>5 million timestamps</strong>. Sensors readings are at 1Hz sampling frequency. <ul> <li><strong>1 million nominal </strong>observations (the first 1 million datapoints). This is suitable to start learning the "normal" behaviour.</li> <li><strong>4 million</strong> observations that include both <strong>nominal and anomalous segments</strong>. This is suitable to evaluate both semi-supervised approaches (novelty detection) as well as unsupervised approaches (outlier detection).</li> </ul> </li> <li><strong>200 anomalous segments. </strong>One anomalous segment may contain several successive anomalous observations / timestamps. Only the last 4 million observations contain anomalous segments.</li> <li><strong>Different types of anomalies </strong>to understand what anomaly types can be detected by different approaches. The categories are available in the dataset and in the metadata.</li> <li><strong>Fine control over ground truth.</strong> As this is a simulated system with deliberate anomaly injection, the start and end time of the anomalous behaviour is known very precisely. In contrast to real world datasets, there is no risk that the ground truth contains mislabelled segments which is often the case for real data.</li> <li><strong>Suitable for root cause analysis.</strong> In addition to the anomaly category, the time series channel in which the anomaly first developed itself is recorded and made available as part of the metadata. This can be useful to evaluate the performance of algorithm to trace back anomalies to the right root cause channel.</li> <li><strong>Affected channels.</strong> In addition to the knowledge of the root cause channel in which the anomaly first developed itself, we provide information of channels possibly affected by the anomaly. This can also be useful to evaluate the explainability of anomaly detection systems which may point out to the anomalous channels (root cause and affected).</li> <li><strong>Obvious anomalies.</strong> The simulated anomalies have been designed to be "easy" to be detected for human eyes (i.e., there are very large spikes or oscillations), hence also detectable for most algorithms. It makes this synthetic dataset useful for screening tasks (i.e., to eliminate algorithms that are not capable to detect those obvious anomalies). However, during our initial experiments, the dataset turned out to be challenging enough even for state-of-the-art anomaly detection approaches, making it suitable also for regular benchmark studies.</li> <li><strong>Context provided. </strong>Some variables can only be considered anomalous in relation to other behaviours. A typical example consists of a light and switch pair. The light being either on or off is nominal, the same goes for the switch, but having the switch on and the light off shall be considered anomalous. In the CATS dataset, users can choose (or not) to use the available context, and external stimuli, to test the usefulness of the context for detecting anomalies in this simulation.</li> <li><strong>Pure signal ideal for robustness-to-noise analysis.</strong> The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.</li> <li><strong>No missing data.</strong> You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.</li> </ul> <p><strong>Change Log</strong></p> <p>Version 2</p> <ul> <li><strong>Metadata:</strong> we include a metadata.csv with information about: <ul> <li>Anomaly categories</li> <li>Root cause channel (signal in which the anomaly is first visible)</li> <li>Affected channel (signal in which the anomaly might propagate) through coupled system dynamics</li> </ul> </li> <li><strong>Removal of anomaly overlaps:</strong> version 1 contained anomalies which overlapped with each other resulting in only 190 distinct anomalous segments. Now, there are no more anomaly overlaps.</li> <li><strong>Two data files: </strong>CSV and parquet for convenience.</li> </ul> <p>[1] Example Benchmark of Anomaly Detection in Time Series: “Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock. Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB, 15(9): 1779 - 1797, 2022. doi:10.14778/3538598.3538602”</p> <p><strong>About Solenix</strong></p> <p>Solenix is an international company providing software engineering, consulting services and software products for the space market. Solenix is a dynamic company that brings innovative technologies and concepts to the aerospace market, keeping up to date with technical advancements and actively promoting spin-in and spin-out technology activities. We combine modern solutions which complement conventional practices. We aspire to achieve maximum customer satisfaction by fostering collaboration, constructivism, and flexibility.</p>
CAT 4.6 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly
<p>Taxonomic binning of the gold standard pooled assembly<br> <strong>Software: </strong>CAT<br> <strong>SoftwareVersion: </strong>4.6<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://github.com/dutilh/CAT<br> <strong>ReferenceDatabase:</strong> prebuilt 2018-12-12<br> <strong>Taxonomy:</strong> NCBI 2018-12-12<br> <strong>ShortReadsUsed:</strong> False<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> CAT contigs -c anonymous_gsa_pooled.fasta -d CAT_prepare_20181212/2018-12-12_CAT_database/ -t CAT_prepare_20181212/2018-12-12_taxonomy/ --tmpdir tmp --nproc 16</p>
Data of "Deterministic creation of entangled atom–light Schrödinger-cat states"
<p>Data published in "<em>Deterministic creation of entangled atom–light Schrödinger-cat states</em>"</p> <p>Nature Photonics <strong>volume 13</strong>, pages110–115(2019)</p>
Fig. 5 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 5. Records of the Hemilienardia ocellata species complex, based on material examined in the present paper. Filled cycles = H. ocellata (Jousseaume, 1884); triangles = H. acinonyx sp. nov.; black square = H. lynx sp. nov.; grey square = H. cf. lynx sp. nov.; diamonds = H. pardus sp. nov.
Fig. 4 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 4. Marginal radular teeth of some species of Hemilienardia. A. H. malleti (Récluz, 1852) (from Kantor & Taylor 2002). B–C. H. ocellata (Jousseaume, 1884). Specimen from the Loyalty Islands, Lifou, Baie du Santal, Atelier LIFOU 2000, stn 1429, 20°47.5' S, 167°07.1' E, 8–18 m, 4.4 mm long. D. H. acinonyx sp. nov. Specimen from the Loyalty Islands, Lifou, Baie du Santal, Atelier LIFOU 2000, stn 1448, 20°45.8' S, 167°01.65' E, 20 m, 5.0 mm long.
Fig. 3 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 3. Protoconch and shell morphology in the Hemilienardia ocellata complex. A–C. H. ocellata (Jousseaume, 1884). A. Specimen from the Maldives, Ari Atoll, Maagau Kandu, 25 m, 3.1 mm long. B–C. Specimen from New Caledonia, Expedition MONTROUZIER, stn 1319, 20°44.7' S, 164°15.5' E, 15–20 m, 3.6 mm long. D–E. H. acinonyx sp. nov. Specimen from the Loyalty Islands, Lifou, Baie du Santal, Atelier Lifou 2000, stn 1448, 20°45.8' S, 167°01.65' E, 20 m, 5.0 mm long. F–G. H. lynx sp. nov. Holotype, MNHN IM-2013-5489, Madang District, off Kranket Island, PAPUA NIUGINI stn PP14, 05°12' S, 145°50' E, 100–120 m, 2.75 mm long. H–I. H. pardus sp. nov. Specimen from the Loyalty Islands, Lifou, Baie du Santal, Atelier LIFOU 2000, stn 1454, 20°56.65' S, 167°02.0' E, 15–18 m, 5.2 mm long.
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.