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832 results for “automatic”
Data set of labeled scenes in a barn in front of automatic milking system
<p>This data set includes supplementary files to our article \emph{"Deep learning image recognition of cow behavior near an automatic milking robot with an open data set"} (Leonardo Santiago Benitez Pereira, Olli Koskela, Ilpo Pölönen, Ilmo Aronen and Iivari Kunttu, in review, 2020). We acquired continuous video data over two-month period of cows in front of Automatic Milking Station (AMS). Each frame of the video is labeled as a single image belonging to one of classes described below.</p> <p>This data set includes 253 files of video data having in total \num{1526473} labeled frames. The data set used in the article consisted of 280 videos, but for privacy reasons, 27 video files including persons were removed from this data set, but their Java Script Object Notation (JSON) label files are left for, e.g., temporal analyses.</p>
Mere presence of co-eater automatically shifts foraging tactics toward "fast and easy" food in humans
Competition for food resources is widespread in nature. The foraging behavior of social animals should thus be adapted to potential food competition. We hypothesized that in the presence of co-foragers, animals would shift their tactics to forage more frequently for smaller food. Because smaller foods are more abundant in nature and allow faster consumption, such tactics should allow animals to consume food more securely against scrounging. We tested whether humans would exhibit such a shift. To prevent subjects from having rivalry, they were instructed to engage in a "taste test" in a laboratory, alone or in pairs. Even though the other subject was merely present and there was no real competition for food, subjects in pairs immediately exhibited a systematic behavioral shift to reaching for smaller food amounts more frequently, which was clearly distinct from their reaching patterns both when eating alone and when simply weighing the same food without eating any. These patterns suggest that behavioral shifts in the presence of others may be built-in tactics in gregarious animals to adapt to potential food competition in social foraging.
voiceHome-2 corpus - automatic speech recognition baseline - acoustic model
<p>This entry contains the acoustic model used for evaluation of distant-microphone speech recognition performance in:</p> <p>Nancy Bertin, Ewen Camberlein, Romain Lebarbenchon, Emmanuel Vincent, Sunit Sivasankaran, Irina Illina, Frédéric Bimbot<br> <a href="https://hal.inria.fr/hal-01923108">VoiceHome-2, an extended corpus for multichannel speech processing in real homes</a><br> <em>Speech Communication</em>, 2019, 106, pp.68-78. <a href="https://dx.doi.org/10.1016/j.specom.2018.11.002">⟨10.1016/j.specom.2018.11.002⟩</a></p>
Model Age Derivation of Large Martian Impact Craters, using automatic crater counting methods / Dataset
<ul> <li>"counting_area" folder: shapefiles of the mapped ejecta layers considered in this study</li> <li>"scc" folder: .scc files readable on CraterStats listing the size and location of craters detected by our CDA and recognized as primaries by the ASCI. The counting area considered for each crater slightly vary from the area indicated in the shapefile due to the removal of Thiessen polygons associated to secondary craters by the ASCI.</li> </ul> <p>The ASCI code and toolbox implementable to ESRI ArcGIS (10.6) is discoverable here: https://github.com/curtin-crater-detection/secondary-crater-removal<br> </p>
Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"
<p>This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. main_code folder</strong></p> <ul> <li><em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020)</li> <li><em>EASE2020 - 650 non-decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020)</li> <li><em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work.</li> <li><em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019)</li> </ul> <p><strong>3. RQ1 folder</strong></p> <ul> <li><em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>4. RQ2 folder</strong></p> <ul> <li><em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>5. RQ3 folder</strong></p> <ul> <li><em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul>
An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times
<p>Catalog of 440,697 earthquakes of the 2016-2017 Central Italy seismic sequence semi-automatically generated by Spallarossa et al. (2020). The catalogue covers one year of aftershocks following the first mainshock of the sequence (from 08242016 to 08312017).</p> <p>The catalog has been generated using the Complete Automatic Seismic Processor (CASP) procedure (Scafidi et al., 2019) to detect the events and an advanced picker engine (RSNI-Picker<sub>2</sub>; Scafidi et al., 2018; Spallarossa et al., 2014) to determine their phase arrival times. The final set of about 7 million P- and 10 million S-wave arrival times have been used to locate the events using a non-linear location algorithm (NonLinLoc; Lomax et al. 2000), with a 1D velocity model calibrated for the area (De Luca et al., 2009) and station corrections. For each event, also local magnitudes (M<sub>L</sub>) has been calculated as well as a locations quality.</p> <p>Earthquake locations quality has been classified by means of the procedure proposed by Michele et al., (2019) consisting of the combination of diverse uncertainty parameters provided by the NonLinLoc location code. Locations quality is provided in terms of a unique numeric normalized value, named quality factor, varying between qf=0 (best quality location) and qf=1 (worst quality location). Then locations have been assigned to a quality class depending on the qf parameter value according to the following scheme: A-class (0 < qf ≤ 0.25), B-class (0.25 < qf ≤ 0.50), C-class (0.50 < qf ≤ 0.75), and D-class (0.75 < qf < 1.00). The earthquake locations are distributed between the quality classes as A-30.6%, B-31.4%, C-18.6%, and D-19.4% (details in Spallarossa et al., 2020).</p> <p>We accompanied the catalogue with the 30 events with M>3.5 missed by our procedure (bring the total number of events to 440,727), including the first Amatrice mainshock (M<sub>W</sub>6.0; see Spallarossa et al., 2020). These 30 missing events recognisable by the ID starting with ISI), have been taken from INGV bulletin (<a href="http://terremoti.ingv.it">http://terremoti.ingv.it</a>; ISIDe Working Group., 2007), manually generated. These additional events report INGV locations and magnitude parameters while are missing related quality factors and quality class, being generated by a different procedure.</p> <p>We added to the larger events, the available moment magnitudes (M<sub>W</sub>) from Time Domain Moment Tensor catalogue (<a href="http://terremoti.ingv.it/tdmt">http://terremoti.ingv.it/tdmt</a>; Scognamiglio et al., 2006).</p> <p>The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following:</p> <ul> <li>Id-event – ID</li> <li>Latitude (°) expressed in decimal degrees - LAT</li> <li>Longitude (°) expressed in decimal degrees - LON</li> <li>Depth(km) hypocentral depth expressed in kilometres - DEP</li> <li>Year of origin time in the format yyyy - YR</li> <li>Month of origin time in the format mo - MON</li> <li>Day of origin time in the format dd - DY</li> <li>Hour of origin time in the format hh - HR</li> <li>Minute of origin time in the format mi - MIN</li> <li>Second of origin time in the format XX.XXX s - SEC</li> <li>Local Magnitude - ML</li> <li>Standard deviation of the Local Magnitude – STD</li> <li>Moment Magnitude – Mw (from TDMT)</li> <li>Horizontal Error (from NLL output) (km) expressed in kilometres - ERH</li> <li>Vertical Error (from NLL output) (km) expressed in kilometres - ERZ</li> <li>RMS (from NLL output) (s) expressed in seconds - RMS</li> <li>Number of Phases – NPHS</li> <li>Stations Azimuthal GAP (°) expressed in decimal degrees - GAP</li> <li>Quality factor - Qf</li> <li>Quality class - Qc</li> </ul> <p> </p> <p>De Luca G., M. Cattaneo, G. Monachesi and A, Amato (2009). Seismicity in the Umbria-Marche region from the integration of national and regional seismic networks. Tectonophysics, 476(1), 219-231. doi: 10.1016/j.tecto.2008.11.032.</p> <p>ISIDe Working Group. (2007). Italian Seismological Instrumental and Parametric Database (ISIDe). Istituto Nazionale di Geofisica e Vulcanologia (INGV); https://doi.org/10.13127/ISIDE.</p> <p>Lomax, A., J. Virieux, P. Volant, and C. Berge-Thierry (2000). Probabilistic earthquake location in 3D and layered models: introduction of a Metropolis–Gibbs method and comparison with linear locations. In: Advances in seismic event location, ed. C. H. Thurber and N. Rabinowitz, 101–134. Dordrecht and Boston: Kluwer Academic Publishers.</p> <p>Michele, M., Latorre, D., Emolo, A. (2019). An Empirical Formula to Classify the Quality of Earthquake Locations. Bulletin of the Seismological Society of America. Vol. 109, No. 6, pp. 2755–2761, December 2019, doi: 10.1785/0120190144.</p> <p>Scafidi, D., Viganò A., Ferretti G., and Spallarossa D. (2018). Robust picking and accurate location with RSNI-Picker2: real-time automatic monitoring of earthquakes and non-tectonic events, Seismol. Res. Lett, Vol. 89 (4), pp. 1478-1487, doi: 10.1785/0220170206.</p> <p>Scafidi D, Spallarossa D, Ferretti G, Barani S, Castello B, Margheriti L (2019). A complete automatic procedure to compile reliable seismic catalogs and travel-time and strong-motion parameters datasets. Seismol Res Lett 90(3):1308–1317.</p> <p>Scognamiglio, L., Tinti, E., Quintiliani, M. (2006). Time Domain Moment Tensor [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/TDMT.</p> <p>Spallarossa, D., G. Ferretti, D. Scafidi, C. Turino, and M. Pasta (2014). Performance of the RSNI-Picker, Seismol. Res. Lett. 85, 1243–1254.</p> <p>Spallarossa D., Cattaneo M., Scafidi D., Michele M., Chiaraluce L., Segou M. and I. G. Main (2020). An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times. Geophys. J. Int. doi: 10.1093/gji/ggaa604.</p>
Review4Repair: Code Review Aided Automatic Program Repairing
<p>The natural language instructions scripted on the review comments are enormous sources of information about code bug’s nature and expected solutions. In this study, we investigate the performance improvement of repair techniques using code review comments. We train a sequence-to-sequence model on 55,060 code reviews and associated code changes. We also introduce new tokenization and preprocessing approaches that help to achieve significant improvement over state-of-the-art learning-based repair techniques. We boost the top-1 accuracy by 20.33% and top-10 accuracy by 34.82%. We could provide a suggestion for stylistics and non-code errors unaddressed by prior techniques.</p>
Data from paper: Semi-automatic Feedback for Improving Architecture Conformance to Microservice Patterns and Practices
<p>All the data of the study <strong><em>Semi-automatic Feedback for Improving Architecture Conformance to Microservice Patterns and Practices. The file contains all the scripts and models of this study</em></strong></p>
BRAMS Radio Spectrograms and Spectrogram Samples for Automatic Detection of Meteor Echoes
<p>The files in this dataset are based of radio recordings taped by BRAMS (Belgian RAdio Meteor Stations), the Belgian meteor detection network.</p> <p>Included in the dataset are the original BRAMS radio recordings (stored as .wav audio files), the spectrogram data for each radio recording (stored as .csv files) and the meteor and non-meteor samples extracted from the radio spectrograms (stored as .csv files).</p> <p>It should be noted that the the spectrogram data was sampled using a sliding window of size 30x20 pixels and the samples extracted in this manner were further processed by calculating the vertical average of each column in the 30x20 matrixes. The result of this sampling procedure is a set of data vectors containing the average power of the signal found in the original 30x20 spectrogram sample.</p>
Artificial dataset for "Automatic Determination of Parameters Values for Heuristics Miner++"
<p>This set of processes, built for test purposes [1], is composed of 125 process models. These processes were created using PLG [2, 3]. The generation of the random processes is based on some basic “process patterns”, like the AND-split/join, XORsplit/join, the sequence of two activities, and so on.</p> <p>For each of the 125 process models, two logs were generated: one with 250 traces and one with 500 traces. In these logs, the 75% of the activities are expressed as time intervals (the other ones are instantaneous) and 5% of the traces are noise. In this context “noise” is considered either a swap between two activities or removal of an activity.</p> <p> </p> <p><strong>References</strong></p> <ol> <li>A. Burattin, A. Sperduti. "<em>Automatic Determination of Parameters Values' for Heuristics Miner+</em>+". In <em>Proceedings of IEEE Congress on Evolutionary Computation</em> (IEEE WCCI CEC 2010); 10.1109/CEC.2010.5586208</li> <li>A. Burattin. “<em>PLG2: Multiperspective Processes Randomization and Simulation for Online and Offline Settings</em>”. In <em>CoRR</em> abs/1506.08415, Jun. 2015.</li> <li>A. Burattin and A. Sperduti. “<em>PLG: a Framework for the Generation of Business Process Models and their Execution Logs</em>”. In <em>Proc. of the 6th Int. Workshop on Business Process Intelligence</em> (BPI 2010); 2010.10.1007/978-3-642-20511-8_20.</li> </ol>
Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms - DICOM Database
<p>This dataset contains DICOM versions of the 24 anthropomorphic pulmonary CT phantoms accompanying the manuscript "Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms" submitted to PLoS ONE.</p> <p>NRRD versions can be found in http://dx.doi.org/10.5281/zenodo.20766 (doi:10.5281/zenodo.20766).</p>
Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms - NRRD Database
<p>This dataset contains the 24 anthropomorphic pulmonary CT phantoms accompanying the manuscript "Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms" submitted to PLoS ONE.</p> <p>DICOM versions can be found at http://dx.doi.org/10.5281/zenodo.32740 (doi:10.5281/zenodo.32740)</p>
Validity of two automatic artifact reduction software methods in ictal EEG interpretation. Dataset 1
<p>Unprocessed electroencephalogram recordings of seizures from deidentified study patients with epilepsy prior to and following processing using the AR2 (artifact reduction 2) software method. The files are stored in European Data Format (.EDF). "_out" files have been processed by AR2.</p>
Part-by-part interface-based search and automatic reassembly of CAD models for database expansion and model reuse
<p>This dataset contains the generated cad assembly from three differents strategy from the article "Part-by-part interface-based search and automatic reassembly of CAD models for database expansion and model reuse".<br> In ReplacePart strategy, each assembly is available in .FCStd format which has the kinematic constraints in the A2+ workbench. A .STEP format of the assembly as well as a PNG screen of the assembly is also available. For the two other strategies, juste STEP files are availables.</p>
Confidence Matrices of Automatically Recognized Charters of the Monastery of Einsiedeln
<p>The data set consists of data gained through automatic text recognition with HTR+ models.<br>The model is usable within the Transkribus environment: <a href="https://readcoop.eu/model/charter-scripts-german-latin-french/">readcoop.eu/model/charter-scripts-german-latin-french/</a>.</p><p>Each folder is a group of documents, depending on the call-number in the archives (<a href="https://www.klosterarchiv.ch/">https://www.klosterarchiv.ch/</a>). Each sub-folder is a document and each CSV is a line of recognized text in the form of a Confidence Matrix.</p>
Data supporting "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables"
<p>This repository contains the set of data and the code to reproduce the results shown in "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables" published on Journal of Chemical Engineering and Data (DOI: 10.1021/acs.jced.3c00538).</p>
Research data supporting ""Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables""
<p>This repository contains the set of data and the code to reproduce the results shown in "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables" published on Journal of Chemical Engineering and Data (DOI: 10.1021/acs.jced.3c00538).</p>
Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution"
<p><strong>Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution".</strong></p><p>We store values of the basic build-in Python types: numeric (int, float, and complex), boolean (bool), and text sequence (str). We also store values of the types: sequence (list, tuple, and range), set (frozenset and set), and mapping (dict) that include only basic types. To create the dataset, we monitored the test suites of 15 Python Standard Libraries. TestDossier contains 1,234 distinct argument/variable names and 133,169 dis- tinct values, leading to a total of 12,9M individual values.</p>
Exploring the Automatic Classification of Usage Information in Feedback
<p>Datasets and list of relevant papers for the publication "Exploring the Automatic Classification of Usage Information in Feedback".</p><p>Please read the included readMe for more information on formatting of Json-Files</p>
Data from: ASHS-OAP atlas for automatic entorhinal cortex segmentation
<p>Early stages of Alzheimer's disease (AD) are associated with volume reductions in specific subregions of the medial temporal lobe (MTL). Using a manual segmentation method—the Olsen-Amaral-Palombo (OAP) protocol— previous work in healthy older adults showed that reductions in grey matter volumes in MTL subregions were associated with lower scores on the Montreal Cognitive Assessment (MoCA), suggesting atrophy may occur prior to diagnosis of mild cognitive impairment, a condition that often progresses to AD. However, current manual segmentation methods are labour intensive and time consuming. Here, we examined the utility of Automatic Segmentation of Hippocampal Subfields (ASHS) to detect volumetric differences in MTL subregions of healthy older adults who varied in cognitive status as determined by the MoCA. We trained ASHS on the OAP protocol to create the ASHS-OAP atlas, and then examined how well automated segmentation replicated the ground truth of manual segmentation. Volumetric measures obtained from the ASHS-OAP atlas were also contrasted against those from the ASHS-PMC atlas, a widely used atlas provided by the ASHS team. Volumetrics from the ASHS-OAP atlas aligned well with those from manual segmentation, suggesting ASHS-OAP is a viable alternative to current manual segmentation methods. In addition, while some subtle differences were observed, results from the ASHS-PMC and ASHS-OAP atlases aligned well with each other overall. Our findings highlight the utility of automated segmentation methods but still underscore the need for a unified and harmonized MTL segmentation atlas.</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.