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Dataset results
832 results for “automatic”
Pixel-based forest classification of Sentinel-2 images using automatically generated datasets
<p>Contains six training datasets, composed of 800, 1600 and 3200 images. Each training dataset made up of OSM (<em>OpenStreetMap</em>) masks or HRL (<em>Copernicus pan-European High Resolution Layers</em>).</p> <p>Additional 2 evaluation datasets based on OSM and HRL. Composed of 200 evaluation images.</p> <p>For study area <em>lithuania_2018_06.tiff</em> is provided. This contains a fully preprocessed study area (removed clouds, composed mosaic).</p> <p>We provide additionally a merged mosaic of Lithuanian HRL in <em>lithuania_HRL.tiff </em>file.</p> <p><em>OpenStreetMap</em> database is not provided, it can be found at https://planet.openstreetmap.org.</p>
Using photodiodes and supervised Machine Learning for automatic classification of weld defects in laser welding of thin foils copper-to-steel battery tabs
<p>In this folder, excel files are stored with the results of signal processing that supported findings in the following paper:</p> <p>"Using photodiodes and supervised Machine Learning for automatic classification of weld defects in laser welding of thin foils copper-to-steell battery tabs".</p> <p>Matlab scripts and orginal signals will be uploaded soon with more detailed description.</p> <p> </p>
Appendix for "Don't DIY: Automatically transform legacy Python code to support structural pattern matching"
<p>This is the appendix for paper "Don’t DIY: Automatically transform legacy Python code to support structural pattern matching" presented in SCAM 2022.</p> <p><strong>Abstract</strong></p> <p><em>As data becomes more and more complex as technology evolves, the need to support more complex data types in programming languages has grown. However, without proper storage and manipulation capabilities, handling such data can result in hard-to-read, difficult-to-maintain code. Therefore, programming languages continuously evolve to provide more and more ways to handle complex data. Python 3.10 introduced structural pattern matching, which serves this exact purpose: we can split complex data into relevant parts by examining its structure, and store them for later processing. Previously, we could only use the traditional conditional branching, which could have led to long chains of nested conditionals. Maintaining such code fragments can be cumbersome. In this paper, we present a complete framework to solve the aforementioned problem. Our software is capable of examining Python source code and transforming relevant conditionals into structural pattern matching. Moreover, it is able to handle nested conditionals and it is also easily extensible, thus the set of possible transformations can be easily increased.</em></p>
Automatically mapped traces from UAV-acquired images of Loviisa shoreline outcrops
<p>This dataset contains automatically mapped two-dimensional bedrock fracture traces from shoreline outcrop orthomosaics generated from UAV-acquired images (https://doi.org/10.5281/zenodo.7077518). The outcrops are located along the shoreline of Loviisa, South-East Finland. Code for automatic mapping is available on GitHub (https://github.com/nialov/ALSA).</p> <p>Data is published as ESRI Shapefiles in the ETRS-TM35FIN (EPSG:3067) coordinate system. </p> <p>The work in automatic mapping was done as part of a Geological Survey of<br> Finland project, Kallioperän Rikkonaisuus, during 2021-2022.</p>
Automatic segmentation
<p>Automatic segmentation of a point cloud presenting a single tree captured by terrestrial laser scanning. The approach uses the neighbor relation of the surface patches whose center points are presented as spheres in this animation. The process starts from the tree base and advances until all surface patches are processed.</p> <p>At every step, a cut set is defined for the current segment. The cut set is basically a layer of surface patches that are either appended to the current segment or marked as a base of a new segment. Whenever the cut set becomes disconnected, each of its components is inspected in various ways to determine whether it belongs to the current segment or not. A component that is not part of the segment is marked as a new base. A segment is completed when it cannot be extended into any unprocessed direction. Upon completion a new base is selected from the process queue.</p> <p>For details of the segmentation process, see the group homepage or, e.g., the open access journal article "Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data" (http://www.mdpi.com/2072-4292/5/2/491).</p> <p>This animation was produced by the Inverse Problems research group in the Department of Mathematics at Tampere University of Technology (http://math.tut.fi/inversegroup).</p> <p>Animation created using Blender (http://www.blender.org).</p> <p>Music:<br> Firebrand Kevin MacLeod (http://incompetech.com)<br> Licensed under Creative Commons: By Attribution 3.0<br> http://creativecommons.org/licenses/by/3.0/</p> <p>Sound effects:<br> Whooshes - 27 - Wooshes - Dark Rumbling Low.wav<br> Whooshes - 30 - Wooshes - Crackling Energy.wav<br> by koroshiya1 at http://www.freesound.org/<br> Licensed under Creative Commons: By Attribution 3.0<br> http://creativecommons.org/licenses/by/3.0/</p>
Physical Features for my study on Automatic Seismic Event Classification System in Pacific Northwest (Origin time - 50, +100)
<p>These features used the revised version of the feature extraction code. The revision involves changing the envelope filtering options and some minor modifications. </p>
Automatic State Interaction for Large Localized Active Spaces for Multimetallic Systmes
<p>The localized active space self consistent field (LASSCF) method factorizes a complete active space (CAS) wave function into an antisymmetrized product of localized active space wave function fragments. Correlation between fragments is then reintroduced through LAS state interaction (LASSI), in which the Hamiltonian is diagonalized in a model space of LAS states. However, the optimal procedure for defining the LAS fragments and LASSI model space is unknown. We here present an automated framework to explore systematically convergent sets of model spaces, which we call LASSI[$r$,$\nmax$]. This method requires the user to select only $r$, the number of electron hops from one fragment to another and $\nmax$, the number of fragment basis functions per Hilbert space, which converges to CASCI in the limit of $r,\nmax\to\infty$. Numerical tests of this method on the tri-metal complexes [Fe(III)Al(III)Fe(II)($\mu_3$-O)]$^{6+}$ and [Fe(III)$_2$Fe(II)($\mu_3$-O)]$^{6+}$ show efficient convergence to the CASCI limit with 4-10 orders of magnitude fewer states.</p>
Data from: Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy
<p>Mice are the most commonly used model animals for itch research and for the development of anti-itch drugs. Most laboratories manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale genetic or drug screenings. In this study, we developed a new system, Scratch-AID (Automatic Itch Detection), which could automatically identify and quantify mouse scratching behavior with high accuracy. Our system included a custom-designed videotaping box to ensure high-quality and replicable mouse behavior recording and a convolutional recurrent neural network trained with frame-labeled mouse scratching behavior videos, induced by nape injection of chloroquine. The best-trained network achieved 97.6% recall and 96.9% precision on previously unseen test videos. Remarkably, Scratch-AID could reliably identify scratching behavior in other major mouse itch models, including the acute cheek model, the histaminergic model, and the chronic itch model. Moreover, our system detected significant differences in scratching behavior between control and mice treated with an anti-itch drug. Taken together, we have established a novel deep learning-based system that could replace manual quantification for mouse scratching behavior in different itch models and for drug screening. This dataset includes all videos for the study to establish a novel deep learning-based system for automatic mouse scratching behavior quantification.</p>
Unlocking LLM Insights: A Dataset for Automatic Model Card Generation
<p>Language models (LMs) are no longer restricted to the ML community, and instruction-following LMs have led to a rise in autonomous AI agents. As the accessibility of LMs grows, it is imperative that an understanding of their capabilities, intended usage, and development cycle also improves. Model cards are a widespread practice for documenting detailed information about an ML model. To automate model card generation, we introduce a dataset of 500 question-answer pairs for 25 LMs that cover crucial aspects of the model, such as its training configurations, datasets, biases, architecture details, and training resources. We employ annotators to extract the answers from the original paper. Further, we explore the capabilities of LMs in generating model cards by answering questions. We experiment with three configurations: zero-shot generation, retrieval-augmented generation, and fine-tuning on our dataset. The fine-tuned Llama 3 model shows an improvement of 7 points over the retrieval-augmented generation setup. This indicates that our dataset can be used to train models to automatically generate model cards from paper text and reduce the human effort in the model card curation process.</p>
Arrival-time data automatically picked up by PickNet of Pn wave in Tanlu Fault Zone and surrounding areas
<p>This file is the arrival-time data automatically picked up by PickNet of Pn wave in Tanlu Fault Zone and surrounding areas. Only these Pn arrival times are used for tomographic inversion.</p> <p> </p>
Automatic Ontology Population Results
Open the record for dataset details and reuse information.
Dataset for "On Developing an ML-Based Approach for the Automatic Characterization of Behavioral Phenotypes for Dairy Cows Relevant to Thermotolerance"
<p>This dataset consists of 3,421 videos filmed at T & K Dairy in Snyder, TX, over a 24-hour period on March 12-13, 2023. These videos were then used to train, validate, and evaluate a computer vision algorithm that is capable of automatically identifying cows using their coat patterns.</p>
A Comprehensive Central Kurdish Sound Dataset for Robust Automatic Speech Recognition (Part 1).
<p>Exploring the intricacies of Speech Recognition Technology (SRT), our dataset encompasses a wide range of age demographics, spanning from adolescents to individuals in their fifties. This diverse dataset comprises a substantial collection of raw data, amounting to 1,739,089 entries. Within this dataset, a meticulous curation process has yielded a total of 1,683 hours of data, providing a thorough examination of language acquisition patterns across different age cohorts within the Central Kurdish linguistic domain.</p>
Assisting the appraisal of e-mail records with automatic classification
<p>This spreadsheet shows the results of training a series of Support Vector Machine (SVM) classifiers to replicate the decision process for identifying e-mails of business value from among 846 e-mails in two corporate mailboxes. One set of SVMs were trained on emails without including the text of attachments and with the inclusion of attachments. For the sake of comparison, these SVMs were also trained on Spam / Ham and on a randomly selected subset of Enron emails half of which were arbitrarily tagged as "business value".</p>
Automatic registration of magnetic resonance and optoacoustic tomography
<p>optoacoustic / photoacoustic tomography - magnetic resonance imaging registration dataset.</p> <p>1. phantom with fiducial marker inserts</p> <p>2. ex vivo mouse brain</p> <p>3. in vivo mouse brain </p> <p>4. RegOA registration application </p>
Flute audio labelled database for Automatic Music Transcription
<p>Automatic Music Transcription (ATM) is a well-known task in the Music Information Retrieval (MIR) domain and consists on the computation of a symbolic music representation from an audio recording. In this work, our focus is to adapt algorithms that extract musical information from an audio file for a particular instrument. The main objective is to study the automatic transcription of digitized music support systems. Currently, these techniques are applied to a generic sound timbre, to sounds to any instrument for further analysis and conversion to a digital music encoding and final score format. The results of this project add new knowledge in this automatic transcription field, since traverse flute has been selected as the instrument on which to focus all the process and, until now, there is no database of flute sounds for this purpose.</p> <p>For so, we have recorded some sounds, both monophonic and polyphonic music. These audio files have been processed by the chosen transcription algorithm and converted to a digital music encoding format for its posterior alignment with the original recordings. Once all these data have been converted to text, the resulting labeled database its constituted by the initial audios and final aligned files.</p> <p>Furthermore, after this process and from the obtained data, an evaluation of the transcriptor behavior has been made based on two main techniques: note and frame level.</p> <p>This database includes the original audio files (.wav), transcribed MIDI files (.mid), aligned MIDI files (.mid), aligned text files (.txt) and evaluation files (.csv).</p>
Semi-automatic entry of handles
<p>This screencast gives an overview how a script written to run as AutoHotkey script helps our article cataloguing librarians in their daily work.</p>
Dataset from: Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest Biogeosciences 2019
<p>Dataset used for the manuscript <strong>Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest</strong> in Biogesciences, 2019</p>
Data and models for automatic scansion experiment Dutch Song Database
<p>This release contains the <strong>data </strong>used in an <a href="https://github.com/WHaverals/scanner">experiment</a> on automatic scansion for historical Dutch song texts. Aside form the data, two <strong>models </strong>are included in this release as well. One model is essential for running the code that is part of this experiment (model_s); while the other model is an example of an acquired automatic scansion model (best_model).</p> <p><strong>Item descriptions</strong>:</p> <ul> <li><em>meertens-meter-songs.zip</em> → collection of 23,197 historic Dutch songs (xml-format). These files (and the gathered meta-data) stems from a collaboration project between the <em><a href="http://www.liederenbank.nl/index.php?lan=en">Dutch Song Database</a> </em>and the <em><a href="https://dbnl.org">Digital Library for Dutch Literature</a></em>. All files contain meta-data on the number of beats that is present in individual verse lines. Snippet:</li> </ul> <blockquote> <pre><code class="language-xml"><lg> <l id="s1:l1" met="4" type="-+"> Een Meysken op een Rivierken <rhyme label="a" type="m">sadt</rhyme>,</l> <l id="s1:l2" met="2" type="-+"> So schoon zy <rhyme label="b" type="m">was</rhyme>,</l> <l id="s1:l3" met="4" type="-+"> Sy sadt en verbeyde haer soete <rhyme label="c" type="m">Lief</rhyme>,</l> <l id="s1:l4" met="2" type="-+"> Int groene <rhyme label="b" type="m" corresp="#s1:l2">gras</rhyme>.</l> </lg></code></pre> <p> </p> </blockquote> <p> </p> <ul> <li><em>model_s</em> → model used for syllabification and assignment of lexical stress of (historic) Dutch words. The development of this model was part of a <a href="https://github.com/WHaverals/stresser">previous project</a>.</li> </ul> <p> </p> <ul> <li><em>stress_xml.zip</em> → collection of 23,197 historic Dutch songs (xml-format). These are the same songs a the <em>meertens-meter-songs</em>, yet now their individual words are syllabified and annotated for lexical stress. The songs in this folder are used as input during the training process. Snippet:</li> </ul> <blockquote> <pre><code class="language-xml"><l id="s1:l1" met="4" type="-+"> <w token="een"> <s word-stress="1" line-stress="0">een</s> </w> <w token="meysken"> <s word-stress="1" line-stress="0">meys</s> <s word-stress="0" line-stress="0">ken</s> </w> <w token="op"> <s word-stress="1" line-stress="0">op</s> </w> <w token="een"> <s word-stress="1" line-stress="0">een</s> </w> <w token="rivierken"> <s word-stress="0" line-stress="0">ri</s> <s word-stress="1" line-stress="0">vier</s> <s word-stress="0" line-stress="0">ken</s> </w> <rhyme label="a" type="m"> <w token="sadt"> <s word-stress="1" line-stress="0">sadt</s> </w> </rhyme> </l></code></pre> <p> </p> </blockquote> <ul> <li><em>gold_scan.zip</em> → 198 Dutch song files (xml-format). These files have been annotated by an expert for line stress.</li> </ul> <p> </p> <ul> <li><em>eval_splits.zip </em>→ contains the splits made from <em>gold_scan. </em>These are the splits used in the automatic scansion experiment: a development set of 98 songs (used during training), and a test set of 99 songs (used for evaluating the best model after training).</li> </ul> <p> </p> <ul> <li><em>best_model.zip</em> → contains the files of an acquired model for automatic Dutch song scansion.</li> </ul>
InCLosure Code for Real Automatic Differentiation: Supplementary Material for Article "A Consistent and Categorical Axiomatization of Differentiation Arithmetic Applicable to First and Higher Order Derivatives"
<p>InCLosure Code for Real Automatic Differentiation: Supplementary Material for Article "A Consistent and Categorical Axiomatization of Differentiation Arithmetic Applicable to First and Higher Order Derivatives", Punjab University Journal of Mathematics, October 2019. Download latest release of InCLosure via <a href="https://doi.org/10.5281/zenodo.2702404">https://doi.org/10.5281/zenodo.2702404</a></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.