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832 results for “automatic”
Tarmachan Automatic Weather Station (National Trust for Scotland/University of Dundee)
<p>Mountain weather station on the south-facing slopes of Meall nan Tarmachan on the National Trust for Scotland's National Nature Reserve (NNR). The site is at 710 m above sea level, overlooking Loch Tay. Snowcover has historically been an important part of the climate of the site, but in recent years has become increasingly transient.</p> <p>The NNR is the most important in Scotland for its arctic alpine flora, we are noticing changes in species distribution and to habitats indicative of the effects of climate change. Hence the establishment of this weather station to allow changes in climate at altitude to be monitored. This site installed May 2018 ~20 m beyond the headwall of a disused quarry. Winds may cause undercatch of rainfall (though the gauge is of an aerodynamic design) and more particularly snow. Recorded wind speeds may underestimate winds generally around the mountain owing to effects of the quarry wall.</p> <p>An earlier site was operated nearby in the early 2000s but sadly data have been lost.</p> <p>Data are recorded on a Campbell Scientific CR1000 data logger, running with 10 s scan rate and 15 min logging interval.</p> <p>Snow depths may be inferred by examining TCDT (temperature-corrected depth to target) data - obtained from a SR50A sensor on an arm c. 2.3 m above ground. These are available only for limited periods in 2019 and again winter 2021/22, and are now discontinued. However, snow cover can be inferred by examining the differential between air temperature and ground temperature: the ground sensor is insulated when snow covers the ground.</p> <p>Sensor details: see metdata</p> <p>Real-time data are displayed graphically at <a href="https://hydro-data.dundee.ac.uk/tarmachan" target="_blank" rel="noopener">https://hydro-data.dundee.ac.uk/tarmachan</a> (no downloads)</p>
Data and source code for Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary
<p>We present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.</p>
2021 pollen dataset from automatic particle detector in Šiauliai
<p>Dataset acquired from Rapid-E device by testing it with anemophilous plant pollen collected in Lithuania.</p> <p>The sampling methodology can be found in publication "Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps", Šaulienė Ingrida, et al. Atmospheric Measurement Techniques, 2019, 12.6: 3435-3452. <a href="https://doi.org/10.5194/amt-12-3435-2019">https://doi.org/10.5194/amt-12-3435-2019</a></p> <p>The authors would like to hear from you at realtime@sa.vu.lt if you use this dataset.</p>
Podcast annotation dataset for paper "Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts "
<p>Dataset for paper "Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts". Please refer to the paper for details. Compared to the dataset used in the paper, 20 out of the 417 episodes have been removed due to copyright issues. </p> <p>The data file contains the following fields:</p> <p>- "episode_intro_start": the time stamp for episode introduction start (in milliseconds)</p> <p>- "episode_intro_end": the time stamp for episode introduction end (in milliseconds)</p> <p>- "program_intro_start": the time stamp for program introduction start (in milliseconds)</p> <p>- "program_intro_end": the time stamp for program introduction end (in milliseconds)</p> <p>- "program_name": name of the podcast program</p> <p>- "episode_name": name of the podcast episode</p> <p>- "transcription": JSON string containing the transcription, including the timestamps.</p> <p>- "annotator": anonymized annotator ID.</p>
Dvoice : An open source dataset for Automatic Speech Recognition on Moroccan dialectal Arabic
<p>Dialectal Voice is a community project initiated by AIOX Labs to facilitate voice recognition by Intelligent Systems. Today, the need for AI systems capable of recognizing the human voice is increasingly expressed within communities. However, we note that for some languages such as Darija, there are not enough voice technology solutions. To meet this need, we then proposed to establish this program of iterative and interactive construction of a dialectal database open to all in order to help improve models of voice recognition and generation.</p>
A web-based tool for automatically linking clinical trials to their publications - example calculation
<p><strong>Objective</strong>: Evidence synthesis teams, physicians, policy makers, and patients and their families all have an interest in following the outcomes of clinical trials and would benefit from being able to evaluate both the results posted in trial registries and in the publications that arise from them. Manual searching for publications arising from a given trial is a laborious and uncertain process. We sought to create a statistical model to automatically identify PubMed articles likely to report clinical outcome results from each registered trial in ClinicalTrials.gov.</p> <p><strong>Materials and Methods</strong>: A machine learning-based model was trained on pairs (publications linked to specific registered trials). Multiple features were constructed based on the degree of matching between the PubMed article metadata and specific fields of the trial registry, as well as matching with the set of publications already known to be linked to that trial.</p> <p><strong>Results</strong>: Evaluation of the model using NCT-linked articles as gold standard showed that they tend to be top ranked (median best rank = 1.0), and 91% of them are ranked in the top ten.</p> <p><strong>Discussion</strong>: Based on this model, we have created a free, public web based tool at http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/TrialPubLinking/trial_pub_link_start.cgi that, given any registered trial in ClinicalTrials.gov, presents a ranked list of the PubMed articles in order of estimated probability that they report clinical outcome data from that trial. The tool should greatly facilitate studies of trial outcome results and their relation to the original trial designs.</p>
Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks
<p>This is a Fossil Image Dataset, which contains >415000 images. A total of 50 clades were labeled, with a final 90% accuracy. We used the web crawler to download fossil images from the Internet. We declare that all the collected images are used for academic purposes only. If anyone wants to use this dataset, please agree on the Terms of access for the Fossil Image Dataset (FID). We uploaded two datasets: FID (contains 0.415 million images) and reduced-FID (60 thousand images, 1200 for each clade). Requirements of necessary preinstalled Python libraries, algorithms for analysis, and the model weights are available at <a href="https://github.com/XiaokangLiuCUG/Fossil_Image_Dataset">https://github.com/XiaokangLiuCUG/Fossil_Image_Dataset</a>.</p>
FlavoriaFoodWeight1700: Automated Lunch Line Meal Pictures with Automatic Measurement of Weight and Contents
<p>A dataset of around 2000 pictures consisting of both pictures taken on a lunch line and their measured contents based on the lunch line's automation systems.</p> <p>The pictures were automatically taken and linked to data via RFID tags on each meal tray.</p> <p>The pictures are linkable to CSV meal contents. CSV includes explanatory headers.</p> <p> </p>
ReSpa - Towards an automatic requirements classification in a new Spanish dataset
<p>ReSpa (Spanish Dataset for requirements classification) dataset is conformed by requirements collected from final degree projects from one University. It was presented in the paper 'Towards an automatic requirements classification in a new Spanish dataset' and used also in the paper 'Requirements Classification Using FastText and BETO in Spanish Documents'.</p> <p>Cited as:</p> <p>Limaylla-Lunarejo, M. I., Condori-Fernandez, N., & Luaces, M. R. (2022, August). Towards an automatic requirements classification in a new Spanish dataset. In <em>2022 IEEE 30th International Requirements Engineering Conference (RE)</em> (pp. 270-271). IEEE. https://doi.org/10.1109/RE54965.2022.00039</p> <p> </p> <p> </p> <p> </p> <p> </p>
Replication package of `Can We Automatically Generate Class Comments in Pharo?`
<pre><code class="language-markdown"># RP Automatic-comment-generation This folder contains all the material needed to replicate the experiments. ## Content - [RP Automatic-comment-generation](#automatic-comment-generation) - Appendix.pdf - [Content](#content) - [Dataset/](#dataset/) - [Online evaluation/](#online-evaluation/) - [Results/](#results/) - [SI-Approach/](#SI-Approach/) ## Dataset/ This contains the data for the online evaluation - #### Online evaluation/ Contains set of questions used in online evaluation, results of these questions and lists of classes used for each online evaluation - [Class_understanding_questions.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Class_understanding_questions.xlsx) Questions used to determin if a participant understood the functionality of a class. - [Q_and_A_class_comment_characteristics.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Q_and_A_class_comment_characteristics) Questions and possible answers used for the evaluation of the characteristics adequacy, conciseness and comprehensibility of a generated class comment. - [Q_and_A_what_participants_write_and_look_for.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Q_and_A_what_participants_write_and_look_for.xlsx) Questions and possible answers used to determine what participants look for and write in class comments. - [Classes_used_in_evaluation1.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation1.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 1. - [Classes_used_in_evaluation2.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation2.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 2. - [Classes_used_in_evaluation3.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation3.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 3. - [Classes_used_in_evaluation4.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation4.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 4. - [How_often_participants_write_comments.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\How_often_participants_write_comments.xlsx) Results for distribution of how often participants claim to write comments. - [How_participants_follow_class_comment_template.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\How_participants_follow_class_comment_template.xlsx) Results for distribution of how participants follow the class comment template. - [What_participants_look_for_in_comments.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\What_participants_look_for_in_comments.xlsx) Results of evaluation for what participants look for in class comments. - [What_participants_write_in_comments.xlsx](\RP-Automatic-comment-generation\Dataset\Online_evaluation\What_participants_write_in_comments.xlsx) Results of evaluation for what participants write in class comments. ## Results/ - #### SI-Approach/ Contains all the data for the results related to SI-Approach and the scripts used to generate said data. - [Class_stereotype_distribution.xlsx](RP-Automatic-comment-generation\Results\SI-Approach\Class_stereotype_distribution.xlsx) Extracted class stereotypes for 350 random classes in the Pharo base image. Evaluated in 10 classes per step. - [Class_stereotype_script.txt](RP-Automatic-comment-generation\Results\SI-Approach\Class_stereotype_script.txt) Script used in the Playground of Pharo to return counts of how many classes get assigned the specific class stereotypes. Returns numbers for the class stereotypes in alphabetical order. Boundary => Small - [Method_stereotype_dstribution.xlsx](RP-Automatic-comment-generation\Results\SI-Approach\Method_stereotype_distribution.xlsx) Extracted method stereotypes for 500 random classes in the Pharo base image. Evaluated in 20 classes per step. - [Method_stereotype_script.txt](RP-Automatic-comment-generation\Results\SI-Approach\Method_stereotype_script.txt) Script used in the Playground of Pharo to return counts of how many methods get assigned the specific method stereotypes. Returns numbers for the method stereotypes in the order Accessors, Getters, Mutators, Setters, Collaborators, Controllers, Factories and Degenerate. </code></pre> <p>Replication package of 'Can We Automatically Generate Class Comments in Pharo?'</p> <pre><code class="language-markdown"># RP Automatic-comment-generation This folder contains all the material needed to replicate the experiments. ## Content - [RP Automatic-comment-generation](#automatic-comment-generation) - [Content](#content) - [Dataset/](#dataset/) - [Online evaluation/](#online-evaluation/) - [Results/](#results/) - [SI-Approach/](#SI-Approach/) ## Dataset/ This contains the data for the online evaluation - #### Online evaluation/ Contains set of questions used in online evaluation, results of these questions and lists of classes used for each online evaluation - [Class_understanding_questions.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Class_understanding_questions.xlsx) Questions used to determin if a participant understood the functionality of a class. - [Q_and_A_class_comment_characteristics.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Q_and_A_class_comment_characteristics) Questions and possible answers used for the evaluation of the characteristics adequacy, conciseness and comprehensibility of a generated class comment. - [Q_and_A_what_participants_write_and_look_for.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Q_and_A_what_participants_write_and_look_for.xlsx) Questions and possible answers used to determine what participants look for and write in class comments. - [Classes_used_in_evaluation1.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation1.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 1. - [Classes_used_in_evaluation2.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation2.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 2. - [Classes_used_in_evaluation3.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation3.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 3. - [Classes_used_in_evaluation4.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation4.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 4. - [How_often_participants_write_comments.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\How_often_participants_write_comments.xlsx) Results for distribution of how often participants claim to write comments. - [How_participants_follow_class_comment_template.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\How_participants_follow_class_comment_template.xlsx) Results for distribution of how participants follow the class comment template. - [What_participants_look_for_in_comments.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\What_participants_look_for_in_comments.xlsx) Results of evaluation for what participants look for in class comments. - [What_participants_write_in_comments.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\What_participants_write_in_comments.xlsx) Results of evaluation for what participants write in class comments. ## Results/ - #### SI-Approach/ Contains all the data for the results related to SI-Approach and the scripts used to generate said data. - [Class_stereotype_distribution.xlsx](thesis\RP-Automatic-comment-generation\Results\RQ2\Class_stereotype_distribution.xlsx) Extracted class stereotypes for 350 random classes in the Pharo base image. Evaluated in 10 classes per step. - [Class_stereotype_script.txt](thesis\RP-Automatic-comment-generation\Results\RQ2\Class_stereotype_script.txt) Script used in the Playground of Pharo to return counts of how many classes get assigned the specific class stereotypes. Returns numbers for the class stereotypes in alphabetical order. Boundary => Small - [Method_stereotype_dstribution.xlsx](thesis\RP-Automatic-comment-generation\Results\RQ2\Method_stereotype_distribution.xlsx) Extracted method stereotypes for 500 random classes in the Pharo base image. Evaluated in 20 classes per step. - [Method_stereotype_script.txt](thesis\RP-Automatic-comment-generation\Results\RQ2\Method_stereotype_script.txt) Script used in the Playground of Pharo to return counts of how many methods get assigned the specific method stereotypes. Returns numbers for the method stereotypes in the order Accessors, Getters, Mutators, Setters, Collaborators, Controllers, Factories and Degenerate.</code></pre> <p> </p>
Comprehensive Automatic Processing and Analysis of Adaptive Optics Flood Illumination Retinal Images
<p>A collaborative research group has established this database to support AO-FIO image utilization and evaluation of photoreceptor detection. <br> Please cite the following publication when using the database:</p> <p>Eva Valterova, Jan D. Unterlauft, Mike Francke, Toralf Kirsten, Radim Kolar, and Franziska G. Rauscher, "Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects," Biomed. Opt. Express <strong>14</strong>, 945-970 (2023)<br> <br> The database can be utilized in connection with our application MATADOR for AO-FIO image registration and analysis, which is freely available on:</p> <p>https://github.com/evavalterova/MATADOR.git</p> <p>The database includes</p> <ul> <li>over 200 flood illumination adaptive optics images of 10 normal healthy subjects. Each folder includes 10 images of the right eye (denoted by OD) and 10 images of the left eye (denoted by OS) with their preliminary determined retinal position during image acquisition.</li> <li>foveal and peripheral patches. Each consists of 40 cropped regions from the set of 200 images. In each cropped region are manually labeled positions of photoreceptors by three evaluators.</li> <li>axial lengths of 10 subjects in ".xlsx" file<br> </li> </ul>
Data Cleaning, Translation & Split of the Dataset for the Automatic Classification of Documents for the Classification System for the Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft
<ul> <li>Cleaned_Dataset.csv – The combined CSV files of all scraped documents from DABI, e-LiS, o-bib and Springer.</li> <li>Data_Cleaning.ipynb – The Jupyter Notebook with python code for the analysis and cleaning of the original dataset.</li> <li>ger_train.csv – The German training set as CSV file.</li> <li>ger_validation.csv – The German validation set as CSV file.</li> <li>en_test.csv – The English test set as CSV file.</li> <li>en_train.csv – The English training set as CSV file.</li> <li>en_validation.csv – The English validation set as CSV file.</li> <li>splitting.py – The python code for splitting a dataset into train, test and validation set.</li> <li>DataSetTrans_de.csv – The final German dataset as a CSV file.</li> <li>DataSetTrans_en.csv – The final English dataset as a CSV file.</li> <li>translation.py – The python code for translating the cleaned dataset.</li> </ul>
Weights for automatic two-dimensional bedrock fracture trace mapping from outcrop images
<p>This file contains weights for automatic two-dimensional bedrock fracture trace mapping. The weights have been trained on manually mapped traces (https://doi.org/10.5281/zenodo.7077574) that were digitized from UAV-acquired drone orthomosaics from along the shoreline of Loviisa, South-East Finland (https://doi.org/10.5281/zenodo.7077518). Code for automatic mapping and weights generation is available on GitHub (https://github.com/nialov/ALSA).</p> <p>Data is published as Hierarchical Data Format, version 5 (HDF5).</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>
Re-Training Extension of the Benchmark for Automatic Glottis Segmentation (BAGLS-RT)
<p>BAGLS-RT is an extension of the BAGLS dataset (DOI 10.5281/zenodo.3762320) intended for (re-)training glottis segmentation models.</p>
Automatically Annotated Quan Tang Shi and Quand Song Shi
<p>Dataset containing the entire poetry of the Quan Tang Shi 全唐詩 and Quan Song Shi 全宋詩, automatically annotated using a Community annotator. This is supplementary material for "Leveraging graph algorithms to speed up the annotation of large rhymed corpora", CLAO vol 51.</p>
Automatic Levee Detection Inputs
<p>Data associated with automatic levee identification for 6 Japanese cities:</p> <ol> <li>Chiba</li> <li>Kawasaki</li> <li>Kitakyushu</li> <li>Nagano</li> <li>Okayama</li> <li>Okazaki</li> </ol> <p>For each city the following data are included</p> <ul> <li>input_data <ul> <li>Digital elevation map raster file</li> <li>Manually generated levee locations shapefile</li> <li>River flowlines, and OSM roads shapefiles</li> <li>Regions of interest required for reproducing cross-validation results</li> </ul> </li> <li>features: Several raster files with relevant measurements for levee detection</li> </ul>
Automatic camera images of Suldenferner 20150821 to 20150913
<p>Photographs taken by an automatic camera overlooking the debris-covered part of Suldenferner, Ortler Group, Italy.</p> <ul> <li>Format: JPEG, provided in .zip format</li> <li>Filenames: numerical (0015 to 1502)</li> <li>Period covered: 21 August - 13th September 2015</li> <li>Image interval: daily 03:27 then 15 minute increments until 18:42, timestamp in UTC</li> <li>Camera model: Harbortronics Timelapse package F (https://www.harbortronics.com/Products/TimeLapsePackageF/)</li> <li>Camera location: 46°29'27.7"N / 10°35'53.7" E</li> </ul> <p>Timelapse video compiled from these photos in .mp4 format.</p> <p> </p>
Figure 10. Conceptual diagram illustrating vector quantization codeword formation-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>After the conversion process converts the analogue speech signal into a series words,<br> then converting recognized word to facial animation based on VRML.</p>
Figure 9. Mel Cepstral Coefficients in time domain-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>In this section, Feature matching Algorithm has been discussed. The goal of feature<br> matching is to classify objects into one of a number of categories or classes. In this project, Vector<br> Quantization approach will be used and the best matching result will be the desired voice.</p>
Figure 6. Change the frequency at Hertz scale to Mel Scale.-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>Following computing the spectrum power and applying the above equation on frequency<br> axis, some mediating filters equal to identical overlapping are applied on the scaled spectrum and<br> each filters energy is computed as particularity. This is because conception of a particular frequency<br> by the auditory system is affected by a critical band of frequencies surrounding it. Number of filters<br> is usually between 20 and 30. Logarithmic non linear change operations on obtained particulates for<br> adjusting amplified of particularities and their important though coordinating them with the<br> structure of the auditory system following computing energy of each filter is done as follows: in the<br> following equation F1 is filter in I th, and e (i) is logarithm of energy at ith band.</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.