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15 results for “label information”
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
Manually labeled Bird song dataset of 22 species from Xeno-canto to enhance deep learning acoustic classifiers with contextual information.
<p>Data accompanying the paper: Jeantet and Dufourq (2023). Empowering Deep Learning Acoustic Classifiers with Human-like Ability to Utilize Contextual Information for Wildlife Monitoring. <em>Ecological Informatics</em>. 77, 15749541, DOI: 10.1016/j.ecoinf.2023.102256</p> <p> </p> <p>Our investigation contributes to the field of deep learning and bioacoustics by highlighting the potential for improved classification performance through the incorporation of contextual information such as time and location.</p> <p>To test if spatial-temporal information can enhance deep learning classifier, we developed a subset dataset derived from Xeno-Canto that included location metadata as input alongside the spectrogram. We used this dataset with the primary purpose of creating a bird song classification task with species carefully selected to share similar vocal characteristics but from distinct geographical distributions. We only considered the recordings of category `A', corresponding to the best quality score in the database.</p> <p>The dataset contains songs of <strong>22 bird species</strong> from 5 families and genera differents. The recordings were downloaded from the Xeno-canto database in .wav format and each recording was <strong>manually annotated </strong>by labelling the start and stop time for every vocalisation occurrence using Sonic Visualiser. In total, database contained 6537 occurrences of bird songs of various length from <strong>967 file recordings</strong>. A precise description of the distribution by species and country can be found in the associated article.</p> <p> </p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip". The name of each file follows the following nomenclature: Family_genus_species_country of recording_date of recording_ID Xenocanto_type of song.wav/svl. The meta-data information of each file can be find in the csv file provided (Xenocanto_metadata_qualityA_selection) based on the number of the ID Xeno-canto. The annotations can be viewed using the Sonic Visualiser software. The python codes to process these files and train neural networks can be found here : github</p> <p>The files were divided into a <strong>training folder</strong> and a<strong> validation folder</strong> to train and evaluate the efficiency of each method. For each species and country, we randomly selected 70% of the downloaded recordings for the training dataset and kept the remaining 30% for validation.</p> <p><strong>Process to select the species</strong> : We selected the ten most recorded families in the Passeriformes order, the most represented order in Xeno-canto database. From each of the ten families, we again sub-samples the ten most recorded genera. For each genus, we observed the countries of the recordings and the number of available recordings per species and countries. From these observations, we made a self-selection of genera containing species with similar songs but recorded in different regions, with enough recordings available by species and country to form a dataset . At the end, 5 genus were selected containing 22 species. We considered only recordings associated with bird songs, specifically, within Xeno-canto we selected the `song' type. To balance the number of recordings between species of the same genus, we reduced the number of recordings for the most represented species. Thus, for each genus we calculated the average of the number of records available per species and per country and limited the number of recordings for the species/country pairs that were in greater number to this value plus two.</p> <p> </p> <p> </p> <p> </p>
SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation
<p>The data have been generated using scripts developed in Python with Open-Source libraries (GDAL/OGR and MapScript) to rasterize of vector cartography representing the photovoltaic (PV) panels instalations in urban, industrial, and rural areas. This PV panels cartography has been generated by manual digitalizing the PV panels found latest aerial orthofotographs available on June 1, 2021 from Plano Nacional de Ortofotografía Aérea (PNOA), produced by the National Geographic Institute of Spain, using the Web Map Service PNOA-MA.<br> <br> The dataset consists of 239,680 images of 256 × 256 pixels in size, in png format, labelled with Class_1: “Contains PV panel” and Class_2: “Does not contain PV panel”, that were pre-divided with a split criterion of 70:10:20%. in train, validation and test folders, respectively.<br> <br> The structure of the data is as follows:<br> 1-Panels-Ortho and 1-Panels-Mask contain the images featuring PV panels and their corresponding ground truth mask for training the semantic segmentation networks.<br> 1-Panels-Ortho and 2-NoPanels-Ortho contain images containing and not containing PV panels, for the training of binary recognition models of PV panels.<br> <br> Moreover, in each folder the structure is the same: train, test, validation containing 70%, 10% and 20% of the total images and masks of each type.<br> <br> 1-Panels-Ortho<br> |----Train<br> |----Test<br> -----Validation<br> <br> 1-Panels-Mask<br> |----Train<br> |----Test<br> -----Validation<br> <br> 2-NoPanels-Ortho<br> |----Train<br> |----Test<br> -----Validation</p>
Data associated with 'Closing the stellar labels gap: Stellar label independent evidence for [α/M] information in Gaia BP/RP spectra'
<p>We present a stellar label independent model for Gaia BP/RP (XP) spectra, which does not rely on stellar labels to simulate XP spectra. Here, we provide all of the data required to reproduce <a href="https://github.com/AlexLaroche7/xp_vae">our results</a>. All of the data files should be placed in the data folder. Below we brieflty summarize the contents of each:</p> <p>APOGEE/GAIA XP DATA:</p> <ul> <li>xp_apogee_cat.h5: Contains all Gaia XP data and APOGEE stellar labels used in this work.</li> <li>xp_corrs.npz: Contains Gaia XP covariance matrices for a subset of the test data to compute reconstruction errors.</li> </ul> <p>INTERMEDIATE DATA PRODUCTS:</p> <p><em>(All files below can be reproduced with our codebase. We simply include them to speed up runtime for reproducing our results.)</em></p> <ul> <li>lb23_xp_est_labels.npy: <a href="https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.1494L/abstract">Leung & Bovy (2023)</a> XP coeffficient spectra reconstructions (stellar label dependent implementaton)</li> <li>lb23_xp_est_no_labels.npy: Leung & Bovy (2023) XP coeffficient spectra reconstructions (stellar label independent implementaton)</li> <li>xp_wavelength_space.npy: Observed Gaia XP spectra in wavelength space</li> <li>err_wavelength_space.npy: Observed Gaia XP uncertainties in wavelength space</li> <li>apogee_norm.npz: Means and standard deviations which are used to pre-process Gaia XP data before inputting into our model</li> <li>zhang_stellar_params_xmatch.npz: <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.1855Z/abstract">Zhang, Green, Rix (2023)</a> stellar parameter estimates for subset of XP spectra (to compute reconstructions)</li> <li>vae_wavelength_space.npy: Our model XP coefficient reconstructions in wavelength space</li> </ul> <p>If you have any questions please reach out via email: alex.laroche@mail.utoronto.ca</p>
Open-label Study to Assess Usability of the Medical Information Device #1 (MIND1) System in Adults With Schizophrenia On Oral Aripiprazole
ClinicalTrials.gov study NCT02219009. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Are Sugar-Sweetened Beverage Information Labels Well-Targeted
ClinicalTrials.gov study NCT05038163. IPD Sharing: YES. Countries: 1. Publications: 2.
Directed labeled multigraphs and directed labeled multigraphs enriched with information about which graph patterns match a graph and how they match a graph
<p>This dataset contains all directed labeled multigraphs that consist of one or two triples and that were generated from a set of 5 terms.</p> <p>The graphs are represented as RDF data in ntriples format.</p> <p>The graphs 1-125 consist of a single triple. The graphs 126-4625 consist of two triples.</p> <p>Each graph also exists in an enriched version, where each graph pattern that matches the graph is made explicit in the graph and it is also made explicit how it matches the graph.</p> <p>Enrichment means that for each graph pattern that matches a graph, a node is introduced that is labeled with the name of that graph pattern. Each term that occurs in that graph pattern is linked to that graph pattern node with an edge labeled "occurs-in". For each term t in the graph that can be bound to a variable v according to a match of that graph pattern an edge will be created from the node t to the graph pattern node where the edge is labeled with "bound-to-v".</p> <p>The generation of the dataset and the enrichment procedure is described in more detail in a paper that is currently under double-blind submission. We will link to the paper once it is accepted for publication.</p> <p> </p> <p>Example: the graph #100 (file 100.nt)</p> <p><http://ex.org/term/4> <http://ex.org/term/5> <http://ex.org/term/5> .</p> <p>Example: the graph #2000 (file 2000.nt)</p> <p><http://ex.org/term/2> <http://ex.org/term/2> <http://ex.org/term/3> .<br><http://ex.org/term/2> <http://ex.org/term/3> <http://ex.org/term/1> .</p> <p>Example: the enriched graph #100 (file 100-extended.nt)</p> <p><http://ex.org/term/4> <http://ex.org/term/5> <http://ex.org/term/5> .<br><http://ex.org/term/5> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-v1-t5-v2> .<br><http://ex.org/term/4> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-v1-t5-v2> .<br><http://ex.org/term/5> <http://ex.org/pattern/bound-to-v2> <http://ex.org/pattern/p-v1-t5-v2> .<br><http://ex.org/term/4> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-t4-v1-v1> .<br><http://ex.org/term/5> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-t4-v1-v1> .<br><http://ex.org/term/4> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-t4-t5-v1> .<br><http://ex.org/term/5> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-t4-t5-v1> .<br><http://ex.org/term/5> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-t4-t5-v1> .<br><http://ex.org/term/5> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-t4-v1-t5> .<br><http://ex.org/term/4> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-t4-v1-t5> .<br><http://ex.org/term/5> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-t4-v1-t5> .<br><http://ex.org/term/5> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-v1-t5-t5> .<br><http://ex.org/term/4> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-v1-t5-t5> .<br><http://ex.org/term/4> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-v1-v2-v2> .<br><http://ex.org/term/5> <http://ex.org/pattern/bound-to-v2> <http://ex.org/pattern/p-v1-v2-v2> .<br><http://ex.org/term/5> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-v1-v2-t5> .<br><http://ex.org/term/4> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-v1-v2-t5> .<br><http://ex.org/term/5> <http://ex.org/pattern/bound-to-v2> <http://ex.org/pattern/p-v1-v2-t5> .<br><br></p> <p>For each graph pattern exists a CSV file that lists the IDs of all graphs that are matched by that pattern.For example, the file target-p-t3-t4-v1=v1-t1-t2-graph_size_2.csv lists all those graphs that are matched by the pattern t3-t4-v1=v1-t1-t2.</p> <p>Finally, there are four JSON files:</p> <p>plain-graphs_of_size_1.json, plain-graphs_of_size_2.json, enriched-graphs_of_size_1.json, enriched-graphs_of_size_2.json </p> <p>These files contain the same graphs that are also stored in the .nt-files, but allow to conveniently read in a set of graphs at once.</p> <p> </p>
FIGURE. Map of specimens collected for the phylogenetic analysis in this study, excluding Tulipa iliensis and T. altaica, which both lacked GPS information. Populations of the new species T. toktogulica are labelled in order of discovery. in Tulipa toktogulica (Liliaceae), a cryptic, endangered new species from the western Tien-Shan, Kyrgyzstan
FIGURE. Map of specimens collected for the phylogenetic analysis in this study, excluding Tulipa iliensis and T. altaica, which both lacked GPS information. Populations of the new species T. toktogulica are labelled in order of discovery.
Is a Front-of-package Label Contaning Information on Both Nutrient Profile and Ultra-processing Well Understood?
ClinicalTrials.gov study NCT05610930. IPD Sharing: NO. Countries: 1. Publications: 1.
Supplementary material 1 from: Gavrilova A, Gavrilov G (2022) Assessment of morphological pharmacognostic characteristics of the content and label information of dried herbs marketed as food supplements in Bulgaria. Pharmacia 69(3): 865-872. https://doi.org/10.3897/pharmacia.69.e87549
Table S1
Pictorial Warning Labels and Memory for Cigarette Health-risk Information Over Time
ClinicalTrials.gov study NCT03375840. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Labeled Datasets for Research on Information Operations
<h1><strong>Labeled Datasets for Research on Information Operations</strong></h1> <h2><strong>Compliance with Platform Terms<br></strong></h2> <p>To comply with the platform terms, we ask that you download one data file per researcher, per day. By requesting access, you agree to abide by these rules.</p> <h2><strong>Data Sharing Policy</strong></h2> <p>Following university, security, and ethical guidelines, we are unable to respond to data requests from research institutions that are affiliated with foreign military organizations or from a list of countries of concern (currently China, North Korea, Iran, Russia, Cuba, Syria, and Venezuela).</p> <h2><strong>README</strong></h2> <p>19-November-2024<br>Contact: <a href="https://osome.iu.edu/">Observatory on Social Media</a></p> <p><strong>Dataset Articles</strong><br>This dataset is collected and processed according to the paper "<a href="https://doi.org/10.48550/arXiv.2411.10609">Labeled Datasets for Research on Information Operations</a>."</p> <p><strong>Description</strong><br>These datasets contain data curated for research on information operations (IO) and includes both labeled IO and control data. The datasets cover 26 verified IO campaigns from various countries and provide comprehensive records of posts from IO accounts alongside control posts from legitimate accounts discussing similar topics during the same periods. The datasets enable the development and benchmarking of IO detection methods by comparing coordinated versus organic accounts.</p> <p><strong>License</strong><br>This dataset is available under the <a href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Attribution-NonCommercial-NoDerivatives 4.0 International</a> license. If you use this data, please cite the original paper.</p> <p><strong>Dataset Content</strong><br>The dataset includes anonymized fields to preserve privacy, and is structured with the following columns:</p> <ul> <li>postid: Unique identifier for each post within the dataset.</li> <li>post_text: The textual content of the post. The PII inside post_text such as mentions and URLs are hashed</li> <li>application_name: Hashed version of the name of the application or platform from which the post was made.</li> <li>post_language: Language in which the post was written.</li> <li>in_reply_to_postid: Anonymized ID of the post this entry is replying to, if applicable.</li> <li>in_reply_to_accountid: Anonymized ID of the account the post is replying to, if applicable.</li> <li>post_time: Timestamp indicating when the post was made.</li> <li>accountid: Unique anonymized ID for the account that created the post.</li> <li>account_profile_description: Description provided by the account holder in their profile.</li> <li>follower_count: Number of followers the account had at the time of data collection.</li> <li>following_count: Number of accounts the user was following at the time of data collection.</li> <li>account_creation_date: Date when the account was created.</li> <li>is_repost: Boolean indicator if the post is a repost.</li> <li>reposted_accountid: Anonymized ID of the original account that made the reposted post, if applicable.</li> <li>reposted_postid: Anonymized ID of the original post that was reposted, if applicable.</li> <li>hashtags: Hashtags included in the post content, if any.</li> <li>urls: Hashed URLs shared within the post, if any.</li> <li>account_mentions: Anonymized ID of accounts mentioned within the post, if any.</li> <li>is_control: Boolean indicator marking whether the post is from a control (True) or IO (False) account.</li> </ul> <p>Data for different campaigns are organized in separate versions of this repository, which can be also found below or in the excel file shared.</p> <table> <tbody> <tr> <td><strong>Campaign Name</strong></td> <td><strong>URL</strong></td> </tr> <tr> <td>Armenia</td> <td><a href="https://doi.org/10.5281/zenodo.14141550">https://doi.org/10.5281/zenodo.14141550</a></td> </tr> <tr> <td>Bangladesh</td> <td><a href="https://doi.org/10.5281/zenodo.14188947">https://doi.org/10.5281/zenodo.14188947</a></td> </tr> <tr> <td>Catalonia</td> <td><a href="https://doi.org/10.5281/zenodo.14188959">https://doi.org/10.5281/zenodo.14188959</a></td> </tr> <tr> <td>China_1</td> <td><a href="https://doi.org/10.5281/zenodo.14188970">https://doi.org/10.5281/zenodo.14188970</a></td> </tr> <tr> <td>China_2</td> <td><a href="https://doi.org/10.5281/zenodo.14188975">https://doi.org/10.5281/zenodo.14188975</a></td> </tr> <tr> <td>Cuba Part 1</td> <td><a href="https://doi.org/10.5281/zenodo.14188984">https://doi.org/10.5281/zenodo.14188984</a></td> </tr> <tr> <td>Cuba Part 2</td> <td><a href="https://doi.org/10.5281/zenodo.14189008">https://doi.org/10.5281/zenodo.14189008</a></td> </tr> <tr> <td>Ecuador</td> <td><a href="https://doi.org/10.5281/zenodo.14189015">https://doi.org/10.5281/zenodo.14189015</a></td> </tr> <tr> <td>Egypt_UAE</td> <td><a href="https://doi.org/10.5281/zenodo.14189018">https://doi.org/10.5281/zenodo.14189018</a></td> </tr> <tr> <td>Ghana_Nigeria</td> <td><a href="https://doi.org/10.5281/zenodo.14189028">https://doi.org/10.5281/zenodo.14189028</a></td> </tr> <tr> <td>Iran_1</td> <td><a href="https://doi.org/10.5281/zenodo.14189037">https://doi.org/10.5281/zenodo.14189037</a></td> </tr> <tr> <td>Iran_2</td> <td><a href="https://doi.org/10.5281/zenodo.14189038">https://doi.org/10.5281/zenodo.14189038</a></td> </tr> <tr> <td>Iran_3</td> <td><a href="https://doi.org/10.5281/zenodo.14189041">https://doi.org/10.5281/zenodo.14189041</a></td> </tr> <tr> <td>Iran_4</td> <td><a href="https://doi.org/10.5281/zenodo.14189047">https://doi.org/10.5281/zenodo.14189047</a></td> </tr> <tr> <td>Iran_5</td> <td><a href="https://doi.org/10.5281/zenodo.14189048">https://doi.org/10.5281/zenodo.14189048</a></td> </tr> <tr> <td>Iran_6</td> <td><a href="https://doi.org/10.5281/zenodo.14189053">https://doi.org/10.5281/zenodo.14189053</a></td> </tr> <tr> <td>Qatar</td> <td><a href="https://doi.org/10.5281/zenodo.14189058">https://doi.org/10.5281/zenodo.14189058</a></td> </tr> <tr> <td>Russia_1</td> <td><a href="https://doi.org/10.5281/zenodo.14189061">https://doi.org/10.5281/zenodo.14189061</a></td> </tr> <tr> <td>Russia_2</td> <td><a href="https://doi.org/10.5281/zenodo.14189072">https://doi.org/10.5281/zenodo.14189072</a></td> </tr> <tr> <td>Russia_3</td> <td><a href="https://doi.org/10.5281/zenodo.14189075">https://doi.org/10.5281/zenodo.14189075</a></td> </tr> <tr> <td>Russia_4</td> <td><a href="https://doi.org/10.5281/zenodo.14189078">https://doi.org/10.5281/zenodo.14189078</a></td> </tr> <tr> <td>Russia_5</td> <td><a href="https://doi.org/10.5281/zenodo.14189081">https://doi.org/10.5281/zenodo.14189081</a></td> </tr> <tr> <td>Spain</td> <td><a href="https://doi.org/10.5281/zenodo.14189086">https://doi.org/10.5281/zenodo.14189086</a></td> </tr> <tr> <td>Thailand</td> <td><a href="https://doi.org/10.5281/zenodo.14189095">https://doi.org/10.5281/zenodo.14189095</a></td> </tr> <tr> <td>UAE</td> <td><a href="https://doi.org/10.5281/zenodo.14189098">https://doi.org/10.5281/zenodo.14189098</a></td> </tr> <tr> <td>Venezuela_1</td> <td><a href="https://doi.org/10.5281/zenodo.14189107">https://doi.org/10.5281/zenodo.14189107</a></td> </tr> <tr> <td>Venezuela_2</td> <td><a href="https://doi.org/10.5281/zenodo.14189110">https://doi.org/10.5281/zenodo.14189110</a></td> </tr> </tbody> </table>
Pictorial Warning Labels & Memory for Relative & Absolute Cigarette Health-risk Information Over Time in Adult Smokers
ClinicalTrials.gov study NCT03499340. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Pictorial Warning Labels and Memory for Relative and Absolute Cigarette Health-risk Information Over Time in Teens
ClinicalTrials.gov study NCT03500965. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Pictorial Warning Labels, Numeracy, and Memory for Numeric Cigarette Health-risk Information Over Time
ClinicalTrials.gov study NCT03501472. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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.