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24 results for “Automated classification”
Ground Truth and Automated Classification from Copernicus Sentinel-2 Imagery
<p>Ground-Truth and Sentinel2 imagery classification of <em>Trees Outside Forest</em> in an agroforestry landscape in Umbria, Italy.</p> <p>Location: Alfina plains, Castelgiorgio area, Umbria, Italy. Reference system: EPSG:32632 (WGS84, UTM zone 32 North) Extent: West 740609 — East 750828, South 4726490 — North 4737250</p> <p>Dataset format: geopackage, a single file <strong>data.gpkg</strong> containing 9 vector layers (in alphabetical order):</p> <ol> <li>Areas — Areas of interest, 2 polygons</li> <li>Classification — Automated classification from Sentinel2 imagery, 11781 polygons</li> <li>Hedgerows1 — Ground truth, hedgerows of Area1, 148 lines</li> <li>Hedgerows2 — Ground truth, hedgerows of Area2, 135 lines</li> <li>Sentinel2 — Sentinel2 scenes footprint, one polygon</li> <li>Trees1 — Ground truth, isolated trees of Area1, 55 points</li> <li>Trees2 — Ground truth, isolated trees of Area2, 64 points</li> <li>Woods1 — Ground truth, small forest patches of Area1, 33 polygons</li> <li>Woods2 — Ground truth, small forest patches of Area2, 37 polygons</li> </ol> <p>Accompanying map: <strong>map.qgz</strong>, Qgis 3.6 format. The geopackage dataset is supposed to be stored in the same directory of the map (relative path = ./)</p> <p>Dataset description and metadata: <strong>meta.pdf</strong> </p> <p> </p>
Automated MESSENGER Plasma Region Classifications via Unsupervised Transfer Learning
<p>This file contains the 1-minute resolution dataset (“labeled_sunside_data_3labels.csv”) for Toy-Edens et al.’s Automated Classification of MESSENGER Plasma Observations via Unsupervised Transfer Learning. The 1-minute resolution file contains the rolled up 1-minute epoch, features that go into clustering and post-cleaning methods, spacecraft positions (in MSO), total magnetic field, raw and cleaned clustering labels, and raw and cleaned transition name.</p> <p>We ask that if you use any parts of the dataset that you cite Toy-Edens et al.’s Automated Classification of MESSENGER Plasma Observations via Unsupervised Transfer Learning (DOI: 10.3389/fspas.2025.1608091).</p> <p>This work was supported by NASA grants 80NSSC19K0789 and 80NSSC22K0993.</p> <p> </p> <p>The following tables detail the contents of the described files:</p> <p><strong>labeled_sunside_data_3labels.csv description</strong></p> <table style="width: 100.063%; height: 851.2px;"> <tbody> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p><strong>Column Name</strong></p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p><strong>Description</strong></p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> Epoch</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Epoch in datetime (YYYY-MM-DD HH:MM:SS)</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> x_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>x position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> y_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>y position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> z_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>z position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> btot_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Total magnetic field [nT]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> norm_Btot</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Magnitude of the total magnetic field normalized to 150nT. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> ratio_max_width</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Ratio of the width of the most prominent ion spectra peak (in number of energy channels) to max number of energy channels. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> ratio_high_low</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Ratio of the mean of the log intensity of high energies in the ion spectra to the mean of the log intensity of low energies in the ion spectra. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> high_intensity</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Boolean if there is a peak with a higher minimum intensity threshold. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> spectra_counts</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>A ratio of spectra bins with non-zero counts to all possible spectra bins (i.e. way to determine if too much missing spectra data). See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> raw_named_label</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Raw cluster assigned plasma region label (allowed values: magnetosheath, magnetosphere, solar wind)</p> </td> </tr> <tr> <td style="width: 17.3792%;"> <p>intermediate_named_label</p> </td> <td style="width: 78.9512%;"> <p>Cleaned cluster assigned plasma region label with only relabeling rules applied. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> named_label</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Cleaned cluster assigned plasma region label with relabeling rules and post-processing applied (use these unless have a specific reason to use raw labels). See paper for more information</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> raw_transition_name</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Raw transition names (e.g. bow shock, magnetopause) based on "raw_named_label" cluster labels. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> transition_name</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Cleaned transition names (e.g. bow shock, magnetopause) after removing likely transient transitions based on "named_label" cluster labels. See paper for more information</p> </td> </tr> </tbody> </table> <p> </p>
Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses
<p>This repository contains the supplementary file for our study "Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses". The MS Excel file contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals together with self-report ratings of the conversation (Self-Assessment Manikin) and personality trait data (CES-D, BFNES, QCAE). Synchrony features were calculated using code from a previous Zenodo submission (https://zenodo.org/record/7140829).</p>
Data from: Crowdsourcing training material for automated bird sound classification – a pilot study
<p>Data from the manuscript "Crowdsourcing training material for automated bird sound classification – a pilot study" by Petteri Lehikoinen, Meeri Rannisto, Ulisses Camargo, Aki Aintila, Patrik Lauha, Esko Piirainen, Panu Somervuo & Otso Ovaskainen</p>
Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses
<p>This repository contains supplementary files for our study "Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses". The two files are:</p> <p>- ConversationClassification_FeatureTable.xlsx is an MS Excel file that contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals.</p> <p>- ConversationClassification_SynchronyCalculation.zip contains the MATLAB 2021b code used to calculate four physiological synchrony metrics: dynamic time warping, nonlinear interdependence, coherence, and cross-correlation. It also includes some open-source code from other authors that is required for our synchrony calculation code to work. As inputs, the synchrony calculation functions accept 4-minute signal vectors from both participants in the dyad.</p>
Automated mapping and classification of valleys beneath the Greenland Ice Sheet: Datasets
<p><strong>Valley morphology and classification: training dataset and full dataset for Greenland</strong></p> <p>This dataset contains:</p> <ul> <li>TrainingDataValleys.csv: A .csv file of the training dataset used to classify valleys in Greenland as 'fluvial' or 'glacial'. These valleys were extracted from the Copernicus 90 m global digital elevation model.</li> <li>GreenlandValleys.csv: A .csv file of the locations, morphometrics, and class of Greenland valleys identified in Operation IceBridge and CReSIS radio-echo sounding data. These valleys have been classified using a random forest model trained using the above training dataset.</li> </ul>
Automated classification of avian vocal activity using acoustic indices in regional and heterogeneous datasets
<p>Acoustic indices combined with clustering and classification approaches have been increasingly used to automate identification of the presence of vocalizing taxa or acoustic events of interest. While most studies using this approach standardize data collection and study design parameters at the project or study level, recent trends in ecological research are to investigate patterns at regional or continental scales. Large-scale studies often require collaboration between research groups and integration of data from multiple sources to fulfill objectives, which can lead to variation in recording equipment and data collection protocols.</p> <p>Our objectives were to determine how analytical approaches and variation in data collection and processing that is typical of regional acoustic monitoring programs influences accuracy when identifying vocal activity in migratory breeding birds. We used data from three regional datasets in Northern Alberta, Northern British Columbia, and Southern and Central Yukon, Canada to investigate the effect of analytical framework, sample size, local species richness, and data collection variables on classification accuracy.</p> <p>We found supervised classification approaches to be the most effective, with boosted regression trees identifying vocal activity with a 92.0% accuracy and easily able to accommodate variation in data collection and processing parameters. We also provide recommendations on effectively processing large and heterogeneous datasets including sufficient sample size, accommodating nuisance variables, and selecting suitable model training data.</p> <p>The results presented in this study can help inform decisions in data collection, data processing, and study design and analysis, maximize performance and accuracy during analysis, and efficiently process large, heterogeneous datasets to answer questions at scales previously difficult to investigate.</p>
Downsized camera trap images for automated classification
<b>Description: </b><p>Downsized (256x256) camera trap images used for the analyses in "Can CNN-based species classification generalise across variation in habitat within a camera trap survey?", and the dataset composition for each analysis. Note that images tagged as 'human' have been removed from this dataset. Full-size images for the BorneoCam dataset will be made available at LILA.science. The full SAFE camera trap dataset metadata is available at DOI: 10.5281/zenodo.6627707.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://safeproject.net/projects/project_view/203"><b>Machine learning and image recognition to monitor spatio-temporal changes in the behaviour and dynamics of species interactions</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (NERC QMEE CDT Studentship, NE/P012345/1, <a href="http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A">http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://safeproject.net/datasets/xml_metadata?id=6627707">here</a></p><p><b>Files: </b>This dataset consists of 3 files: CT_image_data_info2.xlsx, DN_256x256_image_files.zip, DN_generalisability_code.zip</p><p><b>CT_image_data_info2.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Dataset Images</b> (described in worksheet Dataset_images)</p><p>Description: This worksheet details the composition of each dataset used in the analyses</p><p>Number of fields: 69</p><p>Number of data rows: 270287</p><p>Fields: </p><ul><li><b>filename</b>: Root ID (Field type: id)</li><li><b>camera_trap_site</b>: Site ID for the camera trap location (Field type: location)</li><li><b>taxon</b>: Taxon recorded by camera trap (Field type: taxa)</li><li><b>dist_level</b>: Level of disturbance at site (Field type: ordered categorical)</li><li><b>baseline</b>: Label as to whether image is included in the baseline training, validation (val) or test set, or not included (NA) (Field type: categorical)</li><li><b>increased_cap</b>: Label as to whether image is included in the 'increased cap' training, validation (val) or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_individ_event_level</b>: Label as to whether image is included in the 'individual disturbance level datasets split at event level' training, validation (val) or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_1</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 1' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_2</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 2' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 3' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 4' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 5' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_1_2</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 2 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_1_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 3 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_1_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_1_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_2_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 3 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_2_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_2_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 4 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_2_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 3 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_2_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_2_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_2_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_2_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_2_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_1_2_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3 and 4 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_1_2_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_1_2_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_1_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_2_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_all_1_2_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3, 4 and 5 (all)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_1</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 1' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_2</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 2' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 3' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 4' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 5' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_1_2</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1 and 2 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_1_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1 and 3 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_1_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_1_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_2_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2 and 3 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_2_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_2_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 3 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 3 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 4 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_2_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2 and 3 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_2_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_2_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_2_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_2_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_2_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 3, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_1_2_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2, 3 and 4 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_1_2_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2, 3 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_1_2_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_1_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 3, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_2_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2, 3, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_all_1_2_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2, 3, 4 and 5 (all)' training set, or not included (NA) (Field type: categorical)</li></ul></li></ol><p><b>DN_256x256_image_files.zip</b></p><p>Description: Zip file containing all images used in the analyses</p><p><b>DN_generalisability_code.zip</b></p><p>Description: Zip file containing code for the analyses</p><p><b>Date range: </b>2011-03-19 to 2018-06-12</p><p><b>Latitudinal extent: </b>4.6350 to 4.7538</p><p><b>Longitudinal extent: </b>116.9472 to 117.6253</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Aves <br> -  -  -  -  Galliformes <br> -  -  -  -  Passeriformes <br> -  -  -  -  Cuculiformes <br> -  -  -  -  -  Cuculidae <br> -  -  -  -  Columbiformes <br> -  -  -  -  -  Columbidae <br> -  -  -  -  Gruiformes <br> -  -  -  -  -  Gruidae <br> -  -  -  Mammalia <br> -  -  -  -  Proboscidea <br> -  -  -  -  -  Elephantidae <br> -  -  -  -  -  -  <i>Elephas</i> <br> -  -  -  -  -  -  -  <i>Elephas maximus</i> <br> -  -  -  -  -  -  -  -  <i>Elephas maximus borneensis</i> <br> -  -  -  -  Erinaceomorpha <br> -  -  -  -  -  Erinaceidae <br> -  -  -  -  -  -  <i>Echinosorex</i> <br> -  -  -  -  -  -  -  <i>Echinosorex gymnura</i> <br> -  -  -  -  Pholidota <br> -  -  -  -  -  Manidae <br> -  -  -  -  -  -  <i>Manis</i> <br> -  -  -  -  -  -  -  <i>Manis javanica</i> <br> -  -  -  -  Scandentia <br> -  -  -  -  -  Tupaiidae <br> -  -  -  -  -  -  <i>Tupaia</i> <br> -  -  -  -  -  -  -  <i>Tupaia dorsalis</i> <br> -  -  -  -  -  -  -  <i>Tupaia gracilis</i> <br> -  -  -  -  -  -  -  <i>Tupaia longipes</i> <br> -  -  -  -  -  -  -  <i>Tupaia tana</i> <br> -  -  -  -  Artiodactyla <br> -  -  -  -  -  Cervidae <br> -  -  -  -  -  -  <i>Muntiacus</i> <br> -  -  -  -  -  -  -  <i>Muntiacus atherodes</i> <br> -  -  -  -  -  -  -  <i>Muntiacus muntjak</i> <br> -  -  -  -  -  -  <i>Rusa</i> <br> -  -  -  -  -  -  -  <i>Rusa unicolor</i> <br> -  -  -  -  -  Suidae <br> -  -  -  -  -  -  <i>Sus</i> <br> -  -  -  -  -  -  -  <i>Sus barbatus</i> <br> -  -  -  -  -  Bovidae <br> -  -  -  -  -  -  <i>Bos</i> <br> -  -  -  -  -  -  -  <i>Bos javanicus</i> <br> -  -  -  -  -  Tragulidae <br> -  -  -  -  -  -  <i>Tragulus</i> <br> -  -  -  -  -  -  -  <i>Tragulus kanchil</i> <br> -  -  -  -  -  -  -  <i>Tragulus napu</i> <br> -  -  -  -  Rodentia <br> -  -  -  -  -  Sciuridae <br> -  -  -  -  -  -  <i>Lariscus</i> <br> -  -  -  -  -  -  <i>Sundasciurus</i> <br> -  -  -  -  -  -  -  <i>Sundasciurus hippurus</i> <br> -  -  -  -  -  -  -  <i>Sundasciurus lowii</i> <br> -  -  -  -  -  Hystricidae <br> -  -  -  -  -  -  <i>Hystrix</i> <br> -  -  -  -  -  -  -  <i>Hystrix brachyura</i> <br> -  -  -  -  -  -  -  <i>Hystrix crassispinis</i> <br> -  -  -  -  -  -  <i>Trichys</i> <br> -  -  -  -  -  -  -  <i>Trichys fasciculata</i> <br> -  -  -  -  -  Muridae <br> -  -  -  -  -  -  <i>Sundamys</i> <br> -  -  -  -  -  -  -  <i>Sundamys muelleri</i> <br> -  -  -  -  -  -  <i>Leopoldamys</i> <br> -  -  -  -  -  -  -  <i>Leopoldamys sabanus</i> <br> -  -  -  -  Primates <br> -  -  -  -  -  Hominidae <br> -  -  -  -  -  -  <i>Homo</i> <br> -  -  -  -  -  -  -  <i>Homo sapiens</i> <br> -  -  -  -  -  -  <i>Pongo</i> <br> -  -  -  -  -  -  -  <i>Pongo pygmaeus</i> <br> -  -  -  -  -  Cercopithecidae <br> -  -  -  -  -  -  <i>Presbytis</i> <br> -  -  -  -  -  -  <i>Macaca</i> <br> -  -  -  -  -  -  -  <i>Macaca fascicularis</i> <br> -  -  -  -  -  -  -  <i>Macaca nemestrina</i> <br> -  -  -  -  -  Tarsiidae <br> -  -  -  -  -  -  <i>Cephalopachus</i> <br> -  -  -  -  -  -  -  <i>Cephalopachus bancanus</i> <br> -  -  -  -  Carnivora <br> -  -  -  -  -  Felidae <br> -  -  -  -  -  -  <i>Prionailurus</i> <br> -  -  -  -  -  -  -  <i>Prionailurus bengalensis</i> <br> -  -  -  -  -  -  <i>Pardofelis</i> <br> -  -  -  -  -  -  -  <i>Pardofelis marmorata</i> <br> -  -  -  -  -  -  <i>Neofelis</i> <br> -  -  -  -  -  -  -  <i>Neofelis nebulosa</i> <br> -  -  -  -  -  -  <i>Catopuma</i> <br> -  -  -  -  -  -  -  <i>Catopuma badia</i> <br> -  -  -  -  -  Mustelidae <br> -  -  -  -  -  -  <i>Amblonyx</i> <br> -  -  -  -  -  -  -  <i>Amblonyx cinereus</i> <br> -  -  -  -  -  -  <i>Martes</i> <br> -  -  -  -  -  -  -  <i>Martes flavigula</i> <br> -  -  -  -  -  Mephitidae <br> -  -  -  -  -  -  <i>Mydaus</i> <br> -  -  -  -  -  -  -  <i>Mydaus marchei</i> <br> -  -  -  -  -  Ursidae <br> -  -  -  -  -  -  <i>Helarctos</i> <br> -  -  -  -  -  -  -  <i>Helarctos malayanus</i> <br> -  -  -  -  -  Canidae <br> -  -  -  -  -  -  <i>Canis</i> <br> -  -  -  -  -  -  -  <i>Canis lupus</i> <br> -  -  -  -  -  -  -  -  <i>Canis lupus familiaris</i> <br> -  -  -  -  -  Viverridae <br> -  -  -  -  -  -  <i>Paguma</i> <br> -  -  -  -  -  -  -  <i>Paguma larvata</i> <br> -  -  -  -  -  -  <i>Prionodon</i> <br> -  -  -  -  -  -  -  <i>Prionodon linsang</i> <br> -  -  -  -  -  -  <i>Arctictis</i> <br> -  -  -  -  -  -  -  <i>Arctictis binturong</i> <br> -  -  -  -  -  -  <i>Paradoxurus</i> <br> -  -  -  -  -  -  -  <i>Paradoxurus hermaphroditus</i> <br> -  -  -  -  -  -  <i>Hemigalus</i> <br> -  -  -  -  -  -  -  <i>Hemigalus derbyanus</i> <br> -  -  -  -  -  -  <i>Viverra</i> <br> -  -  -  -  -  -  -  <i>Viverra tangalunga</i> <br> -  -  -  -  -  Herpestidae <br> -  -  -  -  -  -  <i>Herpestes</i> <br> -  -  -  -  -  -  -  <i>Herpestes brachyurus</i> <br> -  -  -  -  -  -  -  <i>Herpestes semitorquatus</i> <br></div><p></p>
Development and Validation of a Comprehensive Classification Automation System for Kidney Allograft Biopsies
ClinicalTrials.gov study NCT05306795. IPD Sharing: UNDECIDED. Countries: 1. Publications: 17.
Automated classification of avian vocal activity using acoustic indices in regional and heterogeneous datasets
Open the record for dataset details and reuse information.
Protein Subcellular localization prediction data used in the article entitled "MSclassifier: Median-Supplement model-based Classification tool for automated knowledge discovery"
<p>This repository contains data used to obtain results from a 5-fold cross-validation testing of how MSclassifier and other packages accurately predict protein subcellular localization in the software article entitled "MSclassifier: median-supplement model-based classification tool for automated knowledge discovery." The data used in the software article is derived from data generated in "G. K. Acquaah-Mensah, S. M. Leach, and C. Guda, Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis, Genomics Proteomics Bioinformatics, 4(2):120-133, 2006, <a href="https://doi.org/10.1016/S1672-0229(06)60023-5">https://doi.org/10.1016/S1672-0229(06)60023-5</a>"</p>
Dataset for Ha and Aylward 'Automated classification of giant virus genomes using a random forest model built on trademark protein families'
<ul><li>Genome sets used for model training and testing</li><li>Custom Python script that generated fragmented genomes at random completeness levels</li></ul>
Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
<p>Occupancy modeling is used to evaluate avian distributions and habitat associations, yet it typically requires extensive survey effort because a minimum of three repeat samples are required for accurate parameter estimation. Autonomous recording units (ARUs) can reduce the need for surveyors on site, yet ARUs utility were limited by hardware costs and the time required to manually annotate recordings. Software that identifies bird vocalizations may reduce expert time needed, if classification is sufficiently accurate. We assessed the performance of BirdNET – an automated classifier capable of identifying vocalizations from >900 North American and European bird species – by comparing automated to manual annotations of recordings of 13 breeding bird species collected in northwestern California. We compared the parameter estimates of occupancy models evaluating habitat associations supplied with manually annotated data (9 min recording segments) to output from models supplied with BirdNET detections. We used three sets of BirdNET output to evaluate the duration of automatic annotation needed to approach manually annotated model parameter estimates: 9-min, 87-min, and 87-min of high-confidence detections. We incorporated 100 3-sec manually validated BirdNET detections per species to estimate true and false positive rates within an occupancy model. BirdNET correctly identified 90% and 65% of the bird species a human detected when data were restricted to detections exceeding a low or high confidence score threshold, respectively. Occupancy estimates, including habitat associations, were similar regardless of method. Precision (proportion of true positives to all detections) was >0.70 for 9 of 13 species, and a low of 0.29. However, processing of longer recordings was needed to rival manually annotated data. We conclude that BirdNET is suitable for annotating multispecies recordings for occupancy modeling when extended recording durations are used. Together, ARUs and BirdNET may benefit monitoring and, ultimately, conservation of bird populations by greatly increasing monitoring opportunities. </p>
Dataset, models and code for "Automating global landslide detection with heterogeneous ensemble deep-learning classification"
Open the record for dataset details and reuse information.
Creating and Assessing a Voice Dataset for Automated Classification of Chronic Obstructive Pulmonary Disease
ClinicalTrials.gov study NCT05897944. IPD Sharing: NO. Countries: 1. Publications: 1.
Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
Open the record for dataset details and reuse information.
Replication Kit: On the Feasibility of Automated Classification of Bug and Non-Bug Issues
<p>This is the replication kit for the manuscript "On the Feasibility of Automated Issue Bug and Non-Bug Issues". </p> <p>The replication kit contains the following data.<br> - results_all_issues contains the csv files with the results of the classifiers we trained with all issues for phases 1 and 2 of the experiment.<br> - results_only_bugs contains the csv files with the results of the classifiers we trained with only bugs for phases 1 and 2 of the experiment.<br> - results_unvalidated contains the csv files with the results for phases 3 and 4 of the experiment<br> - The .p files contain pickled python objects with the issue data. The code for creating this pickles is contained in the evaluation.py.<br> - The evaluation.py creates all results that are missing, i.e., will do nothing if there are already CSV files in the folders! <br> - The evaluation.py also generates the .p files, if they are not available. However, this requires the raw data from Herzig et al (2013), Herbold et al (2020), and Ortu et al (2015) which is not included in this replication kit due to the size of several gigabytes.<br> - The EvaluationNotebook contains the code for the statistical analysis.<br> </p>
Supplementary Materials for "Landscape-aware Automated Algorithm Configuration using Multi-Output Mixed Regression and Classification"
Open the record for dataset details and reuse information.
Automated detection, segmentation and classification of pericardial effusions on chest CT using a deep convolutional neural network
<p>Trainingsdata for chest CT pericard effusion and the finish trained nnU-Net model. </p>
Automated Classification of Estuarine Sub-depositional Environment Using Sediment Texture
<p>The repository corresponding to the paper "Automated classification of estuarine sub-depositional environment using sediment texture" by Houghton et al., 2023. <a href="https://doi.org/10.1029/2022JF006891">https://doi.org/10.1029/2022JF006891</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.