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1,832 results for “Cameras”
Dataset supporting the article "Megapixel camera arrays for high-resolution animal tracking in multiwell plates"
<p>IB and LF contributed equally.</p> <p> </p> <p>This deposition contains the supporting dataset for the article:</p> <p><strong>Megapixel camera arrays for high-resolution animal tracking in multiwell plates</strong></p> <p>Ida Barlow, Luigi Feriani, Eleni Minga, Adam McDermott-Rouse, Thomas J O'Brien, Ziwei Liu, Maximilian Hofbauer, John R Stowers, Erik C Andersen, Siyu S Ding, André EX Brown</p> <p> </p> <p> </p> <p><strong>Acknowledgements</strong>:</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 714853) and was supported by the Medical Research Council through grant MC-A658-5TY30. This work was supported by a Research Grant from HFSP (Ref.-No: RGP0001/2019). AMR was supported by a BBSRC CASE studentship part-funded by Syngenta.</p>
Benchmark Dataset (2D/3D) of an Industrial Rotary Kiln Combustion Chamber with Refuse-Derived Fuel Particles from a Light-Field-Camera
<p>Benchmark dataset for the detection of fuel particles (refuse-derived fuels - RDF) in 2D and 3D image data in a rotary kiln combustion chamber.</p> <p>Organization:<br> 01_Images: 50 Images (2D)<br> 02_Labels: Labeled ground truth image with rotary kiln, burner flame, burning particle in air, non-burning particle in air and particle on wall.<br> 03_Particle List: Lists of the coordinates of the center of gravity of particles in image coordinates.<br> 04_Point Cloud: 3D point cloud for the 50 images.<br> 05_Matlab: Code and visualization examples.<br> 06_All_Data: Images and 3D point cloud for 2010 images of a sequence containing the images with ground truth (see TXT file).</p>
Fig. 2 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 2. General diel activity of otters in the study area.
Fig. 1 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 1. The study area.
Fig. 4 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 4. Otter recordings, correlated with local time and day-night graph.
High-throughput event-based and frame-based convolutions for event-cameras
<p>Event cameras are promising sensors for on-line and real-time vision tasks, due to their high temporal resolution, low latency and the elimination of redundant static data. Many vision algorithms use some form of spatial convolution (i.e. spatial pattern detection) as a fundamental component, but additional consideration must be taken for event cameras, as the visual signal is asynchronous and sparse. While elegant methods have been proposed for event-based convolutions, they are unsuitable for real scenarios due to their inefficient processing pipeline, and subsequent low event-throughput. This paper presents an efficient implementation based on decoupling the event-based computations from the computationally heavy convolution ones, increasing the maximum event processing rate by 15.92x, to over 10 million events/second, while still maintaining the event-based paradigm of asynchronous input and output. Results on public datasets with modern 640x480 event-camera recordings show that the proposed implementation achieves real-time processing with minimal impact in the convolution result, while the prior state-of-the-art results in latency of over 1 second per-event.</p>
Predicting foraging dive outcomes in chinstrap penguins using biologging and animal-borne cameras
<p>Direct observation of foraging behavior is not always possible, especially for marine species that hunt below the surface. However, biologging and tracking devices in particular have provided very detailed information about how various species use their habitat. From these indirect observations, researchers have tried to infer foraging and prey catching events for a more accurate definition of these species' ecological niches. In this study, we deployed video cameras in addition to GPS and time-depth recorders on chinstrap penguins during the brood phase of the 2018-19 breeding season at various colonies on the Gourlay peninsula (South Orkney Islands). More than 57 hours of footage from 16 birds covering 770 dives were scrutinized by two independent observers. The outcome of each dive was classified as unsuccessful, individual krill encounter or krill swarm encounter. In addition, the number of prey items caught was recorded for successful dives. We then used various predicting variables derived from the other logging devices or from the environment to train a machine-learning algorithm to predict the outcome of each dive. Our results show that despite some limitations, the data collected from the footage was reliable as there was a high agreement from both annotators. We also demonstrate that it was possible to accurately predict the outcome of each dive from basic dive patterns and horizontal movement characteristics that have not been used for penguins previously. Finally, we discuss how video footage can help build more accurate habitat models and gain wider knowledge about predator behavior or prey distribution.</p>
Using by-catch camera trapping data for estimating the population size of spotted hyena (Crocuta crocuta)
<p>Spotted hyenas (<em>Crocuta crocuta</em>) are an important carnivore species whose dual role of scavenger and predator is vital to trophic energy flows of systems in which they are found. Where populations of spotted hyenas are small, the environment has few cleaners and carcasses can remain unprocessed. Despite being largely characterized as scavengers, spotted hyenas actively hunt and take down live prey and at high densities can have depressing effects on fragile or choice ungulate populations. In addition, they can alter the structure and composition dynamics of the carnivore guild through direct conflict or indirectly through competition for food and space. Despite their importance to ecosystem function and balance, reliable estimates of spotted hyena densities are rare. This is because unlike lions and leopards, spotted hyenas are generally not regarded as a charismatic species and, as such, survey resources, which are costly, are seldom solely allocated towards surveying them. Nonetheless, being able to confidently estimate spotted hyena numbers is important for the effective management of carnivore and herbivore populations whose dynamics they influence.</p>
Data availability: Random encounter model is a reliable method for estimating population density of multiple species using camera traps
<p>Data of the paper entitled "Random encounter model is a reliable method for estimating population density of multiple species using camera traps" published on Remote Sensing in Ecology and Conservation</p>
Camera trap data used for assessment of movement patterns by injured moose
<p>The data were collected from camera trap recordings of animals on a remote trail in northern British Columbia, Canada, from 2017-2020. The data were used for an assessment of the general fauna composition of the area; and also for assessing movement patterns of moose - especially those with obvious leg injuries. The data set includes 4562 observations - that is, instances of an animal recorded on a given video. For further description see the paper "<strong><span>Performance of Wild Animals with "Broken" Traits: </span></strong><strong><span>Movement Patterns in Nature of Moose That Have Leg Injuries" published in Ecology and Evolution.</span></strong></p>
Temporal data from camera trap captures of raccoons (Procyon lotor) and coyote (Canis latrans) across urban-rural gradient Michigan 2015-2020
<p>Temporal data and trap success for raccoons (<em>Procyon lotor</em>) and coyotes (<em>Canis latrans</em>) across an urban-rural gradient in Michigan, from 2015 to 2020. These data are associated with the article "Temporal refuges of a subordinate carnivore vary across rural-urban gradient" in the journal Ecology and Evolution. </p>
Ecuadorian Plant-Hummingbird interactions over an elevation gradient in the Andes, sampled with camera traps in 11 localities
<p class="MsoNormal"><span>Community ecologists have made great advances in understanding how natural communities can be both diverse and stable by studying communities as interaction networks. However, focus has been on interaction networks aggregated over time, neglecting the consequences of the seasonal organization of interactions, henceforth seasonal structure, for community stability. Here, we extended previous theoretical findings on the topic in two ways: (i) by integrating empirical seasonal structure of 11 plant-hummingbird communities into dynamic models, and (ii) by tackling multiple facets of network stability together. We show that, in a competition context, seasonal structure enhances community stability by allowing diverse and resilient communities while preserving their robustness to species extinctions. The positive effects of empirical seasonal structure on network stability vanished when using randomized seasonal structures, suggesting that eco-evolutionary dynamics produce stabilizing seasonal structures. We also show that the effects of seasonal structure on community stability are mainly mediated by changes in network structure and productivity, suggesting that the seasonal structure of a community is an important and yet neglected aspect in the diversity-stability and diversity-productivity debates.</span></p>
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition
<p>This is a human activity recognition dataset with measurements from both mmWave radar and camera sensor. Meanwhile, we set multiple people scenario to mimic more realistic scenes. The other dataset collected in non-LOS(line-of-sight) environment, you can visit https://zenodo.org/record/7096889#.YynBvuhBwQ8 to get it. The mmWave radar sensors used in our experiments are composed of TI IWR6843ISK-ODS, eradar ESRR(corner radar), eradar EMRR(front radar). We appreciate the support of the eradar company, that provides corner radars and front radars for us, you can visit http://en.eradartech.com/ to get more information. </p>
Context Camera digital elevation models for Aeolis Dorsa, Mars
<p>Mars topography dataset with Context Camera (CTX) digital elevation models produced using the Ames Stereo Pipeline at the Murray Lab at Caltech.</p>
Cross-Camera View-Overlap Recognition
<p>Data accompanying the paper titled <em>Cross-Camera View-Overlap Recognition</em>, published in the proceedings of the European Conference on Computer Vision Workshop and presented used for the evaluation of the framework presented in the publication.</p> <p>The dataset consists of image sequence pairs from four scenarios: two scenarios that were collected with both hand-held and chest-mounted cameras – <em>gate</em> and <em>backyard</em> of four sequences each – and two publicly available datasets – TUM-RGB-D SLAM and <em>courtyard</em> from <a href="https://ieeexplore.ieee.org/document/6193110">CoSLAM</a> – for a total of ∼28,000 frames (∼25 minutes).</p> <p>The data consisting of images, annotations, and scripts to process existing public sequences.</p> <p>Image sequences are provided for the collected scenarios <em>gate</em> and <em>backyard</em>. We sub-sampled <em>backyard</em> from 30 to 10 fps for annotation purposes.</p> <p>Image sequences for the scenario <em>office</em> can be found at <a href="https://vision.in.tum.de/data/datasets/rgbd-dataset/download">TUM RGB-D SLAM</a> (fr1_desk, fr1_desk2, fr1_room). Scripts to process these sequences as used in the work are provided.</p> <p>The <em>courtyard</em> scenario consists of four sequences. We sub-sampled courtyard from 50 to 25 fps for annotation purposes. Original sequences are available at CoSLAM project <a href="http://drone.sjtu.edu.cn/dpzou/dataset/CoSLAM">website</a>.</p> <p>For all scenarios, we provide i) the annotation of angular distances, Euclidean distances, and overlap ratio of each view pair across camera sequences; ii) the annotation of the calibration (intrinsic) parameters; and iii) the annotation of the camera poses over time for each camera sequence, as automatically reconstructed with the structure-from-motion pipeline, <a href="https://colmap.github.io/">COLMAP</a>, or exploiting the depth data for the <em>office</em> scenario.</p> <p> </p> <p>Camera poses are saved as .txt file for each sequence using the <a href="https://www.cvlibs.net/datasets/kitti/eval_odometry.php">KITTI</a> format. The pose of each frame is represented as a 3x4 matrix (12 parameters) that is converted into a vector by horizontally concatenating the rows of the matrix:<br> [r11 r12 r13 tx<br> r21 r22 r23 ty => [r11 r12 r13 tx r21 r22 r23 ty r31 r32 r33 tz]<br> r31 r32 r33 tz]</p> <p>Values of the parameters are saved in 6 digit floating point numbers as exponential notation.</p> <p> </p> <p>Along with the dataset, we also provide the global features computed by using <a href="https://doi.org/10.1109/TPAMI.2018.2833865">DeepBit</a> [<a href="https://github.com/kevinlin311tw/cvpr16-deepbit">code</a>] and <a href="https://doi.org/10.1109/TPAMI.2017.2711011">NetVLAD</a> [<a href="https://github.com/Relja/netvlad">code</a>] for each image of all camera sequences.</p> <p> </p> <p>If you use the data, please cite:<br> <br> A. Xompero and A. Cavallaro, <a href="http://www.eecs.qmul.ac.uk/~ax300/xview/"><strong>Cross-camera view-overlap recognition</strong></a>, International Workshop on Distributed Smart Cameras (IWDSC), European Conference on Computer Vision Workshops, 24 October 2022.</p> <p>ArXiv: <a href="https://arxiv.org/abs/2208.11661">https://arxiv.org/abs/2208.11661</a><br> Webpage: <a href="http://www.eecs.qmul.ac.uk/~ax300/xview/">http://www.eecs.qmul.ac.uk/~ax300/xview/</a></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>
Daguerreotype camera (TM18868)
During the experimental period of the 1840s, different types of cameras appeared. The size changed and gave pictures in different formats. Daguerreotypy required special knowledge and the new media technique was aimed at professional practitioners. This camera was purchased in 1852 by the Royal Artillery and Engineering School in Stockholm. It is a sliding box model and intended for use with tripod. The distance was set with scale and visor disc. The camera lacks aperture and shutter. Source: Objaverse 1.0 / Sketchfab
Twin lens camera
祖父が使用していた古いカメラです。 日本のメーカー、太陽堂光機製ですが、詳しい型番については分かりません。 フォトグラメトリモデルです。 This is an old camera that my grandfather used. It was made by Taiyodo Koki, a Japanese manufacturer, but I don't know the detailed model number. It is a photogrammetry model. Camera:SONY alpha Software:Agisoft Metashape Source: Objaverse 1.0 / Sketchfab
Kiev 60 Film Camera - Artec Spider 3D Scan
This is a 3D scan of a 1990's Soviet-era Kiev 60, a medium format SLR film camera manufactured by the Arsenal Factory in Kiev, Ukraine. Produced between 1984-99, it was inspired by the Pentacon Six. But contrary to wide-spread claims, it is not a Soviet copy, it was substantially re-designed and is an improvement on the Pentacon Six. A great find for anyone wanting to do amateur medium format photography. For more information on the scanner, please visit: https://gomeasure3d.com/artec/spider Source: Objaverse 1.0 / Sketchfab
Sanderson de luxe camera (TM30762)
This camera was in Heinrich Karl Hugo Goodwin's ownership. The model was manufactured between 1902-1939. Goodwin (1878-1931), also known as "The Prince of Photographers"; born in Munich and doctorate in linguistics 1903 in Leipzig. He came to Sweden for a lecturer in Uppsala 1905-1909. After that he moved to Stockholm and worked as a lexicographer at Nordstedt & Söner in 1912. In 1913 he moved from amateur to professional man as he opened his own studio called "Kamerabilden" at Strandvägen 7. He had a successful start with a wealthy clientele who gladly paid his often high prices. Read more about this [camera](https://digitaltmuseum.se/021026331313/kamera?i=1&aq=owner%3F%3A%22S-TEK%22+text%3A%22tm30762%22"camera") Source: Objaverse 1.0 / Sketchfab
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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.