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1,832 results for “Cameras”
Combining local ecological knowledge with camera traps to assess the link between African mammal life history traits and their occurrence in anthropogenic landscapes
<p>Understanding what influences species and trait composition is critical for predicting changes in communities driven by landscape transformation. </p> <p>We explored how life history traits are associated with the persistence of mammal species in human-dominated habitats within the Garden Route Biosphere Reserve, South Africa. We combined data from a camera trap and a local ecological knowledge-based survey in an integrated occupancy model to analyze species occurrence along a gradient of anthropogenic landscape transformation. </p> <p>Results confirmed that mammal occurrence in human-modified habitats was related to specific life history traits. Species with more specialist diets, as well as larger body mass species were more likely to stay in protected areas. Species with slow reproductive strategies occupied more natural areas. </p> <p>Our study also showed that combining different monitoring methods enabled us to increase spatial coverage and mammal sighting numbers. This approach fostered research participation by various stakeholders, an important step for co-designing wildlife-friendly anthropogenic spaces. </p> <p><strong>Synthesis and applications: </strong>Integrating data from a standard ecological protocol and structured participatory citizen knowledge allowed us to identify the species functional traits associated with mammal species occurrence in anthropogenic landscapes at a local scale. These results advocate for wisely combining methods, and will guide conservation orientated land-use planning towards the protection of natural habitats in the Garden Route Biosphere Reserve. This methodological approach will enable managers and conservationists to use data obtain from diverse protocols. This should catalyze the involvement of citizens in biodiversity monitoring and conservation.</p>
ENDGAME - Laboratory Experiment 2023-06-23 Exp. 004 - High Speed Camera data
<div> <p>Shock-tube experiments. </p> <p>The shocktube setup consists of a high-pressure reservoir connected with a cylindrical tube through a diaphragm pulse valve which allows a fast release of pressurized gas into the ambient pressure tube. The high-pressure reservoir is filled with compressed air at a given overpressure with respect to ambient pressure (up to 8 bar). The pipe was either empty (i.e. with air at ambient conditions) or filled with a given amount of fluids (water or viscous fluid) or small particles. We adopted pipes with different inner diameters (3 and 4 cm) and different lengths (30 and 80 cm). Images were collected at a frame rate of 50000 fps.</p> <p>When the valve is open, a jet flow is produced, with shock and acoustic waves propagating in the atmosphere.</p> <p> </p> </div>
Jingju (Beijing opera) "Remorse at death" multi-camera teaching and performing videos
<p>此文件包含梅派京剧曲目《生死恨》多视角教学、演出视频,由中国戏曲学院摄制。 本素材属于由中国戏曲学院张晶老师负责的北京市社会科学基金研究基地项目 -- 《梅派唱腔的音视频与电脑辅助教学研究》(项目号 16JDYTA016)成果的一部分。Music Technology Group, Universitat Pompeu Fabra, Barcelona作为项目参与方负责讲此视频素材封装在iOS app中。</p> <p>This file contains the multi-camera teaching and performing videos of Mei school jingju (Beijing opera) play "Remorse at death", shot and edited by National Academy of Chinese Theatre Arts (NACTA). These video materials are a part of the outcome of the Beijing City Social Science Foundation project -- "Audio-Visual and computer-aided research on Mei school singing teaching" (Num. 16JDYTA016), directed by professor ZHANG Jin in NACTA. As a collaborator, Music Technology Group, Universitat Pompeu Fabra, Barcelona is in charge of encapsulating these videos materials into an iOS app.</p>
Videos of artificially released SO2 puffs recorded simultaneously with six UV SO2 cameras
<p>The six videos show the temporal evolution of six artifially released SO2 puffs. Each video covers the same 1-minute time period and is recorded by one of six UV SO2 cameras (UV1 - UV6) at different positions around the source location.<br> The videos have been created in the scope of the Comtessa project and show data from the first field campaign in July 2017.</p>
Associated raw data to the PhD thesis: Design and evaluation of a camera-based indoor positioning system for forklift trucks
<p>This is a test data set for marker-based augmented reality algorithms used to locate ground conveyors in an industrial environment. It was recorded in the testing area of the chair fml at TUM to develop and evaluate algorithms for locating forklift trucks in my PhD thesis "Entwicklung und Evaluierung einer kamerabasierten Lokalisierungsmethode für Flurförderzeuge" (see https://mediatum.ub.tum.de/?id=1395267 available in German only).</p>
Associated raw data to the publication: An accurate and efficient camera-based indoor positioning approach for intralogistic environments (MHCL 2015)
<p>This is a test data set for marker-based augmented reality algorithms used to locate ground conveyors in an industrial environment. It was recorded in the testing area of the chair fml at TUM to develop and evaluate algorithms for locating forklift trucks in the publication "An accurate and efficient camera-based indoor positioning approach for intralogistic environments" at MHCL 2015 conference (see https://mediatum.ub.tum.de/1286589 and http://www.fml.mw.tum.de/fml/images/Publikationen/MHCL_2015_jung_submitted.pdf). Originally these files were recorded and used as uncompressed 8-bit grayscale bitmaps. The images were losslessly compressed to png files in order to reduce the test set file size (by approx. factor 3.5)</p>
Long term bearded pig camera trap data across the SAFE landscape.
<p><strong>Description: </strong></p> <p>Data on camera trap surveys and capture events for bearded pigs across the SAFE landscape from 2011-2017.<br> Data was collected by Dr Oliver Wearn from 2011 to 2014, by Phil Chapman from 2015 to 2016 and by Charlie Davison in 2017.<br> Used to assess how bearded pigs are responding to land-use change in Sabah. NB: These data are a subset of the full SAFE Project core mammal trapping data, but include additional details about bearded pig social structure and abundances.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/173"><strong>Group Dynamics of Bornean Bearded Pigs: the advantages of behavioural plasticity in changeable landscapes.</strong></a></p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=312">here</a></p> <p><strong>Files: </strong>This consists of 1 file: DavisonBeardedPigs.xlsx</p> <p><strong>DavisonBeardedPigs.xlsx</strong></p> <p>This file contains dataset metadata and 2 data tables:</p> <ol> <li> <p><strong>Deployments</strong> (described in worksheet Deployments)</p> <p>Description: Data relating to all random camera trap deployments</p> <p>Number of fields: 5</p> <p>Number of data rows: 833</p> <p>Fields:</p> <ul> <li><strong>TrapID</strong>: Camera placement point on SAFE project core grids (Field type: Location)</li> <li><strong>Date.On</strong>: Date survey started (Field type: Date)</li> <li><strong>Date.Off</strong>: Date survey ended (Field type: Date)</li> <li><strong>CTNs</strong>: Length of survey (camera trap nights). Zero if camera was faulty. (Field type: Numeric)</li> <li><strong>Landuse</strong>: Land-use type (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Records</strong> (described in worksheet Records)</p> <p>Description: Data relating to all camera trap records of bearded pigs across all land-uses, and humans and domestic dogs in Oil palm; data generated from individual camera trap images</p> <p>Number of fields: 13</p> <p>Number of data rows: 4236</p> <p>Fields:</p> <ul> <li><strong>TrapID</strong>: Camera placement location on SAFE project core grids (Field type: Location)</li> <li><strong>Date</strong>: Date of photo capture (Field type: Date)</li> <li><strong>Time</strong>: Time of photo capture (Field type: Time)</li> <li><strong>Ambient.Temp</strong>: Temperature at the time of photo capture (Field type: Numeric)</li> <li><strong>Moon.Phase</strong>: Moonphase at time of photo capture (Field type: Categorical)</li> <li><strong>Species</strong>: Identity of the individual(s) (Field type: Taxa)</li> <li><strong>Soc.Str</strong>: Social structure (Field type: Categorical)</li> <li><strong>Sex</strong>: Sex of the individual (Field type: Categorical)</li> <li><strong>No.Individuals</strong>: Number of individuals in survey (Field type: Abundance)</li> <li><strong>No.Juveniles</strong>: Number of adults in survey (Field type: Abundance)</li> <li><strong>No.Subadults</strong>: Number of subadults in survey (Field type: Abundance)</li> <li><strong>No.Adults</strong>: Number of juveniles in survey (Field type: Abundance)</li> <li><strong>Land-use</strong>: Land use type (Field type: Categorical)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2011-04-30 to 2018-04-01</p> <p><strong>Latitudinal extent: </strong>4.6350 to 4.7538</p> <p><strong>Longitudinal extent: </strong>116.9472 to 117.6253</p> <p><strong>Taxonomic coverage: </strong><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> <p>Animalia<br>  - Chordata<br>  -  - Mammalia<br>  -  -  - Artiodactyla<br>  -  -  -  - Suidae<br>  -  -  -  -  - <em>Sus</em><br>  -  -  -  -  -  - <em>Sus barbatus</em><br>  -  -  - Carnivora<br>  -  -  -  - Canidae<br>  -  -  -  -  - <em>Canis</em><br>  -  -  -  -  -  - <em>Canis lupus</em><br>  -  -  -  -  -  -  - <em>Canis lupus familiaris</em><br>  -  -  - Primates<br>  -  -  -  - Hominidae<br>  -  -  -  -  - <em>Homo</em><br>  -  -  -  -  -  - <em>Homo sapiens</em></p> <p> </p>
Camera trap image of Phylloscopus collybita (2016-01-18T19:05:44Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Lepus europaeus (2019-02-01T14:20:22Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Felis silvestris catus (2017-09-11T12:16:10Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Capreolus capreolus (2017-11-19T11:04:43Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Vulpes vulpes (2017-09-18T07:57:01Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Capreolus capreolus (2018-07-18T14:24:48Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Turdus philomelos (2017-06-08T11:44:49Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Capreolus capreolus (2018-02-17T10:15:52Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Vulpes vulpes (2018-07-07T17:20:31Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Turdus merula (2018-03-13T15:07:40Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Mustela putorius (2018-08-20T09:59:09Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Sitta europaea (2019-02-03T12:53:45Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Turdus philomelos (2018-04-16T14:08:42Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.