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2,809 results for “photos”
Irrawady Dolphin at Chilika Lake (Photo: Chandra et al., 2021). in Faunal Composition of Ramsar Wetlands from India: An Analysis
Irrawady Dolphin at Chilika Lake (Photo: Chandra et al., 2021).
Pneumatopteris glandulifera, Kolombangara,Solomon Islands. Photo by Cheng-Wei Chen. in A Generic Classification of the Thelypteridaceae
Pneumatopteris glandulifera, Kolombangara,Solomon Islands. Photo by Cheng-Wei Chen.
PM_110143_Antique_Photo_camera
<u>File Name</u>: PM_110143_Antique_Photo_camera.jpg <br><u>Sublocation</u>: None <br><u>Location</u>: None <br><u>Province</u>: None <br><u>Country</u>: None <br><u>Header</u>: Antiek fototoestel, Zeiss-Ikon, 6x9cm; Privécollectie Paul M.R; Maeyaert; <br><u>Description</u>: Photo camera Zeiss-Ikon, 6x9cm Private collection Paul M. R Maeyaert <br><u>Keywords</u>: Antique photo camera/cinema, Cultural heritage, Museum/private collection, Thematic, privé, privé collecties <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_110133_Antique_Photo_camera
<u>File Name</u>: PM_110133_Antique_Photo_camera.jpg <br><u>Sublocation</u>: None <br><u>Location</u>: None <br><u>Province</u>: None <br><u>Country</u>: None <br><u>Header</u>: Antiek fototoestel, Kodak, Retina Reflex S (type 034), 1959-1960; Privécollectie Paul M.R; Maeyaert; <br><u>Description</u>: Photo camera Kodak, Retina Reflex S (type 034), 1959-1960 Private collection Paul M. R Maeyaert <br><u>Keywords</u>: Antique photo camera/cinema, Cultural heritage, Museum/private collection, Thematic, privé, privé collecties <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
Figure 6 – Live photo A in First records of two non-indigenous tineid species from St Helena Island (Tineioidia: Tineidae)
Figure 6 – Live photo A. siccata, Burnt Rock, on soil (L. Fowler).
Figure 8 – Live photo S in First records of two non-indigenous tineid species from St Helena Island (Tineioidia: Tineidae)
Figure 8 – Live photo S. rutella, wall at Cole's courtyard, Jamestown (T. Karisch)
Drones' footage and Photos from floods in Sardinia, Italy (November, December 2020)
<p>This dataset was gathered during the deadly flooding that happened in 2020 in Bitti, a municipality in Sardinia. The event took place on November 28th, 2020. A destructive massive mudslide, caused by the heavy rains, poured along the main street in the town, and its height was 4 meters. Some of the residents have been moved to avoid the worst. The drone flights captured and collected data (videos and photos) from the day after the storm, November 29th, up to December 4th. There is a sequence of operative actions undertaken by the people involved, such as the Civil Protection Department, Firefighters, Police, Carabinieri, volunteered associations, and many more. </p> <p> </p>
A photo of the typical view of organic, permaculture and conventional horticultural farms and a photo of a typical soil profile in a core sampler for each
<p>The pdf file contains an introduction slide (Slide 1) with the list of the introduced farms.</p> <p>There are 15 slides following the introductory file.</p> <p>Each slide has a photo of a horticultural farm, its code and a photo of one of its typical soil profiles.</p> <p>The photo of the profile was made of a soil core sampler that is 100 cm long.</p> <p>In some of the farms, there were multiple profiles revealed and photos were made, here we just show one of these.</p> <p>The majority of the soils are Luvisols but we have some 2 Fluvisols, 2 Chernozems and 2 Fluvisols.</p> <p>More information can be found in a published article: Szilágyi, A.; Plachi, E.; Nagy, P.; Simon, B.; Centeri, C. Assessing Earthworm Populations in Some Hungarian Horticultural Farms: Comparison of Conventional, Organic and Permaculture Farming. <em>Biol. Life Sci. Forum</em> <strong>2021</strong>, <em>2</em>, 11. https://doi.org/10.3390/BDEE2021-09416</p> <p>The purpose of the recent pdf is to provide information for an upcoming article in the journal of Diversity.</p> <p>All soil laboratory analyses have already been published for this purpose:</p> <p>https://zenodo.org/record/5717449#.YeoUYv7MJPY</p>
Doodleverse/Segmentation Zoo Res-UNet models for identifying coins in photos of sediment.
<p><strong>Doodleverse/Segmentation Zoo models for identifying coins in photos of sediment.</strong></p> <p>These model data are based on images of sand and coins and associated labels. Models have been fitted to the following types of data</p> <p>1. RGB (3 band): red, green, blue</p> <p>Classes are: {0: other, 1: coin}.</p> <p>These files are used in conjunction with Segmentation Zoo*</p> <p>For each model, there are 3 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p> </p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images</p> <p> </p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p> </p> <p>References</p> <p>* https://github.com/Doodleverse/segmentation_zoo</p> <p>** https://github.com/Doodleverse/segmentation_gym</p>
Doodleverse/Segmentation Zoo Res-UNet models for identifying water in oblique aerial photos of coasts.
<p><strong>Doodleverse/Segmentation Zoo Res-UNet models for identifying water in oblique aerial photos of coasts.</strong></p> <p>These model data are based on images of coasts and associated labels. Models have been fitted to the following types of data</p> <p>1. RGB (3 band): red, green, blue</p> <p>Classes are: {0: water, 1: land}.</p> <p>These files are used in conjunction with Segmentation Zoo*</p> <p>For each model, there are 3 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p> </p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images</p> <p> </p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p> </p> <p>References</p> <p>* https://github.com/Doodleverse/segmentation_zoo</p> <p>** https://github.com/Doodleverse/segmentation_gym</p>
Doodleverse/Segmentation Zoo UNet models for identifying water in orthomosaic photos of coasts.
<p><strong>Doodleverse/Segmentation Zoo UNet models for identifying water in orthomosaic photos of coasts.</strong></p> <p> </p> <p><strong>Based on Coast Train data</strong></p> <p>Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/P91NP87I">https://doi.org/10.5066/P91NP87I</a>.</p> <p>These model data are based on images of coasts and associated labels. Models have been fitted to the following types of data</p> <p>1. RGB (3 band): red, green, blue</p> <p>Classes are: {0: null, 1: water}.</p> <p>These files are used in conjunction with Segmentation Zoo*</p> <p>For each model, there are 3 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p> </p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images</p> <p> </p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p> </p> <p>References</p> <p>* https://github.com/Doodleverse/segmentation_zoo</p> <p>** https://github.com/Doodleverse/segmentation_gym</p>
Photos of participants of Turin Workshop (11 and 12 April 2022): Skull collections. Series, standardization and instruments
<p>During the two days of the workshop in the Museum of Human Anatomy was inaugurated the Exhibition: "The art of trepanning".</p> <p>The artworks of the Peruvian artist Fernando Bedoya, exhibited near the showcase where the two South American mummies are preserved, are intended to dialogue with the human findings as well as evoking the ancient surgical practices of the Incas, known as trepanation, which consisted of the perforation of the skull.</p> <p>Bedoya, through his ceramic replicas of ancient heads, imitates the act of trepanation by perforating twice the head. One hole is left open, following the sign of the surgical intervention, while the other hole is covered with a bottle cap of Coca-Cola, a brand of the modern global exploiter.</p> <p>On display are also four of his engravings from the “Fardos” series, where the use of collage and silkscreen techniques bind together like the bandaging of a mummy.</p>
Specimen photos of DNA sample MHNG Hydrozoa DNA1159
<p><strong>Sample data</strong></p> <p>sample collected by: P. Schuchert & A. Hosia</p> <p>collection locality: Raunefjord, Norway</p> <p>WGS84: 60.2575, 05.1393</p> <p>collection date: 14-06-2016</p> <p>depth: 0 – 200m</p> <p>gear: plankton net</p> <p>number of individuals or colonies: 1</p> <p>subsequent preservation: 95 % ethanol</p> <p>voucher specimen deposited: no</p> <p>figured specimen used for DNA extraction: yes</p> <p>part used for DNA extraction: entire</p> <p>DNA extraction date: 04-07-2016</p> <p> </p> <p><strong>Taxonomy</strong></p> <p>Phylum: Cnidaria</p> <p>Class: Hydrozoa</p> <p>Family: Margelopsidae</p> <p>Species: <em>Margelopsis hartlaubii </em>Browne, 1903</p> <p>life stage: medusa</p> <p>identified by: P. Schuchert</p> <p> </p>
Supplementary material (Sanger sequencing photo) of the case of THRB mosaicism presented in Clinically Symptomatic Resistance to Thyroid Hormone β Syndrome Because of THRB Gene Mosaicism of Donnars et al. (https://doi.org/10.1210/clinem/dgac347)
<p>Supplementary material (Sanger photo) of the case of THRB mosaicism presented in "Clinically Symptomatic Resistance to Thyroid Hormone β Syndrome Because of THRB Gene Mosaicism", by Donnars et al. (https://doi.org/10.1210/clinem/dgac347)</p>
Zdjęcia oprawione w okno // Window with photos
Nie było wówczas jeszcze ani Stowarzyszenia, ani Atelier w sercu miasta. Okno znalezione zostało nad ranem w konterze remontowym przy ulicy Garbary. To okno zainspirowało Macieja Krajewskiego vel Łazęga Poznański do ocalania starych kamienicznych ram okiennych i oprawiania w nie zdjęć z włóczęg po Poznaniu. W to okno oprawione są dwa zdjęcia przedstawiające klatki schodowe z ulicy Wronieckiej obok budynku dawnej synagogii, większości Poznaniaków kojarzy się on wciąż jako pływalnia miejska... Source: Objaverse 1.0 / Sketchfab
Photos de panneaux d'informations sur la plage d'Audinghen
<p>Photos de panneaux d'informations sur la plage d'Audinghen. Ces panneaux, pour certains bilingues, fournissent des informations et recommandations aux usagers du littoral. Un panneau montre l'interdiction de la cueillette de moules dans les zones d'élevage. Ces photos ont été prises au mois de mai 2017.</p>
Photos de la plage d'Audinghen
<p>Photos destinées à apprécier la topographie et l'organisation du lieu. Les photos ont été prises en mai 2017.</p>
Photos d'un concours de pêche sur la plage de Boulogne-sur-Mer
<p>Photos prises lors d'un concours de Surf-Casting le 7 mai sur la plage de Boulogne-sur-Mer. Le premier plan des photos montre la plage, sur laquelle sont installés des pêcheurs avec leurs cannes à pêche. L'arrière plan montre la mer ou le port de la ville en fonction de l'angle de prise de vue. </p>
Photos de la digue Carnot à Boulogne-sur-Mer
<p>Ces trois photos ont été prises au mois de mai 2017 sur la digue Carnot à Boulogne-sur-Mer. </p> <p>Elles montrent les traces des activités humaines : restes de coquillage destinés à servir d'appât, arrêté préfectoral arraché, poubelle remplie de détritus. </p>
Specimen photos of DNA sample MHNG Hydrozoa DNA915
<p><strong>collection locality: Norway, Raunefjord</strong></p> <p><strong>GPS: </strong><strong>60.275 5.200</strong></p> <p><strong>collection date: 22.05.2012</strong></p> <p><strong>depth: 10 m</strong></p> <p><strong>gear: plankton net</strong></p> <p><strong>number of individuals or colonies: 1</strong></p> <p><strong>subsequent preservation: 95 % ethanol</strong></p> <p><strong>figured specimen used for DNA extraction: yes</strong></p> <p><strong>part used for DNA extraction: whole medusa</strong></p> <p><strong>DNA extraction: </strong></p> <p><strong>voucher specimen deposited: no</strong></p> <p><strong>Phylum: Cnidaria</strong></p> <p><strong>Class: Hydrozoa</strong></p> <p><strong>Family: Pandeidae</strong></p> <p><strong>Species: <em>Leuckartiara octona</em> (Fleming, 1823)</strong></p> <p><strong>life stage: medusa</strong></p> <p><strong>identified by: P. Schuchert</strong></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.