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130 results for “My photography”
Landscape Phenology from Unmanned Aerial Vehicle Photography at Harvard Forest 2013
This data set contains orthophotos in the vicinity of the EMS tower at Harvard Forest, as well as the flight logs from the unmanned aerial vehicle (UAV) used to obtain the digital images used in orthophoto creation. Orthophotos were created by mosaicking approximately 200 JPEG images from each date of observation. The orthophotos cover the spatial extent of the 250 meter resolution MODIS pixel that contains the EMS tower. Land cover types in the area of photography include deciduous and evergreen forest, and wetlands. The research goal of data collection for this data set was to observe spatial variance in plant phenology. Therefore, photos were taken from before leaf out until after leaf drop. Orthophotos were collected approximately every 5 days during spring and weekly during fall; see filenames for specific dates. The nominal spatial resolution of the orthophotos is 6 cm, however due to various factors including inaccuracy of the onboard GPS, wind-blown motion of trees, the automated orthophoto mosaicking process, and user error in final georeferencing, image analysis has been conducted at 10 m resolution. The orthophotos are available as GeoTIFF files.
Presence/absence of new snow-fall scored from time-lapse photography collected near Toolik Field Station, Alaska, summers 2012-2016
This data set describes the presence/absence of new snowfall approximated daily using time -lapse photography images near Toolik Field Station during summers from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). Additional cameras funded by other grants were also used for scoring including multiple Toolik EDC timelapse images taken at Toolik, Atigun Ridge, and Imnavait. Additional data were scored from time lapse photography taken by the Deegan and Urban labs (Office of Polar Programs #0902153 and #1417664). All data are associated with publication DOI: 10.1111/jav.01712.
Repeat photography of tidal fresh forest trees along the salinity gradient of the Altamaha River, GA
We established a transect of 42 stations for repeat photography of tidal fresh forest trees along the salinity gradient of the Altamaha River estuary. Target trees are located approximately every km on both the north and south banks, beginning at km 20 (with 0 at the mouth of the estuary) up to km 41, for a total of 21 km. We used a small boat to travel to each station and take digital photographs of target trees facing the river in Nov 2017, Feb, May, Aug of 2018. In Oct 2018 we extended the transect an additional 5 km downstream so that it now starts at km 16, with 5 more stations on the north bank and 5 on the south bank. All 52 stations were photographed in Oct 2018 and Oct 2019. These photos will be used to distinguish healthy, stressed and dead trees in each image and how they change over time.
Typical Village UAV Aerial Photography Dataset in northeastern Tibetan Plateau (2022)
<p>This dataset was collected during a field survey in the Hehuang Valley, located in the northeastern Tibetan Plateau, in July and August 2022. Using a DJI Mavic 2 Pro equipped with a Hasselblad L1D-20c camera, over 4,600 aerial photographs were captured from 55 typical villages across the region. These images were processed into high-resolution orthophotos using Agisoft PhotoScan 1.25 software, resulting in ultra-high-precision orthophoto data for the 55 villages. The "Village Information" section provides detailed information on each village, including its full name, abbreviation, latitude and longitude coordinates, and elevation. This dataset accurately reflects the overall situation, spatial patterns, and surrounding environment of the typical villages, offering a high-resolution data source for spatial structure analysis, land use mapping, and correction tasks.</p>
Historical digital elevation models (DEMs) and orthoimage mosaics for North American Glacier Aerial Photography (NAGAP) program, version 1.0
<p>This data archive contains digital elevation models (DEMs) and orthoimages generated from scanned historical aerial photographs from the North American Glacier Aerial Photography program available from the NSF Arctic Data Center (ADC, arcticdata.io). </p> <p>The scanned images were preprocessed using the <a href="https://github.com/friedrichknuth/hipp">Historical Image Pre-Processing</a> v0.1 software. Photogrammetric processing was performed with the <a href="https://github.com/friedrichknuth/hsfm">Historical Structure from Motion</a> v0.1 software. </p> <p>All DEM and orthoimage products are provided in the UTM Zone 10N (EPSG:32610) projected coordinate system. Elevation values are in meters above the WGS84 ellipsoid. </p> <p>See <a href="https://www.sciencedirect.com/science/article/pii/S0034425722004850">manuscript</a> and <a href="https://ars.els-cdn.com/content/image/1-s2.0-S0034425722004850-mmc1.pdf">supplement</a> for processing details and further dataset description.</p> <p>This release contains data products for two study sites in Washington state, USA:</p> <p><strong>Mount Baker</strong><br> 1970-09-09<br> 1970-09-29<br> 1974-08-10<br> 1977-09-27<br> 1979-10-06<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-09-15<br> 1992-09-18</p> <p><strong>South Cascade</strong><br> 1967-09-21<br> 1970-09-29<br> 1974-08-10<br> 1977-10-03<br> 1979-08-20<br> 1979-10-06<br> 1984-08-14<br> 1986-09-05<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-07-28<br> 1992-09-15<br> 1992-09-18<br> 1992-10-06<br> 1994-09-06<br> 1996-09-10<br> 1997-09-23</p> <p>The 00_thumbnails.jpg provides a quicklook overview at both sites.</p> <p><strong>The DEM and ortho file names are structured as follows:</strong><br> hsfm_NAGAP_[site-name]_[date]_[type].tif</p> <p><strong>For example:</strong><br> hsfm_NAGAP_south-cascade_19670921_ortho.tif</p> <p><strong>Where:</strong><br> [site-name] = Either mount-baker or south-cascade<br> [date] = Image acquisition date in YYYYMMDD format<br> [type] = File type</p> <p><strong>For each DEM and ortho pair, we provide the following:</strong><br> _1m_dem.tif = Digital elevation model posted at 1 m resolution <br> _ortho.tif = Orthoimage mosaic posted at the median image ground sample distance, rounded up to the nearest second decimal place.<br> _metadata.tar.gz = Metadata tarball containing:<br> _ortho_footprints.geojson = Orthoimage mosaic footprint polygons provided in GeoJSON format (EPSG:4326)<br> _dem_footprints.geojson = DEM footprint polygons provided in GeoJSON format (EPSG:4326)<br> _cameras.csv = Image file names, positions, and orientations</p>
Night Photography Rendering Challenge 2024 Dataset
<p>The procedure of capturing and processing images taken by cameras involves employing onboard processing to transform raw sensor images into the final, polished photographs, subsequently encoding them in a standard color space like sRGB.</p> <p>However, capturing images at night presents distinct challenges not typically encountered in daylight shots. Unlike daytime images, where assuming a single global illumination is often adequate, night images frequently feature multiple illuminants, many of which are visible in the scene. This complexity makes it difficult to determine the optimal illumination correction for rendering night images. Additionally, commonly used tone curves and photo-finishing techniques for daytime images may not be suitable for night photography. Furthermore, widely employed image metrics like SSIM and LPIPS may not effectively evaluate night images. The absence of established best practices and limited research in the realm of night photography pose significant challenges.</p> <p>The main objective of addressing this challenge is to stimulate research and advance the field of image processing specifically tailored for night photography.</p> <p>The participants in this challenge will be granted access to raw-RGB images of night scenes, which have been captured using the <strong>Huawei Mate 40 Pro</strong> sensor type and are encoded in 16-bit PNG files. Accompanying these images will be additional meta-data, provided in JSON files. As an added resource, the organizers have also provided code for a baseline algorithm, as well as a demonstration, on <a href="https://github.com/createcolor/nightimaging24">GitHub</a>.</p> <p>Dataset includes data postprocessed by baseline algorithm and resulting images processed by competitors` algorithms.</p> <p>Mean opinion scores are obtained through visual comparison, carried out using Yandex Tasks (similar to Mechanical Turk). Yandex Tasks users ranked the solutions in a forced-choice manner. It is important to note that Yandex Tasks primarily relies on observers from Eastern Europe and and Central Asia to perform the image ranking, and as a result, there may be a cultural bias in terms of the preferred image aesthetics by the observers. Yandex Tasks users were not aware of the identity of the participants. An example of this evaluation process can be found at a <a href="https://nightimaging.org/challenges/2022/ranking/index.html">specified link</a>.</p>
Infrared thermography of turbulence patterns of operational wind turbine rotor blades supported with high-resolution photography: KI-VISIR Dataset
<h2>Abstract</h2> <p><span>With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.</span></p>
Text-fig. 4. Porechye open pit coal mine. a: photography of the Lipovtsy Formation section; b: stratigraphic column, 1 – sandstone, 2 – conglomerate, 3 – coal, 4 – tuff dated by U-Th-Pb geochronology, 5 – siltstone, 6 – mudstone, 7 – palynological samples with angiosperm pollen, 8 – dispersed angiosperm cuticles. in Angiosperm Diversification In The Early Cretaceous Of Primorye, Far East Of Russia
Text-fig. 4. Porechye open pit coal mine. a: photography of the Lipovtsy Formation section; b: stratigraphic column, 1 – sandstone, 2 – conglomerate, 3 – coal, 4 – tuff dated by U-Th-Pb geochronology, 5 – siltstone, 6 – mudstone, 7 – palynological samples with angiosperm pollen, 8 – dispersed angiosperm cuticles.
Fig. 3 in Photography in the ultraviolet and visible violet spectra: Unravelling methods and applications in palaeontology
Fig. 3. Bivalves and gastropods photographed under visible, UV, and VV light. A. Venerid bivalve Chamelea gallina (Linnaeus, 1758) from the Lower Pleistocene Arda River section, Italy. MPUM 12161 (ACG204), right valve in external view. In the white rectangles is highlighted a fine zig zag colour pattern. B. Arcoid bivalve Glycymeris nummaria (Linnaeus, 1758) from the Lower Pleistocene Arda River section, Italy. MPUM 12159 (ACG204-4), left valve in external view. C. Conid gastropod Conus sp. from the Holocene Inqitat Khor Rori Archaeological Park, Oman. MPUM 12163 (BS-148) in apical view. These specimens experienced a 72 h immersion in 50% diluted bleach (MPUM 12161 [ACG204]) or pure bleach (MPUM 12159 [ACG204-4], MPUM 12163 [BS-148]). Visible light, without treatment (A1–C1); visible light, bleach treatment (A2–C2); 365 nm, bleach treatment (A3–C3); 440 nm, bleach treatment (A4–C4).
Text-fig. 5. Free living colonies, showing a mode of preservation which does not allow for precise determination but clearly exhibiting features characteristic for Smittipora and/or Cupuladria and/or Reusirella. (note the clear intrazooecial buds). Specimen deposited in NM Prague under number T 3319. A – imprint, B – counterpart to fig A. C – Specimen deposited in SNM under number Z 37724. Optic photography. Scale bar 1 mm. in The Priabonian Bryozoan-Decapod Association From The Borové Formation (The Ďurkovec Quarry, Ne Slovakia) And Its Palaeoecological Implications
Text-fig. 5. Free living colonies, showing a mode of preservation which does not allow for precise determination but clearly exhibiting features characteristic for Smittipora and/or Cupuladria and/or Reusirella. (note the clear intrazooecial buds). Specimen deposited in NM Prague under number T 3319. A – imprint, B – counterpart to fig A. C – Specimen deposited in SNM under number Z 37724. Optic photography. Scale bar 1 mm.
Text-fig. 4. Reteporella sp., deposited in NM Prague under number T 3318. A – Large colony suggesting very short transport. Scale bar 10 mm. Optic photography. B – the detail of branch showing the mode of preservation (no original skeleton preserved). Scale bar 1 mm. SEM photography (BSE detector). in The Priabonian Bryozoan-Decapod Association From The Borové Formation (The Ďurkovec Quarry, Ne Slovakia) And Its Palaeoecological Implications
Text-fig. 4. Reteporella sp., deposited in NM Prague under number T 3318. A – Large colony suggesting very short transport. Scale bar 10 mm. Optic photography. B – the detail of branch showing the mode of preservation (no original skeleton preserved). Scale bar 1 mm. SEM photography (BSE detector).
Text-fig. 6. Lunulites(?), deposited in NM Prague under number T 3320. A – optic (scale bar 1 mm) and B – SEM (BSE detector) photography (scale bar 100 µm) showing characters suggesting determination as Lunulites (square shape and linear arrangement of autozooecia, short cryptocyst and presence of vibracularia). in The Priabonian Bryozoan-Decapod Association From The Borové Formation (The Ďurkovec Quarry, Ne Slovakia) And Its Palaeoecological Implications
Text-fig. 6. Lunulites(?), deposited in NM Prague under number T 3320. A – optic (scale bar 1 mm) and B – SEM (BSE detector) photography (scale bar 100 µm) showing characters suggesting determination as Lunulites (square shape and linear arrangement of autozooecia, short cryptocyst and presence of vibracularia).
Text-fig. 3. Coeloma vigil A. MILNE-EDWARDS. Nearly complete specimens from several different layers. A – layer 15; B – layer 11; C – layer 16. All specimens are deposited at KGP MH. Key: ch=chelipeds; es=eye stalks; p=pereiopods. Scale bar 10 mm. Specimens were covered with ammonium chloride prior to photography. in The Priabonian Bryozoan-Decapod Association From The Borové Formation (The Ďurkovec Quarry, Ne Slovakia) And Its Palaeoecological Implications
Text-fig. 3. Coeloma vigil A. MILNE-EDWARDS. Nearly complete specimens from several different layers. A – layer 15; B – layer 11; C – layer 16. All specimens are deposited at KGP MH. Key: ch=chelipeds; es=eye stalks; p=pereiopods. Scale bar 10 mm. Specimens were covered with ammonium chloride prior to photography.
Linked collectors and determiners for: Taxonomy and Biogeography without frontiers – WhatsApp, Facebook and smartphone digital photography let citizen scientists in more remote localities step out of the dark.
Natural history specimen data linked to collectors and determiners held within, "Taxonomy and Biogeography without frontiers – WhatsApp, Facebook and smartphone digital photography let citizen scientists in more remote localities step out of the dark". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/d9953b25-4b14-437c-aecf-1ccb60e8d698">https://bionomia.net/dataset/d9953b25-4b14-437c-aecf-1ccb60e8d698</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d9953b25-4b14-437c-aecf-1ccb60e8d698">https://gbif.org/dataset/d9953b25-4b14-437c-aecf-1ccb60e8d698</a>. Formatted as a Frictionless Data package.
Paired Waikato Region Aerial Photography and Sentinel-2 Imagery
<p>This dataset contains paired high resolution orthorectified aerial photography provided by the Waikato Region Aerial Photography initiative, paired with Sentinel-2 satellite images. These images were collected as part of a satellite imagery super-resolution research project. The aerial photograph was down-sampled to a spatial resolution of 2.5m per pixel, while the satellite images were taken at a spatial resolution of 10m per pixel. The satellite images were taken from 8 fly-bys between 16th January to 2nd March 2019 so that it is temporally consistent with the aerial photographs taken in February of 2019.</p>
FIG. 4. — Photographie d in Les hétéroptères (Insecta, Hemiptera) décrits par Paul Dispons. Inventaire commenté des spécimens types conservés au Muséum national d'Histoire naturelle, Paris
FIG. 4. — Photographie d'une page du registre de chasse de P. Dispons correspondant à des récoltes faites en avril 1954 dans le Sud marocain (Tiznit, Arak).
Repeat photography of a joint military operation involving the Ethiopian and Eritrean armies in Inderta, Tigray, Ethiopia (November 2020)
<p>The Eritrean PFDJ government has actively participated in the conflict against Ethiopia's Tigray region since it began in November 2020, supporting the Ethiopian government. Regularly, Eritrea dispatched Eritrean Defence Force (EDF) troops to aid the Ethiopian National Defence Forces (ENDF) in their conflict with the Tigray region. Following the Eritrean admission of its involvement, the Ethiopian Prime Minister Abiy Ahmed, however, minimized the scope of the Eritrean participation throughout the conflict and tended to confine it to the Ethio-Eritrean border regions. Here we present a dataset of repeated video images, allowing the examination of the coordination between the Ethiopian and Eritrean armies in the war in late 2020, deep within Tigray. The warfare imagery was accurately placed near Miheni in Inderta district, and shows unequivocally that Eritrean forces participated in fighting in Tigray in close coordination with the Ethiopian army. It is 180 kilometers, or 102 kilometers as the crow flies, to the Eritrean border from Miheni.</p>
Cataloguing the Landscape: Photography in 1970s America
<p>4<sup>th</sup> Presentation</p>
Complete Scan Raw Photography for the Ruthwell Cross - Visionary Cross Project
<p>This zip file include the complete photography from photographic scan of Ruthwell Cross - one of the objects of the cultural mix being constructed in 3D model for the Visionary Cross project. </p>
Complete Scan Raw Photography for the Brussels Cross - Visionary Cross Project
<p>This .zip file include the complete photography from photographic scan of the Brussels Cross - one of the objects of the cultural mix being constructed in 3D model for the Visionary Cross project. </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.