Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
130
datasets available to search
ShareScore release 0.9.0
Dataset results
130 results for “Time-lapse”
Time-lapse camera (phenocam) imagery of black sand extended growing season length experiment, 2022 - 2023.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and potentially earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows in a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot of each block by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to these plots after snow had naturally melted. This dataset includes phenocam images from 2022-2023.
Time-lapse camera (phenocam) imagery of sensor network plots, 2017 - ongoing.
Images from time-lapse cameras were analyzed to track the greenness curves of 16 plots in the Sensor Network at Niwot Ridge. Images were taken every 30 minutes during daylight hours throughout the growing season. Cameras were angled to view 1m^2 vegetation plots located at each sensor node. Pixels in the portion of the image capturing the vegetation plot were used to calculate the green chromatic coordinate (GCC). The change in GCC over the growing season represents the growth and phenology of the plant communities captured.
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.
Time-lapse 3D confocal microscopy videos of mitochondrial dynamics in human alveolar epithelial cells (A549-DsRed) infected with Mycobacterium marinum (Mmar) strains
<div>The dataset consists of time-lapse, 3D confocal images of mitochondrial dynamics in human alveolar epithelial cells (A549-DsRed) infected with Mycobacterium marinum (Mmar) strains. Images were captured at 60X magnification in an environmental chamber at 35°C for live-cell imaging. Host cell mitochondria were labeled with red fluorescent protein (RFP) and infected with both wildtype (wt) and ESAT-6 operon knockout mutant labeled with green fluorescent protein (GFP) at MOI of 100 for 24 hours at 35°C. Infected cells were identified and analyzed to explore the effect of pathogenic mycobacteria on mitochondrial morphology over time.</div> <div> </div> <div>More details available in this preprint: <a href="https://doi.org/10.48550/arXiv.2411.06035">https://doi.org/10.48550/arXiv.2411.06035</a></div>
Time-lapse electrical resistivity tomography and seismic reflection imaging of a shallow ground-water aquifer (0-50 m): Mississippi River levee seepage across the Duncan Point bar, Baton Rouge, Louisiana, U.S.A.
<p>The electrical resisitivity raw data files are slightly processed to remove bad data points but can be inverted using tomographic inversion code. </p> <p>The seismic data were assembled in Seismic Unix format, a shortened version of the SEG-Y format (Society of Exploration Geophysicists Exchange Format-Y https: //seg. org/Publications/SEG-Technical-Standards), that has the 3200-byte EBCDIC and 400-byte tape header removed. The data uploaded online (<a href="https://zenodo.org/records/14776025">https://zenodo.org/records/14776025</a>) is a CMP brute-stacked seismic section. </p> <p>During data collection, shotpoint location changed proceeding along a 136-degree azimuth (south-easterly direction), and spaced every 1 m.</p> <p>A total of 48, horizontal-component 28-Hz nominal geophones were placed every one meter and shotpoints were located half-way between geophones. Geophones remained fixed at their locations throughout the survey and so the CMP spacing is nominally 0.5-m but fold varies linearly from a value of 1 from either side of the survey to a central maximum of 24. The seismic source consisted of a partially buried 20-lb steel I-beam struck repeatedly on either side three times by an 8-lb sledge hammer. Data of the same striking polarity were added in-phase in the field. Data with opposing polarity at each shotpoint location were subtracted later to enhance SH-wave data and suppress converted SH-to-P waves.</p> <p>Seismic processing is minimal and consists of standard surface-wave muting, elimination of bad seismic traces, normal moveout, bandpass filtering (between 12 Hz and 50 Hz) and preliminary stacking with trace mixing every 3 CMPs. The data were stacked with a single velocity throughout that ranged from 80 m/s (Vs) at 0.2 s, to 100 m/s at 0.35 s and reached 180 m/s at 0.5 s of two-way traveltime.</p> <p> </p>
BioTISR: a time-lapse biological image dataset for super-resolution microscopy
<p>BioTISR is a biological image dataset for super-resolution microscopy, currently including 2D and 3D time-lapse image pairs of low-and-high resolution images of a variety of biology structures, aiming to provide a high-quality dataset of time-lapse biological SR images for the community to spark more developments of computational SR methods.</p> <p>At present, 2D dataset includes five specimens (clathrin-coated pits, lysosomes, outer mitochondrial membrane, microtubules, and F-actin) acquired with the GI/TIRF-SIM mode and nonlinear SIM mode of our Multi-SIM system, and 3D dataset includes three specimens (outer mitochondrial membrane, microtubules, and F-actin) acquired with 3D-SIM mode of the Multi-SIM system. For each type of specimen and each imaging modality, we acquired the raw data from at least 50 distinct regions-of-interest (ROI). For each ROI, we acquired two (3D data) or three (2D data) groups of N-phase × M-orientation × T-timepoint raw images with a constant exposure time but increasing the excitation light intensity, where (N, M, T) are (3, 3, 20) for TIRF-SIM and GI-SIM, (5, 5, 10) for nonlinear SIM, and (3, 5, 10) for 3D-SIM. Specific imaging conditions and scripts for reading MRC file are provided in Supplement Files.</p> <p>The BioTISR dataset is related to the following paper:<a href="https://www.nature.com/articles/s41587-025-02553-8#citeas">Qiao, C., Liu, S., Wang, Y. <em>et al.</em> A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification. <em>Nat Biotechnol</em> (2025). https://doi.org/10.1038/s41587-025-02553-8</a>, which is an extension of our previously published <a href="https://doi.org/10.6084/m9.figshare.13264793.v9">BioSR dataset</a> (https://www.nature.com/articles/s41592-020-01048-5).</p> <p>Limited by quota, the original images uploaded in the current 3D dataset are wide-field images obtained after averaging 15 images, where (N, M, T) are (1, 1, 10). We will update them to raw SIM images after the quota is expanded.</p> <p>2D dataset's url:</p> <p><a href="https://doi.org/10.5281/zenodo.13843670" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13843670</a></p> <p>3D dataset's urls:</p> <p>F-actin:</p> <p>WF input: <a href="https://doi.org/10.5281/zenodo.13843673">https://doi.org/10.5281/zenodo.13843673</a></p> <p>Raw SIM input:<a href="https://doi.org/10.5281/zenodo.13994464" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13994464</a></p> <p>Microtubules:</p> <p>WF input: <a href="https://doi.org/10.5281/zenodo.13932988" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13932988</a></p> <p>Raw SIM input: <a href="https://doi.org/10.5281/zenodo.13989327" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13989327</a></p> <p>Mitochondria:</p> <p>WF input: <a href="https://doi.org/10.5281/zenodo.13843183" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13843183</a></p> <p>Raw SIM input: <a href="https://doi.org/10.5281/zenodo.14000502" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14000502</a></p> <p> </p> <p>Update 2025.5.6</p> <p>Add optical transfer function(OTF) of the microscopy system and the pixel size of each data to the supplementary files.</p>
Lookout Fire Time-Lapse Video Taken from the Roswell Communication Tower for the Period of Record Aug. 10 - Oct. 23, 2023.
<p>A lightning strike fire started on Saturday, August 5, within the H.J. Andrews Experimental Forest, between the base of Lookout cliff and the ridge dividing Lookout and Mack Creek drainages. As of today, August 7, the fire is 2.5 acres. Two helicopters are traveling between the Blue River reservoir and the fire carrying water. The plan is to keep knocking back the fire until ground crews can get a line around it and contain it. It is burning in steep terrain, in old growth with dense understory, which is making it a challenge for experienced ground crews. In addition to the helicopters, a hotshot crew has been assigned to the fire, most likely starting August 8.</p><p>These images were taken from remote based StarDot and NetCam IP cameras positioned 13 meters up the Roswell Communication tower located in the north east side of the HJ Andrews Experimental Forest. The HJAHQCAM video was recorded from a StarDot camera positioned on top of the HJ Andrews main office building. Andrews Forest LTER collected and manged these data in real-time and compiled a final time-lapse video of each camera using a multi-threaded python program.</p><p>Additional Resources:<br><a href="https://andrewsforest.oregonstate.edu/about/news-events/lookout-fire-updates-2023">Andrews Forest LTER</a><br><a href="https://www.youtube.com/@AndrewsForest/playlists">Andrews Forest Youtube</a><br><a href="https://inciweb.wildfire.gov/incident-information/orwif-lookout-fire">InciWeb</a></p><p><strong>This material is based upon work supported by the H.J. Andrews Experimental Forest and Long Term Ecological Research (LTER) program under the NSF grant LTER8 DEB-2025755.</strong></p>
Svalbard time-lapse cameras
<p>Time-lapse cameras are important data sources enabling us to observe changes in the Svalbard environment in an efficient and economically favorable way. Focusing on snow cover monitoring using cameras, it is important to identify potential image providers, archived imagery, and processed datasets.</p>
AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery
<p><span>This repository contains data and scripts used in the study titled 'AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery' published as a <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2570/" target="_blank" rel="noopener">preprint </a>in Earth Surface Dynamcis (EGU) . Please check the README.docx for </span><span>folder structure with descriptions of each folder and file.</span></p>
Time-lapse of AARTFAAC detections of radio meteors in Perseids 2020
<p>LOFAR AARTFAAC images (integrated over all observing bands) during the Perseids meteor shower in the night 2020 August 12 --13. The large-scale diffuse emission is the Galactic plane. The brightest radio sources Cassiopeia~A and Cygnus~A have been subtracted, sometimes leaving some artefacts. In red, trajectories as computed from the CAMS BeNeLux optical observations are overlaid.</p> <p>Observing frequencies were in the range 30 to 60 MHz.</p>
Phase Contrast Time-Lapse and F-actin Imaging of Mechanically Compressed or Irradiated Pseudostratified Human Bronchial Epithelial Cells
<p><strong>Overview</strong></p> <p>This dataset includes phase contrast time-lapse imaging of <em>in vitro</em> pseudostratified airway epithelial cells to visualize their collective cellular migration after exposure to mechanical compression (mimicking bronchoconstriction) or irradiation. Additionally, the cells were fixed and stained for F-actin to visualize the apical cell boundaries, basal cell boundaries, and basal cell stress fibers.</p> <p><strong>Cell Culture and Treatment</strong></p> <p>Primary human bronchial epithelial cells (from a single donor) were grown on transwells in air-liquid interface (ALI) culture for 14 days to model a well-differentiated, pseudostratified airway epithelium. Cells were then exposed to either mechanical compression (30 cmH2O for 3 hours) mimicking asthmatic bronchoconstriction or irradiation (1Gy of ionizing radiation using a RS 2000 Biological Research Irradiator (RadSource) on ALI days 7, 10, and 14).</p> <p><strong>Phase Contrast Time-Lapse Imaging</strong></p> <p>At 24 or 72 hours after final treatment, cells were imaged to visualize collective cellular migration. For each independent experimental replicate (2 transwells per treatment per timepoint), six fields of view per well were imaged every 6 minutes over 1.5 hours. The imaging chamber was supplied with 37°C, 5% CO2, humidified air on a Zeiss Axio Observer Z1 to collect phase contrast images. <em>The image resolution is 0.586 µm/pixel.</em></p> <p><strong>Immunofluorescence Imaging</strong></p> <p>Cells were fixed (4% PFA for 30 minutes) at 24 or 72 hours after final treatment (and after phase contrast time-lapse imaging). Fixed transwells were stained for F-actin (Alexa fluor 488-Phalloidin, ThermoFisher Scientific, diluted 1:40, 30 minutes). Transwell membranes were cut from the plastic support and mounted on glass slides. Slides were imaged using a Zeiss Axio Observer Z1 with an apotome module controlled using Zen Blue 2.0 software. Five random fields of view were imaged from each transwell membrane in a z-stack from substrate to apical cell surface. To visualize various planes through the pseudostratified epithelial layer (apical cell boundaries, basal cell boundaries, and basal cell stress fibers), maximum intensity projections were generated from regions of interest through the z-stack. <em>The image resolution is 0.293 µm/pixel.</em></p> <p><strong>Dataset</strong></p> <p>Phase contrast time-lapse movies are provided as *.avi files. Immunofluorescence images are provided as *.tif files. For an individual transwell, the imaging dataset includes:</p> <ul> <li>6 phase contrast time-lapse movies</li> <li>5 immunofluorescence images of apical cell boundaries</li> <li>5 immunofluorescence images of basal cell boundaries</li> <li>5 immunofluorescence images of basal cell stress fibers</li> </ul> <p>Phase contrast time-lapse filenames contain</p> <ul> <li>Donor: U13</li> <li>Timepoint: 24 or 72 hours</li> <li>Treatment & Well: control (C), mechanical compression (P), or irradiation (R); well 1 or 2</li> <li>Field of View: (1) – (6)</li> </ul> <p>Immunofluorescence image filenames contain:</p> <ul> <li>Donor: <strong>U13</strong></li> <li>Timepoint: <strong>24</strong> or <strong>72</strong> hours</li> <li>Treatment & Well: control (<strong>C</strong>), mechanical compression (<strong>P</strong>), or irradiation (<strong>R</strong>); well <strong>1</strong> or <strong>2</strong></li> <li>Field of View: <strong>1-5</strong></li> <li>Region of Interest: apical cell boundaries (<strong>ACB</strong>), basal cell boundaries (<strong>BCB</strong>), or basal stress fibers (<strong>SF</strong>)</li> </ul> <p>Phase contrast time-lapse and immunofluorescence from the same transwell will all start with the same “Donor_Timepoint_Treatment/Well...” (i.e. U13_24_C1…). <strong>Note that the images from phase contrast and immunofluorescence are not necessarily from matched locations within the transwell and are at different spatial scales.</strong></p> <p>Immunofluorescence images from the same z-stack field of view will start with the same “Donor_Timepoint_Treatment/Well_FieldofView…” (i.e. U13_24_C1_1…).</p>
Data and scripts for 'Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry'
<p>This repository contains three zipped elements used in the study <em>Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry (</em>https://doi.org/10.5194/tc-2022-81):</p> <p>1. The time-lapse DEMs (original and flow-corrected), orthomosaics (not flow-corrected) and cliff outlines (original and flow-corrected) of the 24K and Langtang survey areas used in this study. The spatial resolution is the same as used in the analysis. These zipped files also contain a .csv file (time_selection.csv) indicating for each index (indicated in the file name) the serial date number in days (date origin January 0, 0000).</p> <p>2. The R and Python scripts (Scripts_final.zip) used to process the DEMs and orthomosaics from the time-lapse images as well as the script to calculate the slope-perpendicular melt. These scripts come with .csv and .txt files that serve as template for the required input data format.</p>
Strain gauge platforms: Time-lapse microscopy dataset of engineered cardiac microbundles
<p>This dataset is a "part I" extension of the "<a href="https://doi.org/10.5061/dryad.5x69p8d8g">Engineered cardiac microbundle time-lapse microscopy image dataset</a>" and contains 732 experimental time-lapse image sequences of beating hiPSC-based cardiac microbundles using microbundle strain gauge platforms [1] ("Type1"). In the "part II" extension, we included 808 experimental time-lapse image sequences of beating hiPSC-based cardiac microbundles using FibroTUG platforms [2] ("Type2"). </p> <p>References:</p> <p>[1] Zhang, K., Cloonan, P. E., Sundaram, S., Liu, F., Das, S. L., Ewoldt, J. K., ... & Chen, C. S. (2021). Plakophilin-2 truncating variants impair cardiac contractility by disrupting sarcomere stability and organization. <em>Science Advances</em>, <em>7</em>(42), eabh3995.</p> <p>[2] DePalma, S. J., Davidson, C. D., Stis, A. E., Helms, A. S., & Baker, B. M. (2021). Microenvironmental determinants of organized iPSC-cardiomyocyte tissues on synthetic fibrous matrices. <em>Biomaterials science</em>, <em>9</em>(1), 93-107.</p>
Time-lapse (4D) volumetric fluorescence microscopy image sequence of a living zebrafish embryo
<p>The dataset contains a time-lapse (4D) volumetric fluorescence microscopy image sequence of a living zebrafish embryo (cxcr4aMO). The sequence has been captured with a confocal laser-scanning microscope during zebrafish gastrulation and shows endodermal cells that have been fluorescently labelled.</p> <p>The sequence is best viewed with Fiji (https://fiji.sc/) and can be loaded into Matlab with tiffread.m (http://www.cytosim.org/misc/index.html).</p> <p>For the treatment of the specimen see:</p> <p>S. Nair and T. F. Schilling. Chemokine signaling controls endodermal migration during zebrafish gastrulation. Science, 322(5898):89–92, October 2008.</p>
Datasets for time-lapse camera monitoring of insects and their floral environments
<p>Contains the dataset for training and validation of models to estimate flower cover and identify taxa of arthropods in time-lapse camera recordings described in the paper:</p> <p>Kim Bjerge, Henrik Karstoft, Hjalte M. R. Mann, Toke T. Høye, A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments, 2024, bioRxiv, <a href="https://doi.org/10.1101/2024.04.12.589205" rel="noopener">https://doi.org/10.1101/2024.04.12.589205</a></p> <p>The zip files contain the needed files and directory structure to train the models in Python code published at: <a href="https://github.com/kimbjerge/insectsFlowers">https://github.com/kimbjerge/insectsFlowers</a></p> <p>Content of zip files:<br>===============</p> <p>insects.zip: Contains images and labels in YOLO format: <a href="https://github.com/ultralytics/yolov5/issues/2293">https://github.com/ultralytics/yolov5/issues/2293</a></p> <p>trainI21m contains the images and labels to train the insect detector with YOLOv5. Contains only the motion-informed enhanced images (MIE).<br>testI21m contains the images and labels to test the insect detector trained with YOLOv5. Contains only the motion-informed enhanced images (MIE).</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Flowers.zip: contains the images of plants and flowers with black and white masks to train the DeepLabv3 flower semantic segmentation model.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>NI2-19cls.zip: contains images for training and validation of the arthropod classifiers </p> <p>Image crops of arthropods are organized in 19 subdirectories one for each class.</p> <p>A1-Coccinellidae<br>B2-Coleoptera<br>C3-Background<br>D4-Bombus<br>E5-Syrphidae<br>F6-Lepidoptera<br>G7-Aranaeae<br>H8-Formidicidae<br>I9-Diptera<br>J10-Hemiptera<br>K11-Isopoda<br>L12-Uspecificerede<br>N13-Hymenoptera<br>O14-Orthoptera<br>P15-Rhagonycha_fulva<br>Q16-Satyrinae<br>R17-Aglais_urticea<br>S18-Odonata<br>T19-Apis_mellifera</p>
NCAS CAO NLC-Camera Time-Lapse Video starting at 2020-06-21 20:00 UTC
<p>A time-lapse video showing Noctilucent Clouds (NLCs) seen from southern England (51.15°N,-1.44°E) during the night of 21st/22nd June 2020. NLCs are a seasonal wonder of the natural world. They can only be seen from upper-middle and high latitudes during the mid-summer months (between mid May and mid August in the northern hemisphere). They are the result of ice crystals forming at the extraordinarily high altitude of around 82 km. This is 70 km higher than virtually all other clouds seen at these latitudes and qualifies as being at the edge of space (the atmospheric density and pressure are approximately 100,000th of their values at sea level). NLCs can only be seen during twilight hours, hence the name noctilucent, which means night-shining. In this video there is a mild display of NLCs during the dusk followed by a much more impressive display during the dawn. Note that British Summer Time (BST) is one hour ahead of Coordinated Universal Time (UTC). The solar elevation angles do not take account of atmospheric refraction, which is only noticeable when the sun is close to the horizon.</p>
NERC MSTRF Sky-Camera Time-Lapse Video for 2010-09-07 16:50 UTC (showing Cumulonimbus cloud development at sunset).
<p>A time-lapse video showing Cumulonimbus cloud development at sunset. Most of the first Cumulonimbus cloud is obscured by the smaller Cumulus clouds in the foreground. Its anvil is clearly visible. Three further Cumulonimbus clouds cast crepuscular rays from the light of the setting sun. Their anvils merge together. This video has been created from images taken by the NERC MST Radar Facility's Sky-Camera, which is located near Aberystwyth in West Wales. The images are freely available, under an Open (UK) Government License, from http://tinyurl.com/nerc-mstrf-sky-camera/ . For an explanation of the atmospheric phenomena that can be seen, download the resource available at http://cedadocs.ceda.ac.uk/1259/ .</p>
NERC MSTRF Sky-Camera Time-Lapse Video for 2007-09-30 14:30 UTC (showing Altocumulus clouds displaying both undulatus and lenticularis features).
<p>A time-lapse video showing Altocumulus clouds displaying both undulatus and lenticularis features. A lower cloud layer can be seen moving in a different direction at the beginning of the sequence. Contrails and cirrus clouds can be seen at higher levels. This video has been created from images taken by the NERC MST Radar Facility's Sky-Camera, which is located near Aberystwyth in West Wales. The images are freely available, under an Open (UK) Government License, from http://tinyurl.com/nerc-mstrf-sky-camera/ . For an explanation of the atmospheric phenomena that can be seen, download the resource available at http://cedadocs.ceda.ac.uk/1259/ .</p>
NERC MSTRF Sky-Camera Time-Lapse Video for 2008-07-13 18:00 UTC.
<p>A time-lapse video showing Cirrostratus clouds giving rise to a 22° Halo around the Sun. A number of aircraft Contrails pass through the field of view and a Sun Dog can be seen briefly towards the end of the sequence. This video has been created from images taken by the NERC MST Radar Facility's Sky-Camera, which is located near Aberystwyth in West Wales. The images are freely available, under an Open (UK) Government License, from http://tinyurl.com/nerc-mstrf-sky-camera/ . For an explanation of the atmospheric phenomena that can be seen, download the resource available at http://cedadocs.ceda.ac.uk/1259/ .</p>
NCAS CDAO Sky-Camera Time-Lapse Video for 2007-10-04 05:30 UTC (showing Cumulus mediocris clouds).
<p>A time-lapse video showing Cumulus mediocris clouds (with occasional Cirrus clouds and contrails at a higher level). The Cumulus clouds are in a continuous state of change. Many can be seen to form and/or to evaporate within the field of view. The evaporating clouds give rise to ragged Cumulus fractus formations. This video has been created from images taken by the Sky-Camera at the National Centre for Atmospheric Science (NCAS) Capel Dewi Atmospheric Observatory (CDAO) near Aberystwyth, UK - formerly known as the Natural Environment Research Council (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility. The images are freely available under a UK Open Government Licence from http://tinyurl.com/nerc-mstrf-sky-camera/ . For more details visit http://mst.nerc.ac.uk .</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.