Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

[Dataset] Spatial and temporal variability of the 365-nm albedo of Venus observed by the camera on board Venus Express

This is the derived data, presented in a publication entitled "Spatial and temporal variability of the 365-nm albedo of Venus observed by the camera on board Venus Express" (JGR:Planet, doi: 10.1029/2019JE006271). See the paper for details. See 'Readme.txt' for the file descriptions.

Fig. 3 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania

Fig. 3. The activity pattern of the otter in the three protected areas, based on the number of otter recordings at the observation sites during March 2011–April 2016.

Fig. 6 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania

Fig. 6. Seasonal activity patterns of Lutra lutra in the study area during the study period based on the number of otter crossings through the observation sites.

opencc-by-4.0Jun 2022View details →

Sprites observed with Global Meteor Network camera DE000C on 2022-06-30

This dataset contains compressed video observations of sprites, made with one low-light video camera of the Global Meteor Network. The camera, DE000C, is located in Sörup, Northern Germany, and has pointing azimuth 217˚ (so South-West), elevation 39˚. The files are 10.24 second videos compressed in the four-frame format. They are just FITS files with four frames, containing per pixel 1) the maximum value over 256 frames 2) the frame nr (between 0 and 255) where the maximum occurred 3) the mean value of all 256 frames and 4) the RMS of the 256 values. The platepar files contain calibration data that can be read with the sofware at https://github.com/CroatianMeteorNetwork/RMS/. The astrometry contained in the fits files is derived from this and may be less accurate. A stack of the maxpixel images is also contained.

opencc-zeroMay 2024View details →

Spatial behavior and diet data for discrete-choice analyses: data observed and classified from GPS video camera collars worn by female members of the Fortymile Caribou Herd across Alaska, USA, and Yukon, Canada

Competition for resources and space can drive forage selection of large herbivores from the bite through the landscape scale. Animal behavior and foraging patterns are also influenced by abiotic and biotic factors. Fine-scale mechanisms of density-dependent foraging at the bite scale are likely consistent with density-dependent behavioral patterns observed at broader scales, but few studies have directly tested this assertion. Here, we tested if space use intensity, a proxy of spatiotemporal density, affects foraging mechanisms at fine spatial scales similarly to density-dependent effects observed at broader scales in caribou. We specifically assessed how behavioral choices are affected by space use intensity and environmental processes using behavioral state and forage selection data from caribou (Rangifer tarandus granti) observed from GPS video-camera collars using a multivariate discrete-choice modeling framework. We found that the probability of eating shrubs increased with increasing caribou space use intensity and cover of Salix spp. shrubs, whereas the probability of eating lichen decreased. Insects also affected fine-scale foraging behavior by reducing the overall probability of eating. Strong eastward winds mitigated the negative effects of insects and resulted in higher probabilities of eating lichen. Lastly, caribou exhibited foraging functional responses wherein their probability of selecting each food type increased as the availability (% cover) of that food increased. Space use intensity signals of fine-scale foraging were consistent with density-dependent responses observed at larger scales and with recent evidence suggesting declining reproductive rates in the same caribou population. Our results highlight the potential risks of overgrazing on sensitive forage species such as lichen. Remote investigation of the functional responses of foraging behaviors provides exciting future applications where spatial models can identify high-quality habitats for conservation.

opencc-by-4.0May 2022View details →

The Longyearbyen all-sky camera full resolution image data (movie and four ASC data plots) used in the paper entitled "Auroral Morphological Changes to the Formation of Auroral Spiral during the Late Substorm Recovery Phase: Polar UVI and Ground All-Sky Camera Observations"

The uploaded movie is an animation of the Longyearbyen all-sky camera (ASC) full resolution image data from 20:00 UT to 22:00 UT, which is including all ASC snapshots used in Figure 2. This movie file is the same as Movie S1.  The uploaded four png files are the Longyearbyen all-sky camera (ASC) full resolution image snapshots, which were used in Figure S3. The numerical ASCII data to make the four ASC full resolution image snapshots are also uploaded; the count number of data detected by the ASC and associated latitude and longitude information in geographical coordinates of the ASC field of view.

opencc-zeroJul 2021View details →

MICA - Muskrat and coypu camera trap observations in Belgium, the Netherlands and Germany

MICA - Muskrat and coypu camera trap observations in Belgium, the Netherlands and Germany is a camera trap observations dataset published by the <a href="https://inbo.be">Research Institute of Nature and Forest (INBO)</a>. It is part of the <a href="https://lifemica.eu/">LIFE project MICA</a>, in which innovative techniques are tested for a more efficient control of muskrat and coypu populations, both invasive species. The dataset contains camera trap observations of muskrat and coypu, as well as many other observed species. Data in this package are exported from the camera trap management system Agouti (<a href="https://agouti.eu">https://agouti.eu</a>) and formatted as a <a href="https://tdwg.github.io/camtrap-dp/">Camera Trap Data Package (Camtrap DP)</a>. Files Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/record/5590881/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>. <ul> <li>datapackage.json: technical description of the data files.</li> <li>deployments.csv: camera trap deployments. Includes <code>deploymentID</code>, start, end, location and camera setup information.</li> <li>media.csv: media files (images/videos) captured by the camera traps. Associated with deployments (<code>deploymentID</code>) and organized in sequences (<code>sequenceID</code>). Includes timestamp and file path.</li> <li>observations.csv: observations based on the media files. Associated with deployments (<code>deploymentID</code>) and sequences (<code>sequenceID</code>). Observations can mark non-animal events (camera setup, human, blank) or one or more animal observations (<code>observationType</code> = <code>animal</code>) of a certain taxon, count, age, sex, behaviour and/or individual.</li> </ul>

opencc-by-4.0Mar 2023View details →

Data files for Atmospheric Gravity Wave and Instability Observations from the International Space Station using the Near InfraRed Airglow Camera (NIRAC)

The files in this set are data obtained from  the NIRAC airglow imager on the International Space Station. The files are  named for a JGR paper by J. Hecht et al.  entitled  Atmospheric Gravity Wave and Instability Observations from the International Space Station using the Near InfraRed Airglow Camera (NIRAC).  These files are for plots in Figures 5,7,10,11,17,18, and 19 in the submitted paper. The files are published here so as to be available for review. This paper should appear in JGR Atmospheres sometime in late 2023 or early 2024. The files that  are text files are meant to be  read with IDL as discussed in the readme file.

opencc-zeroJun 2023View details →

Data for: Home security cameras as a tool for behavior observations and science equity

Reliably capturing transient animal behavior in the field and laboratory remains a logistical and financial challenge, especially for small ectotherms. Here, we present a camera system that is affordable, accessible, and suitable to monitor small, cold-blooded animals historically overlooked by commercial camera traps, such as small amphibians. The system is weather-resistant, can operate offline or online, and allows collection of time-sensitive behavioral data in laboratory and field conditions with continuous data storage for up to four weeks. The lightweight camera can also utilize phone notifications over Wi-Fi so that observers can be alerted when animals enter a space of interest, enabling sample collection at proper time periods. We present our findings, both technological and scientific, in an effort to elevate tools that enable researchers to maximize use of their research budgets. We discuss the relative affordability of our system for researchers in South America, which is home to the largest population of ectotherm diversity.

publicJun 2023View details →

Data for: Home security cameras as a tool for behavior observations and science equity

Open the record for dataset details and reuse information.

publicMay 2024View details →

Spatial behavior and diet data for discrete-choice analyses: data observed and classified from GPS video camera collars worn by female members of the Fortymile Caribou Herd across Alaska, USA, and Yukon, Canada

Open the record for dataset details and reuse information.

opencc-by-4.0Jun 2020View details →

Statistics of turbulent tracer dispersion from UV camera observations of SO 2, Data set

LES and radiation transport data sets used to produce the figures in the manuscript: Kylling, A., Ardeshiri, H., Cassiani, M., Dinger, A. S., Park, S.-Y., Pisso, I., Schmidbauer, N., Stebel, K., and Stohl, A.: Can statistics of turbulent tracer dispersion be inferred from camera observations of SO2 in the ultraviolet?, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-286, in review, 2019 See README file for further description of files.

opencc-by-4.0Jan 2024View details →

AUV-Based Multi-Sensor Dataset: Forward-Looking Camera (FLC) and Forward-Looking Sonar (FLS) Observations in the Red Sea

Context This dataset is the first part of a dataset collection comprised of forward-looking sonar (FLS) and forward-looking camera (FLC) underwater images. The entire data was collected during the years 2021-2023 using 2 underwater vehicles in both the Red Sea and the Mediterranean along the Israeli shoreline, depicting both man-made and natural underwater environments. The data is part of a research project aimed at developing fusion models for improved obstacle detection and navigation in autonomous underwater vehicles. Content This dataset consists of FLC and FLS images and their metadata, collected by the ALICE-AUV. Both sensors were installed in the front payload section in a configuration having aligned fields of view to achieve matching pairs of data. The data was collected to train and evaluate a complete perception and obstacle avoidance framework. A series of diving sessions were performed in the Red Sea, off the coast of Eilat, Israel. The experiments focused on two main sites: A "Sunboat" shipwreck and the Eilat-Ashkelon Pipeline Company (EAPC) pier pillars. The "Sunboat" shipwreck is a 40-meter long vessel resting at a depth of approximately 12 meters, with the surrounding seabed at a depth of 18-24 meters. This dataset contains approximately 8,000 FLC-FLS sample pairs from the first session conducted at the "Sun boat" shipwreck site on September 3, 2023. The data was recorded at depths ranging from 10 to 15 meters. The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, the data is further categorized into modalities: camera (FLC images), sonar (FLS images), and navigation (dead reckoning data). The navigation data is derived from a combination of GPS, DVL, and IMU sensors, providing estimated positions when GPS is unavailable. Inside each modality directory, you will find the corresponding data files in PNG format for images and CSV format for navigation data. The file names follow a sequential numbering scheme (e.g., 00001.png, 00002.png, etc.). Each modality directory also contains a CSV file (e.g., camera.csv) that maps each data file to its respective timestamp. Additionally, the samples.json file documents the relationship between uni-modal and multi-modal samples, allowing for easy association of data from different modalities. By providing synchronized and aligned camera and sonar imagery, along with corresponding navigation data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles. Technical Details <ul> <li>Sonar: Blueprint Oculus M1200d <ul> <li>Operating frequency: 1.2 MHz (low frequency mode)</li> <li>Maximum range: 40 m (set to 20 m for this dataset)</li> <li>Horizontal aperture: 130°</li> <li>Vertical aperture: 20°</li> <li>Number of beams: 512</li> <li>Angular resolution: 0.6°</li> <li>Beam separation: 0.25°</li> <li>Image resolution: 902x497 pixels</li> <li>Coordinate system: Polar</li> </ul> </li> <li>Camera: Allied-Vision Manta G-917 <ul> <li>Image dimensions: 3384x2710 pixels (downscaled to 1692x1355 for this dataset)</li> <li>Sensor type: CCD Progressive</li> <li>Sensor bit depth: 12-bit</li> <li>Captured bit depth: 8-bit</li> <li>Camera model: Pinhole with Plumb Bob (Brown–Conrady) distortion coefficients</li> <li>Focal length (fx, fy): (1638.36157, 1641.95202)</li> <li>Principal point (cx, cy): (1705.03529, 1380.27954)</li> <li>Radial distortion coefficients (k1, k2, k3): (-0.124823, 0.048851, 0.000000)</li> <li>Tangential distortion coefficients (p1, p2): (0.000259, -0.002945)</li> </ul> </li> <li>Navigation: <ul> <li>Data format: CSV</li> <li>Contains fused dead reckoning data based on GPS, DVL, and IMU sensors</li> <li>Columns: <ul> <li>timestamp: Unix timestamp (seconds)</li> <li>latitude: Latitude (degrees)</li> <li>longitude: Longitude (degrees)</li> <li>altitude: Altitude (meters)</li> <li>yaw: Yaw angle (degrees)</li> <li>pitch: Pitch angle (degrees)</li> <li>roll: Roll angle (degrees)</li> <li>velocity_x: Velocity along the x-axis (meters per second)</li> <li>velocity_y: Velocity along the y-axis (meters per second)</li> <li>velocity_z: Velocity along the z-axis (meters per second)</li> <li>depth: Depth (meters)</li> </ul> </li> </ul> </li> <li>Frame rate: 2 Hz for both sonar and camera</li> </ul> More datasets from this collection will be uploaded in the future, and a link to access them will be provided on this page. Acknowledgements The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.

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.