Find research datasets worth reusing

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-by-4.0Feb 2023View details →

Fig. 1 in Camera traps and genetic identification of faecal samples for detection and monitoring of an endangered ungulate

Fig. 1. Distribution of deployed camera traps showing presence (black circles) and non-detection (purple circles) and genetic sampling locations showing presence (black triangles) and non-detection (purple triangles) of Eld's deer. Inset map shows the location of Chhaeb Wildlife Sanctuary in Cambodia (black rectangle). Background shows proportion of tree cover from WorldCover land cover map (© ESA WorldCover project 2020 / Contains modified Copernicus Sentinel data (2020) processed by ESA WorldCover consortium).

opencc-by-4.0Apr 2023View details →

TSlam-optitrack-camera-recordings

The dataset contains the recordings from an Optitrack system sync to the capturing of a monocular camera. The data is used to evaluate the TSlam system developed at IBOIS for augmented carpentry.

opencc-by-4.0Apr 2023View details →

ADASIND: A Diverse Wide Angle Fisheye Camera Dataset for Autonomous Driving

This dataset comprises images collected from a fisheye lens on Indian roads to capture a variety of driving scenarios in a real-world environment, with a focus on semi-urban roads that don't have dividers.. The dataset includes 10,000 RGB images and has well labeled annotations for 2d bounding box, moving object detection, calibration of the fisheye lens containing parameters, ground truth of the RGB images and previous image of the corresponding frames. With this dataset, we would like to encourage the community to adapt computer vision models for the fisheye camera instead of using naive rectification.  The Adasind dataset was collected by recording a video of more than 10,000 seconds as a vehicle drove across the Silchar city, starting from the main gate of the National Institute of Technology Silchar during different time periods of the day considering traffic variations. To capture the video, we used a smartphone with a 64-megapixel camera, which was attached to a fisheye lens and a tripod stand. The smartphone was firmly held from the backside of the motor vehicle, ensuring that it was stable and wouldn't move during the recording process. The vehicle drove through the city at a steady pace, capturing video footage at approximately 30 frames per second (fps). The annotations for various tasks is available in their respective folder and described in details below: <ol> <li> 2d box annotations : This folder contains the object label, x-y coordinates, width and height of the various objects within the image in the .txt file. Here, the Yolov5 algorithm is used to perform object detection on a set of input images which uses a deep neural network to detect objects within an image. The files of this folder are in the same sequence as that of the original RGB image with the same file names. The use of 2D bounding box annotations has several benefits in computer vision, including object detection. 2D bounding boxes can be used to train object detection models, which are designed to identify the location and extent of objects within an image. </li> <li> Motion instance annotations : This folder contains the annotations for the moving objects which have relative motion with respect to the motion of the vehicle. Those objects create bias in specific tasks like depth estimation and have to be masked before feeding into the network. The files in the folder are in the same sequence as the original image and can be masked pixel wise. </li> <li> Calibration : The process of turning the 3D world into a 2D image is done by a camera and this process is said to be calibration which can be done using the camera parameters namely the intrinsic and extrinsic parameters. As of now only a single json file is dumped with the parametric values of the camera and users can make separate copies of the file for calibration tasks related to each original image. Together, the intrinsic and extrinsic parameters are used to model the relationship between the 3D world and the 2D image captured by the camera. This information is essential in many computer vision applications, such as 3D reconstruction, object tracking, and augmented reality. </li> <li> Ground Truth : This is a subfolder inside the "Motion instance annotations" folder that contains the correct labels or annotations for each image or object in a dataset. These labels are in the form of images and the filenames are the same as those of the original images. Ground truth labels are used during evaluation to assess the accuracy and performance of the machine learning or deep learning models. The accuracy of a model is measured by comparing its predicted labels to the ground truth labels, using evaluation metrics. </li> <li> RGB images: This folder contains the RGB images extracted from the video recording (30 fps) by considering one frame  per second with 30 frame interval. A total of  10,000 RGB images are there in the folder indexed sequentially as  appeared in the video recording.  </li> <li> Previous Image (not provided) :  The folder for the previous images corresponding to each original image may be prepared as per the requirement using the original images present in the "RGB image" folder.  The use of previous images is especially relevant in computer vision tasks that involve motion prediction or scene understanding, as it enables algorithms to better understand the dynamics of the scene and predict future states.   </li> </ol> The original images can be converted to 3d tensors with RGB channels or grayscale channels. The 2d box annotated values can be fed to the network for better understanding of the objects, the RGB image tensor values can be matrix multiplied with intrinsic parameters for converting from 3d to 2d and vice-versa, motion images can be multiplied with their respective original image so the moving objects is not taken into consideration, evaluation for the model with tensors from the ground truth can be done and at last previous image tensors can also be fetched along with the current image for upcoming predicted tasks.

opencc-by-4.0May 2023View details →

Transit Bus Left Side Camera Video Traffic Monitoring Dataset

This dataset contains the raw data used in the research and development study reported in “Automated Traffic Surveillance Using Existing Cameras on Transit Buses”. This dataset consists of 11 video clips, each approximately 20 min. in duration, taken from the driver (left) side read camera of an in-service Ohio State University (OSU) Campus Area Bus Service (CABS) 40-foot transit bus running the West Campus Loop route. One set, consisting of 7 videos, was collected on a sunny day in October 2019. A second set, consisting of 3 videos, was collected during and after periods of rain and heavy rain in March 2022. Each video is accompanied by manually extracted ground truth of vehicles, and some other objects, that are in the roadway and observed by the camera.

opencc-zeroJun 2023View details →

Crowdsourced dataset of firefly trajectories obtained by automated stereo calibration of 360-degree cameras

Advancements in animal tracking techniques, spanning from migrating mammals to swarming insects, have resulted in remarkable progress in the fields of behavioral ecology and conservation science. Recently, we have devised a method for tracking luminous fireflies in their natural habitat using stereoscopic pairs of 360-degree cameras. This method offers affordability, versatility, and ease of setup; however, the process of camera calibration has remained tedious and time-consuming. Now, we have introduced an enhanced algorithm that achieves spatial and temporal stereo calibration directly from the data, eliminating the need for manual procedures both in the field and during video processing. The algorithm relies on cross-correlation of flashing patterns and numerical estimation of camera pose. Utilizing this improved protocol and processing software, we have compiled an extensive dataset comprising over 100 reconstructed firefly swarms of various species. This data was gathered throughout the United States by numerous contributors following a straightforward protocol. The dataset holds significant potential for advancing our comprehension of firefly collective behavior, facilitating population monitoring, and expanding citizen science initiatives.

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.

opencc-by-4.0Sep 2023View details →

Finite Element Analysis perturbation files for Rubin Observatory Simonyi Survey Telescope and LSST Camera

## Notes on FEA files   # M1M3 Bending modes   M1M3_1um_156_grid.fits.gz - source = IM/data/M1M3/M1M3_1um_156_grid.txt - shape = (5256, 159) - Each row is one of 5256 FEA nodes. - 0th column is M1M3 disambiguator - 1st and 2nd columns are FEA node x and y in M1M3 CS - Last 156 columns are bending modes; the z-displacement of each node for each mode.   M1M3_1um_156_force.fits.gz - source = IM/data/M1M3/M1M3_1um_156_force.txt - shape = (156, 159) - Each row is one of 156 bending modes. - 0th column is actuator ID - 1st and 2nd columns are actuator x and y in M1M3 CS - Last 156 columns are forces in Newtons for each mode.   # M1M3 print through   M1M3_dxdydz_zenith.fits.gz - source = IM/data/M1M3/M1M3_dxdydz_zenith.npy - shape = (5256, 3) - Each row is one of 5256 FEA nodes. - Columns are dx, dy, dz in M1M3 CS. - This is the gravitational "print through" when mirror is zenith pointing   M1M3_dxdydz_horizon.fits.gz - source = IM/data/M1M3/M1M3_dxdydz_horizon.npy - shape = (5256, 3) - Each row is one of 5256 FEA nodes. - Columns are dx, dy, dz in M1M3 CS. - This is the gravitational "print through" when mirror is horizon pointing   M1M3_force_zenith.fits.gz - source = IM/data/M1M3/M1M3_force_zenith.npy - shape = (256,) - Each row is one of 256 actuators. (So we consider the x and y actuators here too.) - Columns are forces in Newtons. - These are the mirror support forces when the mirror is zenith pointing. (Is this after optimization? Include LUT or not?)   M1M3_force_horizon.fits.gz - source = IM/data/M1M3/M1M3_force_horizon.npy - shape = (256,) - Each row is one of 256 actuators. (So we consider the x and y actuators here too.) - Columns are forces in Newtons. - These are the mirror support forces when the mirror is horizon pointing. (Is this after optimization? Include LUT or not?)   # M1M3 Thermal   M1M3_thermal_FEA.fits.gz - source = IM/data/M1M3/M1M3_thermal_FEA.npy - shape = (5244, 7) - Each row is one of 5244 FEA nodes. (Why aren't these the same as above? I don't know.) - Columns are: - 0: Unit-Normalized FEA x - 1: Unit-Normalized FEA y - 2: Bulk temperature dz coefficient - 3: x temperature gradient dz coefficient - 3: y temperature gradient dz coefficient - 3: z temperature gradient dz coefficient - 3: r temperature gradient dz coefficient   # M1M3 Miscellany   M1M3_influence_256.fits.gz - source = IM/data/M1M3/M1M3_influence_256.npy - shape = (5256, 256) - Each row is one of 5256 FEA nodes. - Each column is one of 256 actuators. - Values are dz/dF for each actuator/node.   M1M3_LUT.fits.gz - source = IM/data/M1M3/M1M3_LUT.txt - shape = (257, 91) - First column is index in degrees (0-90 inclusive). Last 256 columns are forces in Newtons. - Each column is LUT for one value of the elevation index.   M1M3_1000N_UL_shape_156.fits.gz - source = IM/data/M1M3/M1M3_1000N_UL_shape_156.npy - shape = (5256, 156) - Rows must be FEA nodes, columns must be bending modes. - Not sure what the purpose is of this one.   # M2 Bending modes   M2_1um_grid.fits.gz - source = IM/data/M2/M2_1um_grid.DAT - shape = (15984, 75) - Each row is one of 15984 FEA nodes. - 0th column is node index ? - 1st and 2nd columns are FEA node x and y in M2 CS - Last 72 columns are bending modes; the z-displacement of each node for each mode.   M2_1um_force.fits.gz - source = IM/data/M2/M2_1um_force.DAT - shape = (72, 75) - Each row is one of 72 bending modes. - 0th column is actuator ID - 1st and 2nd columns are actuator x and y in M2 CS - Last 72 columns are forces in Newtons for each mode.   # M2 print through / thermal   M2_GT_FEA.fits.gz - source = IM/data/M2/M2_GT_FEA.txt - shape = (9084, 6) - Each row is one of 9084 FEA nodes. (Why aren't these the same as above? I don't know.) - Columns are: - 0: Unit-Normalized FEA x - 1: Unit-Normalized FEA y - 2: Zenith print through dz coefficient - 3: Horizon print through dz coefficient - 4: z temperature gradient dz coefficient - 5: r temperature gradient dz coefficient

opencc-by-4.0Dec 2019View details →

"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice

"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17

opencc-by-4.0Dec 2019View details →

"The sound comes from a meadow in the Sierra Nevada Mountains in California. The meadow is at an elevation of 2400 meters near a mountain named Olancha Peak, which is 3700 meters in altitude. Ihave a group of friends with which Ibackpack (trek) into the mountains. Our goal was to spend some time in the mountains and hike to the top of Olancha Peak (…) By the time we reached the meadow, we were in a forest and there was still snow on the ground in some places. We took the trip in June of 2006. The Sierra Nevada Mountains are a large mountain range. Much of the range is protected by national parks or preserved areas we call 'wilderness areas' (…) Ihave been backpacking for nearly 40 years and Iwill hopefully continue with this challenging activity for 40 years more! Many of my friends are much younger than Iam and it gives me much satisfaction to be able to have as much or more stamina for this activity than they have! When we are on these trips, we hike up peaks, catch fish, drink some whiskey around campfires and enjoy our time in the beautiful solitude. My memories of this trip were of the steep, hot hike from the desert to the cool meadow; the overall beauty of the nature, the absolute solitude of our campsite near the meadow; the strenuous hike to the top of Olancha Peak; the camaraderie of my friends; and, of course the sound of the frogs in the meadow. The frog sounds were astounding to me and Iwould listen in awe of the creature's instinctual desire to reproduce and continue the existence of their kind. Surely there were different species in the meadow for some of the frog sounds were different than others. The sounds only occurred after the Sun went down for the evening. Istood next to the creek in the meadow and recorded the sounds using my digital camera." [Peter/plentz1960]16 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice

"The sound comes from a meadow in the Sierra Nevada Mountains in California. The meadow is at an elevation of 2400 meters near a mountain named Olancha Peak, which is 3700 meters in altitude. Ihave a group of friends with which Ibackpack (trek) into the mountains. Our goal was to spend some time in the mountains and hike to the top of Olancha Peak (…) By the time we reached the meadow, we were in a forest and there was still snow on the ground in some places. We took the trip in June of 2006. The Sierra Nevada Mountains are a large mountain range. Much of the range is protected by national parks or preserved areas we call 'wilderness areas' (…) Ihave been backpacking for nearly 40 years and Iwill hopefully continue with this challenging activity for 40 years more! Many of my friends are much younger than Iam and it gives me much satisfaction to be able to have as much or more stamina for this activity than they have! When we are on these trips, we hike up peaks, catch fish, drink some whiskey around campfires and enjoy our time in the beautiful solitude. My memories of this trip were of the steep, hot hike from the desert to the cool meadow; the overall beauty of the nature, the absolute solitude of our campsite near the meadow; the strenuous hike to the top of Olancha Peak; the camaraderie of my friends; and, of course the sound of the frogs in the meadow. The frog sounds were astounding to me and Iwould listen in awe of the creature's instinctual desire to reproduce and continue the existence of their kind. Surely there were different species in the meadow for some of the frog sounds were different than others. The sounds only occurred after the Sun went down for the evening. Istood next to the creek in the meadow and recorded the sounds using my digital camera." [Peter/plentz1960]16

Phenological time lapse images from canopy camera MC100 in Tammela Spruce stand

This record contains phenological time lapse images from camera Tammela Spruce stand. Camera was mounted at canopy view level at location 60.64598306; 23.80650111(N;E, WGS84). First set of images were taken between 31.03.2014--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record. Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist. More details on the camera installations and operation history can be found at  http://doi.org/10.5281/zenodo.777952 . The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi. For further information contact Mikko Peltoniemi (mikko.peltoniemi@luke.fi)

Phenological time lapse images from ground camera MC111 in Sodankylä, peatland Peatland

This record contains phenological time lapse images from camera Sodankylä, peatland Peatland. Camera was mounted at ground view level at location 67.368517;26.654483(N;E, WGS84). First set of images were taken between 22.05.2014--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record. Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist. More details on the camera installations and operation history can be found at doi 10.5281/zenodo.777952 The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi. For further information contact mika.aurela@fmi.fi

Phenological time lapse images from ground camera MC121 in Värriö Pine stand

This record contains phenological time lapse images from camera Värriö Pine stand. Camera was mounted at ground view level at location 67.75492;29.60989(N;E, WGS84). First set of images were taken between 13.04.2015--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record. Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist. More details on the camera installations and operation history can be found at doi 10.5281/zenodo.777952 The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi. For further information contact Pasi Kolari (pasi.kolari@helsinki.fi)

Phenological time lapse images from ground camera MC128 in Kaamanen Peatland

This record contains phenological time lapse images from camera Kaamanen Peatland. Camera was mounted at ground view level at location 69.140583;27.269817(N;E, WGS84). First set of images were taken between 25.03.2015--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record. Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist. More details on the camera installations and operation history can be found at doi 10.5281/zenodo.777952 The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi. For further information contact mika.aurela@fmi.fi

Phenological time lapse images from ground camera MC101 in Tammela Spruce stand

This record contains phenological time lapse images from camera Tammela Spruce stand. Camera was mounted at ground view level at location 60.64598306; 23.80650111(N;E, WGS84). First set of images were taken between 31.03.2014--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record. Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist. More details on the camera installations and operation history can be found at 10.5281/zenodo.777952 The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi. For further information contact Mikko Peltoniemi (mikko.peltoniemi@luke.fi)

Phenological time lapse images from ground camera MC107 in Hyytiälä Pine stand

This record contains phenological time lapse images from camera Hyytiälä Pine stand. Camera was mounted at ground view level at location 61.84769;24.29496(N;E, WGS84). First set of images were taken between 14.03.2014--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record. Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist. More details on the camera installations and operation history can be found at doi 10.5281/zenodo.777952 The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi. For further information contact Pasi Kolari (pasi.kolari@helsinki.fi)

Phenological time lapse images from ground camera MC127 in Lammi Birch stand

This record contains phenological time lapse images from camera Lammi Birch stand. Camera was mounted at ground view level at location 61.05211; 25.04180(N;E, WGS84). First set of images were taken between 11.12.2015--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record. Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist. More details on the camera installations and operation history can be found at http://doi...(camera information sheet). The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi. For further information contact john.loehr@helsinki.fi

Phenological time lapse images from ground camera MC110 in Sodankylä Pine stand

This record contains phenological time lapse images from camera Sodankylä Pine stand. Camera was mounted at ground view level at location 67.3618;26.638167(N;E, WGS84). First set of images were taken between 22.05.2014--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record. Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist. More details on the camera installations and operation history can be found at doi 10.5281/zenodo.777952 The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi. For further information contact mika.aurela@fmi.fi

publicOct 2023View details →

Data from: Holistic monitoring of aquatic and terrestrial vertebrates by camera trapping and aquatic environmental DNA

Open the record for dataset details and reuse information.