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.
1,506
datasets available to search
ShareScore release 0.9.0
Dataset results
1,506 results for “objects”
Data set: Quantifying the Accuracy of Collaborative IoT and Robot Sensing in Indoor Settings of Rigid Objects
<p>This is the data set accompanying the paper "Quantifying the Accuracy of Collaborative IoT and Robot Sensing in Indoor Settings of Rigid Objects" by Sune L. Sørensen and Mikkel Baun Kjærgaard. Please refer to the paper for a description of the hardware used to record the data and how it is recorded.</p> <p>It consists of the following files:</p> <p><em>IoT camera images</em>: RBG images, named img_aa_bbb_0.jpg, where aa is the setup ID, bb is the camera ID (101, 102, 103 or 104).</p> <p><em>Robot RGB images</em>: RBG images, named aa0.png where aa is the setup ID.</p> <p><em>Robot point clouds</em>: pcd-files, named aa0.pcd where aa is the setup ID.</p> <p>The transformation from the IoT coordinate system to the robot coordinate system is:</p> <p>robotTiot = np.array([[0.914428, 0.134934, -0.378832, 3.76475],</p> <p>[0.393661, -0.49845, 0.772371, 0.791051],</p> <p>[-0.0846896, -0.855336, -0.509056, 2.37154],</p> <p>[0.0, 0.0, 0.0, 1.0]])</p> <p>Example, tranforming a pose in IoT coordinates to robot coordinates: p_rob = robotTiot * p_iot</p>
Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs
<p>The data comes from common guillemots studied at Stora Karlsö, Sweden between 2019 and 2021. The common guillemots breed at an artificial cliff, and has been filmed continusly from above over three breeding seasons. Using the video material, a YOLOv5 model has been trained to detect adult birds, chicks and eggs. The dataset contains annotations (bounding boxes) used for training the model, the model itself, and outputs from the model (object detections).</p> <p>The data can be used and shared freely.</p>
SEM database of spherical objects
<p>The UWB scattered Field from 5 different classes of spheres is computed in the far-field region from 0.01 to 5 GHz, for both mono and bi-static configurations, using Mie series algorithm implemented in Matlab. The scattered field is recovered for both field components Etheta and Ephi. It is recovered for multiple observation angles where θ varies from 0° to 180° with 5° step and ϕ from -180° to 180° with 10° step. The pre-processing of the scattered Field is done by extracting the natural frequencies and their associated residues from the frequency response of each simulated object using Vector Fitting algorithm implemented in MATLAB (<a href="https://www.sintef.no/projectweb/vectorfitting/">https://www.sintef.no/projectweb/vectorfitting/</a>).</p> <p>The 5 classes of spheres are: PEC sphere, sphere with a relative permittivity er= 4 and conductivity σ= 0.5, three lossless dielectric spheres with er equal to 2, 4 and 9. They are enumerated as classes 0 to 4 respectively. To reduce the size of the datasets, we include data from only three main planes; ϕ=0° and θ varies (XoZ plane), ϕ =90° and θ varies (YoZ plane), θ=90° and ϕ varies (XoY plane).</p> <p>The datasets contain four parameters : natural frequencies, Q-factors and their respective residues</p>
Preserving and sharing born-digital and hybrid objects from and across the National Collection (Decision-Making Model)
<p>When considering the complex challenges faced by cultural heritage organisations in collecting, preserving and sharing born digital and hybrid objects, it becomes clear that the process of defining solutions as a community of practice is in its early probing phase: characterised as tentative, exploratory, questioning, experimental. The workshops within this Preserving and sharing born-digital and hybrid objects from and across the National Collection project, which examined the case studies from multiple angles, yielded a richly discursive examination of the main considerations.</p> <p>This Decision Model represents an attempt to create a structured representation of those main considerations and the discourse from the workshops, to codify the main decision-making processes that an organisation may go through when assessing an acquisition of such an object, categorised into high level areas. It attempts to create a traversable system that could be used by collections professionals in their work - policy makers, managers, collections management or digital preservation practitioners, conservators.</p>
Preserving and sharing born-digital and hybrid objects from and across the National Collection (January 2022)
<p>This report is one of a set of outputs from the Arts and Humanities Research Council funded project ‘Preserving and sharing born-digital and hybrid objects from and across the National Collection’. It has been designed to provide an extensive account of the project research activities and findings, to be useful to museum, heritage, and preservation professionals, as well as to scholars interested in born-digital materials.</p>
Born Digital, Born Slippery - Data Modelling for Born Digital/Hybrid objects (March 2022)
<p>The report Preserving and sharing born-digital and hybrid objects from and across the National Collection discusses the challenges for cultural heritage institutions in cataloguing, preserving, and providing access to four born digital and hybrid objects/projects. This response looks at the issues brought up by those discussions, in particular for the purpose of cataloguing, and the extent, within existing cataloguing standards, they can be supported. Where there does not appear to be support within current standards, model development activity is proposed to allow the expansion of the current `born physical` focused cataloguing standards to also handle born digital and hybrid object cataloguing.</p>
Object neophilia in wild herring gulls in urban and rural locations
<p>Living with increasing urbanisation and human populations requires resourcefulness and flexibility in wild animals' behaviour. Animals have to adapt to anthropogenic novelty in habitat structure and resources that may not resemble, or be as beneficial as, natural resources. Herring gulls (Larus argentatus) increasingly reside in towns and cities to breed and forage, yet how gulls are adjusting their behaviour to life in urban areas is not yet fully understood. This study investigated wild herring gulls' responses to novel and common anthropogenic objects in urban and rural locations. We also examined whether gulls' age influenced their object response behaviour. We found that, out of the 126 individual gulls presented with objects, 34% approached them. This suggests that the majority of targeted gulls were wary or lacked interest in the experimental set-up. Of the 43 gulls that approached the objects, we found that those tested in urban locations approached more slowly than their rural counterparts. Overall, gulls showed no preference for either novel or common anthropogenic objects, and age did not influence likelihood of approach, approach speed or object choice. Individuals paid most attention to the object they approached first, potentially indicative of individual preferences. Our findings indicate that most herring gulls are not as attracted to anthropogenic objects as anecdotal reports have suggested. Covering up obvious food rewards may thus help mitigate human-gull conflict over anthropogenic food sources.</p>
Initial relevant routes and geospatial objects for refugees and asylum seekers in MS
<p>Open geospatial dataset with an initial collection of routes, landmarks, and decision and confirmation points relevant for young refugees and asylum seekers arriving to Münster (MS), Germany. The information here collected were the results of participatory workshops done with young forced migrants in 2016.</p> <p>The information of the routes, landmarks (reference objects), points (origin, destination, decision, and confirmation points) and the relationship between points and reference objects is available in .JSON format. It has as an example, the images collected for one of the relevant routes (R2) identified by the group of young forced migrants. This route is the one from the main mall downtown (Arkaden) MS to the central train station. The pictures are available in .zip format.</p> <p> </p>
The RBO Dataset of Articulated Objects and Interactions
<p>The RBO dataset of articulated objects and interactions is a collection of 358 RGB-D video sequences (67:18 minutes) of humans manipulating 14 articulated objects under varying conditions (light, perspective, background, interaction). All sequences are annotated with ground truth of the poses of the rigid parts and the kinematic state of the articulated object (joint states) obtained with a motion capture system. We also provide complete kinematic models of these objects (kinematic structure and three-dimensional textured shape models). In 78 sequences the contact wrenches during the manipulation are also provided.</p> <p>For more information, please refer to https://tu-rbo.github.io/articulated-objects/</p>
Figure 1. Associations between objects and words-Intelligent Agent for Acquisition of the Mother Tongue Vocabulary
<p>So, the learning process is very complex, and it is a probabilistic one. Thus, a second<br> example is required, and even more than that. Normally, the mother speaks naturally to her child,<br> she speaks with love and affection, she doesn’t “judge” or “program” what to say to her child.<br> Therefore, another day she will tell her child, for example, “My darling son, let’s drink the milk<br> from the cup.”</p>
Data associated with Versatile Multiple Object Tracking in Sparse 2D/3D Videos via Deformable Image Registration (2024)
<p>This includes a volumetric whole-brain calcium recording of a freely behaving worm (<em>C. elegans</em>) captured at 4 Hz with tracked fluorescent neuronal nuclei, used to demonstrate the performance of a multi-object tracking algorithm (ZephIR) described in the associated publication.</p>
Canola seedling object detection dataset
<div> <div>This is a single-class object detection dataset containing aerial images of early-season canola fields. The objects of interest are canola seedlings. The images were acquired by a Hasselblad L1D-20c camera mounted on a DJI Mavic 2 Pro Drone, which was flown at a height of 2 metres. The dataset contains 431 images and 77059 canola seedling bounding box annotations.</div> </div>
An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data
<p>This repository contains the data used in "An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data" by Oettli and Kotsuki (submitted to Journal of Geophysical Research: Atmospheres).</p>
Segmentation masks of ZooScan images focusing on images with several objects separated by a human operator
<p>All information is available from the original publication page: https://doi.org/10.17882/99663</p>
The Potential of Deep Learning Object Detection in Citizen-Driven Snail Host Monitoring to Map Putative Disease Transmission Sites
<p><a name="_Hlk158802145"></a><span>Schistosomiasis is a neglected tropical disease caused by parasitic flukes transmitted by freshwater snails. Despite increasing efforts of mass drug administration, schistosomiasis remains a public health concern and the World Health Organization recommends complementary snail control. To address the need of broad-scale and actual snail distribution data to guide snail control, we adopted a citizen science approach and recruited citizen scientists (CS) to perform weekly snail sampling in the endemic setting in Uganda. Snails were identified, sorted and counted according to genus, photographed and uploaded for expert-led validation and feedback. However, expert validation is time-consuming and introduces a delay in verified data output. Thus, artificial intelligence could provide a solution by means of automated detection and counting of multiple snails collected from the field. Trained on approximately 2500 citizen-collected images, the resulting model can simultaneously detect and count Biomphalaria and Radix snails with average precision of 98.1% and 98.8% respectively. The object detection model also agreed with the expert’s decision averagely for 98.8% of the test images and could be ran in real-time (24.6 images per second). We conclude that the automatic and instant detection can rapidly and reliably validate data submitted by CS in the field, ultimately minimizing the expert validation efforts and thereby facilitating the mapping of putative schistosomiasis transmission sites. An extension to a mobile application could equip citizen scientists in remote areas with instant learning opportunities and expert-like identification skills, overcoming the need for on-site training and extensive expert intervention. </span></p>
Figure 8. F1 scores for YOLOv5 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 8. F1 scores for YOLOv5 indicating the harmonic mean between precision and recall scores. Data indicated that detection results for both species would be best at a confidence threshold of 0.298.
Figure 11. YOLOv5 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 11. YOLOv5 precision (A), recall (B), and F1 score (C) changes as a function of Amoronthus polmeri density (plants m−2).
Figure 10. Detection results for YOLOv5 with a in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 10. Detection results for YOLOv5 with a confidence interval of 0.15. False-positive detections of Mollugo verticillata and Abutilon theophrasti as Amoronthus polmeri are denoted by arrows pointing from "A" and "B," respectively.
Figure 9. YOLOv5 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 9. YOLOv5 detection results for Amoronthus polmeri and soybean using confidence thresholds of 0.15 (A) and 0.70 (B). The likelihood of false-negative (FN) detections increases as confidence thresholds increase, as can be seen in B. Objects assigned a confidence interval of less than 0.70 are not detected in B. FN A. palmeri and soybean detections in B are indicated by the orange and white arrows, respectively.
Figure 7 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 7. Image annotation of soybean at the R2 growth stage. As soybean populations were much higher than Amoronthus polmeri populations, there was a high level of soybean overlap. Therefore, it was necessary to include multiple soybean plants in each image. However, A. polmeri plants typically did not have as much overlap, and in most cases, it was much easier to identify and label individual plants.
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.