Find research datasets worth reusing

opencc-by-4.0Jun 2024View details →

Real-world grasp data of a dual-arm Yumi robot with a parallel gripper and suction cup end-effectors

The attached txt file contains indexes to a cleaner subset of the data issued in the first version. Note:  Version 1 contains samples with failure cases due to environment constraints, which work well for the platform used in GraspAgent 1.0 (https://doi.org/10.1109/LRA.2024.3502066). However, this can degrade the performance if used on another platform with different constraints. To solve this, version 2 reports a subset of the raw data, excluding the failure modes due to environmental causes.

opencc-by-sa-4.0Jul 2024View details →

MicroCT slices of reproductions of Minoan ceramic cups

MicroCT data have been acquired at the Multidisciplinary Laboratory of the Abdus Salam International Centre for Theoretical Physics. The microCT scans were carried out by using a sealed X-ray source (Hamamatsu L8121–03) with a focal spot size of 5 μm and a flat panel detector (Hamamatsu C7942SK-25; pixel size of 50μm) according to the following parameters: 110kV, 90μA, exposure time/projection of 2 s, 1440 projections of the samples over 360°. The X-ray beam was filtered by a 0.01 mm-thick copper absorber. The final slices were reconstructed using the commercial software DigiXCT (Digisens) in 32-bit format at an isotropic voxel size of 40 μm. The uploaded slices have been converted in 8-bit format.

opencc-by-4.0Jul 2021View details →

3D Archaeological Greek Pottery: MHNC-UP-020060, Gnathian cup-skyphos, Laurel Spray Group

This 3D dataset is related to the publication: <ul> <li>Moitinho de Almeida, V. (2023). "<a href="https://www.researchgate.net/publication/353038967_Contributions_of_3D_digital_methods_and_techniques_to_the_study_of_ancient_pottery">Contributions of 3D digital methods and techniques to the study of ancient pottery</a>". In Myths, Gods, and Heroes. Greek vase collections in Portugal / Mitos, Deuses e Heróis. As coleções de vasos gregos em Portugal. R. Morais, R. Centeno, D. Ferreira (eds.). Câmara Municipal de Santa Maria da Feira - Museu Convento dos Lóios; Reitoria da Universidade do Porto; Faculdade de Letras da Universidade do Porto; Imprensa da Universidade de Coimbra. Pp.269-291. (ISBN: 978-989-8183-25-5)</li> </ul> 3D processed dataset for object MHNC-UP-020060 in the Museu de História Natural e da Ciência da Universidade do Porto (MHNC-UP), Portugal. CC BY-NC-SA 4.0 license. MHNC-UP-020060 is a Gnathian cup-skyphos, Laurel Spray Group, dating from c. 330 BCE and unknown provenance (Rocha Pereira & Morais, 2007; Morais, 2019; Morais et al., 2021). Aims: 3D digital documentation; morphological characterization; technological and functional analysis of archaeological Greek pottery. Data acquisition: at the MHNC-UP, with a portable non-contact structured white light scanner, Breukmann smartSCAN3D-HE, equipped with stereo colour cameras at 250 mm FOV. Additional metadata included in associated spreadsheet. Data processing: 24 scans aligned and merged. MHNC-UP-020060_3D01.ply: non-manifold edges, self-intersections, small components, and small tunnels in the mesh automatically fixed, noise data removed; orientation and position normalised. MHNC-UP-020060_3D01-holesFilled.ply: holes filled for calculation of material density, filling volume, and centre of mass. Additional metadata included in associated spreadsheet. Access to the MHNC-UP-020060 was granted by the MHNC-UP. When citing this material: please include the original inventory ID (MHNC-UP-020060) reference to the physical object.

opencc-by-4.0Feb 2023View details →

Human-to-robot Handovers of Cups with Water

If you use this dataset in your work, please cite the following publication (accepted to IEEE iROS 2023): Lastrico, Linda, Nuno Ferreira Duarte, Alessandro Carfí, Francesco Rea, Alessandra Sciutti, Fulvio Mastrogiovanni, and José Santos-Victor. "Expressing and inferring action carefulness in human-to-robot handovers." In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9824-9831. IEEE, 2023. Setup Description: The experiment is presented as a collaborative task, where the human should help the robot clean the table by handing over the cups, from the rightmost cup to the leftmost, one at a time. The robot receives the cup; in case the cup contains water, it pours the content into the orange bucket, and finally, it places the empty cup in the blue drawer. Participants stand in front of a table with four identical plastic cups placed in a row, equidistant from each other. These cups differ in content, being two empty and two filled with water almost to the brim, constituting two types of objects to be handover: empty or full. Participants faced a Kinova Gen3 robot fixed to a table with two distinct recipients at the robot side. On the left side of the robot, there is an orange bucket meant to contain water, while the blue drawer on the right stores the empty (or emptied) cups. We adopted a within-subject study design where participants are exposed, in a randomized order, to two conditions associated with the controller used by the robot to complete the task: a neutral motion (NEU) and an expressive motion (GAN). The neutral is a simple PID controller and the expressive is Generative Adversarial Network (GAN) which generates robot trajectories that are human-inspired from a previous dataset of humans handing over cups with water and without. Our study involved 15 right-handed participants (8 females, 7 males, average of 26.6 (+-6.2) years old) who provided written informed consent. They were all naive regarding the purpose of the experiments and not directly involved in our research. The self-reported level of knowledge in robotics was: 40.0% professional or advanced, 33.3% average, and 26.7% little or none. 360 actions (15 participants x 12 handovers x 2 conditions) were recorded and performed successfully without dropping the cup or spilling the content. Data Description: All the data is synchronize using ROS timestamps. - motion-tracking: Motion Capture data for head, shoulder, and wrist from OptiTrack at 120 Hz + IMU wrist data at 400 Hz. Inside each participant P## folder you will find two other folders P##_neu and P##_gan related to the two interaction conditions where the kinova motion controller changed during the cup pouring and cup placing. Inside each subfolder you will find the following motion tracking data: head.csv, shoulder.csv, wrist.csv, robot.csv (from OptiTrack markers, the robot.csv is the robot's base), imu.csv (from IMU in the wrist), pupil.csv (Pupil ROS node), key.csv (manual labels). All these files have ROS_timestamps that can be used to find the matching frames for each of the sensors. The key.csv are manually picked time flags we marked to define specific moments in the experiment (you can the meaning in the additional notes below). - eye-tracking_#: Pupil-Labs head-mounted eye-trackers at 120Hz for pupil infra-red cameras, and 30 Hz for forward RGB camera. All 16 participants (P##) are present for both robot motion controllers (neutral NEU, and GAN). - go_pro_#: GoPro 1080p video of the size view of the Human-to-robot handovers experiments at 60 Hz. Note that there were 16 participants in this experiment but 3 participants did not give permission to make their image public so we removed the following participants videos: P01, P15, P16.

opencc-by-4.0May 2020View details →

Mogha resevoir (मोघा जलाशय, near देवरी, उदयपुरा तहसील, रायसेन ज़िला, Madhya Pradesh). Bhairava ghat, slab with cup marks.

Mogha resevoir (मोघा जलाशय, near देवरी, उदयपुरा तहसील, रायसेन ज़िला, Madhya Pradesh). Bhairava ghat, slab with cup marks.

opencc-by-4.0Nov 2021View details →

Model weights for a Weather4cast 2021 Challenge IEEE Big Data Cup Stage solution

This repository contains the pre-trained model weights for the TensorFlow/Keras models used in the <a href="https://www.iarai.ac.at/weather4cast/2021-competition/challenge/">Weather4cast 2021 Challenge IEEE Big Data Cup Stage</a> by the team "antfugue". The model code can be found in <a href="https://github.com/jleinonen/weather4cast-bigdata">https://github.com/jleinonen/weather4cast-bigdata</a> along with instructions on where to extract the weights.

opencc-by-4.0Jun 2022View details →

YOGData: Labelled data (YOLO and Mask R-CNN) for yogurt cup identification within production lines

Data abstract: The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.)    Paper abstract: The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model,  the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%.

opencc-by-4.0Aug 2022View details →

Text-fig. 8. a–e: Quercus aff. cerris. a: Cup, Oriolo MSF 984. b: Cup, Oriolo MSF 982. c: Leaf, Oriolo MSF 689. d: Leaf, Oriolo MSF 659. e: Oriolo MSF 657. f: Quercus aff. pubescens Oriolo MSF 688. g: Quercus sect. Quercus acorn, Oriolo MSF 658. h–j: Quercus iberica. h: Oriolo MSF 694. i: Oriolo MSF 639. j: Oriolo MSF 698. Scale bars 10 mm (a, b, g), 50 mm (c–f, h–j). in The Late Early Pleistocene Flora Of Oriolo, Faenza (Italy): Assembly Of The Modern Forest Biome

Text-fig. 8. a–e: Quercus aff. cerris. a: Cup, Oriolo MSF 984. b: Cup, Oriolo MSF 982. c: Leaf, Oriolo MSF 689. d: Leaf, Oriolo MSF 659. e: Oriolo MSF 657. f: Quercus aff. pubescens Oriolo MSF 688. g: Quercus sect. Quercus acorn, Oriolo MSF 658. h–j: Quercus iberica. h: Oriolo MSF 694. i: Oriolo MSF 639. j: Oriolo MSF 698. Scale bars 10 mm (a, b, g), 50 mm (c–f, h–j).

opencc-by-4.0Dec 2021View details →

X-ray scattering Datasets of gold and silver nanoparticle composites, relating to the publication "Gold and silver dichroic nanocomposite in the quest for 3D printing the Lycurgus cup"

Wide-range X-ray scattering datasets and analyses for all samples described in the 2020 publication "Gold and silver dichroic nanocomposite in the quest for 3D printing the Lycurgus cup". These datasets are composed by combining multiple small-angle x-ray scattering and wide-angle x-ray scattering curves into a single dataset. They have been analyzed using McSAS to extract polydispersities and volume fractions. They have been collected using the MOUSE project (instrument and methodology).

opencc-by-4.0May 2024View details →

CUP-RNA

CUP-RNA data for BPformer

Figure 9 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 9. Simulation results for the lift forces acting on the Model A and B tags (top panel) in constant flow (5.6 m/s) as a function of orientation (–20º to 180º). Measured results as a function of orientation (–20º to 90º) in constant 5.6 m/s flow are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).

Figure 8 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 8. Simulation results for the drag forces acting on the Model A and B tags (top panel) in constant flow (5.6 m/s) as a function of orientation (–20º to 180º). Measured results as a function of orientation (–20º to 90º) in constant 5.6 m/s flow are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).

Figure 4. CFD simulation results for Models A in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 4. CFD simulation results for Models A (panels A and C) and B (panels B and D) in steady 5.6 m/s flow. The blue, yellow, and green regions are areas of reduced flow speed that generate forces on the tags. The upper panels show the flow speed over a horizontal cross-section at the tag midline. The lower panels show flow speed over a vertical cross-section at the centerline of the tag. The improved flow around Model B is evident in the smaller magnitude of blue coloration in the wake behind the tag.

Figure 2 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 2. An illustration of the Model A tag in the computational domain used for the simulations of all tag designs. The fluid flow is from left to right and representative orientations of the tag to the flow are shown at the bottom of the figure.

Figure 7 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 7. Simulation results for the lift forces acting on the Model A and B tags (top panel) in variable flow (0.25–10 m/s). Measured results in variable flow speed (1–5.6 m/s) are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).

Figure 10 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 10. Experimental load testing of a Model A tag attached at three test sites on a common dolphin cadaver using four silicone suction cups. Applied force vs. calculated total cup attachment force at the four silicone suction cups are shown for lift (bottom panel) and drag (top panel) loading. The average pressure difference for the four cups is also shown on the right hand axis, where 1 atmosphere is approximately 100 kPa. Forces were applied via a line attached to the tag by pulling either perpendicular to the body (lift) or parallel (drag). The curves end where the cup attachment failed or the cups began to slide. The average of two trials at each site is shown in each panel.

Figure 6 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 6. Simulation results for the drag forces acting on the Model A and B tags (top panel) in variable flow (0.25–10 m/s) with fixed orientation (0º). Measured results in variable flow speed (1–5.6 m/s) are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).