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Fig. 7 in Hands, feet, and behaviour in Pinacosaurus (Dinosauria: Ankylosauridae)

Fig. 7. Ankylosaurid dinosaur Pinacosaurus (MPC 100/1339) from the Alagteeg Formation (Upper Cretaceous) of Alag Teeg, Mongolia. Right fibula (A) and tibia (B) in anterior view.

Fig. 6. Ankylosaurid dinosaur Pinacosaurus grangeri Gilmore, 1933 in Hands, feet, and behaviour in Pinacosaurus (Dinosauria: Ankylosauridae)

Fig. 6. Ankylosaurid dinosaur Pinacosaurus grangeri Gilmore, 1933 (MPC 100/1310) from the Alagteeg Formation (Upper Cretaceous) of Alag Teeg, Mongolia. Outline of left metacarpus and proximal phalanges in proximal (A) and anterior (B) views.

Fig. 10 in Hands, feet, and behaviour in Pinacosaurus (Dinosauria: Ankylosauridae)

Fig. 10. Ankylosaurid dinosaur Pinacosaurus from the Alagteeg Formation (Upper Cretaceous) of Alag Teeg, Mongolia. A. MPC 100/1339, right foot. B. Dorsal view of MPC 100/1320 (left tarsals, metatarsals and phalanges).

Fig. 4 in Hands, feet, and behaviour in Pinacosaurus (Dinosauria: Ankylosauridae)

Fig. 4. Ankylosaurid dinosaur Pinacosaurus, limb elements from the Alagteeg Formation (Upper Cretaceous) of Alag Teeg, Mongolia. A. Humerus (MPC 100/1310) in proximal (A1), dorsal (A2), lateral (A3), ventral (A4), and distal (A5) views. B. Femur (MPC 100/1322) in proximal (B1), posterior (B2), and distal (B3) views. C. Radius, ulna (MPC 100/1323) in proximal (C1), medial (C2), lateral (C3), and distal (C4) views. Comparison of outlines of proximal ends of ulna (light gray) and radius (dark gray) of MPC 100/1323 (D) and reversed image of MPC 100/1326 (E).

Fig. 3 in Hands, feet, and behaviour in Pinacosaurus (Dinosauria: Ankylosauridae)

Fig. 3. Ankylosaurid dinosaur Pinacosaurus from Upper Cretaceous of Bayan Mandahu, China (all specimens in IVPP). Quarry diagram showing the alignment of juvenile skeletons, all of which were upright with limbs positioned underneath their bodies.

Fig. 3. Demodex midae n in A new species of Demodex (Acari: Demodecidae) from the skin of golden-handed tamarins, Saguinus midas (Primates: Cebidae)

Fig. 3. Demodex midae n. sp. (A) Holotype male, ventral view. (B) Holotype male, dorsal view. (C) Spines on the right palpus, male, ventral view. (D) Supracoxal spine, male, dorsal view, arrows indicate the orientation of the left spine as a view on the gnathosoma. (E) Claw on the leg of tarsi, male. (F) Aedeagus. (G) Female, ventral view. (H) Egg. ae: aedeagus, op: opisthosomal organ, sg: subgnathosomal setae (setae n), sc: supracoxal spine (setae elc.p), vu: vulva. Scale bars = 20 μm (A, B, G, H) and 1 μm (C–F).

Fig. 2 in A new species of Demodex (Acari: Demodecidae) from the skin of golden-handed tamarins, Saguinus midas (Primates: Cebidae)

Fig. 2. Demodectic mange in a male golden-handed tamarin (case 1). (A) Macrophage/histiocyte accumulate and form nodules around the follicles that have filled and expanded with demodecid mites. H&E. Scale bar = 1 mm. (B) Dilated hair follicles are filled with transverse or cross sections of demodecid mites. The monotonous infiltrating macrophage/histiocyte surrounding the follicle has round to slightly irregular nuclei and abundant eosinophilic cytoplasm. Numerous mitotic figures are seen. H&E. Scale bar = 50 μm. (C) Almost all macrophage/histiocytes are strongly immunolabeled with Iba-1. Scale bar = 1 mm. (D) Numerous macrophage/histiocytes are immunolabeled for Ki-67 with a Ki-67 labeling index of 85.5%. Scale bar = 50 μm. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4 in A new species of Demodex (Acari: Demodecidae) from the skin of golden-handed tamarins, Saguinus midas (Primates: Cebidae)

Fig. 4. Maximum likelihood phylogenetic trees of Demodex (Trombidiformes: Cheyletoidea: Demodecidae). (A) 18S sequence-based rooted phylogenetic tree with Myobia (Trombidiformes: Prostigmata: Myobiidae) as outgroup and Cheletomimus and Neochelacheles (Trombidiformes: Cheyletoidea: Cheyletidae) as related species of Demodex. (B) 16S sequence-based midpoint-rooting tree. Accession numbers are shown in roman next to the taxa names. Nodes are labeled with bootstrap values greater than 60%. Scale bars represent substitutions per site.

Fig. 1 in A new species of Demodex (Acari: Demodecidae) from the skin of golden-handed tamarins, Saguinus midas (Primates: Cebidae)

Fig. 1. Photographs of a male red-handed tamarin of case 1. (A and B) On initial examination, hair loss, and scale are found on the face, especially severe on the left temple, with numerous psoriasis-like plaques. (C and D) Findings of five months after treatment with fluralaner showing resolution of related lesions. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opencc-by-4.0Aug 2024View details →

Biomechanical filtering supports efficient tactile encoding in the human hand

This repository contains the data and code used to produce the results in the publication "Biomechanical filtering supports efficient tactile encoding in the human hand." If you use these data or code, please cite our publication. Full citation: N. Tummala, G. Reardon, B. Dandu, Y. Shao, H. P. Saal, and Y. Visell, "Biomechanical filtering supports efficient tactile encoding in the human hand". bioRxiv, 2024. doi: 10.1101/2023.11.10.565040   Abstract From Manuscript Touching an object elicits skin oscillations that are biomechanically transmitted throughout the hand, driving responses in thousands of tactile receptors, including numerous exquisitely sensitive Pacinian corpuscles (PCs). Accepted descriptions of PC functionality characterize their response properties as highly stereotyped, based on experimental data gathered when stimuli are applied near the receptor. However, during natural touch, spiking activity in the majority of PCs is evoked by transmitted skin oscillations that are modified by biomechanical filtering. This filtering mechanism, stemming from dispersive wave dynamics in the skin, bears some similarity to the pre-neuronal filtering of auditory signals by the basilar membrane, a mechanical process that is instrumental to perception. Thus, we sought to clarify how skin biomechanics might influence tactile information encoding in the periphery. We used vibrometry imaging and computational neural experiments to examine the influence of biomechanical filtering on neural activity in whole-hand PC populations. We observed complex, location- and frequency-dependent patterns of filtering that were shaped by tissue mechanics and hand morphology. This source of biomechanical modulation diversified PC population spiking activity and enhanced tactile information encoding efficiency. These findings indicate that biomechanics furnishes a pre-neuronal mechanism that facilitates efficient tactile encoding and processing.

opencc-by-4.0Sep 2024View details →

Tobii Pro Spectrum hand-labelled data set

For recording this data set, the Tobii Pro Lab software and a Tobii Pro Spectrum eye tracking device with sampling frequencies up to 600 Hz were used. The provided monitor had a size of 23.8 inches and a 16:9 aspect ratio. The exact procedure for generating this data is described in (1). Three recordings at 300 Hz and three recordings at 600 Hz are extracted from the data in (1).  From this data, time spans of about 20 seconds are cut out and the fixations and saccades are labelled by hand.  (1) Timur Ezer, Matthias Greiner, Lisa Grabinger, Florian Hauser, and Jürgen Mottok. Eye tracking as technology in education: Data quality analysis and improvements. In ICERI2023 Proceedings, 16th annual International Conference of Education, Research and Innovation, pages 4500–4509, Valencia, Spain, 13-15 November, 2023 2023. IATED. ISBN 978-84-09-55942-8. doi: 10.21125/iceri.2023.1127. URL https://doi.org/10.21125/iceri.2023.1127

opencc-by-4.0Sep 2024View details →

HAND Skills demOnstrated by Multi-subjEcts (HANDSOME) Dataset

The HANDSOME (HAND Skills demOnstrated by Multi-subjEcts) dataset is designed to provide reliable hands and objects motion data during human demonstrations of manual activities. This dataset was originally collected to study interactions between hands and objects in various contexts and automatically map the resulting task representations into robot plans. However, it can be utilized for any application requiring hands and objects detection from RGB video. The setup involved an RGB camera (Intel RealSense D435i) positioned in a top-down (bird's eye) view, with the image plane aligned parallel to the working plane. To enable robust detection of the 3D pose of objects and hands, we employed a marker-based detection system. ArUco markers were attached to the back of the hand and strategically positioned on the objects, preserving natural movements during manipulation. We involved 10 participants, comprising 5 males and 5 females with an average age of 28.4 +/- 2.4 years. Among them, 8 were right-handed and 2 were left-handed. We asked subjects to perform both unimanual and bimanual activities, for a total of 400 recordings, in two different contexts (kitchen and workshop). The whole experimental procedure was carried out in accordance with the Declaration of Helsinki and the protocol was approved by the ethics committee azienda sanitaria locale (ASL) Genovese N.3 (Protocol IIT_HRII_ERGOLEAN 156/2020).

opencc-by-4.0Nov 2024View details →

Results of the ROSCon24 Workshop Survey 'Hands-on with ROS2 Deliberation Technologies'

Results of the ROSCon24 Deliberation Workshop Survey: The members of the ROS deliberation community group (ros-wg-delib) hosted a 'Hands-on with ROS2 Deliberation Technologies' workshop at ROSCon24 in Odense. The workshop aimed to showcase the diversity of deliberation technologies, including behaviour trees, finite-state machines, task planning, and verification and validation systems. Participants had the opportunity to tackle predefined tasks using deliberation tools such as BehaviorTree.CPP, FlexBE, ros_bt_py, SkiROS2, and the AS2FM tool by CONVINCE. Guided by the tools' creators, they explored how different technologies can be applied to solve various robotic challenges. A survey was conducted at the end of the workshop to gather feedback from participants about the tools they explored. The survey results highlight the strengths and limitations of each tool, offering insights into areas for improvement, and are particularly valuable for the newly introduced tools developed by the CONVINCE consortium.

opencc-by-4.0Sep 2021View details →

Hand Written Roman Numerals Dataset

The dataset contains 2621 images of handwritten Roman numerals from I to X, collected from 131 individuals. Inside the main folder, there are 10 subfolders corresponding to each Roman numeral. There is an additional folder called "edge cases" (96 images) containing <a href="https://en.wikipedia.org/wiki/Roman_numerals#Variant_forms">non-standard forms</a> of the Roman numerals. <ul> <li>iij (3)</li> <li>IIII (4)</li> <li>IIIII (5)</li> <li>A (5)</li> <li>IIIIII (6)</li> <li>ↅ (6)</li> <li>vij (7)</li> <li>IIX (8)</li> <li>VIIII (9)</li> </ul>

opencc-by-4.0Nov 2022View details →

SYNTHETIC dataset attached to the paper "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis"

SYNTHETIC dataset to replicate the results in "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis", accepted to IEEE/RSJ IROS 2022. In order to fully reproduce the experiments, download also the REAL dataset.  To automatically download the REAL and SYNTHETIC dataset, run the script provided at the link below. Code to replicate the results available at: https://github.com/hsp-iit/prosthetic-grasping-experiments

opencc-by-4.0Nov 2022View details →

REAL dataset attached to the paper "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis"

REAL dataset to replicate the results in "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis", accepted to IEEE/RSJ IROS 2022. In order to fully reproduce the experiments, download also the SYNTHETIC dataset.  To automatically download the REAL and SYNTHETIC dataset, run the script provided at the link below. Code to replicate the results available at: https://github.com/hsp-iit/prosthetic-grasping-experiments

opencc-by-4.0Dec 2022View details →

Synthetic Hand Recognition Dataset

This is the synthetic hand recognition dataset generated with the  Synthetic Human Dataset Generator (available on github). It contains 90,000 images of 10 different virtual humans (5 female, 5 male) in various poses. All images are annotated with the color coded segmented hands.

opencc-by-4.0May 2023View details →

BUCSS22 hands-on data

This is the dataset used in all hands-on sessions of the 4th edition of BUCSS. This Bochum Urban Climate Summer School took place from 26 to 29 September 2022 at <a href="https://www.ruhr-uni-bochum.de/en">Ruhr-University Bochum (Germany)</a>, and aimed at 1) providing a general introduction to different facets of urban climatology with a special focus on urban climate informatics, 2) providing structured information and skill-building capabilities related to urban climate monitoring, remote sensing and modelling, and 3) strengthening an active pool of young scientists to tackle the major urban sustainability challenges of future generations. The programme consisted out of state-of-the-art lectures and hands-on tutorials on remote sensing in urban areas, crowdsourcing and urban climate modelling. All of these teaching resources are available here: <a href="https://github.com/RUBclim/BUCSS22">https://github.com/RUBclim/BUCSS22</a>

opencc-by-4.0Aug 2021View details →

Hand-BCG & SCG signals dataset

General description This repository contains Ballistocardiogram signals collected from multiple users while they were holding a mobile phone in hand and Seismocardiogram signals collected with the phone placed on their chest. It consists of 3 different datasets made up of multiple recordings from a total of 272 different users. The main characteristics of these datasets are given below. +---------------+-------------+-------------+-----------------+------------------------------+-----------------------+--------------------+------------+-----------+ |                    | Loc.        | # users   | Age range  | # sessions per user | session length | Phone            | Acc. Fs | Gyr. Fs | +---------------+-------------+-------------+-----------------+------------------------------+-----------------------+--------------------+------------+-----------+ | Dataset A | Hand      | 112         | 20-60          | 10                               | 30s                     | Samsung S8 | 100 Hz | 500 Hz | +---------------+-------------+-------------+-----------------+------------------------------+-----------------------+--------------------+------------+-----------+ | Dataset B | Hand      | 105         | 20-32          | 1                                 | 30s                     | Google Pixel  | 50 Hz    | 400 Hz | +---------------+-------------+-------------+-----------------+------------------------------+-----------------------+--------------------+------------+-----------+ | Dataset C | Chest     | 55           | 19-52          | 4                                 | 30s                     | Google Pixel  | 50 Hz    | 400 Hz | +---------------+-------------+-------------+-----------------+------------------------------+-----------------------+--------------------+------------+-----------+   Content of each dataset Each dataset is made up of multiple files, with each file corresponding to a given measurement session. The name of a given file indicates some metadata about the measurement session itselft. It is assigned based on the following template: <UserId>-<Gender>-<Age>yrs-<SignalLength>_<Year>-<Month>-<Day>_<Hour>-<Minute>-<Seconds>.csv The content of the file itself corresponds to the sensor readings collected during the measurement session. It is a collection of timestamped sensor values, reported in a comma-separated format. The first column represent the timestamp of the sensor event (in nanoseconds), the second one the type of the considered sensor and the 3 following ones the actual values along the different axis. Please refer to the official Android SensorEvent documentation for more details.