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.
258
datasets available to search
ShareScore release 0.9.0
Dataset results
258 results for “phantom”
Extracted sinograms for VQC Phantom: GE SIGNA PET/MR
<p>This dataset includes:</p> <p>1. Extracted Emission Sinogram: sino_f2g1d0b0.hs; sino_f2g1d0b0.s</p> <p>2. Extracted Randoms Sinogram: randoms.hs; randoms.s</p> <p>3. Extracted Normalisation Sinogram: normsino.hs; normsino.s</p> <p>4. Extracted Geometric Sinogram: geo.hs; geo.s</p> <p>5. Normalisation Complete Sinogram (Normalisation+ Geometric correction effects accounted for in this sinogram): normgeo.hs; normgeo.s </p> <p> </p>
Phase contrast mammography phantom
<p>Mammography phantom for phase-contrast simulations. For details, see our GitHub page <a href="https://github.com/ilianhaggmark/phase-contrast-phantom">here.</a></p> <p>The phantom is stored as ten separate material files (format .mat). Each file contains a matrix (type single) which is 24640 by 60160 pixels.</p> <p>Material indices:</p> <p>000 - Air<br> 001 - Adipose tissue<br> 002 - Skin tissue<br> 029 - Glandular tissue<br> 088 - Connective tissue<br> 095 - Terminal duct<br> 125 - Duct<br> 151 - Blood<br> 200 - Mass<br> 250 - Calcium oxalate<br> </p> <p>Please cite: <strong>In Silico Phase-Contrast X-Ray Imaging of Anthropomorphic Voxel-Based Phantoms</strong>, Ilian Häggmark, Kian Shaker, and Hans M. Hertz, <a href="https://doi.org/10.1109/TMI.2020.3031318">IEEE Transactions on Medical Imaging 40(2) 539-548 (2021)</a>.</p>
Data from: BCI training to move a virtual hand reduces phantom limb pain: a randomized crossover trial
<p>Objective: To determine whether training with a brain–computer interface (BCI) to control an image of a phantom hand, which moves based on cortical currents estimated from magnetoencephalographic signals, reduces phantom limb pain.</p> <p>Methods: Twelve patients with chronic phantom limb pain of the upper limb due to amputation or brachial plexus root avulsion participated in a randomized single-blinded crossover trial. Patients were trained to move the virtual hand image controlled by the BCI with a real decoder, which was constructed to classify intact hand movements from motor cortical currents, by moving their phantom hands for 3 days ("real training"). Pain was evaluated using a visual analogue scale (VAS) before and after training, and at follow-up for an additional 16 days. As a control, patients engaged in the training with the same hand image controlled by randomly changing values ("random training"). The two trainings were randomly assigned to the patients. This trial is registered at UMIN-CTR (UMIN000013608).</p> <p>Results: VAS at day 4 was significantly reduced from the baseline after real training (45.3 [24.2] to 30.9 [20.6], 1/100mm, mean [SDs]; P=0.009<0.025), but not after random training (P=0.047>0.025). Compared to VAS at day 1, VAS at days 4 and 8 was significantly reduced by 32% and 36%, respectively, after real training and was significantly lower than VAS after random training (P<0.01).</p> <p>Conclusion: Three-day training to move the hand images controlled by BCI significantly reduced pain for one week.<br> Classification of evidence: This study provides Class &#8546; evidence that BCI reduces phantom limb pain.</p> <p> </p>
Phantom Armor
Low poly modeled for videogames, Animated in mixamo Source: Objaverse 1.0 / Sketchfab
FIGURE 3 in Apertochrysa (Neuroptera: Chrysopidae): A heterogeneric phantom?
FIGURE 3. Phylogenetic tree for all chrysopid taxa used in this study. The total sequence length of the alignment was 1926 bp, comprising 483 bp of PepCK, 525 bp of wg, and 918 bp of ATPase. The tree is inferred from a combined analysis of all sequences in all species except Apertochrysa umbrosa, for which only ATPase sequence was available. Values placed on each node are Bayesian posterior probabilities (PP) ± 0.80 / ML bootstrap values ± 50%. Dashes (-) mark branches with less than 0.80 PP or less than 50% bootstrap support. Stars (*) on branches represent both 1.00 PP and 100% bootstrap value.
FIGURE 1. Wing venation. a in Apertochrysa (Neuroptera: Chrysopidae): A heterogeneric phantom?
FIGURE 1. Wing venation. a: Cunctochrysa kannemeyeri, the inner gradate touches the pseudomedia. b: Pseudomallada zelleri (Schneider 1851), the inner gradate does not touch the pseudomedia. gr: inner gradate, pm: pseudomedia.
FIGURE 4. Gonapsis shapes. a in Apertochrysa (Neuroptera: Chrysopidae): A heterogeneric phantom?
FIGURE 4. Gonapsis shapes. a: Icon for the shape characteristic of the alcestes-group, one of the four large species-groups in the Pseudomallada complex (Duelli et al. 2016). b and c: Gonapsis of two males of Apertochrysa edwardsi (Western Australia). d and e: Gonapsis of two males of A. eurydera (Ghana), a species in the alcestes-group.
FIGURE 2. Larval setation. a in Apertochrysa (Neuroptera: Chrysopidae): A heterogeneric phantom?
FIGURE 2. Larval setation. a: Dorsal view of thorax showing two setae per submedian tubercle on pro- and mesothorax, and a double row of strong setae on pro-, meso- and metathorax. b: Dorsal view of thorax showing one seta per submedian tubercle and a single row of strong setae on meso- and metathorax. c: Lateral view of 1st abdominal segment showing setae hooked both forward (pointed towards head) and backward (pointed towards tail). d: Lateral view of 1st abdominal segment showing setae hooked only backward. smt: submedian tubercle, R: row of setae.
FIGURE 5 in Apertochrysa (Neuroptera: Chrysopidae): A heterogeneric phantom?
FIGURE 5. Gonapsis shapes of Apertochrysa umbrosa. a: Redrawn from fig. 234 in Brooks and Barnard 1990. b: Redrawn from fig. 1753 in Tjeder 1966. c and d: Two males from Wendo Genet, Ethiopia. e: Lateral view of d.
Simulation of a realistic brain phantom for Susceptibility Tensor Imaging
<p>The susceptibility tensor brain phantom is an in-silico brain phantom that serves as ground-truth for new and existing Susceptibility Tensor Imaging algorithms. It contains two different brain phantoms with diffusion (chi_dti.nii.gz) and susceptibility (chi_sti.nii.gz) information each one respectively. Additionally, we upload 2 different local phase simulations for each case:</p> <ul> <li>Angles 1 and Angles 2: Simulations of Gradient Echo acquisitions at two different ranges of rotation angles.</li> <li>phi_6_orientations.nii.gz: Local phase of the 6 different acquisitions</li> <li>phi_12_orientations.nii.gz: Local phase of the 12 different acquisitions</li> <li>angles_6.mat; Angles of the simulations at 6 different acquisitions</li> <li>angles_12.mat: Angles of the simulations at 12 different acquisitions </li> </ul>
Ac-225 Scans: 34 Repeated Phantom Acquisitions
<p>This dataset consists of 34 repeated acquisitions of an Ac-225 cylindrical phantom with three spheres (60mm/28mm/22mm) filled at a 10:1 background. The spheres were initially filled at an activity concentration of 1.37kBq/mL. Imaging parameters were 4.8mmx4.8mm resolution @ 128x128 matrix size, 96 projections, and 60s per projection. There is also an associated CT image taken at the beginning of all acquisitions.</p>
53 Repeated Acquisitions of a Lu-177 NEMA Phantom
<p>This dataset consists of 53 repeated acquisitions of an Lu-177 NEMA phantom with six spheres (37mm, 28mm, 22mm, 17mm, 13mm, 10mm) filled at a 10:1 background. The spheres were initially filled at an activity concentration of 0.89MBq/mL. Imaging parameters were 4.8mmx4.8mm resolution @ 128x128 matrix size, 96 projections, and 15s per projection. There is also an associated CT image taken at the beginning of all acquisitions.</p>
NEMA phantom 2min and 60 min acquistion with Raytest ClearPET
<p>NEMA phantom files from Raytest ClearPET camera.</p> <p>2min acquisition and 60 min acquisition</p> <p>coincidences</p> <p>sinograms</p> <p>reconstructed image </p>
Developing an in-depth understanding of the prevalence, risk factors and treatment recommendations for phantom limb pain, and patient-generated care priorities for people who have undergone lower limb amputations.
<p>The file holds data collected for a series of four studies on phantom limb pain. </p>
Multicenter CT phantoms public dataset for radiomics reproducibility tests
<p>No description provided.</p>
Repeated CT for an anthropomorphic phantom with different nasal cavity fillings
<p>Anthropomorphic head-and-neck phantom, developed at Paul Scherrer Institute in collaboration with CIRS (Computerized Imaging Reference Systems, Inc., Norfolk, USA) and made from a material equivalent to human tissue. To simulate different anatomical changes, the nasal cavities can be filled with a material equivalent to mucus, and a layer of fat can be applied to the neck to simulate weight gain/loss. The phantom has been first introduced here: DOI 10.1088/1361-6560/ac2b84</p> <p>In this repository, we include 26 different CTs presenting anatomical and/or positioning variations with respect to the reference CT. A <em>readme</em> file provides more information regarding the different CT images included in this dataset.</p>
The phantom array experiment at the CSTB feat. Xiangzhen Kong and Anders Thorseth, supervised by Christophe Martinsons during the MetTLM project.
<p>This video shows the phantom array experiment carried out at the CSTB in Grenoble, France, featuring Xiangzhen Kong (the experimenter) and Anders Thorseth (the test subject), supervised by Christophe Martinsons (CSTB researcher) during the EMPIR MetTLM project funded by EURAMET.</p> <p>This experiment was designed to measure the sensitivity of people to the phantom array effect. The phantom array effect is a undesirable visual effect caused by temporal light modulation (TLM) occuring when high modulation frequencies are present in the light source while the observer moves their eyes. It is more visible in the dark than in bright ambient conditions. The highest visibility happens at around 600 Hz (peak of the sensitivity curve) but the phenomenon is visible from 80 Hz up to several kHz depending on the visual acuity of the observer.</p> <p>In this experiment, the light source was a fine slit. Its modulation frequency was slowed down to a few hertz in order to be seen easily on the video. The observer had to move their eyes back and forth between fixed points. Two sucessive stimuli were presented in a random order, a modulated stimulus and a steady-state stimulus (no TLM). The protocol was based on randomized, counterbalanced, repeated measures using a two-interval forced choice (2IFC) procedure optimized with an adaptive procedure called QUEST+ developed by Andrew Watson for psychovisual experiments. </p> <p>The experiment used an eye-tracing device to monitor the saccade speed and amplitude during the experiment.</p>
Data file needed for implementation of the Helmholtz equation of state in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** this file is downloaded automatically on running Phantom **<br><br>This is a datafile containing information needed to utilise the Helmholtz equation of state (<a href="http://adsabs.harvard.edu/abs/2000ApJS..126..501T">Timmes & Swesty 2000</a>) implemented in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code (<a href="http://adsabs.harvard.edu/abs/2018PASA...35...31P">Price et al. 2018</a>). </p> <p>Primarily used to model degenerate matter in white dwarfs</p> <p>For information about how to read the file and its contents, refer to the relevant module in the phantom source code (<a href="https://github.com/danieljprice/phantom/blob/master/src/main/eos_helmholtz.f90">eos_helmholtz.f90</a>)</p>
LGE CMR SAX Phantom Images and Segmentation Masks
<h2>Synthetic imaging data used for analysis of Radiomic feature comparability</h2> <p>Phantom (synthetic) images of a single short-axis cardiovascular MRI slice of the heart (showing only the left ventricular myocardium and bloodpool), and corresponding segmentation masks in nifti format for whole myocardium and 17-segment AHA model. </p> <p>The phantom exists at a range of sizes (myocardial diameter between 28 and 99mm) and a range of resolutions (between 0.6 and 2.2mm isotropic voxel-size), as well as at resolutions corresponding to voxel-densities between 28 and 76 voxels per myocardial diameter.</p> <p>LGE patterns have been added to the healthy myocardium: global mesocardial LGE, global subendocardial LGE, global subepicardial LGE, inferolateral transmural LGE, and a patchy pattern. In each case, extent of LGE is 30% of the entire myocardium.</p> <p> </p> <p>Corresponding code can be found at: https://github.com/annplaube/mykkeLGEradiomics</p> <p> </p> <p>Naming conventions:</p> <p>Phantom: phantom_{LGE_pattern}_d{size}_{resampling_mode}.nii</p> <p>Label: label_d{size}_{resampling_mode}.nii</p> <p>AHA Label: label_aha_d{size}_{resampling_mode}.nii</p>
Data from: A phantom ultrasonic insect chorus repels low-flying bats, but most are undeterred
<p><b>Abstract</b></p> <p>1. The acoustic environment can serve as a niche axis, structuring animal behaviour by providing or obscuring salient information. Meadow katydid choruses occupy the ultrasonic, less studied, realm of this acoustic milieu, form dense populations in some habitats, and present a potential sensory challenge to co-occurring ultrasonic-hearing animals. Aerial-hawking insectivorous bats foraging immediately over vegetation must listen for echoes of their prey and other cues amidst the chorus din.</p> <p>2. We experimentally created the cacophony of a katydid chorus in a katydid-free rice paddy using an aggregation of 100 ultrasonic speakers in a 25 x 25 m grid to test the hypothesis that aerially hawking bats are averse to this noise source. We alternated between chorus-on and chorus-off hourly, and acoustically monitored bat activity and arthropod prey abundance.</p> <p>3. We found that our phantom katydid chorus reduced bat activity nearest the sound source by 39.3% (95% CI: 7.8 - 60.0%) for species whose call spectrum fully overlapped with the chorus, and elicited marginal reductions in activity in species with only partial spectral overlap.</p> <p>4. Our study suggests that ultrasonic insect choruses degrade foraging habitat, potentially suppressing bats' ecosystem services as consumers of pests; and, given the global distribution of meadow katydids, may provide an underappreciated force modifying animal behaviour in other grassland habitats.</p>
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.