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258 results for “phantom”

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zenodo36/100

DICOM images of abdomen CT phantom in axial, helicoid, iterative and dual energy scans.

The two ZIP files contain the images of a CT phantom of abdomen. DualEnergy.zip contains files of acquisition at 80 kVp and 140 kVp. The second file HelicalAxialIterative.zip contains acquisition done in axial, helicoidal (aka spiral) and iterative reconstruction (Saphire) modes.  Axial images have AbdSeq in the name, Helicoidal have Abdomen and B in the filter name, Iterative have Abdomen and I in the filter name. You can compare Exposure in mAs for each acquisition modality, how it diminishes from sequential/helicoidal/axial through helicoidal/spiral to iterative. This scanner if programmed with iterative acquisition can still  reduce exposure from helicoidal scan. The acquisition was done on Siemens-Healthineers Somatom Confidence CT scanner.  Antonio Ortiz Lora antonio.ortiz.lora.sspa@juntadeandalucia.es Marcin Balcerzyk mbalcerzyk@us.es <div class="ms-editor-squiggler"> </div>

opencc-by-4.0Jan 2021View details →

dryad36/100

Data from: Large-scale manipulation of the acoustic environment can alter the abundance of breeding birds: evidence from a phantom natural gas field

1. Altered animal distributions are a consequence of human expansion and development. Anthropogenic noise can be an important predictor of abundance declines near human infrastructure, yet more information is needed to understand noise impacts at the spatial and temporal scales necessary to alter populations. 2. Energy development and associated anthropogenic noise are globally pervasive, and expanding. For example, 600,000 new natural gas wells have been drilled across central North America in less than twenty years. 3. We experimentally broadcast energy sector noise (recordings of compressor engines) in Southwest Idaho (USA). We placed arrays of speakers creating a "phantom natural gas field" in a large-scale experiment, and tested the effects of noise alone on breeding songbird abundance. To examine variation in human-caused noise, we broadcast two types of compressor noise, one with a slightly higher sound intensity and greater bandwidth than the other. 4. Our phantom natural gas field encompassed approximately 100 km2. We broadcast noise for over three continuous months, for each of two seasons, and quantified over 20,000 hours of background sound levels. 5. Brewer's sparrows (Spizella breweri) were affected by our narrowband playback, declining 30% 50 m from the speaker arrays. During our broadband playback, all species combined and Brewer's sparrows decreased 20% and 33% respectively at the scale of our sites (~0.5 km2; up to 400 m from speaker arrays). 6. Our results show the importance of incorporating the acoustic structure of noise when estimating the cost of noise exposure for populations and suggest an urgent need for noise mitigation, such as quieting compressor station noise, in energy extraction fields and natural areas broadly.

opencc-zeroJun 2019View details →

zenodo36/100

Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms - DICOM Database

This dataset contains DICOM versions of the 24 anthropomorphic pulmonary CT phantoms accompanying the manuscript "Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms" submitted to PLoS ONE. NRRD versions can be found in http://dx.doi.org/10.5281/zenodo.20766 (doi:10.5281/zenodo.20766).

opencc-by-sa-4.0Oct 2015View details →

zenodo36/100

Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms - NRRD Database

This dataset contains the 24 anthropomorphic pulmonary CT phantoms accompanying the manuscript "Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms" submitted to PLoS ONE. DICOM versions can be found at http://dx.doi.org/10.5281/zenodo.32740  (doi:10.5281/zenodo.32740)

opencc-by-sa-4.0Jul 2015View details →

zenodo36/100

Multi-modal phantom experiments, mimicking flow through the mitral heart valve

In this repository all experimental data obtained from a phantom study on the left heart, including a deformable mitral valve, is reported. Using invasive catheter pressure measurements, magnetic resonance imaging and ultrasound imaging, several parameters relevant to diagnosing heart valve disease were measured on the phantom. The data is reported in MS excel files (xlsx format) and the geometry of the phantom is reported in STL and STEP file format, together with an exploded view, indicating how the parts are put together.

opencc-by-4.0Nov 2023View details →

zenodo36/100

Pulseq multi-slice radial 2D phantom data

Multi-slice radial2D Pulseq sequence and acquired phantom data including the trajectory information in the ISMRM raw data format.

opencc-by-4.0Nov 2024View details →

zenodo36/100

Chinese Reference Population: open-source age-dependent computational phantoms of reference Chinese population

The Chinese Reference Population (CRP) phantoms dataset encompass 30 phantoms available in both voxel and NURBS formats, with age in 0, 1, 2, 3, 4, 5, 6, 8, 10, 12, 15, 18 years and adult male and female, as well as 4 pregnant women and fetus in early pregnancy, first trimester, second trimester and third trimester. <ul> <li>Voxelized phantoms are accessible in NII format : "XXX.nii", which could be opened in AMIDE software.</li> <li>Excel file containing organ masses and other descriptive information : "CRP_descriptive_Info.xlsx"</li> <li>In the application of F18−FDG dose calculation, organ absorbed doses per unit activity administered is provided in an Excel file : "Application_F18-FDG.xlsx"</li> </ul> All data are stored on Zenodo and can be publicly accessed.

opencc-by-4.0Sep 2024View details →

zenodo36/100

Phantom ISMRMRD datasets for "MaxGIRF: Image Reconstruction Incorporating Concomitant Field and Gradient Impulse Response Function Effects"

Phantom ISMRMRD datasets for "MaxGIRF: Image Reconstruction Incorporating Concomitant Field and Gradient Impulse Response Function Effects". Code to process and reconstruct the data is available here: <a href="https://github.com/usc-mrel/lowfield_maxgirf">https://github.com/usc-mrel/lowfield_maxgirf</a>

opencc-by-4.0Jan 2022View details →

zenodo36/100

Patient breast MRI images and computational breast phantom data for research in patient-derived realistic breast modelling

The data is comprised of two parts: 1) patient DICOM MRI images and 2) 3D matrix of a computational breast phantom. <ol> <li>The DICOM images are anonymised patient breast MRI images of a female patient diagnosed with invasive ductal carcinoma. The obtaining of the patients’ DICOM images is approved by the Ethics Committee of Medical University of Varna. The acquisition was performed with GE Signa HDxt MRI scanner. The images are from a T1-weigthed Axial multi-phase VIBRANT (3-phase) sequence and with voxel size of 0.7 mm x 0.7 mm x 0.8 mm. Contrast agent is present. The image set can be opened with any standard DICOM reader.</li> <li>The computational breast phantom is derived from the above mentioned dataset. The phantom is in the form of a 3D matrix saved as a MATLAB data file (.mat file). Each voxel has an assigned Hounsfield Unit value depending on its classification: air = 0, adipose tissue = -152, glandular tissue = 42, tumour = 64, skin = 108. The data file can be opened with MATLAB or Octave.</li> </ol>

openSep 2021View details →

zenodo36/100

Modular Torso Motion Phantom for Magnetic Resonance Imaging (MRI)

This data set contains CAD models, code and information for the construction of a Modular Torso Motion Phantom for Magnetic Resonance Imaging (MRI).

opencc-by-nc-4.0Apr 2024View details →

zenodo36/100

Cone beam computed tomography dataset of a knee phantom

This is a cone beam computed tomography dataset of a custom-made knee phantom. The data was collected with Planmeca Viso G7 scanner. A total of 500 projections were collected. 100 kV with 80 mAs was used during the measurement. The data is stored in a mat-file that can be easily accessed in MATLAB, GNU Octave, Python, Julia, C/C++ and other languages. Only the flat field correction has been preapplied to the projection images, no other corrections have been done. Included are the FOV size, size of one detector pixel, the flat value, number of columns and rows in each projection image, the offset value for the object, the radians for the rotation of the panel (yaw/roll), the projection images themselves, the projection angles, the source to center of rotation and source to panel distances and the coordinates for the source and the center of the panel for each projection. The OMEGA software includes an example on how to use this dataset (all CBCT examples) in MATLAB/Octave and Python.

opencc-by-4.0Jul 2024View details →

zenodo36/100

Data file needed for star cluster setup in Phantom smoothed particle hydrodynamics and magnetohydrodynamics code

** this file is automatically downloaded by Phantom when running the starcluster setup ** This is a small ascii file containing positions and velocities of stars utilised in the "starcluster" configuration in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code (<a href="http://adsabs.harvard.edu/abs/2018PASA...35...31P">Price et al. 2018</a>). It is used to set up a collection of N-body particles. The star cluster setup (and the data file) were written by Yann Bernard as part of his PhD thesis at Université Grenoble Alpes. The datafile is published here so it can be used in the automated code testing via github actions.  The columns are mass, position (x,y,z) and velocity (vx,vy,vz) for all of the stars in the simulation

opencc-by-4.0Apr 2024View details →

zenodo36/100

Binary data file used for analysis_common_envelope unit testing in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code

** this file is automatically downloaded as part of the Phantom github actions tests ** This is an example snapshot from a Phantom simulation of a common envelope interaction, taken from the paper by <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.3181G">González-Bolívar et al. (2022)</a>. It is posted here primarily in order to perform unit and regression testing on the <a href="https://github.com/danieljprice/phantom/blob/master/src/utils/analysis_common_envelope.f90">analysis_common_envelope</a> module in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code (<a href="http://adsabs.harvard.edu/abs/2018PASA...35...31P">Price et al. 2018</a>).

opencc-by-4.0Mar 2023View details →

zenodo36/100

PTB 2D GRAPPA Acquisitions of ACR Resolution Phantom

ACR resolution phantom data acquired on the Siemens Verio 3T MR scanner using a FLASH sequence. Three datasets were acquired with a fully sampled k-space, GRAPPA 2 and GRAPPA 4 acceleration. The data is available in ISMRMRD format.

opencc-by-4.0Apr 2019View details →

zenodo36/100

Recipes for different tissue phantoms at narrowband and broadband frequency ranges

This dataset includes: <ul> <li>A PDF document explaining the performed permittivity measurements of different salt solutions (PBS and EBSS) and cell culture media (DMEM, DMEM+10% FBS, RPMI and RPMI + 10% FBS) that are often used in cell culture experiments as well as the recipes for tissue phantoms of blood, cartilage, cerebrospinal fluid, colon, cornea, heart, kidney, liver, muscle, pancreas, small intestine and stomach at room temperature for different frequency bands.</li> <li>CSV and EXCEL files with the permittivity measurements of the different salt solutions (PBS and EBSS) and cell culture media (DMEM, DMEM+10% FBS, RPMI and RPMI + 10% FBS) at storage (9ºC), room (21ºC) and culture (37ºC) temperature.</li> <li>CSV and EXCEL files with the permittivity measurements of the proposed tissue phantoms of blood, cartilage, cerebrospinal fluid, colon, cornea, heart, kidney, liver, muscle, pancreas, small intestine and stomach at room temperature for different frequency bands.</li> </ul>

opencc-by-4.0Jun 2024View details →

zenodo36/100

The Phantom EEG Dataset

When you use this dataset, please cite this paper. More information about this dataset could also be found in this paper. Xu, X., Wang, B., Xiao, B., Niu, Y., Wang, Y., Wu, X., & Chen, J. (2024). Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals. arXiv preprint arXiv:2405.17024. <h1>1 Metadata</h1> <h2>Brief introduction</h2> The present work aims to demonstrate that temporal autocorrelations (TA) significantly impacts various BCI tasks even in conditions without neural activity. We used the watermelon as the phantom head and found that we could get the pitfall of overestimated decoding performance if continuous EEG data with the same class label were split into training and test sets. More details can be found in Motivation. As watermelons cannot perform any experimental tasks, we can reorganize it to the format of various actual EEG dataset without the need to collect EEG data as previous work did (examples in Domain Studied). <h2>Measurement devices</h2> Manufacturers: NeuroScan SynAmps2 system (Compumedics Limited, Victoria, Australia) Configuration: 64-channel Ag/AgCl electrode cap with a 10/20 layout <h2>Species</h2> Watermelons. Ten watermelons served as phantom heads. <h2>Domain Studied</h2> Overestimated Decoding Performance in EEG decoding. Following BCI datasets in various BCI tasks have been reorganized using the Phantom EEG Dataset. The pitfall has been found in four of five tasks. -        CVPR dataset [1] for image decoding task. -        DEAP dataset [2] for emotion recognition task. -        KUL dataset [3] for auditory spatial attention decoding task. -        BCIIV2a dataset [4] for motor imagery task (the pitfalls were absent due to the use of rapid-design paradigm during EEG recording). -        SIENA dataset [5] for epilepsy detection task. <h2>Tasks Completed</h2> Resting State but you could reorganize it to any task in BCI. <h2>Dataset Name</h2> The Phantom EEG Dataset <h2>Dataset license</h2> Creative Commons Attribution 4.0 International <h2>Code</h2> Your could get the code to read the data files (.cnt or .set) in the “code” folder. To run the codes, you should install the mne and numpy package. You could install via pip pip install mne==1.3.1 pip install numpy Then, you could use “BID2WMCVPR.py” to convert the BID dataset to the WM-CVPR dataset. You could also use “CNTK2WMCVPR.py” to convert the CNT dataset to the WM-CVPR dataset. The codes to reorganize other datasets other than CVPR [1] will be released on github after reviewing. <h2>Data information</h2> -        CNT: the raw data. Each Subject (S*.cnt) contains the following information: EEG.data: EEG data (samples X channels) EEG.srate: Sampling frequency of the saved data EEG.chanlocs : channel numbers (1 to 68, ‘EKG’ ‘EMG’ 'VEO' 'HEO' were not recorded)   -        BIDS: an extension to the brain imaging data structure for electroencephalography. BIDS primarily addresses the heterogeneity of data organization by following the FAIR principles [6]. Each Subject (sub-S*/eeg/) contains the following information: sub-S*_task-RestingState_channels.tsv: channel numbers (1 to 68, ‘EKG’ ‘EMG’ 'VEO' 'HEO' were not recorded) sub-S*_task-RestingState_eeg.json: Some information about the dataset. sub-S*_task-RestingState_eeg.set: EEG data (samples X channels) sub-S*_task-RestingState_events.tsv: the event during recording. We organized events using block-design and rapid-event-design. However, it is important to note that this does not need to be considered in any subsequent data reorganization, as watermelons cannot follow any experimental instructions.   -        code: more information on Code.   -        readme.md: the information about the dataset. <h2>Recordings</h2> An additional electrode was placed on the lower part of the watermelon as the physiological reference, and the forehead served as the ground site. The inter-electrode impedances were maintained under 20 kOhm. Data were recorded at a sampling rate of 1000 Hz. EEG recordings for each watermelon lasted for more than 1 hour to ensure sufficient data for the decoding task.   <h2>Citation and more information</h2> Citation will be updated after the review period is completed. We will provide more information about this dataset (e.g. the units of the captured data) once our work is accepted. This is because our work is currently under review, and we are not allowed to disclose more information according to the relevant requirements. All metadata will be provided as a backup on Github and will be available after the review period is completed.   <h1>2 Motivation</h1> Researchers have reported high decoding accuracy (>95%) using non-invasive Electroencephalogram (EEG) signals for brain-computer interface (BCI) decoding tasks like image decoding, emotion recognition, auditory spatial attention detection, epilepsy detection, etc. Since these EEG data were usually collected with well-designed paradigms in labs, the reliability and robustness of the corresponding decoding methods were doubted by some researchers, and they proposed that such decoding accuracy was overestimated due to the inherent temporal autocorrelations (TA) of EEG signals [7]–[9]. However, the coupling between the stimulus-driven neural responses and the EEG temporal autocorrelations makes it difficult to confirm whether this overestimation exists in truth. Some researchers also argue that the effect of TA in EEG data on decoding is negligible and that it becomes a significant problem only under specific experimental designs in which subjects do not have enough resting time [10], [11]. Due to a lack of problem formulation previous studies [7]–[9] only proposed that block-design should not be used to avoid the pitfall. However, the impact of TA could be avoided only when the trial of EEG was not further segmented into several samples. Otherwise, the overfitting or pitfall would still occur. In contrast, when the correct data splitting strategy was used (e.g. separating training and test data in time), the pitfall could also be avoided even when block-design was used. In our framework, we proposed the concept of "domain" to represent the EEG patterns resulting from TA and then used phantom EEG to remove stimulus-driven neural responses for verification. The results confirmed that the TA, always existing in the EEG data, added unique domain features to a continuous segment of EEG. The specific finding is that when the segment of EEG data with the same class label is split into multiple samples, the classifier will associate the sample's class label with the domain features, interfering with the learning of class-related features. This leads to an overestimation of decoding performance for test samples from the domains seen during training, and results in poor accuracy for test samples from unseen domains (as in real-world applications). Importantly, our work suggests that the key to reducing the impact of EEG TA on BCI decoding is to decouple class-related features from domain features in the actual EEG dataset. Our proposed unified framework serves as a reminder to BCI researchers of the impact of TA on their specific BCI tasks and is intended to guide them in selecting the appropriate experimental design, splitting strategy and model construction. <h1>3 The rationality for using watermelon as the phantom head</h1> We must point out that the "phantom EEG" indeed does not contain any "EEG" but records only noise, a watermelon is not a brain and does not generate any electrical signals. Therefore, the recorded electrical noises, even when amplified using equipment typically used for EEG, do not constitute EEG data when considering the definition of EEG. This is why previous researchers called it "phantom EEG". Some researchers may therefore think that it is questionable to use watermelon to get the phantom EEG. However, the usage of the phantom head allows researchers to evaluate the performance of neural-recording equipment and proposed algorithms without the effects of neural activity variability, artifacts, and potential ethical issues. Phantom heads used in previous studies include digital models [12]–[14], real human skulls [15]–[17], artificial physical phantoms [18]–[24] and watermelons [25]–[40]. Due to their similar conductivity to human tissue, similar size and shape to the human head, and ease of acquisition, watermelons are widely used as "phantom heads". Most works tried to use watermelon as a phantom head and found that the results analyzed using the neural signals from human subjects could not be obtained when using the phantom head, thus proving that the achieved results were indeed caused by neural signals. For example, Mutanen et.al [35] proposed that “the fact that the phantom head stimulation did not evoke similar biphasic artifacts excludes the possibility that residual induced artifacts, with the current TMS-compatible EEG system, could explain these components”. Our work differs significantly from most previous works. It is firstly found in our work that the phantom EEG exhibits the effect of TA on BCI decoding even when only noise was recorded, indicating the inherent existence of TA in the EEG data. The conclusion we hope to draw is that some current works may not truly use stimulus-driven neural responses to obtain the overestimated decoding performance. Similar logic may be found in a neuroscience review article [41], they proposed that EEG recordings from phantom head (watermelon) remind us that background noise may appear as positive results without proper statistical precautions.     <h1>Reference</h1> [1]   C. Spampinato, S. Palazzo, I. Kavasidis, D. Giordano, N. Souly, and M. Shah, “Deep Learning Human Mind for Automated Visual Classification,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, pp. 4503–4511. [2]   S. Koelstra et al., “DEAP: A Database for Emotion Analysis ;Using Physiological Signals,” IEEE Transactions on Affective Computing, vol. 3, no. 1, pp. 18–31, 2012. [3]   N. Das, T. Francart, and A. Bertrand, “Auditory Attention Detection Dataset KULeuven.” Zenodo, Aug. 27, 2020. [4]   M. Tangermann et al., “Review of the BCI Competition IV,” Front. Neurosci., vol. 6, 2012. [5]   P. Detti, G. Vatti, and G. Zabalo Manrique de Lara, “EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings,” Processes, vol. 8, no. 7, Art. no. 7, 2020. [6]   C. R. Pernet et al., “EEG-BIDS, an extension to the brain imaging data structure for electroencephalography,” Sci Data, vol. 6, no. 1, p. 103, 2019. [7]   R. Li et al., “The Perils and Pitfalls of Block Design for EEG Classification Experiments,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 1, pp. 316–333, 2021. [8]   H. Ahmed, R. B. Wilbur, H. M. Bharadwaj, and J. M. Siskind, “Object classification from randomized EEG trials,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2021, pp. 3844–3853. [9]   H. M. Bharadwaj, R. B. Wilbur, and J. M. Siskind, “Still an Ineffective Method With Supertrials/ERPs—Comments on ‘Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features,’” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 11, pp. 14052–14054, 2023. [10] S. Palazzo, C. Spampinato, I. Kavasidis, D. Giordano, J. Schmidt, and M. Shah, “The effects of experiment duration and supertrial analysis on EEG classification methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–3, 2024. [11] S. Palazzo, C. Spampinato, J. Schmidt, I. Kavasidis, D. Giordano, and M. Shah, “Correct block-design experiments mitigate temporal correlation bias in EEG classification.” arXiv, Nov. 25, 2020. [12] C. H. Wolters, A. Anwander, X. Tricoche, D. Weinstein, M. A. Koch, and R. S. MacLeod, “Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling,” NeuroImage, vol. 30, no. 3, pp. 813–826, 2006. [13] D. L. Collins et al., “Design and construction of a realistic digital brain phantom,” IEEE Trans. Med. Imaging, vol. 17, no. 3, pp. 463–468, 1998. [14] K. Miller, K. Chinzei, G. Orssengo, and P. Bednarz, “Mechanical properties of brain tissue in-vivo: experiment and computer simulation,” Journal of Biomechanics, vol. 33, no. 11, pp. 1369–1376, 2000. [15] S. Baillet, J. J. Riera, G. Marin, J. F. Mangin, J. Aubert, and L. Garnero, “Evaluation of inverse methods and head models for EEG source localization using a human skull phantom,” Phys. Med. Biol., vol. 46, no. 1, p. 77, 2001. [16] L. Gavit, S. Baillet, J.-F. Mangin, J. Pescatore, and L. Garnero, “A multiresolution framework to MEG/EEG source imaging,” IEEE Trans. Biomed. Eng., vol. 48, no. 10, pp. 1080–1087, 2001. [17] R. M. Leahy, J. C. Mosher, M. E. Spencer, M. X. Huang, and J. D. Lewine, “A study of dipole localization accuracy for MEG and EEG using a human skull phantom,” Electroencephalography and Clinical Neurophysiology, vol. 107, no. 2, pp. 159–173, 1998. [18] T. J. Collier, D. B. Kynor, J. Bieszczad, W. E. Audette, E. J. Kobylarz, and S. G. Diamond, “Creation of a Human Head Phantom for Testing of Electroencephalography Equipment and Techniques,” IEEE Trans. Biomed. Eng., vol. 59, no. 9, pp. 2628–2634, 2012. [19] R. J. Cooper, R. Eames, J. Brunker, L. C. Enfield, A. P. Gibson, and J. C. Hebden, “A tissue equivalent phantom for simultaneous near-infrared optical tomography and EEG,” Biomed. Opt. Express, vol. 1, no. 2, p. 425, 2010. [20] C. K. Looi and Z. N. Chen, “Design of a human-head-equivalent phantom for ISM 2.4-GHz applications,” Microw. Opt. Technol. Lett., vol. 47, no. 2, pp. 163–166, 2005. [21] A. S. Oliveira, B. R. Schlink, W. D. Hairston, P. König, and D. P. Ferris, “Induction and separation of motion artifacts in EEG data using a mobile phantom head device,” J. Neural Eng., vol. 13, no. 3, p. 036014, 2016. [22] J. R. Rice, R. H. Milbrandt, E. L. Madsen, G. R. Frank, E. J. Boote, and J. C. Blechinger, “Anthropomorphic 1H MRS head phantom,” Med. Phys., vol. 25, no. 7, pp. 1145–1156, 1998. [23] A. J. Riordan, M. Prokop, M. A. Viergever, J. W. Dankbaar, E. J. Smit, and H. W. A. M. De Jong, “Validation of CT brain perfusion methods using a realistic dynamic head phantom: Digital dynamic head phantom for CT brain perfusion,” Med. Phys., vol. 38, no. 6Part1, pp. 3212–3221, 2011. [24] K. Shmueli, D. L. Thomas, and R. J. Ordidge, “Design, construction and evaluation of an anthropomorphic head phantom with realistic susceptibility artifacts,” Magnetic Resonance Imaging, vol. 26, no. 1, pp. 202–207, 2007. [25] I. Akhoun et al., “Speech auditory brainstem response (speech ABR) characteristics depending on recording conditions, and hearing status,” Journal of Neuroscience Methods, vol. 175, no. 2, pp. 196–205, 2008. [26] M. Balasubramanian, W. M. Wells, J. R. Ives, P. Britz, R. V. Mulkern, and D. B. Orbach, “RF Heating of Gold Cup and Conductive Plastic Electrodes during Simultaneous EEG and MRI,” The Neurodiagnostic Journal, vol. 57, no. 1, pp. 69–83, 2017. [27] V. V. M. Dattada, S. Sasidharan, A. Hojlund, and K. S. Sridharan, “How Does Deep Brain Stimulation Affect Magnetoencephalography Data?,” in 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Nitte, India, Nov. 2021, pp. 307–312. [28] M. K. Egan, R. Larsen, J. Wirsich, B. P. Sutton, and S. Sadaghiani, “Safety and data quality of EEG recorded simultaneously with multi-band fMRI,” PLOS ONE, vol. 16, no. 7, p. e0238485, 2021. [29] T. Eggert, H. Dorn, C. Sauter, G. Schmid, and H. Danker-Hopfe, “RF-EMF exposure effects on sleep – Age doesn’t matter in men!,” Environmental Research, vol. 191, p. 110173, 2020. [30] D. Freche, J. Naim-Feil, A. Peled, N. Levit-Binnun, and E. Moses, “A quantitative physical model of the TMS-induced discharge artifacts in EEG,” PLoS Comput Biol, vol. 14, no. 7, p. e1006177, 2018. [31] F. Kruggel, C. j. Wiggins, C. s. Herrmann, and D. y. von Cramon, “Recording of the event-related potentials during functional MRI at 3.0 Tesla field strength,” Magnetic Resonance in Medicine, vol. 44, no. 2, pp. 277–282, 2000. [32] H. Mandelkow et al., “Heart beats brain: The problem of detecting alpha waves by neuronal current imaging in joint EEG–MRI experiments,” NeuroImage, vol. 37, no. 1, pp. 149–163, 2007. [33] M. I. Miga, T. K. Sinha, D. M. Cash, R. L. Galloway, and R. J. Weil, “Cortical surface registration for image-guided neurosurgery using laser-range scanning,” IEEE Transactions on Medical Imaging, vol. 22, no. 8, pp. 973–985, 2003. [34] J. Modolo, M. Hassan, G. Ruffini, and A. Legros, “Probing the circuits of conscious perception with magnetophosphenes,” J. Neural Eng., vol. 17, no. 3, p. 036034, 2020. [35] T. Mutanen, H. Mäki, and R. J. Ilmoniemi, “The Effect of Stimulus Parameters on TMS–EEG Muscle Artifacts,” Brain Stimulation, vol. 6, no. 3, pp. 371–376, 2013. [36] J. Peeters et al., “Current Steering Using Multiple Independent Current Control Deep Brain Stimulation Technology Results in Distinct Neurophysiological Responses in Parkinson’s Disease Patients,” Front. Hum. Neurosci., vol. 16, 2022. [37] N. Perentos, R. J. Croft, R. J. McKenzie, and I. Cosic, “The Alpha Band of the Resting Electroencephalogram Under Pulsed and Continuous Radio Frequency Exposures,” IEEE Trans. Biomed. Eng., vol. 60, no. 6, pp. 1702–1710, 2013. [38] R. S. Schaefer, J. Farquhar, Y. Blokland, M. Sadakata, and P. Desain, “Name that tune: Decoding music from the listening brain,” NeuroImage, vol. 56, no. 2, pp. 843–849, 2011. [39] L. Sun and H. Hinrichs, “Simultaneously recorded EEG–fMRI: Removal of gradient artifacts by subtraction of head movement related average artifact waveforms,” Human Brain Mapping, vol. 30, no. 10, pp. 3361–3377, 2009. [40] J. N. van der Meer, Y. B. Eisma, R. Meester, M. Jacobs, and A. J. Nederveen, “Effects of mobile phone electromagnetic fields on brain waves in healthy volunteers,” Sci Rep, vol. 13, no. 1, p. 21758, 2023. [41] A. Peterson, D. Cruse, L. Naci, C. Weijer, and A. M. Owen, “Risk, diagnostic error, and the clinical science of consciousness,” NeuroImage: Clinical, vol. 7, pp. 588–597, 2015.

opencc-by-4.0Aug 2024View details →

zenodo36/100

Diffusion-relaxation MRI data from biomimetic phantoms

Diffusion-relaxation MRI data and tools to estimate the inner fibre radius of biomimetic phantoms, as reported here: <blockquote> Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI. Erick J. Canales-Rodríguez, Marco Pizzolato, Feng-Lei Zhou, Muhamed Barakovic, Jean-Philippe Thiran, Derek K. Jones, Geoffrey J.M. Parker, Tim B. Dyrby. Magn Reson Med. 2024; 91: 2579-2596. doi: 10.1002/mrm.29991 <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29991" rel="nofollow">https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29991</a> </blockquote> Run the code to replicate the figures reported in the paper. We provide the raw diffusion-relaxation data (spherical mean signal) to facilitate future evaluations and developments.

opencc-by-4.0Jun 2024View details →

zenodo36/100

MetTLM 20NRM01 TU/e Dataset: Dependence of Temporal Frequency and Chromaticity on the Visibility of the Phantom Array Effect

The dataset has the following format: 20 (Participants) by 18 (= 3 Chromaticities × 6 Temporal Frequencies) Chromaticities:              Red (R); Green (G); Warm White (W) Temporal Frequencies: F1 = 80 Hz; F2 = 300 Hz; F3 = 600 Hz; F4 = 900 Hz; F5 = 1200 Hz; F6 = 1800 Hz. <table> <tbody> <tr> <td> </td> <td>  R F1 </td> <td> R F2 </td> <td> R F3 </td> <td> R F4 </td> <td> R F5 </td> <td> R F6 </td> <td> G F1 </td> <td> G F2 </td> <td> G F3 </td> <td> G F4 </td> <td> G F5 </td> <td> G F6 </td> <td> W F1 </td> <td> W F2 </td> <td> W F3 </td> <td> W F4 </td> <td> W F5 </td> <td> W F6 </td> </tr> <tr> <td>P01</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>P02</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>P03</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>P04</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>...</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>P19</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>P20</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table> Due to the fractional factorial 3 (colour) × 6 (temporal frequency) mixed design, there are some empty cells. The details are described in Table 2 of the publication. The values in the table represent the visibility thresholds in Modulation Depth (MD). For example, the values of 0.05, 0.1, and 1 means a MD of 5%, 10% and 100% respectively.

opencc-by-4.0Jul 2024View details →

zenodo36/100

DATASET: Water Phantom Characterization of a Novel Optical Fiber Sensor for LDR Brachytherapy

Dataset for the journal article "Water Phantom Characterization of a Novel Optical Fiber Sensor for LDR Brachytherapy"

opencc-by-4.0Jan 2023View details →

zenodo36/100

Fig. 3. Isolated type A coprolites viewed using transmitted-light microscopy. Scale bars 100 in The ecological role of immature phantom midges (Diptera: Chaoboridae) in the Eocene Lake Messel, Germany

Fig. 3. Isolated type A coprolites viewed using transmitted-light microscopy. Scale bars 100 µm.

opencc-by-4.0Apr 2007View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record