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Inner Speech Recognition for Mutism and Speech Disorder Using Brain-Computer Interface
<p>Four native English speakers, right-handed and healthy individuals participated in collecting EEG-based inner speech data in this study, all of them had no neurological or movement disorders, no hearing loss, and no speech loss. The participants were two males and two females aged 20 to 56 and were named (sub-01) for the first subject (sub-02) for the second subject and so on. A total of 400 recordings sessions were successfully completed, 100 recordings for the command Up, 100 recordings for Down, 100 recordings for the command Left, and 100 recordings for Right.</p>
Figure 1. A in Anatomical study of two previously undescribed specimens of Clevosaurus hudsoni (Lepidosauria: Rhynchocephalia) from Cromhall Quarry, UK, aided by computed tomography, yields additional information on the skeleton and hitherto undescribed bones
Figure 1. A, palaeogeographical map showing the principal Late Triassic/Early Jurassic tetrapod-bearing fissure deposits near Bristol, with current coastline superimposed (modified from Whiteside & Marshall, 2008). B, simplified geology map of the area around Cromhall Quarry. Mercia Mudstone Group is Triassic, Old Red Sandstone is Devonian and the other labelled strata are Carboniferous (geology map and legend derived from BGS website Open Science data, last accessed 29 October 2017).
Figure 11 in Anatomical study of two previously undescribed specimens of Clevosaurus hudsoni (Lepidosauria: Rhynchocephalia) from Cromhall Quarry, UK, aided by computed tomography, yields additional information on the skeleton and hitherto undescribed bones
Figure 11. Photographs and surface models of Clevosaurus hudsoni specimen NHMUK PV R36832. Left humerus in (A) anterodorsal, (B) dorsal and (C) ventral views. Left ulna in (D) dorsolateral, (E) anterior and (F) posterior views. G, left distal forelimb bones in lateral view. H, isolated distal left forelimb bones in lateral view.
Data for computing sequentiality
<p>Paper: Adaptive erasure of spurious sequences in sensory cortical circuits</p>
KLM computer centre
<u>Source</u>: Europeana <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1652249755.2162.jpg">https://4dcity.org/imgupload/1652249755.2162.jpg</a> <br><u>Original Image URL</u>: <a href="https://api.europeana.eu/thumbnail/v2/url.json?uri=https%3A%2F%2Fwww.openbeelden.nl%2Fimages%2F672618%2FKLM_computercentrum_%25281_24%2529.png&type=VIDEO">https://api.europeana.eu/thumbnail/v2/url.json?uri=https%3A%2F%2Fwww.openbeelden.nl%2Fimages%2F672618%2FKLM_computercentrum_%25281_24%2529.png&type=VIDEO</a> <br><br><u>Image-Metadata:</u><br>Filename: 1652249755.2162.jpg<br>Image Dimensions: 360x288<br>Megapixels: 0.10 MP<br>Filesize: 38.72 KB<br>
SynthRAD2023 Grand Challenge test dataset: synthetizing computed tomography for radiotherapy
<div> <p><strong>DATASET STRUCTURE</strong></p> <p>The dataset can be downloaded from <a href="https://doi.org/10.5281/zenodo.7260705">https://doi.org/10.5281/zenodo.10514185</a> and a detailed description is offered at "synthRAD2023_dataset_description.pdf".</p> <p>The<strong> test datasets</strong> for Task1 is in Task1.tar.zst, while for Task2 in Task2.tar.zst. After unzipping, each Task is organized according to the following folder structure:</p> <p>Task1<br>└── mr<br>│ ├── brain<br>│ │ ├── 1BA003<br>│ │ │ ├── mask.nii.gz<br>│ │ │ └── mr.nii.gz<br>│ │ ├── 1BA029<br>│ │ │ ├── mask.nii.gz<br>│ │ │ └── mr.nii.gz<br>│ │ ├── ...<br>│ └── pelvis<br>│ ├── 1PA003<br>│ │ ├── mask.nii.gz<br>│ │ └── mr.nii.gz<br>│ ├── 1PA006<br>│ │ ├── mask.nii.gz<br>│ │ └── mr.nii.gz<br>│ ├── ...<br>├── ct<br>│ ├── 1BA003.nii.gz<br>│ ├── 1BA029.nii.gz<br>│ ├── 1BA063.nii.gz<br>│ ├── ...<br>├── doseplanning<br>│ ├── 1BA003<br>│ │ ├── 1BA003_RTplan_photons.mat<br>│ │ └── 1BA003_RTplan_protons.mat<br>│ ├── 1BA029<br>│ │ ├── 1BA029_RTplan_photons.mat<br>│ │ ├── 1BA029_RTplan_protons.mat<br>│ ├── ...<br><br></p> <p>Task2<br>├── cbct<br>│ ├── brain<br>│ │ ├── 2BA019<br>│ │ │ ├── cbct.nii.gz<br>│ │ │ └── mask.nii.gz<br>│ │ ├── 2BA021<br>│ │ │ ├── cbct.nii.gz<br>│ │ │ └── mask.nii.gz<br>│ │ ├── ...<br>│ └── pelvis<br>│ ├── 2PA022<br>│ │ ├── cbct.nii.gz<br>│ │ └── mask.nii.gz<br>│ ├── 2PA023<br>│ │ ├── cbct.nii.gz<br>│ │ └── mask.nii.gz<br>│ ├── ... <br>├── ct<br>│ ├── 2BA019.nii.gz<br>│ ├── 2BA021.nii.gz<br>│ ├── 2BA022.nii.gz<br>│ ├── ....<br>├── doseplanning<br>│ ├── 2BA019<br>│ │ ├── 2BA019_RTplan_photons.mat<br>│ │ └── 2BA019_RTplan_protons.mat<br>│ ├── 2BA021<br>│ │ ├── 2BA021_RTplan_photons.mat<br>│ │ └── 2BA021_RTplan_protons.mat<br>│ ├── ...<br><br></p> <p>Each patient folder has a unique name that contains information about the task, anatomy, center and a patient ID. The naming follows the convention below:</p> <table> <tbody> <tr> <td>[Task]</td> <td>[Anatomy]</td> <td>[Center]</td> <td>[PatientID]</td> </tr> <tr> <td>1</td> <td>B</td> <td>A</td> <td>001</td> </tr> </tbody> </table> <p>The zip contains the following structure: </p> <ul> <li> <p>ct/<patient code>.nii.gz: The gold-standard CT image.</p> </li> <li> <p>doseplanning/<patient code>/<patient code>_RTplan_[photons/protons].mat: The matRad photon and proton doseplans for this particular patient. </p> </li> <li> <p>[mr/cbct]/[brain/pelvis]/<patient code>/[mr/cbct].nii.gz: The corresponding CBCT or MR image.</p> </li> <li> <p>[mr/cbct]/[brain/pelvis]/<patient code>/mask.nii.gz: image containing a binary mask of the dilated patient outline.</p> </li> </ul> <p><strong>DATASET DESCRIPTION</strong></p> <p>This challenge dataset contains imaging data of patients who underwent radiotherapy in the brain or pelvis region. Overall, the population is predominantly adult and no gender restrictions were considered during data collection. For Task 1, the inclusion criteria were the acquisition of a CT and MRI during treatment planning while for task 2, acquisitions of a CT and CBCT, used for patient positioning, were required. Datasets for task 1 and 2 do not necessarily contain the same patients, given the different image acquisitions for the different tasks.</p> <p>Data was collected at 3 Dutch university medical centers:</p> <ul> <li> <p>Radboud University Medical Center</p> </li> <li> <p>University Medical Center Utrecht</p> </li> <li> <p>University Medical Center Groningen</p> </li> </ul> <p>For anonymization purposes, from here on, institution names are substituted with A, B and C, without specifying which institute each letter refers to.</p> <p>The following number of patients is available in the training set.</p> <p><strong>Training</strong></p> <table> <tbody> <tr> <td> </td> <td> <p><strong>Brain</strong></p> </td> <td> <p><strong>Pelvis</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Total</strong></p> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Tota</strong>l</p> </td> </tr> <tr> <td> <p><strong>Task 1</strong></p> </td> <td> <p>60</p> </td> <td> <p>60</p> </td> <td> <p>60</p> </td> <td> <p>180</p> </td> <td> <p>120</p> </td> <td> <p>0</p> </td> <td> <p>60</p> </td> <td> <p>180</p> </td> </tr> <tr> <td> <p><strong>Task 2</strong></p> </td> <td> <p>60</p> </td> <td> <p>60</p> </td> <td> <p>60</p> </td> <td> <p>180</p> </td> <td> <p>60</p> </td> <td> <p>60</p> </td> <td> <p>60</p> </td> <td> <p>180</p> </td> </tr> </tbody> </table> <p>Each subset generally contains equal amounts of patients from each center, except for task 1 brain, where center B had no MR scans available. To compensate for this, center A provided twice the number of patients than in other subsets.</p> <p><strong>Validation</strong></p> <table> <tbody> <tr> <td> </td> <td> <p><strong>Brain</strong></p> </td> <td> <p><strong>Pelvis</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Total</strong></p> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Tota</strong>l</p> </td> </tr> <tr> <td> <p><strong>Task 1</strong></p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> <td> <p>20</p> </td> <td> <p>0</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> </tr> <tr> <td> <p><strong>Task 2</strong></p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> </tr> </tbody> </table> <p><strong>Testing</strong></p> <table> <tbody> <tr> <td> </td> <td> <p><strong>Brain</strong></p> </td> <td> <p><strong>Pelvis</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Total</strong></p> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Total</strong></p> </td> </tr> <tr> <td> <p><strong>Task 1</strong></p> </td> <td> <p>20</p> </td> <td> <p>20</p> </td> <td> <p>20</p> </td> <td> <p>60</p> </td> <td> <p>40</p> </td> <td> <p>0</p> </td> <td> <p>20</p> </td> <td> <p>60</p> </td> </tr> <tr> <td> <p><strong>Task 2</strong></p> </td> <td> <p>20</p> </td> <td> <p>20</p> </td> <td> <p>20</p> </td> <td> <p>60</p> </td> <td> <p>20</p> </td> <td> <p>20</p> </td> <td> <p>20</p> </td> <td> <p>60</p> </td> </tr> </tbody> </table> <p>In total, for all tasks and anatomies combined, 1080 image pairs (720 training, 120 validation, 240 testing) are available in this dataset. <strong>This repository only contains the test data.</strong></p> <p>All images were acquired with the clinically used scanners and imaging protocols of the respective centers and reflect typical images found in clinical routine. As a result, imaging protocols and scanner can vary between patients. A detailed description of the imaging protocol for each image, can be found in spreadsheets that are part of the dataset release (see dataset structure).</p> <p>Data was acquired with the following scanners:</p> <ul> <li> <p>Center A:</p> <ul> <li> <p>MRI: Philips Ingenia 1.5T/3.0T</p> </li> <li> <p>CT: Philips Brilliance Big Bore or Siemens Biograph20 PET-CT</p> </li> <li> <p>CBCT: Elekta XVI</p> </li> </ul> </li> <li> <p>Center B:</p> <ul> <li> <p>MRI: Siemens MAGNETOM Aera 1.5T or MAGNETOM Avanto_fit 1.5T</p> </li> <li> <p>CT: Siemens SOMATOM Definition AS</p> </li> <li> <p>CBCT: IBA Proteus+ or Elekta XVI</p> </li> </ul> </li> <li> <p>Center C:</p> <ul> <li> <p>MRI: Siemens Avanto fit 1.5T or Siemens MAGNETOM Vida fit 3.0T</p> </li> <li> <p>CT: Philips Brilliance Big Bore</p> </li> <li> <p>CBCT: Elekta XVI</p> </li> </ul> </li> </ul> <p>For task 1, MRIs were acquired with a T1-weighted gradient echo or an inversion prepared - turbo field echo (TFE) sequence and collected along with the corresponding planning CTs for all subjects. The exact acquisition parameters vary between patients and centers. For centers B and C, selected MRIs were acquired with Gadolinium contrast, while the selected MRIs of center A were acquired without contrast.</p> <p>For task 2, the CBCTs used for image-guided radiotherapy ensuring accurate patient position were selected for all subjects along with the corresponding planning CT.</p> <p>The following pre-processing steps were performed on the data:</p> <ul> <li> <p>Conversion from dicom to compressed nifti (nii.gz)</p> </li> <li> <p>Rigid registration between CT and MR/CBCT</p> </li> <li> <p>Anonymization (face removal, only for brain patients)</p> </li> <li> <p>Patient outline segmentation (provided as a binary mask)</p> </li> <li> <p>Crop MR/CBCT, CT and mask to remove background and reduce file sizes</p> </li> </ul> <p>The code used to preprocess the images can be found at: <a href="https://github.com/SynthRAD2023/">https://github.com/SynthRAD2023/</a>. Detailed information about the dataset are provided in SynthRAD2023_dataset_description.pdf published here along with the data and will also be submitted to Medical Physics.</p> <p><strong>ETHICAL APPROVAL</strong></p> <p>Each institution received ethical approval from their internal review board/Medical Ethical committee:</p> <ul> <li> <p>UMC Utrecht approved not-WMO on 4/03/2022 with number 22/474 entitled: “Synthetizing computed tomography for radiotherapy Grand Challenge (SynthRAD)”.</p> </li> <li> <p>UMC Groningen approved not-WMO on 20/07/2022 with number 202200310 entitled: “Synthesizing computed tomography for radiotherapy - Grand Challenge”.</p> </li> <li> <p>Radboud UMC declared the study not-WMO on 17/10/2022 with number 2022-15950 entitled “Synthetizing computed tomography for radiotherapy Grand Challenge”.</p> </li> </ul> <p><strong>CHALLENGE DESIGN</strong></p> <p>The overall challenge design can be found at <a href="https://doi.org/10.5281/zenodo.7746020">https://doi.org/10.5281/zenodo.7746020</a>. </p> <h2>Notes</h2> <div>FUNDING BODIES: The challenge has been funded thanks to the support of the Seed Fund provided by the " EWUU Alliance TU/e, WUR, UU, UMCU" https://ewuu.nl/en/collaboration/seed-fund/.</div> </div>
Computer Science Research 2011-2018
<p><strong>Location of the institution</strong></p> <p>The location of the institution has been established by a 2-step process. First, a custom web-scraping tool has been used to find the latitude-longitude data of the institution at Wikipedia. For the institutions that were not successfully identified at Wikipedia, the location was attached using GeoPy (Bing location service).</p> <p>The following columns are related to location:</p> <p>LatLonReady: Latitude and longitude data</p> <p>country: name of the country</p> <p>continent: name of the continent</p> <p>cblock: same as country, EU countries as EU28</p> <p><strong>Business institution</strong></p> <p>Using the scraping tool developed for Wikipedia search, institutions were identified as business units if Wiki page contained information on Industry / HQ/ Product</p> <p>business: dummy variable</p> <p> </p> <p> </p>
FIG. 5 in Description of a New Blind and Rare Species of Xyliphius (Siluriformes: Aspredinidae) from the Amazon Basin Using High-Resolution Computed Tomography
FIG. 5. HRXCT model of skull and anterior body of Xyliphius sofiae, ANSP 182322, holotype, 44.1 mm SL. (A) Ventral view. (B) Mesial view of left side. ach: anterior ceratohyal; bb: basibranchials; bo: basioccipital; br: branchiostegal rays; cl: cleithrum; co: scapulocoracoid; cv: complex vertebrae; den: dentary; epo: epioccipital; exo: exoccipital; fr: frontal; let: lateral ethmoid; mc: mandibular canal tubules; mes: mesethmoid; mnp: middle nuchal plate; mx: maxilla; orb: orbitosphenoid; pal: autopalatine; pas: parasphenoid; pch: posterior ceratohyal; pfr: pectoral-fin rays; pmx: premaxilla; pro: prootic; ps: pectoral-fin spine; pte: pterosphenoid; pto: pterotic; rad: pectoral-fin radial; ret: retroarticular; sc: posttemporal-supracleithrum; soc: supraoccipital; spo: sphenotic; tr: tripus; uh: urohyal; v5: vertebrae five; vh: ventral hypohyal. Scale bar ¼ 2 mm.
Dataset related to the article "Computational assessment of fibromuscular dysplasia-related renal artery stenosis: a feasibility study"
<p>The dataset contains:</p> <p>- a folder containing the 3D virtual models of the renal arteries used for the computational fluid dynamic simulations</p> <p>- an excel sheet containing the patient-specific boundary conditions used for the computational fluid dynamic simulations</p>
Fibre Computer Repository
Open the record for dataset details and reuse information.
FIGURE 5 in First fossil representative of Cerylonidae (Coleoptera: Coccinelloidea) described using X-ray micro-computed tomography, from Eocene Baltic amber
FIGURE 5. Protostomopsis pandema gen. et sp. nov., paratype, No AB 058 [CAB]: A – habitus, dorsal view; B – habitus ventral view; C – right metathoracic wing, ventral view. Scale bars represent 0.25 mm for Figs. A and B, 0.1 mm for Fig. C.
Support vector machine Performance for: CIIE 2018 folio number 2018062802133: A Computational Literature Review of Educational Innovation'
<p>Results of support vector machine classification.</p>
Code for the lecture notes: "Green Computing for the Internet of Things"
<p>This dataset contains the supplementary files for the lecture and practical "Green Computing for the Internet of Things" that was given at the SusTrainable Summer School 2022, Rijeka, Croatia, July 4–5, 2022.</p> <p>This work acknowledges the support of the ERASMUS+ project “SusTrainable—Promoting<br> Sustainability as a Fundamental Driver in Software Development Training and Education”, no.<br> 2020–1–PT01–KA203–078646.</p> <p>https://sustrainable.uniri.hr/</p> <p>It was originally uploaded at: https://ftp.cs.ru.nl/Clean/mTask/SUSTRAINABLE2022/</p> <p>The dataset contains the following files:</p> <ul> <li>slides.pdf: the slides for the the lecture.</li> <li>exercises.zip: the installation instructions and assignments.</li> <li>exercises.pdf: the reader for the exercises done during the practical.</li> <li>exercises-oob.zip: a copy of exercises.zip but this includes all packages, dependencies, and binaries for the build system nitrile.</li> </ul>
Does attitude toward programming predict computational thinking? Analysis of gender and programming experience effect among engineering students
<p>This data was generated during the development of the FDPD project. Data were anonymized.</p>
Macintosh 128K Computer (1984)
Modelled accurately to existing product. .zip folder including PBR materials (diffuse,metallic) and original .blend file High poly model. Adjustable Keyboard chord and Mouse chord. Seperate objects (Computer, Keyboard, Mouse). Individual keys. Ready for use in a game/animation scene. [My Portfolio](http://hogandarren01.wixsite.com/portfolio) Source: Objaverse 1.0 / Sketchfab
OLD COMPUTER MEMORY - BRAZILIAN HARDWARE
Source: Objaverse 1.0 / Sketchfab
Computed Tomography Evaluation of Urinary Stones Densities Compared to in Vitro Analysis of Its Chemical Composition
ClinicalTrials.gov study NCT04110574. IPD Sharing: Not stated. Countries: 0. Publications: 0.
The Prevalence of Extra Root Canals and the Root Canal Configuration in Mandibular Anterior Teeth in a Sample of Egyptian Population Using Cone Beam Computed Tomography
ClinicalTrials.gov study NCT03935711. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Computer Driven Management of Weaning Following Prolonged Mechanical Ventilation
ClinicalTrials.gov study NCT00502489. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Radiological Re-evaluation of Failed Anastomotic Uretheroplasty for PFUDDI Using Computed Tomographic Urethrography.
ClinicalTrials.gov study NCT03328000. IPD Sharing: Not stated. Countries: 0. Publications: 0.
ScienceDex guides
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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.