Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Generated Metastatic CT 3D Femurs with lesions segmentation 1/3

This file regroups the 5675 synthetic femurs used and described in the paper: "Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models" Saillard et al.  Three repositories are needed to form the full archive. When all are downloaded, they can be deflated with the following bash command :  <pre><code>cat Generated_3DCT_Metastatic_Femurs.tar.gz* | tar faxv -</code></pre>   This is the first part (1/3) part 2/3: <a title="Opens in new tab" href="https://doi.org/10.5281/zenodo.13824177" target="_blank" rel="noopener"> 10.5281/zenodo.13824177 </a> part 3/3: <a title="Opens in new tab" href="https://doi.org/10.5281/zenodo.13824179" target="_blank" rel="noopener"> 10.5281/zenodo.13824179  </a>   Generated CT Scans are organized in two directories: one for files generated with DDPM this one) and one without DDPM. ├── Generated_3DCT_Metastatic_Femurs_with_DDPM  │   ├── img │   ├── lbl │   └── msk └── Generated_3DCT_Metastatic_Femurs_NO_DDPM     ├── img     ├── lbl     └── msk The content of each directory is as follows:  <ul> <li>img : the generated 3D CT scan (nii.gz format) from the 26 healthy femurs</li> <li>lbl : the lesions segmentation (nii.gz format) of the generated 3D CT scan</li> <li>msk : the segmentations of the 26 healthy femurs (nii.gz)</li> </ul> A given filename corresponds to in img and lbl directories : <ul> <li>./lbl/MEK03les0MEK14.nii.gz is the lesions segmentation of ./img/MEK03les0MEK14.nii.gz.</li> <li>./lbl/MEK03les197MEK28.nii.gz is the lesions segmentation of  ./img/MEK03les197MEK28.nii.gz</li> </ul> But in msk, the corresponding femur mask for these examples is ./msk/MEK03.nii.gz

opencc-by-4.0Sep 2024View details →

Left Atrium 3D Models Extracted from Static CT Images (AF and SR Models)

A cohort of 45 x 2 patient-specific 3D-models from static computed tomography (CT) images provided by the University Medical Center Hamburg-Eppendorf in Germany. The study complies with EU Regulation 2016/679 and has recieved ethical approval from the regional committee.  The cohort has been used in two research papers: <ol> <li>Kjeldsberg et al. (2024), Estimation of inlet flow rate in simulations of left atrial flows: A proposed optimized and reference-based algorithm with application to sinus rhythm and atrial fibrillation, J Biomech [Accepted]</li> <li>Kjeldsberg et al. (2024), Beyond CHA2DS2–VASc: hemodynamic and morphologic discriminants for thrombus formation and stroke in atrial fibrillation patients, Ann. Biomed. Eng. [Submitted]</li> </ol> models_af.zip contains 45 3D surface models (.vtp) extracted during onset of atrial diastole where Atrial Fibrillation movement was applied using a motion algorithm [*] models_sr.zip contains 45 3D surface models (.vtp) extracted during onset of atrial diastole where Sinus Rhytmn movement was applied using a motion algorithm [*]   [*] For details on the motion algorithm, see <a href="Harrison, J., 2024. Medical Imaging, Shapes and Statistics for Stroke Prediction in Atrial Fibrillation (Doctoral dissertation, Inria & Université Cote d'Azur, CNRS, I3S, Sophia Antipolis, France).">Harrison – Medical Imaging, Shapes and Statistics for Stroke Prediction in Atrial Fibrillation </a>

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

CT-EBM-SP - Corpus of Clinical Trials for Evidence-Based-Medicine in Spanish (version 2)

A collection of 1200 texts (292173 tokens) about clinical trials studies and clinical trials announcements in Spanish: - 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO). - 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos. Texts were annotated with the following entities types: - Semantic groups from the Unified Medical Language System:    • ANAT: anatomy   • CHEM: pharmacological and chemical substances   • DEVI: medical devices   • DISO: pathologic conditions    • LIVB: living beings, included the human being   • PHYS: physiological processes   • PROC: lab tests, diagnostic or therapeutic procedures - Medical drug information:   • Contraindicated: a contraindicated drug or treatment   • Dose: dose or strength   • Form: dosage form   • Route: administration route or mode - Temporal expressions     • Age   • Date   • Duration   • Frequency   • Time - Miscellaneous medical entities:    • Concept: abstract concepts, statistical tests or measurement scales   • Food: foods or drinks   • Observation: medical observations or clinical findings   • Quantifier_or_Qualifier: quantifier or qualifier adjective   • Result_or_Value: result or value of a measurement, laboratory analysis or procedure - Negation/Speculation:     • Neg_cue: negation cue   • Negated: negated event   • Spec_cue: speculation cue   • Speculated: speculated or uncertain event - Attributes:    • Temporality:     ◦ History_of: past event     ◦ Future: future event   • Experiencer:     ◦ Patient: patient or participant on a clinical trial     ◦ Family_member     ◦ Other: other person different from the patient or the family member 86 389 entities and 16 590 attributes were annotated. 10% of the corpus was doubly annotated, and high inter-annotator agreement (IAA) values were achieved: F1-score = 0.84% for entities; and F1-score = 0.88% for attributes (both in strict match).  The dataset includes the texts and annotations used for the human evaluation of the medical named entity tool: - 100 clinical trial announcements from EudraCT not used for system development: we provide files of the version revised by medical professionals (Reference folder) - 100 clinical cases with Creative Commons license: we provide files with the files revised by medical professionals (Reference folder). These data come from:    • Urgencias Bidasoa (https://urgenciasbidasoa.wordpress.com/casos-clinicos-3/)    • Hipocampo.org (https://www.hipocampo.org/)    • Cases published by Sociedad Andaluza de Medicina Familiar y Comunitaria (SAMFyC): we are greatly thankful for giving us permission to use these cases and we acknowledge that the copyright belongs to the authors' contents. Clinical cases were extracted from books published from 2016 to 2022 (https://www.samfyc.es/tipos-publicacion/publicaciones/).     If you use these data, please, acknowledge the copyright and intellectual property rights to the authors' contents. The dataset is freely distributed for research and educational purposes under a Creative Commons Non-Commercial Attribution (CC-BY-NC-A) License. If you use the CT-EBM-SP vs. 2 dataset, please, cite as follows: Campillos-Llanos, L., A. Valverde-Mateos & A. Capllonch-Carrion (2024) Hybrid natural language processing tool for semantic annotation of medical texts in Spanish. BMC Bioinformatics. BioMed Central.

opencc-by-4.0Sep 2021View details →

CT Fishing Report Occurrences

Dataset of gamefish occurrences as compiled from the Connecticut Fishing Report (2006-2018) and Trophy Fish Report (2009-2018), both published by the Connecticut Department of Energy and Environmental Protection. Compiled as thesis project by Rebecca Hedreen for a Masters of Science in Biology from Southern Connecticut State University, with advisor Dr. Sean Grace.

opencc-by-2.5Oct 2022View details →

CT-OCR-2022 (folded002, part2)

CT-OCR-2022 dataset CT-OCR-2022 dataset contains optically scanned images for source paper document, X-ray projections, CT-reconstructed cross-sections and  segmentation markups for model objects. Description of the structure of the dataset, contacts and information about the reference to the dataset see at <a href="https://doi.org/10.5281/zenodo.7123495">https://doi.org/10.5281/zenodo.7123495</a>.

opencc-by-4.0Jul 2023View details →

Dataset of CT scans, slice photographs, and visual browning scores of 120 'Kanzi' apples

SummaryThis dataset is a collection of CT scans, slice photographs, and visual browning scores of 120 'Kanzi' apples. DescriptionSample informationIn 2022, 120 'Kanzi' apples that had been stored under CA conditions (4 °C, 1 kPa O2, 1.5 kPa CO2) for 8 months were obtained from FruitMasters, The Netherlands. The fruit was grown in orchards surrounding Geldermalsen, the Netherlands, and harvested at physiological maturity in 2021.CT acquisitionThe dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].  A cone beam geometry with a circular trajectory was used to acquire 1440 projection images at an exposure time of 100ms, a tube peak voltage of 90kV, a current of 550uA, and 2 times binning, halving the detector resolution. Volumes were reconstructed with the FDK algorithm and a voxel size of 129.3um. Beam hardening correction was used from the FleXbox package [Kostenko 2020]. To make sure that the grey values could be compared between scans the spectral sensitivity of the scanner was first estimated for each scan individually and the average of these estimates was used for beam hardening correction on all CT scans. All apples were scanned with the stem side on top. Moreover, a line was drawn on all apples from the stem to the calyx. The apples were put in the CT scanner so that the line was facing the X-ray source.The CT volumes are saved as .tiff stacks. All volumes have been cropped to remove the background.Slicing and photograph acquisitionOne day after CT scanning, the apples were sliced using a modified meat-slicing machine (CaterChef, house brand of EMGA, Mijdrecht, The Netherlands), which is illustrated in the file slicing_machine_labels.png. The sliding surface of the meat-slicing machine was replaced by a transparent acrylic sheet, and a camera was placed behind the slicing surface. While in the machine, each apple was kept in place by a suction cup so that it could not rotate during the slicing. All apples were sliced from the stem end to the calyx end, with a slice thickness of roughly 4mm. Every time before slicing, a picture was taken of the remaining part of the apple through the transparent sliding surface. To ensure that all apples were roughly aligned to the CT scans, the apples were oriented so that the line drawn earlier was on top.The slice photographs are saved as .png files. All photographs have been cropped to remove the background and to center the apple in the image.Visual browning scoresAfter each apple was sliced it was also visually inspected, and a score from one to ten was given to describe the amount of browning in the apple.Related paperWhen using this dataset please consider citing the following paper. It explains how the dataset was collected and used for the first time:Dirk Elias Schut, Rachael Maree Wood, Anna Katharina Trull, Rob Schouten, Robert van Liere, Tristan van Leeuwen, Kees Joost Batenburg, "Detecting internal disorders in fruit by CT. Part 1: Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit", 2023, <a href="https://arxiv.org/abs/2310.01987">arXiv preprint arXiv:2310.01987</a> Research group This dataset was produced in a collaboration between the Computational Imaging group at Centrum Wiskunde & Informatica (CWI), and GREEFA.<a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a> <a href="https://www.greefa.com/nl/">https://www.greefa.com/nl/</a>Contact details dirk [dot] schut [at] cwi [dot] nlAcknowledgments This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003). The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.

opencc-by-4.0Jul 2018View details →

Artifacts supplementing the RSA-CT 2018 paper "Why Johnny the Developer Can't Work with Public Key Certificates"

Supplemental materials for the paper "Why Johnny the Developer Can't Work with Public Key Certificates" (DOI 10.1007/978-3-319-76953-0_3, more details at https://crocs.fi.muni.cz/public/papers/rsa2018) contain the following: <ul> <li>Informed consent participants had to sign (experiment design approved by Research Ethics Committee of Masaryk University)</li> <li>General questionnaire & System usability scale questionnaire</li> <li>User tasks & certificates to validate</li> </ul>

opencc-by-4.0Aug 2023View details →

Human Inner Ear Anatomy: Labeled Volume CT Data of Inner Ear Fluid Space and Anatomical Landmarks

The provided dataset comprises 43 instances of temporal bone volume CT scans. The scans were performed on human cadaveric specimen with a resulting isotropic voxel size of \(99 \times 99 \times 99 \, \, \mathrm{\mu m}^3\). Voxel-wise image labels of the fluid space of the bony labyrinth, subdivided in the three semantic classes cochlear volume, vestibular volume and semicircular canal volume are provided. In addition, each dataset contains JSON-like descriptor data defining the voxel coordinates of the anatomical landmarks: (1) apex of the cochlea, (2) oval window and (3) round window. The dataset can be used to train and evaluate algorithmic machine learning models for automated innear ear analysis in the context of the supervised learning paradigm.   Usage Notes The datasets are formatted in the HDF5 format developed by the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5 Group</a>. We utilized and thus recommend the usage of Python bindings <a href="https://www.h5py.org/">pyHDF</a> to handle the datasets. The flat-panel volume CT raw data, labels and landmarks are saved in the HDF5-internal file structure using the respective group and datasets: <pre><code>raw/raw-0 label/label-0 landmark/landmark-0 landmark/landmark-1 landmark/landmark-2</code></pre> Array raw and label data can be read from the file by indexing into an opened h5py file handle, for example as numpy.ndarray. Further metadata is contained in the attribute dictionaries of the raw and label datasets. Landmark coordinate data is available as an attribute dict and contains the coordinate system (LPS or RAS), IJK voxel coordinates and label information. The helicotrema or cochlea top is globally saved in landmark 0, the oval window in landmark 1 and the round window in landmark 2. Read as a Python dictionary, exemplary landmark information for a dataset may reads as follows: <pre><code class="language-python">{'coordsys': 'LPS', 'id': 1, 'ijk_position': array([181, 188, 100]), 'label': 'CochleaTop', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 44.21109689, -139.38058589, -183.48249736])}</code></pre>   <pre><code class="language-python">{'coordsys': 'LPS', 'id': 2, 'ijk_position': array([222, 182, 145]), 'label': 'OvalWindow', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 48.27890112, -139.95991131, -179.04103763])}</code></pre>   <pre><code class="language-python">{'coordsys': 'LPS', 'id': 3, 'ijk_position': array([223, 209, 147]), 'label': 'RoundWindow', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 48.33120126, -137.27135678, -178.8665465 ])}</code></pre>

opencc-by-4.0Aug 2023View details →

Hyperspectral 2D fan-beam X-ray CT dataset of 5 materials

Hyperspectral X-ray CT dataset acquired at the DTU 3D imaging center. The phantom consists of 5 materials: Aluminium (10 mm) and PVC (7.8 mm) in solid blocks. Sugar, H2O2, and H2O in circular glass containers. 3D array with dimension: 128 x 370 x 258 < channel, angle, horizontal >   Detector parameters: Number of detector pixels: 258 (concatenated from 2 detector modules with 128 pixels each and 2 pixel interpolated across a gap between detectors) Pixel size: 0.077 cm Sep=0.153  Pixels' gap length (cm) det_space=(ndet)*pixel_size+Sep # physical width of detector in cm (pixels*pixel_size), including the gap   Acquisition Parameters 360 # Angular span of projections in degrees 370 # Number of projections. note: last projection taken is not a duplicate of the first projection. At angle: 360/370 degrees from first projection. 115.0 # Source-Detector distance in cm 0 # Vertical source shift from perfect placement 0 # Vertical detector shift from perfect placement 57.5 # Source-AxisOfRotation distance in cm   rot_axis_x = 0 # x-position offset of AxisOfRotation rot_axis_y = 0 # y-position offset of AxisOfRotation

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Visualization of Marsh Grass Roots and Rhizomes by CT imaging: VCR salt marshes, summer 2012

Computer-aided tomography and image processing previously has been used to accurately and rapidly quantify coarse root mass in coastal wetlands (Davey et al. 2011. Ecological Applications). The data in this data base are being used to develop the technique to allow for resolution of fine roots. The contribution of Spartina alterniflora roots-and-rhizomes (hereafter, roots) to soil volume is measured in VCR mainland marsh soils. Soil cores were collected several different marshes with differing soil types (mineral vs peaty). The cores were scanned by computer-aided tomography and image processing was used to determine the volume of living roots. Our results show that CT imaging may also be used to quantify coarse and fine root volume in salt marsh soils and that in peaty soils, coarse roots make an important contribution to soil volume (up to 53% of the soil volume is live roots). The dataset includes an extensive manual of methods, along with instructional videos.

openCustomJun 2020View details →

opencc-zeroJun 2019View details →

IG. 6. — A, Trunk vertebra of Alsophis sp. 2 from Pointe du Helleux archaeological site (Square 2 – crab layer) located on Grande-Terre Island; B, trunk vertebra of Erythrolamprus juliae cf. copeae (Parker, 1936) from Sainte-Rose La Ramée archaeological site (US 2058) located on Basse-Terre Island. Abbreviations: cd., condyle; ct., cotyle; di., diapophysis; h. k., hemal keel; m. c., medial constriction; n. a., neural arch; n. s., neural spine; p. c., precondylar constriction; p. d., paracotylar depression; p. n., postero-medial notch of the zygantrum; pa., parapophysis; pz. f., prezygapophyseal facet; pz. p., prezygapophyseal process; s. d., subcentral depression; s. r., subcentral ridge; s. t., sub-cotylar tubercle; zs., zygosphene. Scale bars: 4 mm in Fossil dipsadid snakes from the Guadeloupe Islands (French West-Indies) and their interactions with past human populations

IG. 6. — A, Trunk vertebra of Alsophis sp. 2 from Pointe du Helleux archaeological site (Square 2 – crab layer) located on Grande-Terre Island; B, trunk vertebra of Erythrolamprus juliae cf. copeae (Parker, 1936) from Sainte-Rose La Ramée archaeological site (US 2058) located on Basse-Terre Island. Abbreviations: cd., condyle; ct., cotyle; di., diapophysis; h. k., hemal keel; m. c., medial constriction; n. a., neural arch; n. s., neural spine; p. c., precondylar constriction; p. d., paracotylar depression; p. n., postero-medial notch of the zygantrum; pa., parapophysis; pz. f., prezygapophyseal facet; pz. p., prezygapophyseal process; s. d., subcentral depression; s. r., subcentral ridge; s. t., sub-cotylar tubercle; zs., zygosphene. Scale bars: 4 mm

Fig. 14 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features

Fig. 14. Copionodon exotatos, holotype, MZUSP 120631, CT-scan image of posterior part of skull and Weberian capsule, dorsal view. Abbreviations: FR1, First Ray 1; CC, complex centrum; WC, Weberian capsule; VC, vertebrae centrum; PR, pleural ribs.

Fig. 15 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features

Fig. 15. Copionodon exotatos, holotype, MZUSP 120631, CT-scan images of left palatine and associated entopterygoid: a. dorsal view, b. ventral view, c. with entopterygoid removed. Abbreviations: PAL, palatine; EN, entopterygoid.

Fig. 13 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features

Fig. 13. Copionodon exotatos, holotype, MZUSP 120631, CT-scan image of opercle and interopercle, right side. Abbreviations: VOD, vestigial odontodes; OP, opercle; INT, interopercle. Lateral view.

Fig. 9 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features

Fig. 9. Copionodon exotatos, holotype, MZUSP 120631, CT-scan image of anterior portion of skull, dorsal view. Abbreviations: PMX, premaxilla; MX, maxilla; QUA, quadrate; PO, preopercle; ME, mesethmoid; AF, anterior fontanel; FR, frontal; DEN, dentary; PAL, palatine; AA, anguloarticular; LE, lateral ethmoid; HYO, hyomandibula.

Fig. 8 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features

Fig. 8. Collection site of Copionodon exotatos, right-hand branch of Riacho do Mosquito (trib. to rio Santo Antônio, rio Paraguaçu drainage), immediately upstream from Cachoeira do Mosquito (12º21'59.51"S, 41 ºS 22"19.37"W) at exit of rock-enclosed sector.

Fig. 5 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features

Fig. 5. Copionodon exotatos, paratype, MZUSP 121656, basibranchials and hypobranchials, dorsal view. Grey areas represent cartilage. Abbreviations: BB2-4, basibranchials 2 to 4; HB1-3, hypobranchials 1 to 3. Scale bar = 1 mm.