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.
3,688
datasets available to search
ShareScore release 0.7.1
Dataset results
3,688 results for “Computer”
Computer-assisted abridgment of a classification scheme
<p>Work is under way to define and test a model for semi-automatic abridgment of the Dewey Decimal Classification (DDC) system. This abridgment is guided by data in the current full and abridged editions and by principles and practices that inform development and maintenance of the classification. The model is immediately applicable to the development of the next abridged edition of the DDC; other applications include derivation of different views of the DDC and management of multilingual content at various levels of development.</p>
AMPSphere pre-computed resources and auxiliary files for the manuscript codes
<p>AMPSphere is a comprehensive catalog of antimicrobial peptides predicted using Macrel (DOI: 10.7717/peerj.10555) from 63,410 public metagenomes, ProGenomes v2.2 database (82,400 high-quality microbial genomes), and c.a. 4k non-whitelisted microbial genomes from NCBI. Currently, AMPSphere is available as a web resource at https://ampsphere.big-data-biology.org/. AMPSphere v.2022-03 contains 863,498 sequences (avg length: 36 amino acids, range 8-98). DRAMP database was used to find confirmed sequences with strict homology to reference. This approach showed that 2,488 peptides were previously confirmed in our dataset. The present repository is a data dump for the precomputed resources and files needed for its generation and analysis as a complement to the GitHub repository. The complementary documentation is also available for each one of the files. To use the files just download them and apply the command `untar` to decompress the folders.</p>
Computational results and python files for the work "Divergence-conforming velocity and vorticity approximations for incompressible fluids obtained with minimal facet coupling"
<p><br> This repository contains data accompanying the paper "Divergence-conforming velocity and vorticity approximations for incompressible fluids obtained with minimal facet coupling".</p> <p>The implementation is based on the python-interface of the NGSolve open source Finite Element library (ngsolve.org).</p> <p>The file solve_problem_allione.py represents a minimum working example where the proposed MCS/HDG (set the use_MCS flag) method is used to solve the problem from the numerics section of the paper.</p> <p>The files FlowTemplates.py and krylovspace_extension.py contain a somewhat larger and more modular implementation of the proposed method that also features preconditioned iterative solvers, including support for the NgsAMG NGSolve extension library as well as the NGSolve-PETSc interface.</p> <p>The files errors_hdg.pickle, errors_mcs.pickle and kappas.pickle contain the raw data the tables and pictures in the paper were generated from.</p> <p>This data was generated with the scripts conv3d_hdg.py, conv3d_mcs.py and calc_kappas.py which use the FlowTemplates.py infrastructure.</p>
SynthRAD2023 Grand Challenge dataset: synthetizing computed tomography for radiotherapy
<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.7260705</a> and a detailed description is offered at "synthRAD2023_dataset_description.pdf".</p> <p>The<strong> training datasets</strong> for Task1 is in Task1.zip, while for Task2 in Task2.zip. After unzipping, each Task is organized according to the following folder structure:</p> <p>Task1.zip/</p> <p>├── Task1</p> <p> ├── brain</p> <p> ├── 1Bxxxx</p> <p> ├── mr.nii.gz</p> <p> ├── ct.nii.gz</p> <p> └── mask.nii.gz</p> <p> ├── ...</p> <p>└── overview</p> <p> ├── 1_brain_train.xlsx</p> <p> ├── 1Bxxxx_train.png</p> <p> └── ... </p> <p> └── pelvis</p> <p> ├── 1Pxxxx</p> <p> ├── mr.nii.gz</p> <p> ├── ct.nii.gz</p> <p> ├── mask.nii.gz</p> <p> ├── ...</p> <p>└── overview</p> <p> ├── 1_pelvis_train.xlsx</p> <p> ├── 1Pxxxx_train.png</p> <p> └── ....</p> <p>Task2.zip/</p> <p>├──Task2</p> <p> ├── brain</p> <p> ├── 2Bxxxx</p> <p> ├── cbct.nii.gz</p> <p> ├── ct.nii.gz</p> <p> └── mask.nii.gz</p> <p> ├── ...</p> <p>└── overview</p> <p> ├── 2_brain_train.xlsx</p> <p> ├── 2Bxxxx_train.png</p> <p> └── ... </p> <p> └── pelvis</p> <p> ├── 2Pxxxx</p> <p> ├── cbct.nii.gz</p> <p> ├── ct.nii.gz</p> <p> ├── mask.nii.gz</p> <p>├── ...</p> <p>└── overview</p> <p> ├── 2_pelvis_train.xlsx</p> <p> ├── 2Pxxxx_train.png</p> <p> └── ....</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> <p>[Task] [Anatomy] [Center] [PatientID]</p> <p>1 B A 001</p> <p>In each patient folder, three files can be found: </p> <ul> <li> <p>ct.nii.gz: CT image </p> </li> <li> <p>mr.nii.gz or cbct.nii.gz (depending on the task): CBCT/MR image</p> </li> <li> <p>mask.nii.gz:image containing a binary mask of the dilated patient outline </p> </li> </ul> <p>For each task and anatomy, an overview folder is provided which contains the following files:</p> <ul> <li> <p>[task]_[anatomy]_train.xlsx: This file contains information about the image acquisition protocol for each patient.</p> </li> <li> <p>[task][anatomy][center][PatientID]_train.png: For each patient a png showing axial, coronal and sagittal slices of CBCT/MR, CT, mask and the difference between CBCT/MR and CT is provided. These images are meant to provide a quick visual overview of the data.</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 training 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>
Computational Data for "Synthesis, Structures and Photophysical Properties of Tetra- and Hexanuclear Zinc Complexes Supported by Tridentate Schiff Base Ligands"
<p>Computational data used for the article "Synthesis, Structures and Photophysical Properties of Tetra- and Hexanuclear Zinc Complexes Supported by Tridentate Schiff Base Ligands". This Includes the geometries, optimizations, and the TD-DFT calculations of all mentioned structures and simulated UV-Vis spectra.</p>
Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge Dataset (MICCAI 2023)
<p>Image quality assessment (IQA) is extremely important in computed tomography (CT) imaging, since it facilitates the optimization of radiation dose and the development of novel algorithms in medical imaging, such as restoration. In addition, since an excessive dose of radiation can cause harmful effects in patients, generating high- quality images from low-dose images is a popular topic in the medical domain. However, even though peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are the most widely used evaluation metrics for these algorithms, their correlation with radiologists’ opinion of the image quality has been proven to be insufficient in previous studies, since they calculate the image score based on numeric pixel values (1-3). In addition, the need for pristine reference images to calculate these metrics makes them ineffective in real clinical environments, considering that pristine, high-quality images are often impossible to obtain due to the risk posed to patients as a result of radiation dosage. To overcome these limitations, several studies have aimed to develop a no-reference novel image quality metric that correlates well with radiologists’ opinion on image quality without any reference images (2, 4, 5).</p> <p>Nevertheless, due to the lack of open-source datasets specifically for CT IQA, experiments have been conducted with datasets that differ from each other, rendering their results incomparable and introducing difficulties in determining a standard image quality metric for CT imaging. Besides, unlike real low-dose CT images with quality degradation due to various combinations of artifacts, most studies are conducted with only one type of artifact (e.g., low-dose noise (6-11), view aliasing (12), metal artifacts (13), scattering (14-16), motion artifacts (17-22), etc.). Therefore, this challenge aims to 1) evaluate various NR-IQA models on CT images containing complex noise/artifacts, 2) to compare their correlations with scores produced by radiologists, and 3) to grant insights into the determination of the best-performing metric of CT imaging in terms of correlating with the perception of radiologists’.</p> <p>Furthermore, considering that low-dose CT images are achieved by reducing the number of projections per rotation and by reducing the X-ray current, the combination of two major artifacts, namely the sparse view streak and noise generated by these methods, is dealt with in this challenge so that the best-performing IQA model applicable in real clinical environments can be verified.</p> <p> </p> <p><strong>Funding Declaration:</strong></p> <p>This research was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155966, Artificial Intelligence Convergence Innovation Human Resources Development (Ewha Womans University)), and by the National Research Foundation of Korea (NRF-2022R1A2C1092072), and by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711174276, RS-2020-KD000016).</p> <p> </p> <p><strong>References:</strong></p> <ol> <li>Lee W, Cho E, Kim W, Choi J-H. Performance evaluation of image quality metrics for perceptual assessment of low-dose computed tomography images. Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment: SPIE, 2022.</li> <li>Lee W, Cho E, Kim W, Choi H, Beck KS, Yoon HJ, Baek J, Choi J-H. No-reference perceptual CT image quality assessment based on a self-supervised learning framework. Machine Learning: Science and Technology 2022.</li> <li>Choi D, Kim W, Lee J, Han M, Baek J, Choi J-H. Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT. Machine Vision and Applications 2021;32(116):1-14.</li> <li>Pal D, Patel B, Wang A. SSIQA: Multi-task learning for non-reference CT image quality assessment with self-supervised noise level prediction. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI): IEEE, 2021; p. 1962-1965.</li> <li>Mittal A, Moorthy AK, Bovik AC. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 2012;21(12):4695-4708. doi: 10.1109/TIP.2012.2214050</li> <li>Lee J-YK, Wonjin; Lee, Yebin; Lee, Ji-Yeon; Ko, Eunji; Choi, Jang-Hwan. Unsupervised Domain Adaptation for Low-dose Computed Tomography Denoising. IEEE Access 2022.</li> <li>Jeon S-Y, Kim W, Choi J-H. MM-Net: Multi-frame and Multi-mask-based Unsupervised Deep Denoising for Low-dose Computed Tomography. IEEE Transactions on Radiation and Plasma Medical Sciences 2022.</li> <li>Kim W, Lee J, Kang M, Kim JS, Choi J-H. Wavelet subband-specific learning for low-dose computed tomography denoising. PloS one 2022;17(9):e0274308.</li> <li>Han M, Shim H, Baek J. Low-dose CT denoising via convolutional neural network with an observer loss function. Med Phys 2021;48(10):5727-5742. doi: 10.1002/mp.15161</li> <li>Kim B, Shim H, Baek J. Weakly-supervised progressive denoising with unpaired CT images. Med Image Anal 2021;71:102065. doi: 10.1016/j.media.2021.102065</li> <li>Wagner F, Thies M, Gu M, Huang Y, Pechmann S, Patwari M, Ploner S, Aust O, Uderhardt S, Schett G, Christiansen S, Maier A. Ultralow-parameter denoising: Trainable bilateral filter layers in computed tomography. Med Phys 2022;49(8):5107-5120. doi: 10.1002/mp.15718</li> <li>Kim B, Shim H, Baek J. A streak artifact reduction algorithm in sparse-view CT using a self-supervised neural representation. Med Phys 2022. doi: 10.1002/mp.15885</li> <li>Kim S, Ahn J, Kim B, Kim C, Baek J. Convolutional neural network-based metal and streak artifacts reduction in dental CT images with sparse-view sampling scheme. Med Phys 2022;49(9):6253-6277. doi: 10.1002/mp.15884</li> <li>Bier B, Berger M, Maier A, Kachelrieß M, Ritschl L, Müller K, Choi JH, Fahrig R. Scatter correction using a primary modulator on a clinical angiography Carm CT system. Med Phys 2017;44(9):e125-e137.</li> <li>Maul N, Roser P, Birkhold A, Kowarschik M, Zhong X, Strobel N, Maier A. Learning-based occupational x-ray scatter estimation. Phys Med Biol 2022;67(7). doi: 10.1088/1361-6560/ac58dc</li> <li>Roser P, Birkhold A, Preuhs A, Syben C, Felsner L, Hoppe E, Strobel N, Kowarschik M, Fahrig R, Maier A. X-Ray Scatter Estimation Using Deep Splines. IEEE Trans Med Imaging 2021;40(9):2272-2283. doi: 10.1109/TMI.2021.3074712</li> <li>Maier J, Nitschke M, Choi JH, Gold G, Fahrig R, Eskofier BM, Maier A. Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements. IEEE Trans Biomed Eng 2022;69(5):1608-1619. doi: 10.1109/TBME.2021.3123673</li> <li>Choi JH, Maier A, Keil A, Pal S, McWalter EJ, Beaupré GS, Gold GE, Fahrig R. Fiducial markerbased correction for involuntary motion in weightbearing Carm CT scanning of knees. II. Experiment. Med Phys 2014;41(6Part1):061902.</li> <li>Choi JH, Fahrig R, Keil A, Besier TF, Pal S, McWalter EJ, Beaupré GS, Maier A. Fiducial markerbased correction for involuntary motion in weightbearing Carm CT scanning of knees. Part I. Numerical modelbased optimization. Med Phys 2013;40(9):091905.</li> <li>Berger M, Muller K, Aichert A, Unberath M, Thies J, Choi JH, Fahrig R, Maier A. Marker-free motion correction in weight-bearing cone-beam CT of the knee joint. Med Phys 2016;43(3):1235-1248. doi: 10.1118/1.4941012</li> <li>Ko Y, Moon S, Baek J, Shim H. Rigid and non-rigid motion artifact reduction in X-ray CT using attention module. Med Image Anal 2021;67:101883. doi: 10.1016/j.media.2020.101883</li> <li>Preuhs A, Manhart M, Roser P, Hoppe E, Huang Y, Psychogios M, Kowarschik M, Maier A. Appearance Learning for Image-Based Motion Estimation in Tomography. IEEE Trans Med Imaging 2020;39(11):3667-3678. doi: 10.1109/TMI.2020.3002695</li> </ol>
Comparison between ozone column depths and methane lifetimes computed by 1-D and 3-D models at different atmospheric O2 Levels
<p>Recently, Cooke et al. (2022) used a 3-D coupled chemistry-climate model (WACCM6) to calculate ozone column depths at varied atmospheric O<sub>2</sub> levels. They argued that previous 1-D photochemical model studies, e.g., Segura et al. (2003), may have overestimated the ozone column depth at low pO<sub>2</sub>, and hence also overestimated the lifetime of methane. We have compared new simulations from an updated version of the Segura et al. model with those from WACCM6, together with some results from another 1-D and 3-D model. The discrepancy in ozone column depths is likely due to multiple interacting parameters, including lower boundary conditions, vertical and meridional transport rates, and different chemical mechanisms, especially the treatment of O<sub>2</sub> photolysis in the Schumann-Runge (SR) bands (175-205 nm). The discrepancy in tropospheric OH concentrations and methane lifetime between WACCM6 and the 1-D model at low pO<sub>2</sub> is reduced when absorption from CO<sub>2</sub> and H<sub>2</sub>O in this wavelength region is included in WACCM6. Including scattering in the SR bands may further reduce this difference. Resolving these issues can be accomplished by developing an accurate parameterization for O<sub>2</sub> photolysis in the SR bands and then repeating these calculations in the various models. Work is already underway to this end.</p>
Graft‐host coupling changes can lead to engraftment arrhythmia: A computational study
<p>This dataset contains examples and raw data related to the cited publication (doi: 10.1113/jp284244). Computational models derived from histological images are provided. Raw values in the data spreadsheet are given as fraction of simulations for a particular configureation that resulted in graft-initiated host excitation.</p>
Computational workflows for perovskites: Case study for lanthanide manganites
<p>Supplemental material for the above manuscript. Revised version (v2.0).</p> <p>Includes the complete code archive, including all Quantum ESPRESSO calculation input and output files, as well as postprocessing scripts used to generate the figures in this manuscript.</p>
Free Lunch in Evolutionary Embodied Computation in Modular Robotics
<p>We demonstrate, based on anecdotal experimental results, that physical constraints (e.g., in physics-based simulations of evolutionary robotics) can significantly increase the diversity of results obtained by evolutionary computation methods.</p>
Promiscuity and quantitative contribution of UGT2B17 in drug and steroid metabolism determined by experimental and computational approaches
<p>Input and parameter files used for the publication "Promiscuity and quantitative contribution of UGT2B17 in drug and steroid metabolism determined by experimental and computational approaches"</p> <p>#1. Smile notations of UGT2B17 substrates and non-substrates</p> <p>#2. Pharmacophore(.ph4) hypothesis files for representative compounds; Dicofenac, vorinostat and carvedilol.</p> <p>#3. Simulation input files - topology and molecular dynamics parameters (Vorinostat as a representative example).</p>
Datasets for publication: FREEDA: an automated computational pipeline guides experimental testing of protein innovation
<p>Supplementary materials related to the article: Dudka D, Akins RB, Lampson MA (2023) FREEDA: an automated computational pipeline guides experimental testing of protein innovation. Journal of Cell Biology.</p> <p> </p> <p>The file includes FREEDA pipeline validation results: orthologue detection, comparison with previously published datasets, analysis of rodent centromere proteins and additional analyses of KIF4A, KIF4B, histone H4, MICA, MICB, NUP73 and HERC5. Manually aligned structural prediction models for MIS18A, MIS18B, AURKC, CENP-O and CENP-P are also included.</p>
CARP: Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning
<p>The CARP dataset consists of 4,886 middle school computation-intensive algebra problems, and each problem is associated with a natural language solution and an annotated EFG.<br> Our annotated EFG explicitly depicts the step-by-step reasoning process of a math problem in a readable and concise format.</p>
2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 3,001-4,000 (reference reconstructions and segmentations)
<p>This upload contains the reference reconstructions and segmentation of slices 3,001 – 4,000 from the data collection described in</p> <p>Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “"2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6"><em>Sci Data</em> <strong>10</strong>, 576 (2023)</a> or <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a></p> <p>Abstract:<br> "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline."</p> <p>The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels, <span class="math-tex">\(74.8\mu m^2\)</span> each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete dataset can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8014758">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8014766">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8014787">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8014829">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8014874">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8014907">OOD</a>.<br> The reference reconstructions and segmentations can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8017583">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8017604">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8017612">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8017618">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8017624">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8017653">OOD</a>.</p> <p>The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on <a href="https://github.com/mbkiss/2DeteCTcodes">github</a>. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well.</p> <p>Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”.</p> <p>For more information or guidance in using the data collection, please get in touch with</p> <p> Maximilian.Kiss [at] cwi.nl</p> <p> Felix.Lucka [at] cwi.nl</p>
2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices OOD (reference reconstructions and segmentations)
<p>This upload contains the reference reconstructions and segmentation of the out-of-distribution slices (OOD) from the data collection described in</p> <p>Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “"2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6"><em>Sci Data</em> <strong>10</strong>, 576 (2023)</a> or <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a></p> <p>Abstract:<br> "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline."</p> <p>The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels, <span class="math-tex">\(74.8\mu m^2\)</span> each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete dataset can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8014758">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8014766">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8014787">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8014829">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8014874">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8014907">OOD</a>.<br> The reference reconstructions and segmentations can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8017583">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8017604">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8017612">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8017618">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8017624">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8017653">OOD</a>.</p> <p>The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on <a href="https://github.com/mbkiss/2DeteCTcodes">github</a>. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well.</p> <p>Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”.</p> <p>For more information or guidance in using the data collection, please get in touch with</p> <p> Maximilian.Kiss [at] cwi.nl</p> <p> Felix.Lucka [at] cwi.nl</p>
2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 1,001-2,000 (reference reconstructions and segmentations)
<p>This upload contains the reference reconstructions and segmentation of slices 1,001 – 2,000 from the data collection described in</p> <p>Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “"2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6"><em>Sci Data</em> <strong>10</strong>, 576 (2023)</a> or <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a></p> <p>Abstract:<br> "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline."</p> <p>The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels, <span class="math-tex">\(74.8\mu m^2\)</span> each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete dataset can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8014758">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8014766">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8014787">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8014829">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8014874">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8014907">OOD</a>.<br> The reference reconstructions and segmentations can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8017583">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8017604">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8017612">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8017618">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8017624">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8017653">OOD</a>.</p> <p>The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on <a href="https://github.com/mbkiss/2DeteCTcodes">github</a>. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well.</p> <p>Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”.</p> <p>For more information or guidance in using the data collection, please get in touch with</p> <p> Maximilian.Kiss [at] cwi.nl</p> <p> Felix.Lucka [at] cwi.nl</p>
2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 4,001-5,000
<p>This upload contains slices 4,001 – 5,000 from the data collection described in</p> <p>Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “"2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6"><em>Sci Data</em> <strong>10</strong>, 576 (2023)</a> or <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a></p> <p>Abstract:<br> "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline."</p> <p>The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels, <span class="math-tex">\(74.8\mu m^2\)</span> each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete dataset can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8014758">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8014766">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8014787">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8014829">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8014874">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8014907">OOD</a>.<br> The reference reconstructions and segmentations can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8017583">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8017604">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8017612">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8017618">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8017624">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8017653">OOD</a>.</p> <p>The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on <a href="https://github.com/mbkiss/2DeteCTcodes">github</a>. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well.</p> <p>Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”.</p> <p>For more information or guidance in using the data collection, please get in touch with</p> <p> Maximilian.Kiss [at] cwi.nl</p> <p> Felix.Lucka [at] cwi.nl</p>
2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 3,001-4,000
<p>This upload contains slices 3,001 – 4,000 from the data collection described in</p> <p>Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “"2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6"><em>Sci Data</em> <strong>10</strong>, 576 (2023)</a> or <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a></p> <p>Abstract:<br> "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline."</p> <p>The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels, <span class="math-tex">\(74.8\mu m^2\)</span> each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete dataset can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8014758">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8014766">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8014787">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8014829">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8014874">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8014907">OOD</a>.<br> The reference reconstructions and segmentations can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8017583">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8017604">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8017612">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8017618">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8017624">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8017653">OOD</a>.</p> <p>The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on <a href="https://github.com/mbkiss/2DeteCTcodes">github</a>. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well.</p> <p>Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”.</p> <p>For more information or guidance in using the data collection, please get in touch with</p> <p> Maximilian.Kiss [at] cwi.nl</p> <p> Felix.Lucka [at] cwi.nl</p>
2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 1-1,000
<p>This upload contains slices 1 – 1,000 from the data collection described in</p> <p>Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “"2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6"><em>Sci Data</em> <strong>10</strong>, 576 (2023)</a> or <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a></p> <p>Abstract:<br> "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline."</p> <p>The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels, <span class="math-tex">\(74.8\mu m^2\)</span> each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete dataset can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8014758">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8014766">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8014787">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8014829">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8014874">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8014907">OOD</a>.<br> The reference reconstructions and segmentations can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8017583">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8017604">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8017612">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8017618">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8017624">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8017653">OOD</a>.</p> <p>The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on <a href="https://github.com/mbkiss/2DeteCTcodes">github</a>. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well.</p> <p>Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”.</p> <p>For more information or guidance in using the data collection, please get in touch with</p> <p> Maximilian.Kiss [at] cwi.nl</p> <p> Felix.Lucka [at] cwi.nl</p>
2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 1-1,000 (reference reconstructions and segmentations)
<p>This upload contains the reference reconstructions and segmentation of slices 1 – 1,000 from the data collection described in</p> <p>Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “"2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6"><em>Sci Data</em> <strong>10</strong>, 576 (2023)</a> or <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a></p> <p>Abstract:<br> "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline."</p> <p>The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels, <span class="math-tex">\(74.8\mu m^2\)</span> each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete dataset can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8014758">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8014766">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8014787">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8014829">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8014874">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8014907">OOD</a>.<br> The reference reconstructions and segmentations can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8017583">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8017604">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8017612">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8017618">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8017624">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8017653">OOD</a>.</p> <p>The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on <a href="https://github.com/mbkiss/2DeteCTcodes">github</a>. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well.</p> <p>Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”.</p> <p>For more information or guidance in using the data collection, please get in touch with</p> <p> Maximilian.Kiss [at] cwi.nl</p> <p> Felix.Lucka [at] cwi.nl</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.