Find research datasets worth reusing

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices OOD

This upload contains the out-of-distribution slices (OOD) from the data collection described in 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">Sci Data 10, 576 (2023)</a> or  <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a> Abstract: "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." 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,  \(74.8\mu m^2\) 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. Please refer to the paper for all further technical details. 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>. 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>. 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. 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”. For more information or guidance in using the data collection, please get in touch with     Maximilian.Kiss [at] cwi.nl     Felix.Lucka [at] cwi.nl

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 2,001-3,000 (reference reconstructions and segmentations)

This upload contains the reference reconstructions and segmentation of slices 2,001 – 3,000 from the data collection described in 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">Sci Data 10, 576 (2023)</a> or  <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a> Abstract: "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." 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,  \(74.8\mu m^2\) 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. Please refer to the paper for all further technical details. 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>. 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>. 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. 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”. For more information or guidance in using the data collection, please get in touch with     Maximilian.Kiss [at] cwi.nl     Felix.Lucka [at] cwi.nl

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 2,001-3,000

This upload contains slices 2,001 – 3,000 from the data collection described in 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">Sci Data 10, 576 (2023)</a> or  <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a> Abstract: "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." 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,  \(74.8\mu m^2\) 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. Please refer to the paper for all further technical details. 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>. 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>. 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. 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”. For more information or guidance in using the data collection, please get in touch with     Maximilian.Kiss [at] cwi.nl     Felix.Lucka [at] cwi.nl

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 1,001-2,000

This upload contains slices 1,001 – 2,000 from the data collection described in 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">Sci Data 10, 576 (2023)</a> or  <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a> Abstract: "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." 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,  \(74.8\mu m^2\) 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. Please refer to the paper for all further technical details. 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>. 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>. 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. 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”. For more information or guidance in using the data collection, please get in touch with     Maximilian.Kiss [at] cwi.nl     Felix.Lucka [at] cwi.nl

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 4,001-5,000 (reference reconstructions and segmentations)

This upload contains the reference reconstructions and segmentation of slices 4,001 – 5,000 from the data collection described in 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">Sci Data 10, 576 (2023)</a> or  <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a> Abstract: "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." 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,  \(74.8\mu m^2\) 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. Please refer to the paper for all further technical details. 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>. 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>. 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. 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”. For more information or guidance in using the data collection, please get in touch with     Maximilian.Kiss [at] cwi.nl     Felix.Lucka [at] cwi.nl

International E- Conference on "Recent Trends in Chemical Science, Physical Science, Life Science and Computer Technology (ICRTCPLCT–2022)

International E- Conference on “Recent Trends in Chemical Science, Physical Science, Life Science and Computer Technology (ICRTCPLCT–2022)”  By Anjuman Islam Janjira Degree College of Science, Murud on 29th March 2022.

Brain volumes computed with FreeSurfer 6 for all ABIDE I subjects

# ABIDE I FreeSurfer 6 'brain volume' data This archive contains the following ABIDE I FreeSurfer 6 volumes: mri/brain.mgz, mri/brain_mask.mgz, mri/aseg.mgz, mri/wm.mgz. ## Credits This data is derived from the MRI scans of the ABIDE I dataset: * ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/ Quoting from that website:     "The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first      ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17      international sites, sharing previously collected resting state functional      magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets      made available for data sharing with the broader scientific community.      This effort yielded 1112 dataset, including 539 from individuals with      ASD and 573 from typical controls (ages 7-64 years, median 14.7 years      across groups). This aggregate was released in August 2012. Its      establishment demonstrated the feasibility of aggregating resting      state fMRI and structural MRI data across sites; the rate of these      data use and resulting publications (see Manuscripts) have shown its      utility for capturing whole brain and regional properties of the brain      connectome in Autism Spectrum Disorder (ASD). In accordance with      HIPAA guidelines and 1000 Functional Connectomes Project / INDI      protocols, all datasets have been anonymized, with no protected      health information included." Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014).           The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667. ## How this data was produced The following steps were used to create the data: * We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects). * We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.     - We did not run any quality metrics on the scans or exclude any subjects. ## Contained files * In order to reduce the size of this dataset, for each subject, we only included the following files:    - <subject>/mri/brain.mgz: the full brain, in FreeSurfer standard orientation ("conformed")     - <subject>/mri/brain_mask.mgz: binary mask separating brain from background     - <subject>/mri/aseg.mgz: brain segmentation, assigning voxels to regions. The region code for the voxel values can be found in the FreeSurferColorLUT.txt file that comes with FreeSurfer 6.     - <subject>/mri/wm.mgz: binary mask separating white matter from everything else All files are in FreeSurfer MGZ format. ## What is NOT contained * The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website. ## Author and License Note: For the authors of the original ABIDE I dataset, see the Credits section above. This mri volume data was created by:     Dr. Tim Schäfer     Postdoc Computational Neuroimaging     Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy     University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany     http://rcmd.org/ts The data is published under the following license: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code. See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

Surface transforms (sphere.reg) computed with FreeSurfer 6 for all ABIDE I subjects

# ABIDE I FreeSurfer 6 'surface transforms' data This archive contains files needed to map surface-based subject data to other subjects, templates or spaces. ## Credits This data is derived from the MRI scans of the ABIDE I dataset: * ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/ Quoting from that website:     "The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first      ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17      international sites, sharing previously collected resting state functional      magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets      made available for data sharing with the broader scientific community.      This effort yielded 1112 dataset, including 539 from individuals with      ASD and 573 from typical controls (ages 7-64 years, median 14.7 years      across groups). This aggregate was released in August 2012. Its      establishment demonstrated the feasibility of aggregating resting      state fMRI and structural MRI data across sites; the rate of these      data use and resulting publications (see Manuscripts) have shown its      utility for capturing whole brain and regional properties of the brain      connectome in Autism Spectrum Disorder (ASD). In accordance with      HIPAA guidelines and 1000 Functional Connectomes Project / INDI      protocols, all datasets have been anonymized, with no protected      health information included." Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014).           The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667. ## How this data was produced The following steps were used to create the data: * We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects). * We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.     - We did not run any quality metrics on the scans or exclude any subjects. ## Contained files * In order to reduce the size of this dataset, for each subject, we only included the following files:     - <subject>/surf/lh.sphere.reg: spherical registration information for left hemisphere     - <subject>/surf/rh.sphere.reg: spherical registration information for right hemisphere ## What is NOT contained * The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website. ## Author and License Note: For the authors of the original ABIDE I dataset, see the Credits section above. This data was created by:     Dr. Tim Schäfer     Postdoc Computational Neuroimaging     Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy     University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany     http://rcmd.org/ts The data is published under the following license: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code. See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

opencc-by-4.0Apr 2023View details →

Computational redesign of cytochrome P450 CYP102A1 for highly stereoselective omeprazole hydroxylation by UniDesign

To make it consistent with our manuscript, in this version of Zenodo dataset, all "conformers" have been changed to "poses" in the file names and the contents of the Perl scripts (in Scripts.zip).

opencc-by-4.0Dec 2022View details →

Computational 3D resolution enhancement for optical coherence tomography with a narrowband visible light source

This repository contains the code and data underlying the publication "Computational 3D resolution enhancement for optical coherence tomography with a narrowband visible light source" in Biomedical Optics Express 14, 3532-3554 (2023) (doi.org/10.1364/BOE.487345). The reader is free to use the scripts and data in this depository, as long as the manuscript is correctly cited in their work. For further questions, please contact the corresponding author.  Description of the code and datasets Table 1 describes all the Matlab and Python scripts in this depository. Table 2 describes the datasets. The input datasets are the phase corrected datasets, as the raw data is large in size and phase correction using a coverslip as reference is rather straightforward. Processed datasets are also added to the repository to allow for running only a limited number of scripts, or to obtain for example the aberration corrected data without the need to use python. Note that the simulation input data (input_simulations_pointscatters_SLDshape_98zf_noise75.mat) is generated with random noise, so if this is overwritten de results may slightly vary. Also the aberration correction is done with random apertures, so the processed aberration corrected data (exp_pointscat_image_MIAA_ISAM_CAO.mat and exp_leaf_image_MIAA_ISAM_CAO.mat) will also slightly change if the aberration correction script is run anew. The current processed datasets are used as basis for the figures in the publication. For details on the implementation we refer to the publication. <table> <caption>Table 1: The Matlab and Python scripts with their description</caption> <tbody> <tr> <td>Script name</td> <td>Description</td> </tr> <tr> <td>MIAA_ISAM_processing.m</td> <td>This scripts performs the DFT, RFIAA and MIAA processing of the phase-corrected data that can be loaded from the datasets. Afterwards it also applies ISAM on the DFT and MIAA data and plots the results in a figure (via the scripts plot_figure3, plot_figure5 and plot_simulationdatafigure).</td> </tr> <tr> <td>resolution_analysis_figure4.m</td> <td>This figure loads the data from the point scatterers (absolute amplitude data), seeks the point scatterrers and fits them to obtain the resolution data. Finally it plots figure 4 of the publication.</td> </tr> <tr> <td>fiaa_oct_c1.m, oct_iaa_c1.m, rec_fiaa_oct_c1.m, rfiaa_oct_c1.m </td> <td>These four functions are used to apply fast IAA and MIAA. See script MIAA_ISAM_processing.m for their usage.</td> </tr> <tr> <td>viridis.m, morgenstemning.m</td> <td>These scripts define the colormaps for the figures.</td> </tr> <tr> <td>plot_figure3.m, plot_figure5.m, plot_simulationdatafigure.m</td> <td>These scripts are used to plot the figures 3 and 5 and a figure with simulation data. These scripts are executed at the end of script MIAA_ISAM_processing.m.</td> </tr> <tr> <td>Python script: computational_adaptive_optics_script.py</td> <td>Python script that applied computational adaptive optics to obtain the data for figure 6 of the manuscript.</td> </tr> <tr> <td>Python script: zernike_functions2.py</td> <td>Python script that gives the values and carthesian derrivatives of the Zernike polynomials.</td> </tr> <tr> <td>figure6_ComputationalAdaptiveOptics.m</td> <td>Script that loads the CAO data that was saved in Python, analyzes the resolution, and plots figure 6.</td> </tr> <tr> <td>Python script: OCTsimulations_3D_script2.py</td> <td>Python script simulates OCT data, adds noise and saves it as .mat file for use in the matlab script above.</td> </tr> <tr> <td>Python script: OCTsimulations2.py</td> <td>Module that contains a python class that can be used to simulate 3D OCT datasets based on a Gaussian beam.</td> </tr> <tr> <td>Matlab toolbox DIPimage 2.9.zip</td> <td>Dipimage is used in the scripts. The toolbox can be downloaded online or this zip can be used.</td> </tr> </tbody> </table> <table> <caption>The datasets in this Zenodo repository</caption> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>input_leafdisc_phasecorrected.mat</td> <td>Phase corrected input image of the leaf disc (used in figure 5).</td> </tr> <tr> <td>input_TiO2gelatin_004_phasecorrected.mat</td> <td>Phase corrected input image of the TiO2 in gelatin sample.</td> </tr> <tr> <td>input_simulations_pointscatters_SLDshape_98zf_noise75</td> <td>Input simulation data that, once processed, is used in figure 4.</td> </tr> <tr> <td> exp_pointscat_image_DFT.mat exp_pointscat_image_DFT_ISAM.mat exp_pointscat_image_RFIAA.mat exp_pointscat_image_MIAA_ISAM.mat exp_pointscat_image_MIAA_ISAM_CAO.mat </td> <td>Processed experimental amplitude data for the TiO2 point scattering sample with respectively DFT, DFT+ISAM, RFIAA, MIAA+ISAM and MIAA+ISAM+CAO. These datasets are used for fitting in figure 4 (except for CAO), and MIAA_ISAM and MIAA_ISAM_CAO are used for figure 6.</td> </tr> <tr> <td> simu_pointscat_image_DFT.mat simu_pointscat_image_RFIAA.mat simu_pointscat_image_DFT_ISAM.mat simu_pointscat_image_MIAA_ISAM.mat </td> <td>Processed amplitude data from the simulation dataset, which is used in the script for figure 4 for the resolution analysis.</td> </tr> <tr> <td> exp_leaf_image_MIAA_ISAM.mat exp_leaf_image_MIAA_ISAM_CAO.mat </td> <td>Processed amplitude data from the leaf sample, with and without aberration correction which is used to produce figure 6.</td> </tr> <tr> <td> exp_leaf_zernike_coefficients_CAO_normal_wmaf.mat exp_pointscat_zernike_coefficients_CAO_normal_wmaf.mat </td> <td>Estimated Zernike coefficients and the weighted moving average of them that is used for the computational aberration correction. Some of this data is plotted in Figure 6 of the manuscript.</td> </tr> <tr> <td>input_zernike_modes.mat</td> <td>The reference Zernike modes corresponding to the data that is loaded to give the modes the proper name.</td> </tr> <tr> <td> exp_pointscat_MIAA_ISAM_complex.mat exp_leaf_MIAA_ISAM_complex </td> <td>Complex MIAA+ISAM processed data that is used as input for the computational aberration correction. </td> </tr> </tbody> </table>

dryad36/100

Data from: Quantum computation of frequency-domain molecular response properties using a three-qubit iToffoli gate

The quantum computation of molecular response properties on near-term quantum hardware is a topic of substantial interest. Computing these properties directly in the frequency domain is desirable, but the circuits require large depth if the typical hardware gate set consisting of single- and two-qubit gates is used. Here, we report the application of a high-fidelity multipartite gate, the iToffoli gate, to the computation of frequency-domain response properties of diatomic molecules. The iToffoli gate enables a ~50% reduction in circuit depth and ~40% reduction in circuit execution time compared to the traditional gate set. We show that the molecular properties obtained with the iToffoli gate exhibit comparable or better agreement with theory than those obtained with the native CZ gates. Our work is among the first demonstrations of the practical usage of a native multi-qubit gate in quantum simulation, with diverse potential applications to near-term quantum computation.

opencc-zeroJul 2023View details →

Datasets to compute species traits for Neotropical anurans

This dataset has harmonized data for modeling three species traits: phenology, physiology, and acoustic characteristics. The selected variables express intraspecific trait variation across the species' geographic range and provide a deeper understanding of the species-level capacity to deal with climate change. Besides, they supply data to document key, primarily unknown, life-history traits of a highly endangered group of vertebrates in a hyperdiverse region.

Dataset for the paper "How to Work on Equality and Inclusion when Introducing Computational Thinking and Educational Robotics in Early Childhood Education: A Systematic Review"

Resources for the Systematic Literature Review (SLR) about Computational Thinking and Educational Robotics in Early Childhood Education for fostering equality and inclusion. The SLR is related to the project "COEDUIN-Alfabetización digital y STEAM en edades tempranas: propuesta co-educativa inclusiva" funded by Fundación Caja Canarias and Fundación La Caixa (ref. 2020EDU08). The SLR covers papers in WoS and Scopus from 2011 to 2022.

A 3-dimensional histology computer model of malignant melanoma

This dataset contains 66 slices of a human melanoma cut in sequential order and the reconstructed model stored in the NIfTI file format. The model can be viewed in the web browser by accessing https://dbo-dkfz.github.io/niivue-ui-fork/ and opening the downloaded file.

Computer Code and Data - Determination of server location in emergency care systems: an index proposal using Data Envelopment Analysis and the Hypercube Queuing Model

Computer code and data related to the research project "Determination of server location in emergency care systems: an index proposal using Data Envelopment Analysis and the Hypercube Queuing Model".

MD preview for: Fighting Celiac Disease: Improvement of pH Stability of Cathepsin L In Vitro by Computational Design

Dataset structure: This is MD preview (some initial and final files, along with light versions of principal MD trajectories) for wild-type (WT) and acidophilic mutant (V277A) cathepsin L versions. Each folder contains three variants of pH calculations: <ul> <li>pH 7</li> <li>pH 2 (considering all ionizable residues)</li> <li>pH 2 (considering only His 275)</li> </ul> See paper text for details. Each subfolder contains seven MD-related files: <ul> <li>md.mdp: Gromacs options file</li> <li>topol.top: system topology, including ionization states of the charged residues</li> <li>em.gro: system coordinates before MD</li> <li>md.gro: system coordinates after MD</li> <li>md.tpr: Gromacs tpr file required for MD start</li> <li>md_view.gro: system coordinates after MD without water and ions. Required for MD preview using the next trajectory file</li> <li>md_view.xtc: "light" MD trajectory file without water and ions with coordinates saved each 100 ps (gmx trjconv "-dt 100" option). Use two latter files for MD preview in software like VMD or Pymol.</li> </ul>

opencc-by-4.0Aug 2023View details →

Supplemental Material: Computational Experiments in Computer Science Research: A literature survey

This laboratory package contains supplemental material from the study: "Supplemental Material: Computational Experiments in Computer Science Research: A literature survey". The supplementary material contains: 1. The list of the 26 primary studies. 2. The .xlsx file of the dataset used to analyze the RQ. 3. The list of figures published in the scientific article.

opencc-by-4.0Aug 2023View details →

Data and software for "Mental Tasks for Controlling Cursor Movements in a Brain Computer Interface"

All data for the experimental subjects on the examined mental tasks is contained in the "BCI_IFS_RECORDING_2012MARCH~APRIL_GroupIII" Zip folder. The software that was used to process the data is contained in the "IETE3.2_CROSS_VALID_AUTO" Zip folder.

opencc-by-4.0Mar 2023View details →

Exploring the effect of microstructure and surface recombination on hydrogen effusion in Zn-Ni coated martensitic steels by advanced computational modelling

Raw data pertaining to the publication "Exploring the effect of microstructure and surface recombination on hydrogen effusion in Zn-Ni coated martensitic steels by advanced computational modelling".