Find research datasets worth reusing

A metadata-based approach for research discipline prediction using machine learning techniques and distance metrics

The dataset is based on the paper:  Hoang-Son Pham, Hanne Poelmans and Amr Ali-Eldin ‘’A metadata-based approach for research discipline prediction using machine learning techniques and distance metrics’’, IEEE Access (2023). The dataset includes:  1. a list of project metadata extracted from FRIS portal 2. a list of VODS disciplines 3. a distance matrix   * Kindly refer to our paper for more details on the dataset. https://ieeexplore.ieee.org/document/10156853

opencc-zeroMay 2023View details →

dryad36/100

Data from: Behaviour-specific spatiotemporal patterns of habitat use by sea turtles revealed using biologging and supervised machine learning

<ol> <li>Conservation of threatened species and anthropogenic threat mitigation commonly rely on spatially managed areas selected according to habitat preference. Since the impact of threats can be behaviour-specific, such information could be incorporated into spatial management to improve conservation outcomes. However, collecting spatially explicit behavioural data is challenging.</li> <li>Using multi-sensor biologging tags containing high-resolution movement sensors (e.g., accelerometer, magnetometer, GPS) and animal-borne video cameras, combined with supervised machine learning, we developed a method to automatically identify and geolocate typically ambiguous behaviours for the poorly understood flatback turtle Natator depressus. Subsequently, we evaluated behaviour-specific spatiotemporal patterns of habitat use.</li> <li>Boosted regression trees successfully identified the presence of foraging and resting in 7074 dives (AUC > 0.9), using dive features representing characteristics of locomotory activity, body posture, and three-dimensional dive paths validated by ancillary video data. Foraging was characterised by dives with longer duration, variable depth, tortuous bottom phases; resting was characterised by dives with decreased locomotory activity and longer duration bottom phases.</li> <li>Foraging and resting showed minimal spatial segregation based on 50% and 95% utilisation distributions. Expected diel patterns of behaviour-specific habitat use were superseded by the extreme tides at the near-shore study site. Turtles rested in areas close to the subtidal and intertidal boundary within larger overlapping foraging areas, allowing efficient access to intertidal food resources upon inundation at high tides when foraging was ~25% more likely.</li> <li> Synthesis and applications: Using supervised machine learning and biologging tools, we show the potential for dynamic spatial management of flatback turtles to mitigate behaviour-specific threats by prioritising protection of important locations at pertinent times. Although results are a species-specific response to a super-tidal environment, our approach can be generalised to a broad range of taxa and study systems, facilitating a conceptual advance in spatial management.</li> </ol>

Comparison between ribosomal assembly and machine learning tools for microbial identification of organisms with different characteristics

DNABERT+DeLUCS_notebooks.zip <ul> <li>Code notebooks for running DNABERT and DeLUCS</li> </ul>   images-20230519T015235Z-001.zip <ul> <li>Heatmaps</li> <li>Factor plots</li> </ul>   Data-20230518T191203Z-003.zip <ul> <li>MBARC and Hot Springs datasets <ul> <li>Reference genomes</li> <li>16S sequences (barrnap)</li> </ul> </li> </ul>   hot-springs-reads.gz <ul> <li>Reads data for Hot Springs dataset</li> </ul>   mbarc-reads-download.txt <ul> <li>Reads data for MBARC dataset <ul> <li>Link to download from NCBI</li> </ul> </li> </ul>   assemblies.zip <ul> <li>Megahit and MetaSPAdes assemblies for both MBARC and Hot Springs</li> </ul>

opencc-by-4.0Apr 2023View details →

Generative Machine Learning for Detector Response Modeling with a Conditional Normalizing Flow

The samples are datasets used for testing in the scenarios: baseline (corr0.hdf5), correlation of 0.5 (corr50.hdf5), correlation of 1.0 (corr100.hdf5), and asymmetric detector responses (asymmetric.hdf5). Each file contains the conditional variables, generated and simulated detector responses, and generated and simulated reconstruction-level variables.

Data and code example for the article: "Massively parallel hybrid quantum-classical machine learning for kernelized time-series classification"

Data needed to reproduce the figures of <a href="https://arxiv.org/abs/2305.05881">https://arxiv.org/abs/2305.05881</a> and a simple code example of a quantum-convex-classical neural network used to train a sine versus cosine classification problem.

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 3,001-4,000 (reference reconstructions and segmentations)

This upload contains the reference reconstructions and segmentation of slices 3,001 – 4,000 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 OOD (reference reconstructions and segmentations)

This upload contains the reference reconstructions and segmentation of 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 1,001-2,000 (reference reconstructions and segmentations)

This upload contains the reference reconstructions and segmentation of 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

This upload contains 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

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 3,001-4,000

This upload contains slices 3,001 – 4,000 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 1-1,000

This upload contains slices 1 – 1,000 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 1-1,000 (reference reconstructions and segmentations)

This upload contains the reference reconstructions and segmentation of slices 1 – 1,000 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 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

A dataset on in-situ electromagnetic wave integrity control of selective laser fusion printed parts using Machine Learning

DeepSLM Dataset This dataset contains signals from metal parts printed in 3D using SLM (Selective laser melting) technology, produced as part of the DeepSLM project: In-situ monitoring of Selective-Laser-Melted Ti6Al4V Parts Using Eddy Current Testing and Machine Learning. Sensor and data collection An impedance-based non-destructive testing device is installed on the LPBF coating device. It is in the form of sensors with the following characteristics: Spectrum of analysis of the sensors <ul> <li>Minimal length: 3-5 mm</li> <li>Minimal width acquisition: 2-3 mm (Without borders effects)</li> <li>Minimal number of layers: 50 layers</li> <li>Signal minimal penetration depth (TiAl6V4/875kHz) = 1.5 mm</li> </ul> The sensors record the signal during printing, while simultaneously sending it to a computer via Bluetooth. The signals are first stored in the database. Next, a dataset is constructed from the extracted signals related to each part, and from the  added semi-automatic annotations which describe the porosity of the part and layers. Annotations In the annotation process, there are 2 different types of labels and a metadata summary for these prints: 1. Archimedean porosity measurement for the whole part 2. Layer porosity obtained after image processing applied on the resulting metallography images 3. Print-related metadata such as print parameters, part dimensions, layer thickness, etc Size: 66 parts Set of printing parameters: 250  -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Dataset organization The dataset is composed of 7 different printings and is organized as follows: - signals: contains collected signals - metallography: contains porosity extracted by metallography - archimedean: contains porosity extracted by archimedean weighing - metallography-images: contains metallography process images - metadata.csv: contains print metadata (print parameter, part name, strategy, etc.) Each printed part has a unique ID ranging from 0 to 65. Each sub-part has an ID which is the ID of the base part suffixed with the sub-part position: 14_1 is the first sub-part of part 14. This nomenclature is uniform throughout the dataset. The README.md file in the dataset provides more detailed information on the structure of the dataset and the format of each of the files making up the dataset.   Relative article <a title="https://doi.org/10.1007/978-3-031-47784-3_18" href="https://doi.org/10.1007/978-3-031-47784-3_18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-47784-3_18</a> Sallem, H., Ghorbel, H., Goffinet, E., Cinna, A., Pralong, J., Wicht, J., & Revaz, B. (2023, May). In-Situ Monitoring of Selective Laser Melted Ti–6Al–4V Parts Using Eddy Current Testing and Machine Learning. In Advances In Additive Manufacturing Conference (pp. 139-148). Cham: Springer Nature Switzerland.

opencc-zeroDec 2022View details →

Supporting data for "Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis"

Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naïve (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. MLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at <a href="https://github.com/FunctionalUrology/MLme">https://github.com/FunctionalUrology/MLme</a>.