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opencc-zeroMay 2023View details →

Mesopelagic fish reference specimen photographic library

Mesopelagic fishes are a crucial component of the world's oceans in terms of their abundance, biomass, and ecosystem function. These fishes are important contributors to the biological carbon pump via their feeding and behaviors, whereby they facilitate the transfer of carbon from shallow waters to the deep sea. Several species undertake diel vertical migration, feeding in shallower waters at night and moving to deeper waters during the day. This process actively expedites the downward flux of carbon. However, carbon budgets and climate models require accurate information regarding the depth distributions and migration patterns of these fishes, and environmental DNA (eDNA) analyses can provide this information. Here, we utilize eDNA approaches, generating taxonomically-informative COI and 12S reference barcodes for 80 species of mesopelagic fishes, which can be used to for species-level identification of eDNA sequences. Using these, along with a publicly available barcode database, we compare results from eDNA analysis with traditional net sampling and explore the ability of eDNA techniques to detect diel vertical migration in fishes from samples collected in Northwest Atlantic Slope Water. We found that eDNA and net samples often resulted in different species identifications, demonstrating that eDNA can detect species that would otherwise be missed with traditional methods. In our eDNA samples, we also detected more species (12) in our shallowest depth category (0–100 m) from nighttime samples than from daytime samples (3). This is consistent with increased diversity in shallow waters at night due to diel vertical migration. Based on the variability observed in sample duplicates, we suggest that future mesopelagic eDNA studies incorporate larger sample volumes and scaled-up sampling efforts. We also note the potential for eDNA analysis to address ecological questions such as predator-prey relationships and identification of foraging hotspots, yielding insights into carbon flow through the ocean's midwaters.

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 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 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 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

opencc-zeroJun 2023View details →

Reference data and simulated communities for 16S rRNA GCN prediction

16S rRNA gene has been widely used in microbial diversity studies to determine the community composition and structure. 16S rRNA gene copy number (16S GCN) varies among microbial species and this variation introduces biases to the relative cell abundance estimated using 16S rRNA read counts. To correct the biases, methods (e.g., PICRUST2) have been developed to predict 16S GCN. 16S GCN predictions come with inherent uncertainty, which is often ignored in the downstream analyses. However, a recent study suggests that the uncertainty can be so great that copy number correction is not justified in practice. Despite the significant implications in 16S rRNA-based microbial diversity studies, the uncertainty associated with 16S GCN predictions has not been well characterized. Here we develop a novel method to better model and capture the inherent uncertainty. Using cross-validation, we show that our method provides robust confidence estimates for the GCN predictions and outperforms PICRUST2 in both precision and recall. We found that 16S GCN correction should improve compositional and functional profiles estimated using 16S rRNA reads. On the other hand, we found that GCN variation has limited impacts on PCoA, PERMANOVA and random forest test, and 16S rRNA GCN correction is unnecessary in beta-diversity analyses.

Results from participants in interlaboratory analysis of a candidate of reference material for hydrogen cryoadsorption

Isothermal data from participants in an interlaboratory analysis of adsorption at 77 K of a candidate reference material for hydrogen cryoadsorption in the framework of the MefHySto project (Metrology for advance hydrogen storage solutions).

opencc-zeroJun 2023View details →

Creating, curating, and evaluating a mitogenomic reference database to improve regional species identification using environmental DNA

Species detection using eDNA is revolutionizing global capacity to monitor biodiversity. However, the lack of regional, vouchered, genomic sequence information—especially sequence information that includes intraspecific variation—creates a bottleneck for management agencies wanting to harness the complete power of eDNA to monitor taxa and implement eDNA analyses. eDNA studies depend upon regional databases of mitogenomic sequence information to evaluate the effectiveness of such data to detect and identify taxa. We created the Oregon Biodiversity Genome Project to create a database of complete, nearly error-free mitogenomic sequences for all of Oregon's fishes. We have successfully assembled the complete mitogenomes of 313 specimens of freshwater, anadromous, and estuarine fishes representing 24 families, 55 genera, and 129 species and lineages. Comparative analyses of these sequences illustrate that many regions of the mitogenome are taxonomically informative, that the short (~150 bp) mitochondrial "barcode" regions typically used for eDNA assays do not consistently diagnose for species, and that complete single or multiple genes of the mitogenome are preferable for identifying Oregon's fishes. This project provides a blueprint for other researchers to follow as they build regional databases, illustrates the taxonomic value and limits of complete mitogenomic sequences, and offers clues as to how current eDNA assays and environmental genomics methods of the future can best leverage this information.

Validation of references provided by ChatGPT

Validation of references provided by ChatGPT for the generation of communication strategies for the long-term involvement of participants in Citizen Science projects. Article: Unlocking long-term engagement with citizen science: communication strategies driven by complex thinking under an AI-assisted approach.

European Reference Genome Atlas Community - Phase 1 Members - 2020-2023

This dataset presents a comprehensive list of members registered as part of the European Reference Genome Atlas (ERGA, erga-biodiversity.eu) Community during ERGA Phase 1, which spanned from 2020 to 2023. The dataset includes information on the various roles undertaken by these members, particularly those who played a key role in establishing ERGA. Founding members are individuals who joined ERGA prior to the first leadership elections in February 2021, and they were instrumental in building the initial structure of ERGA. Some of these founding members were also involved in the establishment of different ERGA Committees. The dataset further includes details about the current and former core members and chairs of the ERGA committees. Additionally, it provides a list of the current (2023) and former Council members, along with the countries or regions they represent. The dataset is organised in alphabetical order for ease of reference. The co-authors of this dataset encompass both current and former ERGA Council members, listed in alphabetical order, and the current ERGA chair, as the last author.

Massive Chinese domestic pigs provide missing sequences in reference genome and reveal non-coding sequence variations regulating gene expression across Eurasian boars

This dataset contains novel sequences in Chinese domestic pigs but is absent in Sscrofa 11.1 reference genome. The detailed information for each file is recorded in the README file.

China Seismological Reference Model (CSRM) - Models and Datasets

Reference Models: <ul> <li>CSRM-1.0 [<a href="https://doi.org/10.5281/zenodo.11098135">Link</a>]</li> </ul> Pre-CSRM and CSRM-derived models: <ul> <li>Seismic model of the shallow crust in continental China [<a href="https://doi.org/10.5281/zenodo.8099245">Link</a>]</li> <li>An uppermost mantle seismic Pn-velocity in continental China [<a href="https://doi.org/10.5281/zenodo.8112759">Link</a>]</li> <li>Crustal average Vp/Vs model in continental China [<a href="../records/12188026">Link</a>]</li> <li>A composition model of crust beneath continental China [<a href="https://doi.org/10.5281/zenodo.11107654">Link</a>]</li> </ul> Database (Level 2): Level 2 database contains processed seismic data and seismic constraints used for the construction of the "China Seismological Reference Model". The Level 2 data are for personal use only. They should not be redistributed and should not be used for any commercial purposes. <ul> <li>P-wave polarization angle [<a href="https://doi.org/10.5281/zenodo.8099308">Link</a>]</li> <li>Short-period Rayleigh wave ellipticity (4 - 8 s) [<a href="https://doi.org/10.5281/zenodo.8101105">Link</a>]</li> <li>Stacked receiver function [<a href="https://doi.org/10.5281/zenodo.8101006">Link</a>]</li> <li>Pn travel time [<a href="https://doi.org/10.5281/zenodo.8112291">Link</a>]</li> <li>Pn travel time in the crust beneath station [<a href="https://doi.org/10.5281/zenodo.8121110">Link</a>]</li> <li>Pn travel time in the crust beneath seismic event [<a href="https://doi.org/10.5281/zenodo.8269721">Link</a>]</li> <li>Epicenter location of event initiation point [<a href="https://doi.org/10.5281/zenodo.8121146">Link</a>]</li> <li>Long-period Rayleigh wave ellipticity (20 - 80 s) [<a href="https://doi.org/10.5281/zenodo.11098003">Link</a>]</li> <li>Isotropic receiver function (2 - 12 s) [<a href="https://doi.org/10.5281/zenodo.11096746">Link</a>]</li> <li>Inter-station empirical Green's functions [<a href="https://doi.org/10.5281/zenodo.13764584">Link</a>]</li> <li>Event-station Rayleigh wave phase and group velocity dispersion curves [<a href="https://doi.org/10.5281/zenodo.11107377">Link</a>]</li> <li>Rayleigh wave phase/group velocity maps (8 - 70 s) [<a href="https://doi.org/10.5281/zenodo.11107525">Link</a>]</li> <li>Raw receiver functions (0 - 35 s) [<a href="../records/12179171">Link</a>]</li> <li>Seismometer orientation measurements [<a href="https://zenodo.org/records/14170435">Link</a>]</li> </ul> Database (Level 1) [<a href="http://chinageorefmodel.org/wp-content/uploads/2017/04/%E4%B8%AD%E5%9B%BD%E5%8C%BA%E5%9F%9F%E5%9C%B0%E9%9C%87%E5%AD%A6%E5%8F%82%E8%80%83%E6%A8%A1%E5%9E%8B%E8%B5%84%E6%96%99%E7%AE%A1%E7%90%86%E8%A7%84%E5%88%99.pdf">Policy of data usage</a>] Level 1 database contains the original seismic data that Level 2 datasets were derived from. <ul> <li>P-wave polarization angle <a href="https://www.cenc.ac.cn/">[Link</a>]</li> <li>Short-period Rayleigh wave ellipticity (4 - 8 s) [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Stacked receiver function [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Pn travel time [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Pn travel time in the crust beneath station [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Pn travel time in the crust beneath seismic event [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Epicenter location of event initiation point [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Long-period Rayleigh wave ellipticity (20 - 80 s) [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Isotropic receiver function (2 - 12 s) [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Inter-station empirical Green's functions [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Event-station Rayleigh wave phase and group velocity dispersion curves [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Rayleigh wave phase/group velocity maps (8 - 70 s) [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Raw receiver functions (0 - 35 s) [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Seismometer orientation measurements [<a href="https://www.cenc.ac.cn/">Link</a>]</li> </ul> Citation of the Level 2 database: Wen, L. X., and Yu, S. (2023). The China Seismological Reference Model project. Earth Planet. Phys., 7(5), 521–532. doi: <a href="http://dx.doi.org/10.26464/epp2023078" target="_blank" rel="noopener">10.26464/epp2023078</a> _______________________________________________________________________________________________________________________ 参考模型: <ul> <li>CSRM-1.0 [<a href="https://doi.org/10.5281/zenodo.11098135">链接</a>]</li> </ul> 预构建模型和导出模型: <ul> <li>中国大陆浅层地壳地震学模型 [<a href="https://doi.org/10.5281/zenodo.8099245">链接</a>]</li> <li>中国大陆上地幔顶部 Pn 速度模型 [<a href="https://doi.org/10.5281/zenodo.8112759">链接</a>]</li> <li>中国大陆地壳平均 Vp/Vs 模型 [<a href="../records/12188026">链接</a>]</li> <li>中国大陆地壳成分模型 [<a href="https://doi.org/10.5281/zenodo.11107654">链接</a>]</li> </ul> Level 2 数据库: Level 2 数据库包含经过处理的地震数据和用于构建 “中国区域地震学参考模型” 的地震学约束。Level 2 数据库仅限于个人使用,不得分发和用于任何商业目的。 <ul> <li>P 波偏振角度 [<a href="https://doi.org/10.5281/zenodo.8099308">链接</a>]</li> <li>短周期瑞利波椭率 (4 - 8 s) [<a href="https://doi.org/10.5281/zenodo.8101105">链接</a>]</li> <li>叠加接收函数 [<a href="https://doi.org/10.5281/zenodo.8101006">链接</a>]</li> <li>Pn 走时 [<a href="https://doi.org/10.5281/zenodo.8112291">链接</a>]</li> <li>台站下方壳内 Pn 波走时 [<a href="https://doi.org/10.5281/zenodo.8121110">链接</a>]</li> <li>震源下方壳内 Pn 波走时 [<a href="http://doi.org/10.5281/zenodo.8269721">链接</a>]</li> <li>地震起破点水平位置 [<a href="https://doi.org/10.5281/zenodo.8121146">链接</a>]</li> <li>长周期瑞利波椭率 (20 - 80 s) [<a href="https://doi.org/10.5281/zenodo.11098003">链接</a>]</li> <li>各向同性接收函数 (2 - 12 s) [<a href="https://doi.org/10.5281/zenodo.11096746">链接</a>]</li> <li>台站间经验格林函数 [<a href="https://doi.org/10.5281/zenodo.13764584">链接</a>]</li> <li>事件-台站间瑞利波相/群速度频散曲线 [<a href="https://doi.org/10.5281/zenodo.11107377">链接</a>]</li> <li>瑞利波相/群速度图 (8 - 70 s) [<a href="https://doi.org/10.5281/zenodo.11107525">链接</a>]</li> <li>原始接收函数 (0 - 35 s) [<a href="../records/12179171">链接</a>]</li> <li>地震计方位测量 [<a href="https://zenodo.org/records/14170435">链接</a>]</li> </ul> Level 1 数据库 [<a href="http://chinageorefmodel.org/wp-content/uploads/2017/04/%E4%B8%AD%E5%9B%BD%E5%8C%BA%E5%9F%9F%E5%9C%B0%E9%9C%87%E5%AD%A6%E5%8F%82%E8%80%83%E6%A8%A1%E5%9E%8B%E8%B5%84%E6%96%99%E7%AE%A1%E7%90%86%E8%A7%84%E5%88%99.pdf">数据使用政策</a>] Level 1 数据库包含 Level 2 数据库中用到的原始地震数据。 <ul> <li>P 波偏振角度 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>短周期瑞利波椭率 (4 - 8 s) [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>叠加接收函数 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>Pn 走时 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>台站下方壳内 Pn 波走时 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>震源下方壳内 Pn 波走时 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>地震起破点水平位置 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>长周期瑞利波椭率 (20 - 80 s) [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>各向同性接收函数 (2 - 12 s) [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>台站间经验格林函数 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>事件-台站间瑞利波相/群速度频散曲线 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>瑞利波相/群速度图 (8 - 70 s) [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>原始接收函数 (0 - 35 s) [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>地震计方位测量 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> </ul> Level 2 数据库引用: Wen, L. X., and Yu, S. (2023). The China Seismological Reference Model project. Earth Planet. Phys., 7(5), 521–532. doi: <a href="http://dx.doi.org/10.26464/epp2023078" target="_blank" rel="noopener">10.26464/epp2023078</a>

opencc-by-4.0Nov 2021View details →

SARS-CoV-2 Viral Samples and Reference Genome for Galaxy Training Network SARS with Galaxy on AnVIL Tutorial

In the lab activity, we'll see if there are genomic differences in the collected sample compared to the original SARS-CoV-2 genome (the reference). We need three files to do this: <ul> <li>SARS-CoV-2_reference_genome.fasta : the reference genome</li> <li>VA_sample_forward_reads.fastq.gz: 1 of 2 raw read data files</li> <li>VA_sample_reverse_reads.fastq.gz: 2 of 2 raw read data files</li> </ul> The sample for this activity was derived from data collected at Virginia Commonwealth University in Richmond, VA. The researchers collected the sample with the goal of being able to track the spread and evolution of this virus state-wide, nationally, and internationally. You can download the original data <a href="https://www.ncbi.nlm.nih.gov/sra/?term=XGTK449087">here</a>. You can download the reference genome <a href="https://www.ncbi.nlm.nih.gov/nuccore/1798174254">here</a>.

opencc-by-4.0Jul 2023View details →

Candidate High-Resolution Mass Spectrometry-Based Reference Method for the Quantification of Procalcitonin in Human Serum Using a Characterized Recombinant Protein as a Primary Calibrator

Dataset related to Huu-Hien Huynh, Vincent Delatour, Maxence Derbez-Morin, Qinde Liu, Amandine Boeuf, and Joëlle Vinh, (2022) Candidate High-Resolution Mass Spectrometry-Based Reference Method for the Quantification of Procalcitonin in Human Serum Using a Characterized Recombinant Protein as a Primary Calibrator. Anal. Chem. 2022, 94, 10, 4146–4154.

opencc-by-4.0Aug 2023View details →

Lineage And Outgroup Reference Set

This is a reference set that includes genomes of specific lineages as well as outgroups for the building of the phylogenetic trees that are  produced by the MAGMA pipeline

opencc-by-4.0Aug 2023View details →

A reference dataset of global gridded average-state specific yield

The uploaded data is related with our manuscript "A reference dataset of global gridded average-state specific yield".

opencc-by-4.0Jul 2023View details →

Reference atlases used in ProjectSVR paper

Reference atlases used in ProjectSVR paper.