Find research datasets worth reusing

opencc-by-nc-nd-4.0Jan 2019View details →

3D cranial landmark coordinates from the Athens Collection. A modern Greek population reference dataset for testing 3D-ID software

The present dataset comprises the landmark coordinates of 158 intact crania (80 males and 78 females) of adult individuals from the Athens Collection. The 3D coordinates of up to 34 landmarks have been extracted from high quality textured 3D models produced with photogrammetry. The dataset aims to evaluate the correct classification performance of 3D-ID software. Hence, the dataset contains the landmark 3D coordinates both in Meshlab's PickedPoints files (.pp) but also in 3D-ID's input text format (.3did). The dataset is accompanied by certain GNU Octave scripts and functions used for data conversion and integrity check. For more details see the Dataset Description pdf.

opencc-by-4.0Oct 2018View details →

From fine sand to boulders: examining the relationship between beach-face slope and sediment size. Dataset and references.

A collection of 2144 pairs of beach-face slope and median sediment size measurements gathered from 78 publications that covers the whole range of coastal sediments.   Size-Slope-Data-Points.ods : Size/slope values and their references. The 'Type' column separates beaches from boulder ridges. Size-Slope-References.ods : Details of each reference used.

opencc-by-4.0Aug 2024View details →

An analysis of peak fitting in reference material spectra for calibration of Raman spectroscopy instruments (Dataset)

tbd NeXus format

opencc-by-4.0Oct 2024View details →

Molecular surface coverage standards by reference-free GIXRF supporting SERS and SEIRA substrate benchmarking - Dataset

This is the dataset of "Molecular surface coverage standards by reference-free GIXRF supporting SERS and SEIRA substrate benchmarking".  <a href="https://doi.org/10.1515/nanoph-2024-0222" target="_blank" rel="noopener">https://doi.org/10.1515/nanoph-2024-0222</a> Part of this work was supported by the European project OpMetBat, code 21GRD01. The project has received funding from the European Partnership on Metrology, cofinanced from the the European Union's Horizon Europe Research and Innovation Programme, and by Participating States.

dryad36/100

Dataset from: A curated DNA barcode reference library for parasitoids of northern European cyclically outbreaking geometrid moths

Large areas of forests are annually damaged or destroyed by outbreaking insect pests. Understanding the factors that trigger and terminate such population eruptions has become crucially important, as plants, plant-feeding insects, and their natural enemies may respond differentially to the ongoing changes in the global climate. In northernmost Europe, climate-driven range expansions of the geometrid moths Epirrita autumnata and Operophtera brumata have resulted in overlapping and increasingly severe outbreaks. Delayed density-dependent responses of parasitoids are a plausible explanation for the ten-year population cycles of these moth species, but the impact of parasitoids on geometrid outbreak dynamics is unclear due to a lack of knowledge on the host ranges and prevalences of parasitoids attacking the moths in nature. To overcome these problems, we reviewed the literature on parasitism in the focal geometrid species in their outbreak range, and then constructed a DNA barcode reference library for all relevant parasitoid species based on reared specimens and sequences obtained from public databases. The combined parasitoid community of E. autumnata and O. brumata consists of 32 hymenopteran species, all of which can be reliably identified based on their barcode sequences. The curated barcode library presented here opens up new opportunities for estimating the abundance and community composition of parasitoids across populations and ecosystems based on mass barcoding and metabarcoding approaches. Such information can be used for elucidating the role of parasitoids in moth population control, possibly also for devising methods for reducing the extent, intensity, and duration of outbreaks.

opencc-zeroOct 2022View details →

opencc-by-4.0Feb 2023View details →

Pento-DIARef: A Diagnostic Dataset for Learning the Incremental Algorithm for Referring Expression Generation from Examples

We present a Diagnostic dataset of IA References in a Pentomino domain (Pento-DIARef) that ties extensional and intensional definitions more closely together, insofar as the latter is the generative process creating the former. We create a novel synthetic dataset of examples that pairs visual scenes with generated referring expressions; examine two variants of the dataset, representing two different ways to exemplify the underlying task; and evaluate an LSTM-based baseline, a transformer and a modified version with region embeddings on them. See https://github.com/clp-research/pento-diaref for more information.

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

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-4.0Aug 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-4.0Dec 2023View details →

Dataset for "Comprehensive Identification of NUMTs in the Human Reference Genome through Pan-Mitogenome"

存放"Comprehensive Identification of NUMTs in the Human Reference Genome through Pan-Mitogenome"文章中的相关数据。 包括blastn出来的原始output文件;mtDNA-like short segments fastq文件;ATAC-seq的fastq文件; 以及文章中提及的supplementary 表格和bed文件

Ultrasonic Pulse Transmission Tests: Datasets - Test Series 3, Reference Tests on Air

The test series was created to receive information about the impact of the behaviour of two different piezoelectric shear wave sensors on the measurement results of ultrasonic pulse transmission tests. The approach for the test series was to vary the parameters pulse-width and the shear-wave-sensor-type. This results in a two-dimensional test parameter grid (pulse-width versus shear-wave-sensor-type). Tests were performed for each grid point in order to receive information needed to optimize the pulse width, but also a possible dependency between material and sensor behaviour. The material tested was the air of the laboratory environment. The test method used was the ultrasonic pulse transmission method with combined compression- and shear wave measurements. All test data and metadata are summarized into datasets using GNU Octave's open binary file format.

Ultrasonic Pulse Transmission Tests: Datasets - Test Series 7, Reference Tests on Aluminium Cylinder

The test series was created in order to validate the stability and functionality of the test device and to create a data reference for metals (aluminium cylinder). The approach for the test series was to vary the parameters number-of-samples-recorded and pulse-voltage. This results in a two-dimensional test parameter grid. Tests were performed several times for each grid point (number-of-samples-recorded versus pulse-voltage) in order to receive statistical information about the stability of the test results. The material tested was an aluminium cylinder with a diameter of 50 millimetres and a height of 50 millimetres. The test method used was the ultrasonic pulse transmission method with combined compression- and shear wave measurements. All test data and metadata are summarized into datasets using GNU Octave's open binary file format.

Ultrasonic Pulse Transmission Tests: Datasets - Test Series 6, Reference Tests on Water

The test series was created in order to validate the stability and functionality of the test device and to create a data reference for liquid materials (water). The approach for the test series was to vary the parameters number-of-samples-recorded, pulse-voltage and distance-between-actuator-and-sensor. This results in a three-dimensional test parameter grid. Tests were performed several times for each grid point (number-of-samples-recorded versus pulse-voltage versus distance-between-actuator-and-sensor) in order to receive statistical information about the stability of the test results. The material tested was water from the tap of the laboratory water supply system. The test method used was the ultrasonic pulse transmission method with combined compression- and shear wave measurements. All test data and metadata are summarized into datasets using GNU Octave's open binary file format.

Ultrasonic Pulse Transmission Tests: Datasets - Test Series 5, Reference Tests on Air

The test series was created in order to validate the stability and functionality of the test device and to create a data reference for aeriform materials (air). The approach for the test series was to vary the parameters number-of-samples-recorded, pulse-voltage and distance-between-actuator-and-sensor. This results in a three-dimensional test parameter grid. Tests were performed several times for each grid point (number-of-samples-recorded versus pulse-voltage versus distance-between-actuator-and-sensor) in order to receive statistical information about the stability of the test results. The material tested was the air in the laboratory room. The test method used was the ultrasonic pulse transmission method with combined compression- and shear wave measurements. All test data and metadata are summarized into datasets using GNU Octave's open binary file format.