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209 results for “cylinders”
Numerical simulation data of a two-dimensional flow around a fixed circular cylinder
<p>Numerical results of a two-dimensional flow past a fixed circular cylinder in the vortex shedding regime. The solver is Cadyf, an in-house fluid-structure interaction code using finite element method (<a href="https://doi.org/10.1016/j.jcp.2008.11.032">doi.org/10.1016/j.jcp.2008.11.032</a>). The Reynolds number is Re = 100. The boundary conditions and numerical integration are described in <a href="https://doi.org/10.1017/jfm.2021.252">doi.org/10.1017/jfm.2021.252</a>.</p> <p>The fixed_cylinder_atRe100 raw file contains the values of the flow velocity, pressure, and node coordinates at each time step. Tables of the results can be extracted using the file processing code in text_flow.py.</p> <p>The Re100_fixe00.reactions raw file contains the values of the total force and torque applied on the cylinder at each time step. Tables of the results can be extracted using the file processing code in reactions_process.py.</p>
Images of Cylinders Transported on a Conveyor Belt - Recording 11
<p>This data set comprises images of cylinders on a conveyor belt. The images were recorded on the small-scale optical belt sorter Tablesort. A thorough description of the Tablesort system can be found in</p> <ul> <li><em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer,</em> <strong>Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking</strong>, Transactions on Industrial Electronics, February 2020.</li> <li>See also the <a href="https://www.iosb.fraunhofer.de/en/projects-and-products/inside-schuettgut.html">project website</a>.</li> </ul> <p>This dataset is part of a batch of recordings on optical sorters. Please use the search function with the keyword "Tobias Hornberger" (in quotes) to find them or use the list at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a> (conveyor belt data sets only).</p> <p>The camera was recorded on a Bonito CL-400C. The calibration image for the extrinsic parameters can be found in calibration_extrinsics.png for the extrinsics and calibration_color.png for the color calibration. Please see the debayer script on <a href="https://github.com/geomai/debayer_bonito">GitHub</a><strong>.</strong> Each pixel is approximately 0.056 mm long in world coordinates. The frame rate is 192.9 Hz.</p> <p>Algorithms for two key challenges can be developed and evaluated on the data sets:</p> <ol> <li>Multitarget tracking for predicting the particle’s motion. This can be used to enhance the separation of optical sorters. For further details on this, see the publications <ul> <li><em>Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer,</em><br> <strong>TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials,</strong><br> Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015.</li> <li><em>Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer,</em><br> <strong>Predictive Tracking with Improved Motion Models for Optical Belt Sorting</strong>,<br> at – Automatisierungstechnik, April 2020.</li> </ul> </li> <li>Classification of particles. The classification may use a multitarget tracker to accumulate visual features over time. One can also use the information on the trajectory to classify the particles. For information on this, refer to <ul> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information,</strong><br> Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany, March 2017.</li> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Motion-Based Material Characterization in Sensor-Based Sorting,</strong> <br> tm – Technisches Messen, De Gruyter, October 2017.</li> </ul> </li> </ol> <p><br> To this date, publications that used these data include</p> <ul> <li><em>Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck,</em><br> <strong>Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking</strong>,<br> Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, September 2020.</li> </ul> <p>CSV-files with already associated particle tracks are available at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a>.</p> <p><strong>Acknowledgment</strong></p> <p>The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Energy on the basis of a resolution of the German Bundestag.</p>
Images of Cylinders Transported on a Conveyor Belt - Recording 8
<p>This data set comprises images of cylinders on a conveyor belt. The images were recorded on the small-scale optical belt sorter Tablesort. A thorough description of the Tablesort system can be found in</p> <ul> <li><em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer,</em> <strong>Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking</strong>, Transactions on Industrial Electronics, February 2020.</li> <li>See also the <a href="https://www.iosb.fraunhofer.de/en/projects-and-products/inside-schuettgut.html">project website</a>.</li> </ul> <p>This dataset is part of a batch of recordings on optical sorters. Please use the search function with the keyword "Tobias Hornberger" (in quotes) to find them or use the list at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a> (conveyor belt data sets only).</p> <p>The camera was recorded on a Bonito CL-400C. The calibration image for the extrinsic parameters can be found in calibration_extrinsics.png for the extrinsics and calibration_color.png for the color calibration. Please see the debayer script on <a href="https://github.com/geomai/debayer_bonito">GitHub</a><strong>.</strong> Each pixel is approximately 0.056 mm long in world coordinates. The frame rate is 192.9 Hz.</p> <p>Algorithms for two key challenges can be developed and evaluated on the data sets:</p> <ol> <li>Multitarget tracking for predicting the particle’s motion. This can be used to enhance the separation of optical sorters. For further details on this, see the publications <ul> <li><em>Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer,</em><br> <strong>TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials,</strong><br> Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015.</li> <li><em>Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer,</em><br> <strong>Predictive Tracking with Improved Motion Models for Optical Belt Sorting</strong>,<br> at – Automatisierungstechnik, April 2020.</li> </ul> </li> <li>Classification of particles. The classification may use a multitarget tracker to accumulate visual features over time. One can also use the information on the trajectory to classify the particles. For information on this, refer to <ul> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information,</strong><br> Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany, March 2017.</li> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Motion-Based Material Characterization in Sensor-Based Sorting,</strong> <br> tm – Technisches Messen, De Gruyter, October 2017.</li> </ul> </li> </ol> <p><br> To this date, publications that used these data include</p> <ul> <li><em>Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck,</em><br> <strong>Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking</strong>,<br> Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, September 2020.</li> </ul> <p>CSV-files with already associated particle tracks are available at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a>.</p> <p><strong>Acknowledgment</strong></p> <p>The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Energy on the basis of a resolution of the German Bundestag.</p>
Images of Cylinders Transported on a Conveyor Belt - Recording 9
<p>This data set comprises images of cylinders on a conveyor belt. The images were recorded on the small-scale optical belt sorter Tablesort. A thorough description of the Tablesort system can be found in</p> <ul> <li><em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer,</em> <strong>Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking</strong>, Transactions on Industrial Electronics, February 2020.</li> <li>See also the <a href="https://www.iosb.fraunhofer.de/en/projects-and-products/inside-schuettgut.html">project website</a>.</li> </ul> <p>This dataset is part of a batch of recordings on optical sorters. Please use the search function with the keyword "Tobias Hornberger" (in quotes) to find them or use the list at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a> (conveyor belt data sets only).</p> <p>The camera was recorded on a Bonito CL-400C. The calibration image for the extrinsic parameters can be found in calibration_extrinsics.png for the extrinsics and calibration_color.png for the color calibration. Please see the debayer script on <a href="https://github.com/geomai/debayer_bonito">GitHub</a><strong>.</strong> Each pixel is approximately 0.056 mm long in world coordinates. The frame rate is 192.9 Hz.</p> <p>Algorithms for two key challenges can be developed and evaluated on the data sets:</p> <ol> <li>Multitarget tracking for predicting the particle’s motion. This can be used to enhance the separation of optical sorters. For further details on this, see the publications <ul> <li><em>Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer,</em><br> <strong>TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials,</strong><br> Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015.</li> <li><em>Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer,</em><br> <strong>Predictive Tracking with Improved Motion Models for Optical Belt Sorting</strong>,<br> at – Automatisierungstechnik, April 2020.</li> </ul> </li> <li>Classification of particles. The classification may use a multitarget tracker to accumulate visual features over time. One can also use the information on the trajectory to classify the particles. For information on this, refer to <ul> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information,</strong><br> Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany, March 2017.</li> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Motion-Based Material Characterization in Sensor-Based Sorting,</strong> <br> tm – Technisches Messen, De Gruyter, October 2017.</li> </ul> </li> </ol> <p><br> To this date, publications that used these data include</p> <ul> <li><em>Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck,</em><br> <strong>Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking</strong>,<br> Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, September 2020.</li> </ul> <p>CSV-files with already associated particle tracks are available at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a>.</p> <p><strong>Acknowledgment</strong></p> <p>The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Energy on the basis of a resolution of the German Bundestag.</p>
Images of Cylinders Transported on a Conveyor Belt - Recording 7
<p>This data set comprises images of cylinders on a conveyor belt. The images were recorded on the small-scale optical belt sorter Tablesort. A thorough description of the Tablesort system can be found in</p> <ul> <li><em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer,</em> <strong>Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking</strong>, Transactions on Industrial Electronics, February 2020.</li> <li>See also the <a href="https://www.iosb.fraunhofer.de/en/projects-and-products/inside-schuettgut.html">project website</a>.</li> </ul> <p>This dataset is part of a batch of recordings on optical sorters. Please use the search function with the keyword "Tobias Hornberger" (in quotes) to find them or use the list at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a> (conveyor belt data sets only).</p> <p>The camera was recorded on a Bonito CL-400C. The calibration image for the extrinsic parameters can be found in calibration_extrinsics.png for the extrinsics and calibration_color.png for the color calibration. Please see the debayer script on <a href="https://github.com/geomai/debayer_bonito">GitHub</a><strong>.</strong> Each pixel is approximately 0.056 mm long in world coordinates. The frame rate is 192.9 Hz.</p> <p>Algorithms for two key challenges can be developed and evaluated on the data sets:</p> <ol> <li>Multitarget tracking for predicting the particle’s motion. This can be used to enhance the separation of optical sorters. For further details on this, see the publications <ul> <li><em>Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer,</em><br> <strong>TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials,</strong><br> Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015.</li> <li><em>Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer,</em><br> <strong>Predictive Tracking with Improved Motion Models for Optical Belt Sorting</strong>,<br> at – Automatisierungstechnik, April 2020.</li> </ul> </li> <li>Classification of particles. The classification may use a multitarget tracker to accumulate visual features over time. One can also use the information on the trajectory to classify the particles. For information on this, refer to <ul> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information,</strong><br> Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany, March 2017.</li> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Motion-Based Material Characterization in Sensor-Based Sorting,</strong> <br> tm – Technisches Messen, De Gruyter, October 2017.</li> </ul> </li> </ol> <p><br> To this date, publications that used these data include</p> <ul> <li><em>Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck,</em><br> <strong>Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking</strong>,<br> Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, September 2020.</li> </ul> <p>CSV-files with already associated particle tracks are available at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a>.</p> <p><strong>Acknowledgment</strong></p> <p>The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Energy on the basis of a resolution of the German Bundestag.</p>
Images of Cylinders Transported on a Conveyor Belt - Recording 6
<p>This data set comprises images of cylinders on a conveyor belt. The images were recorded on the small-scale optical belt sorter Tablesort. A thorough description of the Tablesort system can be found in</p> <ul> <li><em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer,</em> <strong>Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking</strong>, Transactions on Industrial Electronics, February 2020.</li> <li>See also the <a href="https://www.iosb.fraunhofer.de/en/projects-and-products/inside-schuettgut.html">project website</a>.</li> </ul> <p>This dataset is part of a batch of recordings on optical sorters. Please use the search function with the keyword "Tobias Hornberger" (in quotes) to find them or use the list at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a> (conveyor belt data sets only).</p> <p>The camera was recorded on a Bonito CL-400C. The calibration image for the extrinsic parameters can be found in calibration_extrinsics.png for the extrinsics and calibration_color.png for the color calibration. Please see the debayer script on <a href="https://github.com/geomai/debayer_bonito">GitHub</a><strong>.</strong> Each pixel is approximately 0.056 mm long in world coordinates. The frame rate is 192.9 Hz.</p> <p>Algorithms for two key challenges can be developed and evaluated on the data sets:</p> <ol> <li>Multitarget tracking for predicting the particle’s motion. This can be used to enhance the separation of optical sorters. For further details on this, see the publications <ul> <li><em>Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer,</em><br> <strong>TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials,</strong><br> Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015.</li> <li><em>Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer,</em><br> <strong>Predictive Tracking with Improved Motion Models for Optical Belt Sorting</strong>,<br> at – Automatisierungstechnik, April 2020.</li> </ul> </li> <li>Classification of particles. The classification may use a multitarget tracker to accumulate visual features over time. One can also use the information on the trajectory to classify the particles. For information on this, refer to <ul> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information,</strong><br> Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany, March 2017.</li> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Motion-Based Material Characterization in Sensor-Based Sorting,</strong> <br> tm – Technisches Messen, De Gruyter, October 2017.</li> </ul> </li> </ol> <p><br> To this date, publications that used these data include</p> <ul> <li><em>Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck,</em><br> <strong>Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking</strong>,<br> Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, September 2020.</li> </ul> <p>CSV-files with already associated particle tracks are available at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a>.</p> <p><strong>Acknowledgment</strong></p> <p>The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Energy on the basis of a resolution of the German Bundestag.</p>
Images of Cylinders Transported on a Conveyor Belt - Recording 5
<p>This data set comprises images of cylinders on a conveyor belt. The images were recorded on the small-scale optical belt sorter Tablesort. A thorough description of the Tablesort system can be found in</p> <ul> <li><em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer,</em> <strong>Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking</strong>, Transactions on Industrial Electronics, February 2020.</li> <li>See also the <a href="https://www.iosb.fraunhofer.de/en/projects-and-products/inside-schuettgut.html">project website</a>.</li> </ul> <p>This dataset is part of a batch of recordings on optical sorters. Please use the search function with the keyword "Tobias Hornberger" (in quotes) to find them or use the list at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a> (conveyor belt data sets only).</p> <p>The camera was recorded on a Bonito CL-400C. The calibration image for the extrinsic parameters can be found in calibration_extrinsics.png for the extrinsics and calibration_color.png for the color calibration. Please see the debayer script on <a href="https://github.com/geomai/debayer_bonito">GitHub</a><strong>.</strong> Each pixel is approximately 0.056 mm long in world coordinates. The frame rate is 192.9 Hz.</p> <p>Algorithms for two key challenges can be developed and evaluated on the data sets:</p> <ol> <li>Multitarget tracking for predicting the particle’s motion. This can be used to enhance the separation of optical sorters. For further details on this, see the publications <ul> <li><em>Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer,</em><br> <strong>TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials,</strong><br> Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015.</li> <li><em>Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer,</em><br> <strong>Predictive Tracking with Improved Motion Models for Optical Belt Sorting</strong>,<br> at – Automatisierungstechnik, April 2020.</li> </ul> </li> <li>Classification of particles. The classification may use a multitarget tracker to accumulate visual features over time. One can also use the information on the trajectory to classify the particles. For information on this, refer to <ul> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information,</strong><br> Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany, March 2017.</li> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Motion-Based Material Characterization in Sensor-Based Sorting,</strong> <br> tm – Technisches Messen, De Gruyter, October 2017.</li> </ul> </li> </ol> <p><br> To this date, publications that used these data include</p> <ul> <li><em>Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck,</em><br> <strong>Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking</strong>,<br> Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, September 2020.</li> </ul> <p>CSV-files with already associated particle tracks are available at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a>.</p> <p><strong>Acknowledgment</strong></p> <p>The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Energy on the basis of a resolution of the German Bundestag.</p>
Images of Cylinders Transported on a Conveyor Belt - Recording 4
<p>This data set comprises images of cylinders on a conveyor belt. The images were recorded on the small-scale optical belt sorter Tablesort. A thorough description of the Tablesort system can be found in</p> <ul> <li><em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer,</em> <strong>Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking</strong>, Transactions on Industrial Electronics, February 2020.</li> <li>See also the <a href="https://www.iosb.fraunhofer.de/en/projects-and-products/inside-schuettgut.html">project website</a>.</li> </ul> <p>This dataset is part of a batch of recordings on optical sorters. Please use the search function with the keyword "Tobias Hornberger" (in quotes) to find them or use the list at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a> (conveyor belt data sets only).</p> <p>The camera was recorded on a Bonito CL-400C. The calibration image for the extrinsic parameters can be found in calibration_extrinsics.png for the extrinsics and calibration_color.png for the color calibration. Please see the debayer script on <a href="https://github.com/geomai/debayer_bonito">GitHub</a><strong>.</strong> Each pixel is approximately 0.056 mm long in world coordinates. The frame rate is 192.9 Hz.</p> <p>Algorithms for two key challenges can be developed and evaluated on the data sets:</p> <ol> <li>Multitarget tracking for predicting the particle’s motion. This can be used to enhance the separation of optical sorters. For further details on this, see the publications <ul> <li><em>Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer,</em><br> <strong>TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials,</strong><br> Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015.</li> <li><em>Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer,</em><br> <strong>Predictive Tracking with Improved Motion Models for Optical Belt Sorting</strong>,<br> at – Automatisierungstechnik, April 2020.</li> </ul> </li> <li>Classification of particles. The classification may use a multitarget tracker to accumulate visual features over time. One can also use the information on the trajectory to classify the particles. For information on this, refer to <ul> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information,</strong><br> Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany, March 2017.</li> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Motion-Based Material Characterization in Sensor-Based Sorting,</strong> <br> tm – Technisches Messen, De Gruyter, October 2017.</li> </ul> </li> </ol> <p><br> To this date, publications that used these data include</p> <ul> <li><em>Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck,</em><br> <strong>Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking</strong>,<br> Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, September 2020.</li> </ul> <p>CSV-files with already associated particle tracks are available at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a>.</p> <p><strong>Acknowledgment</strong></p> <p>The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Energy on the basis of a resolution of the German Bundestag.</p>
Images of Cylinders Transported on a Conveyor Belt - Recording 3
<p>This data set comprises images of cylinders on a conveyor belt. The images were recorded on the small-scale optical belt sorter Tablesort. A thorough description of the Tablesort system can be found in</p> <ul> <li><em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer,</em> <strong>Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking</strong>, Transactions on Industrial Electronics, February 2020.</li> <li>See also the <a href="https://www.iosb.fraunhofer.de/en/projects-and-products/inside-schuettgut.html">project website</a>.</li> </ul> <p>This dataset is part of a batch of recordings on optical sorters. Please use the search function with the keyword "Tobias Hornberger" (in quotes) to find them or use the list at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a> (conveyor belt data sets only).</p> <p>The camera was recorded on a Bonito CL-400C. The calibration image for the extrinsic parameters can be found in calibration_extrinsics.png for the extrinsics and calibration_color.png for the color calibration. Please see the debayer script on <a href="https://github.com/geomai/debayer_bonito">GitHub</a><strong>.</strong> Each pixel is approximately 0.056 mm long in world coordinates. The frame rate is 192.9 Hz.</p> <p>Algorithms for two key challenges can be developed and evaluated on the data sets:</p> <ol> <li>Multitarget tracking for predicting the particle’s motion. This can be used to enhance the separation of optical sorters. For further details on this, see the publications <ul> <li><em>Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer,</em><br> <strong>TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials,</strong><br> Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015.</li> <li><em>Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer,</em><br> <strong>Predictive Tracking with Improved Motion Models for Optical Belt Sorting</strong>,<br> at – Automatisierungstechnik, April 2020.</li> </ul> </li> <li>Classification of particles. The classification may use a multitarget tracker to accumulate visual features over time. One can also use the information on the trajectory to classify the particles. For information on this, refer to <ul> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information,</strong><br> Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany, March 2017.</li> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Motion-Based Material Characterization in Sensor-Based Sorting,</strong> <br> tm – Technisches Messen, De Gruyter, October 2017.</li> </ul> </li> </ol> <p><br> To this date, publications that used these data include</p> <ul> <li><em>Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck,</em><br> <strong>Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking</strong>,<br> Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, September 2020.</li> </ul> <p>CSV-files with already associated particle tracks are available at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a>.</p> <p><strong>Acknowledgment</strong></p> <p>The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Energy on the basis of a resolution of the German Bundestag.</p>
Images of Cylinders Transported on a Conveyor Belt - Recording 1
<p>This data set comprises images of cylinders on a conveyor belt. The images were recorded on the small-scale optical belt sorter Tablesort. A thorough description of the Tablesort system can be found in</p> <ul> <li><em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer,</em> <strong>Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking</strong>, Transactions on Industrial Electronics, February 2020.</li> <li>See also the <a href="https://www.iosb.fraunhofer.de/en/projects-and-products/inside-schuettgut.html">project website</a>.</li> </ul> <p>This dataset is part of a batch of recordings on optical sorters. Please use the search function with the keyword "Tobias Hornberger" (in quotes) to find them or use the list at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a> (conveyor belt data sets only).</p> <p>The camera was recorded on a Bonito CL-400C. The calibration image for the extrinsic parameters can be found in calibration_extrinsics.png for the extrinsics and calibration_color.png for the color calibration. Please see the debayer script on <a href="https://github.com/geomai/debayer_bonito">GitHub</a><strong>.</strong> Each pixel is approximately 0.056 mm long in world coordinates. The frame rate is 192.9 Hz.</p> <p>Algorithms for two key challenges can be developed and evaluated on the data sets:</p> <ol> <li>Multitarget tracking for predicting the particle’s motion. This can be used to enhance the separation of optical sorters. For further details on this, see the publications <ul> <li><em>Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer,</em><br> <strong>TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials,</strong><br> Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015.</li> <li><em>Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer,</em><br> <strong>Predictive Tracking with Improved Motion Models for Optical Belt Sorting</strong>,<br> at – Automatisierungstechnik, April 2020.</li> </ul> </li> <li>Classification of particles. The classification may use a multitarget tracker to accumulate visual features over time. One can also use the information on the trajectory to classify the particles. For information on this, refer to <ul> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information,</strong><br> Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany, March 2017.</li> <li><em>Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,</em><br> <strong>Motion-Based Material Characterization in Sensor-Based Sorting,</strong> <br> tm – Technisches Messen, De Gruyter, October 2017.</li> </ul> </li> </ol> <p><br> To this date, publications that used these data include</p> <ul> <li><em>Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck,</em><br> <strong>Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking</strong>,<br> Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, September 2020.</li> </ul> <p>CSV-files with already associated particle tracks are available at <a href="https://doi.org/10.5281/zenodo.5506551">https://doi.org/10.5281/zenodo.5506551</a>.</p> <p><strong>Acknowledgment</strong></p> <p>The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Energy on the basis of a resolution of the German Bundestag.</p>
Dataset for 3D shape factor between two cylinders
<p>Dataset of shape factor between two cylinders randomly oriented in three-dimensional space computed using finite element method.</p>
Dataset for "Formation of a Scour Funnel Upstream of an Orifice Affected by a Bottom-mounted Cylinder"
<p>The time-averaged velocity, TKE and bed topographies</p>
Data for "The DESI One-Percent Survey: Evidence for Assembly Bias from Low-Redshift Counts-in-Cylinders Measurements"
<p>Summary statistics, covariance matrices, and MCMC results for each of our HOD samples. For links to the original data catalogs and instructions to reproduce the analysis, see the README at: <a href="https://github.com/AlanPearl/galtab/tree/main/galtab/paper2/">https://github.com/AlanPearl/galtab/tree/main/galtab/paper2/</a></p> <p>In brief, the desi_observations/desi_obs_*.npz files contain information about each threshold/redshift sample. Data is loaded via `obs_data = np.load(filename, allow_pickle=True)`, and all available fields can be shown via `obs_data.keys()`. Most importantly, our target data and its corresponding covariance matrix can be accessed with the "mean" and "cov" keys, respectively. These arrays can be sliced into our three observables using the slice objects accessed with the "slice_n", "slice_wp", and "slice_cic" keys.<br><br>The emcee MCMC chains for each sample can be found under desi_results/results_*/emcee_backend.h5. To access the chain data (i.e., to construct corner plots of our HOD parameters), you can either follow our paper plot notebooks linked in the README above, or see the <a href="https://emcee.readthedocs.io/en/stable/">emcee documentation</a>.</p>
Ultrasonic Pulse Transmission Tests: Datasets - Test Series 7, Reference Tests on Aluminium Cylinder
<p>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.</p>
Experimental recordings of cross-flow vortex-induced vibrations on slender free-clamped ends cylinder and cylinder with a pair of branches in constant uniform flow for different reduced velocities.
<p>Dataset of the experiments done on a cylinder (N=0) and a coral model (N=1) 3D-printed with SLS out of TPE material. Cross-flow VIV are recorder with a GoPro in a test section of a water tunnel for different reduced velocities (Ur). </p> <p>This was made for our publication "Modelling vortex-induced vibrations of branched structures by coupling a 3D-corotational frame finite element formulation with wake-oscillators" in the Journal of Fluids and Structures.</p>
156670 cylinder seal
**Photogrammetric model with virtual impression of carved surface**<br> Catalog Number: 156670.nosub[1]<br> Description: seal<br> Notes: Bearded sun god seated on a backed throne, from each shoulder spring three rays, right hand raised in greeting; he is approached by a god clad in long garment and flat horned cap leading a supplicant by the hand, and followed by another supplicant carrying a sacrificial animal (young antelope kid?); between the two supplicants is a mace like standard.<br> Materials: shell<br> Time Period: Akkadian<br> Accession Number: [1497] Excavations at Kish, Iraq (Expedition)<br> Accession Year: 1924<br> Other Numbers: X-385, 88263<br> City/Town: Kish<br> Collector/Source: Excavations at Kish, Iraq, Excavations at Kish, Iraq<br> <br> https://collections-anthropology.fieldmuseum.org/catalogue/1366111<br> <br> Photography: JP Brown <br> Image generation: Sam<br> Metashape Pro: Sam<br> Meshlab 2016.12: Sam<br> ZBrush: JP Brown<br> Source: Objaverse 1.0 / Sketchfab
156701 cylinder seal
**Photogrammetric model with virtual impression of carved surface**<br> Catalog Number: 156701.nosub[1]<br> Description: cylinder seal<br> Notes: almost indistinguishable, probably rampant animals.<br> Materials: shell<br> Time Period: Early Dynastic<br> Accession Number: [1497] Excavations at Kish, Iraq (Expedition)<br> Accession Year: 1924<br> Other Numbers: X-424<br> City/Town: Kish<br> Collector/Source: Excavations at Kish, Iraq, Excavations at Kish, Iraq<br> <br> https://collections-anthropology.fieldmuseum.org/catalogue/1059181<br> <br> Photography: JP Brown <br> Image generation: Sam<br> Metashape Pro: Sam<br> Meshlab 2016.12: Sam<br> ZBrush: JP Brown<br> Source: Objaverse 1.0 / Sketchfab
156735 cylinder seal
**Photogrammetric model with virtual impression of carved surface**<br> <br> Catalog Number: 156735.nosub[1]<br> Description: cylinder seal<br> Notes: <br> Materials: stone; sandstone<br> Time Period: Early Dynastic<br> Accession Number: [1497] Excavations at Kish, Iraq (Expedition)<br> Accession Year: 1924<br> City/Town: Kish<br> Collector/Source: Excavations at Kish, Iraq, Excavations at Kish, Iraq<br> <br> https://collections-anthropology.fieldmuseum.org/catalogue/1058548<br> <br> Photography: JP Brown <br> Image generation: Olivia<br> Metashape Pro: Olivia<br> Meshlab 2016.12: Olivia<br> ZBrush: Olivia<br> Source: Objaverse 1.0 / Sketchfab
228752 cylinder seal
**Photogrammetric model with virtual impression of carved surface**<br> Catalog Number: 228752<br> Description: cylinder seal<br> Notes: design indistinguishable.<br> Materials: shell<br> Accession Number: [1497] Excavations at Kish, Iraq (Expedition)<br> Accession Year: 1924<br> City/Town: Kish<br> Collector/Source: Excavations at Kish, Iraq<br> <br> [https://collections-anthropology.fieldmuseum.org/catalogue/1135326](http://)<br> <br> Photography: JP Brown, JD <br> Image generation: JP Brown<br> Metashape Pro: JD<br> Meshlab 2016.12: JD<br> ZBrush: JP Brown<br> Source: Objaverse 1.0 / Sketchfab
156684 cylinder seal
**Photogrammetric model with virtual impression of carved surface**<br> Catalog Number: 156684.nosub[1]<br> Description: cylinder seal<br> Notes: indistinguishable<br> Materials: shell<br> Accession Number: [1497] Excavations at Kish, Iraq (Expedition)<br> Accession Year: 1924<br> Other Numbers: X-392<br> City/Town: Kish<br> Collector/Source: Excavations at Kish, Iraq, Excavations at Kish, Iraq<br> <br> https://collections-anthropology.fieldmuseum.org/catalogue/1059323<br> <br> Photography: JP Brown <br> Image generation: Sam<br> Metashape Pro: Sam<br> Meshlab 2016.12: Sam<br> ZBrush: JP Brown<br> Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.