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629 results for “Clay”
Clay Pipe (2094a2187)
**Clay smoking pipe** Location: Warren Wilson site (31Bn29), Buncombe County, North Carolina. Period: Mississippian, Pisgah phase (AD 1000-1400) Material: ceramic. Dimensions: length, 50.3 mm; width, 33.5 mm; height, 42.5 mm. Notes: Catalog no. 2094a2187, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Abigail Gancz. Source: Objaverse 1.0 / Sketchfab
Clay rattle
ID no.: MAK/N/84/1 Archaeological Museum in Kraków https://muzea.malopolska.pl/en/objects-list/1470 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
clay owl whistle
Old owl whistle made of clay. Shot with a old Lumix find in the attic. 43 shots and Photoscan 1.4 Source: Objaverse 1.0 / Sketchfab
Clay pipe
Decorative clay pipe found at Esk Valley ironstone mine. Often a decorative design was made at the point the moulds joined. It is rare to find a pipe with stem. Source: Objaverse 1.0 / Sketchfab
Clay Pipe stem
Fragment of clay pipe stem. On the fragment one can discern the words "DUNDEE" and, on the opposite face, the letters "P McLE.....". (See MRM model https://skfb.ly/6RGxK for comparison) An internet search reveals there was a Dundee tobacconist named Peter McLean who had premises at 16 High Street and 57 Commercial Street. Peter McLean was born in Glasgow in 1830 and moved with his parents to Dundee in 1832, where his father opened a pipemaking business. Following time at sea, Peter returned to Dundee and started his own tobacconsit business in 1854. In 1903, Peter's Commercial Street business was damaged, when fire broke out in the cellars. Peter died in 1906, in Monifieth. Source: Objaverse 1.0 / Sketchfab
Savitaulu, clay tablet KM13631:8
Muinaisbabylonialainen savitaulu. Nuolenpääkirjoitus on Aplumin ja Ubarrumin kirje. Huonosti säilyneen savitaulun tekstiä ei ole kokonaisuudessaan käännetty. 1900-1600 eaa. Old-Babylonian clay tablet. Letter of Aplum and Ubarrum, only partially translated because some parts are badly preserved. 1900-1600 BCE. 3D-digitoitu osana Making Home Abroad / Kotona kulttuurissa -tutkimushanketta 2020-2021, käyttäen digitaalista fotogrammetriaa. 3D-digitointia on yksinkertaistettu huomattavasti katselukokemuksen sujuvoittamiseksi. 3D digitized as part of the Making Home Abroad / Kotona kulttuurissa research project 2020-2021, using digital photogrammetry. The 3D digitization is strongly simplified to ensure a smooth online visualization. Source: Objaverse 1.0 / Sketchfab
Clay Pipe (2378a3305)
**Clay smoking pipe** Location: Fredricks site (31Or231), Orange County, North Carolina. Period: Historic (AD 1700). Material: ceramic. Dimensions: length, 56.0 mm; diameter, 26.1 mm. Notes: Catalog no. 2378a3305, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Steve Davis. Source: Objaverse 1.0 / Sketchfab
Ancestral Puebloan Clay Bowl High Res
Ancestral Puebloan clay bowl on display at the Hutchings Museum. 173 images, Canon EOS 80D, 35mm, F/16, ISO 100, Agisoft Metashape, Windows 10. Source: Objaverse 1.0 / Sketchfab
Clay pitcher
ID no.: MAK/N/71:2 Archaeological Museum in Kraków https://muzea.malopolska.pl/en/objects-list/1472 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Clay Dipper (2241a327)
**Clay dipper** Location: Warren Wilson site (31Bn29), Buncombe County, North Carolina. Period: Mississippian, Pisgah phase (AD 1000-1400) Material: ceramic. Dimensions: length, 65.8 mm; width, 42.5 mm; height, 22.1 mm. Notes: Catalog no. 2241a327, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Abigail Gancz. Source: Objaverse 1.0 / Sketchfab
Clay Figurine
Clay figurine on display at the Visitor Information Center in St. Augustine Excavated by City of St. Augustine Archaeology Program Archaeologists To learn more about the City of St. Augustine Archaeology Program, visit: https://www.citystaug.com/Archaeology Model by Emma Dietrich This model was made using RealityCapture software by Capturing Reality Source: Objaverse 1.0 / Sketchfab
Clay Pipe (2389a1483)
**Clay smoking pipe** Location: Lower Saratown site (31Rk1), Rockingham County, North Carolina. Period: Historic (AD 1620-1670). Material: ceramic. Dimensions: length, 81.0 mm; diameter, 22.8 mm. Notes: Catalog no. 2389a1483, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Steve Davis. Source: Objaverse 1.0 / Sketchfab
Clay Pipe (2101a19)
**Clay smoking pipe** Location: Site 31Dv20, Davidson County, North Carolina. Period: Late Woodland (AD 1000-1400) Material: ceramic. Dimensions: length, 86.1 mm; width, 32.5 mm; height, 49.4 mm. Notes: Catalog no. 2101a19, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Chris LaMack. Source: Objaverse 1.0 / Sketchfab
Clay vessel from Bilche Zolote
ID no.: MAK/8475 Archaeological Museum in Kraków https://muzea.malopolska.pl/en/objects-list/636 Digitalisation: RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
Clay Pipe (2378a65)
**Clay smoking pipe** Location: Fredricks site (31Or231), Orange County, North Carolina. Period: Historic (AD 1700). Material: ceramic. Dimensions: length, 73.2 mm; diameter, 28.6 mm. Notes: Catalog no. 2378a65, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Steve Davis. Source: Objaverse 1.0 / Sketchfab
Frog Clay Sculpture
Clay sculpture scanned by Thunk3D Fisher W Whatsapp/phone/wechat: +86 18518781107 Email:daicy@thunk3d.com Facebook:www.facebook.com/qinqin.li.77 Linkedin: https://www.linkedin.com/in/daicy-li-399b82119/ Titter: https://twitter.com/DaicyLi Source: Objaverse 1.0 / Sketchfab
data supporting ''Tuning the mechanical properties of organophilic clay dispersions: Particle composition and preshear history effects''
Open the record for dataset details and reuse information.
A collection of X-ray projections of 131 pieces of modeling clay containing stones for machine learning-driven object detection
<p><strong>Summary</strong></p> <p>This submission contains a collection of 235800 X-ray projections of 131 pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as an extensive and easy-to-use training dataset for supervised machine learning driven object detection. The ground truth locations of the stones are included. The data is supplementary material to the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022].</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections have been corrected with flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images). Both the X-ray projections and the ground truth images are resized to 128x128 pixels. The raw data is made available in another (larger) submission for complete reproduction (<a href="https://zenodo.org/record/5866228">https://zenodo.org/record/5866228</a>). All images are stored in .tif format. The data for samples with 5-8 stones are put in a separate folder from the data with 0-3 stones. The size of the completely unpacked dataset is 19.6 GB.</p> <p><strong>NOTE</strong>: Because the dataset consists of 471600 files, fully extracting the dataset may take a while. Therefore, an additional and significantly smaller zip-file is included for previewing the data, with one X-ray projection for each sample.</p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow enabling deep learning for X-ray based foreign object detection", 2022 (in preparation)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 5 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a> <strong>(this upload)</strong></p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 4 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a> <strong>(this upload)</strong><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a></p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
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