cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.2687896
title: Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 33-37

description: This upload contains samples 33 - 37 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). https://doi.org/10.1038/s41597-019-0235-y or arXiv:1905.04787 (2019) Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by XRE nvand located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: 1-8, 9-16, 17-24, 25-32, 33-37, 38-42 The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on github For more information or guidance in using these dataset, please get in touch with henri.dersarkissian [at] gmail.com Felix.Lucka [at] cwi.nl
DOI: 10.5281/zenodo.2687897
title: Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 33-37

description: This upload contains samples 33 - 37 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). https://doi.org/10.1038/s41597-019-0235-y or arXiv:1905.04787 (2019) Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by XRE nvand located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: 1-8, 9-16, 17-24, 25-32, 33-37, 38-42 The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on github For more information or guidance in using these dataset, please get in touch with henri.dersarkissian [at] gmail.com Felix.Lucka [at] cwi.nl
DOI: 10.26304/366-u1491a-gra
title: Expedition 366 Hole U1491A Bulk density (GRA) data file

description: Hole U1491A GRA - Bulk density (GRA) for Expedition 366 Hole U1491A
DOI: 10.7910/dvn/t1koju
title: Check File PID, Will Delete [Kevin]

description: 1
DOI: 10.7910/dvn/t1koju/owrba5
title: ShortFile.txt

description: :unav
DOI: 10.5281/zenodo.2686970
title: Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 9-16

description: This upload contains samples 9 - 16 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). https://doi.org/10.1038/s41597-019-0235-y or arXiv:1905.04787 (2019) Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by XRE nvand located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: 1-8, 9-16, 17-24, 25-32, 33-37, 38-42 The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on github For more information or guidance in using these dataset, please get in touch with henri.dersarkissian [at] gmail.com Felix.Lucka [at] cwi.nl
DOI: 10.5281/zenodo.2686971
title: Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 9-16

description: This upload contains samples 9 - 16 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). https://doi.org/10.1038/s41597-019-0235-y or arXiv:1905.04787 (2019) Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by XRE nvand located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: 1-8, 9-16, 17-24, 25-32, 33-37, 38-42 The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on github For more information or guidance in using these dataset, please get in touch with henri.dersarkissian [at] gmail.com Felix.Lucka [at] cwi.nl
DOI: 10.7910/dvn/nqhifs
title: Check File PID 2, Will Delete [Kevin]

description: 1
DOI: 10.7910/dvn/nqhifs/ulc2kd
title: ShortFile.txt

description: :unav
DOI: 10.17632/rj8657z4d2.1
title: Data for: Shape variability within Astarte (Class: Bivalvia) from the Pliocene of the Atlantic Coastal Plain, U.S.A.: a study using geometric morphometrics

description: Raw X and Y measurements of Astarte shells from the Pliocene of the Atlantic Coastal Plain of North America. The measurements were taken from digital photographs, and were used in a 2D morphometrics analysis.
DOI: 10.5281/zenodo.2687864
title: IsoSeq transcriptome of C. laxum, D. oligosanthes and H. amplexicaulis

description: IsoSeq leaf transcriptome dataset of three grass species (Chasmanthium laxum, Dichanthelium oligosanthes and Hymenachne amplexicaulis). Mature leaves were used.
DOI: 10.5281/zenodo.2687865
title: IsoSeq transcriptome of C. laxum, D. oligosanthes and H. amplexicaulis

description: IsoSeq leaf transcriptome dataset of three grass species (Chasmanthium laxum, Dichanthelium oligosanthes and Hymenachne amplexicaulis). Mature leaves were used.
DOI: 10.5281/zenodo.2688111
title: Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 38-42

description: This upload contains samples 38 - 42 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). https://doi.org/10.1038/s41597-019-0235-y or arXiv:1905.04787 (2019) Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by XRE nvand located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: 1-8, 9-16, 17-24, 25-32, 33-37, 38-42 The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on github For more information or guidance in using these dataset, please get in touch with henri.dersarkissian [at] gmail.com Felix.Lucka [at] cwi.nl
DOI: 10.5281/zenodo.2688112
title: Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 38-42

description: This upload contains samples 38 - 42 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). https://doi.org/10.1038/s41597-019-0235-y or arXiv:1905.04787 (2019) Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by XRE nvand located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: 1-8, 9-16, 17-24, 25-32, 33-37, 38-42 The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on github For more information or guidance in using these dataset, please get in touch with henri.dersarkissian [at] gmail.com Felix.Lucka [at] cwi.nl
DOI: 10.5061/dryad.cb7tp6m/1
title: Functional_Ecology_2019_Chen_et_al
DOI: 10.7910/dvn/uj61ha/1z20xf
title: hartwig(20190505).wf1

description: :unav
DOI: 10.7910/dvn/uj61ha/q5xekt
title: README.rtf

description: :unav
DOI: 10.5066/p9m1rjj5
title: Turbidity and water temperature profile data in Don Pedro Reservoir, California, 2015-2016

description: This dataset includes water temperature and turbidity profiles collected in Don Pedro Reservoir, California, during 2015-2016.
DOI: 10.15139/s3/unvijf
title: Monmouth University California Poll, Number 178

description: This survey was conducted among likely voters in California’s 48th Congressional District.
DOI: 10.7910/dvn/n7xqmy/t7vabm
title: CFR_replication_data.tab

description: :unav
DOI: 10.7910/dvn/n7xqmy/y8yumj
title: AJPS_replication_code.R

description: :unav
DOI: 10.7910/dvn/n7xqmy/hbaoor
title: readme.txt

description: :unav
DOI: 10.7910/dvn/n7xqmy/hekcf7
title: petition_replication_data.tab

description: :unav
DOI: 10.7910/dvn/n7xqmy/jwdtdb
title: Codebook for Petition Dataset.pdf

description: :unav
DOI: 10.7910/dvn/n7xqmy/lhizl0
title: Codebook For CFR Dataset.pdf

description: :unav
DOI: 10.7910/dvn/n7xqmy/ncdvki
title: CFR-1996.pdf

description: :unav
DOI: 10.7910/dvn/n7xqmy/oyzuau
title: CFR-2015.pdf

description: :unav
DOI: 10.5066/p9wqif0w
title: Landsat 4-8 Collection 1 Level-2 U.S. Albers Scene Bundles

description: Albers Scene Bundles contain 30-meter Collection 1 Level-2 products including Top of Atmosphere (TOA) Reflectance, Surface Reflectance (SR), Top of Atmosphere Brightness Temperature (BT), Surface Temperature (ST), Dynamic Surface Water Extent (DSWE), and Quality Assessment (QA) projected to the Albers Equal Area (AEA) map projection.
DOI: 10.7910/dvn/zaz7q1/xh4dlh
title: TMS_DIENE.txt

description: TMS diene DFT B3LYP 6-31G(d) energy output data.
DOI: 10.7910/dvn/zaz7q1/cm62ys
title: TMS_DIENE.37

description: TMS diene DFT B3LYP 6-31G(d) equilibrium geometry NBO output data (NBO).
DOI: 10.17632/vncpxt4b4z.2
title: Data for: A novel approach for fouling mitigation in anaerobic-anoxic-oxic membrane bioreactor (A2O-MBR) by integrating worm predation

description: Graph
DOI: 10.7910/dvn/zaz7q1/92vwro
title: TMS_DIENE.40

description: TMS diene DFT B3LYP 6-31G(d) equilibrium geometry NBO output data (MO).
DOI: 10.7910/dvn/zaz7q1/ownczz
title: JMiller_Publication.docx

description: Discussion, theoretical chemistry and experimental (laboratory experiments from PhD thesis).
DOI: 10.7910/dvn/i4hkoo/kucdke
title: examiner-date-text.v5.tab
DOI: 10.7910/dvn/i4hkoo/oa8xgh
title: examiner-date-text.v5.tab
DOI: 10.7910/dvn/zaz7q1/gvqoyp
title: PM3_CYCLOADDUCT.txt

description: Endo-cycloadduct from the reaction of naphthazarin with Danishefsky's diene. Semi-empirical PM3 equilibrium geometry energy and frequency analysis data.
DOI: 10.7910/dvn/zaz7q1/nqfc7w
title: TMS_DIENE.xyz

description: TMS diene DFT B3LYP 6-31G(d) equilibrium geometry coordinate data.
DOI: 10.7910/dvn/zaz7q1/pdnsyf
title: TMS_DIENE.46

description: TMS diene DFT B3LYP 6-31G(d) equilibrium geometry NBO  output data.
DOI: 10.7910/dvn/cx9hwd/mcq46f
title: Swissprot_TransFinal.txt

description: Transcriptome for Porites astreoides coral larvae, that was annotated in the Uniprot SwissProt database (downloaded 4/25/19). Blastx, Evalue cutoff 10e-5.
DOI: 10.7910/dvn/ybavzf/m4yxni
title: Au_L3_edge_in_AuPd.chi

description: :unav
DOI: 10.7910/dvn/ybavzf/vy5x8t
title: Pt_L3_edge_in_Pt.prj

description: :unav
DOI: 10.7910/dvn/zaz7q1/cvngvp
title: JMiller_Publication.pdf

description: Discussion, theoretical chemistry and experimental (laboratory experiments from PhD thesis).
DOI: 10.7910/dvn/zaz7q1/pdq4kc
title: TMS_DIENE.jpg

description: TMS diene DFT B3LYP 6-31G(d) equilibrium geometry (image).
DOI: 10.7910/dvn/zaz7q1/m7pnzc
title: TMS_DIENE.31

description: TMS diene DFT B3LYP 6-31G(d) equilibrium geometry NBO output data.
DOI: 10.7910/dvn/ybavzf/5hque0
title: Pd_K_edge_in_AuPd.chi

description: :unav
DOI: 10.7910/dvn/ybavzf/jppfan
title: Au_L3_edge_in_AuPd.prj

description: :unav
DOI: 10.7910/dvn/ybavzf/ynwe5g
title: readme.txt

description: :unav
DOI: 10.7910/dvn/qvmfpz/9h337u
title: alloy_feature_extraction.ipynb

description: :unav
DOI: 10.7910/dvn/ybavzf/7hw6cq
title: Pt_L3_edge_in_Pt.chi

description: :unav
DOI: 10.7910/dvn/ybavzf/apj0eb
title: Pd_K_edge_in_AuPd.prj

description: :unav
DOI: 10.5066/p9cbcjgz
title: SPOT Historical

description: The SPOT Historical archive holdings were received from the SPOT IMAGE Corporation in 1999. The French space agency, Centre National d'Etudes Spatiales (CNES), owns and operates the SPOT satellite system and worldwide commercial operations are anchored by private companies (i.e., SPOT IMAGE Corp. of the United States and other distributors around the world).
DOI: 10.5281/zenodo.2690085
title: PVT dataset to EOG_RAW

description: EOG_Raw EOG_Raw data from J!NS MEME glasses, self-assessments, and PVT recordings collected in-the-wild If you are using this data and publish work of any kind, please make sure to cite the following paper: https://dl.acm.org/citation.cfm?id=3300694 DOWNLOAD INSTRUCTIONS: EOG_Raw Data archive is very big (10.2GB) Please download it from: https://zenodo.org/record/2532900/files/EOG%20Raw%20Data.zip?download=1
DOI: 10.5281/zenodo.2690086
title: PVT dataset to EOG_RAW

description: EOG_Raw EOG_Raw data from J!NS MEME glasses, self-assessments, and PVT recordings collected in-the-wild If you are using this data and publish work of any kind, please make sure to cite the following paper: https://dl.acm.org/citation.cfm?id=3300694 DOWNLOAD INSTRUCTIONS: EOG_Raw Data archive is very big (10.2GB) Please download it from: https://zenodo.org/record/2532900/files/EOG%20Raw%20Data.zip?download=1