cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.2686725
title: Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 1-8

description: This upload contains samples 1 - 8 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.2686726
title: Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 1-8

description: This upload contains samples 1 - 8 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.1202314
title: CarloTopo synthetic GeoModeller model and relevant MCUE outputs

description: Companion dataset to the paper:

<em>Topological Analysis in Monte Carlo Simulation for Uncertainty Estimation</em><em>, </em>Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, Mark Jessell

This dataset contains twoarchives pertaining to Monte Carlo Uncertainty Estimation (MCUE) in implicit 3D geological models:


	CarloTopo.zip contains a synthetic GeoModeller three dimensional model.
	High_confidence_CarloTopo.zip contains MCUE outputs for Gaussian perturbation and topological clustering.
	Low_confidence_CarloTopo.zip contains MCUE outputs for Uniform perturbation and topological clustering.


In the output archive files:


	xml files are the GeoModeller project saves, these can be opened with Intrepid's free viewer for GeoModeller 3.3 3.4 (available @: http://www.intrepid-geophysics.com/ig/index.php?page=geomodeller-update)
	.vo, .isi and .vop1 files are the lithological voxet exports in binary GOCAD format, these can be opened with the aforementionned viewer or Geocando (available @: https://sourceforge.net/projects/geocando)
	.vtk files are the MCUE uncertainty indexes outputs, these can be opened with Paraview (available @:https://www.paraview.org/download)
	the frequency.csv file is the MCUE probabilistic model output, it can be opened with any text editor /spreadsheet
	.csv files are the probabilistic geological models
	.task files are GeoModeller's API files
	.txt files are logs from CURE
DOI: 10.5281/zenodo.1202313
title: CarloTopo synthetic GeoModeller model and relevant MCUE outputs

description: Companion dataset to the paper:

<em>Topological Analysis in Monte Carlo Simulation for Uncertainty Estimation</em><em>, </em>Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, Mark Jessell

This dataset contains twoarchives pertaining to Monte Carlo Uncertainty Estimation (MCUE) in implicit 3D geological models:


	CarloTopo.zip contains a synthetic GeoModeller three dimensional model.
	High_confidence_CarloTopo.zip contains MCUE outputs for Gaussian perturbation and topological clustering.
	Low_confidence_CarloTopo.zip contains MCUE outputs for Uniform perturbation and topological clustering.


In the output archive files:


	xml files are the GeoModeller project saves, these can be opened with Intrepid's free viewer for GeoModeller 3.3 3.4 (available @: http://www.intrepid-geophysics.com/ig/index.php?page=geomodeller-update)
	.vo, .isi and .vop1 files are the lithological voxet exports in binary GOCAD format, these can be opened with the aforementionned viewer or Geocando (available @: https://sourceforge.net/projects/geocando)
	.vtk files are the MCUE uncertainty indexes outputs, these can be opened with Paraview (available @:https://www.paraview.org/download)
	the frequency.csv file is the MCUE probabilistic model output, it can be opened with any text editor /spreadsheet
	.csv files are the probabilistic geological models
	.task files are GeoModeller's API files
	.txt files are logs from CURE
DOI: 10.4231/fe1m-v172
title: test

description: test
DOI: 10.5281/zenodo.2686861
title: Cashtag Piggybacking dataset - Twitter dataset enriched with financial data

description: This dataset is composed of 


	Twitter dataset of ~9M tweets mentioning stocks (cashtags) traded on the most important US markets, shared between May and September 2017 (users data enriched with bot classification label)
	Financial information about ~30k companies found in those tweets, retrieved from Google Finance


Refer to the paper below for more details.

Cresci, S., Lillo, F., Regoli, D., Tardelli, S., & Tesconi, M. (2019). Cashtag Piggybacking: Uncovering Spam and Bot Activity in Stock Microblogs on Twitter. <em>ACM Transactions on the Web (TWEB)</em>, <em>13</em>(2), 11.
DOI: 10.5281/zenodo.2686862
title: Cashtag Piggybacking dataset - Twitter dataset enriched with financial data

description: This dataset is composed of 


	Twitter dataset of ~9M tweets mentioning stocks (cashtags) traded on the most important US markets, shared between May and September 2017 (users data enriched with bot classification label)
	Financial information about ~30k companies found in those tweets, retrieved from Google Finance


Refer to the paper below for more details.

Cresci, S., Lillo, F., Regoli, D., Tardelli, S., & Tesconi, M. (2019). Cashtag Piggybacking: Uncovering Spam and Bot Activity in Stock Microblogs on Twitter. <em>ACM Transactions on the Web (TWEB)</em>, <em>13</em>(2), 11.
DOI: 10.5281/zenodo.2686210
title: World Happiness 2015

description: This is a shameless rip-off from https://www.kaggle.com/unsdsn/world-happiness
DOI: 10.5281/zenodo.2686211
title: World Happiness 2015

description: This is a shameless rip-off from https://www.kaggle.com/unsdsn/world-happiness
DOI: 10.5281/zenodo.2683323
title: Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ

description: This repository contains data reported in the below study:

Atherton, J., Liu, W. and Porcar-Castell, A., 2019. Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ. <em>Remote Sensing of Environment</em>.

Each text file contains the data-set used to produce the relevant figure (see file name). You can find the data to produce A.4. online at  https://avaa.tdata.fi/web/smart/smear/ 

Please pay attention to the following before using this data.


	<strong>Figure2_lampRadPanel_Wm2srnm.txt</strong>: Note that the shapes are of interest here. The magnitude is not the same as the incident light at top of canopy, as these spectra were measured in a laboratory. See paper section A.1. for more details. 
	<strong>Figure3_LEDIFspectra_Wm2srnm.txt</strong>: This data contains the whole observed spectrum including the non-fluorescence regions, which were saturated (warped) in the visible. The fluorescence region is approximately &gt; 650 nm.   
	<strong>Figure4_AQYspectra_nm.txt</strong>: As with Figure3 the whole spectrum is included here.
	<strong>FigureA3_repLEDIFspectra_[pmay/psep/usep]._nm.txt</strong>:  Data from which the mean spectra (Figure3) were calculated, including the uncorrected red spectra. I have split these by canopy type to avoid name conflicts.
description: {"references": ["Atherton, J., Liu, W. and Porcar-Castell, A., 2019. Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ."]}
DOI: 10.5281/zenodo.2687634
title: Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 25-32

description: This upload contains samples 25 - 32 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.2687635
title: Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 25-32

description: This upload contains samples 25 - 32 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.2683322
title: Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ

description: This repository contains data reported in the below study:

Atherton, J., Liu, W. and Porcar-Castell, A., 2019. Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ. <em>Remote Sensing of Environment</em>.

Each text file contains the data-set used to produce the relevant figure (see file name). You can find the data to produce A.4. online at  https://avaa.tdata.fi/web/smart/smear/ 

Please pay attention to the following before using this data.


	<strong>Figure2_lampRadPanel_Wm2srnm.txt</strong>: Note that the shapes are of interest here. The magnitude is not the same as the incident light at top of canopy, as these spectra were measured in a laboratory. See paper section A.1. for more details. 
	<strong>Figure3_LEDIFspectra_Wm2srnm.txt</strong>: This data contains the whole observed spectrum including the non-fluorescence regions, which were saturated (warped) in the visible. The fluorescence region is approximately &gt; 650 nm.   
	<strong>Figure4_AQYspectra_nm.txt</strong>: As with Figure3 the whole spectrum is included here.
	<strong>FigureA3_repLEDIFspectra_[pmay/psep/usep]._nm.txt</strong>:  Data from which the mean spectra (Figure3) were calculated, including the uncorrected red spectra. I have split these by canopy type to avoid name conflicts.
description: {"references": ["Atherton, J., Liu, W. and Porcar-Castell, A., 2019. Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ."]}
DOI: 10.17632/5vskf7brh4.1
title: Data for: High compression leg bandaging prevents seated postural hypotension among elderly hospitalized patients

description: Primary database
DOI: 10.5061/dryad.1t2p835/1
title: Dryad Item 10.5061/DRYAD.1T2P835/1

description: (:tba)
DOI: 10.5281/zenodo.2546505
title: An in-silico analysis of information sharing systems for adaptable resources management: a case study of oyster farmers

description: Model and data outcomes --&gt; We developed an agent-based models involving oyster farmers sharing information to adapt to an ill-understood virus. Various scenarios of heterogeneity and information sharing (through social networks and centralized information system) are simulated.
DOI: 10.5281/zenodo.2546506
title: An in-silico analysis of information sharing systems for adaptable resources management: a case study of oyster farmers

description: Model and data outcomes --&gt; We developed an agent-based models involving oyster farmers sharing information to adapt to an ill-understood virus. Various scenarios of heterogeneity and information sharing (through social networks and centralized information system) are simulated.
DOI: 10.17034/d1c3bbe8-48c0-47e3-9e80-52aa5d3f74e8
title: Experimental observation of a current-driven instability in a neutral electron-positron plasma

description: The dataset shows typical radiographic images of the background plasma taken with a laser-driven proton beam. The features described in the article, namely the magnetic fields left in the wake of the electron-positron beam, are extracted from the detailed analysis of these images aided by particle-tracing simulations. 
The dataset relates to the article: “Experimental observation of a current-driven instability in a neutral electron-positron beam” currently accepted for publication in Physical Review Letters.
DOI: 10.5281/zenodo.2661107
title: Best fit Orphan stream model in the presence of the LMC

description: We give the best fit stream model and orbit for Orphan in the presence of the LMC from the work of Erkal et al. 2019 (https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.2685E). As described in section 4 of that work, this best fit was achieved in a prolate Milky Way halo which responds to the LMC. See the table in the appendix of that work (A1) for the parameters used in the fit. The file containing the orbit (orphan_orbit.txt) gives the position of the Orphan progenitor, LMC, and Milky Way for the past 5 Gyr. The coordinates are Galactocentric in a right-handed system (i..e the Sun is at (-8.1,0,0) kpc and predominantly moving in the positive y direction). The file containing the stream (orphan_stream.txt) gives the position of the Orphan stream particles in Galactocentric coordinates as well as observables (ra,dec,distance,vr,pmra,pmdec) assuming the Sun is at (-8.1,0,0) kpc and moving with a velocity of (11.1,245,7.3) km/s. The stream is generated using the Lagrange point stripping technique as described in the paper. Please cite Erkal et al. 2019 if you make use of this simulation.
DOI: 10.5281/zenodo.2661108
title: Best fit Orphan stream model in the presence of the LMC

description: We give the best fit stream model and orbit for Orphan in the presence of the LMC from the work of Erkal et al. 2019 (https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.2685E). As described in section 4 of that work, this best fit was achieved in a prolate Milky Way halo which responds to the LMC. See the table in the appendix of that work (A1) for the parameters used in the fit. The file containing the orbit (orphan_orbit.txt) gives the position of the Orphan progenitor, LMC, and Milky Way for the past 5 Gyr. The coordinates are Galactocentric in a right-handed system (i..e the Sun is at (-8.1,0,0) kpc and predominantly moving in the positive y direction). The file containing the stream (orphan_stream.txt) gives the position of the Orphan stream particles in Galactocentric coordinates as well as observables (ra,dec,distance,vr,pmra,pmdec) assuming the Sun is at (-8.1,0,0) kpc and moving with a velocity of (11.1,245,7.3) km/s. The stream is generated using the Lagrange point stripping technique as described in the paper. Please cite Erkal et al. 2019 if you make use of this simulation.