cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18154/rwth-2022-11460
title: Supplementary Dataset — Spatially-Resolved experimental investigations of combustion characteristics in a solid fuel doped methane swirl flame and the influence on the formation of ultrafine particulate matter

description: The formation of inhalable fine particles ( dp ≤10 μm) is an undesirable side effect of solid fuel combustion processes. These particles can accumulate in the human respiratory system and thus cause severe lung damage. Therefore, an understanding of the formation of these particles is of crucial importance to avoid or to reduce the amount of fine particulate matter (PM) released into the atmosphere. Until now, most of the studies have been carried out for laminar and early-stage solid fuel combustion under simplified conditions (e.g. flat flame burner with radial almost homogeneous conditions) and only a few studies have been carried out with more complex turbulent flame structures. As the majority of real applications on an industrial scale involves turbulent conditions, research in this area is highly important. For the investigation of particulate matter formation, a swirled methane assisted pulverized solid fuel combustion test rig with full optical access which allows intrusive and non-intrusive measurements in different heights and radial positions was developed. In order to study the local influences of flame characteristics on the formation of ultrafine particulate matter, results from laser Doppler velocimetry (LDV), suction pyrometer, Fourier transform infrared (FTIR) spectroscopy, and scanning mobility particle sizer (SMPS) spectrometer measurements are presented and discussed. It is shown that the local differences - such as gas temperature, the velocity of the solid fuel particles, and concentrations species - have an influence on the formation of particulate matter. Especially in regions with high local velocities and shear zones, PM coagulates strongly due to the increased turbulence in this area. The data presented in this paper are an important foundation for further studies to validate CFD simulations using the measurements and to further develop existing PM formation models for turbulent cases.
DOI: 10.15468/n3nbjs
title: Nansen Legacy Q2 ice algae biodiversity slurpgun

description: The data was collected during the Nansen Legacy Seasonal Study Q2 from 27 April - 20 May 2021 on research vessel Kronprins Haakon (cruise number 2021704), along a transect in the northern Barents Sea from 76N to 82N. The dataset contains an abundance of ice algae protists, including phytoplankton (autotrophic) and protozooplankton (heterotrophic). Protists were identified and counted with light microscopy using the Utermöhl method and the result are given as cells per liter (cells/L).
DOI: 10.5281/zenodo.7467057
title: Data for "Noise-correlation spectrum for a pair of spin qubits in silicon"

description: This dataset contains the data files for "Noise-correlation spectrum for a pair of spin qubits in silicon".
DOI: 10.5281/zenodo.7467056
title: Data for "Noise-correlation spectrum for a pair of spin qubits in silicon"

description: This dataset contains the data files for "Noise-correlation spectrum for a pair of spin qubits in silicon".
DOI: 10.15468/kfnq8e
title: Nansen Legacy JC2-2 ice algae biodiversity

description: The data was collected during the Nansen Legacy Joint Cruise 2-2 from 24 August - 29 September 2021 on the research vessel RV Kronprins Haakon (cruise number 2021710), along a transect in the northern Barents Sea from 76N to 82N. The dataset contains an abundance of ice algae protists, including phytoplankton (autotrophic) and protozooplankton (heterotrophic). Protists were identified and counted with light microscopy using the Utermöhl method and the result are given as cells per liter (cells/L).
DOI: 10.5281/zenodo.7467369
title: AISA-XMI-Mapper

description: The AISA-XMI-Mapper maps selected classes of UML class diagrams to RDF Schema (RDFS) and Shape Constraint Language (SHACL) documents. RDFS defines the vocabulary of the domain which is described by the UML class diagrams, i.e. classes and class hierarchies. SHACL defines structural constraints of the domain. The mapper is created with the aim of mapping aeronautical UML models (AIXM 5.1.1., FIXM 3.0.1. SESAR) which adhere to a specific modelling style. Therefore, models provided to the mapper must fulfill certain semantic and syntactic requirements.
DOI: 10.5281/zenodo.7467368
title: AISA-XMI-Mapper

description: The AISA-XMI-Mapper maps selected classes of UML class diagrams to RDF Schema (RDFS) and Shape Constraint Language (SHACL) documents. RDFS defines the vocabulary of the domain which is described by the UML class diagrams, i.e. classes and class hierarchies. SHACL defines structural constraints of the domain. The mapper is created with the aim of mapping aeronautical UML models (AIXM 5.1.1., FIXM 3.0.1. SESAR) which adhere to a specific modelling style. Therefore, models provided to the mapper must fulfill certain semantic and syntactic requirements.
DOI: 10.5281/zenodo.6466768
title: Document Liveness Challenge (DLC-2021) - part 1 (or, cg)

description: Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies.
DOI: 10.5281/zenodo.6469588
title: Document Liveness Challenge (DLC-2021) - part 1 (or, cg)

description: Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies.
DOI: 10.5281/zenodo.6586764
title: Document Liveness Challenge (DLC-2021) - part 1 (or, cg)

description: Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies.
DOI: 10.5281/zenodo.6792397
title: Document Liveness Challenge (DLC-2021) - part 1 (or, cg)

description: Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies. <strong>Share and Cite</strong> <em>MDPI and ACS Style</em> Polevoy, D.V.; Sigareva, I.V.; Ershova, D.M.; Arlazarov, V.V.; Nikolaev, D.P.; Ming, Z.; Luqman, M.M.; Burie, J.-C. Document Liveness Challenge Dataset (DLC-2021). <em>J. Imaging</em> <strong>2022</strong>, <em>8</em>, 181. https://doi.org/10.3390/jimaging8070181 <em>AMA Style</em> Polevoy DV, Sigareva IV, Ershova DM, Arlazarov VV, Nikolaev DP, Ming Z, Luqman MM, Burie J-C. Document Liveness Challenge Dataset (DLC-2021). <em>Journal of Imaging</em>. 2022; 8(7):181. https://doi.org/10.3390/jimaging8070181 <em>Chicago/Turabian Style</em> Polevoy, Dmitry V., Irina V. Sigareva, Daria M. Ershova, Vladimir V. Arlazarov, Dmitry P. Nikolaev, Zuheng Ming, Muhammad M. Luqman, and Jean-Christophe Burie. 2022. "Document Liveness Challenge Dataset (DLC-2021)" <em>Journal of Imaging</em> 8, no. 7: 181. https://doi.org/10.3390/jimaging8070181
DOI: 10.5281/zenodo.6466767
title: Document Liveness Challenge (DLC-2021) - part 1 (or, cg)

description: Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies. <strong>Share and Cite</strong> <em>MDPI and ACS Style</em> Polevoy, D.V.; Sigareva, I.V.; Ershova, D.M.; Arlazarov, V.V.; Nikolaev, D.P.; Ming, Z.; Luqman, M.M.; Burie, J.-C. Document Liveness Challenge Dataset (DLC-2021). <em>J. Imaging</em> <strong>2022</strong>, <em>8</em>, 181. https://doi.org/10.3390/jimaging8070181 <em>AMA Style</em> Polevoy DV, Sigareva IV, Ershova DM, Arlazarov VV, Nikolaev DP, Ming Z, Luqman MM, Burie J-C. Document Liveness Challenge Dataset (DLC-2021). <em>Journal of Imaging</em>. 2022; 8(7):181. https://doi.org/10.3390/jimaging8070181 <em>Chicago/Turabian Style</em> Polevoy, Dmitry V., Irina V. Sigareva, Daria M. Ershova, Vladimir V. Arlazarov, Dmitry P. Nikolaev, Zuheng Ming, Muhammad M. Luqman, and Jean-Christophe Burie. 2022. "Document Liveness Challenge Dataset (DLC-2021)" <em>Journal of Imaging</em> 8, no. 7: 181. https://doi.org/10.3390/jimaging8070181
description: fix: dlc-2021.csv (lva_passport/03.re0003;android;hp -&gt; lva_passport/03.re0006;android;hp), thanks Stevie Dale fix: experimental_baseline/graycopy_positive_test.lst, thanks Irina Kunina
DOI: 10.5281/zenodo.7467028
title: Document Liveness Challenge (DLC-2021) - part 1 (or, cg)

description: Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies. <strong>Share and Cite</strong> <em>MDPI and ACS Style</em> Polevoy, D.V.; Sigareva, I.V.; Ershova, D.M.; Arlazarov, V.V.; Nikolaev, D.P.; Ming, Z.; Luqman, M.M.; Burie, J.-C. Document Liveness Challenge Dataset (DLC-2021). <em>J. Imaging</em> <strong>2022</strong>, <em>8</em>, 181. https://doi.org/10.3390/jimaging8070181 <em>AMA Style</em> Polevoy DV, Sigareva IV, Ershova DM, Arlazarov VV, Nikolaev DP, Ming Z, Luqman MM, Burie J-C. Document Liveness Challenge Dataset (DLC-2021). <em>Journal of Imaging</em>. 2022; 8(7):181. https://doi.org/10.3390/jimaging8070181 <em>Chicago/Turabian Style</em> Polevoy, Dmitry V., Irina V. Sigareva, Daria M. Ershova, Vladimir V. Arlazarov, Dmitry P. Nikolaev, Zuheng Ming, Muhammad M. Luqman, and Jean-Christophe Burie. 2022. "Document Liveness Challenge Dataset (DLC-2021)" <em>Journal of Imaging</em> 8, no. 7: 181. https://doi.org/10.3390/jimaging8070181
description: fix: dlc-2021.csv (lva_passport/03.re0003;android;hp -&gt; lva_passport/03.re0006;android;hp), thanks Stevie Dale fix: experimental_baseline/graycopy_positive_test.lst, thanks Irina Kunina
DOI: 10.5281/zenodo.6466769
title: Document Liveness Challenge (DLC-2021) - part 2 (re)

description: Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies. <strong>Share and Cite</strong> <em>MDPI and ACS Style</em> Polevoy, D.V.; Sigareva, I.V.; Ershova, D.M.; Arlazarov, V.V.; Nikolaev, D.P.; Ming, Z.; Luqman, M.M.; Burie, J.-C. Document Liveness Challenge Dataset (DLC-2021). <em>J. Imaging</em> <strong>2022</strong>, <em>8</em>, 181. https://doi.org/10.3390/jimaging8070181 <em>AMA Style</em> Polevoy DV, Sigareva IV, Ershova DM, Arlazarov VV, Nikolaev DP, Ming Z, Luqman MM, Burie J-C. Document Liveness Challenge Dataset (DLC-2021). <em>Journal of Imaging</em>. 2022; 8(7):181. https://doi.org/10.3390/jimaging8070181 <em>Chicago/Turabian Style</em> Polevoy, Dmitry V., Irina V. Sigareva, Daria M. Ershova, Vladimir V. Arlazarov, Dmitry P. Nikolaev, Zuheng Ming, Muhammad M. Luqman, and Jean-Christophe Burie. 2022. "Document Liveness Challenge Dataset (DLC-2021)" <em>Journal of Imaging</em> 8, no. 7: 181. https://doi.org/10.3390/jimaging8070181
description: fix: dlc-2021.csv (lva_passport/03.re0003;android;hp -&gt; lva_passport/03.re0006;android;hp), thanks Stevie Dale
DOI: 10.5281/zenodo.6466770
title: Document Liveness Challenge (DLC-2021) - part 2 (re)

description: Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies.
DOI: 10.5281/zenodo.6792396
title: Document Liveness Challenge (DLC-2021) - part 2 (re)

description: Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies. <strong>Share and Cite</strong> <em>MDPI and ACS Style</em> Polevoy, D.V.; Sigareva, I.V.; Ershova, D.M.; Arlazarov, V.V.; Nikolaev, D.P.; Ming, Z.; Luqman, M.M.; Burie, J.-C. Document Liveness Challenge Dataset (DLC-2021). <em>J. Imaging</em> <strong>2022</strong>, <em>8</em>, 181. https://doi.org/10.3390/jimaging8070181 <em>AMA Style</em> Polevoy DV, Sigareva IV, Ershova DM, Arlazarov VV, Nikolaev DP, Ming Z, Luqman MM, Burie J-C. Document Liveness Challenge Dataset (DLC-2021). <em>Journal of Imaging</em>. 2022; 8(7):181. https://doi.org/10.3390/jimaging8070181 <em>Chicago/Turabian Style</em> Polevoy, Dmitry V., Irina V. Sigareva, Daria M. Ershova, Vladimir V. Arlazarov, Dmitry P. Nikolaev, Zuheng Ming, Muhammad M. Luqman, and Jean-Christophe Burie. 2022. "Document Liveness Challenge Dataset (DLC-2021)" <em>Journal of Imaging</em> 8, no. 7: 181. https://doi.org/10.3390/jimaging8070181
DOI: 10.5281/zenodo.7467004
title: Document Liveness Challenge (DLC-2021) - part 2 (re)

description: Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies. <strong>Share and Cite</strong> <em>MDPI and ACS Style</em> Polevoy, D.V.; Sigareva, I.V.; Ershova, D.M.; Arlazarov, V.V.; Nikolaev, D.P.; Ming, Z.; Luqman, M.M.; Burie, J.-C. Document Liveness Challenge Dataset (DLC-2021). <em>J. Imaging</em> <strong>2022</strong>, <em>8</em>, 181. https://doi.org/10.3390/jimaging8070181 <em>AMA Style</em> Polevoy DV, Sigareva IV, Ershova DM, Arlazarov VV, Nikolaev DP, Ming Z, Luqman MM, Burie J-C. Document Liveness Challenge Dataset (DLC-2021). <em>Journal of Imaging</em>. 2022; 8(7):181. https://doi.org/10.3390/jimaging8070181 <em>Chicago/Turabian Style</em> Polevoy, Dmitry V., Irina V. Sigareva, Daria M. Ershova, Vladimir V. Arlazarov, Dmitry P. Nikolaev, Zuheng Ming, Muhammad M. Luqman, and Jean-Christophe Burie. 2022. "Document Liveness Challenge Dataset (DLC-2021)" <em>Journal of Imaging</em> 8, no. 7: 181. https://doi.org/10.3390/jimaging8070181
description: fix: dlc-2021.csv (lva_passport/03.re0003;android;hp -&gt; lva_passport/03.re0006;android;hp), thanks Stevie Dale
DOI: 10.5281/zenodo.6466763
title: Document Liveness Challenge (DLC-2021) - part 3 (cc)

description: Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies. <strong>Share and Cite</strong> <em>MDPI and ACS Style</em> Polevoy, D.V.; Sigareva, I.V.; Ershova, D.M.; Arlazarov, V.V.; Nikolaev, D.P.; Ming, Z.; Luqman, M.M.; Burie, J.-C. Document Liveness Challenge Dataset (DLC-2021). <em>J. Imaging</em> <strong>2022</strong>, <em>8</em>, 181. https://doi.org/10.3390/jimaging8070181 <em>AMA Style</em> Polevoy DV, Sigareva IV, Ershova DM, Arlazarov VV, Nikolaev DP, Ming Z, Luqman MM, Burie J-C. Document Liveness Challenge Dataset (DLC-2021). <em>Journal of Imaging</em>. 2022; 8(7):181. https://doi.org/10.3390/jimaging8070181 <em>Chicago/Turabian Style</em> Polevoy, Dmitry V., Irina V. Sigareva, Daria M. Ershova, Vladimir V. Arlazarov, Dmitry P. Nikolaev, Zuheng Ming, Muhammad M. Luqman, and Jean-Christophe Burie. 2022. "Document Liveness Challenge Dataset (DLC-2021)" <em>Journal of Imaging</em> 8, no. 7: 181. https://doi.org/10.3390/jimaging8070181
description: fix: dlc-2021.csv (lva_passport/03.re0003;android;hp -&gt; lva_passport/03.re0006;android;hp), thanks Stevie Dale