cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26126/intervals.bd3hgv
title: Lung untargeted proteomics (iTRAQ) [Inferential statistics]

description: This dataset was generated in an in vivo systems toxicology study during which A/J mice were exposed to cigarette smoke or aerosol of a heat-not-burn platform, THS 2.2, for up to 18 months (please refer to the study page for more detail on the study design).
The lung proteome was characterized using iTRAQ (untargeted analysis). The full dataset is available in PRIDE under the accession number PXD016460. There is only data from the whole (left) lung for one time point (month 1).
This dataset contains the statistics resulting from the comparison of treatment and control groups.
DOI: 10.26126/intervals.hgsn7m
title: Lung untargeted proteomics (iTRAQ) [Protein expression level per animal]

description: This dataset was generated in an in vivo systems toxicology study during which A/J mice were exposed to cigarette smoke or aerosol of a heat-not-burn platform, THS 2.2, for up to 18 months (please refer to the study page for more detail on the study design).
The lung proteome was characterized using iTRAQ (untargeted analysis). The full dataset is available in PRIDE under the accession number PXD016460. There is only data from the whole (left) lung for one time point (month 1).
This dataset is the normalized intensity values for each identified protein and each sample.
DOI: 10.26126/intervals.3kso7v
title: Nasal epithelium miRNA [Inferential statistics]

description: This dataset was generated in an in vivo systems toxicology study during which A/J mice were exposed to cigarette smoke or aerosol of a heat-not-burn platform, THS 2.2, for up to 18 months (please refer to the study page for more detail on the study design).
The nasal epithellium miRNome was characterized using Affymetrix GeneChip™ miRNA 4.0 Array. The full dataset is available from ArrayExpress under the identifier: E-MTAB-8541.
This dataset contains the statistics resulting from the comparison of treatment and control groups.
DOI: 10.26126/intervals.3jr8ly
title: Nasal epithelium miRNA [miRNA expression levels per animal]

description: This dataset was generated in an in vivo systems toxicology study during which A/J mice were exposed to cigarette smoke or aerosol of a heat-not-burn platform, THS 2.2, for up to 18 months (please refer to the study page for more detail on the study design).
The nasal epithellium miRNome was characterized using Affymetrix GeneChip™ miRNA 4.0 Array. The full dataset is available from ArrayExpress under the identifier: E-MTAB-8541.
This dataset is the normalized intensity values for each miRNA and each sample.
DOI: 10.26126/intervals.my2iqt
title: Nasal epithelium mRNA [Gene expression levels per animal]

description: This dataset was generated in an in vivo systems toxicology study during which A/J mice were exposed to cigarette smoke or aerosol of a heat-not-burn platform, THS 2.2, for up to 18 months (please refer to the study page for more detail on the study design).
The nasal epithelium transcriptome was characterized using Affymetrix GeneChip® Mouse 430 Array. The full dataset is available from ArrayExpress under the identifier: E-MTAB-8540.
This dataset is the normalized intensity values for each gene and each sample.
DOI: 10.26126/intervals.hq6gnz
title: Nasal epithelium mRNA [Inferential statistics]

description: This dataset was generated in an in vivo systems toxicology study during which A/J mice were exposed to cigarette smoke or aerosol of a heat-not-burn platform, THS 2.2, for up to 18 months (please refer to the study page for more detail on the study design).
The nasal epithelium transcriptome was characterized using Affymetrix GeneChip® Mouse 430 Array. The full dataset is available from ArrayExpress under the identifier: E-MTAB-8540.
This dataset contains the statistics resulting from the comparison of treatment and control groups.
DOI: 10.26126/intervals.dwx8kd
title: Nasal epithelium untargeted proteomics (iTRAQ) [Inferential statistics]

description: This dataset was generated in an in vivo systems toxicology study during which A/J mice were exposed to cigarette smoke or aerosol of a heat-not-burn platform, THS 2.2, for up to 18 months (please refer to the study page for more detail on the study design).
The nasal epithelium proteome was characterized using iTRAQ (untargeted analysis). The full dataset is available in PRIDE under the accession number PXD016466.
This dataset contains the statistics resulting from the comparison of treatment and control groups.
DOI: 10.26126/intervals.x7nndc
title: Nasal epithelium untargeted proteomics (iTRAQ) [Protein expression level per animal]

description: This dataset was generated in an in vivo systems toxicology study during which A/J mice were exposed to cigarette smoke or aerosol of a heat-not-burn platform, THS 2.2, for up to 18 months (please refer to the study page for more detail on the study design).
The nasal epithelium proteome was characterized using iTRAQ (untargeted analysis). The full dataset is available in PRIDE under the accession number PXD016466.
This dataset is the normalized intensity values for each identified protein and each sample.
DOI: 10.26126/intervals.oer3bl
title: Organ weight [Individual measurements]

description: This dataset was generated in an in vivo systems toxicology study during which A/J mice were exposed to cigarette smoke or aerosol of a heat-not-burn platform, THS 2.2, for up to 18 months (please refer to the study page for more detail on the study design).
Gross pathology, including organ weight was assessed.
This dataset includes single measurements for each animal.
DOI: 10.26126/intervals.zv7et6
title: Urinalysis and fecal corticosterone [Individual measurements]

description: This dataset was generated in an in vivo systems toxicology study during which A/J mice were exposed to cigarette smoke or aerosol of a heat-not-burn platform, THS 2.2, for up to 18 months (please refer to the study page for more detail on the study design).
Urinalysis and fecal corticosterone measurements were perfomed.
This dataset includes single measurements for each animal.
DOI: 10.5281/zenodo.3980648
title: Pictogramas ARASAAC Color, Part 3 of 16

description: Black and white pictograms created by Sergio Palao (http://www.palao.es) for ARASAAC (http://www.arasac.org).
DOI: 10.5281/zenodo.3980647
title: Pictogramas ARASAAC Color, Part 3 of 16

description: Black and white pictograms created by Sergio Palao (http://www.palao.es) for ARASAAC (http://www.arasac.org).
DOI: 10.15161/oar.it/30617
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5517/ccdc.csd.cc20kqbn
title: CCDC 1864071: Experimental Crystal Structure Determination

description: Related Article: Shuo Wu, Min Li, Zhengyi Yang, Zhengqiang Xia, Bin Liu, Qi Yang, Qing Wei, Gang Xie, Sanping Chen, Shengli Gao, Jack Y. Lu|2020|Green Chemistry|22|5050|doi:10.1039/D0GC01594F
DOI: 10.5517/ccdc.csd.cc22z2ms
title: CCDC 1935535: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z2nt
title: CCDC 1935536: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z2pv
title: CCDC 1935537: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z2qw
title: CCDC 1935538: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z2rx
title: CCDC 1935539: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z2sy
title: CCDC 1935540: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z2tz
title: CCDC 1935541: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z2v0
title: CCDC 1935542: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z2w1
title: CCDC 1935543: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z2x2
title: CCDC 1935544: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z2y3
title: CCDC 1935545: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z2z4
title: CCDC 1935546: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z306
title: CCDC 1935547: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc22z317
title: CCDC 1935548: Experimental Crystal Structure Determination

description: Related Article: Jermaine A. Smith, Marvadeen A. Singh-Wilmot, Korey P. Carter, Christopher L. Cahill, J. August Ridenour|2020|New J.Chem.|44|12317|doi:10.1039/D0NJ02604B
DOI: 10.5517/ccdc.csd.cc249rcl
title: CCDC 1975579: Experimental Crystal Structure Determination

description: Related Article: Bo-Kai Ling, Junhao Li, Yuan-Qi Zhai, Hung-Kai Hsu, Yi-Tsu Chan, Wei-Peng Chen, Tian Han, Yan-Zhen Zheng|2020|Chem.Commun.|56|9130|doi:10.1039/D0CC02898C
DOI: 10.5285/e2386bf1-926d-4c32-9b54-a3cf8f143cc6
title: Ground Penetrating Radar Data from BGS Iceland Glacier Observatory Project, 2012-2014

description: These files contain ground penetrating radar (GPR) data collected from the glacier margins and forelands of Falljökull and of Kvíárjökull, south-east Iceland, between 2012 and 2014.  The data were collected using a Sensors and Software PulseEKKO Pro GPR system.  For each glacier the data are stored in folders that indicate the month and year in which the surveys were conducted.   Each GPR profile has a Sensors and Software GPR (.DT1) file, and associated header (.HD) and GPS (.GPS) files.   The .HD files (which can be opened as text files) give the parameters and equipment used for each profile.  GPS files are not available for some of the profiles collected on Falljökull in April 2013 (due to damage that occurred to the GPS linked with the PulseEKKO Pro system).  For these profiles start, finish, and mid profile positions were recorded using differential GPS, and locations of these profiles are instead given by GIS shapefiles in the relevant folders.

These datasets have been used in the publications listed below.   Further information relating to the data collection methodology can be found therein. 

Phillips, Emrys; Everest, Jez; Evans, David J.A.; Finlayson, Andrew; Ewertowski, Marek; Guild, Ailsa; Jones, Lee. 2017 Concentrated, ‘pulsed’ axial glacier flow: structural glaciological evidence from Kvíárjökull in SE Iceland. Earth Surface Processes and Landforms, 42 (13). 1901-1922. https://doi.org/10.1002/esp.4145 

Phillips, Emrys; Finlayson, Andrew; Bradwell, Tom; Everest, Jez; Jones, Lee. 2014 Structural evolution triggers a dynamic reduction in active glacier length during rapid retreat: evidence from Falljökull, SE Iceland. Journal of Geophysical Research: Earth Surface, 119 (10). 2194-2208. https://doi.org/10.1002/2014JF003165

Phillips, Emrys; Finlayson, Andrew; Jones, Lee. 2013 Fracturing, block-faulting and moulin development associated with progressive collapse and retreat of a polar maritime glacier: Virkisjokul-Falljokull, SE Iceland. Journal of Geophysical Research: Earth Surface, 118 (3). 1545-1561. https://doi.org/10.1002/jgrf.20116

Flett, Verity; Maurice, Louise; Finlayson, Andrew; Black, Andrew; MacDonald, Alan; Everest, Jez; Kirkbride, Martin.  2017. Meltwater flow through a rapidly deglaciating glacier and foreland catchment system: Virkisjökull, SE Iceland.  Hydrology Research, 48 (6). 1666-1681. https://doi.org/10.2166/nh.2017.205
DOI: 10.5281/zenodo.3980685
title: Pictogramas ARASAAC Color, Part 4 of 16

description: Black and white pictograms created by Sergio Palao (http://www.palao.es) for ARASAAC (http://www.arasac.org).
DOI: 10.5281/zenodo.3980684
title: Pictogramas ARASAAC Color, Part 4 of 16

description: Black and white pictograms created by Sergio Palao (http://www.palao.es) for ARASAAC (http://www.arasac.org).
DOI: 10.21227/9wbt-6s35
title: Fast Localization with Unknown Transmit Power and Path-Loss Exponent in WSNs Based on RSS Measurements

description: The data has been used to generate some of the figures presented in "Fast Localization with Unknown Transmit Power and Path-Loss Exponent in WSNs Based on RSS Measurements".The measurement data belongs to the authors of "VOR base stations for indoor 802.11 positioning"
DOI: 10.21227/5gww-s624
title: Fast Localization with Unknown Transmit Power and Path-Loss Exponent in WSNs Based on RSS Measurements

description: The data has been used to generate some of the figures presented in "Fast Localization with Unknown Transmit Power and Path-Loss Exponent in WSNs Based on RSS Measurements".The measurement data belongs to the authors of "VOR base stations for indoor 802.11 positioning"
DOI: 10.25592/uhhfdm.1373
title: 3D Model of Xeroderma pigmentosum

description: 3D Model of Xeroderma pigmentosum This 3D scan was made possible within the framework of the Hamburg Open Science programme by funding from the Ministry of Science, Research and Equality of the Free and Hanseatic City of Hamburg.
DOI: 10.5281/zenodo.3980739
title: Pictogramas ARASAAC Color, Part 5 of 16

description: Black and white pictograms created by Sergio Palao (http://www.palao.es) for ARASAAC (http://www.arasac.org).
DOI: 10.5281/zenodo.3980738
title: Pictogramas ARASAAC Color, Part 5 of 16

description: Black and white pictograms created by Sergio Palao (http://www.palao.es) for ARASAAC (http://www.arasac.org).
DOI: 10.17638/datacat.liverpool.ac.uk/996
title: Amino acid residues determine the response of flexible metal-organic frameworks to guests
DOI: 10.5281/zenodo.3966850
title: Landmark Data of Objects as Basis for Navigation and Wayfinding Applications

description: This dataset is the result of a data collection project and includes objects of the inner city of Augsburg and their so-called landmark dimensions consisting of visual, semantic, and structural dimensions. All the attributes of these dimensions are available for 47 objects - mainly buildings but also fountains and monuments - located at street intersections. For 302 further objects the attributes color and surface area of the visual dimension are available. The dataset for the landmark dimensions is based on OpenStreetMap data and additional data which was collected during a field study. The dataset might be used as a basis for landmark identification, validation of landmark models, as well as for landmark integration as a basis for navigation and wayfinding applications.
DOI: 10.5281/zenodo.3966849
title: Landmark Data of Objects as Basis for Navigation and Wayfinding Applications

description: This dataset is the result of a data collection project and includes objects of the inner city of Augsburg and their so-called landmark dimensions consisting of visual, semantic, and structural dimensions. All the attributes of these dimensions are available for 47 objects - mainly buildings but also fountains and monuments - located at street intersections. For 302 further objects the attributes color and surface area of the visual dimension are available. The dataset for the landmark dimensions is based on OpenStreetMap data and additional data which was collected during a field study. The dataset might be used as a basis for landmark identification, validation of landmark models, as well as for landmark integration as a basis for navigation and wayfinding applications.
DOI: 10.5281/zenodo.1100964
title: TUT Acoustic scenes 2017, Evaluation & Development datasets, processed image

description: Unseparated Pulse Energy Spectrogram Processed audio data. Sound source separation is a <strong>preliminary</strong> for <strong>acoustic scene classification</strong>. It can be argued that rare sound detection can be performed without separation, but in most cases it also depends on it. I have come up with the theory that the full <strong>time-domain</strong>, or if assumptions are made on the amplitude-waveform or the phase-profile, even the <strong>sequence</strong> of events can be <strong>discarded</strong> for acoustic scene classification. For short time frame bins, a <strong>statistical representation</strong> should be enough to correctly identify the scene. Even more so, if deep learning methods are applied. I have also come up with the theory that <strong>energy</strong> scalograms are applied <strong>pulse-length</strong> or waveform/profile-length wise. This can enhance the input representation for machine learning. Furthermore I have used derivatives of the time signal and applied similar signal processing methods to them. For visualisation I have added them to the original scalogram in different colors. The use of <strong>derivatives</strong> is very much <strong>distorted</strong>, if the sound is not separated.
DOI: 10.5281/zenodo.1100963
title: TUT Acoustic scenes 2017, Evaluation & Development datasets, processed image

description: Unseparated Pulse Energy Spectrogram Processed audio data. Sound source separation is a <strong>preliminary</strong> for <strong>acoustic scene classification</strong>. It can be argued that rare sound detection can be performed without separation, but in most cases it also depends on it. I have come up with the theory that the full <strong>time-domain</strong>, or if assumptions are made on the amplitude-waveform or the phase-profile, even the <strong>sequence</strong> of events can be <strong>discarded</strong> for acoustic scene classification. For short time frame bins, a <strong>statistical representation</strong> should be enough to correctly identify the scene. Even more so, if deep learning methods are applied. I have also come up with the theory that <strong>energy</strong> scalograms are applied <strong>pulse-length</strong> or waveform/profile-length wise. This can enhance the input representation for machine learning. Furthermore I have used derivatives of the time signal and applied similar signal processing methods to them. For visualisation I have added them to the original scalogram in different colors. The use of <strong>derivatives</strong> is very much <strong>distorted</strong>, if the sound is not separated.
DOI: 10.5517/ccdc.csd.cc218wv2
title: CCDC 1885384: Experimental Crystal Structure Determination

description: Related Article: Maria Książek, Marek Weselski, Agnieszka Dreczko, Vladyslav Maliuzhenko, Marcin Kaźmierczak, Aleksandra Tołoczko, Joachim Kusz, Robert Bronisz|2020|Dalton Trans.|49|9811|doi:10.1039/D0DT01696A
DOI: 10.5517/ccdc.csd.cc23ry83
title: CCDC 1959425: Experimental Crystal Structure Determination

description: Related Article: Dragiša Obradović, Stefan Nikolić, Ivana Milenković, Marina Milenković, Predrag Jovanović, Vladimir Savić, Alexander Roller, Marija Đorđić Crnogorac, Tatjana Stanojković, Sanja Grgurić-Šipka|2020|J.Inorg.Biochem.|210|111164|doi:10.1016/j.jinorgbio.2020.111164
DOI: 10.5517/ccdc.csd.cc23xdn3
title: CCDC 1963715: Experimental Crystal Structure Determination

description: Related Article: Yong-Sheng Shi, Qing-Qing Xiao, Lianshe Fu, Guang-Hua Cui|2020|CrystEngComm|22|4875|doi:10.1039/D0CE00594K
DOI: 10.5517/ccdc.csd.cc23xdp4
title: CCDC 1963716: Experimental Crystal Structure Determination

description: Related Article: Yong-Sheng Shi, Qing-Qing Xiao, Lianshe Fu, Guang-Hua Cui|2020|CrystEngComm|22|4875|doi:10.1039/D0CE00594K
DOI: 10.5517/ccdc.csd.cc249kdf
title: CCDC 1975394: Experimental Crystal Structure Determination

description: Related Article: Rui Wang, Min Zhang, Wenfeng Wang, Xucheng Wang, Yaofeng Yuan, Junjian Li|2020|J.Mol.Struct.|1222|128826|doi:10.1016/j.molstruc.2020.128826
DOI: 10.5517/ccdc.csd.cc24cvp2
title: CCDC 1977604: Experimental Crystal Structure Determination

description: Related Article: Zhiping Yang, Xiaodong Gu, Li-Biao Han, Jun (Joelle) Wang|2020|Chemical Science|11|7451|doi:10.1039/D0SC01049A
DOI: 10.5517/ccdc.csd.cc24nlm1
title: CCDC 1986003: Experimental Crystal Structure Determination

description: Related Article: Lídia Bofill, Dafne de Sande, Rafael Barbas, Antonio Frontera, Rafel Prohens|2020|CrystEngComm|22|4680|doi:10.1039/D0CE00837K
DOI: 10.5517/ccdc.csd.cc24zjh5
title: CCDC 1995547: Experimental Crystal Structure Determination

description: Related Article: Parvin Safari, Stephen A. Moggach, Paul J. Low|2020|Dalton Trans.|49|9835|doi:10.1039/D0DT01794A
DOI: 10.5517/ccdc.csd.cc250t12
title: CCDC 1996773: Experimental Crystal Structure Determination

description: Related Article: Manpreet Singh, Avijit Kumar Paul, Virender Singh|2020|New J.Chem.|44|12370|doi:10.1039/D0NJ02315A
DOI: 10.5517/ccdc.csd.cc251whm
title: CCDC 1997810: Experimental Crystal Structure Determination

description: Related Article: Xiuling Zhang, Feng Jin|2020|Inorg.Chem.Commun.|119|108075|doi:10.1016/j.inoche.2020.108075
DOI: 10.5517/ccdc.csd.cc2524dt
title: CCDC 1998055: Experimental Crystal Structure Determination

description: Related Article: Sanaz Malali, Jan Chyba, Michal Knor, Michal Horní, Marek Nečas, Jan Novotný, Radek Marek|2020|Inorg.Chem.|59|10185|doi:10.1021/acs.inorgchem.0c01328
DOI: 10.5517/ccdc.csd.cc2524gw
title: CCDC 1998057: Experimental Crystal Structure Determination

description: Related Article: Sanaz Malali, Jan Chyba, Michal Knor, Michal Horní, Marek Nečas, Jan Novotný, Radek Marek|2020|Inorg.Chem.|59|10185|doi:10.1021/acs.inorgchem.0c01328