cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25403/upresearchdata.21710243.v1
title: The impact of agro-input among smallholder farmers in Sironko district, Uganda

description: Dataset for a cross-sectional survey of 249 participants. The data was collected on 126 savings groups (SGs) participants and 123 non-participants.This study took place in Uganda in the district of Sironko. The study was a cross sectional survey conducted using a face-to-face structured questionnaire. The study had two populations, the population of savings group members, and that of non-savings group members. Within the population of savings group members, there were strata, that is, the savings groups were composed of between 4 to 90 individuals. While the population of the non-savings group members was uniform. Within the population of savings group members, stratified random sampling was used to obtain the respondents. All 300 SGs gathered from the Sironko district local government and non-governmental organisations were tabulated in the Excel program with their corresponding contact details and addresses. The groups were sampled by simple random sampling using the Excel function (=RAND). This function assigns a random number to each row within the table. The table was then put in descending order using the random numbers column. The first 125 groups were picked to be interviewed for the study. Only one participant was interviewed from each SG. The data was entered and coded using Excel before exporting to STATA 15, the software package used for analysis.
DOI: 10.6084/m9.figshare.21755804
title: data.zip

description: the spatial distribution of Nucleic Acid Testing sites in the inner city of Wuhan,China, which contains the addresses, coordinates, and service hours.
DOI: 10.6084/m9.figshare.21755804.v1
title: data.zip

description: the spatial distribution of Nucleic Acid Testing sites in the inner city of Wuhan,China, which contains the addresses, coordinates, and service hours.
DOI: 10.6084/m9.figshare.21755843
title: dunes_migration.csv

description: Migration directions of barchan dunes on Mars
DOI: 10.5281/zenodo.6912146
title: Test

description: Test for KPR
DOI: 10.5281/zenodo.6912145
title: Test

description: Test for KPR
DOI: 10.5517/ccdc.csd.cc25lvl7
title: CCDC 2014119: Experimental Crystal Structure Determination

description: Related Article: Swadhin Garain, Suman Kuila, Bidhan Chandra Garain, Meenal Kataria, Aditya Borah, Swapan K. Pati, Subi J. George|2021|Angew.Chem.,Int.Ed.|60|12323|doi:10.1002/anie.202101538
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc25lvrd
title: CCDC 2014124: Experimental Crystal Structure Determination

description: Related Article: Swadhin Garain, Suman Kuila, Bidhan Chandra Garain, Meenal Kataria, Aditya Borah, Swapan K. Pati, Subi J. George|2021|Angew.Chem.,Int.Ed.|60|12323|doi:10.1002/anie.202101538
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc25qvd5
title: CCDC 2017957: Experimental Crystal Structure Determination

description: Related Article: E. Carolina Sañudo, Guillem Gabarró-Riera, Jonay Gonzalez, Pablo Sevilla, Jesus Jover, Jorge Echeverria, Sara Fuertes, Ana Arauzo, Elena Bartolomé|2021|Angew.Chem.,Int.Ed.|60|12001|doi:10.1002/anie.202100507
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2613m0
title: CSD 2026861: Experimental Crystal Structure Determination

description: Related Article: Florian Puls, Philipp Linke, Olga Kataeva, Hans-Joachim Knölker|2021|Angew.Chem.,Int.Ed.|60|14083|doi:10.1002/anie.202103222
description: Related Article: Elvis Shoko, Yun Dang, Guopeng Han, Benjamin B. Duff, Matthew S. Dyer, Luke M. Daniels, Ruiyong Chen, Frédéric Blanc, John B. Claridge, Matthew J. Rosseinsky|2021|Inorg.Chem.|60|14083|doi:10.1021/acs.inorgchem.1c01396
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2669g6
title: CSD 2031847: Experimental Crystal Structure Determination

description: Related Article: Hong-Xin Tang, Rui-Biao Fu, Zu-Ju Ma, Xin-Tao Wu|2021|Inorg.Chem.|60|17364|doi:10.1021/acs.inorgchem.1c02861
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc26c693
title: CSD 2036554: Experimental Crystal Structure Determination

description: Related Article: Yang Zhou, Xiaomeng Liu, Zheshuai Lin, Yanqiang Li, Qingran Ding, Youchao Liu, Yangxin Chen, Sangen Zhao, Maochun Hong, Junhua Luo|2021|Inorg.Chem.|60|18950|doi:10.1021/acs.inorgchem.1c02764
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc26c6c5
title: CSD 2036556: Experimental Crystal Structure Determination

description: Related Article: Yang Zhou, Xiaomeng Liu, Zheshuai Lin, Yanqiang Li, Qingran Ding, Youchao Liu, Yangxin Chen, Sangen Zhao, Maochun Hong, Junhua Luo|2021|Inorg.Chem.|60|18950|doi:10.1021/acs.inorgchem.1c02764
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc26gmfr
title: CSD 2039844: Experimental Crystal Structure Determination

description: Related Article: Joscha Breibeck, Elias Tanuhadi, Nadiia I. Gumerova, Gerald Giester, Alexander Prado-Roller, Annette Rompel|2021|Inorg.Chem.|60|15096|doi:10.1021/acs.inorgchem.1c02005
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc26gmp0
title: CSD 2039852: Experimental Crystal Structure Determination

description: Related Article: Joscha Breibeck, Elias Tanuhadi, Nadiia I. Gumerova, Gerald Giester, Alexander Prado-Roller, Annette Rompel|2021|Inorg.Chem.|60|15096|doi:10.1021/acs.inorgchem.1c02005
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc267n26
title: CSD 2033137: Experimental Crystal Structure Determination

description: Related Article: Tuba Iftikhar, Natalya V. Izarova, Jan Leusen, Paul Kögerler|2021|Chem.-Eur.J.|27|8500|doi:10.1002/chem.202004894
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc267n37
title: CSD 2033138: Experimental Crystal Structure Determination

description: Related Article: Tuba Iftikhar, Natalya V. Izarova, Jan Leusen, Paul Kögerler|2021|Chem.-Eur.J.|27|8500|doi:10.1002/chem.202004894
description: Related Article: Tuba Iftikhar, Natalya V. Izarova, Jan Leusen, Paul Kögerler|2021|Chem.-Eur.J.|27|8500|doi:10.1002/chem.202004894
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc26nmj1
title: CSD 2045613: Experimental Crystal Structure Determination

description: Related Article: Manuel Löber, Markus Ströbele, Klaus Eichele, Carl P. Romao, Hans-Jürgen Meyer|2021|Eur.J.Inorg.Chem.|2021|4929|doi:10.1002/ejic.202100771
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc26p5hl
title: CSD 2046139: Experimental Crystal Structure Determination

description: Related Article: E. Carolina Sañudo, Guillem Gabarró-Riera, Jonay Gonzalez, Pablo Sevilla, Jesus Jover, Jorge Echeverria, Sara Fuertes, Ana Arauzo, Elena Bartolomé|2021|Angew.Chem.,Int.Ed.|60|12001|doi:10.1002/anie.202100507
description: Related Article: Tianyu Zhu, Olivier Mentré, Haoming Yang, Yong Jin, Xinan Zhang, Ángel M. Arévalo-López, Clemens Ritter, Kwang-Yong Choi, Minfeng Lü|2021|Inorg.Chem.|60|12001|doi:10.1021/acs.inorgchem.1c01074
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc26p5jm
title: CSD 2046140: Experimental Crystal Structure Determination

description: Related Article: E. Carolina Sañudo, Guillem Gabarró-Riera, Jonay Gonzalez, Pablo Sevilla, Jesus Jover, Jorge Echeverria, Sara Fuertes, Ana Arauzo, Elena Bartolomé|2021|Angew.Chem.,Int.Ed.|60|12001|doi:10.1002/anie.202100507
description: Related Article: Tianyu Zhu, Olivier Mentré, Haoming Yang, Yong Jin, Xinan Zhang, Ángel M. Arévalo-López, Clemens Ritter, Kwang-Yong Choi, Minfeng Lü|2021|Inorg.Chem.|60|12001|doi:10.1021/acs.inorgchem.1c01074
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7458663
title: FORAIR Dataset

description: The dataset generated by FARM (Flexible Air quality Regional Model) from January 2019 to December 2021. It's composed of numpy (npy) files, which collect air pollutant concentration levels, i.e. carbon monoxide, nitrogen dioxide and ozone, at the sea level. Overall, there are two types of FARM data, the low and the high resolution one. Low resolution (LR) data have 56 points as longitudes and 62 points as latitudes, representing the atmospheric concentrations on a lattice of 20 Km. Instead, high resolution (HR) data have 276 points as longitudes and 306 points as latitudes, representing the reticular atmospheric concentrations on a lattice of 4 Km. Furthermore, there are two different dataset. The first one, is based on considering separately the three variables and is called <em>1D</em>. The second one considers the three variables together and is called <em>3D</em>.
DOI: 10.5281/zenodo.7458662
title: FORAIR Dataset

description: The dataset generated by FARM (Flexible Air quality Regional Model) from January 2019 to December 2021. It's composed of numpy (npy) files, which collect air pollutant concentration levels, i.e. carbon monoxide, nitrogen dioxide and ozone, at the sea level. Overall, there are two types of FARM data, the low and the high resolution one. Low resolution (LR) data have 56 points as longitudes and 62 points as latitudes, representing the atmospheric concentrations on a lattice of 20 Km. Instead, high resolution (HR) data have 276 points as longitudes and 306 points as latitudes, representing the reticular atmospheric concentrations on a lattice of 4 Km. Furthermore, there are two different dataset. The first one, is based on considering separately the three variables and is called <em>1D</em>. The second one considers the three variables together and is called <em>3D</em>.
DOI: 10.5281/zenodo.7268848
title: Substrate-Binding Guides Individual Melibiose Permeases MelB to Structurally Soften and to Destabilize Cytoplasmic Middle-Loop C3. Blaimschein et al.: Data sets

description: Data sets used in the manuscript 'Substrate-Binding Guides Individual Melibiose Permeases MelB to Structurally Soften and to Destabilize Cytoplasmic Middle-Loop C3' by Blaimschein et al. The analysis scripts can be found at https://doi.org/10.5281/zenodo.7268838
DOI: 10.5281/zenodo.7268847
title: Substrate-Binding Guides Individual Melibiose Permeases MelB to Structurally Soften and to Destabilize Cytoplasmic Middle-Loop C3. Blaimschein et al.: Data sets

description: Data sets used in the manuscript 'Substrate-Binding Guides Individual Melibiose Permeases MelB to Structurally Soften and to Destabilize Cytoplasmic Middle-Loop C3' by Blaimschein et al. The analysis scripts can be found at https://doi.org/10.5281/zenodo.7268838
DOI: 10.34740/kaggle/dsv/4747910
title: AutoXGB
DOI: 10.5281/zenodo.7426548
title: Data complementing the Avian influenza overview September– December 2022

description: Annex A – Characteristics of the HPAI A(H5Nx)-positive poultry establishments The Annex contains table with the characteristics of the HPAI A(H5Nx)-positive poultry establishments by affected EU Member State from 3 September- 18 November 2022. In addition, the Netherlands reported on the characteristics of the HPAI A(H5Nx)-positive poultry establishments affected before 3 September that were not included in the previous published report; this information is also included in Annex A. Annex B – Applied prevention and control measures on avian influenza The Annex contains an overview of specific prevention and control measures applied in Belgium, Bulgaria, Croatia, Denmark, France, Germany, Hungary, Ireland, Moldova, Norway, the Netherlands, Poland, Portugal, Spain from 3 September- 18 November 2022. in relation to HPAI outbreaks in poultry and in wild birds. Annex C – Data on wild birds The Annex contains tables and plots on HPAI virus notifications in wild birds in Europe.
description: EU; en; pdf; biohaw@efsa.europa.eu
DOI: 10.5281/zenodo.7426549
title: Data complementing the Avian influenza overview September– December 2022

description: Annex A – Characteristics of the HPAI A(H5Nx)-positive poultry establishments The Annex contains table with the characteristics of the HPAI A(H5Nx)-positive poultry establishments by affected EU Member State from 3 September- 18 November 2022. In addition, the Netherlands reported on the characteristics of the HPAI A(H5Nx)-positive poultry establishments affected before 3 September that were not included in the previous published report; this information is also included in Annex A. Annex B – Applied prevention and control measures on avian influenza The Annex contains an overview of specific prevention and control measures applied in Belgium, Bulgaria, Croatia, Denmark, France, Germany, Hungary, Ireland, Moldova, Norway, the Netherlands, Poland, Portugal, Spain from 3 September- 18 November 2022. in relation to HPAI outbreaks in poultry and in wild birds. Annex C – Data on wild birds The Annex contains tables and plots on HPAI virus notifications in wild birds in Europe.
description: EU; en; pdf; biohaw@efsa.europa.eu
DOI: 10.5281/zenodo.7395772
title: Dataset related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment"

description: This record contains raw data related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment" Abstract Background Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED). Methods In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi<sup>42</sup>(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability. Results In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF − 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s). Conclusions Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.
DOI: 10.5281/zenodo.7395773
title: Dataset related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment"

description: This record contains raw data related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment" Abstract Background Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED). Methods In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi<sup>42</sup>(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability. Results In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF − 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s). Conclusions Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.