cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2b9mt3
title: CCDC 2154215: Experimental Crystal Structure Determination

description: Related Article: Liane Hildegard Staub, Jessica Lambert, Carsten Müller, Bernd Morgenstern, Michael Zimmer, Joshua Warken, Aylin Koldemir, Theresa Block, Rainer Pöttgen, André Schäfer|2022|Dalton Trans.|51|10714|doi:10.1039/D2DT00582D
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.cc2b9mv4
title: CCDC 2154216: Experimental Crystal Structure Determination

description: Related Article: Liane Hildegard Staub, Jessica Lambert, Carsten Müller, Bernd Morgenstern, Michael Zimmer, Joshua Warken, Aylin Koldemir, Theresa Block, Rainer Pöttgen, André Schäfer|2022|Dalton Trans.|51|10714|doi:10.1039/D2DT00582D
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.cc2b9mw5
title: CCDC 2154217: Experimental Crystal Structure Determination

description: Related Article: Liane Hildegard Staub, Jessica Lambert, Carsten Müller, Bernd Morgenstern, Michael Zimmer, Joshua Warken, Aylin Koldemir, Theresa Block, Rainer Pöttgen, André Schäfer|2022|Dalton Trans.|51|10714|doi:10.1039/D2DT00582D
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.cc2bbhfm
title: CCDC 2155040: Experimental Crystal Structure Determination

description: Related Article: Zhou Lu, Yudan Liao, Weigang Fan, Shengyu Dai|2022|Polym.Chem.|13|4090|doi:10.1039/D2PY00282E
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.cc2bn8pz
title: CCDC 2164441: Experimental Crystal Structure Determination

description: Related Article: Saly Hawila, Ahmad Abdallah, Jean-Luc Rukemampunzi, Alexandra Fateeva, Gilles Ledoux, Régis Debord, Stéphane Paihlès, Nathalie Guillou, Florian Massuyeau, Romain Gautier, Adel Mesbah, Aude Demessence|2022|Chem.Commun.|58|8081|doi:10.1039/D2CC02114E
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.cc2bqf5p
title: CCDC 2166502: Experimental Crystal Structure Determination

description: Related Article: Taichi Koike, Takeaki Iwamoto|2022|Chem.Commun.|58|8061|doi:10.1039/D2CC02160A
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.cc2bqf6q
title: CCDC 2166503: Experimental Crystal Structure Determination

description: Related Article: Taichi Koike, Takeaki Iwamoto|2022|Chem.Commun.|58|8061|doi:10.1039/D2CC02160A
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.cc2bqf7r
title: CCDC 2166504: Experimental Crystal Structure Determination

description: Related Article: Taichi Koike, Takeaki Iwamoto|2022|Chem.Commun.|58|8061|doi:10.1039/D2CC02160A
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.cc1jt8zl
title: CCDC 1424480: Experimental Crystal Structure Determination

description: Related Article: Romain Gautier, Michael Paris, Florian Massuyeau|2022|Dalton Trans.|51|10758|doi:10.1039/D2DT00614F
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.cc1jt94s
title: CCDC 1424485: Experimental Crystal Structure Determination

description: Related Article: Romain Gautier, Michael Paris, Florian Massuyeau|2022|Dalton Trans.|51|10758|doi:10.1039/D2DT00614F
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.cc1jt95t
title: CCDC 1424486: Experimental Crystal Structure Determination

description: Related Article: Romain Gautier, Michael Paris, Florian Massuyeau|2022|Dalton Trans.|51|10758|doi:10.1039/D2DT00614F
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.cc1jt96v
title: CCDC 1424487: Experimental Crystal Structure Determination

description: Related Article: Romain Gautier, Michael Paris, Florian Massuyeau|2022|Dalton Trans.|51|10758|doi:10.1039/D2DT00614F
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: 10.5281/zenodo.6862040
title: The conventional versus a constructionist Scratch programming and first-year students' achievements in higher education classes: experimental data.

description: Globally, learning or teaching the first programming (popularly called CS1) remains a significant educational challenge. Indicators such as CS1 students' engagement, failure and attrition rates, and lack of diversity, continue to show the need for innovating the learning or teaching of novice computer science students. To ease initiating novices to programming, Scratch, a visual programming language, has become a staple of K-12 CS1 classes. As outcomes of a research project aiming to explore a constructionist Scratch pedagogy with novice CS students in higher education, we present these datasets. In the research lasting two successive academic sessions, we conducted two quasi-experimental studies involving four intact CS1 classes in selected public polytechnic in the north central Nigeria. In each study, we randomly assigned the classes to the experimental and control groups, constituting the constructionist Scratch and the conventional CS1 classes, respectively. Instruments for collecting data include a student profile questionnaire, a pretest, and posttest. Sequel to ethical clearance and permission from the selected schools, we conducted each study during the first semester of each academic session, in the first seven to eight weeks. During the first to second week, we administered students who consented to take part with the questionnaire and the pretest. Learning or teaching in the two classes lasted six weeks. Then both classes took the posttest. An independent CS educator who is not part of this research marked all the achievement tests, following a rubric prepared by the first author. To strengthen the research design and the possibility of arriving at valid causal evidence, we employed a Coarsened Exact Matching (CEM) algorithm to generate matched samples of experimental and control data, which we used in the analysis. Data presented here includes the raw, unmatched and matched experimental datasets from both studies. A researcher can make use of the data: To explore if some background variables not addressed in the original research may moderate CS1 students' achievements. For instance, their prior achievements in mathematics, physics, or English. To uncover some interesting patterns using machine learning algorithms. To validate the outcome of the original experiment by using the unmatched, matched or newly generated matched samples. The authors welcome further research collaborations in using the data or the accompanying research instruments. Enable Ginger<em>Cannot connect to Ginger</em> Check your internet connection<br> or reload the browserDisable in this text fieldRephraseRephrase current sentence4Edit in Ginger×
DOI: 10.5281/zenodo.6861843
title: Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

description: <strong>Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"</strong> <em>cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</em> These are CSV files of data in the figures and tables published in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19". <strong>50-e0diffT.csv</strong> Figure 1: Life expectancy changes 2019/20 and 2020/21 across countries. The countries are ordered by increasing cumulative life expectancy losses since 2019. Grey dots indicate the average annual LE changes over the years 2015 through 2019. <strong>51-arriagaT.csv</strong> Figure 2: Age contributions to life expectancy changes since 2019 separated for 2020 and 2021. The position of the arrowhead indicates the total contribution of mortality changes in a given age group to the change in life expectancy at birth since 2019. The discontinuity in the arrow indicates those contributions separately for the years 2020 and 2021. Annual contributions can compound or reverse. The total life expectancy change from 2019 to 2021 in a given country is the sum of the arrowhead positions across age. <strong>52-sexdiff.csv</strong> Figure 3: Change in the female life expectancy advantage from 2019 through 2021. Blue colors indicate an increase and red colors a decrease in the female life expectancy advantage. Muted colors indicate non-significant changes. <strong>53-e0diffcodT.csv</strong> Figure 4: Life expectancy deficit in 2021 decomposed into contributions by age and cause of death. LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued. <strong>55-vaxe0.csv</strong> Figure 5: Years of life expectancy deficit during October through December 2021 contributed by ages &lt;60 and 60+ against % of population twice vaccinated by October 1st in the respective age groups. LE deficit is defined as the counterfactual LE from a Lee-Carter mortality forecast based on death rates for the fourth quarter of the years 2015 to 2019 minus observed LE. <strong>54-tab_arriaga.csv</strong> Table 1: Months of life expectancy (LE) changes and deficits (labelled ES) since the start of the pandemic attributed to age-specific mortality changes (labelled AT). LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.
DOI: 10.5281/zenodo.6224375
title: Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

description: <strong>Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"</strong> <em>cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</em> These are CSV files of data in the figures and tables published in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19". <strong>50-e0diffT.csv</strong> Figure 1: Life expectancy changes 2019/20 and 2020/21 across countries. The countries are ordered by increasing cumulative life expectancy losses since 2019. Grey dots indicate the average annual LE changes over the years 2015 through 2019. <strong>51-arriagaT.csv</strong> Figure 2: Age contributions to life expectancy changes since 2019 separated for 2020 and 2021. The position of the arrowhead indicates the total contribution of mortality changes in a given age group to the change in life expectancy at birth since 2019. The discontinuity in the arrow indicates those contributions separately for the years 2020 and 2021. Annual contributions can compound or reverse. The total life expectancy change from 2019 to 2021 in a given country is the sum of the arrowhead positions across age. <strong>52-sexdiff.csv</strong> Figure 3: Change in the female life expectancy advantage from 2019 through 2021. Blue colors indicate an increase and red colors a decrease in the female life expectancy advantage. Muted colors indicate non-significant changes. <strong>53-e0diffcodT.csv</strong> Figure 4: Life expectancy deficit in 2021 decomposed into contributions by age and cause of death. LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued. <strong>55-vaxe0.csv</strong> Figure 5: Years of life expectancy deficit during October through December 2021 contributed by ages &lt;60 and 60+ against % of population twice vaccinated by October 1st in the respective age groups. LE deficit is defined as the counterfactual LE from a Lee-Carter mortality forecast based on death rates for the fourth quarter of the years 2015 to 2019 minus observed LE. <strong>54-tab_arriaga.csv</strong> Table 1: Months of life expectancy (LE) changes and deficits (labelled ES) since the start of the pandemic attributed to age-specific mortality changes (labelled AT). LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.
DOI: 10.6084/m9.figshare.20267196
title: Test

description: Images with rotation-determinism
DOI: 10.6084/m9.figshare.20267196.v2
title: Test

description: Images with rotation-determinism
DOI: 10.6084/m9.figshare.20267196.v1
title: Test

description: Images with rotation-determinism
DOI: 10.6084/m9.figshare.20338542.v2
title: Dataset for "On the role of gravity in dissolving horizontal fractures" by Hu et al.

description: This is the dataset for "On the role of gravity in dissolving horizontal fractures" by Hu et al., including experimental data and numerical results.
DOI: 10.6084/m9.figshare.20338542.v1
title: Dataset for "On the role of gravity in dissolving horizontal fractures" by Hu et al.

description: This is the dataset for "On the role of gravity in dissolving horizontal fractures" by Hu et al., including experimental data and numerical results.
DOI: 10.6084/m9.figshare.20338587
title: EBVaGC tumors were sequenced on a targeted panel..docx

description: EBVaGC tumors were sequenced on a targeted panel
DOI: 10.6084/m9.figshare.20338587.v1
title: EBVaGC tumors were sequenced on a targeted panel..docx

description: EBVaGC tumors were sequenced on a targeted panel
DOI: 10.5287/bodleian:qxbyeo4da
title: Rheological properties of porcine organs: measurements and fractional viscoelastic model
DOI: 10.5281/zenodo.3904584
title: 7T MRSI (HFMRC, MU Vienna) Example Data: Raw Spectra

description: Data from the Vienna 7T MRSI group<br> Contact: wolfgang.bogner@meduniwien.ac.at , gilbert.hangel@meduniwien.ac.at Combinded and preprocessed data after spatial FFT, in the time domain. <br> 64x64x39 matrix, 15 min<br> Method details: Hingerl et al 2020, doi: 10.1097/RLI.0000000000000626 <br> Healthy_Volunteer.mat<br> Female, 24y, measured 2019 <br> GBM_patient.mat<br> Glioblastoma WHO Grade 4, with IDH1 mutation, male, 52y
DOI: 10.5281/zenodo.3904583
title: 7T MRSI (HFMRC, MU Vienna) Example Data: Raw Spectra

description: Data from the Vienna 7T MRSI group<br> Contact: wolfgang.bogner@meduniwien.ac.at , gilbert.hangel@meduniwien.ac.at Combinded and preprocessed data after spatial FFT, in the time domain. <br> 64x64x39 matrix, 15 min<br> Method details: Hingerl et al 2020, doi: 10.1097/RLI.0000000000000626 <br> Healthy_Volunteer.mat<br> Female, 24y, measured 2019 <br> GBM_patient.mat<br> Glioblastoma WHO Grade 4, with IDH1 mutation, male, 52y
DOI: 10.5281/zenodo.5006923
title: Supplementary MRSI data for "Inter-subject stability and regional concentration estimates of 3D-FID-MRSI in the human brain at 7T"

description: Intended as supplementary data to the manuscript "Inter-subject stability and regional concentration estimates of 3D-FID-MRSI in the human brain at 7T" Authors:<br> Gilbert Hangel, Benjamin Spurny, Philipp Lazen, Cornelius Cadrien, Sukrit Sharma, Lukas Hingerl, Eva Hečková, Bernhard Strasser, Stanislav Motyka, Alexandra Lipka, Stephan Gruber, Christoph Brandner, Rupert Lanzenberger, Karl Rössler, Siegfried Trattnig, Wolfgang Bogner ###################################################<br> MRSI Maps from the Vienna 7T scanner in MINC/NIFTI format<br> ################################################### Contact: wolfgang.bogner@meduniwien.ac.at , gilbert.hangel@meduniwien.ac.at<br> https://hfmr.meduniwien.ac.at/ For use with and MINC- and NIFTI-displaying software. MRSI method published as Hingerl et al 2020, doi: 10.1097/RLI.0000000000000626 Volunteers 7,11,13,19,23 as described in Tables 1 and 2.
DOI: 10.5281/zenodo.4382175
title: Supplementary MRSI data for "Inter-subject stability and regional concentration estimates of 3D-FID-MRSI in the human brain at 7T"

description: Intended as supplementary data to the manuscript "Inter-subject stability and regional concentration estimates of 3D-FID-MRSI in the human brain at 7T" Authors:<br> Gilbert Hangel, Benjamin Spurny, Philipp Lazen, Cornelius Cadrien, Sukrit Sharma, Lukas Hingerl, Eva Hečková, Bernhard Strasser, Stanislav Motyka, Alexandra Lipka, Stephan Gruber, Christoph Brandner, Rupert Lanzenberger, Karl Rössler, Siegfried Trattnig, Wolfgang Bogner ###################################################<br> MRSI Maps from the Vienna 7T scanner in MINC/NIFTI format<br> ################################################### Contact: wolfgang.bogner@meduniwien.ac.at , gilbert.hangel@meduniwien.ac.at<br> https://hfmr.meduniwien.ac.at/ For use with and MINC- and NIFTI-displaying software. MRSI method published as Hingerl et al 2020, doi: 10.1097/RLI.0000000000000626 Volunteers 7,11,13,19,23 as described in Tables 1 and 2.
DOI: 10.5281/zenodo.3981030
title: Supplementary MRSI Data for "High-Resolution Metabolic Imaging of High-Grade Gliomas using 7T-CRT-FID-MRSI" - NIFTI Format FIXED

description: Intended as supplementary data to the manuscript "High-Resolution Metabolic Imaging of High-Grade Gliomas using 7T-CRT-FID-MRSI" Authors:<br> Gilbert Hangel, Cornelius Cadrien, Philipp Lazen, Julia Furtner, Alexandra Lipka, Eva Hečková, Lukas Hingerl, Stanislav Motyka, Stephan Gruber, Bernhard Strasser, Barbara Kiesel, Mario Mischkulnig, Matthias Preusser, Thomas Roetzer, Adelheid Wöhrer, Georg Widhalm, Karl Rössler, Siegfried Trattnig and Wolfgang Bogner ###################################################<br> MRSI Maps from the Vienna 7T scanner in MINC format<br> ################################################### Contact: wolfgang.bogner@meduniwien.ac.at , gilbert.hangel@meduniwien.ac.at<br> https://hfmr.meduniwien.ac.at/ For use with NIFTI-displaying software. MRSI method published as Hingerl et al 2020, doi: 10.1097/RLI.0000000000000626 Patient 1:<br> Glioblastoma WHO Grade 4, with IDH1 mutation, male<br> Patient 2:<br> Glioblastoma WHO Grade 4, with IDH1 mutation, male<br> (Same numbers as in the manuscript)
DOI: 10.5281/zenodo.3903459
title: Supplementary MRSI Data for "High-Resolution Metabolic Imaging of High-Grade Gliomas using 7T-CRT-FID-MRSI" - NIFTI Format FIXED

description: Intended as supplementary data to the manuscript "High-Resolution Metabolic Imaging of High-Grade Gliomas using 7T-CRT-FID-MRSI" Authors:<br> Gilbert Hangel, Cornelius Cadrien, Philipp Lazen, Julia Furtner, Alexandra Lipka, Eva Hečková, Lukas Hingerl, Stanislav Motyka, Stephan Gruber, Bernhard Strasser, Barbara Kiesel, Mario Mischkulnig, Matthias Preusser, Thomas Roetzer, Adelheid Wöhrer, Georg Widhalm, Karl Rössler, Siegfried Trattnig and Wolfgang Bogner ###################################################<br> MRSI Maps from the Vienna 7T scanner in MINC format<br> ################################################### Contact: wolfgang.bogner@meduniwien.ac.at , gilbert.hangel@meduniwien.ac.at<br> https://hfmr.meduniwien.ac.at/ For use with NIFTI-displaying software. MRSI method published as Hingerl et al 2020, doi: 10.1097/RLI.0000000000000626 Patient 1:<br> Glioblastoma WHO Grade 4, with IDH1 mutation, male<br> Patient 2:<br> Glioblastoma WHO Grade 4, with IDH1 mutation, male<br> (Same numbers as in the manuscript)
DOI: 10.6084/m9.figshare.19095300
title: Dietary disparities among adolescents according to individual and school socioeconomic status: a multilevel analysis

description: This study aimed to estimate disparities in dietary habits according to the individual and contextual socioeconomic status (SES), while taking into account school nutrition-related characteristics. Data came from the 2018 cross-sectional “Health Behaviour in School-aged Children” (HBSC) survey. Multilevel multiple logistic regressions were performed. Over two-thirds of the observed variance was explained by individual and school characteristics, with SES being the main contributors. For example, 76.9% of the variance in daily sugar-sweetened beverage (SSB) intake was explained by individual and school characteristics. Adolescents of a secondary or lower parental education level were more likely to consume SSB daily than those of a post-secondary level (aOR = 1.46 (1.29–1.66)). Compared to those in a high SES school, the odds to consume SSB daily was higher for adolescents in low SES schools (aOR = 2.37(1.90–2.96)). These findings support the need for schools to pursue a consistent nutrition policy, with an increased support in low socioeconomic populations.