cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc28j1pk
title: CCDC 2100798: Experimental Crystal Structure Determination

description: Related Article: Vibe Boel Jakobsen, Elzbieta Trzop, Emiel Dobbelaar, Laurence C. Gavin, Shalinee Chikara, Xiaxin Ding, Minseong Lee, Kane Esien, Helge Müller-Bunz, Solveig Felton, Eric Collet, Michael A. Carpenter, Vivien S. Zapf, Grace G. Morgan|2021|J.Am.Chem.Soc.|144|195|doi:10.1021/jacs.1c08214
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.7220682
title: ABS spin

description: Data and code for "Spin-filtered measurements of Andreev Bound States" van Driel, David; Wang, Guanzhong; Dvir, Tom This folder contains the raw data and code used to generate the plots for the paper <em>Spin-filtered measurements of Andreev Bound States</em> (arXiv: ??). To run the Jupyter notebook, install Anaconda and execute: <pre><code>conda env create -f environment.yml</code></pre> followed by: <pre><code>conda activate spinABS</code></pre> Finally, <pre><code>jupyter notebook</code></pre> to launch the notebook called 'zenodo_notebook.ipynb'. Raw data are stored in netCDF (.nc) format. The files are exported by the data acquisition package QCoDeS and can be read as an xarray Dataset.
DOI: 10.5281/zenodo.7220681
title: ABS spin

description: Data and code for "Spin-filtered measurements of Andreev Bound States" van Driel, David; Wang, Guanzhong; Dvir, Tom This folder contains the raw data and code used to generate the plots for the paper <em>Spin-filtered measurements of Andreev Bound States</em> (arXiv: ??). To run the Jupyter notebook, install Anaconda and execute: <pre><code>conda env create -f environment.yml</code></pre> followed by: <pre><code>conda activate spinABS</code></pre> Finally, <pre><code>jupyter notebook</code></pre> to launch the notebook called 'zenodo_notebook.ipynb'. Raw data are stored in netCDF (.nc) format. The files are exported by the data acquisition package QCoDeS and can be read as an xarray Dataset.
DOI: 10.5281/zenodo.7575731
title: Bio: Apotheker/in aus Frankfurt/O

description: Questionnaire/s and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Fragebogen und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): https://kalliope-verbund.info/ead?ead.id=DE-611-BF-7096/ Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht veröffentlicht werden dürfen).
DOI: 10.48326/idoc.medoc.spice.1.0
title: Data issued from SPICE instrument on Solar Orbiter : 1st data release
DOI: 10.6084/m9.figshare.21921813
title: Supplementray Files.zip

description: Supplementary files: 1. Supplementary materials: <strong>Figure S1:</strong> Protein-Protein Interaction Networks (PPI) of 310 genes from 20 AAMR-related gene sets; <strong>Figure S2: </strong>Heatmap and correlation analysis between TMEM251 and enrichment scores; <strong>Figure S3: </strong>Connections between hub genes and clinicopathological factors; <strong>Figure S4:</strong> Significantly enriched AAMR-related gene sets in GSEA; <strong>Figure S5: </strong>Immune infiltration heatmap of different risk groups; <strong>Figure S6: </strong>Gene mutation patterns of samples. <strong>(A) </strong>Gene mutation patterns of all samples (n = 330). <strong>(B)</strong> Gene mutation patterns of the low-risk group. <strong>(C)</strong> Gene mutation patterns of the high-risk group. <strong>Datasheet S1: </strong>Pearson correlation analysis between TMEM251 and other genes;<strong> Datasheet S2: </strong>GO analysis results (BP); <strong>Datasheet S3:</strong> GO analysis results (CC); <strong>Datasheet S4: </strong>GO analysis results (MF); <strong>Datasheet S5:</strong> KEGG analysis results;<strong> Datasheet S6: </strong>310 research genes from the 20 enriched gene sets; <strong>Datasheet S7: </strong>Unsupervised cluster analysis based on 1234 TMEM251-coexpression genes; <strong>Datasheet S8: </strong>Unsupervised cluster analysis based on 310 genes from the 20 enriched gene sets; <strong>Datasheet S9: </strong>Unsupervised cluster analysis based on GSVA enrichment scores; <strong>Datasheet S10: </strong>Differential gene expression analysis between cancer and normal samples;<strong> Datasheet S11: </strong>The intersection of 310 genes and DEGs; <strong>Datasheet S12:</strong> LASSO regression analysis of 55 research genes;<strong> Datasheet S13: </strong>Stepwise multivariate Cox regression analysis for the predictive model; <strong>Datasheet S14: </strong>Multivariate Cox proportional hazard regression model (without N stage); <strong>Datasheet S15:</strong> Gene Set Enrichment Analysis (GSEA) results between different risk groups; <strong>Datasheet S16: </strong>Immune infiltration analysis (CIBERSORT) results; <strong>Datasheet S17:</strong> Tumor mutation burden (TMB) scores of all samples. 2. Supplementary other files: <strong>Raw data: </strong>All raw data included in this article.<strong> Code and datasheet: </strong>Software codes and associated datasheet.
DOI: 10.6084/m9.figshare.21921813.v2
title: Supplementray Files.zip

description: Supplementary files: 1. Supplementary materials: <strong>Figure S1:</strong> Protein-Protein Interaction Networks (PPI) of 310 genes from 20 AAMR-related gene sets; <strong>Figure S2: </strong>Heatmap and correlation analysis between TMEM251 and enrichment scores; <strong>Figure S3: </strong>Connections between hub genes and clinicopathological factors; <strong>Figure S4:</strong> Significantly enriched AAMR-related gene sets in GSEA; <strong>Figure S5: </strong>Immune infiltration heatmap of different risk groups; <strong>Figure S6: </strong>Gene mutation patterns of samples. <strong>(A) </strong>Gene mutation patterns of all samples (n = 330). <strong>(B)</strong> Gene mutation patterns of the low-risk group. <strong>(C)</strong> Gene mutation patterns of the high-risk group. <strong>Datasheet S1: </strong>Pearson correlation analysis between TMEM251 and other genes;<strong> Datasheet S2: </strong>GO analysis results (BP); <strong>Datasheet S3:</strong> GO analysis results (CC); <strong>Datasheet S4: </strong>GO analysis results (MF); <strong>Datasheet S5:</strong> KEGG analysis results;<strong> Datasheet S6: </strong>310 research genes from the 20 enriched gene sets; <strong>Datasheet S7: </strong>Unsupervised cluster analysis based on 1234 TMEM251-coexpression genes; <strong>Datasheet S8: </strong>Unsupervised cluster analysis based on 310 genes from the 20 enriched gene sets; <strong>Datasheet S9: </strong>Unsupervised cluster analysis based on GSVA enrichment scores; <strong>Datasheet S10: </strong>Differential gene expression analysis between cancer and normal samples;<strong> Datasheet S11: </strong>The intersection of 310 genes and DEGs; <strong>Datasheet S12:</strong> LASSO regression analysis of 55 research genes;<strong> Datasheet S13: </strong>Stepwise multivariate Cox regression analysis for the predictive model; <strong>Datasheet S14: </strong>Multivariate Cox proportional hazard regression model (without N stage); <strong>Datasheet S15:</strong> Gene Set Enrichment Analysis (GSEA) results between different risk groups; <strong>Datasheet S16: </strong>Immune infiltration analysis (CIBERSORT) results; <strong>Datasheet S17:</strong> Tumor mutation burden (TMB) scores of all samples. 2. Supplementary other files: <strong>Raw data: </strong>All raw data included in this article.<strong> Code and datasheet: </strong>Software codes and associated datasheet.
DOI: 10.6084/m9.figshare.21921813.v1
title: Supplementray Files.zip

description: Supplementary files: 1. Supplementary materials: <strong>Data Sheet S1:</strong> Gene Set Enrichment Analysis (GSEA) results between different risk groups; <strong>Figure S1:</strong> Protein-Protein Interaction Networks (PPI) of 310 genes from 20 AAMR-related gene sets; <strong>Figure S2: </strong>Heatmap and correlation analysis between TMEM251 and enrichment scores;<strong> Figure S3: </strong>Connections between hub genes and clinicopathological factors; <strong>Figure S4:</strong> Significantly enriched AAMR-related gene sets in GSEA; <strong>Figure S5: </strong>Immune infiltration heatmap of different risk groups; <strong>Figure S6: </strong>Gene mutation patterns of samples. <strong>(A) </strong>Gene mutation patterns of all samples (n = 330). <strong>(B)</strong> Gene mutation patterns of the low-risk group. <strong>(C)</strong> Gene mutation patterns of the high-risk group. <strong>Table S1: </strong>Pearson correlation analysis between TMEM251 and other genes;<strong> Table S2: </strong>GO analysis results (BP); Table S3: GO analysis results (CC); <strong>Table S4: </strong>GO analysis results (MF); <strong>Table S5:</strong> KEGG analysis results;<strong> Table S6: </strong>310 research genes from the 20 enriched gene sets; <strong>Table S7: </strong>Unsupervised cluster analysis based on 1234 TMEM251-coexpression genes; <strong>Table S8: </strong>Unsupervised cluster analysis based on 310 genes from the 20 enriched gene sets; <strong>Table S9: </strong>Unsupervised cluster analysis based on GSVA enrichment scores; <strong>Table S10: </strong>Differential gene expression analysis between cancer and normal samples;<strong> Table S11: </strong>The intersection of 310 genes and DEGs; <strong>Table S12:</strong> LASSO regression analysis of 55 research genes;<strong> Table S13: </strong>Stepwise multivariate Cox regression analysis for the predictive model; <strong>Table S14: </strong>Multivariate Cox proportional hazard regression model (without N stage); <strong>Table S15: </strong>Immune infiltration analysis (CIBERSORT) results; <strong>Table S16:</strong> Tumor mutation burden (TMB) scores of all samples. 2. Supplementary other files: <strong>Raw data: </strong>All raw data included in this article.<strong> Code and datasheet: </strong>Software codes and associated datasheet.
DOI: 10.5281/zenodo.7575740
title: Bio: Apotheker/in aus Frankfurt/O

description: Questionnaire/s and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Fragebogen und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): https://kalliope-verbund.info/ead?ead.id=DE-611-BF-7096/ Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht veröffentlicht werden dürfen).
DOI: 10.25402/cer.21967235
title: Real-world comparison between weekly versus biweekly dosing of cetuximab for metastatic colorectal cancer: supplementary materials

description: Aim: This real-world study aims to compare overall survival (OS) associated with biweekly (Q2W) versus weekly (Q1W) cetuximab dosing regimens for metastatic colorectal cancer (mCRC) treatment in the US. Methods: Adult patients with KRAS wild-type mCRC who received cetuximab ± chemotherapy from 2013 to 2019 were selected using Flatiron Health’s electronic health records database. Propensity score matching was used to balance Q2W and Q1W cohorts on baseline patient characteristics. The Kaplan– Meier method was used for survival analyses. Several sensitivity analyses were conducted to assess the robustness of findings from the main analysis. Results: Of 1075 patients in the study, 60.7% received cetuximab Q1W and 39.3% Q2W. Median OS (95% confidence interval) in months was 17.2 (15.3, 18.8) for Q2W versus 14.3 (12.8, 16.0) for Q1W; p = 0.246. Similar OS between the dosing cohorts was observed in sensitivity analyses. Conclusion: Weekly and biweekly cetuximab had comparable effectiveness in this real-world study.
DOI: 10.25402/cer.21967235.v1
title: Real-world comparison between weekly versus biweekly dosing of cetuximab for metastatic colorectal cancer: supplementary materials

description: Aim: This real-world study aims to compare overall survival (OS) associated with biweekly (Q2W) versus weekly (Q1W) cetuximab dosing regimens for metastatic colorectal cancer (mCRC) treatment in the US. Methods: Adult patients with KRAS wild-type mCRC who received cetuximab ± chemotherapy from 2013 to 2019 were selected using Flatiron Health’s electronic health records database. Propensity score matching was used to balance Q2W and Q1W cohorts on baseline patient characteristics. The Kaplan– Meier method was used for survival analyses. Several sensitivity analyses were conducted to assess the robustness of findings from the main analysis. Results: Of 1075 patients in the study, 60.7% received cetuximab Q1W and 39.3% Q2W. Median OS (95% confidence interval) in months was 17.2 (15.3, 18.8) for Q2W versus 14.3 (12.8, 16.0) for Q1W; p = 0.246. Similar OS between the dosing cohorts was observed in sensitivity analyses. Conclusion: Weekly and biweekly cetuximab had comparable effectiveness in this real-world study.
DOI: 10.5281/zenodo.7572385
title: Examining the Influence of Focal Point Location on Heat Transfer Dynamics in Phase Change Material: A Dataset Publication

description: Dataset including experimental data from small-scale solar facade module that consists of several components: Fresnel lens; cone-shaped air gap; aerogel (semi-transparent) as insulation layer; PCM container. Variations of the experimental settings in test setups are different cone diameter (2, 3 and 4 cm) and insulation layer thickness is (3,5 and 7cm). Nine different compositions were tested in laboratory to evaluate the ompact of focal point location. The focal point has been described with three different values that reflect its location on the inner and outer surfaces and in the middle of the PCM container. The corresponding figures can be found in the paper The Effect of Fresnel Lens Focal Point Location on Heat Transfer in Phase Change Material (PCM) Enhanced Dynamic Solar Facade. This study has been supported by Fundamental and Applied Research project ‘Smart building EnVElope with solaR Energy STorage (EVEREST)’, project No. lzp-2019/1-0363, funded by the Latvian Council of Science.
description: Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.
DOI: 10.5281/zenodo.7572384
title: Examining the Influence of Focal Point Location on Heat Transfer Dynamics in Phase Change Material: A Dataset Publication

description: Dataset including experimental data from small-scale solar facade module that consists of several components: Fresnel lens; cone-shaped air gap; aerogel (semi-transparent) as insulation layer; PCM container. Variations of the experimental settings in test setups are different cone diameter (2, 3 and 4 cm) and insulation layer thickness is (3,5 and 7cm). Nine different compositions were tested in laboratory to evaluate the ompact of focal point location. The focal point has been described with three different values that reflect its location on the inner and outer surfaces and in the middle of the PCM container. The corresponding figures can be found in the paper The Effect of Fresnel Lens Focal Point Location on Heat Transfer in Phase Change Material (PCM) Enhanced Dynamic Solar Facade. This study has been supported by Fundamental and Applied Research project ‘Smart building EnVElope with solaR Energy STorage (EVEREST)’, project No. lzp-2019/1-0363, funded by the Latvian Council of Science.
description: Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.
DOI: 10.5281/zenodo.7575691
title: Lena Delta habitat disturbance regimes

description: In the Lena Delta, mainly annual flooding, but also local rapid thaw processes on the land surface of the terraces with ice-rich permafrost, result in disturbance regimes forming distinct habitat types. The floodplains experience seasonal flooding as a regularly occurring disturbance in spring after ice-break up (the spring flood). Very high disturbance regimes due to the most intense scour, erosion and sedimentation result in barren sandbanks or in early-stage plant communities equalling the ‘sparsely vegetated’ habitat class. ‘moist to wet sedge communities’<em>, ‘</em>wet sedge communities’<em>, ‘</em>moist equisetum and shrubs’<em>, ‘</em>dry shrub communities’<em>, ‘</em>dry grass to wet sedge communities’ forms the mid to advanced successional stages on the floodplain (high disturbance regime) with shifting habitat types according to Stanford et al., (2005) and Driscoll and Hauer, (2019). In contrast to the floodplain, habitats on the first, second and third delta terraces are less extensively disturbed (low disturbance) allowing the development of the typical mature-state tundra plant communities: ‘polygonal tundra complex’, ‘tussock tundra’, and ‘dwarsh shrub herb communities’. However, locally, high disturbance occurs by rapid thaw processes of ice-rich permafrost (first and third delta terraces) with habitats characterized by mid to advanced-stage plant succession: ‘moist to wet sedge communities’<em>, ‘</em>wet sedge communities’, ‘dry shrub communities’, and <em>‘</em>dry grass to wet sedge’ communities. Very high disturbance due to intense rapid thaw processes occurs at eroding cliffs and lake margins, in steep valleys and actively developing gullies resulting in barren surfaces with rims of sparsely vegetated transition zones. Given the link between plant communities and flooding as well as rapid thaw processes, we characterised the disturbance regimes for each habitat class (see legend in table) and provide an upscaling product in the form of a disturbance map for the entire Lena Delta. References: Stanford, J. A., Lorang, M. S., and Hauer, F. R.: The shifting habitat mosaic of river ecosystems, SIL Proceedings, 1922-2010, 29, 123-136, 10.1080/03680770.2005.11901979, 2005. Driscoll, K. P. and Hauer, F. R.: Seasonal flooding affects habitat and landscape dynamics of a gravel-bed river floodplain, Freshwater Science, 38, 510-526, 10.1086/704826, 2019.
DOI: 10.5281/zenodo.7575690
title: Lena Delta habitat disturbance regimes

description: In the Lena Delta, mainly annual flooding, but also local rapid thaw processes on the land surface of the terraces with ice-rich permafrost, result in disturbance regimes forming distinct habitat types. The floodplains experience seasonal flooding as a regularly occurring disturbance in spring after ice-break up (the spring flood). Very high disturbance regimes due to the most intense scour, erosion and sedimentation result in barren sandbanks or in early-stage plant communities equalling the ‘sparsely vegetated’ habitat class. ‘moist to wet sedge communities’<em>, ‘</em>wet sedge communities’<em>, ‘</em>moist equisetum and shrubs’<em>, ‘</em>dry shrub communities’<em>, ‘</em>dry grass to wet sedge communities’ forms the mid to advanced successional stages on the floodplain (high disturbance regime) with shifting habitat types according to Stanford et al., (2005) and Driscoll and Hauer, (2019). In contrast to the floodplain, habitats on the first, second and third delta terraces are less extensively disturbed (low disturbance) allowing the development of the typical mature-state tundra plant communities: ‘polygonal tundra complex’, ‘tussock tundra’, and ‘dwarsh shrub herb communities’. However, locally, high disturbance occurs by rapid thaw processes of ice-rich permafrost (first and third delta terraces) with habitats characterized by mid to advanced-stage plant succession: ‘moist to wet sedge communities’<em>, ‘</em>wet sedge communities’, ‘dry shrub communities’, and <em>‘</em>dry grass to wet sedge’ communities. Very high disturbance due to intense rapid thaw processes occurs at eroding cliffs and lake margins, in steep valleys and actively developing gullies resulting in barren surfaces with rims of sparsely vegetated transition zones. Given the link between plant communities and flooding as well as rapid thaw processes, we characterised the disturbance regimes for each habitat class (see legend in table) and provide an upscaling product in the form of a disturbance map for the entire Lena Delta. References: Stanford, J. A., Lorang, M. S., and Hauer, F. R.: The shifting habitat mosaic of river ecosystems, SIL Proceedings, 1922-2010, 29, 123-136, 10.1080/03680770.2005.11901979, 2005. Driscoll, K. P. and Hauer, F. R.: Seasonal flooding affects habitat and landscape dynamics of a gravel-bed river floodplain, Freshwater Science, 38, 510-526, 10.1086/704826, 2019.
DOI: 10.34740/kaggle/dsv/4904978
title: YouTube Daily
DOI: 10.14278/rodare.1544
title: Research data: Terahertz-wave decoding of femtosecond extreme-ultraviolet light pulses

description: This repository entry contains the research data used for generating the publication "Terahertz-wave decoding of femtosecond extreme-ultraviolet light pulses".
DOI: 10.14278/rodare.1543
title: Research data: Terahertz-wave decoding of femtosecond extreme-ultraviolet light pulses

description: This repository entry contains the research data used for generating the publication "Terahertz-wave decoding of femtosecond extreme-ultraviolet light pulses".
DOI: 10.6084/m9.figshare.21967277
title: Is it possible to regulate concentrations and ratios of stream nitrogen and phosphorous through management of soil ecological stoichiometry?

description: Supplementary information for " <strong>Is it possible to regulate concentrations and ratios of stream nitrogen and phosphorous through management of soil ecological stoichiometry?</strong>"
DOI: 10.6084/m9.figshare.21967277.v2
title: Is it possible to regulate concentrations and ratios of stream nitrogen and phosphorous through management of soil ecological stoichiometry?

description: Supplementary information for " <strong>Is it possible to regulate concentrations and ratios of stream nitrogen and phosphorous through management of soil ecological stoichiometry?</strong>"
DOI: 10.6084/m9.figshare.21967277.v1
title: Is it possible to regulate concentrations and ratios of stream nitrogen and phosphorous through management of soil ecological stoichiometry?

description: Supplementary information for " <strong>Is it possible to regulate concentrations and ratios of stream nitrogen and phosphorous through management of soil ecological stoichiometry?</strong>"
DOI: 10.34740/kaggle/dsv/4905054
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.34740/kaggle/dsv/4905064
title: UNET Lung Segmentation Weights for Chest X Rays