cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25505/fiz.icsd.cc2btzpv
title: CSD 2169928: Experimental Crystal Structure Determination

description: Related Article: Sivaprasad Ghanta, Amit Mondal, Anustoop Das, Riju Dey, Mayra D. Silva, Joyce Pham, Anup Kumar Bera, Bikash Saha, S.M. Yusuf, Janez Dolinšek, Partha P. Jana|2022|J.Alloys Compd.|932|167599|doi:10.1016/j.jallcom.2022.167599
description: Related Article: Sivaprasad Ghanta, Anustoop Das, Partha Pratim Jana, Stanislav Vrtnik, Darja Gačnik, Jože Luzar, Andreja Jelen, Primož Koželj, Magdalena Wencka, Janez Dolinšek|2021|Inorg.Chem.|60|12226|doi:10.1021/acs.inorgchem.1c01418
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: 10.5517/ccdc.csd.cc2bvs99
title: CCDC 2170691: Experimental Crystal Structure Determination

description: Related Article: ping he|2022|CSD Communication|||
description: Related Article: Ping He, Yan Chen, Xiao-Na Li, Ying-Ying Yan, Chun Liu|2022|Dalton Trans.|52|128|doi:10.1039/D2DT03537E
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.cc2bwmpk
title: CCDC 2171509: Experimental Crystal Structure Determination

description: Related Article: Meng Li, Guang-Rui Peng, Xuan Yang, Zhen-Ning Ma, Jian-Bo Xie|2023|Org.Biomol.Chem.|21|53|doi:10.1039/D2OB01928K
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.6366587
title: Data for: What's in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed

description: The aggregate data in these datasets were used in analyses for "What’s in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed".
description: This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) through Alexander Graham Bell Canada Graduate Scholarships to the lead author Shaylyn Kress and co-author Josh Neudorf and a Discovery Grant (18968-2013-25)
DOI: 10.5281/zenodo.4900958
title: Data for: What's in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed

description: The aggregate data in these datasets were used in analyses for "What’s in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed".
description: This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) through Alexander Graham Bell Canada Graduate Scholarships to the lead author Shaylyn Kress and co-author Josh Neudorf and a Discovery Grant (18968-2013-25)
DOI: 10.25452/figshare.plus.21375565.v4
title: Full scale, microscopically resolved tomographies of sandstone and carbonate rocks augmented by experimental porosity and permeability values

description: <strong>About the Data</strong> We report a dataset containing full-scale, three-dimensional images of rock plugs for application in digital rock and capillary network analysis. Specifically, we have acquired microscopically resolved tomography datasets of 18 cylindrical sandstone and carbonate rock samples having lengths of 25.4 mm and diameters of 9.5 mm, respectively. The rock plug samples measured in this work have porosity and permeability values ranging from 8% to 30% and 0.4 mD to above 5D, respectively. <br> <strong>Dataset Structure</strong> Researchers interested in using this dataset will find five different volume types and formats for each sample. The suffix inside the parenthesis designates the naming scheme used for the dataset files: <br> <strong>Full Frame</strong> <em><strong>(_grayscale_full</strong></em><strong>):</strong> Data obtained from the reconstruction of the µCT projections. During reconstruction, the volume edges are removed but the largest inscribed parallelepiped within the plug is retained, thus leading to different sized parallelepipeds. <strong>Standard</strong> <em><strong>(_grayscale_standard</strong></em><strong>):</strong> Volume cropped into a standard size of 2500x2500x7500 voxels. <strong>Cropped</strong> <strong>cubes (_grayscale_ROI-X) :</strong> 2500x2500x2500 voxel cubes extracted from the standard volume. The X designates the number of the cube, with values ranging from 1 to 3, cut top-down from the parallelepiped. <strong>Filtered cubes (_grayscale_filtered_ROI-X):</strong> Data obtained from the grayscale cubes through the application of contrast enhancement and noise reduction filters. The X designates the number of the cube, with values ranging from 1 to 3, cut top-down from the parallelepiped. <strong>Binarized cubes (_binary_ROI-X):</strong> Binary image data obtained from the filtered grayscale cubes. Each grayscale cube was segmented at threshold level calculated using the Multi-Otsu algorithm with a number of classes set to three. The X designates the number of the cube, with values ranging from 1 to 3, cut top-down from the parallelepiped. Alongside the volumes described above, the following supporting information is also provided: <strong>HDR file:</strong> File containing the cube size information for each sample. File Naming The <strong>Dataset_Structure.xlsx</strong> file contains the file naming scheme for each file type of each sample. <br> After extracting the compressed files from the original <strong>.tar.bz2 </strong>formats, the filename may change. Please refer to the table in case there is any problems identifying the files. Further Information For further information on the methods used in this work, please check our related research paper at: <strong>TBA</strong> <br>
DOI: 10.5281/zenodo.7470323
title: Data extraction Diabetes cochrane review

description: This is the data that was extracted using Covidence. please note that some changes to risk of bias assessments were made later on review of the ratings by the lead author, upon discussion and agreement with the raters. These changes are noted directly in the Cochrane review.
DOI: 10.5281/zenodo.7470322
title: Data extraction Diabetes cochrane review

description: This is the data that was extracted using Covidence. please note that some changes to risk of bias assessments were made later on review of the ratings by the lead author, upon discussion and agreement with the raters. These changes are noted directly in the Cochrane review.
DOI: 10.5281/zenodo.7470036
title: Long-term snow chemical composition monitoring - Hansbreen glacier (Hornsund) - raw data

description: During the accumulation season, snow samples were taken on Hansbreen Glacier. Several times per season. Snow samples were collected in polyethylene sterile bags and transported to the Polish Polar Station Hornsund. After melting at room temperature, the pH, conductivity and chemical composition (major ions) were analysed in the chemical laboratory of the Polish Polar Station.<br> Snow chemical composition: major ions, HCO3-, pH, conductivity Presented data from 2015 to 2019 The data has not been checked, which means that it is raw data. Principal Investigator (PI) Adam Nawrot
description: The data presented were collected as part of the environmental monitoring programme conducted by the Polish Polar Station Hornsund (Spitsbergen) and the Institute of Geophysics of the Polish Academy of Sciences. Data owner name: Institute of Geophysics, Polish Academy of Sciences Owner PIC (Participant Identification Code): 996625337 Owner address: Ksiecia Janusza 64, 01-452 Warszawa, Poland. Dataset PI: Adam Nawrot; anawrot@igf.edu.pl
DOI: 10.5281/zenodo.7470035
title: Long-term snow chemical composition monitoring - Hansbreen glacier (Hornsund) - raw data

description: During the accumulation season, snow samples were taken on Hansbreen Glacier. Several times per season. Snow samples were collected in polyethylene sterile bags and transported to the Polish Polar Station Hornsund. After melting at room temperature, the pH, conductivity and chemical composition (major ions) were analysed in the chemical laboratory of the Polish Polar Station.<br> Snow chemical composition: major ions, HCO3-, pH, conductivity Presented data from 2015 to 2019 The data has not been checked, which means that it is raw data. Principal Investigator (PI) Adam Nawrot
description: The data presented were collected as part of the environmental monitoring programme conducted by the Polish Polar Station Hornsund (Spitsbergen) and the Institute of Geophysics of the Polish Academy of Sciences. Data owner name: Institute of Geophysics, Polish Academy of Sciences Owner PIC (Participant Identification Code): 996625337 Owner address: Ksiecia Janusza 64, 01-452 Warszawa, Poland. Dataset PI: Adam Nawrot; anawrot@igf.edu.pl
DOI: 10.57833/cidacs/wrkjrs
title: Informações sobre os povos indígenas e quilombolas por município

description: Conjunto de informações cadastrais, organizadas por município, sobre as localidades indígenas e quilombolas estimadas pelo IBGE para a realização dos censos e pesquisas. Este dataset contém informações a cerca de: pessoas declaradas e consideradas indígenas (2010), pessoas declaradas e consideradas indígenas em terras indígenas (2010), estimativa de domicílios ocupados em setores censitários indígenas (2019) e estimativa de localidades indígenas e quilombolas (2019).
DOI: 10.57833/cidacs/vrhca4
title: Informações Demográficas por Terra Indígena - 2010

description: Conjunto de informações cadastrais, organizadas por município, sobre as localidades indígenas estimadas pelo IBGE para a realização dos censos e pesquisas. Este dataset contém informações demográficas por terra indígena oficialmente delimitada, tais como: população total, população por faixa etária, número de domicílios particulares permanentes e média de moradores por domicílio.
DOI: 10.57833/cidacs/rb3vvp
title: População Indígena por Etnia - 2010

description: Conjunto de informações cadastrais sobre as localidades indígenas estimadas pelo IBGE para a realização dos censos e pesquisas. Este dataset contém a população indígena segundo etnia.
DOI: 10.57833/cidacs/aabpwx
title: Localidades Quilombolas - 2019

description: Conjunto de informações cadastrais, organizadas por município, sobre as localidades quilombolas estimadas pelo IBGE para a realização dos censos e pesquisas. Este dataset contém o cadastro de localidades quilombolas (2019): território quilombola oficialmente delimitado, agrupamento quilombola e localidade quilombola identificada por registros administrativos.
DOI: 10.57833/cidacs/edvxwv
title: Localidades Indígenas - 2019

description: Conjunto de informações cadastrais, organizadas por município, sobre as localidades indígenas estimadas pelo IBGE para a realização dos censos e pesquisas. Este dataset contém o cadastro de localidades indígenas (2019): terra indígena oficialmente delimitada, agrupamento indígena e localidade indígena não setorizada.
DOI: 10.25810/rxtv-eb29.56
title: German Newspaper Coverage of Climate Change or Global Warming, 2004-2022 - November 2022

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.25934/pr00007826
title: Efficacy and safety of TNF inhibitors in seronegative rheumatoid arthritis patients who are inadequate responders to csDMARDs. A systematic literature review with meta-analysis.

description: Rheumatoid arthritis (RA) is a chronic autoimmune disease that is characterised by circulating autoantibodies, rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA). These are antibodies that target proteins belonging to the patient and are thought to be involved in the disease mechanism. However, about 20% of patients with RA do not have RF and ACPA in their blood. Therefore, RA is classified as seropositive or seronegative according to the presence or absence of RF and ACPA, respectively. Seropositive and seronegative RA are not exactly the same disease: for example, seropositive RA is more severe than seronegative RA. Tumor necrosis factor (TNF) is a body protein involved in the inflammatory process of RA. TNF inhibitors (TNFi) are drugs that target TNF and are an established treatment for RA, since randomised clinical trials (RCTs) showed their efficacy and safety in patients with this condition. However, the great majority of patients that are included in RCTs have seropositive RA and therefore we do not know whether TNFi are effective in seronegative RA as well. We would like to fill this knowledge gap by performing a systematic literature review with meta-analysis of RCTs, since this is the best way to assess such clinical question. We have already performed a literature search showing that there are no RCTs specifically on seronegative RA. We will proceed by requesting data of seronegative patients from the existing RCTs on RA. Afterwards, we will pool the data in a meta-analysis to assess RA related clinical outcomes. The generated evidence will be summarised and qualitatively judged according to Cochrane methodology.
DOI: 10.25810/6qjv-1296.56
title: Indian Newspaper Coverage of Climate Change or Global Warming, 2000-2022 - November 2022

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.6084/m9.figshare.21657629
title: DKK3 protective role in prostate cancer is partly due to the modulation of immune-related pathways

description: While it is considered one of the most common cancers and the leading cause of death in men worldwide, prognostic stratification and treatment modalities are still limited for patients with prostate cancer (PCa). Recently, the introduction of genomic profiling, as well as, the use of new techniques like next-generation sequencing (NGS) in many cancers provides novel tools for the discovery of new molecular targets that might improve our understanding of the genomic aberrations in PCa and the discovery of novel prognostic and therapeutic targets. In this study, we investigated the possible mechanisms through which <em>Dickkopf-3</em> (DKK3) produces its possible protective role in prostate cancer using NGS in both the DKK3 overexpression PCa cell line (PC3) model, as well as, our patients’ cohort consisting of 9 prostate cancer and 5 benign prostatic hyperplasia. Interestingly, our results have shown that DKK3 transfection-modulated genes are involved in the regulation of cell motility, senescence-associated secretory phenotype (SASP), and cytokine signaling in the immune system, as well as, in the regulation of adaptive immune response. Further analysis of our NGS using our in vitro model revealed the presence of 36 differentially expressed genes between DKK3 transfected cells and PC3 empty vector. In addition, both <em>CP</em> and <em>ACE2 </em>genes were differentially expressed not only between the transfected and empty group but also between the transfected and mock cells. The top common differentially expressed genes (DEGs) between the DKK3 overexpression cell line and in our patients’ cohort showed the list of the following; <em>IL32, IRAK1, RIOK1, HIST1H2BB, SNORA31, AKR1B1,</em> <em>ACE2</em> and <em>CP</em>. The upregulated genes including <em>IL32</em>, <em>HIST1H2BB</em> and <em>SNORA31</em> showed tumor suppressor functions in various cancers including PCa. On the other hand, both <em>IRAK1</em> and <em>RIOK1</em> were downregulated and involved in tumor initiation, tumor progression, poor outcome and radiotherapy resistance. Together, our results highlighted the possible role of the DKK3 related genes in protecting against PCa initiation and progression.
DOI: 10.6084/m9.figshare.21657629.v6
title: DKK3 protective role in prostate cancer is partly due to the modulation of immune-related pathways

description: While it is considered one of the most common cancers and the leading cause of death in men worldwide, prognostic stratification and treatment modalities are still limited for patients with prostate cancer (PCa). Recently, the introduction of genomic profiling, as well as, the use of new techniques like next-generation sequencing (NGS) in many cancers provides novel tools for the discovery of new molecular targets that might improve our understanding of the genomic aberrations in PCa and the discovery of novel prognostic and therapeutic targets. In this study, we investigated the possible mechanisms through which <em>Dickkopf-3</em> (DKK3) produces its possible protective role in prostate cancer using NGS in both the DKK3 overexpression PCa cell line (PC3) model, as well as, our patients’ cohort consisting of 9 prostate cancer and 5 benign prostatic hyperplasia. Interestingly, our results have shown that DKK3 transfection-modulated genes are involved in the regulation of cell motility, senescence-associated secretory phenotype (SASP), and cytokine signaling in the immune system, as well as, in the regulation of adaptive immune response. Further analysis of our NGS using our in vitro model revealed the presence of 36 differentially expressed genes between DKK3 transfected cells and PC3 empty vector. In addition, both <em>CP</em> and <em>ACE2 </em>genes were differentially expressed not only between the transfected and empty group but also between the transfected and mock cells. The top common differentially expressed genes (DEGs) between the DKK3 overexpression cell line and in our patients’ cohort showed the list of the following; <em>IL32, IRAK1, RIOK1, HIST1H2BB, SNORA31, AKR1B1,</em> <em>ACE2</em> and <em>CP</em>. The upregulated genes including <em>IL32</em>, <em>HIST1H2BB</em> and <em>SNORA31</em> showed tumor suppressor functions in various cancers including PCa. On the other hand, both <em>IRAK1</em> and <em>RIOK1</em> were downregulated and involved in tumor initiation, tumor progression, poor outcome and radiotherapy resistance. Together, our results highlighted the possible role of the DKK3 related genes in protecting against PCa initiation and progression.
DOI: 10.25919/jr32-yq58
title: TERN Digital Soil Mapping Raster Covariate Stacks

description: There are over 150 national GeoTIFF rasters representing the SCORPAN factors across climate, parent material, biology, relief, soil and location. Covariate rasters (stored as Cloud Optimised GeoTIFF files) are available as full national mosaics. The mosaics are available at 90m (3 arcsec) resolution and at 30m (1 arcsec) resolution. General information about this data set can be found at - https://aussoilsdsm.esoil.io/dsm-covariates/covariate-data The covariates are also available as Principal Components (PCA). Details of the PCA can be found at - https://aussoilsdsm.esoil.io/dsm-covariates/covariate-data-pca These DSM covariate datasets are available for download at the TERN Landscape File Download Site - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html A detailed metadata listing for the covariate data is available at - https://shiny.esoil.io/Apps/Covariates/
DOI: 10.25452/figshare.plus.19629864.v2
title: RNA-seq Titration Results supporting "Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously"

description: This data accompanies the manuscript "Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously" by Foltz, Taroni, and Greene. Please refer to our github page. The file contains all the raw input data, output files needed for plotting, and the intermediate files (including models and normalized data) from one repeat (seed 3274). <br> Abstract: Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, the majority of available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. The data structure and distributions between the platforms differ, making it challenging to combine them directly. Here we perform supervised and unsupervised machine learning evaluations to assess which existing normalization methods are best suited for combining microarray and RNA-seq data. We find that quantile and Training Distribution Matching normalization allow for supervised and unsupervised model training on microarray and RNA-seq data simultaneously. Nonparanormal normalization and z-scores are also appropriate for some applications, including pathway analysis with Pathway-Level Information Extractor (PLIER). We demonstrate that it is possible to perform effective cross-platform normalization using existing methods to combine microarray and RNA-seq data for machine learning applications.
DOI: 10.25810/fj1y-8c74.47
title: International Wire Services Coverage of Climate Change or Global Warming, 2004-2022 - November 2022

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5517/ccdc.csd.cc2141cm
title: CCDC 1880719: Experimental Crystal Structure Determination

description: Related Article: Madavi S. Prasad, Sankar Bharani, Syed Mastan Sharief, Mudavath Ravi, Murugesan Sivaprakash, Biplob Borah, L. Raju Chowhan|2022|RSC Advances|12|34941|doi:10.1039/D2RA07141J
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.cc250qvs
title: CCDC 1996705: Experimental Crystal Structure Determination

description: Related Article: Lucius F.O.Bomfim Filho, Marden C.F. Teixeira, Bernardo L. Rodrigues, Letícia R. Teixeira|2023|Chem. Data Collect.|43|100974|doi:10.1016/j.cdc.2022.100974
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.