cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8061424
title: Unbiased single-cell morphology with self-supervised vision transformers -- WTC11

description: The data necessary to reproduce the WTC11 results in the paper Unbiased single-cell morphology with self-supervised vision transformers.
DOI: 10.5281/zenodo.8061422
title: Unbiased single-cell morphology with self-supervised vision transformers -- WTC11

description: The data necessary to reproduce the WTC11 results in the paper Unbiased single-cell morphology with self-supervised vision transformers.
DOI: 10.5281/zenodo.7490245
title: CLImate for Maize OMICS: CLIM4OMICS Analytics and Database

description: CLIM4OMICS Analytics and Database is Improved database of G2F data repository that contains OMICs (genetic and phenotypic) and environmental data for maize yield predictability across 84 experimental fields in the U.S. and province of ON in Canada between 2014-2021. The goal of this pipeline is to aggregate, improve, and synthesize multi-dimensional G2F data including Geno-type, Phenotype and Environmental data for GxE modeling. This dataset contains 79,122 phenotype measurements, 378 genotypes of maize lines, environmental data of 178 locations and Python Scripts for Quality control (QC), Consistency control (CC) steps and ML models for GxE interactions. The Environmental data is extracted from NWS, DayMet and NSRDB databases and processed for QC and CC. The environmental dataset contains the minimum temperature (<em>T<sub>min</sub></em>)<em>,</em> average temperature (<em>T<sub>mean</sub></em>)<em>, </em>maximum temperature (<em>T<sub>max</sub></em>)<em>,</em> minimum dew point (<em>DP<sub>min</sub></em>)<em>,</em> average dew point (<em>DP<sub>mean</sub></em>)<em>, </em>maximum dew point (<em>DP<sub>max</sub></em>)<em>, </em>minimum relative humidity (<em>RH<sub>min</sub></em>)<em>, </em>average relative humidity (<em>RH<sub>mean</sub></em>)<em>, </em>maximum relative humidity (<em>RH<sub>max</sub></em>)<em>, </em>minimum solar radiation (<em>SR<sub>min</sub></em>)<em>, </em>average solar radiation (<em>SR<sub>mean</sub></em>)<em>, </em>maximum solar radiation (<em>SR<sub>max</sub></em>)<em>, </em>accumulative rainfall (<em>R<sub>acc</sub></em>)<em>, </em>average wind speed (<em>WS<sub>mean</sub></em>), and average wind direction (<em>WD<sub>mean</sub></em>). This package also contains the raw G2F data and preprocessing pipeline.
description: This work was supported by the Agriculture and Food Research Initiative Grant number NEB-21-176 and NEB-21-166 from the USDA National Institute of Food and Agriculture, Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production. In addition, we thank the Genomes to Fields (G2F) Initiative for providing the database; and Quantifying Life Sciences Initiative at the University of Nebraska-Lincoln.
description: {"references": ["Sarzaeim, P., Munoz-Arriola, F., Jarquin, D., Aslam, H., and De Leon Gatti, N.: CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for Maize phenotype predictability in the U.S. and Canada, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-11, in review, 2023.", "https://github.com/HasnatJutt/CLImate-for-Maize-OMICS_CLIM4OMICS-Analytics-and-Database"]}
DOI: 10.5281/zenodo.7490246
title: CLImate for Maize OMICS: CLIM4OMICS Analytics and Database

description: CLIM4OMICS Analytics and Database is Improved database of G2F data repository that contains OMICs (genetic and phenotypic) and environmental data for maize yield predictability across 84 experimental fields in the U.S. and province of ON in Canada between 2014-2017. The goal of this pipeline is to aggregate, improve, and synthesize multi-dimensional G2F data including Geno-type, Phenotype and Environmental data for GxE modeling. This dataset contains 8,171 phenotype measurements, 376 genotypes of maize lines, environmental data of 84 locations and Python Scripts for Quality control (QC), Consistency control (CC) steps and ML models for GxE interactions. The Environmental data is extracted from NWS, DayMet and NSRDB databases and processed for QC and CC. The environmental dataset contains the minimum temperature (<em>T<sub>min</sub></em>)<em>,</em> average temperature (<em>T<sub>mean</sub></em>)<em>, </em>maximum temperature (<em>T<sub>max</sub></em>)<em>,</em> minimum dew point (<em>DP<sub>min</sub></em>)<em>,</em> average dew point (<em>DP<sub>mean</sub></em>)<em>, </em>maximum dew point (<em>DP<sub>max</sub></em>)<em>, </em>minimum relative humidity (<em>RH<sub>min</sub></em>)<em>, </em>average relative humidity (<em>RH<sub>mean</sub></em>)<em>, </em>maximum relative humidity (<em>RH<sub>max</sub></em>)<em>, </em>minimum solar radiation (<em>SR<sub>min</sub></em>)<em>, </em>average solar radiation (<em>SR<sub>mean</sub></em>)<em>, </em>maximum solar radiation (<em>SR<sub>max</sub></em>)<em>, </em>accumulative rainfall (<em>R<sub>acc</sub></em>)<em>, </em>average wind speed (<em>WS<sub>mean</sub></em>), and average wind direction (<em>WD<sub>mean</sub></em>). This package also contains the raw G2F data and preprocessing pipeline.
description: This work was supported by the Agriculture and Food Research Initiative Grant number NEB-21-176 and NEB-21-166 from the USDA National Institute of Food and Agriculture, Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production. In addition, we thank the Genomes to Fields (G2F) Initiative for providing the database; and Quantifying Life Sciences Initiative at the University of Nebraska-Lincoln.
DOI: 10.5281/zenodo.8002909
title: CLImate for Maize OMICS: CLIM4OMICS Analytics and Database

description: CLIM4OMICS Analytics and Database is Improved database of G2F data repository that contains OMICs (genetic and phenotypic) and environmental data for maize yield predictability across 84 experimental fields in the U.S. and province of ON in Canada between 2014-2021. The goal of this pipeline is to aggregate, improve, and synthesize multi-dimensional G2F data including Geno-type, Phenotype and Environmental data for GxE modeling. This dataset contains 79,122 phenotype measurements, 378 genotypes of maize lines, environmental data of 178 locations and Python Scripts for Quality control (QC), Consistency control (CC) steps and ML models for GxE interactions. The Environmental data is extracted from NWS, DayMet and NSRDB databases and processed for QC and CC. The environmental dataset contains the minimum temperature (<em>T<sub>min</sub></em>)<em>,</em> average temperature (<em>T<sub>mean</sub></em>)<em>, </em>maximum temperature (<em>T<sub>max</sub></em>)<em>,</em> minimum dew point (<em>DP<sub>min</sub></em>)<em>,</em> average dew point (<em>DP<sub>mean</sub></em>)<em>, </em>maximum dew point (<em>DP<sub>max</sub></em>)<em>, </em>minimum relative humidity (<em>RH<sub>min</sub></em>)<em>, </em>average relative humidity (<em>RH<sub>mean</sub></em>)<em>, </em>maximum relative humidity (<em>RH<sub>max</sub></em>)<em>, </em>minimum solar radiation (<em>SR<sub>min</sub></em>)<em>, </em>average solar radiation (<em>SR<sub>mean</sub></em>)<em>, </em>maximum solar radiation (<em>SR<sub>max</sub></em>)<em>, </em>accumulative rainfall (<em>R<sub>acc</sub></em>)<em>, </em>average wind speed (<em>WS<sub>mean</sub></em>), and average wind direction (<em>WD<sub>mean</sub></em>). This package also contains the raw G2F data and preprocessing pipeline.
description: This work was supported by the Agriculture and Food Research Initiative Grant number NEB-21-176 and NEB-21-166 from the USDA National Institute of Food and Agriculture, Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production. In addition, we thank the Genomes to Fields (G2F) Initiative for providing the database; and Quantifying Life Sciences Initiative at the University of Nebraska-Lincoln.
description: {"references": ["Sarzaeim, P., Munoz-Arriola, F., Jarquin, D., Aslam, H., and De Leon Gatti, N.: CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for Maize phenotype predictability in the U.S. and Canada, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-11, in review, 2023.", "https://github.com/HasnatJutt/CLImate-for-Maize-OMICS_CLIM4OMICS-Analytics-and-Database"]}
DOI: 10.6084/m9.figshare.19236920.v1
title: Using Support Vector Machines for Facet Partitioning in Multidimensional Scaling

description: In this article we focus on interpreting multidimensional scaling (MDS) configurations using facet theory. The facet theory approach is attempting to partition a representational space, facet by facet, into regions with certain simplifying constraints on the regions’ boundaries (e.g., concentric circular sub-spaces). A long-standing problem has been the lack of computational methods for optimal facet-based partitioning. We propose using support vector machines (SVM) to perform this task. SVM is highly attractive for this purpose as they allow for linear as well as nonlinear classification boundaries in any dimensionality. Using various classical examples from the facet theory literature we elaborate on the combined use of MDS and SVM for facet-based partitioning. Different types of MDS are discussed, and options for SVM kernel specification, tuning, and performance evaluation are illustrated.
DOI: 10.5281/zenodo.8061428
title: Unbiased single-cell morphology with self-supervised vision transformers -- Cell Painting

description: The data necessary to reproduce the Cell Painting results in the paper Unbiased single-cell morphology with self-supervised vision transformers.
DOI: 10.5281/zenodo.8061427
title: Unbiased single-cell morphology with self-supervised vision transformers -- Cell Painting

description: The data necessary to reproduce the Cell Painting results in the paper Unbiased single-cell morphology with self-supervised vision transformers.
DOI: 10.5281/zenodo.8061426
title: Unbiased single-cell morphology with self-supervised vision transformers -- HPA single cells

description: The data necessary to reproduce the HPA single cells results in the paper Unbiased single-cell morphology with self-supervised vision transformers.
DOI: 10.5281/zenodo.8061425
title: Unbiased single-cell morphology with self-supervised vision transformers -- HPA single cells

description: The data necessary to reproduce the HPA single cells results in the paper Unbiased single-cell morphology with self-supervised vision transformers.
DOI: 10.5281/zenodo.8061392
title: Unbiased single-cell morphology with self-supervised vision transformers -- HPA FOV

description: The data necessary to reproduce the HPA FOV results in the paper Unbiased single-cell morphology with self-supervised vision transformers.
DOI: 10.5281/zenodo.8061391
title: Unbiased single-cell morphology with self-supervised vision transformers -- HPA FOV

description: The data necessary to reproduce the HPA FOV results in the paper Unbiased single-cell morphology with self-supervised vision transformers.
DOI: 10.25921/5mm1-vq62
title: ME70 Water Column Sonar Data Collected During PC1901
DOI: 10.5281/zenodo.8061609
title: Mapping of aridity and its connections with climate classes and climate desertification in future scenarios – Brazilian semi-arid region

description: This database comes from the article entitled ''Mapping of aridity and its connections with climate classes and climate desertification in future scenarios –Brazilian semi-arid region'' (https://seer.ufu.br/index.php/sociedadenatureza /article/view/67666/36193). We provide data on aridity and desertification index for the current scenario and future projections considering changes in climate.
DOI: 10.5281/zenodo.8061608
title: Mapping of aridity and its connections with climate classes and climate desertification in future scenarios – Brazilian semi-arid region

description: This database comes from the article entitled ''Mapping of aridity and its connections with climate classes and climate desertification in future scenarios –Brazilian semi-arid region'' (https://seer.ufu.br/index.php/sociedadenatureza /article/view/67666/36193). We provide data on aridity and desertification index for the current scenario and future projections considering changes in climate.
DOI: 10.5517/ccdc.csd.cc2fnf7s
title: CCDC 2253955: Experimental Crystal Structure Determination

description: Related Article: Tapaswini Sethi, Debarati Bhattacharya, Dinabandhu Das|2023|CrystEngComm|25|3356|doi:10.1039/D3CE00334E
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.cc2fnf8t
title: CCDC 2253956: Experimental Crystal Structure Determination

description: Related Article: Tapaswini Sethi, Debarati Bhattacharya, Dinabandhu Das|2023|CrystEngComm|25|3356|doi:10.1039/D3CE00334E
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.cc2fnf9v
title: CCDC 2253957: Experimental Crystal Structure Determination

description: Related Article: Tapaswini Sethi, Debarati Bhattacharya, Dinabandhu Das|2023|CrystEngComm|25|3356|doi:10.1039/D3CE00334E
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.cc2fnfbw
title: CCDC 2253958: Experimental Crystal Structure Determination

description: Related Article: Tapaswini Sethi, Debarati Bhattacharya, Dinabandhu Das|2023|CrystEngComm|25|3356|doi:10.1039/D3CE00334E
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.cc2fnfcx
title: CCDC 2253959: Experimental Crystal Structure Determination

description: Related Article: Tapaswini Sethi, Debarati Bhattacharya, Dinabandhu Das|2023|CrystEngComm|25|3356|doi:10.1039/D3CE00334E
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.cc2fnfdy
title: CCDC 2253960: Experimental Crystal Structure Determination

description: Related Article: Tapaswini Sethi, Debarati Bhattacharya, Dinabandhu Das|2023|CrystEngComm|25|3356|doi:10.1039/D3CE00334E
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.cc2fnffz
title: CCDC 2253961: Experimental Crystal Structure Determination

description: Related Article: Tapaswini Sethi, Debarati Bhattacharya, Dinabandhu Das|2023|CrystEngComm|25|3356|doi:10.1039/D3CE00334E
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.cc2fnfg0
title: CCDC 2253962: Experimental Crystal Structure Determination

description: Related Article: Tapaswini Sethi, Debarati Bhattacharya, Dinabandhu Das|2023|CrystEngComm|25|3356|doi:10.1039/D3CE00334E
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.cc2fpcp6
title: CCDC 2254868: Experimental Crystal Structure Determination

description: Related Article: Qin Zhou, Yong-Shuai Tao, Jin-Bao Qiao, Yu-Ming Zhao|2023|Org.Lett.|25|4066|doi:10.1021/acs.orglett.3c01254
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.cc242x16
title: CCDC 1968997: Experimental Crystal Structure Determination

description: Related Article: Ayşen Orhan Erkovan, Azam Seifi, Burcu Topaloğlu Aksoy, Alireza Khataee, Yunus Zorlu, Bünyemin Çoşut|2023|Polyhedron|241|116472|doi:10.1016/j.poly.2023.116472
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.cc2fpcq7
title: CCDC 2254869: Experimental Crystal Structure Determination

description: Related Article: Qin Zhou, Yong-Shuai Tao, Jin-Bao Qiao, Yu-Ming Zhao|2023|Org.Lett.|25|4066|doi:10.1021/acs.orglett.3c01254
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.cc2fg705
title: CCDC 2247996: Experimental Crystal Structure Determination

description: Related Article: Min Meng, Lingrong Liao, Qi Zheng, Weimin Xuan|2023|New J.Chem.|47|11312|doi:10.1039/D3NJ01172K
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: 10.25345/c5ff3m933
title: MassIVE MSV000092219 - Comparative proteomic approach of Arabidopsis GNOM ARF-GEF line in response to cold stress
title: Gain-of-function that results in GNOM-overexpression, shows contrasting response to its loss-of-function (gnomB/E) for cold stress suggesting GNOM is a crucial regulator of cold response. However, a complete picture of the regulators that are involved in GNOM-mediated cold response pathway remains elusive. To elucidate the regulatory components of this pathway, we carried out a comparative proteomic analysis using Col-0, GNOM-overexpression, GNOM loss-of-function (gnomB/E) line, from which a total of 2704 proteins were identified. A total of 400 proteins were differentially expressed (DEPs) in these three lines of which 243 upregulated and 157 downregulated. However, there is 50 differentially upregulated proteins present only in GNOM line, that might be crucial to the cold stress tolerance of GNOM.
DOI: 10.5281/zenodo.8061669
title: [Datasets] Evaluación de la capacidad predictiva de modelos de aprendizaje supervisado para la clasificación de pacientes con cáncer colorrectal

description: <strong>DATASETS INFO</strong> Dataset on colorectal cancer and hydroxymethylation levels ready to be used in machine learning algorithms. This dataset was generated using data from Walker NJ, Rashid M, Yu S, et al. [Dataset] Hydroxymethylation profile of cell free DNA is a biomarker for early colorectal cancer. Accessed May 17, 2023. https://zenodo.org/record/5170265#.ZGSpgHZBxD-. <strong>ABSTRACT</strong><br> Colorectal cancer (CRC) is the second most common cause of cancer death, accounting for 9.5% of all cancer deaths. In addition to patient age, other potential risk factors should be considered to correctly identify the target population for CRC screening programmes. The identification of these risk factors would allow a personalised and accurate approach for each patient that would help improve the survival rate. Thus, the main objective of this study was to identify useful risk biomarkers for the early detection of CRC using machine learning algorithms. For this purpose, we compared the predictive ability of different supervised machine learning models, such as gradient boosting, support vector machines (SVM) or random forest, using a public dataset on hydroxymethylation levels in the enhancer regions in CRC patients, AAR and controls. In addition, we evaluated the suitability of K-means for the identification of CRC patient subgroups using this dataset. The results of this work suggested that the best supervised model to differentiate CRC patients from controls, using hydroxymethylation data, was a SVM model with linear kernel, whose sensitivity was 58% after setting the specificity to 95%, improving the model presented in the article from which the dataset was extracted. In addition, enhancers that regulate the expression of genes such as MYSM1 or SP1, or those that regulate genes encoding proteins involved in pathways such as TGF-β and integrin pathways, were identified as the most relevant enhancers when classifying samples into CRC or control. On the other hand, the use of K-means identified 6 clusters among the samples in the hydroxymethylation dataset. Two of these clusters were mainly composed of samples with CCR, however, these clusters were not associated with a specific stage of development, and the differentiation between clusters was not clear, obtaining very close clusters. Thus, we can conclude that hydroxymethylation data were useful for the identification of CRC biomarkers, obtaining promising results by supervised machine learning approaches. However, these results should be interpreted as preliminary, requiring validation in an external cohort and molecular analysis of the biomarkers identified.