cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2d2y3k
title: CCDC 2207358: Experimental Crystal Structure Determination

description: Related Article: Joseph P. Tuccinardi, John L. Wood|2022|J.Am.Chem.Soc.|144|20539|doi:10.1021/jacs.2c09826
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: 10.6084/m9.figshare.19224693
title: US_HDW

description: The influence in single, extreme climate event on crop yield loss has been verified in previous studies, while the whether the compound climate events such as combined high temperature, low relative humidity, and high wind [hereafter referred to as hot-dry-windy events (HDW)] aggravates wheat yield has not been investigated. In this study, we demonstrate the impacts of an increased number of HDW events on winter wheat yield in the U.S. Great Plains from 1982 to 2020. We conclude that HDW were the most influential drivers for yield loss, accounting for a 4% yield reduction per 10 hours of HDW during heading to maturity.
DOI: 10.6084/m9.figshare.19224693.v4
title: US_HDW

description: The influence in single, extreme climate event on crop yield loss has been verified in previous studies, while the whether the compound climate events such as combined high temperature, low relative humidity, and high wind [hereafter referred to as hot-dry-windy events (HDW)] aggravates wheat yield has not been investigated. In this study, we demonstrate the impacts of an increased number of HDW events on hard winter wheat yield in the U.S. Great Plains from 1982 to 2020. We conclude that HDW were the most influential drivers for yield loss, accounting for a 5% yield reduction per 10 hours of HDW during heading to maturity.
DOI: 10.6084/m9.figshare.21525753.v1
title: Data S3

description: Original data of the survey results
DOI: 10.6084/m9.figshare.21525816.v1
title: Word file S1

description: Specific content of the survey
DOI: 10.5519/qd.bntq6hhf
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.21530036
title: Additional file 4 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

description: Additional file 4: Data S1. Subset of genes for gene expression profiles and description of GEO datasets with manual binary annotations.
DOI: 10.6084/m9.figshare.21530036.v1
title: Additional file 4 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

description: Additional file 4: Data S1. Subset of genes for gene expression profiles and description of GEO datasets with manual binary annotations.
DOI: 10.6084/m9.figshare.21530042
title: Additional file 6 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

description: Additional file 6: Data S2. GRN inference and validation. (Microsoft Excel Worksheet). The GRN inference with the 3 algorithms and the consensus matrix are reported. We have set stringent rules and only interactions that were inferred by the 3 algorithms were included in the mechanistic model: those interactions are all reported in Fig. 2D. The validity of the inferred interactions was investigated by searching the GeneCard database. The relevant GeneHancer IDs are reported when supportive evidence was found.
DOI: 10.6084/m9.figshare.21530042.v1
title: Additional file 6 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

description: Additional file 6: Data S2. GRN inference and validation. (Microsoft Excel Worksheet). The GRN inference with the 3 algorithms and the consensus matrix are reported. We have set stringent rules and only interactions that were inferred by the 3 algorithms were included in the mechanistic model: those interactions are all reported in Fig. 2D. The validity of the inferred interactions was investigated by searching the GeneCard database. The relevant GeneHancer IDs are reported when supportive evidence was found.
DOI: 10.6084/m9.figshare.21530048
title: Additional file 8 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

description: Additional file 8: Data S3. Attractors complete protein and gene expression profiles. (Microsoft Excel Worksheet). The predicted profiles of the 3 attractors are reported for all variables. The dataset contains the values of the fast sub-variables (i.e. protein subnetwork), slow sub-variables (i.e. gene subnetwork) and the global functional activity.
DOI: 10.6084/m9.figshare.21530048.v1
title: Additional file 8 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

description: Additional file 8: Data S3. Attractors complete protein and gene expression profiles. (Microsoft Excel Worksheet). The predicted profiles of the 3 attractors are reported for all variables. The dataset contains the values of the fast sub-variables (i.e. protein subnetwork), slow sub-variables (i.e. gene subnetwork) and the global functional activity.
DOI: 10.6084/m9.figshare.21530051
title: Additional file 9 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

description: Additional file 9: Data S4. In silico screening predictions of combinatorial treatments. (Microsoft Excel Worksheet). This file reports the results of a screening of combinatorial perturbations. More particularly, it reports all conditions that lead to a transition towards a healthy chondrocyte (SOX9+) when starting from a hypertrophic-like chondrocyte (Runx2+). The first sheet reports conditions leading to such transition 100% of the time, between 99 and 90% of the time for the next sheet and so on. The TFs sheet references all the transcription factors of the model that could be retrieved in the result and that are less convenient to target experimentally (to define exclusion criteria for the experimental validation). The ‘Summary’ sheet, reports a list of conditions that might be interesting to test. It excludes conditions involving TFs.
DOI: 10.6084/m9.figshare.21530051.v1
title: Additional file 9 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

description: Additional file 9: Data S4. In silico screening predictions of combinatorial treatments. (Microsoft Excel Worksheet). This file reports the results of a screening of combinatorial perturbations. More particularly, it reports all conditions that lead to a transition towards a healthy chondrocyte (SOX9+) when starting from a hypertrophic-like chondrocyte (Runx2+). The first sheet reports conditions leading to such transition 100% of the time, between 99 and 90% of the time for the next sheet and so on. The TFs sheet references all the transcription factors of the model that could be retrieved in the result and that are less convenient to target experimentally (to define exclusion criteria for the experimental validation). The ‘Summary’ sheet, reports a list of conditions that might be interesting to test. It excludes conditions involving TFs.
DOI: 10.6084/m9.figshare.21530055
title: Additional file 1 of CALN1 hypomethylation as a biomarker for high-risk bladder cancer

description: Additional file 1. Demographicaland clinical information of the subjectsparticipating in this study.
DOI: 10.6084/m9.figshare.21530055.v1
title: Additional file 1 of CALN1 hypomethylation as a biomarker for high-risk bladder cancer

description: Additional file 1. Demographicaland clinical information of the subjectsparticipating in this study.
DOI: 10.6084/m9.figshare.21530058
title: Additional file 2 of CALN1 hypomethylation as a biomarker for high-risk bladder cancer

description: Additional file 2. Raw dataof relationship between CALN1 mRNA expression and CALN1 methylation percentage.
DOI: 10.6084/m9.figshare.21530058.v1
title: Additional file 2 of CALN1 hypomethylation as a biomarker for high-risk bladder cancer

description: Additional file 2. Raw dataof relationship between CALN1 mRNA expression and CALN1 methylation percentage.
DOI: 10.6084/m9.figshare.21529812
title: supersecondary_structures_dataset.zip

description: Training dataset for classifying different types of SSSs: βαβ-unit, α-hairpin, β-hairpin, and αα-corner Dataset extracted from public Protein Data Bank
DOI: 10.6084/m9.figshare.21529812.v1
title: supersecondary_structures_dataset.zip

description: Training dataset for classifying different types of SSSs: βαβ-unit, α-hairpin, β-hairpin, and αα-corner Dataset extracted from public Protein Data Bank
DOI: 10.5517/ccdc.csd.cc2cjj4l
title: CCDC 2190619: Experimental Crystal Structure Determination

description: Related Article: Nouria Al-Awadi|2022|CSD Communication|||
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.cc25y23b
title: CCDC 2023931: Experimental Crystal Structure Determination

description: Related Article: yanjie liu|2022|CSD Communication|||
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.cc2cjj5m
title: CCDC 2190620: Experimental Crystal Structure Determination

description: Related Article: Nouria Al-Awadi|2022|CSD Communication|||
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.cc2ck2pq
title: CCDC 2191163: Experimental Crystal Structure Determination

description: Related Article: Mengxin Xia|2022|CSD Communication|||
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.cc2ck2qr
title: CCDC 2191164: Experimental Crystal Structure Determination

description: Related Article: Mengxin Xia|2022|CSD Communication|||
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.cc2ck515
title: CCDC 2191236: Experimental Crystal Structure Determination

description: Related Article: Jérémy Forté, Mickaël Méphon and Vincent Corcé|2022|CSD Communication|||
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.cc2ck526
title: CCDC 2191237: Experimental Crystal Structure Determination

description: Related Article: Jérémy Forté, Mickaël Méphon and Vincent Corcé|2022|CSD Communication|||
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.cc2ck548
title: CCDC 2191239: Experimental Crystal Structure Determination

description: Related Article: Jérémy Forté, Émilie Dutreix and Vincent Corcé|2022|CSD Communication|||
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.cc2ck8qy
title: CCDC 2191350: Experimental Crystal Structure Determination

description: Related Article: Francisca Mayara Santos de Alencar, Luiz Gonzaga de França Lopes, Eduardo Henrique Silva de Sousa, Alejandro Pedro Ayala|2022|CSD Communication|||
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.cc2ck8rz
title: CCDC 2191351: Experimental Crystal Structure Determination

description: Related Article: Francisca Mayara Santos de Alencar, Luiz Gonzaga de França Lopes, Eduardo Henrique Silva de Sousa, Alejandro Pedro Ayala|2022|CSD Communication|||
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.cc2ck8s0
title: CCDC 2191352: Experimental Crystal Structure Determination

description: Related Article: Francisca Mayara Santos de Alencar, Luiz Gonzaga de França Lopes, Eduardo Henrique Silva de Sousa, Alejandro Pedro Ayala|2022|CSD Communication|||
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.cc2chzf9
title: CCDC 2190101: Experimental Crystal Structure Determination

description: Related Article: K. Mandal, S. Sarkar, P. Ghosh, V.R. Hathwar, D. Chopra|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|597|doi:10.1107/S2053229622009469
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.cc2bk6kp
title: CCDC 2161492: Experimental Crystal Structure Determination

description: Related Article: Krzesimir Korona, Arkadiusz Kornowicz, Iwona Justyniak, Michał Terlecki, Artur Błachowski, Janusz Lewiński|2022|Dalton Trans.|51|16557|doi:10.1039/D2DT02274E
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.cc2bk6lq
title: CCDC 2161493: Experimental Crystal Structure Determination

description: Related Article: Krzesimir Korona, Arkadiusz Kornowicz, Iwona Justyniak, Michał Terlecki, Artur Błachowski, Janusz Lewiński|2022|Dalton Trans.|51|16557|doi:10.1039/D2DT02274E
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.cc2c11wc
title: CCDC 2175762: Experimental Crystal Structure Determination

description: Related Article: Krzesimir Korona, Arkadiusz Kornowicz, Iwona Justyniak, Michał Terlecki, Artur Błachowski, Janusz Lewiński|2022|Dalton Trans.|51|16557|doi:10.1039/D2DT02274E
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.cc2bk6jn
title: CCDC 2161491: Experimental Crystal Structure Determination

description: Related Article: Krzesimir Korona, Arkadiusz Kornowicz, Iwona Justyniak, Michał Terlecki, Artur Błachowski, Janusz Lewiński|2022|Dalton Trans.|51|16557|doi:10.1039/D2DT02274E
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.cc2c3rz7
title: CCDC 2178369: Experimental Crystal Structure Determination

description: Related Article: Xiaodong Liu, Chen Zhao, Jinjiang Wu, Zhenhua Zhu, Jinkui Tang|2022|Dalton Trans.|51|16444|doi:10.1039/D2DT02902B
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.cc2c3ry6
title: CCDC 2178368: Experimental Crystal Structure Determination

description: Related Article: Xiaodong Liu, Chen Zhao, Jinjiang Wu, Zhenhua Zhu, Jinkui Tang|2022|Dalton Trans.|51|16444|doi:10.1039/D2DT02902B
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.7309315
title: A New Tracking Algorithm Based on Foshan Total Lightning Data and the Influence of Historical Velocity and Matching Method on the Results

description: This paper introduces a new tracking algorithm (V2CM) based on total lightning data from the Foshan Total Lightning Location System (FTLLS), and the other four algorithms are used to test the influence of historical velocity and matching method on the results during the formation of V2CM. Different methods are used to quantitatively evaluate the performance of five tracking methods. Additionally, the number of splits and merges along a track is used as a new index to describe the complexity of the track. It is found that the best tracking method V2CM has the highest probability of detection (POD), the false alarm rate (FAR) and the critical success index (CSI) being 65.6%, 39.2%, 46.4%, respectively. It also produces longer and more coherent tracks than those of other four methods. The average number of splits and merges of the best method is 2.40 and lightning clusters matching method between two successive time intervals has a greater impact on the evaluation results than the historical velocity. In addition, lightning clusters move in the northeast direction, with a mean speed of 48.48 km/h and a median speed of 39.08 km/h. Complex tracks, involving splitting or merging, move faster than simple track.
DOI: 10.5281/zenodo.7309314
title: A New Tracking Algorithm Based on Foshan Total Lightning Data and the Influence of Historical Velocity and Matching Method on the Results

description: This paper introduces a new tracking algorithm (V2CM) based on total lightning data from the Foshan Total Lightning Location System (FTLLS), and the other four algorithms are used to test the influence of historical velocity and matching method on the results during the formation of V2CM. Different methods are used to quantitatively evaluate the performance of five tracking methods. Additionally, the number of splits and merges along a track is used as a new index to describe the complexity of the track. It is found that the best tracking method V2CM has the highest probability of detection (POD), the false alarm rate (FAR) and the critical success index (CSI) being 65.6%, 39.2%, 46.4%, respectively. It also produces longer and more coherent tracks than those of other four methods. The average number of splits and merges of the best method is 2.40 and lightning clusters matching method between two successive time intervals has a greater impact on the evaluation results than the historical velocity. In addition, lightning clusters move in the northeast direction, with a mean speed of 48.48 km/h and a median speed of 39.08 km/h. Complex tracks, involving splitting or merging, move faster than simple track.
DOI: 10.6084/m9.figshare.21294885
title: liver-1.5-2051.csv

description: DEGs from liver RNA-Seq <br>
DOI: 10.6084/m9.figshare.21294885.v1
title: liver-1.5-2051.csv

description: DEGs from liver RNA-Seq <br>
DOI: 10.6084/m9.figshare.21294876
title: intestine-1.5-1155.csv

description: DEGs from instine RNA-Seq <br>
DOI: 10.6084/m9.figshare.21294876.v1
title: intestine-1.5-1155.csv

description: DEGs from instine RNA-Seq <br>
DOI: 10.5281/zenodo.7306771
title: HG005_Son.R1.fastq.gz (GIAB_ChineseTrio_30X)

description: This is a sub-sampled version (30X) of ChineseTrio HG005(son)_R1 fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> As the total size of these data amounts to more than 200GB, we also prepared 10X fastq files. The original data is available under:https://github.com/genome-in-a-bottle/giab_data_indexes<br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. <em>et al.</em> High-coverage, long-read sequencing of Han Chinese trio reference samples. <em>Sci Data</em> <strong>6</strong>, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2<br> We are permitted to published sub-sampled versions by Genome in a Bottle project.
description: {"references": ["Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2", "Zook, J. M. et al. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci. Data 3:160025 doi: 10.1038/sdata.2016.25 (2016)."]}