cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22253275
title: List of included studies

description: Folie a deux is a rare psychotic disorder in which two people share the same delusional beliefs. It is also known as "shared psychosis" or "induced psychosis." One person, the "primary case," experiences delusions or hallucinations, and the other person, the "secondary case," comes to adopt the same delusions or hallucinations as a result of their close relationship with the primary case
DOI: 10.6084/m9.figshare.22253275.v1
title: List of included studies

description: Folie a deux is a rare psychotic disorder in which two people share the same delusional beliefs. It is also known as "shared psychosis" or "induced psychosis." One person, the "primary case," experiences delusions or hallucinations, and the other person, the "secondary case," comes to adopt the same delusions or hallucinations as a result of their close relationship with the primary case
DOI: 10.15151/esrf-es-1102673032
title: Structural Biology at EMBL Grenoble, IBS-CIBB and IAB

description: This proposal clusters crystallography projects at the EMBL Grenoble, IBS and IAB working on different topics with a focus on nucleic acid-protein complexes and host-pathogen interactions. The proposers have long-standing experience in the use, as well as the development of beam lines for macromolecular crystallography and have participated in successive BAG applications over the years, mostly at the ESRF but also at other synchrotrons (i.e. Soleil, Petra II, Diamond, SLS)
DOI: 10.6084/m9.figshare.22257859
title: ENCODE_HepG2_eCLIP_peak.annotation

description: ENCODE HepG2 peaks annotation using HOMER
DOI: 10.6084/m9.figshare.22257871
title: ENCODE_K562_eCLIP_peak.annotation

description: ENCODE K562 peaks annotation using HOMER
DOI: 10.6084/m9.figshare.22257727
title: Longitudinal Temperature Measurement Can Determine Humane Endpoints in BALB/c Mouse Models of ESKAPEE Infection - Supplemental Data 1

description: Antimicrobial resistance (AMR) is a worldwide problem, which is driving more preclinical research to find new treatments and countermeasures for drug-resistant bacteria. However, translational models in the preclinical space have remained static for years. To improve animal use ethical considerations, we assessed novel methods to evaluate survival after lethal infection with ESKAPEE pathogens (<em>Enterococcus faecium, Staphylococcus aureus</em>, <em>Klebsiella pneumoniae</em>, <em>Acinetobacter baumannii</em>, <em>Pseudomonas aeruginosa</em>, <em>Enterobacter cloacae</em>, and <em>Escherichia coli</em>) in pulmonary models of infection. Consistent with published lung infection models often used for novel antimicrobial development, BALB/c mice were immunosuppressed with cyclophosphamide and inoculated intranasally with individual ESKAPEE pathogens or sterile saline. Observations were recorded at frequent intervals to determine predictive thresholds for humane endpoint decision-making. Internal temperature was measured via implanted IPTT300 microchips, and external temperature was measured using a non-contact, infrared thermometer. Additionally, clinical scores were evaluated based on animal appearance, behavior, hydration status, respiration, and body weight. Internal temperature differences between survivors and non-survivors were statistically significant for <em>E. faecium, S. aureus</em>, <em>K. pneumoniae</em>, <em>A. baumannii</em>, <em>E. cloacae</em>, and <em>E. coli</em>, and external temperature differences were statistically significant for <em>S. aureus</em>, <em>K. pneumoniae</em>, <em>E. cloacae</em>, and <em>E. coli</em>. Internal temperature more precisely predicted mortality compared to external temperature, indicating that a threshold of 85ºF (29.4ºC) was 86.0% predictive of mortality and 98.7% predictive of survival. Based on our findings, we recommend future studies involving BALB/c mice ESKAPEE pathogen infection use temperature monitoring as a humane endpoint threshold.
DOI: 10.6084/m9.figshare.22257913
title: Lista de estudios incluidos.pdf

description: Borderline personality disorder is a mental illness characterized by impairment of a person's ability to control their emotions. These people can feel insecure about how they perceive the outside world and especially how they perceive themselves, seriously affecting their relationships with others where they can quickly change their mood and go from closeness to extreme aversion. These people tend to go to extremes, where they are cheered up and then they turn extremely sad.
DOI: 10.6084/m9.figshare.22236898
title: NLRP3_CodingSequencesALIGNED.fasta

description: This file is a FASTA-formatted file containing the aligned protein-coding sequences of the NLRP3 receptor for our species of interest (human, cat, mouse, pig, chimp, bat, mink, rabbit) obtained from Ensembl containing reference UniProt identifiers.
DOI: 10.6084/m9.figshare.22236898.v1
title: NLRP3_CodingSequencesALIGNED.fasta

description: This file is a FASTA-formatted file containing the aligned protein-coding sequences of the NLRP3 receptor for our species of interest (human, cat, mouse, pig, chimp, bat, mink, rabbit) obtained from Ensembl containing reference UniProt identifiers.
DOI: 10.6084/m9.figshare.22236895
title: ACE2_CodingSequencesALIGNED.fasta

description: This file is a FASTA-formatted file containing the aligned protein-coding sequences of the ACE2 receptor for our species of interest (human, cat, mouse, pig, chimp, bat, mink, rabbit) obtained from Ensembl containing reference UniProt identifiers.
DOI: 10.6084/m9.figshare.22236895.v1
title: ACE2_CodingSequencesALIGNED.fasta

description: This file is a FASTA-formatted file containing the aligned protein-coding sequences of the ACE2 receptor for our species of interest (human, cat, mouse, pig, chimp, bat, mink, rabbit) obtained from Ensembl containing reference UniProt identifiers.
DOI: 10.34740/kaggle/dsv/5148873
title: arXiv Dataset
DOI: 10.5281/zenodo.7566673
title: Snapshot Testing Dataset - Repositories using Jest

description: This is a dataset of GitHub repositories that were tagged with Jest, that used Snapshot Testing. Information on all repositories is available in the file "0_Snapshot Testing Dataset.xlsx" (named to be the very first file). All other files represent the repository packed in targz format as "&lt;user&gt;_&lt;repository_name&gt;.tar.gz". We split large repositories (&gt;50MB) using the "split" command on Unix (use cat to rejoin them). In total there are 685 repositories. We collected only public repositories that were tagged with the Jest keyword, had at least 1 star, and at least 1 snapshot file. The spreadsheet data was collected on July 13, 2022.
DOI: 10.5281/zenodo.7571230
title: Snapshot Testing Dataset - Repositories using Jest

description: This is a dataset of GitHub repositories that were tagged with Jest, that used Snapshot Testing. Information on all repositories is available in the file "0_Snapshot Testing Dataset.xlsx" (named to be the very first file). All other files represent the repository packed in targz format as "&lt;user&gt;_&lt;repository_name&gt;.tar.gz". We split large repositories (&gt;50MB) using the "split" command on Unix (use cat to rejoin them). In total there are 686 repositories. We collected only public repositories that were tagged with the Jest keyword, had at least 1 star, and at least 1 snapshot file. The spreadsheet data was collected on July 13, 2022. Please check our GitHub repository for the scripts used to collect this data &lt;https://github.com/hscrocha/SnapshotTestingDataset&gt;. Since in GitHub, it is easier to update code artifacts.
DOI: 10.5281/zenodo.7574358
title: Snapshot Testing Dataset - Repositories using Jest

description: This is a dataset of GitHub repositories that were tagged with Jest, for JavaScript and TypeScript languages, that used Snapshot Testing. Information on all repositories is available in the file "0_Snapshot Testing Dataset.xlsx" (named to be the very first file). Most files represent the repository packed in targz format as "&lt;user&gt;_&lt;repository_name&gt;.tar.gz". We split large repositories (&gt;50MB) using the "split" command on Unix (use cat to rejoin them). In total there are 686 repositories. We collected only public repositories that were tagged with the Jest keyword, for JavaScript and TypeScript, had at least 1 star, and at least 1 snapshot file. The spreadsheet data was collected on July 13, 2022. We also have all scripts used to gather this data. Here, "python_scripts.zip" has all python scripts to find repositories based on queries and save their attributes, and "node_and_shell_scripts.zip" contain the node and shell scripts to download a tarball of the repository. Therefore you should first use the python scripts to collect repositories & their attribute, and later use the node & shell to download a copy of the repositories. Moreover, inside each script folder/zip there is a Readme file with instructions and examples. Our GitHub repository is an exact copy of this dataset &lt;https://github.com/hscrocha/SnapshotTestingDataset&gt;, but it is much better organized into folders and the README files for the scripts will be nicely displayed on it.
DOI: 10.5281/zenodo.7724641
title: Snapshot Testing Dataset - Repositories using Jest

description: This is a dataset of GitHub repositories that were tagged with Jest, for JavaScript and TypeScript languages, that used Snapshot Testing. Information on all repositories is available in the file "0_Snapshot Testing Dataset.xlsx" (named to be the very first file). Most files represent the repository packed in targz format as "&lt;user&gt;_&lt;repository_name&gt;.tar.gz". We split large repositories (&gt;50MB) using the "split" command on Unix (use cat to rejoin them). In total there are 686 repositories. We collected only public repositories that were tagged with the Jest keyword, for JavaScript and TypeScript, had at least 1 star, and at least 1 snapshot file. The spreadsheet data was collected on July 13, 2022. We also have all scripts used to gather this data. Here, "python_scripts.zip" has all python scripts to find repositories based on queries and save their attributes, and "node_and_shell_scripts.zip" contain the node and shell scripts to download a tarball of the repository. Therefore you should first use the python scripts to collect repositories & their attribute, and later use the node & shell to download a copy of the repositories. Moreover, inside each script folder/zip there is a Readme file with instructions and examples. Our GitHub repository is an exact copy of this dataset &lt;https://github.com/hscrocha/SnapshotTestingDataset&gt;, but it is much better organized into folders and the README files for the scripts will be nicely displayed on it.
DOI: 10.5281/zenodo.7566672
title: Snapshot Testing Dataset - Repositories using Jest

description: This is a dataset of GitHub repositories that were tagged with Jest, for JavaScript and TypeScript languages, that used Snapshot Testing. Information on all repositories is available in the file "0_Snapshot Testing Dataset.xlsx" (named to be the very first file). Most files represent the repository packed in targz format as "&lt;user&gt;_&lt;repository_name&gt;.tar.gz". We split large repositories (&gt;50MB) using the "split" command on Unix (use cat to rejoin them). In total there are 686 repositories. We collected only public repositories that were tagged with the Jest keyword, for JavaScript and TypeScript, had at least 1 star, and at least 1 snapshot file. The spreadsheet data was collected on July 13, 2022. We also have all scripts used to gather this data. Here, "python_scripts.zip" has all python scripts to find repositories based on queries and save their attributes, and "node_and_shell_scripts.zip" contain the node and shell scripts to download a tarball of the repository. Therefore you should first use the python scripts to collect repositories & their attribute, and later use the node & shell to download a copy of the repositories. Moreover, inside each script folder/zip there is a Readme file with instructions and examples. Our GitHub repository is an exact copy of this dataset &lt;https://github.com/hscrocha/SnapshotTestingDataset&gt;, but it is much better organized into folders and the README files for the scripts will be nicely displayed on it.
DOI: 10.5517/ccdc.csd.cc2690j2
title: CCDC 2034453: Experimental Crystal Structure Determination

description: Related Article: Qin Huang, Meng-Jiao Hao, Lai-You Wang, Feng Wu, Hou-Jin Li, Jie Yuan, Jun Xu, Taifo Mahmud, Wen-Jian Lan|2023|Phytochemistry|209|113612|doi:10.1016/j.phytochem.2023.113612
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.cc276bf7
title: CCDC 2061668: Experimental Crystal Structure Determination

description: Related Article: Xueyu Wang, Lianjun Song, Qiao Yu, Qiuju Li, Lanlan He, Xiao Xiao, Qingjiang Pan, Yanqiu Yang, Songdong Ding|2023|Inorg.Chem.|62|3916|doi:10.1021/acs.inorgchem.2c04311
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.cc27wjnb
title: CCDC 2082042: Experimental Crystal Structure Determination

description: Related Article: Qin Huang, Meng-Jiao Hao, Lai-You Wang, Feng Wu, Hou-Jin Li, Jie Yuan, Jun Xu, Taifo Mahmud, Wen-Jian Lan|2023|Phytochemistry|209|113612|doi:10.1016/j.phytochem.2023.113612
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.cc28qtmg
title: CCDC 2107306: Experimental Crystal Structure Determination

description: Related Article: Da Li, Ailan Liu, Yifan Xing, Zhijun Li, Yang Luo, Shijie Zhao, Linlin Dong, Tianyou Xie, Kunpeng Guo, Jie Li|2023|Dyes Pigm.|213|111180|doi:10.1016/j.dyepig.2023.111180
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.7722964
title: Mechanism of α and β diversity during succession

description: This data set contains key data for the article
DOI: 10.5281/zenodo.7722963
title: Mechanism of α and β diversity during succession

description: This data set contains key data for the article
DOI: 10.6084/m9.figshare.21587145
title: Increases in intraspecific body size variation are common amongst North American mammals and birds between 1880 and 2020

description: Many studies have documented the average body size of animals declining over time. Compared to mean body size, less is known about long-term changes in intraspecific trait variation (ITV), which is also important to understanding species’ ability to cope with environmental challenges. Based on 393,499 specimen records from 380 species collected in North America between 1880-2020, we found that body size ITV increased by 9.59% for mammals (n=302) and 30.67% for birds (n=78); human-harvested species had higher probability of ITV increase. The observed increasing ITV in many species suggests possible niche expansion and potential buffering effects against downsizing, but it risks increased maladaptation to rapidly changing environments. The results demonstrate that trait mean and variance do not necessarily respond in similar ways to anthropogenic pressures, and both should be considered.
DOI: 10.5517/ccdc.csd.cc2cy31h
title: CCDC 2202706: Experimental Crystal Structure Determination

description: Related Article: Kazutoshi Nishimura, Shibo Xu, Yuji Nishii, Koji Hirano|2023|Org.Lett.|25|1503|doi:10.1021/acs.orglett.3c00263
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.cc21z9gt
title: CCDC 1905956: Experimental Crystal Structure Determination

description: Related Article: Xueyu Wang, Lianjun Song, Qiao Yu, Qiuju Li, Lanlan He, Xiao Xiao, Qingjiang Pan, Yanqiu Yang, Songdong Ding|2023|Inorg.Chem.|62|3916|doi:10.1021/acs.inorgchem.2c04311
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.cc2d4mhp
title: CCDC 2208982: Experimental Crystal Structure Determination

description: Related Article: Xueyu Wang, Lianjun Song, Qiao Yu, Qiuju Li, Lanlan He, Xiao Xiao, Qingjiang Pan, Yanqiu Yang, Songdong Ding|2023|Inorg.Chem.|62|3916|doi:10.1021/acs.inorgchem.2c04311
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.cc2d4mjq
title: CCDC 2208983: Experimental Crystal Structure Determination

description: Related Article: Xueyu Wang, Lianjun Song, Qiao Yu, Qiuju Li, Lanlan He, Xiao Xiao, Qingjiang Pan, Yanqiu Yang, Songdong Ding|2023|Inorg.Chem.|62|3916|doi:10.1021/acs.inorgchem.2c04311
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.cc2d4mkr
title: CCDC 2208984: Experimental Crystal Structure Determination

description: Related Article: Xueyu Wang, Lianjun Song, Qiao Yu, Qiuju Li, Lanlan He, Xiao Xiao, Qingjiang Pan, Yanqiu Yang, Songdong Ding|2023|Inorg.Chem.|62|3916|doi:10.1021/acs.inorgchem.2c04311
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.cc2d5wbt
title: CCDC 2210186: Experimental Crystal Structure Determination

description: Related Article: Kazutoshi Nishimura, Shibo Xu, Yuji Nishii, Koji Hirano|2023|Org.Lett.|25|1503|doi:10.1021/acs.orglett.3c00263
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.cc2d5wcv
title: CCDC 2210187: Experimental Crystal Structure Determination

description: Related Article: Kazutoshi Nishimura, Shibo Xu, Yuji Nishii, Koji Hirano|2023|Org.Lett.|25|1503|doi:10.1021/acs.orglett.3c00263
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.cc2dc6dd
title: CCDC 2215303: Experimental Crystal Structure Determination

description: Related Article: Xinmiao Huang, Ling Tang, Zhiyong Song, Shuangshuang Jiang, Xianmao Liu, Ming Ma, Bo Chen, Yuanhong Ma|2023|Org.Lett.|25|1198|doi:10.1021/acs.orglett.3c00185
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.cc2dfv6x
title: CCDC 2217839: Experimental Crystal Structure Determination

description: Related Article: Xueyu Wang, Lianjun Song, Qiao Yu, Qiuju Li, Lanlan He, Xiao Xiao, Qingjiang Pan, Yanqiu Yang, Songdong Ding|2023|Inorg.Chem.|62|3916|doi:10.1021/acs.inorgchem.2c04311
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.7724799
title: MITO training datasets for live cell anatomy segmentation

description: Training dataset acquired from two microscopes for the segmentation of mitochondria.
DOI: 10.5281/zenodo.7724798
title: MITO training datasets for live cell anatomy segmentation

description: Training dataset acquired from two microscopes for the segmentation of mitochondria.
DOI: 10.5281/zenodo.7724778
title: Pretrained models and test examples of live cell anatomy segmentation

description: Pretrained models of live cell anatomy segmentation and test examples of the segmentation of 15 structures
DOI: 10.5281/zenodo.7724779
title: Pretrained models of live cell anatomy segmentation

description: Pretrained models of live cell anatomy segmentation
DOI: 10.5281/zenodo.7724828
title: Pretrained models and test examples of live cell anatomy segmentation

description: Pretrained models of live cell anatomy segmentation and test examples of the segmentation of 15 structures
DOI: 10.34740/kaggle/dsv/5149173
title: 🇺🇦 Ukraine Conflict Twitter Dataset