cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2fbbty
title: CCDC 2244270: Experimental Crystal Structure Determination

description: Related Article: Emilay B. T. Diogo, Fábio G. Delolo, Gabriela A. P. Graça, Esther R. S. Paz, Ícaro A. O. Bozzi, Renata Diniz, Juliano P. Passos, Tulio Matencio, Liane K. Soares, Diego Alves, Pedro M. S. Costa, Claudia Pessoa, Cynthia L. M. Pereira, Lutz Ackermann, Eufrânio N. da Silva Júnior|2023|Eur.J.Org.Chem.|26|e202300506|doi:10.1002/ejoc.202300506
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.cc2fbbx1
title: CCDC 2244273: Experimental Crystal Structure Determination

description: Related Article: Emilay B. T. Diogo, Fábio G. Delolo, Gabriela A. P. Graça, Esther R. S. Paz, Ícaro A. O. Bozzi, Renata Diniz, Juliano P. Passos, Tulio Matencio, Liane K. Soares, Diego Alves, Pedro M. S. Costa, Claudia Pessoa, Cynthia L. M. Pereira, Lutz Ackermann, Eufrânio N. da Silva Júnior|2023|Eur.J.Org.Chem.|26|e202300506|doi:10.1002/ejoc.202300506
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.cc2fbbw0
title: CCDC 2244272: Experimental Crystal Structure Determination

description: Related Article: Emilay B. T. Diogo, Fábio G. Delolo, Gabriela A. P. Graça, Esther R. S. Paz, Ícaro A. O. Bozzi, Renata Diniz, Juliano P. Passos, Tulio Matencio, Liane K. Soares, Diego Alves, Pedro M. S. Costa, Claudia Pessoa, Cynthia L. M. Pereira, Lutz Ackermann, Eufrânio N. da Silva Júnior|2023|Eur.J.Org.Chem.|26|e202300506|doi:10.1002/ejoc.202300506
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.cc2fspdb
title: CCDC 2258052: Experimental Crystal Structure Determination

description: Related Article: Rahul Kumar Saini, Paran J. Borpatra, Satyendra Kumar Pandey|2023|Eur.J.Org.Chem.|26|e202300693|doi:10.1002/ejoc.202300693
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.cc2g42sh
title: CCDC 2268046: Experimental Crystal Structure Determination

description: Related Article: Nikita A. Shekhovtsov, Sofia Vorob’eva, Elena B. Nikolaenkova, Alexey A. Ryadun, Viktor P. Krivopalov, Christophe Gourlaouen, Mark B. Bushuev|2023|Inorg.Chem.|62|16734|doi:10.1021/acs.inorgchem.3c02036
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.cc2g5gqv
title: CCDC 2269377: Experimental Crystal Structure Determination

description: Related Article: Jian-Zhen Liao, Shu-Jie Liu, Hua Ke|2023|Inorg.Chem.|62|16825|doi:10.1021/acs.inorgchem.3c02271
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.cc2ggtyq
title: CCDC 2278374: Experimental Crystal Structure Determination

description: Related Article: Emilay B. T. Diogo, Fábio G. Delolo, Gabriela A. P. Graça, Esther R. S. Paz, Ícaro A. O. Bozzi, Renata Diniz, Juliano P. Passos, Tulio Matencio, Liane K. Soares, Diego Alves, Pedro M. S. Costa, Claudia Pessoa, Cynthia L. M. Pereira, Lutz Ackermann, Eufrânio N. da Silva Júnior|2023|Eur.J.Org.Chem.|26|e202300506|doi:10.1002/ejoc.202300506
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.10009112
title: Hyperspectral Oblique Plane Microscopy -- heart

description: Data and postprocessing codes for the zebrafish embryo heart experiment
DOI: 10.5281/zenodo.8368476
title: Hyperspectral Oblique Plane Microscopy - microparticles

description: Processed spectra data for microparticle classification and raw hyperspectral images from mixture of microparticles
DOI: 10.6084/m9.figshare.24442876
title: Oxygen supersaturation does not protect aquatic ectotherms from acute warming

description: These are data to accompany a manuscript about the effect of oxygen supersaturation on CTmax in 11 aquatic ectotherms.
DOI: 10.6084/m9.figshare.24442876.v1
title: Oxygen supersaturation does not protect aquatic ectotherms from acute warming

description: These are data to accompany a manuscript about the effect of oxygen supersaturation on CTmax in 11 aquatic ectotherms.
DOI: 10.17633/rd.brunel.24442468
title: Cambridge Gambling Task Data

description: Background: Late-life onset psychosis and milder delusion-like ideation are known risk factors for cognitive decline and dementia. The Mild Behavioral Impairment (MBI) framework was developed to capture specific psychotic-like symptoms relevant to dementia prognosis in older adults. This study aims to investigate the cognitive deficits associated with MBI-psychosis and their implications for understanding the underlying mechanisms and potential treatment targets.Method: The study recruited participants between November 2021 and July 2022 from the PROTECT study registry. Participants completed the Cambridge Gambling Task, Stroop, Trail Making, Paired Associates Learning, Verbal Reasoning, Digit Span and Self-Ordered Search. Psychotic symptom status was assessed using the Mild Behavioral Impairment Checklist (MBI-C), with participants categorized as MBI-psychosis if they or their study partner reported any psychotic symptoms.Results: Out of 2,111 eligible participants invited, 417 consented to participate. There were no significant differences in age, sex, education level, or mental health history between the MBI-psychosis and No Psychosis groups. Participants with MBI-psychosis exhibited significantly worse performance on the Stroop task (p=0.0002, Cohen’s d=0.37) compared to those without psychosis. There was also some evidence of impairment in verbal reasoning, though it did not reach significance after Bonferroni correction. No significant differences were found on other cognitive measures.Discussion: This cross-sectional study provides insight into the cognitive deficits associated with MBI-psychosis. The finding of impaired Stroop task performance in individuals with MBI-psychosis is noteworthy, as this deficit is commonly observed in earlier-life major psychotic disorders. Further research is needed to explore the neural underpinnings of these deficits and to determine whether they represent early markers of neurodegenerative disease or other factors.
DOI: 10.17633/rd.brunel.24442468.v1
title: Cambridge Gambling Task Data

description: Background: Late-life onset psychosis and milder delusion-like ideation are known risk factors for cognitive decline and dementia. The Mild Behavioral Impairment (MBI) framework was developed to capture specific psychotic-like symptoms relevant to dementia prognosis in older adults. This study aims to investigate the cognitive deficits associated with MBI-psychosis and their implications for understanding the underlying mechanisms and potential treatment targets.Method: The study recruited participants between November 2021 and July 2022 from the PROTECT study registry. Participants completed the Cambridge Gambling Task, Stroop, Trail Making, Paired Associates Learning, Verbal Reasoning, Digit Span and Self-Ordered Search. Psychotic symptom status was assessed using the Mild Behavioral Impairment Checklist (MBI-C), with participants categorized as MBI-psychosis if they or their study partner reported any psychotic symptoms.Results: Out of 2,111 eligible participants invited, 417 consented to participate. There were no significant differences in age, sex, education level, or mental health history between the MBI-psychosis and No Psychosis groups. Participants with MBI-psychosis exhibited significantly worse performance on the Stroop task (p=0.0002, Cohen’s d=0.37) compared to those without psychosis. There was also some evidence of impairment in verbal reasoning, though it did not reach significance after Bonferroni correction. No significant differences were found on other cognitive measures.Discussion: This cross-sectional study provides insight into the cognitive deficits associated with MBI-psychosis. The finding of impaired Stroop task performance in individuals with MBI-psychosis is noteworthy, as this deficit is commonly observed in earlier-life major psychotic disorders. Further research is needed to explore the neural underpinnings of these deficits and to determine whether they represent early markers of neurodegenerative disease or other factors.
DOI: 10.18120/tapm-7p97
title: SCS LowerLimb Sensory Restoration
DOI: 10.5517/ccdc.csd.cc2dhsqf
title: CCDC 2219715: Experimental Crystal Structure Determination

description: Related Article: Masashi Hasegawa, Yuki Nojima, Yuya Nagata, Kazuteru Usui, Ken-ichi Sugiura, Yasuhiro Mazaki|2023|Eur.J.Org.Chem.|26|e202300656|doi:10.1002/ejoc.202300656
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.cc2fjnc0
title: CCDC 2250332: Experimental Crystal Structure Determination

description: Related Article: Shuaibo Zhang, Dong Li, Bangzhong Wang, Wuding Sun, Haojun Ma, Kai Di, Luyang Sun, Jinfeng Zhao, Jingping Qu, Yuhan Zhou|2023|Eur.J.Org.Chem.|26|e202300593|doi:10.1002/ejoc.202300593
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.cc2gfsl9
title: CCDC 2277371: Experimental Crystal Structure Determination

description: Related Article: Megan L. Rammer, Emily R. Gonnering, Zakarias L. Driscoll, Herh G. Vang, Eric Bosch, Nathan P. Bowling|2023|Eur.J.Org.Chem.|26|e202300716|doi:10.1002/ejoc.202300716
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.cc2gfsmb
title: CCDC 2277372: Experimental Crystal Structure Determination

description: Related Article: Megan L. Rammer, Emily R. Gonnering, Zakarias L. Driscoll, Herh G. Vang, Eric Bosch, Nathan P. Bowling|2023|Eur.J.Org.Chem.|26|e202300716|doi:10.1002/ejoc.202300716
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.10044949
title: BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification

description: The dramatic increase in the use of social media platforms for information sharing has also driven a significant rise in online abuse. A simple yet effective method of targeting individuals or communities involves creating memes, often combining an image with a brief piece of text overlaid on top. These harmful elements are widely used and pose a threat to online safety. Therefore, developing efficient models for detecting and flagging abusive memes is essential. This challenge becomes even more demanding in a low-resource setting, such as Bengali memes (i.e., images with Bengali text embedded in them), due to the absence of benchmark datasets on which AI models can be trained. This work addresses this gap by creating a Bengali meme dataset of 4K data points.
This upload includes the labeled Bengali meme dataset obtained from the web, described in the paper 'BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification.'
DOI: 10.5281/zenodo.10044948
title: BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification

description: The dramatic increase in the use of social media platforms for information sharing has also driven a significant rise in online abuse. A simple yet effective method of targeting individuals or communities involves creating memes, often combining an image with a brief piece of text overlaid on top. These harmful elements are widely used and pose a threat to online safety. Therefore, developing efficient models for detecting and flagging abusive memes is essential. This challenge becomes even more demanding in a low-resource setting, such as Bengali memes (i.e., images with Bengali text embedded in them), due to the absence of benchmark datasets on which AI models can be trained. This work addresses this gap by creating a Bengali meme dataset of 4K data points.
This upload includes the labeled Bengali meme dataset obtained from the web, described in the paper 'BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification.'
DOI: 10.34740/kaggle/dsv/6803023
title: 📒Meta Kaggle ported to SQLITE DB
DOI: 10.6084/m9.figshare.24444106
title: MSGR Faculty Evaluation of the MSGR Course

description: Likert style data of faculty evaluations of the MSGR course
DOI: 10.6084/m9.figshare.24444106.v1
title: MSGR Faculty Evaluation of the MSGR Course

description: Likert style data of faculty evaluations of the MSGR course
DOI: 10.25345/c5cf9jh7j
title: MassIVE MSV000093203 - An Improved Sample Preparation Method for Protein and Peptide Identification from Human Hair
title: A fast and sensitive Direct Extraction (DE) method developed in our group can efficiently extract proteins in 30 min from a 5 cm-long hair strand. Previously we coupled DE to downstream analysis using gel electrophoresis followed by in-gel digestion, which can be time-consuming. In searching for a better alternative, we found that a combination of DE with a bead-based method (SP3) can lead to significant improvements in protein discovery from the human hair. Since SP3 is designed for general applications, we optimized it to process hair proteins following DE and compared it to several other in-solution-digestion methods. Of particular concern are genetically variant peptides (GVPs), which can be used for human identification in forensic analysis. Here we demonstrated improved GVP discovery with the DE and SP3 workflow which was three times faster than the previous in-gel digestion method and required significantly less instrument time depending on the number of gel slices processed. Additionally, it led to increased numbers of identified proteins and GVPs. Among the tested in-solution digestion methods, DE combined with SP3 showed the highest sequence coverage with higher abundances of the identified peptides. This provides a significantly enhanced means for identifying proteins and GVPs in human hair.
DOI: 10.6084/m9.figshare.22083050.v2
title: Listing and pictures of Diptera WIPs

description: Pictures of Wing interferential patterns (WIPs) from various Dipteran insects, process according to the methodology described in (Cannet, A., Simon-Chane, C., Akhoundi, M. <em>et al.</em> Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (<em>Glossina </em>spp.) identification. <em>Sci Rep</em> <strong>12</strong>, 20086 (2022). https://doi.org/10.1038/s41598-022-24522-w).
DOI: 10.6084/m9.figshare.24445129
title: sharedata-to-reproduce-LACL

description: You can download datasets used in the paper and extract the zip file under <code>./data</code> folder in the code base repository https://github.com/parkyjmit/LACL. Both QM9 and QMugs should be saved in the folder under their name. Conformations of all the data is pickled after preprocessing.QM9<code>qm9_all.pickle</code><br>List of dictionaries with properties. One dictionary corresponds to one molecule. It also contains cartesian coordinates of MMFF conformations and MMFF potential.<br><code>qm9_all_cgcf.pkl</code><br>List of rdkit molecules with cartesian coordinates of CGCF-ConfGen conformations. They were calculated by the official implement of CGCF-ConfGen.QMugs<code>QMugs_20_energy.pkl</code><br>List of dataframes containing identifiers, properties, SMILES, and rdkit mols with less than or equal to 20 number of heavy atoms.<br><code>QMugs_20_energy_mmff.pkl</code><br>List of rdkit molecules with cartesian coordinates of MMFF conformations. They were calculated by rdkit MMFF optimization.<br><code>QMugs_20_energy_cgcf.pkl</code><br>List of rdkit molecules with cartesian coordinates of CGCF-ConfGen conformations. They were calculated by the official implement of CGCF-ConfGen.<code>QMugs_{num}_energy_test.pkl</code><br>List of dataframes containing identifiers, properties, SMILES, and rdkit mols with <code>num</code> number of heavy atoms. <code>ex</code> means mols with more than 40 heavy atoms<br><code>QMugs_{num}_energy_mmff.pkl</code><br>List of rdkit molecules including <code>num</code> number of heavy atoms with cartesian coordinates of MMFF conformations.
DOI: 10.6084/m9.figshare.24445129.v1
title: sharedata-to-reproduce-LACL

description: You can download datasets used in the paper and extract the zip file under <code>./data</code> folder in the code base repository https://github.com/parkyjmit/LACL. Both QM9 and QMugs should be saved in the folder under their name. Conformations of all the data is pickled after preprocessing.QM9<code>qm9_all.pickle</code><br>List of dictionaries with properties. One dictionary corresponds to one molecule. It also contains cartesian coordinates of MMFF conformations and MMFF potential.<br><code>qm9_all_cgcf.pkl</code><br>List of rdkit molecules with cartesian coordinates of CGCF-ConfGen conformations. They were calculated by the official implement of CGCF-ConfGen.QMugs<code>QMugs_20_energy.pkl</code><br>List of dataframes containing identifiers, properties, SMILES, and rdkit mols with less than or equal to 20 number of heavy atoms.<br><code>QMugs_20_energy_mmff.pkl</code><br>List of rdkit molecules with cartesian coordinates of MMFF conformations. They were calculated by rdkit MMFF optimization.<br><code>QMugs_20_energy_cgcf.pkl</code><br>List of rdkit molecules with cartesian coordinates of CGCF-ConfGen conformations. They were calculated by the official implement of CGCF-ConfGen.<code>QMugs_{num}_energy_test.pkl</code><br>List of dataframes containing identifiers, properties, SMILES, and rdkit mols with <code>num</code> number of heavy atoms. <code>ex</code> means mols with more than 40 heavy atoms<br><code>QMugs_{num}_energy_mmff.pkl</code><br>List of rdkit molecules including <code>num</code> number of heavy atoms with cartesian coordinates of MMFF conformations.
DOI: 10.34740/kaggle/dsv/6801499
title: Factors Affecting Children Anemia Level
DOI: 10.5063/f13x854k
title: The Association of Vertebrate Species with Artificial Shrubs

description: The Association of Vertebrate Species with Artificial Shrubs within a desert environment in Southern California. Camera traps were deployed for a 30 day field season in 2023. A total of 58 Cameras were deployed between the Cuyama Valley and The Carrizo Plane National Monument. Shrub densities were calculated in a 20m buffer around each shrub, open, and artificial shrub. Temperature was recorded through the use of OMEGA temperature pendants deployed at each sample site.
DOI: 10.5281/zenodo.3822650
title: CICE tx1 JRA55 Forcing Data 2020.05.12

description: This file contains the tx1 JRA55 forcing data for CICE. The latest information about CICE forcing data and files can be found at the GitHub Resource Index: https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index under the "Input Data" link. These data are provided by the CICE Consortium (https://github.com/CICE-Consortium).
DOI: 10.5281/zenodo.10044769
title: CICE tx1 JRA55 Forcing Data 2020.09.19

description: This file contains the tx1 JRA55 forcing data for CICE. 
The latest information about CICE forcing data and files can be found at the GitHub Resource Index: https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index under the "Input Data" link.
These data are provided by the CICE Consortium (https://github.com/CICE-Consortium).
*Note that there was a bug found in the previous version. This version 2023-09-19 has been fixed.
DOI: 10.5281/zenodo.3822649
title: CICE tx1 JRA55 Forcing Data 2020.09.19

description: This file contains the tx1 JRA55 forcing data for CICE. 
The latest information about CICE forcing data and files can be found at the GitHub Resource Index: https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index under the "Input Data" link.
These data are provided by the CICE Consortium (https://github.com/CICE-Consortium).
*Note that there was a bug found in the previous version. This version 2023-09-19 has been fixed.