cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2bf41z
title: CCDC 2157570: Experimental Crystal Structure Determination

description: Related Article: Juliana Mota, Catarina Bravo, Catarina Santos, Paula C. Alves, Patrícia Rijo, Alexandra M. Antunes, Liliana Grenho, M. Helena Fernandes, Marta M. Alves, Vânia André|2023|Colloids and Surfaces B: Biointerfaces|221|113008|doi:10.1016/j.colsurfb.2022.113008
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.cc2bfsyj
title: CCDC 2158218: Experimental Crystal Structure Determination

description: Related Article: Thipashini Ganesan, Prakash Muthudoss, Raja Sekhar Voguri, Subhas Ghosal, Eddy Yii Chung Ann, Jayson Kwok, Sajid Syed Shahnawaz, Muhammad Firdaus Omar, Ravikiran Allada, Hong Heng See|2022|J.Pharm.Sci.|111|3318|doi:10.1016/j.xphs.2022.08.022
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.cc2bl74b
title: CCDC 2162471: Experimental Crystal Structure Determination

description: Related Article: Prince, Sandeep Kumar, Ram Sunil Kumar Lalji, Mohit Gupta, Prashant Kumar, Ravindra Kumar, Brajendra K. Singh|2022|Org.Biomol.Chem.|20|8944|doi:10.1039/D2OB01451C
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.cc2bvf1p
title: CCDC 2170342: Experimental Crystal Structure Determination

description: Related Article: Wang Wang, Yanyao Cai, Renyu Guo, M. Kevin Brown|2022|Chemical Science|13|13582|doi:10.1039/D2SC04789F
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.cc2bvf2q
title: CCDC 2170343: Experimental Crystal Structure Determination

description: Related Article: Wang Wang, Yanyao Cai, Renyu Guo, M. Kevin Brown|2022|Chemical Science|13|13582|doi:10.1039/D2SC04789F
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.cc2bvqb8
title: CCDC 2170630: Experimental Crystal Structure Determination

description: Related Article: Wang Wang, Yanyao Cai, Renyu Guo, M. Kevin Brown|2022|Chemical Science|13|13582|doi:10.1039/D2SC04789F
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.cc29llyg
title: CCDC 2133046: Experimental Crystal Structure Determination

description: Related Article: Deboshmita Mukherjee, Sahin Reja, Kaushik Sarkar, T.K.S. Fayaz, Pranesh Kumar, Ambica Kejriwal, Purak Das, Palash Sanphui, Rajesh Kumar Das|2022|Inorg.Chem.Commun.|146|110190|doi:10.1016/j.inoche.2022.110190
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.cc2c034n
title: CCDC 2174840: Experimental Crystal Structure Determination

description: Related Article: Suvam Bhattacharjee, Sudip Laru, Alakananda Hajra|2022|Org.Biomol.Chem.|20|8893|doi:10.1039/D2OB01776H
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.cc2c1dpj
title: CCDC 2176097: Experimental Crystal Structure Determination

description: Related Article: Praseetha Mathoor Illam, Chandra Shekhar Tiwari, Arnab Rit|2022|Chemical Science|13|13387|doi:10.1039/D2SC05024B
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.cc2c1dsm
title: CCDC 2176100: Experimental Crystal Structure Determination

description: Related Article: Praseetha Mathoor Illam, Chandra Shekhar Tiwari, Arnab Rit|2022|Chemical Science|13|13387|doi:10.1039/D2SC05024B
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.cc2c1dtn
title: CCDC 2176101: Experimental Crystal Structure Determination

description: Related Article: Praseetha Mathoor Illam, Chandra Shekhar Tiwari, Arnab Rit|2022|Chemical Science|13|13387|doi:10.1039/D2SC05024B
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.cc2c1dvp
title: CCDC 2176102: Experimental Crystal Structure Determination

description: Related Article: Praseetha Mathoor Illam, Chandra Shekhar Tiwari, Arnab Rit|2022|Chemical Science|13|13387|doi:10.1039/D2SC05024B
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.cc2c1dwq
title: CCDC 2176103: Experimental Crystal Structure Determination

description: Related Article: Praseetha Mathoor Illam, Chandra Shekhar Tiwari, Arnab Rit|2022|Chemical Science|13|13387|doi:10.1039/D2SC05024B
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.cc2c1dxr
title: CCDC 2176104: Experimental Crystal Structure Determination

description: Related Article: Praseetha Mathoor Illam, Chandra Shekhar Tiwari, Arnab Rit|2022|Chemical Science|13|13387|doi:10.1039/D2SC05024B
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.cc2c1dys
title: CCDC 2176105: Experimental Crystal Structure Determination

description: Related Article: Praseetha Mathoor Illam, Chandra Shekhar Tiwari, Arnab Rit|2022|Chemical Science|13|13387|doi:10.1039/D2SC05024B
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.7349375
title: Arabic Raw Text Corpus (unfiltered, extracted from the Clueweb WARC Files)

description: The corpus is created from 29,192,662 ClueWeb html. Initially archived in WARC files. Each text file contains in average 30,000 different webpages. The size of the corpus is 18,482,719 terms.
DOI: 10.5281/zenodo.7349376
title: Arabic Raw Text Corpus (unfiltered, extracted from the Clueweb WARC Files)

description: The corpus is created from 29,192,662 ClueWeb html. Initially archived in WARC files. Each text file contains in average 30,000 different webpages. The size of the corpus is 18,482,719 terms.
DOI: 10.5281/zenodo.7352382
title: Supplementary data files for exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins

description: This dataset includes 20 supplementary data files for manuscript <em>Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins </em>by Jakub W. Wojciechowski, Emirhan Tekoglu, Marlena Gąsior-Głogowska, Virginie Coustou, Natalia Szulc, Monika Szefczyk, Marta Kopaczyńska, Sven J. Saupe, and Witold Dyrka (under revision). SF2. Profile HMMs of NLR effector domains. The file includes previously unpublished models. SF3. Multiple sequence alignments of N-termini clusters. SF4. Tabularized results of N-termini annotation. SF5. Structure prediction of HeLo-/Goodbye-/MLKL-like domains. Full AlphaFold2/ColabFold outputs. SF6. Structure prediction of previously unannotated domains. Full AlphaFold2/ColabFold outputs. SF7. PCFGs for BASS. The file includes previously unpublished grammars and a sample scanning configuration. SF8. Candidate short NLR N-termini with ASMs. The FASTA file includes sequences from clusters with high content of ASM-like sequences, according to the BASS PCFGs (SF7). SF9. Profile HMMs of ASMs found in short NLR N-termini. SF10. Profile HMM of HeLo-related HRAMs. SF11. Genomic neighbors of candidate short N-termini NLRs with ASMs The list includes accessions of proteins encoded by genes within the neighborhood of 20kbp of genes encoding the query proteins (SF8). SF12. Short C-termini of 200–400 aa long proteins genomically neighboring candidate short NLR N-termini with ASMs. The FASTA file concerns target proteins listed in SF11. SF13. Pairwise hits of the same ASMs in N-termini of NLRs and C-termini of genomically neighboring proteins. The table is based on SF8–9 and SF11–12. SF14. Lists of HMMER domain hits of effector domain profiles. The lists were obtained through iterative searches in NCBI “nr” starting from Pfam profiles of known NLR effector domains. SF15. Short C-termini of effector proteins. The FASTA file concerns target proteins listed in SF14. SF16. Short N-termini of Pfam NACHT and NB-ARC proteins. The FASTA file concerns proteins from NCBI “nr” associated with the two families in the Pfam database. SF17. Profile HMMs of ASMs found both in effector C-termini and NLR N-termini of genomically neighboring proteins. SF18. Genomic neighbors of candidate short N-termini Pfam NACHT and NB-ARC proteins. The list includes accessions of proteins encoded by genes within the neighborhood of 20kbp of genes encoding the query proteins (SF16). SF19. Pairwise hits of the same ASMs in N-termini of NACHT/NB-ARC NLRs and C-termini of genomically neighboring effector proteins. The table is based on SF15–18. SF20. Pairwise hits of the same ASMs in N-termini of NLRs and C-termini of genomically co-occurring effector proteins. The table is based on SF8–9 and SF15. SF21. BaMLKL homologs identified with hmmsearch in Basidiomycota. A FASTA file.
DOI: 10.5281/zenodo.7352381
title: Supplementary data files for exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins

description: This dataset includes 20 supplementary data files for manuscript <em>Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins </em>by Jakub W. Wojciechowski, Emirhan Tekoglu, Marlena Gąsior-Głogowska, Virginie Coustou, Natalia Szulc, Monika Szefczyk, Marta Kopaczyńska, Sven J. Saupe, and Witold Dyrka (under revision). SF2. Profile HMMs of NLR effector domains. The file includes previously unpublished models. SF3. Multiple sequence alignments of N-termini clusters. SF4. Tabularized results of N-termini annotation. SF5. Structure prediction of HeLo-/Goodbye-/MLKL-like domains. Full AlphaFold2/ColabFold outputs. SF6. Structure prediction of previously unannotated domains. Full AlphaFold2/ColabFold outputs. SF7. PCFGs for BASS. The file includes previously unpublished grammars and a sample scanning configuration. SF8. Candidate short NLR N-termini with ASMs. The FASTA file includes sequences from clusters with high content of ASM-like sequences, according to the BASS PCFGs (SF7). SF9. Profile HMMs of ASMs found in short NLR N-termini. SF10. Profile HMM of HeLo-related HRAMs. SF11. Genomic neighbors of candidate short N-termini NLRs with ASMs The list includes accessions of proteins encoded by genes within the neighborhood of 20kbp of genes encoding the query proteins (SF8). SF12. Short C-termini of 200–400 aa long proteins genomically neighboring candidate short NLR N-termini with ASMs. The FASTA file concerns target proteins listed in SF11. SF13. Pairwise hits of the same ASMs in N-termini of NLRs and C-termini of genomically neighboring proteins. The table is based on SF8–9 and SF11–12. SF14. Lists of HMMER domain hits of effector domain profiles. The lists were obtained through iterative searches in NCBI “nr” starting from Pfam profiles of known NLR effector domains. SF15. Short C-termini of effector proteins. The FASTA file concerns target proteins listed in SF14. SF16. Short N-termini of Pfam NACHT and NB-ARC proteins. The FASTA file concerns proteins from NCBI “nr” associated with the two families in the Pfam database. SF17. Profile HMMs of ASMs found both in effector C-termini and NLR N-termini of genomically neighboring proteins. SF18. Genomic neighbors of candidate short N-termini Pfam NACHT and NB-ARC proteins. The list includes accessions of proteins encoded by genes within the neighborhood of 20kbp of genes encoding the query proteins (SF16). SF19. Pairwise hits of the same ASMs in N-termini of NACHT/NB-ARC NLRs and C-termini of genomically neighboring effector proteins. The table is based on SF15–18. SF20. Pairwise hits of the same ASMs in N-termini of NLRs and C-termini of genomically co-occurring effector proteins. The table is based on SF8–9 and SF15. SF21. BaMLKL homologs identified with hmmsearch in Basidiomycota. A FASTA file.
DOI: 10.5066/p99i27t4
title: Cyanotoxin, chlorophyll-a, and cyanobacterial toxin genetic data for samples collected at twelve large river sites throughout the United States, June through October 2019

description: This U.S. Geological Survey (USGS) data release provides discretely measured cyanotoxin, chlorophyll-a, and cyanobacterial toxin genetic data for samples collected from twelve large river sites throughout the United States, from June through October 2019. Discrete water-quality samples were analyzed for cyanotoxins (anatoxin, cylindrospermopsin, microcystin, and saxitoxin), chlorophyll-a, and cyanobacterial toxin genetics as part of a National Water-Quality Assessment Project pilot study to describe cyanobacteria and cyanotoxin occurrence in the Nation's large rivers.�The data release contains the genetic data (in .csv and .xlsx formats), qPCR standard curve information (in .csv and .xlsx formats), and a readme file explaining the data headers for the genetic and standard curve files. �
DOI: 10.5281/zenodo.6824280
title: Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 RGB images of coasts.

description: <strong>Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 RGB images of coasts.</strong> <strong>Based on SWED*** data</strong> https://openmldata.ukho.gov.uk/ These Residual-UNet model data are based on images of coasts and associated labels. Models have been fitted to the following types of data 1. RGB (3 band): red, green, blue Classes are: {0: null, 1: water}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images 3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym *** https://www.sciencedirect.com/science/article/abs/pii/S0034425722001584
DOI: 10.5281/zenodo.6824384
title: Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 RGB images of coasts.

description: <strong>Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 RGB images of coasts.</strong> <strong>Based on SWED*** data</strong> https://openmldata.ukho.gov.uk/ These Residual-UNet model data are based on images of coasts and associated labels. Models have been fitted to the following types of data 1. RGB (3 band): red, green, blue Classes are: {0: null, 1: water}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images 3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym *** https://www.sciencedirect.com/science/article/abs/pii/S0034425722001584
DOI: 10.6084/m9.figshare.7294874
title: Supplemental material for Going Beyond Convergence in Bayesian Estimation: Why Precision Matters too and How to Assess It (published in Structural Equation Modeling)

description: The following tables provide detailed results from the simulation studies. Tables S1 and S2 show comparisons of the estimates of the regression coefficient in the macro-micro design (Study 1) for the default in Mplus (i.e, PSR &lt; 1:05 for models with only one parameter, and PSR &lt; 1:10 for models with many parameters) with the ESS criteria (e.g., ESS &gt; 400) with respect to the relative bias and the relative RMSE. Table S3 compares the coverage rates of Mplus’ default criterion with those of the ESS criteria in Study 1. The remaining tables present the detailed results from Study 2 (micro-macro design) as well as those from the additional study.
DOI: 10.6084/m9.figshare.7294874.v1
title: Supplemental material for going beyond convergence in Bayesian estimation: Why precision matters too and how to assess It (published in Structural Equation Modeling)

description: The following tables provide detailed results from the simulation studies. Tables S1 and S2 show comparisons of the estimates of the regression coefficient in the macro-micro design (Study 1) for the default in Mplus (i.e, PSR &lt; 1:05 for models with only one parameter, and PSR &lt; 1:10 for models with many parameters) with the ESS criteria (e.g., ESS &gt; 400) with respect to the relative bias and the relative RMSE. Table S3 compares the coverage rates of Mplus’ default criterion with those of the ESS criteria in Study 1. The remaining tables present the detailed results from Study 2 (micro-macro design) as well as those from the additional study.
DOI: 10.6084/m9.figshare.21604014
title: Root-Pattern Morphological Dataset

description: Morphological Database resulted in using three different morphological parser and stemmer (Arabic Textware and Alkalil morphological analyzers, and khoja stemmer). Data are organized word, stem, word, and corresponding patterns. Stems need to be cleaned. <br>
DOI: 10.6084/m9.figshare.21604014.v1
title: Root-Pattern Morphological Dataset

description: Morphological Database resulted in using three different morphological parser and stemmer (Arabic Textware and Alkalil morphological analyzers, and khoja stemmer). Data are organized word, stem, word, and corresponding patterns. Stems need to be cleaned. <br>
DOI: 10.6084/m9.figshare.21586326
title: Language Data Model for Arabic based Root-Pattern MLE (Bi-gram) Analysis

description: Language Data Model for Arabic considering Arabic morphological analysis and its impact on cognitive aspects on Arabic word perception. MLE of Bi-gram statistical analysis of a corpus containing around 18.49-million-word types.