cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2023-02.2744024/2308677
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744195/2308678
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2745110/2308679
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744684/2308680
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744877/2308681
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744003/2308682
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2745002/2308683
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744130/2308684
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744546/2308685
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2743139/2308686
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2745396/2308687
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2745026/2308688
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2745441/2308689
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744643/2308690
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2745440/2308691
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744028/2308692
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744523/2308693
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2743211/2308694
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744967/2308695
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2745734/2308696
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2743027/2308697
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744168/2308698
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744561/2308699
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2744825/2308700
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2744405/2308701
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2745282/2308702
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2745281/2308703
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2745703/2308704
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2745704/2308705
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2744406/2308706
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2745280/2308707
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2744404/2308708
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2745706/2308709
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2744824/2308710
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2745283/2308711
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2744823/2308712
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2744826/2308713
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2744407/2308714
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2023-02.2745705/2308715
title: Data for EMSL Project 51127 from February 2023
DOI: 10.25582/data.2024-02.3033168/2308716
title: Data for EMSL Project 60846 from February 2024
DOI: 10.25582/data.2024-02.3033161/2308717
title: Data for EMSL Project 60846 from February 2024
DOI: 10.25582/data.2023-02.2744380/2308718
title: Data for EMSL Project 51609 from February 2023
DOI: 10.25582/data.2023-02.2745260/2308719
title: Data for EMSL Project 51609 from February 2023
DOI: 10.25582/data.2023-02.2744795/2308720
title: Data for EMSL Project 51609 from February 2023
DOI: 10.25582/data.2023-02.2745676/2308721
title: Data for EMSL Project 51609 from February 2023
DOI: 10.25582/data.2023-02.2744383/2308722
title: Data for EMSL Project 51663 from February 2023
DOI: 10.25582/data.2023-02.2744798/2308723
title: Data for EMSL Project 51663 from February 2023
DOI: 10.25582/data.2023-02.2745263/2308724
title: Data for EMSL Project 51663 from February 2023
DOI: 10.25582/data.2023-02.2745679/2308725
title: Data for EMSL Project 51663 from February 2023
DOI: 10.25582/data.2023-02.2745248/2308726
title: Data for EMSL Project 51597 from February 2023
DOI: 10.25582/data.2023-02.2745664/2308727
title: Data for EMSL Project 51597 from February 2023
DOI: 10.25582/data.2023-02.2744783/2308728
title: Data for EMSL Project 51597 from February 2023
DOI: 10.25582/data.2023-02.2744368/2308729
title: Data for EMSL Project 51597 from February 2023
DOI: 10.25582/data.2023-02.2743251/2308730
title: Data for PNCC Project 160210 from February 2023
DOI: 10.25582/data.2023-02.2743253/2308731
title: Data for PNCC Project 160210 from February 2023
DOI: 10.25582/data.2023-02.2743255/2308732
title: Data for PNCC Project 160210 from February 2023
DOI: 10.25582/data.2023-02.2743254/2308733
title: Data for PNCC Project 160210 from February 2023
DOI: 10.25582/data.2023-02.2743252/2308734
title: Data for PNCC Project 160210 from February 2023
DOI: 10.25582/data.2023-02.2743256/2308735
title: Data for PNCC Project 160210 from February 2023
DOI: 10.25582/data.2023-02.2745634/2308736
title: Data for EMSL Project 50291 from February 2023
DOI: 10.25582/data.2023-02.2745218/2308737
title: Data for EMSL Project 50291 from February 2023
DOI: 10.25582/data.2023-02.2744338/2308738
title: Data for EMSL Project 50291 from February 2023
DOI: 10.5281/zenodo.7885102
title: High-resolution Gridded Emission Database for India

description: The gridded high-resolution (0.1°×0.1°) surface chemistry data for the Indian subcontinent includes the emission of Particulate Matter (PM2.5 and PM10) from a broad spectrum of 17 major and unattended Anthropogenic sources for the most recent base year 2020. Units are in tons/grid/yr. The files are given in .shp file format suitable for GIS software.
DOI: 10.5281/zenodo.7885103
title: High-resolution Gridded Emission Database for India

description: The gridded high-resolution (0.1°×0.1°) surface chemistry data for the Indian subcontinent includes the emission of Particulate Matter (PM2.5 and PM10) from a broad spectrum of 17 major and unattended Anthropogenic sources for the most recent base year 2020. Units are in tons/grid/yr. The files are given in .shp file format suitable for GIS software.
DOI: 10.25582/data.2023-02.2744753/2308739
title: Data for EMSL Project 50291 from February 2023
DOI: 10.25582/data.2023-02.2745711/2308740
title: Data for EMSL Project 51514 from February 2023
DOI: 10.25582/data.2023-02.2745288/2308741
title: Data for EMSL Project 51514 from February 2023
DOI: 10.25582/data.2023-02.2744831/2308742
title: Data for EMSL Project 51514 from February 2023
DOI: 10.25582/data.2023-02.2744412/2308743
title: Data for EMSL Project 51514 from February 2023
DOI: 10.25582/data.2023-02.2743477/2308744
title: Data for EMSL Project 60240 from February 2023
DOI: 10.5281/zenodo.10682055
title: Data set for genome sequence and mutation rate analysis
DOI: 10.5281/zenodo.10682056
title: Data set for genome sequence and mutation rate analysis
DOI: 10.25582/data.2024-02.3033920/2308849
title: Data for EMSL Project 60846 from February 2024
DOI: 10.25582/data.2024-02.3033929/2308850
title: Data for EMSL Project 60846 from February 2024
DOI: 10.25582/data.2024-02.3033943/2308851
title: Data for EMSL Project 60846 from February 2024
DOI: 10.5281/zenodo.8016202
title: TACoS Speech Caption Dataset

description: This dataset is an extension of TACoS dataset, where audio is read from text using machine simulation method "microsoft/speecht5_tts"
description: {"references": ["Rohrbach, Marcus, et al. \"Script data for attribute-based recognition of composite activities.\" Computer Vision\u2013ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I 12. Springer Berlin Heidelberg, 2012."]}
DOI: 10.6084/m9.figshare.25248985
title: Data for 'Body size as a metric for the affordable world'

description: The original data for the results reported in the manuscript 'Body size as a metric for the affordable world'. For detailed description, please see README and the methods section.
DOI: 10.26182/jvkn-dm13
title: Iron and Women's Health: A Multidimensional Exploration of Iron Deficiency, Therapy, and Perceptions in Active Females
DOI: 10.5281/zenodo.10649195
title: Active Site Variants of CPIII ferrochelatase
DOI: 10.5281/zenodo.10649196
title: Active Site Variants of CPIII ferrochelatase
DOI: 10.5281/zenodo.10682116
title: Active Site Variants of CPIII ferrochelatase
DOI: 10.6084/m9.figshare.25249078
title: Supplementary Material for: Transvaginal Sonography for the preoperative assessment of parametrial deep infiltrating endometriosis: a diagnostic accuracy study

description: Objective: To evaluate the diagnostic accuracy for parametria endometriosis (PE) of transvaginal sonography (TVS) performed following a systematic approach for the assessment of the lateral parametria.
Design: Diagnostic accuracy study based on a prospective observational design
Participants: All consecutive patients who underwent laparoscopic surgery for endometriosis between January 2016 and December 2020.
Setting: Endometriosis referral hospital
Methods: We prospectively collected clinical, imaging, and surgical data of all consecutive patients who underwent laparoscopic surgery for endometriosis between January 2016 and December 2020. A standardized technique with a systematic approach for the assessment of the lateral parametria following specific anatomic landmarks was used for the TVS. The diagnostic accuracy for PE of TVS was assessed using the intraoperative and pathologic diagnosis of PE as the gold standard.
Results: In 476 patients who underwent surgery, PE was identified in 114 out of 476 patients (23.95%): 91 left and 54 right PE out of 476 surgical procedures were identified (19.12% vs. 11.34%; p=0.001); bilateral involvement in 27.19% (31/114 patients) cases. The sensitivity of TVS for PE was 90.74% (79.70%-96.92%, 95% CI) for the right side and 87.91% (79.40%-93.81%, 95% CI) for the left side. The specificity was almost identical for both sides (98.58% vs. 98.18%). For the right parametrium, the positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 63.82 (28.70-141.90, 95% CI) and 0.09 (0.04-0.22, 95% CI), respectively. On the left parametrium, the PLR and NLR were 48.35 (23.12-101.4, 95% CI) and 0.12 (0.07-0.21, 95% CI), respectively. The diagnostic accuracy for right and left PE was 97.69% (95.90%-98.84%, 95%CI) and 96.22% (94.04%-97.74%, 95% CI), respectively.
Limitations: The principal limit is the high dependence of TVS on the operator experience. Therefore, although a standardized approach following precise definitions of anatomical landmarks was used, we cannot conclude that the observed accuracy of TVS for PE is the same for all sonographers. In this regard, the learning curve was not assessed. In the case of negative TVS for parametrial involvement with an absent intraoperative suspect, a complete dissection of the parametrium was not performed to avoid surgical complications; therefore, cases of minor PE may be missed, underestimating false negatives.
Conclusions: TVS performed following a systematic approach for assessing the lateral parametria seems to have good diagnostic accuracy for PE with large changes in posttest probability of parametrial involvement based on the TVS evaluation. Considering the clinical and surgical implications of PE, further studies implementing a standardized approach for assessing the parametrium by TVS are recommended to confirm our observations and implement a standardized protocol in clinical practice.
DOI: 10.6084/m9.figshare.25249078.v1
title: Supplementary Material for: Transvaginal Sonography for the preoperative assessment of parametrial deep infiltrating endometriosis: a diagnostic accuracy study

description: Objective: To evaluate the diagnostic accuracy for parametria endometriosis (PE) of transvaginal sonography (TVS) performed following a systematic approach for the assessment of the lateral parametria.
Design: Diagnostic accuracy study based on a prospective observational design
Participants: All consecutive patients who underwent laparoscopic surgery for endometriosis between January 2016 and December 2020.
Setting: Endometriosis referral hospital
Methods: We prospectively collected clinical, imaging, and surgical data of all consecutive patients who underwent laparoscopic surgery for endometriosis between January 2016 and December 2020. A standardized technique with a systematic approach for the assessment of the lateral parametria following specific anatomic landmarks was used for the TVS. The diagnostic accuracy for PE of TVS was assessed using the intraoperative and pathologic diagnosis of PE as the gold standard.
Results: In 476 patients who underwent surgery, PE was identified in 114 out of 476 patients (23.95%): 91 left and 54 right PE out of 476 surgical procedures were identified (19.12% vs. 11.34%; p=0.001); bilateral involvement in 27.19% (31/114 patients) cases. The sensitivity of TVS for PE was 90.74% (79.70%-96.92%, 95% CI) for the right side and 87.91% (79.40%-93.81%, 95% CI) for the left side. The specificity was almost identical for both sides (98.58% vs. 98.18%). For the right parametrium, the positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 63.82 (28.70-141.90, 95% CI) and 0.09 (0.04-0.22, 95% CI), respectively. On the left parametrium, the PLR and NLR were 48.35 (23.12-101.4, 95% CI) and 0.12 (0.07-0.21, 95% CI), respectively. The diagnostic accuracy for right and left PE was 97.69% (95.90%-98.84%, 95%CI) and 96.22% (94.04%-97.74%, 95% CI), respectively.
Limitations: The principal limit is the high dependence of TVS on the operator experience. Therefore, although a standardized approach following precise definitions of anatomical landmarks was used, we cannot conclude that the observed accuracy of TVS for PE is the same for all sonographers. In this regard, the learning curve was not assessed. In the case of negative TVS for parametrial involvement with an absent intraoperative suspect, a complete dissection of the parametrium was not performed to avoid surgical complications; therefore, cases of minor PE may be missed, underestimating false negatives.
Conclusions: TVS performed following a systematic approach for assessing the lateral parametria seems to have good diagnostic accuracy for PE with large changes in posttest probability of parametrial involvement based on the TVS evaluation. Considering the clinical and surgical implications of PE, further studies implementing a standardized approach for assessing the parametrium by TVS are recommended to confirm our observations and implement a standardized protocol in clinical practice.
DOI: 10.34740/kaggle/ds/4330260
title: Dataset for academic insight survey
DOI: 10.6084/m9.figshare.25248712
title: Modeling and Representing Translation-based Classic Transmission Knowledge

description: Translating classical texts plays a pivotal role in cultural transmission, significantly facilitating the exchange and dissemination of ideas worldwide and fostering the collaborative development of diverse cultures. To facilitate a better understanding of classic transmission based on translations and promote the integration of fragmented knowledge on classic transmission, this study employed ontology and knowledge graphs for modeling and representing translation-based classic transmission knowledge from a multi-disciplinary perspective by integrating history, culture, geography, and communication knowledge. Centering on 'events', the proposed translation-based classic transmission model (TCTM) provides a detailed representation of various entities, attributes, and complex interrelationships and interactions. Using <i>Tao Te Ching</i> as an example, this study conducted the instantiation based on TCTM with multiple data sources to generate a knowledge graph and introduced a method for evaluating the effects of classic transmission for validating the model. The results indicate that the approach is able to dissect the process of the <i>Tao Te Ching</i>'s effect on various countries. The findings suggest that the proposed multi-disciplinary model offers valuable insights into the dynamics of classic transmission and is effective in uncovering latent knowledge within these processes.
DOI: 10.6084/m9.figshare.25248712.v1
title: Modeling and Representing Translation-based Classic Transmission Knowledge

description: Translating classical texts plays a pivotal role in cultural transmission, significantly facilitating the exchange and dissemination of ideas worldwide and fostering the collaborative development of diverse cultures. To facilitate a better understanding of classic transmission based on translations and promote the integration of fragmented knowledge on classic transmission, this study employed ontology and knowledge graphs for modeling and representing translation-based classic transmission knowledge from a multi-disciplinary perspective by integrating history, culture, geography, and communication knowledge. Centering on 'events', the proposed translation-based classic transmission model (TCTM) provides a detailed representation of various entities, attributes, and complex interrelationships and interactions. Using <i>Tao Te Ching</i> as an example, this study conducted the instantiation based on TCTM with multiple data sources to generate a knowledge graph and introduced a method for evaluating the effects of classic transmission for validating the model. The results indicate that the approach is able to dissect the process of the <i>Tao Te Ching</i>'s effect on various countries. The findings suggest that the proposed multi-disciplinary model offers valuable insights into the dynamics of classic transmission and is effective in uncovering latent knowledge within these processes.
DOI: 10.6084/m9.figshare.25249051
title: Tomato Dataset YOLOV5

description: Mohsin, Abu (2024). Tomato Dataset YOLOV5. figshare. . https://doi.org/10.6084/m9.figshare.25249051Tomato dataset
DOI: 10.21227/rqh3-h568
title: Grand Challenge on Respiratory Sound Classification for SPRSound Dataset

description: Globally, respiratory diseases are the leading cause of death, making it essential to develop an automatic respiratory sounds software to speed up diagnosis and reduce physician workload. A recent line of attempts have been proposed to predict accurately, but they have yet been able to provide a satisfactory generalization performance. In this contest, we invited the community to develop more accurate and generalized respiratory sound algorithms. A starter code is provided to standardize the submissions and lower the barrier. New testing set is prepared to evaluate the generalization performance of the submissions. Top 3 teams will present their work at IEEE BioCAS 2023 conference.
DOI: 10.6084/m9.figshare.25249108
title: Systematic review and meta-analysis of <i>V. parahaemolyticus </i>

description: Meta-analysis and Systematic Review of Phenotypic and Genotypic Antimicrobial Resistance and Virulence Factors of <i>Vibrio parahaemolyticus</i> Isolated from Shrimp
DOI: 10.6084/m9.figshare.25249108.v1
title: Systematic review and meta-analysis of <i>V. parahaemolyticus </i>

description: Meta-analysis and Systematic Review of Phenotypic and Genotypic Antimicrobial Resistance and Virulence Factors of <i>Vibrio parahaemolyticus</i> Isolated from Shrimp
DOI: 10.6084/m9.figshare.25249105
title: Zesta all timepoints merged on common genes

description: Zesta dataset stereoseq timepoints from BGI, merged all timepoints on common genes
DOI: 10.5281/zenodo.10610414
title: OseMOSYS clicSAND Visualisation Template

description: The current visualisation template is the updated version of the older clicSAND template with some additional functionalities.
DOI: 10.5281/zenodo.10610415
title: OseMOSYS clicSAND Visualisation Template

description: The current visualisation template is the updated version of the older clicSAND template with some additional functionalities.
DOI: 10.6084/m9.figshare.24947651
title: Genetic polymorphisms in CYP2B6 may be associated with lung cancer risk in the Chinese Han population

description: Our study aimed to elucidate the association between single nucleotide polymorphisms (SNPs) in CYP2B6 gene and susceptibility to lung cancer (LC). Five SNPs in CYP2B6 were genotyped in Chinese Han population (507 cases and 505 controls) utilizing Agena MassARRAY. The relationship between these SNPs and LC susceptibility was assessed using odds ratios, 95% confidence intervals, and χ2 tests. Additionally, multifactor dimensionality reduction was employed to analyze SNP-SNP interactions. Bioinformatics methods were applied to investigate the function of these SNPs. We found that rs2099361 was associated with an increased susceptibility to LC in the codominant model (OR = 1.31, <i>p</i> = 0.045). Stratification analysis revealed the allele G at rs4803418 and the allele T at rs4803420 of CYP2B6 (BMI &gt;24 kg/m<sup>2</sup>) were significantly linked to decreased susceptibility of LC. Conversely, the allele C at rs12979270 (BMI &gt;24 kg/m<sup>2</sup>) showed increased susceptibility to LC. Moreover, a robust redundant relationship between rs12979270 and rs4803420 was identified in the study. According to the VannoPortal database, we found that rs4803420, rs12979270 and rs2099361 may modulate the binding affinity of LMNB1, SP1 and HDAC2, respectively. Our results suggest that SNPs in the CYP2B6 gene play crucial roles in LC susceptibility.
DOI: 10.6084/m9.figshare.24947651.v1
title: Genetic polymorphisms in CYP2B6 may be associated with lung cancer risk in the Chinese Han population

description: Our study aimed to elucidate the association between single nucleotide polymorphisms (SNPs) in CYP2B6 gene and susceptibility to lung cancer (LC). Five SNPs in CYP2B6 were genotyped in Chinese Han population (507 cases and 505 controls) utilizing Agena MassARRAY. The relationship between these SNPs and LC susceptibility was assessed using odds ratios, 95% confidence intervals, and χ2 tests. Additionally, multifactor dimensionality reduction was employed to analyze SNP-SNP interactions. Bioinformatics methods were applied to investigate the function of these SNPs. We found that rs2099361 was associated with an increased susceptibility to LC in the codominant model (OR = 1.31, <i>p</i> = 0.045). Stratification analysis revealed the allele G at rs4803418 and the allele T at rs4803420 of CYP2B6 (BMI &gt;24 kg/m<sup>2</sup>) were significantly linked to decreased susceptibility of LC. Conversely, the allele C at rs12979270 (BMI &gt;24 kg/m<sup>2</sup>) showed increased susceptibility to LC. Moreover, a robust redundant relationship between rs12979270 and rs4803420 was identified in the study. According to the VannoPortal database, we found that rs4803420, rs12979270 and rs2099361 may modulate the binding affinity of LMNB1, SP1 and HDAC2, respectively. Our results suggest that SNPs in the CYP2B6 gene play crucial roles in LC susceptibility.