cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2ch467
title: CCDC 2189288: Experimental Crystal Structure Determination

description: Related Article: Rouzbeh Samii, Anton Fransson, Pamburayi Mpofu, Pentti Niiranen, Lars Ojamäe, Vadim Kessler, Nathan J. O’Brien|2022|Inorg.Chem.|61|20804|doi:10.1021/acs.inorgchem.2c03071
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.cc2ch9ls
title: CCDC 2189455: Experimental Crystal Structure Determination

description: Related Article: Amit Kumar, Pragati Pandey, Michael R. Gau, Patrick J. Carroll, Eric J. Schelter|2022|Inorg.Chem.|61|20785|doi:10.1021/acs.inorgchem.2c02629
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.cc2ch9nv
title: CCDC 2189457: Experimental Crystal Structure Determination

description: Related Article: Amit Kumar, Pragati Pandey, Michael R. Gau, Patrick J. Carroll, Eric J. Schelter|2022|Inorg.Chem.|61|20785|doi:10.1021/acs.inorgchem.2c02629
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.cc2ch9pw
title: CCDC 2189458: Experimental Crystal Structure Determination

description: Related Article: Amit Kumar, Pragati Pandey, Michael R. Gau, Patrick J. Carroll, Eric J. Schelter|2022|Inorg.Chem.|61|20785|doi:10.1021/acs.inorgchem.2c02629
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.cc2ch9mt
title: CCDC 2189456: Experimental Crystal Structure Determination

description: Related Article: Amit Kumar, Pragati Pandey, Michael R. Gau, Patrick J. Carroll, Eric J. Schelter|2022|Inorg.Chem.|61|20785|doi:10.1021/acs.inorgchem.2c02629
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.cc2cmm9y
title: CCDC 2193600: Experimental Crystal Structure Determination

description: Related Article: Vanda Dašková, Damián Padín, Ben L. Feringa|2022|J.Am.Chem.Soc.|144|23603|doi:10.1021/jacs.2c10911
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.cc2cpczf
title: CCDC 2195295: Experimental Crystal Structure Determination

description: Related Article: Moumita Bera, Simarjeet Kaur, Kritika Keshari, Dooshaye Moonshiram, Sayantan Paria|2022|Inorg.Chem.|61|21035|doi:10.1021/acs.inorgchem.2c03559
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.cc2d5rq2
title: CCDC 2210074: Experimental Crystal Structure Determination

description: Related Article: I. J. Huerfano, Carl A. Laskowski, Maren Pink, Veronica Carta, Gregory L. Hillhouse, Kenneth G. Caulton, Jeremy M. Smith|2022|Inorg.Chem.|61|20986|doi:10.1021/acs.inorgchem.2c03453
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.cc2d5rs4
title: CCDC 2210076: Experimental Crystal Structure Determination

description: Related Article: I. J. Huerfano, Carl A. Laskowski, Maren Pink, Veronica Carta, Gregory L. Hillhouse, Kenneth G. Caulton, Jeremy M. Smith|2022|Inorg.Chem.|61|20986|doi:10.1021/acs.inorgchem.2c03453
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.cc2d5s1f
title: CCDC 2210084: Experimental Crystal Structure Determination

description: Related Article: I. J. Huerfano, Carl A. Laskowski, Maren Pink, Veronica Carta, Gregory L. Hillhouse, Kenneth G. Caulton, Jeremy M. Smith|2022|Inorg.Chem.|61|20986|doi:10.1021/acs.inorgchem.2c03453
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.cc2d5s2g
title: CCDC 2210085: Experimental Crystal Structure Determination

description: Related Article: I. J. Huerfano, Carl A. Laskowski, Maren Pink, Veronica Carta, Gregory L. Hillhouse, Kenneth G. Caulton, Jeremy M. Smith|2022|Inorg.Chem.|61|20986|doi:10.1021/acs.inorgchem.2c03453
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.cc2d5s3h
title: CCDC 2210086: Experimental Crystal Structure Determination

description: Related Article: I. J. Huerfano, Carl A. Laskowski, Maren Pink, Veronica Carta, Gregory L. Hillhouse, Kenneth G. Caulton, Jeremy M. Smith|2022|Inorg.Chem.|61|20986|doi:10.1021/acs.inorgchem.2c03453
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.21441567
title: Structual variations of S. americanum

description: Structural variation information of S. americanum in VCF format using SP1102 as the reference.
DOI: 10.6084/m9.figshare.21441567.v1
title: Variation information of S. americanum

description: Single nucleotide polymorphisms and structural variation information of S. americanum in VCF format using SP1102 as the reference.
DOI: 10.5281/zenodo.7500621
title: Breast self-examination practice and its determinants among women in Indonesia: A systematic review, meta-analysis, and meta-regression

description: Breast self-examination practice and its determinants among women in Indonesia: A systematic review, meta-analysis, and meta-regression
DOI: 10.5281/zenodo.7500620
title: Breast self-examination practice and its determinants among women in Indonesia: A systematic review, meta-analysis, and meta-regression

description: Breast self-examination practice and its determinants among women in Indonesia: A systematic review, meta-analysis, and meta-regression
DOI: 10.6084/m9.figshare.21793196
title: code and data

description: code and data for paper
DOI: 10.6084/m9.figshare.21793196.v4
title: code and data

description: code and data for special source-sink zones detection
DOI: 10.6084/m9.figshare.21805035
title: Questionaire Dimesionality Marketing Performance in SMEs Malang

description: Questionarie research for measurement items for marketing performance for fostered SMEs
DOI: 10.6084/m9.figshare.21805035.v1
title: Questionaire Dimesionality Marketing Performance in SMEs Malang

description: Questionarie research for measurement items for marketing performance for fostered SMEs
DOI: 10.5281/zenodo.7500659
title: Net greenhouse gas balance of fiber wood plantation on peat in Indonesia

description: Tropical peatlands cycle and store large amounts of carbon in their soil and biomass<sup>1</sup><sup>-</sup><sup>5</sup>. Climate and land-use change alter greenhouse gas fluxes of tropical peatlands, but the magnitude of these changes remains highly uncertain<sup>6</sup><sup>-</sup><sup>19</sup>. Here we measure net ecosystem exchanges of carbon dioxide, methane, and soil nitrous oxide fluxes between October 2016 - May 2022 from <em>Acacia</em> <em>crassicarpa</em> plantation, degraded forest and intact forest within the same peat landscape, representing land-cover change trajectories in Sumatra, Indonesia. This allows us to present a full plantation rotation greenhouse gas flux balance in fiber wood plantation on peatland. We find that the <em>Acacia</em> plantation has lower greenhouse gas emissions than the degraded site with a similar average groundwater level, despite more intensive land-use. The greenhouse gas emissions from the <em>Acacia</em> plantation over a full plantation rotation (35.2 ± 4.7 tCO<sub>2</sub>-eq ha<sup>−1</sup> yr<sup>−1</sup>, average ± standard deviation) were around two times higher than those from the intact forest (20.3 ± 3.7 tCO<sub>2</sub>-eq ha<sup>−1</sup> yr<sup>−1</sup>), but only half of the current IPCC Tier 1 emission factor<sup>20</sup> for this land-use. Our results can help to reduce the uncertainty in greenhouse gas emissions estimates, provide an estimate of the impact of land-use change on tropical peat, and develop science-based peatland management practices as nature-based climate solutions.
DOI: 10.5281/zenodo.7500658
title: Net greenhouse gas balance of fiber wood plantation on peat in Indonesia

description: Tropical peatlands cycle and store large amounts of carbon in their soil and biomass<sup>1</sup><sup>-</sup><sup>5</sup>. Climate and land-use change alter greenhouse gas fluxes of tropical peatlands, but the magnitude of these changes remains highly uncertain<sup>6</sup><sup>-</sup><sup>19</sup>. Here we measure net ecosystem exchanges of carbon dioxide, methane, and soil nitrous oxide fluxes between October 2016 - May 2022 from <em>Acacia</em> <em>crassicarpa</em> plantation, degraded forest and intact forest within the same peat landscape, representing land-cover change trajectories in Sumatra, Indonesia. This allows us to present a full plantation rotation greenhouse gas flux balance in fiber wood plantation on peatland. We find that the <em>Acacia</em> plantation has lower greenhouse gas emissions than the degraded site with a similar average groundwater level, despite more intensive land-use. The greenhouse gas emissions from the <em>Acacia</em> plantation over a full plantation rotation (35.2 ± 4.7 tCO<sub>2</sub>-eq ha<sup>−1</sup> yr<sup>−1</sup>, average ± standard deviation) were around two times higher than those from the intact forest (20.3 ± 3.7 tCO<sub>2</sub>-eq ha<sup>−1</sup> yr<sup>−1</sup>), but only half of the current IPCC Tier 1 emission factor<sup>20</sup> for this land-use. Our results can help to reduce the uncertainty in greenhouse gas emissions estimates, provide an estimate of the impact of land-use change on tropical peat, and develop science-based peatland management practices as nature-based climate solutions.
DOI: 10.5281/zenodo.7500655
title: Dataset related to article "External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era "

description: This record contains raw data related to article “External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era" Abstract <strong>Introduction: </strong> To externally validate and directly compare the performance of the Briganti 2012 and Briganti 2019 nomograms as predictors of lymph node invasion (LNI) in a cohort of patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND). <strong>Materials and methods: </strong> After the exclusion of patients with incomplete biopsy, imaging, or clinical data, 752 patients who underwent RARP and ePLND between December 2014 to August 2021 at our center, were included. Among these patients, 327 (43.5%) had undergone multi-parametric MRI (mpMRI) and mpMRI-targeted biopsy. The preoperative risk of LNI was calculated for all patients using the Briganti 2012 nomogram, while the Briganti 2019 nomogram was used only in patients who had performed mpMRI with the combination of targeted and systematic biopsy. The performances of Briganti 2012 and 2019 models were evaluated using the area under the receiver-operating characteristics curve analysis, calibrations plot, and decision curve analysis. <strong>Results: </strong> A median of 13 (IQR 9-18) nodes per patient was removed, and 78 (10.4%) patients had LNI at final pathology. The area under the curves (AUCs) for Briganti 2012 and 2019 were 0.84 and 0.82, respectively. The calibration plots showed a good correlation between the predicted probabilities and the observed proportion of LNI for both models, with a slight tendency to underestimation. The decision curve analysis (DCA) of the two models was similar, with a slightly higher net benefit for Briganti 2012 nomogram. In patients receiving both systematic- and targeted-biopsy, the Briganti 2012 accuracy was 0.85, and no significant difference was found between the AUCs of 2012 and 2019 nomograms (<em>p</em> = 0.296). In the sub-cohort of 518 (68.9%) intermediate-risk PCa patients, the Briganti 2012 nomogram outperforms the 2019 model in terms of accuracy (0.82 vs. 0.77), calibration curve, and net benefit at DCA. <strong>Conclusion: </strong> The direct comparison of the two nomograms showed that the most updated nomogram, which included MRI and MRI-targeted biopsy data, was not significantly more accurate than the 2012 model in the prediction of LNI, suggesting a negligible role of mpMRI in the current population.
DOI: 10.5281/zenodo.7500654
title: Dataset related to article "External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era "

description: This record contains raw data related to article “External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era" Abstract <strong>Introduction: </strong> To externally validate and directly compare the performance of the Briganti 2012 and Briganti 2019 nomograms as predictors of lymph node invasion (LNI) in a cohort of patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND). <strong>Materials and methods: </strong> After the exclusion of patients with incomplete biopsy, imaging, or clinical data, 752 patients who underwent RARP and ePLND between December 2014 to August 2021 at our center, were included. Among these patients, 327 (43.5%) had undergone multi-parametric MRI (mpMRI) and mpMRI-targeted biopsy. The preoperative risk of LNI was calculated for all patients using the Briganti 2012 nomogram, while the Briganti 2019 nomogram was used only in patients who had performed mpMRI with the combination of targeted and systematic biopsy. The performances of Briganti 2012 and 2019 models were evaluated using the area under the receiver-operating characteristics curve analysis, calibrations plot, and decision curve analysis. <strong>Results: </strong> A median of 13 (IQR 9-18) nodes per patient was removed, and 78 (10.4%) patients had LNI at final pathology. The area under the curves (AUCs) for Briganti 2012 and 2019 were 0.84 and 0.82, respectively. The calibration plots showed a good correlation between the predicted probabilities and the observed proportion of LNI for both models, with a slight tendency to underestimation. The decision curve analysis (DCA) of the two models was similar, with a slightly higher net benefit for Briganti 2012 nomogram. In patients receiving both systematic- and targeted-biopsy, the Briganti 2012 accuracy was 0.85, and no significant difference was found between the AUCs of 2012 and 2019 nomograms (<em>p</em> = 0.296). In the sub-cohort of 518 (68.9%) intermediate-risk PCa patients, the Briganti 2012 nomogram outperforms the 2019 model in terms of accuracy (0.82 vs. 0.77), calibration curve, and net benefit at DCA. <strong>Conclusion: </strong> The direct comparison of the two nomograms showed that the most updated nomogram, which included MRI and MRI-targeted biopsy data, was not significantly more accurate than the 2012 model in the prediction of LNI, suggesting a negligible role of mpMRI in the current population.
DOI: 10.34740/kaggle/dsv/4784652
title: wscc9busFaultDataset
DOI: 10.6084/m9.figshare.20479503.v1
title: Decoupling the skull and skeleton in a Cretaceous bird with unique appendicular morphologies

description: R script used to reproduce the results in that paper
DOI: 10.6084/m9.figshare.20479503
title: Decoupling the skull and skeleton in a Cretaceous bird with unique appendicular morphologies

description: R script used to reproduce the results in that paper
DOI: 10.5281/zenodo.7500686
title: Dataset related to article "Intratumoral Switch of Molecular Phenotype and Overall Survival in Muscle Invasive Bladder Cancer"

description: This record contains raw data related to article “Intratumoral Switch of Molecular Phenotype and Overall Survival in Muscle Invasive Bladder Cancer" Abstract In recent years, immunohistochemical protein expression was studied as a surrogate to the molecular classification of bladder cancer, although no tissue biomarkers are available for clinical use to predict survival or the response to neoadjuvant chemotherapy (CT) in UC, as the literature produced conflicting results. This retrospective study included TURB specimens harboring foci of HG pT2 muscle-invasive bladder carcinoma (MIBC) from 251 patients who subsequently underwent radical cystectomy. We performed immunohistochemical analysis on tumor samples, for relevant gene-expression-based markers for basal type (<em>CD44</em>, <em>CK5/6</em>) and luminal type (<em>CK20</em> and <em>pPARγ</em>). Piescore, investigated in both non-muscle-invasive (NMI) and muscle-invasive (MI) components of the tumor, divided basal and luminal UC-types when at least three of the four markers were consistent with a specific phenotype, mixed types if one/two luminal and basal markers were present simultaneously, and neu-like types when all four markers investigated were negative. Eighteen selected cases were also investigated with RT-PCR to validate, and to increase the specificity of, the immunohistochemical results. We observe an immunophenotypical difference in the NMI and MI components in 96/251 UC patients (38.25%): half of tumors (44/96 cases) have a transition to basal, 36.46% (35/96 cases) to neu-like, 12.5% (12/96 cases) to mixed, and 5.2% (5/96 cases) to luminal phenotypes. Mixed tumors in the NMI component are more likely to change phenotype than other groups, particularly compared with basal tumors, which demonstrate greater stability (only 8/96 cases, <em>p</em> &lt; 0.00001). The transition of luminal tumors to basal display a better OS compared with the transition toward neu-like tumors (<em>p</em> = 0.027). Overall, the phenotypical switch does not affect lymphovascular invasion, pT, DFS, or OS compared with non-switched cases. In the MI component, the presence of <em>CD44</em> expression, irrespective of score-related phenotype, shows a protective effect in papillary-type UC (OS <em>p</em> = 0.008, HR 0.453, PFS <em>p</em> = 0.07, HR 0.599), and in UC naïve for CT (<em>p</em> = 0.0479). Piescore immunophenotyping reveals an intratumoral phenotypical transition between the NMI and MI components of the same tumor. The molecular change is a common event in the mixed and luminal categories, but not in basal tumors, which show better phenotypical stability. This phenomenon could partially explain the sensitivity of a subset of luminal UC to chemotherapy: good responders could be "non-real" luminal UC, which acquire nasal markers, such as <em>CD44</em>.