cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2c8y2p
title: CCDC 2183332: Experimental Crystal Structure Determination

description: Related Article: Julia Volk, Myron Heinz, Michael Leibold, Clemens Bruhn, Tobias Bens, Biprajit Sarkar, Max C. Holthausen, Ulrich Siemeling|2022|Chem.Commun.|58|10396|doi:10.1039/D2CC03871D
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.cc2c8y3q
title: CCDC 2183333: Experimental Crystal Structure Determination

description: Related Article: Julia Volk, Myron Heinz, Michael Leibold, Clemens Bruhn, Tobias Bens, Biprajit Sarkar, Max C. Holthausen, Ulrich Siemeling|2022|Chem.Commun.|58|10396|doi:10.1039/D2CC03871D
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.cc2c8y4r
title: CCDC 2183334: Experimental Crystal Structure Determination

description: Related Article: Julia Volk, Myron Heinz, Michael Leibold, Clemens Bruhn, Tobias Bens, Biprajit Sarkar, Max C. Holthausen, Ulrich Siemeling|2022|Chem.Commun.|58|10396|doi:10.1039/D2CC03871D
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.cc2c8y5s
title: CCDC 2183335: Experimental Crystal Structure Determination

description: Related Article: Julia Volk, Myron Heinz, Michael Leibold, Clemens Bruhn, Tobias Bens, Biprajit Sarkar, Max C. Holthausen, Ulrich Siemeling|2022|Chem.Commun.|58|10396|doi:10.1039/D2CC03871D
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.cc2c8y6t
title: CCDC 2183336: Experimental Crystal Structure Determination

description: Related Article: Julia Volk, Myron Heinz, Michael Leibold, Clemens Bruhn, Tobias Bens, Biprajit Sarkar, Max C. Holthausen, Ulrich Siemeling|2022|Chem.Commun.|58|10396|doi:10.1039/D2CC03871D
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.cc2c8y7v
title: CCDC 2183337: Experimental Crystal Structure Determination

description: Related Article: Julia Volk, Myron Heinz, Michael Leibold, Clemens Bruhn, Tobias Bens, Biprajit Sarkar, Max C. Holthausen, Ulrich Siemeling|2022|Chem.Commun.|58|10396|doi:10.1039/D2CC03871D
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.cc2c8y8w
title: CCDC 2183338: Experimental Crystal Structure Determination

description: Related Article: Julia Volk, Myron Heinz, Michael Leibold, Clemens Bruhn, Tobias Bens, Biprajit Sarkar, Max C. Holthausen, Ulrich Siemeling|2022|Chem.Commun.|58|10396|doi:10.1039/D2CC03871D
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.cc2chjp3
title: CCDC 2189675: Experimental Crystal Structure Determination

description: Related Article: Xiao-Shuang Ji, Hang-Dong Zuo, Yi-Ting Shen, Wen-Juan Hao, Shu-Jiang Tu, Bo Jiang|2022|Chem.Commun.|58|10420|doi:10.1039/D2CC03922B
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.cc2bpj2n
title: CCDC 2165631: Experimental Crystal Structure Determination

description: Related Article: Nilanjana Sen, Prachi Gothe, Pallavi Sarkar, Shubhajit Das, Srinu Tothadi, Swapan K. Pati, Shabana Khan|2022|Chem.Commun.|58|10380|doi:10.1039/D2CC03158B
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: 10.25921/fj23-ms13
title: ES60 Water Column Sonar Data Collected During EBS12AL
DOI: 10.15151/esrf-es-895535202
title: PROTEINS RELATED TO PATHOGENESIS OF DISEASES, VIRAL PROTEINS, CELL DIVISION, SIGNALLING AND CHROMATIN PROCESSES

description: Our BAG is composed of eight PIs at six different institutions in Italy and Austria. We focus on a large variety of human, bacterial, viral and synthetic protein targets and address diverse biological contexts by combining structural biology with biochemistry, biophysics, cellular and in vivo investigations.
DOI: 10.34740/kaggle/dsv/4207911
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.21070969
title: Molecular modelling on multiepitope-based vaccine against SARS-CoV-2 using immunoinformatics, molecular docking, and molecular dynamics simulation

description: The pandemic of COVID-19 caused by SARS-CoV-2 has made a worldwide health emergency. Despite the fact that current vaccines are readily available, several SARSCoV-2 variants affecting the existing vaccine are to be less effective due to the mutations in the structural proteins. Furthermore, the appearance of the new variants cannot be easily predicted in the future. Therefore, the attempts to construct new vaccines or to modify the current vaccines are still pivotal works for preventing the spread of the virus. In the present investigation, the computational analysis through immunoinformatics, molecular docking, and molecular dynamics (MD) simulation is employed to construct an effective vaccine against SARS-CoV2. The structural proteins of SARS-CoV2 are utilized to create a multiepitope-based vaccine (MEV). According to our findings presented by systematic procedures in the current investigation, the MEV construct may be able to trigger a strong immunological response against the virus. Therefore, the designed MEV could be a potential vaccine candidate against SARS-CoV-2, and also it is expected to be effective for other variants.
DOI: 10.6084/m9.figshare.21080772
title: Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis

description: The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (<i>r</i><sup>2</sup><sub>VAL</sub> = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.
DOI: 10.6084/m9.figshare.21080772.v1
title: Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis

description: The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (<i>r</i><sup>2</sup><sub>VAL</sub> = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.
DOI: 10.6084/m9.figshare.21070969.v1
title: Molecular modelling on multiepitope-based vaccine against SARS-CoV-2 using immunoinformatics, molecular docking, and molecular dynamics simulation

description: The pandemic of COVID-19 caused by SARS-CoV-2 has made a worldwide health emergency. Despite the fact that current vaccines are readily available, several SARSCoV-2 variants affecting the existing vaccine are to be less effective due to the mutations in the structural proteins. Furthermore, the appearance of the new variants cannot be easily predicted in the future. Therefore, the attempts to construct new vaccines or to modify the current vaccines are still pivotal works for preventing the spread of the virus. In the present investigation, the computational analysis through immunoinformatics, molecular docking, and molecular dynamics (MD) simulation is employed to construct an effective vaccine against SARS-CoV2. The structural proteins of SARS-CoV2 are utilized to create a multiepitope-based vaccine (MEV). According to our findings presented by systematic procedures in the current investigation, the MEV construct may be able to trigger a strong immunological response against the virus. Therefore, the designed MEV could be a potential vaccine candidate against SARS-CoV-2, and also it is expected to be effective for other variants.
DOI: 10.6084/m9.figshare.21120050.v1
title: Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models

description: Spraying repellents on clothing limits toxicity and allergy problems that can occur when the repellents are directly applied to skin. This also allows the use of higher doses to ensure longer lasting effects. As the number of repellents available on the market is limited, it is necessary to propose new ones, especially by using in silico methods that reduce costs and time. In this context SAR models were built from a dataset of 2027 chemicals for which repellent activity on clothing was measured against <i>Aedes aegypti</i>. The interest of using either the ECFP or MACCS fingerprints as input neurons of a three-layer perceptron was evaluated. Transformation of MACCS bit strings into disjunctive tables led to interesting results. Models obtained with both types of fingerprints were compared to a model including physicochemical and topological descriptors.
DOI: 10.6084/m9.figshare.21120050
title: Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models

description: Spraying repellents on clothing limits toxicity and allergy problems that can occur when the repellents are directly applied to skin. This also allows the use of higher doses to ensure longer lasting effects. As the number of repellents available on the market is limited, it is necessary to propose new ones, especially by using in silico methods that reduce costs and time. In this context SAR models were built from a dataset of 2027 chemicals for which repellent activity on clothing was measured against <i>Aedes aegypti</i>. The interest of using either the ECFP or MACCS fingerprints as input neurons of a three-layer perceptron was evaluated. Transformation of MACCS bit strings into disjunctive tables led to interesting results. Models obtained with both types of fingerprints were compared to a model including physicochemical and topological descriptors.
DOI: 10.6084/m9.figshare.21119932.v1
title: Protective effect of morin on dextran sulfate sodium-induced colitis in mice by regulating the NF-κB pathway

description: The document is the original data of the manuscript titled "Protective effect of morin on dextran sulfate sodium-induced colitis in mice by regulating the NF-κB pathway".
DOI: 10.6084/m9.figshare.21119932
title: Protective effect of morin on dextran sulfate sodium-induced colitis in mice by regulating the NF-κB pathway

description: The document is the original data of the manuscript titled "Protective effect of morin on dextran sulfate sodium-induced colitis in mice by regulating the NF-κB pathway".
DOI: 10.5281/zenodo.7019738
title: Kaarten Gobelin - Groenblauwe netwerken in Vlaanderen - Rapport 2

description: Deze dataset ontsluit de kaarten van het Gobelin-project (Groenblauwe netwerken in Vlaanderen). De kaarten werden gebruikt voor de opmaak van het deelrapport 2, Methode voor monitoring (Smets & Stevens, 2019). Het Gobelin-project werd gefinancierd door en uitgevoerd in opdracht van het Vlaams Planbureau voor Omgeving (Vlaamse Overheid, Departement Omgeving). De rapporten van het project zijn beschikbaar op de website van het Departement Omgeving. Het zip-bestand <code>Gobelin_kaarten.zip</code> bevat de geodatabanken met de rasterbestanden van de kaarten. Het bestand <code>kaarten.mxd</code> groepeert alle kaarten met de juiste legende. Het bestand <code>Modellen_ArcGIS.tbx</code> bevat de ArcGIS-modellen waarmee de kaarten gemaakt zijn. Het html-bestand <code>Gobelin_toelichting.html</code> geeft een snelle interactieve inkijk in de kaarten en een beknopte toelichting. Zie Smets & Stevens (2019) voor een beschrijving van de methode en de bespreking van de kaarten.
description: {"references": ["Smets J., Stevens M. (2019). Gobelin rapport N\u00b0 2: Groenblauwe netwerken in Vlaanderen - Methode voor monitoring, uitgevoerd in opdracht van het Vlaams Planbureau voor Omgeving. Rapporten van het Instituut voor Natuur- en Bosonderzoek 2019 (46). INBO, Instituut voor Natuur- en Bosonderzoek. doi.org/10.21436/inbor.16748879"]}
DOI: 10.5281/zenodo.7019737
title: Kaarten Gobelin - Groenblauwe netwerken in Vlaanderen - Rapport 2

description: Deze dataset ontsluit de kaarten van het Gobelin-project (Groenblauwe netwerken in Vlaanderen). De kaarten werden gebruikt voor de opmaak van het deelrapport 2, Methode voor monitoring (Smets & Stevens, 2019). Het Gobelin-project werd gefinancierd door en uitgevoerd in opdracht van het Vlaams Planbureau voor Omgeving (Vlaamse Overheid, Departement Omgeving). De rapporten van het project zijn beschikbaar op de website van het Departement Omgeving. Het zip-bestand <code>Gobelin_kaarten.zip</code> bevat de geodatabanken met de rasterbestanden van de kaarten. Het bestand <code>kaarten.mxd</code> groepeert alle kaarten met de juiste legende. Het bestand <code>Modellen_ArcGIS.tbx</code> bevat de ArcGIS-modellen waarmee de kaarten gemaakt zijn. Het html-bestand <code>Gobelin_toelichting.html</code> geeft een snelle interactieve inkijk in de kaarten en een beknopte toelichting. Zie Smets & Stevens (2019) voor een beschrijving van de methode en de bespreking van de kaarten.
description: {"references": ["Smets J., Stevens M. (2019). Gobelin rapport N\u00b0 2: Groenblauwe netwerken in Vlaanderen - Methode voor monitoring, uitgevoerd in opdracht van het Vlaams Planbureau voor Omgeving. Rapporten van het Instituut voor Natuur- en Bosonderzoek 2019 (46). INBO, Instituut voor Natuur- en Bosonderzoek. doi.org/10.21436/inbor.16748879"]}
DOI: 10.6084/m9.figshare.21090004.v3
title: Supplementary Files

description: <strong>Table S1: </strong>Datasets used in this study <strong>Table S2: </strong>Primer sequences used in this study <strong>Table S3: </strong>125 exosome-related genes from literature and databases <strong>Table S4: </strong>4355 OS-related lncRNAs by univariate cox regression analysis <strong>Table S5: </strong>Genes of 15 exosome-related modules defined by a correlation coefficient over 0.3 in module-trait correlation analysis <strong>Table S6:</strong> 42 hub genes of exosome-related modules defined by gene significance over 0.3 and module membership over 0.8 <strong>Table S7:</strong> Correlation between 26 modeling lncRNAs and 812 FDA approved drug targets. A correlation coefficient over 0.3 is considered exosome-related <strong>Table S8: </strong>Correlation between 26 modeling lncRNAs and 1572 potential drug targets. A correlation coefficient over 0.3 is considered exosome-related <strong>Table S9: </strong>Correlation between 26 modeling lncRNAs and 1055 OS-related RBPs. A correlation coefficient over 0.2 is considered exosome-related <strong>Table S10: </strong>Trend tests for survival analysis in different significance test methods
DOI: 10.6084/m9.figshare.21090004
title: Supplementary Files

description: <strong>Table S1: </strong>Datasets used in this study. <strong>Table S2: </strong>Primer sequences used in this study. <strong>Table S3: </strong>125 exosome-related genes from literature and databases. <strong>Table S4: </strong>4355 OS-related lncRNAs by univariate cox regression analysis. <strong>Table S5: </strong>Genes of 15 exosome-related modules defined by a correlation coefficient over 0.3 in module-trait correlation analysis. <strong>Table S6:</strong> 42 hub genes of exosome-related modules defined by gene significance over 0.3 and module membership over 0.8. <strong>Table S7: </strong>Restricted mean survival time between risk groups at different time points. <strong>Table S8:</strong> Correlation between 26 modeling lncRNAs and 812 FDA approved drug targets. A correlation coefficient over 0.3 is considered exosome-related. <strong>Table S9: </strong>Correlation between 26 modeling lncRNAs and 1572 potential drug targets. A correlation coefficient over 0.3 is considered exosome-related. <strong>Table S10: </strong> Trend tests for survival analysis in different significance test methods. <strong>Table S11: </strong> Correlation between 26 modeling lncRNAs and 1055 OS-related RBPs. A correlation coefficient over 0.2 is considered exosome-related.
DOI: 10.6084/m9.figshare.21090004.v5
title: Supplementary Files

description: <strong>Table S1: </strong>Datasets used in this study. <strong>Table S2: </strong>Primer sequences used in this study. <strong>Table S3: </strong>125 exosome-related genes from literature and databases. <strong>Table S4: </strong>4355 OS-related lncRNAs by univariate cox regression analysis. <strong>Table S5: </strong>Genes of 15 exosome-related modules defined by a correlation coefficient over 0.3 in module-trait correlation analysis. <strong>Table S6:</strong> 42 hub genes of exosome-related modules defined by gene significance over 0.3 and module membership over 0.8. <strong>Table S7: </strong>Restricted mean survival time between risk groups at different time points. <strong>Table S8:</strong> Correlation between 26 modeling lncRNAs and 812 FDA approved drug targets. A correlation coefficient over 0.3 is considered exosome-related. <strong>Table S9: </strong>Correlation between 26 modeling lncRNAs and 1572 potential drug targets. A correlation coefficient over 0.3 is considered exosome-related. <strong>Table S10: </strong> Trend tests for survival analysis in different significance test methods. <strong>Table S11: </strong> Correlation between 26 modeling lncRNAs and 1055 OS-related RBPs. A correlation coefficient over 0.2 is considered exosome-related.
DOI: 10.6084/m9.figshare.21090004.v4
title: Supplementary Files

description: <strong>Table S1: </strong>Datasets used in this study. <strong>Table S2: </strong>Primer sequences used in this study. <strong>Table S3: </strong>125 exosome-related genes from literature and databases. <strong>Table S4: </strong>4355 OS-related lncRNAs by univariate cox regression analysis. <strong>Table S5: </strong>Genes of 15 exosome-related modules defined by a correlation coefficient over 0.3 in module-trait correlation analysis. <strong>Table S6:</strong> 42 hub genes of exosome-related modules defined by gene significance over 0.3 and module membership over 0.8. <strong>Table S7: </strong>Restricted mean survival time between risk groups at different time points. <strong>Table S8:</strong> Correlation between 26 modeling lncRNAs and 812 FDA approved drug targets. A correlation coefficient over 0.3 is considered exosome-related. <strong>Table S9: </strong>Correlation between 26 modeling lncRNAs and 1572 potential drug targets. A correlation coefficient over 0.3 is considered exosome-related. <strong>Table S10: </strong> Trend tests for survival analysis in different significance test methods. <strong>Table S11: </strong> Correlation between 26 modeling lncRNAs and 1055 OS-related RBPs. A correlation coefficient over 0.2 is considered exosome-related.
DOI: 10.25403/upresearchdata.20425386
title: The development of an institutional framework for cooperative coastal governance for the Eastern Cape

description: The datasets are the processed data guided by interview questions posed to participants as part of qualitative research in the development of a thesis. The thesis develops and institutional framework for cooperative coastal governance for the Eastern Cape Province. The data is organised by participant and by applicable responses to interview questions, per question - in order to facilitate ease of reading.