cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25442/hku.22124513
title: Data supporting 'Regulation of endometrial gland secretome sialylation and its implication in the pathogenesis of early-onset preeclampsia’

description: Dataset split into different chapter supporting the thesis. <br> Glycosylation is a post-translational modification that involves the enzymatic attachment of glycans to proteins or other organic molecules. Hormonal regulation of endometrial gland secretome glycosylation during the early placentation has suggested to be crucial for trophoblast functions and placental development. However, direct evidence for the involvement of protein glycosylation on regulating placentation is lacking. Previous studies had linked various pregnancy anomalies to the defects of glycosylation in placenta and uterus. Preeclampsia (PE) is one of these complications characterized by dysregulated invasion and migration of the trophoblast. Therefore, we hypothesize that the glycosylation of endometrial gland secretome is able to modulate trophoblast-regulatory activity of human and contributes to PE pathogenesis. The human endometrial organoids (EO) were derived from endometrial tissues of patients who underwent In vitro fertilisation (IVF) in LH +7/8 stage. EOs were treated with sex hormones estrogen (E2), progesterone (P4), and human chorionic gonadotropin (hCG) to mimic the menstrual cycle and early pregnancy environment. The PE and normotensive (NT) EO culture were also established from the decidual tissues of women diagnosed with PE or not during their cesarean delivery. The organoid secretomes were collected for glycosylation profile by lectin array, lectin binding assay and sialo-proteomics analysis. Their roles on trophoblast functions e.g. differentiation, invasion and migration were also studied. We have cultured EOs in hormonal conditions mimicking the proliferative phase (E2), secretory phase (E2+P4) and early pregnancy state (E2+P4+hCG). <br> The project demonstrated that changes in the sialylation of endometrial gland secretome is associated with the alterations in trophoblast functions, which further indicated the importance of secretome glycoprotein sialylation in the PE pathophysiology.
DOI: 10.25442/hku.22124513.v1
title: Data supporting 'Regulation of endometrial gland secretome sialylation and its implication in the pathogenesis of early-onset preeclampsia’

description: Dataset split into different chapter supporting the thesis. <br> Glycosylation is a post-translational modification that involves the enzymatic attachment of glycans to proteins or other organic molecules. Hormonal regulation of endometrial gland secretome glycosylation during the early placentation has suggested to be crucial for trophoblast functions and placental development. However, direct evidence for the involvement of protein glycosylation on regulating placentation is lacking. Previous studies had linked various pregnancy anomalies to the defects of glycosylation in placenta and uterus. Preeclampsia (PE) is one of these complications characterized by dysregulated invasion and migration of the trophoblast. Therefore, we hypothesize that the glycosylation of endometrial gland secretome is able to modulate trophoblast-regulatory activity of human and contributes to PE pathogenesis. The human endometrial organoids (EO) were derived from endometrial tissues of patients who underwent In vitro fertilisation (IVF) in LH +7/8 stage. EOs were treated with sex hormones estrogen (E2), progesterone (P4), and human chorionic gonadotropin (hCG) to mimic the menstrual cycle and early pregnancy environment. The PE and normotensive (NT) EO culture were also established from the decidual tissues of women diagnosed with PE or not during their cesarean delivery. The organoid secretomes were collected for glycosylation profile by lectin array, lectin binding assay and sialo-proteomics analysis. Their roles on trophoblast functions e.g. differentiation, invasion and migration were also studied. We have cultured EOs in hormonal conditions mimicking the proliferative phase (E2), secretory phase (E2+P4) and early pregnancy state (E2+P4+hCG). <br> The project demonstrated that changes in the sialylation of endometrial gland secretome is associated with the alterations in trophoblast functions, which further indicated the importance of secretome glycoprotein sialylation in the PE pathophysiology.
DOI: 10.25442/hku.22138468
title: Supporting data for "Bivalve shell formation through development and evolution"

description: This repository contains 8 folders. Each data chapter (chapters 3, 4, 5, 6) has 2 folders, one for supporting data and one for supporting scripts for data analysis. Further details regarding the content of each folder are found in the readme file.
DOI: 10.25442/hku.22138468.v1
title: Supporting data for "Bivalve shell formation through development and evolution"

description: This repository contains 8 folders. Each data chapter (chapters 3, 4, 5, 6) has 2 folders, one for supporting data and one for supporting scripts for data analysis. Further details regarding the content of each folder are found in the readme file.
DOI: 10.25442/hku.22147562
title: Supporting data for “Expanding the repertoire of protein disulfide catalyzing machineries: functions of the Scs proteins in Salmonella enterica serovar Typhimurium”.

description: This file includes all the raw data, processed data, and figures for my thesis. It includes the comparative proteomic analysis between WT and Δ<em>scsC</em> cells under anaerobic tetrathionate conditions. The interactome analysis between ScsC and its substrates was performed with pull-down assay and photocrosslinking. The interactions between ScsC and its substrates were verified and modelled by Hdock. The roles of ScsC udder anaerobic respiration were revealed through viability and growth assay. All the raw data, processed data, and figures for my thesis. It is arranged according to data harvested from different methods. The files should be opened with appropriate software as followed: .xlsx (Microsoft Excel),.tif (Windows PhotoViewer), .jpg (Windows PhotoViewer),.zip (Zipped file), .pse(PyMOL Session File).
DOI: 10.25442/hku.22147562.v1
title: Supporting data for “Expanding the repertoire of protein disulfide catalyzing machineries: functions of the Scs proteins in Salmonella enterica serovar Typhimurium”.

description: This file includes all the raw data, processed data, and figures for my thesis. It includes the comparative proteomic analysis between WT and Δ<em>scsC</em> cells under anaerobic tetrathionate conditions. The interactome analysis between ScsC and its substrates was performed with pull-down assay and photocrosslinking. The interactions between ScsC and its substrates were verified and modelled by Hdock. The roles of ScsC udder anaerobic respiration were revealed through viability and growth assay. All the raw data, processed data, and figures for my thesis. It is arranged according to data harvested from different methods. The files should be opened with appropriate software as followed: .xlsx (Microsoft Excel),.tif (Windows PhotoViewer), .jpg (Windows PhotoViewer),.zip (Zipped file), .pse(PyMOL Session File).
DOI: 10.25442/hku.22148696
title: Supporting data for "Plastic degradation in Hong Kong natural open-air and coastal marine environments"

description: In situ environmental degradation data of conventional plastics and bioplastics, including the environmental parameters of the incubation environments, the physical and chemical changes in plastics. <br> Data measuring physical changes in degraded plastics include: Visual inspection records of plastic disintegration and biofouling development Plastic piece mass SEM photographs documenting plastic surface morphology <br> Data measuring chemical changes in degraded plastics include: Oxidation indices including carbonyl, hydroxyl and vinyl index measured by FTIR, and contact angle Crystallinity (measured by FTIR)
DOI: 10.17632/36252jfpky.1
title: Next stop innovation

description: This is a set of data on the impact of the opening of the subway in Beijing on regional innovation.
DOI: 10.17632/xp9pcmjkms.1
title: Table 1. PF in Immunocompromised Patients

description: Pityrosporum Folliculitis in Immunocompromised Populations: A Systematic Review in JAAD March of 2023, Table 1 for Review
DOI: 10.5281/zenodo.7652249
title: SuperDARN Grid data in netCDF format (2002-Dec)

description: 2002-Dec SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7652250
title: SuperDARN Grid data in netCDF format (2002-Dec)

description: 2002-Dec SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7652253
title: SuperDARN Grid data in netCDF format (2002-Nov)

description: 2002-Nov SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7652254
title: SuperDARN Grid data in netCDF format (2002-Nov)

description: 2002-Nov SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7652263
title: SuperDARN Grid data in netCDF format (2002-Oct)

description: 2002-Oct SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7652264
title: SuperDARN Grid data in netCDF format (2002-Oct)

description: 2002-Oct SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7652273
title: SuperDARN Grid data in netCDF format (2002-Sep)

description: 2002-Sep SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7652274
title: SuperDARN Grid data in netCDF format (2002-Sep)

description: 2002-Sep SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7652283
title: SuperDARN Grid data in netCDF format (2002-Aug)

description: 2002-Aug SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7652284
title: SuperDARN Grid data in netCDF format (2002-Aug)

description: 2002-Aug SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7652291
title: SuperDARN Grid data in netCDF format (2002-Jul)

description: 2002-Jul SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'