cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25442/hku.20724088.v1
title: Supporting data for “Uterine Leiomyosarcoma with Homologous Recombination Deficiency is highly sensitive to Polo-Like Kinase 4 inhibitor”

description: This dataset consists of all experimental data for generating the results in the thesis “Uterine Leiomyosarcoma with Homologous Recombination Deficiency is highly sensitive to Polo-Like Kinase 4 inhibitor”. Data in the "Sanger seq" and "WB" confirms the establishment of BRCA-deficient uterine LMS models, while those in "SRB proliferation assay", "AV-PI apoptotic assay", "gH2AX immunofluorescence", and "DSB repair reporter assay" are the evidences that BRCA-deficiency sensitizes uterine LMS to CFI-400945 treatment.
DOI: 10.25442/hku.20732980
title: Supporting data for "Role of N-cadherin in mechano-mediated cell fate control in intervertebral disc homeostasis"

description: The dataset includes all experimental data for my thesis. It contains the compression records for biomechanics studies, AFM and NMR test for biomaterial properties, the computational analysis on single-cell sequencing reads for bioinformatics and Real-time PCR validating the cell estimation function by bioinformatics in the 3-D cell culture system.
DOI: 10.25442/hku.20732980.v1
title: Supporting data for "Role of N-cadherin in mechano-mediated cell fate control in intervertebral disc homeostasis"

description: The dataset includes all experimental data for my thesis. It contains the compression records for biomechanics studies, AFM and NMR test for biomaterial properties, the computational analysis on single-cell sequencing reads for bioinformatics and Real-time PCR validating the cell estimation function by bioinformatics in the 3-D cell culture system.
DOI: 10.25442/hku.20726449
title: Supporting data for "Cenozoic dynamics of shallow marine biodiversity"

description: This is the fossil ostracod census dataset used in my research entitled "Cenozoic dynamics of shallow marine biodiversity". It shows the biodiversity and species compositional changes across the Paleocene-Eocene Thermal Maximum (PETM), and the Neogene period. I have two sediment cores (SDB and MCBR) of the PETM interval from the North Atlantic coastal plain in shallow-marine, subtropical to temperate paleoenvironmental conditions. The SDB and MCBR core were sub-sampled at approximately 12 cm and 10 cm intervals, respectively, and I counted the number of ostracod specimens of different species from each sub-sample to construct the datasets. <br> The file titled "IAA" contains the fossil ostracod census data of 180 outcrop sediment samples from the Indo-Australian Archipelago (IAA). Microfossil ostracod specimens from each sample were counted and recorded. Ages of these samples range from early Miocene to Pleistocene. As the dataset shows the abundance and species richness of ostracods across the Neogene, it has been used to reveal the historical development of tropical marine biodiversity hotspot in the IAA.
DOI: 10.25442/hku.20726449.v1
title: Supporting data for "Cenozoic dynamics of shallow marine biodiversity"

description: This is the fossil ostracod census dataset used in my research entitled "Cenozoic dynamics of shallow marine biodiversity". It shows the biodiversity and species compositional changes across the Paleocene-Eocene Thermal Maximum (PETM), and the Neogene period. I have two sediment cores (SDB and MCBR) of the PETM interval from the North Atlantic coastal plain in shallow-marine, subtropical to temperate paleoenvironmental conditions. The SDB and MCBR core were sub-sampled at approximately 12 cm and 10 cm intervals, respectively, and I counted the number of ostracod specimens of different species from each sub-sample to construct the datasets. <br> The file titled "IAA" contains the fossil ostracod census data of 180 outcrop sediment samples from the Indo-Australian Archipelago (IAA). Microfossil ostracod specimens from each sample were counted and recorded. Ages of these samples range from early Miocene to Pleistocene. As the dataset shows the abundance and species richness of ostracods across the Neogene, it has been used to reveal the historical development of tropical marine biodiversity hotspot in the IAA.
DOI: 10.5281/zenodo.6408057
title: Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma

description: Original file of <em>Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma.</em>
DOI: 10.5281/zenodo.6408058
title: Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma

description: Original file of <em>Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma.</em>
DOI: 10.5281/zenodo.6575229
title: Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma

description: Original file of <em>Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma.</em>
DOI: 10.5281/zenodo.7063501
title: Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma

description: Original file of <em>Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma.</em>
DOI: 10.25442/hku.20726560
title: Supporting data for “Class, ideology and nationalism: The case of Chinese 5G digital infrastructural workers"

description: To understand the relationship between class and nationalism, workers in the making of the national 5G digital infrastructure were recruited and interviewed. They were asked about their job experiences, thoughts on the infrastructure, and perceptions of nationalism. Also, their individual class actions and collective class struggles are discussed during the interview. The interview guide, some interview transcripts and the chat history of an informal group form the first dataset. <br> To understand the general discourses and ideology related to the infrastructure and workers, discussions on Zhihu, a popular Q&A site in China are recorded and analysed. The top five most popular questions and all the answers are documented and contribute to the second dataset.
DOI: 10.25442/hku.20726560.v1
title: Supporting data for “Class, ideology and nationalism: The case of Chinese 5G digital infrastructural workers"

description: To understand the relationship between class and nationalism, workers in the making of the national 5G digital infrastructure were recruited and interviewed. They were asked about their job experiences, thoughts on the infrastructure, and perceptions of nationalism. Also, their individual class actions and collective class struggles are discussed during the interview. The interview guide, some interview transcripts and the chat history of an informal group form the first dataset. <br> To understand the general discourses and ideology related to the infrastructure and workers, discussions on Zhihu, a popular Q&A site in China are recorded and analysed. The top five most popular questions and all the answers are documented and contribute to the second dataset.
DOI: 10.5281/zenodo.7033615
title: Table 1 for Radiocarbon and Stable Carbon Isotope Constraints on the Propagation of Vent CO2 to Fluid in the Acidic Kueishantao Shallow Water Hydrothermal System

description: This table contains radiocarbon (<sup>14</sup>C) and stable carbon isotope (<sup>13</sup>C) compositions of CO<sub>2</sub> in vent gas, dissolved inorganic carbon and particulates of hydrothermal fluid from Kueishantao shallow water hydrothermal system, offshore northeastern Taiwan.
DOI: 10.5281/zenodo.7033614
title: Table 1 for Radiocarbon and Stable Carbon Isotope Constraints on the Propagation of Vent CO2 to Fluid in the Acidic Kueishantao Shallow Water Hydrothermal System

description: This table contains radiocarbon (<sup>14</sup>C) and stable carbon isotope (<sup>13</sup>C) compositions of CO<sub>2</sub> in vent gas, dissolved inorganic carbon and particulates of hydrothermal fluid from Kueishantao shallow water hydrothermal system, offshore northeastern Taiwan.
DOI: 10.5281/zenodo.7050972
title: Bayesian Estimation on Microbial Electrochemical Time Profiles obtained by a High-Throughput Bioelectrochemical Device

description: Bioelectrochemical systems (BESs) are attracting much attention, but the mechanisms are not yet fully clarified. One of the issues for the BESs research is the lack of a high-quality database that is developed through well-controlled experiments with a high-throughput data-collecting system. Here, we developed a new high-throughput potentiostat with 96 well plates with silk-screen-printed electrodes. With this potentiostat, we obtained 576 time-profiles of microbial current production for viewing the complex landscape of the parameters, namely, the redox mediator concentration and the electrode potential. This dataset contains the following materials: Current production profiles for <em>Shewanella </em>(Data with average of four data sets for impact of mediators.zip) Summary of the calculated values (statistics summary.csv) Python code for calculating the maximum slope (Slope EF.py) Python codes for Bayesian estimation (2D Bayesian Optimization mapping.py and 2D Bayesian Optimization_slice.py)
DOI: 10.5281/zenodo.7050971
title: Bayesian Estimation on Microbial Electrochemical Time Profiles obtained by a High-Throughput Bioelectrochemical Device

description: Bioelectrochemical systems (BESs) are attracting much attention, but the mechanisms are not yet fully clarified. One of the issues for the BESs research is the lack of a high-quality database that is developed through well-controlled experiments with a high-throughput data-collecting system. Here, we developed a new high-throughput potentiostat with 96 well plates with silk-screen-printed electrodes. With this potentiostat, we obtained 576 time-profiles of microbial current production for viewing the complex landscape of the parameters, namely, the redox mediator concentration and the electrode potential. This dataset contains the following materials: Current production profiles for <em>Shewanella </em>(Data with average of four data sets for impact of mediators.zip) Summary of the calculated values (statistics summary.csv) Python code for calculating the maximum slope (Slope EF.py) Python codes for Bayesian estimation (2D Bayesian Optimization mapping.py and 2D Bayesian Optimization_slice.py)
DOI: 10.25442/hku.20732791
title: Supporting data for “Everyday life mooring: Hong Kong Elderlies’ (im)mobilities in public open space”

description: This study adopted mobility theories to investigate the relationship between elderly people and public open space in Hong Kong. In doing so, qualitative methods, which are semi-structured interviews and (participant) observations, are adopted for the research methods. The data is collected from two Hong Kong areas, which are Wong Tai Sin District and Fanling / Sheung Shui New Town. The dataset consists of 4 files, including audio-recordings of interview, transcripts of interviews, photos from observation and videos from observation. There are 29 audio-recordings of interview (in Cantonese) and transcripts of interviews (in Chinese) respectively. For the imaging, there are 299 photos and 33 videos.
DOI: 10.25442/hku.20732791.v1
title: Supporting data for “Everyday life mooring: Hong Kong Elderlies’ (im)mobilities in public open space”

description: This study adopted mobility theories to investigate the relationship between elderly people and public open space in Hong Kong. In doing so, qualitative methods, which are semi-structured interviews and (participant) observations, are adopted for the research methods. The data is collected from two Hong Kong areas, which are Wong Tai Sin District and Fanling / Sheung Shui New Town. The dataset consists of 4 files, including audio-recordings of interview, transcripts of interviews, photos from observation and videos from observation. There are 29 audio-recordings of interview (in Cantonese) and transcripts of interviews (in Chinese) respectively. For the imaging, there are 299 photos and 33 videos.
DOI: 10.6084/m9.figshare.14766624
title: YARP reaction database

description: This is a dataset generated by Yet Another Reaction Program (YARP), including pyGSM reaction pathways, Gaussian transitions states optimization file, IRC calculation results, etc. <br> Four systems are provided, 'KHP network' involves reactions of gamma-ketohydroperoxide and it's 12 intended products. 'Z-benchmark' involves reactants obtained from Zimmerman testing set.
DOI: 10.6084/m9.figshare.14766624.v6
title: YARP reaction database

description: This is a dataset generated by Yet Another Reaction Program (YARP), including pyGSM reaction pathways, Gaussian transitions states optimization file, IRC calculation results, etc. <br> Four systems are provided, 'KHP network' involves reactions of gamma-ketohydroperoxide and it's 12 intended products. 'Z-benchmark' involves reactants obtained from Zimmerman testing set.
DOI: 10.6084/m9.figshare.21066028.v1
title: Spreadsheet data for F1000Res

description: The data contains replicate absorbance values for cell viability, DPPH and ABTS. It also contains replicate fluorescent intensity of ROS production, relative mRNA and protein expressions, and original immunoblots.
DOI: 10.34740/kaggle/dsv/4179956
title: Russian Invasion of Ukraine
DOI: 10.6084/m9.figshare.21067888.v1
title: Data for the study entitled "Embedding stakeholder preferences in setting priorities for health research"

description: Data are responses derived from a survey and 1000Minds exercise conducted individually amongst ACTA members. There are 3 files: README describes the variables in the 2 datasets - MAIN and VALUE OF INFORMATION
DOI: 10.6084/m9.figshare.21067888
title: Data for the study entitled "Embedding stakeholder preferences in setting priorities for health research"

description: Data are responses derived from a survey and 1000Minds exercise conducted individually amongst ACTA members. There are 3 files: README describes the variables in the 2 datasets - MAIN and VALUE OF INFORMATION
DOI: 10.5281/zenodo.5153528
title: Longitudinal Single Cell Atlas Identifies Type I Interferon Response as an Early Transient Prognostic Signature of COVID-19 Severity

description: The clinical spectrum of COVID-19 ranges from asymptomatic or mildly symptomatic illness to pneumonia, critical illness and death. Molecular markers early in the disease course (Days 1-8) that identify patients at high risk for progression are scarce, due to the paucity of relevant longitudinal studies. We performed longitudinal single cell RNA-seq on 286 peripheral blood samples from 108 age- and gender-matched COVID-19 patients, 73 of which had at least one early sample. By examining discrete cell subtypes as well as continuous single cell states, we identified upregulation of genes related to type I interferon (IFN) signaling as the predominant cellular prognostic signature of future disease severity. Type I IFN signaling was dynamic and complex, spiking early in Progressors and then receding to the level of Non-Progressors at the very next sampling. Across the entire cohort, expression of type I IFN signaling genes dropped steadily, falling to asymptomatic levels by Day 14 regardless of severity. Moreover, in severe and critical cases, IFN signaling was deficient during Days 5-8, potentially due to upregulation of <em>SOCS3</em> and other negative regulators. In summary, we have identified an early expression signature that may predict COVID-19 severity, as well as potential mechanisms underlying the dysfunctional host immune response in severe disease.
DOI: 10.5281/zenodo.5153527
title: Longitudinal Single Cell Atlas Identifies Type I Interferon Response as an Early Transient Prognostic Signature of COVID-19 Severity

description: The clinical spectrum of COVID-19 ranges from asymptomatic or mildly symptomatic illness to pneumonia, critical illness and death. Molecular markers early in the disease course (Days 1-8) that identify patients at high risk for progression are scarce, due to the paucity of relevant longitudinal studies. We performed longitudinal single cell RNA-seq on 286 peripheral blood samples from 108 age- and gender-matched COVID-19 patients, 73 of which had at least one early sample. By examining discrete cell subtypes as well as continuous single cell states, we identified upregulation of genes related to type I interferon (IFN) signaling as the predominant cellular prognostic signature of future disease severity. Type I IFN signaling was dynamic and complex, spiking early in Progressors and then receding to the level of Non-Progressors at the very next sampling. Across the entire cohort, expression of type I IFN signaling genes dropped steadily, falling to asymptomatic levels by Day 14 regardless of severity. Moreover, in severe and critical cases, IFN signaling was deficient during Days 5-8, potentially due to upregulation of <em>SOCS3</em> and other negative regulators. In summary, we have identified an early expression signature that may predict COVID-19 severity, as well as potential mechanisms underlying the dysfunctional host immune response in severe disease.
DOI: 10.6084/m9.figshare.14473867
title: Anaerobic digestion of urban wastes: integration and benefits of a small-scale system

description: This work presents an integrated approach of anaerobic digestion (AD) at the urban scale, based on on-site collected wastes, experimentation in a pilot reactor, and model approach. To cope with urban waste limitations (season inflow, organic matter decrease, limited area), it was proposed a settlement of wastewater (WW), a drying of food waste (FW), and silage of green waste (GW). The results obtained highlight the performance of these pretreatments to concentrate and/or preserve the organic matter over time. Co-digestion in a 30-liter reactor was then successfully carried out, resulting in an 83% of volatile solids reduction and stable methane production of 321 ml CH<sub>4</sub>/g VS<sub>add</sub>, in 35 days. Finally, we developed a standard neighbourhood model with AD based on these experimental results. It appears that 66% of the primary sludge (PS) and 28% of the FWs produced daily could be treated by the proposal process. 14% of the annual production of GW could thus be processed daily. From an energetic point of view, the process generates a surplus of respectively, 2500 MJ/year and 38,000 MJ/year in terms of electricity and heat. The generation of 15 times more heat energy than electricity, supports an installation of anaerobic digestion, close to homes. Moreover, if these results are encouraging, we suggest different scenarios of co-digestion ratios and operational parameters for their optimization.
DOI: 10.6084/m9.figshare.14473867.v2
title: Anaerobic digestion of urban wastes: integration and benefits of a small-scale system

description: This work presents an integrated approach of anaerobic digestion (AD) at the urban scale, based on on-site collected wastes, experimentation in a pilot reactor, and model approach. To cope with urban waste limitations (season inflow, organic matter decrease, limited area), it was proposed a settlement of wastewater (WW), a drying of food waste (FW), and silage of green waste (GW). The results obtained highlight the performance of these pretreatments to concentrate and/or preserve the organic matter over time. Co-digestion in a 30-liter reactor was then successfully carried out, resulting in an 83% of volatile solids reduction and stable methane production of 321 ml CH<sub>4</sub>/g VS<sub>add</sub>, in 35 days. Finally, we developed a standard neighbourhood model with AD based on these experimental results. It appears that 66% of the primary sludge (PS) and 28% of the FWs produced daily could be treated by the proposal process. 14% of the annual production of GW could thus be processed daily. From an energetic point of view, the process generates a surplus of respectively, 2500 MJ/year and 38,000 MJ/year in terms of electricity and heat. The generation of 15 times more heat energy than electricity, supports an installation of anaerobic digestion, close to homes. Moreover, if these results are encouraging, we suggest different scenarios of co-digestion ratios and operational parameters for their optimization.
DOI: 10.5281/zenodo.7063521
title: Pre-trained Models for SMP Classification and Segmentation

description: This dataset provides access to pre-trained models that were used for SnowMicroPen profile classification and segmentation. The models were trained on a part of the MOSAiC SMP dataset, available on https://doi.pangaea.de/10.1594/PANGAEA.935554. The labeled training data consists mostly of profiles from leg three of the expedition (January - May 2020), some profiles from leg one and two, and no profiles from leg four. Please refer to the snowdragon GitHub repository (https://github.com/liellnima/snowdragon) to access the models' training code and be directed to current publications. The following trained models are available here (alphabetically ordered): Artificial neural networks Bi-directional long short-term memory <em>(blstm.hdf5)</em> Encoder-decoder <em>(enc_dec.hdf5)</em> Long short-term memory <em>(lstm.hdf5)</em> Baseline Majority vote classifier <em>(baseline.model)</em> Semi-supervised models Cluster-then-predict models: Bayesian Gaussian mixture model <em>(gmm.model)</em> Bayesian mixture model <em>(bmm.model)</em> K-means clustering <em>(kmeans.model)</em> Label propagation <em>(label_spreading.model)</em> Self-trained classifier <em>(self_trainer.model)</em> Supervised models Balanced random forest <em>(rf_bal.model)</em> Easy ensemble <em>(easy_ensemble.model)</em> K-nearest neighbors <em>(knn.model)</em> Random forest <em>(rf.model)</em> Support vector machines <em>(svm.model)</em> <br> <em>Loading Instructions:</em><br> The models with the file-ending ".model" are pickeled Python objects and can be loaded with ``pickle.load(your_model.model)``. The random forest must be loaded with ``joblib.load(rf.model)``. All artificial neural networks are h5py.File objects (tf.keras models) and can be loaded with ``tf.keras.models.load_model(your_ann.model)``.
description: Contact julia.kaltenborn[at]mail.mcgill.ca to report problems or request further information.