cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30464177
title: Understanding the influence of staff stress during implementation of an asthma program in early childhood education: a qualitative design

description: To evaluate the role of staff stress in the implementation of a school-based asthma program in early childhood education programs for low-income urban families. Fifteen staff members from four programs participated in focus groups and interviews examining perceptions of the asthma program one year into implementation. Following semi-structured guides, interviews explored general staff workload and stress levels as well as asthma-related stress. Transcripts were coded using thematic analysis with NVivo (Version 13) software. Using thematic analysis, investigators generated four main themes and 12 sub-themes from the data, characterizing staff stress and experiences at work and with the asthma program. The first two themes detail the inherent stress of working in early childhood education and the specific stress related to providing asthma care. Overall work stress sub-themes relate to busy schedules and overworked staff, while asthma-related subthemes pertain to the anxiety and overwhelm associated with the asthma care responsibilities. The third theme described how this stress impacted program implementation with sub-themes related to understaffing and turnover. The fourth theme detailed how staff asthma education reduced stress and concern. Staff described stress reductions through improvements in asthma knowledge, self-efficacy in asthma skills, reducing uncertainty about asthma symptoms, and agency related to asthma management. Although they experience high workplace stress levels, early childhood education staff felt that a school-based asthma program both reduced stress and improved their asthma-management competence. By focusing on practicable skills and building confidence, the implementation of an evidence-based program could effectively improve both child health outcomes and staff stress.
DOI: 10.6084/m9.figshare.30464177.v2
title: Understanding the influence of staff stress during implementation of an asthma program in early childhood education: a qualitative design

description: To evaluate the role of staff stress in the implementation of a school-based asthma program in early childhood education programs for low-income urban families. Fifteen staff members from four programs participated in focus groups and interviews examining perceptions of the asthma program one year into implementation. Following semi-structured guides, interviews explored general staff workload and stress levels as well as asthma-related stress. Transcripts were coded using thematic analysis with NVivo (Version 13) software. Using thematic analysis, investigators generated four main themes and 12 sub-themes from the data, characterizing staff stress and experiences at work and with the asthma program. The first two themes detail the inherent stress of working in early childhood education and the specific stress related to providing asthma care. Overall work stress sub-themes relate to busy schedules and overworked staff, while asthma-related subthemes pertain to the anxiety and overwhelm associated with the asthma care responsibilities. The third theme described how this stress impacted program implementation with sub-themes related to understaffing and turnover. The fourth theme detailed how staff asthma education reduced stress and concern. Staff described stress reductions through improvements in asthma knowledge, self-efficacy in asthma skills, reducing uncertainty about asthma symptoms, and agency related to asthma management. Although they experience high workplace stress levels, early childhood education staff felt that a school-based asthma program both reduced stress and improved their asthma-management competence. By focusing on practicable skills and building confidence, the implementation of an evidence-based program could effectively improve both child health outcomes and staff stress.
DOI: 10.22008/fk2/3tsbf0/mfyikx
title: test_pressure_transducer_depth.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/oehhcd
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.6084/m9.figshare.30217493
title: Shenzhu Pingxiao formula treats allergic asthma through the HSP90AA1/MAPK14/PTGS2 pathway

description: Shenzhu Pingxiao formula is a classic Chinese medicinal prescription used for asthma. Given that its mechanism is unclear, this study aimed to evaluate anti-allergic asthma (AA) properties and predict the targets of Shenzhu Pingxiao formula. Mice were challenged using house dust mites (HDMs). The anti-AA effect of the formula was evaluated by the airway resistance and number of immune cells in bronchoalveolar lavage fluid (BALF). Network pharmacology and molecular docking were used to predict the targets. The real-time reverse-transcription PCR and western blot were applied to analyze key genes and proteins associated with IL-17 signaling pathway. Administration of formula moderated the number of immune cells and reduced the airway hyperresponsiveness. Administration of formula reduced neutrophil recruitment. HSP90AA1, PTGS2, and MAPK14 involved in IL-17 signaling pathway, were predicted to be key targets for this formula. Vina scores in molecular docking were all ≤ −9 for wogonin–HSP90AA1, wogonin–MAPK14, and wogonin–PTGS2 pairs. Shenzhu Pingxiao formula decreased the expression levels of HSP90AA1, PTGS2, and MAPK14 in HDM-challenged mouse lung. Shenzhu Pingxiao formula can reduce neutrophil recruitment and exert anti-AA effects via suppression of HSP90AA1/MAPK14/PTGS2 pathway.
DOI: 10.6084/m9.figshare.30217493.v2
title: Shenzhu Pingxiao formula treats allergic asthma through the HSP90AA1/MAPK14/PTGS2 pathway

description: Shenzhu Pingxiao formula is a classic Chinese medicinal prescription used for asthma. Given that its mechanism is unclear, this study aimed to evaluate anti-allergic asthma (AA) properties and predict the targets of Shenzhu Pingxiao formula. Mice were challenged using house dust mites (HDMs). The anti-AA effect of the formula was evaluated by the airway resistance and number of immune cells in bronchoalveolar lavage fluid (BALF). Network pharmacology and molecular docking were used to predict the targets. The real-time reverse-transcription PCR and western blot were applied to analyze key genes and proteins associated with IL-17 signaling pathway. Administration of formula moderated the number of immune cells and reduced the airway hyperresponsiveness. Administration of formula reduced neutrophil recruitment. HSP90AA1, PTGS2, and MAPK14 involved in IL-17 signaling pathway, were predicted to be key targets for this formula. Vina scores in molecular docking were all ≤ −9 for wogonin–HSP90AA1, wogonin–MAPK14, and wogonin–PTGS2 pairs. Shenzhu Pingxiao formula decreased the expression levels of HSP90AA1, PTGS2, and MAPK14 in HDM-challenged mouse lung. Shenzhu Pingxiao formula can reduce neutrophil recruitment and exert anti-AA effects via suppression of HSP90AA1/MAPK14/PTGS2 pathway.
DOI: 10.6084/m9.figshare.30464177.v1
title: Understanding the influence of staff stress during implementation of an asthma program in early childhood education: A qualitative design

description: Objectives To evaluate the role of staff stress in the implementation of a school-based asthma program in early childhood education programs for low-income urban families. Methods Fifteen staff members from four programs participated in focus groups and interviews examining perceptions of the asthma program one year into implementation. Following semi-structured guides, interviews explored general staff workload and stress levels as well as asthma-related stress. Transcripts were coded using thematic analysis with NVivo (Version 13) software. Results Using thematic analysis, investigators generated four main themes and twelve sub-themes from the data, characterizing staff stress and experiences at work and with the asthma program. The first two themes detail the inherent stress of working in early childhood education and the specific stress related to providing asthma care. Overall work stress sub-themes relate to busy schedules and overworked staff, while asthma-related subthemes pertain to the anxiety and overwhelm associated with the asthma care responsibilities. The third theme described how this stress impacted program implementation with sub-themes related to understaffing and turnover. The fourth theme detailed how staff asthma education reduced stress and concern. Staff described stress reductions through improvements in asthma knowledge, self-efficacy in asthma skills, reducing uncertainty about asthma symptoms, and agency related to asthma management. Conclusions Although they experience high workplace stress levels, early childhood education staff felt that a school-based asthma program both reduced stress and improved their asthma-management competence. By focusing on practicable skills and building confidence, the implementation of an evidence-based program could effectively improve both child health outcomes and staff stress.
DOI: 10.6084/m9.figshare.30496665
title: Causal relationship between fatty acid metabolism and allergic asthma: a mendelian randomization and multi-omics study

description: Allergic asthma (AAS) arises from intricate gene-environment interactions, yet its pathophysiological mechanisms remain incompletely understood. This study employs a multi-omics approach to elucidate the regulatory role of metabolites in AAS pathogenesis, aiming to identify novel therapeutic targets and preventive strategies. We conducted bidirectional two-sample Mendelian randomization (MR) analysis on 1,400 serum metabolites, followed by co-localization analysis to validate shared genetic loci. Metabolic pathway enrichment focusing on rate-limiting enzymes was performed, complemented by protein-protein interaction (PPI) network construction. MR was systematically applied to assess the impact of 17 lifestyle factors on AAS-associated metabolites. Six metabolites including Carnitine C14 (OR = 2.660) and 3-hydroxyoleoylcarnitine (OR = 1.620) showed significant associations with AAS after false discovery rate (FDR) correction (FDR &lt; 0.05). Co-localization analysis (PPH3 + PPH4 &gt; 0.9) identified fatty acid metabolism as the central pathway, with ACACB demonstrating significant interaction with salbutamol’s target ADRB3. Lifestyle modulation analysis revealed cereal intake suppressed Carnitine C14 metabolism (<i>p</i> = 0.007), while cheese (<i>p</i> = 0.029) and oily fish consumption (<i>p</i> = 0.015) regulated 3-hydroxyoleoylcarnitine levels. This multi-omics integration study pioneers in delineating fatty acid metabolic reprogramming as a central mechanism in AAS pathogenesis. The identified ACACB-ADRB3 axis presents a novel therapeutic target, while dietary modulations of metabolite profiles offer promising avenues for personalized prevention strategies, advancing precision medicine in asthma management.
DOI: 10.6084/m9.figshare.30496665.v2
title: Causal relationship between fatty acid metabolism and allergic asthma: a mendelian randomization and multi-omics study

description: Allergic asthma (AAS) arises from intricate gene-environment interactions, yet its pathophysiological mechanisms remain incompletely understood. This study employs a multi-omics approach to elucidate the regulatory role of metabolites in AAS pathogenesis, aiming to identify novel therapeutic targets and preventive strategies. We conducted bidirectional two-sample Mendelian randomization (MR) analysis on 1,400 serum metabolites, followed by co-localization analysis to validate shared genetic loci. Metabolic pathway enrichment focusing on rate-limiting enzymes was performed, complemented by protein-protein interaction (PPI) network construction. MR was systematically applied to assess the impact of 17 lifestyle factors on AAS-associated metabolites. Six metabolites including Carnitine C14 (OR = 2.660) and 3-hydroxyoleoylcarnitine (OR = 1.620) showed significant associations with AAS after false discovery rate (FDR) correction (FDR &lt; 0.05). Co-localization analysis (PPH3 + PPH4 &gt; 0.9) identified fatty acid metabolism as the central pathway, with ACACB demonstrating significant interaction with salbutamol’s target ADRB3. Lifestyle modulation analysis revealed cereal intake suppressed Carnitine C14 metabolism (<i>p</i> = 0.007), while cheese (<i>p</i> = 0.029) and oily fish consumption (<i>p</i> = 0.015) regulated 3-hydroxyoleoylcarnitine levels. This multi-omics integration study pioneers in delineating fatty acid metabolic reprogramming as a central mechanism in AAS pathogenesis. The identified ACACB-ADRB3 axis presents a novel therapeutic target, while dietary modulations of metabolite profiles offer promising avenues for personalized prevention strategies, advancing precision medicine in asthma management.
DOI: 10.22008/fk2/3tsbf0/ebxipg
title: test_wind.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/omyjol
title: test_humidity.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.6084/m9.figshare.30217493.v1
title: Shenzhu Pingxiao Formula treats allergic asthma through the HSP90AA1/MAPK14/PTGS2 pathway

description: Shenzhu Pingxiao formula is a classic Chinese medicinal prescription used for asthma. Given that its mechanism is unclear, this study aimed to evaluate anti-allergic asthma properties and predict the targets of Shenzhu Pingxiao formula. Mice were challenged using house dust mites (HDM). The anti-allergic asthma effect of the formula was evaluated by the airway resistance and number of immune cells in bronchoalveolar lavage fluid (BALF). Network pharmacology and molecular docking were used to predict the targets. The real-time reverse-transcription PCR and western blot were applied to analyze key genes and proteins associated with IL-17 signaling pathway. Administration of formula moderated the number of immune cells and reduced the airway hyperresponsiveness. Administration of formula reduced neutrophil recruitment. HSP90AA1, PTGS2, and MAPK14 involved in IL-17 signaling pathway, were predicted to be key targets for this formula. Vina scores in molecular docking were all ≤-9 for wogonin-HSP90AA1, wogonin-MAPK14, and wogonin-PTGS2 pairs. Shenzhu Pingxiao formula decreased the expression levels of HSP90AA1, PTGS2, and MAPK14 in HDM-challenged mouse lung. Shenzhu Pingxiao formula can reduce neutrophil recruitment and exert anti-allergic asthma effects via suppression of HSP90AA1/MAPK14/PTGS2 pathway.
DOI: 10.6084/m9.figshare.30496665.v1
title: Causal Relationship Between Fatty Acid Metabolism and Allergic Asthma: A Mendelian Randomization and Multi-Omics Study

description: Allergic asthma (AAS) arises from intricate gene-environment interactions, yet its pathophysiological mechanisms remain incompletely understood. This study employs a multi-omics approach to elucidate the regulatory role of metabolites in AAS pathogenesis, aiming to identify novel therapeutic targets and preventive strategies. We conducted bidirectional two-sample Mendelian randomization (MR) analysis on 1,400 serum metabolites, followed by co-localization analysis to validate shared genetic loci. Metabolic pathway enrichment focusing on rate-limiting enzymes was performed, complemented by protein-protein interaction (PPI) network construction. MR was systematically applied to assess the impact of 17 lifestyle factors on AAS-associated metabolites. Six metabolites including Carnitine C14 (OR = 2.660) and 3-hydroxyoleoylcarnitine (OR = 1.620) showed significant associations with AAS after false discovery rate (FDR) correction (FDR &lt; 0.05). Co-localization analysis (PPH3 + PPH4 &gt; 0.9) identified fatty acid metabolism as the central pathway, with ACACB demonstrating significant interaction with salbutamol's target ADRB3. Lifestyle modulation analysis revealed cereal intake suppressed Carnitine C14 metabolism (P = 0.007), while cheese (P = 0.029) and oily fish consumption (P = 0.015) regulated 3-hydroxyoleoylcarnitine levels. This multi-omics integration study pioneers in delineating fatty acid metabolic reprogramming as a central mechanism in AAS pathogenesis. The identified ACACB-ADRB3 axis presents a novel therapeutic target, while dietary modulations of metabolite profiles offer promising avenues for personalized prevention strategies, advancing precision medicine in asthma management.
DOI: 10.22008/fk2/3tsbf0/5pjny5
title: test_station_pose.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/qfbjrf
title: test_air_temperature.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/tewzyb
title: test_radiation.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/jjk72u
title: test_station_boom_height.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/wkacqe
title: test_gps.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/ioaez2
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/rrczzd
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/tzix1o
title: test_precipitation.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.6084/m9.figshare.30601838
title: Conditional Data Synthesis Augmentation

description: Reliable machine learning and statistical analysis rely on diverse, well-distributed training data. However, real-world datasets are often limited in size and exhibit underrepresentation across key subpopulations, leading to biased predictions and reduced performance, particularly in supervised tasks such as classification. To address these challenges, we propose Conditional Data Synthesis Augmentation (CoDSA), a novel framework that leverages generative models, such as diffusion models, to synthesize high-fidelity data for improving model performance across multimodal domains, including tabular, textual, and image data. CoDSA generates synthetic samples that faithfully capture the conditional distributions of the original data, with a focus on under-sampled or high-interest regions. Through transfer learning, CoDSA fine-tunes pre-trained generative models to enhance the realism of synthetic data and increase sample density in sparse areas. This process preserves inter-modal relationships, mitigates data imbalance, improves domain adaptation, and boosts generalization. We also introduce a theoretical framework that quantifies the statistical accuracy improvements enabled by CoDSA as a function of synthetic sample volume and targeted region allocation, providing formal guarantees of its effectiveness. Extensive experiments demonstrate that CoDSA consistently outperforms non-adaptive augmentation strategies and state-of-the-art baselines in both supervised and unsupervised settings. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.22008/fk2/3tsbf0/dhbmzx
title: test_ingest.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.6084/m9.figshare.30601838.v1
title: Conditional Data Synthesis Augmentation*

description: Reliable machine learning and statistical analysis rely on diverse, well-distributed training data. However, real-world datasets are often limited in size and exhibit underrepresentation across key subpopulations, leading to biased predictions and reduced performance, particularly in supervised tasks such as classification. To address these challenges, we propose Conditional Data Synthesis Augmentation (CoDSA), a novel framework that leverages generative models, such as diffusion models, to synthesize high-fidelity data for improving model performance across multimodal domains, including tabular, textual, and image data. CoDSA generates synthetic samples that faithfully capture the conditional distributions of the original data, with a focus on under-sampled or high-interest regions. Through transfer learning, CoDSA fine-tunes pre-trained generative models to enhance the realism of synthetic data and increase sample density in sparse areas. This process preserves inter-modal relationships, mitigates data imbalance, improves domain adaptation, and boosts generalization. We also introduce a theoretical framework that quantifies the statistical accuracy improvements enabled by CoDSA as a function of synthetic sample volume and targeted region allocation, providing formal guarantees of its effectiveness. Extensive experiments demonstrate that CoDSA consistently outperforms non-adaptive augmentation strategies and state-of-the-art baselines in both supervised and unsupervised settings.
DOI: 10.6084/m9.figshare.30601838.v2
title: Conditional Data Synthesis Augmentation

description: Reliable machine learning and statistical analysis rely on diverse, well-distributed training data. However, real-world datasets are often limited in size and exhibit underrepresentation across key subpopulations, leading to biased predictions and reduced performance, particularly in supervised tasks such as classification. To address these challenges, we propose Conditional Data Synthesis Augmentation (CoDSA), a novel framework that leverages generative models, such as diffusion models, to synthesize high-fidelity data for improving model performance across multimodal domains, including tabular, textual, and image data. CoDSA generates synthetic samples that faithfully capture the conditional distributions of the original data, with a focus on under-sampled or high-interest regions. Through transfer learning, CoDSA fine-tunes pre-trained generative models to enhance the realism of synthetic data and increase sample density in sparse areas. This process preserves inter-modal relationships, mitigates data imbalance, improves domain adaptation, and boosts generalization. We also introduce a theoretical framework that quantifies the statistical accuracy improvements enabled by CoDSA as a function of synthetic sample volume and targeted region allocation, providing formal guarantees of its effectiveness. Extensive experiments demonstrate that CoDSA consistently outperforms non-adaptive augmentation strategies and state-of-the-art baselines in both supervised and unsupervised settings. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.22008/fk2/3tsbf0/chxo7p
title: test_imap_client.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/tyn43s
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/zrs9fl
title: test_get_l0tx.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/uviqrs
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/yoghnc
title: test_persistence.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.