cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26183/26j0-7w24
title: Their tenacity to just keep going: Nurses’ experiences in medical hotel quarantine during the COVID-19 pandemic dataset

description: To explore how nurses and nursing knowledge contributed to the success of an Australian nurse-led medical hotel quarantine facility during the COVID-19 pandemic. The facility was established to accommodate returning travellers who were COVID-19 positive or at risk of becoming positive, to travellers requiring complex care and expanded to community members who could not quarantine at home. Design: This descriptive qualitative study explored how nurses and nursing knowledge in the quarantine facility contributed to the low transmission incidence of COVID-19 infection. Method: Twelve semi-structured interviews were conducted via ZOOM from February to May 2022 with all levels of nursing staff (nurse managers to assistants in nursing) who had worked in the facility for 3 months or more. The nurses were asked to describe their experiences, the challenges they faced and how they overcame them. The data were rich and were analysed using Braun and Clarke's six-step thematic analysis. Results: Four broad themes were generated demonstrating how nurses played a pivotal role in the success of the facility. First: nursing knowledge was evident through the development of policies, which minimized risks to nurses and patients. Second: a community of learning was developed as nurses supported upskilling and capacity building of staff, particularly new graduates with the facility. Third: a supportive management structure promoted teamwork and a positive workplace culture. And finally: the nurses were encouraged to develop techniques that promoted self-care which enabled them to develop resilience. Conclusion: This was a nurse-led service that developed strategies to manage care delivery and overcame unanticipated difficulties in a unique clinical setting. Reporting Method: The quality of the research design was ensured by using the Consolidated Criteria for Reporting Qualitative Studies (COREQ) checklist. Patient or Public Contribution: No patient or public contribution.
Data contains confidential information and ethics approval was granted subject to the data not being published openly. To discuss the data, please contact Diana Jefferies d.jefferies@westernsydney.edu.au ORCID 0000-0001-5271-2569
DOI: 10.6084/m9.figshare.23714950
title: Matlab and R code from Metacognition in the audiovisual McGurk illusion: perceptual and causal confidence

description: This code was used for the data analysis. Upon request we can also share the code that was used for the stimuli presentation
DOI: 10.6084/m9.figshare.23714950.v1
title: Matlab and R code from Metacognition in the audiovisual McGurk illusion: perceptual and causal confidence

description: This code was used for the data analysis. Upon request we can also share the code that was used for the stimuli presentation
DOI: 10.25345/c5x34n282
title: MassIVE MSV000092486 - Non-severe burns induce a prolonged systemic metabolic phenotype indicative of a sustained inflammatory response to injury
title: Globally, burns are a significant cause of injury that can cause substantial acute trauma as well as lead to increased incidence of chronic co-morbidity and disease. To date, research has primarily focused on the systemic response severe injury, with little in the literature reported on impact of non-severe injuries (&lt;15% total burn surface area; TBSA). To elucidate the metabolic consequences of non-severe burn injury, longitudinal plasma was collected from adults (n=35) who presented at hospital with a non-severe burn injury at admission, and at 6 week follow up. A cross-sectional baseline sample was also collected from non-burn control participants (n=14). Samples underwent multiplatform metabolic phenotyping using 1H nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry to quantify 112 lipoprotein and glycoproteins signatures and 852 lipid species from across 20 subclasses. Multivariate data modelling (Orthogonal projection to latent structures-discriminate analysis) revealed alterations in lipoprotein and lipid metabolism when comparing baseline control to hospital admission samples, with the phenotypic signature found to be sustained at follow up. Univariate (Mann-Whitney U) testing and OPLS-DA indicated specific increases in GlycB (p-value &lt;1.0e-4), low density lipoprotein-2 subfractions (Variable importance in projection score; VIP &gt;6.83e-1) and monoacyglyceride (20:4)(p-value &lt;1.0e-4) and decreases in circulating anti-inflammatory high-density lipoprotein-4 subfractions (VIP &gt;7.75e-1), phosphatidylcholines, phosphatidylglycerols, phosphatidylinositols and phosphatidylserines. The results indicate a persistant systemic metabolic phenotype that occurs even in cases of non-severe burn injury. The phenotype is indicative of an accute inflammatory profile which continues to be sustained post-injury, suggesting an impact on systems health beyond the site of injury. The phenotypes contained metabolic signatures consistent with chronic inflammatory states reported to have elevated incidence post- burn injury. Such phenotypic signatures may provide patient stratification opportunities, to identify individual responses to injury, personalise intervention strtegies and improve acute care, reducing risk of chronic co-morbidity.
DOI: 10.7910/dvn/o274oo
title: Replication Data for: "A Pandemic Crossing the Border: The Impact of Covid-19 in the US on the Mexican Labor Market"

description: The labor markets in the US and Mexico are closely linked through migrant workers and remittances and the changes in remittance flow may alter labor allocations in the origin households. In this paper, we investigate how the prevalence of the Covid-19 epidemic in the US affected the local labor market in Mexico. We construct a Mexican municipality-level measure of the exposure to Covid-19 in the US using data on Covid-19 prevalence across US states and data on migrants' destinations across the US states. We find a positive effect of Covid-19 exposure in the US on the hours worked among workers in Mexico yet no significant effects were found for the local wages. We also find that the effect varies across subgroups which indicates that the responses in hours worked depend on the household dynamics, the nature of the occupation-specific tasks, and the migration intensity. Finally, we document the potential mechanism behind the effect on the hours worked, which is through the changes in remittances sent to the origin municipalities in Mexico.
DOI: 10.6084/m9.figshare.23715222
title: Supplementary Material for: Predicting Alzheimer’s Disease with Interpretable Machine Learning

description: Introduction: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We included 1219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. Shapley additive explanations (SHAP) was applied to identify key predictors of optimal models. Results: The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic and self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, LR performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. Conclusion: Interpretable machine learning showed promise in screening high-risk AD individual, and could further identify key predictors for targeted prevention.
DOI: 10.6084/m9.figshare.23715225
title: Supplementary Material for: Predicting Alzheimer’s Disease with Interpretable Machine Learning

description: Introduction: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We included 1219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. Shapley additive explanations (SHAP) was applied to identify key predictors of optimal models. Results: The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic and self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, LR performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. Conclusion: Interpretable machine learning showed promise in screening high-risk AD individual, and could further identify key predictors for targeted prevention.
DOI: 10.6084/m9.figshare.23715225.v1
title: Supplementary Material for: Predicting Alzheimer’s Disease with Interpretable Machine Learning

description: Introduction: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We included 1219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. Shapley additive explanations (SHAP) was applied to identify key predictors of optimal models. Results: The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic and self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, LR performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. Conclusion: Interpretable machine learning showed promise in screening high-risk AD individual, and could further identify key predictors for targeted prevention.
DOI: 10.6084/m9.figshare.23715228
title: Supplementary Material for: Predicting Alzheimer’s Disease with Interpretable Machine Learning

description: Introduction: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We included 1219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. Shapley additive explanations (SHAP) was applied to identify key predictors of optimal models. Results: The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic and self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, LR performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. Conclusion: Interpretable machine learning showed promise in screening high-risk AD individual, and could further identify key predictors for targeted prevention.
DOI: 10.6084/m9.figshare.23715228.v1
title: Supplementary Material for: Predicting Alzheimer’s Disease with Interpretable Machine Learning

description: Introduction: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We included 1219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. Shapley additive explanations (SHAP) was applied to identify key predictors of optimal models. Results: The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic and self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, LR performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. Conclusion: Interpretable machine learning showed promise in screening high-risk AD individual, and could further identify key predictors for targeted prevention.
DOI: 10.34740/kaggle/ds/3536687
title: Subsidies Schemes under Budget from 2021-24
DOI: 10.6084/m9.figshare.23714961
title: Research Data.7z

description: <strong>Comparative Analysis of Geometrical Differences in Left-Right Goat Bone Morphology and its Implications for Understanding Human Joint Asymmetry</strong>
DOI: 10.6084/m9.figshare.23714961.v1
title: Research Data.7z

description: <strong>Comparative Analysis of Geometrical Differences in Left-Right Goat Bone Morphology and its Implications for Understanding Human Joint Asymmetry</strong>
DOI: 10.26179/9pyk-8m89
title: Gravity Measurements at Casey and Law Dome, 1978
DOI: 10.6084/m9.figshare.23705712
title: OOD-GrainSet (wheat)

description: OOD-GrainSet-wheat for ECAI2023 (Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection) including 180K single-kernel images - 135K healthy kernel images - 45K images containing six types of damaged kernels and impurities, each type has 4k
DOI: 10.6084/m9.figshare.23715291
title: <strong>The Relationship between Care Needs and Quality of Life of Family Caregivers in Patients with Pancreatic Cancer</strong>

description: This study used a cross-sectional survey method combined with a convenience sampling method based on inclusion and exclusion criteria. The primary family caregivers of patients with pancreatic cancer who had completed active surgical treatment and were in the early stage of recovery at the pancreatic centers of two tertiary hospitals in Jiangsu Province were selected as the research subjects. A self-administered general information questionnaire, the Comprehensive Needs Assessment Tool in Cancer for Caregivers, the Positive Aspects of Caregiving, the Simplified Coping Style Questionnaire and the 12-item Short Form Health Survey were used to measure the current status, correlation, interactive relationship and direct effects of care needs and quality of life of this group at this stage, and to further explore the current status of positive experience and coping styles and their influencing mechanisms between care needs and quality of life.
DOI: 10.34740/kaggle/dsv/6162125
title: Facebook dataset
DOI: 10.15151/esrf-es-1117952258
title: Heidelberg BAG (HeidelBAG)

description: The HeidelBAG unites macromolecular crystallography of EMBL Heidelberg and the Heidelberg University Biochemistry Center (BZH). Central to all projects are large assemblies involved in chromosome organization and transcription control, in nuclear and cytoplasmic RNA regulation and modification, in translational control by ribosome associated factors or further in protein targeting, membrane protein insertion or post-translational modification. While O. Barabas from EMBL has left the BAG, Svetlana Dodonova and Sebastian Eustermann at EMBL will join and add projects on chromatin organization. Nearly all projects make use of integrated structural biology techniques (MX, SAXS/SANS, NMR, cryo-EM, AI approaches) that are combined with complementary methods, a strategy that warranties the continuous success of the BAG. More and more the detailed architecture and mechanistic description of entire macromolecular machines come into focus. The BAG reports on 17 publications and 30 PDB entries.
DOI: 10.15151/esrf-es-1118675832
title: Structural Biology BAG - Portugal

description: The Portuguese MX BAG includes 13 PIs from three different institutions. Our research interests include soluble and membrane proteins of biomedical interest from humans or pathogenic organisms, several of which can be drug targets. Focus is also given to structural enzymology, aimed at the elucidation of reaction mechanisms, active sites, and inhibition tolerance in proteins with potential biotechnology applications. In several cases, complementary structural biology approaches are being used, combining MX with SAXS and, more recently, CryoEM.
DOI: 10.15151/esrf-es-1120141538
title: BAG for the Structural Biology Groups in Madrid

description: This proposal is a collaborative effort of the Structural Biology Groups of Madrid with the objective to form a strong core of scientist devoted to this area of research in Spain. It is composed of 18 groups distributed in 4 research Institutes.
DOI: 10.15151/esrf-es-1231863765
title: Multi-scale Multi-techniques investigations of Li-ion batteries: towards a European Battery Hub

description: In this science-driven Long-Term proposal, we propose a 3-years multi-beamline program tackling grand challenges for actual, emerging and future battery technologies. We will deploy advanced methodologies for accelerating research by setting new cooperation modes for multi-techniques multi-scale studies. Three chemistry-neutral scientific cases have been identified to fulfil this LTP: bulk-to-surface mechanisms, high energy system and smart sensing. Multiple, time-coordinated, interoperable spectroscopic, imaging & scattering techniques will be implemented. This proposal is the Pilot Phase towards a European Battery Hub aiming at accelerating workflows in battery research by optimizing the exceptional diversity of means available at ESRF. We expect to demonstrate the added value of a new “proposal” mechanism, e.g. science-driven beamtime allocation group, serving the fast growing, highly competitive and rapidly evolving European battery community.
DOI: 10.15151/esrf-es-1297641834
title: cellulose chains in solution

description: Structures of cellulose chains in liquid ionic solutions will be investigated as a function of temperature between 25 to 353°C, in sealed capillaries
DOI: 10.15151/esrf-es-1226443633
title: Resolving Atomic-Scale Metallic Mixing Peptide-Enabled High Entropy Alloy Nanocatalysts using in-situ HE-XRD coupled to PDF analysis and rev

description: Homogenous alloy of 5 or more elements, commonly referred to as high entropy alloy nanocatalysts (HEAs), have recently demonstrated novel catalytic properties for a wide range of emerging electrocatalytic reactions. However, the absence of advance structural characterization efforts, especially those afforded by atomic-scale structural resolution, hinders our understanding about the true origin of HEAs’ catalytic enhancement. On the back of our group’s recent breakthrough in resolving the atomic structure of HEAs, aided by PDF coupled with reverse Monte Carlo simulations, we are proposing a new set of experiments to study the dynamic structural changes in HEAs under electrochemical conditions. Using in-situ PDF analysis, we aim to uncover the spatiotemporal structural evolution of HEAs during methanol oxidation reaction, allowing us to build accurate structure-property relationships, which in turn will assist in the developing rational design strategies for HEAs.
DOI: 10.26179/zzbk-zq17
title: Magnetic Readings Along Pioneerskaya - Dome C Traverses, 1977 and 1978
DOI: 10.6084/m9.figshare.22774811.v1
title: Data and codes for reproducing the results in Loong and Chang (2023, under review)

description: This link consists of two parts: 1. Naturalistic data for an undergraduate civil engineering course, CIVL 3310 <em>Structural Analysis</em>, taught at the spring semester of academic years 2018 and 2019 at the Hong Kong University of Science and Technology (HKUST). 2. Codes that support the findings in the "Control knowledge tracing: modeling students' learning dynamics from a control-theory perspective". <br> If you have any questions, please contact Cheng Ning Loong via Email at cnloong@connect.ust.hk. <br>
DOI: 10.6084/m9.figshare.23715450
title: Pathway enrichment analysis

description: Pathway enrichment analysis
DOI: 10.6084/m9.figshare.23715450.v1
title: Pathway enrichment analysis

description: Pathway enrichment analysis
DOI: 10.5281/zenodo.8166602
title: Location-Aware Container Scaling (LACS) in Geo-distributed Clouds

description: Datasets and code for the problem of location-aware container scaling (LACS) in geo-distributed clouds: Randomly extracted one day’s workload from WikiBench and NASA HTTP: AppWorkload.py Facebook subscribers by January 2020 to simulate the distribution of application requests among different user regions: FacebookUserData.csv Sprint IP Network location as 82 user regions from 35 countries on 6 continents: SprintLocation.csv Observation on the network latency matrix among 82 user centres: LatencyMatrix.py Representative code using openAI's gym environment: deepscale.py
DOI: 10.5281/zenodo.8166601
title: Location-Aware Container Scaling (LACS) in Geo-distributed Clouds

description: Datasets and code for the problem of location-aware container scaling (LACS) in geo-distributed clouds: Randomly extracted one day’s workload from WikiBench and NASA HTTP: AppWorkload.py Facebook subscribers by January 2020 to simulate the distribution of application requests among different user regions: FacebookUserData.csv Sprint IP Network location as 82 user regions from 35 countries on 6 continents: SprintLocation.csv Observation on the network latency matrix among 82 user centres: LatencyMatrix.py Representative code using openAI's gym environment: deepscale.py