cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16578428.v1
title: wificsi1

description: This directory contains the Channel State Information (CSI) data acquired from WiFi CSI receiver 1 (NUC1) which was facing the CSI transmitter (NUC3) in a 180 degrees configuration. The files are in .mat format and each row in the file corresponds to a received CSI packet. Each packet consists of 270 complex CSI values (3 transmit antennas x 3 receive antennas x 30 OFDM subcarriers). This dataset also contains other information such as the activity being performed, timestamp in milliseconds, experiment number, person id, room id, and ground truth XY coordinates of the person in the room. More details on the CSI system and structure of the CSI data are available in the "Methods" and "Experiment Directory" sections of the Data Descriptor, respectively.
DOI: 10.6084/m9.figshare.24826557.v1
title: Supplementary Material for: Early identification of patients with potential failure of nonoperative management for gastroduodenal peptic ulcer perforation

description: Introduction: We aimed to identify objective factors associated with failure of nonoperative management (NOM) of gastroduodenal peptic ulcer perforation (GDUP) and establish a scoring model for early identification of patients in whom NOM of GDUP may fail.
Methods: A total of 71 patients with GDUP were divided into NOM (cases of NOM success) and operation groups (cases requiring emergency operation or conversion from NOM to operation). Using logistic regression analysis, a scoring model was established based on the independent factors. The patients were stratified into low-risk and high-risk groups according to the scores.
Results: Of the 71 patients, 18 and 53 were in the NOM and operation groups, respectively. Ascites in the pelvic cavity on computed tomography (CT) and sequential organ failure assessment (SOFA) score at admission were identified as independent factors for NOM failure. The scoring model was established based on the presence of ascites in the pelvic cavity on CT and SOFA score ≥2 at admission. The operation rates for GDUP were 28.6% and 86.0% in the low-risk (score, 0) and high-risk groups (scores, 2 and 4), respectively.
Conclusion: Our scoring model may help determine NOM failure or success in patients with GDUP and make decisions regarding initial treatment.
DOI: 10.6084/m9.figshare.27130488.v1
title: Jupyter Notebook V-PFCRC - Formula Generation and Validation

description: This Jupyter Notebook contains comprehensive Python scripts designed to automate the generating and validating V-PFCRC (Volume- and Creatinine-corrected) formulas. These scripts are intended to analyze data from the accompanying Excel file, "Arsenic Raw Data Set 1.xlsx." However, they can easily be adapted to any Excel file that includes creatinine (CRN) values and uncorrected spot urinary analyte data. Modifications to file names, paths, and analyte names within the scripts will be necessary for use with a different dataset.The notebook automates the following key tasks for V-PFCRC formula generation:<b>Generation of V-PFCRC Coefficients</b>: It calculates analyte-specific, sex-differentiated V-PFCRC coefficients based on input datasets.<b>CRN Cut-off Optimization</b>: The notebook identifies the optimal creatinine range for V-PFCRC by evaluating regression curve fits across a broad spectrum of creatinine cut-offs.<b>Comparison of Mean Calculation Methods</b>: It compares different methods for mean calculation (geometric mean, arithmetic mean, and median) and evaluates their effectiveness in improving regression model fits.<b>Validation of the V-PFCRC Formula</b>: The performance of the V-PFCRC formula is validated by comparing residual R² values across datasets that include uncorrected data, conventionally creatinine-corrected data, and V-PFCRC-adjusted data.Additionally, the accompanying Excel file, "Jupyter-Python V-PFCRC Formula Generation Validation Results.xlsx," provides critical regression data used for optimizing and validating the V-PFCRC formulas. Beyond arsenic, four smaller datasets containing data for metals such as Cesium, Molybdenum, Strontium, and Zinc were also analyzed, along with a large dataset for Iodine. The results from these analyses are provided in the file.<br>
DOI: 10.6084/m9.figshare.13674949.v1
title: Supplementary Material for: What We Have Learned from Two Decades of Epidemics and Pandemics: A Systematic Review and Meta-Analysis of the Psychological Burden of Frontline Healthcare Workers

description: In light of the current coronavirus disease 2019 (COVID-19) pandemic and potential future infectious disease outbreaks, a comprehensive understanding of the negative effects of epidemics and pandemics on healthcare workers’ mental health could inform appropriate support interventions. Thus, we aimed to synthesize and quantify the psychological and psychosomatic symptoms among frontline medical staff. We searched four databases up to March 19, 2020 and additional literature, with daily search alerts set up until October 26, 2020. Studies reporting psychological and/or psychosomatic symptoms of healthcare workers caring for patients with severe acute respiratory syndrome, H1N1, Ebola, Middle East respiratory syndrome, or COVID-19 were eligible for inclusion. Two reviewers independently conducted the search, study selection, quality appraisal, data extraction, and synthesis and involved a third reviewer in case of disagreement. We used random effects modeling to estimate the overall prevalence rates of psychological/psychosomatic symptoms and the <i>I</i><sup>2</sup> statistic. We included 86 studies, reporting data from 75,991 participants. Frontline staff showed a wide range of symptoms, including concern about transmitting the virus to the family (60.39%, 95% CI 42.53–76.96), perceived stress (56.77%, 95% CI 34.21–77.95), concerns about own health (45.97%, 95% CI 31.08–61.23), sleeping difficulties (39.88%, 95% CI 27.70–52.72), burnout (31.81%, 95% CI 13.32–53.89), symptoms of depression (25.72%, 95% CI 18.34–33.86), symptoms of anxiety (25.36%, 95% CI 17.90–33.64), symptoms of posttraumatic stress disorder (24.51%, 95% CI 18.16–31.46), mental health issues (23.11%, 95% CI 15.98–31.10), and symptoms of somatization (14.68%, 95% CI 10.67–19.18). We found consistent evidence for the pervasive and profound impact of large-scale outbreaks on the mental health of frontline healthcare workers. As the CO­VID-19 crisis continues to unfold, guaranteeing easy access to support structures for the entire healthcare workforce is vitally important.
DOI: 10.17632/d9f4m2735b
title: CNN model to map vegetation classification in a subalpine coniferous forest using UAV imagery

description: This study reveals the potential of combining deep learning with UAV remote sensing for natural forests. This approach can help to obtain forest information more reliably in inaccessible forest areas, providing an efficient and cost-effective method for monitoring and protecting natural forests. 
The code was written using the interactive programming environment Jupter notebook and developed using the arcgis.learn module in the ArcGIS API for Python (https://developers.arcgis.com/python/api-reference/). The code is easier to use in Jupter notebook, which is built into ArcGIS Pro.
The dataset includes the drone image and the sample set. The UAV image was captured using DJI Mavic 2 (SZ DJI Technology Co., Shenzhen, China). The sample set includes a training set and a test set, which can be tested by building models using the code in the Code folder.
DOI: 10.17632/wg7swwf6jr
title: Prominent CD8 + T cell responses are directed at novel conserved influenza B virus epitopes across anatomical sites and age groups

description: The files in this data repository contain the T cell receptor sequences described in the following study:

Prominent CD8+ T cell responses are directed at novel conserved influenza B virus epitopes across anatomical sites and age groups
Tejas Menon, Patricia T. Illing, Priyanka Chaurasia, Hayley A. McQuilten, Chloe Shepherd, Louise C. Rowntree, Jan Petersen, Dene R. Littler, Grace Khuu, Ziyi Huang, Lilith F. Allen, Steve Rockman, Jane Crowe, Katie L. Flanagan, Linda M. Wakim, Thi H. O. Nguyen, Nicole A. Mifsud, Jamie Rossjohn, Anthony W. Purcell, Carolien E van de Sandt and Katherine Kedzierskafiles in
DOI: 10.25405/data.ncl.8223395.v1
title: AFM_Results

description: AFM dataset used to create figures in paper entitled "The Assessment of Organic Matter Young's Modulus Distribution with Deposition Environment and Maturity".
DOI: 10.6084/m9.figshare.1501021.v7
title: Tracing a late Mesozoic magmatic arc along the Southeast Asian margin from the granitoids drilled from the northern South China Sea

description: The granitoid suites encountered by drilling in the northern South China Sea (SCS) remain important for understanding the evolution of the late Mesozoic Southeast Asian continental margin. They comprise a range of rock types including diorite, tonalite, granodiorite, monzogranite and syenogranite with SiO<sub>2</sub> spanning 56.4–76.8%. Newly acquired secondary ion mass spectrometry (SIMS) U–Pb ages of samples from 14 boreholes indicate two key magmatic episodes: Late Jurassic (161.6–148.2 Ma) and Early Cretaceous (136.5–101.7 Ma). Jurassic magmatism probably began in late Middle Jurassic time, documented by the dates of inherited zircons. The granitoids are dominated by metaluminous to weakly peraluminous I-type granites, are transitional between magnesian and ferroan, and encompass calc-alkaline, high-K calc-alkaline, and shoshonitic series. The geochemical signatures suggest that these granitoids were mostly generated in a normal continental arc environment. Notable features of the I-type samples are well-defined negative Nb–Ta–Ti anomalies typical of arc-related magmas. Taken together, the late Mesozoic arc granites of the SCS, the accretionary wedge of the Palawan terrane to the southeast, and the zone of lithospheric extension north of the SCS throughout Southeast China, define a southeast-to-northwest trench-arc-backarc architecture for the late Mesozoic Southeast Asian continental margin whose geodynamic setting is related to subduction of the Palaeo-Pacific slab beneath the Asian continent. Two key subduction episodes are recognized, one in Late Jurassic and the other in Early Cretaceous time.
DOI: 10.6084/m9.figshare.23614809.v1
title: An Open Dataset and an Algorithm for Niacin Skin-flushing Segmentation with Application in Psychiatric Disorders Classification

description: <strong>About the Dataset</strong> <strong>Summary:</strong> The dataset includes images associated with the Niacin Skin-Flushing Response (NSR), a physiological response connected with mental illnesses such as Depression (DP), Bipolar Disorder (BP), and Schizophrenia (SZ), in addition to images from a healthy control group (HC). The data was initially part of a larger dataset collected from inpatients diagnosed according to the International Classification of Diseases, 10th Revision (ICD10). The HC group includes individuals with no lifetime history of psychiatric illness. A subset of images was selected from the original dataset to create an AI screening tool. These images had areas of interest cut out, and the NSR areas were annotated by five students. A pixel was marked as a flushing pixel if at least two students marked it as such. While source files for the developing segmentation algorithms cannot be provided due to IP issues, the results can be viewed on the benchmark page. For collaboration interests, feel free to get in touch. <strong>Dataset Details:</strong> The dataset includes: Number of images: 600 (Formatted as 1.png, 2.png, ... 600.png) Format of images: PNG Contents of the original image: A common label for reference size and four flushing areas Marked images: Images with human-determined skin-flushing areas (Formatted as 1-marked.png, ..., 600-marked.png) Reference score (info.csv): A human-determined relative size of the flushing area compared to the label. <strong>Ethical Considerations:</strong> The data collection process followed guidelines set by the local ethics committee, with all participants having signed informed consent forms. Strict inclusion criteria were followed to ensure the health and safety of participants. The data has been anonymized and does not contain any personally identifiable information.
DOI: 10.17632/3jx6fdbdw3
title: Causal Associations between Sleep Traits, Sleep Disorders, and Glioblastoma: A Two-Sample Bidirectional Mendelian Randomization Study

description: This study applied Mendelian Randomization (MR) analysis to investigate the bidirectional causal association of sleep traits (chronotype, getting up in the morning, midday nap, sleep duration, and sleep episodes) and sleep disorders (insomnia, narcolepsy, sleep apnea, and general sleep disorders) with GBM, based on their potential links with GBM. 
Study design and data source
GWAS data for GBM was sourced from genome sequencing data of the Finn cohort. Data regarding sleep traits and sleep disorders were collected from the UKB and GWAS Catalog, with sample sizes ranging from 84,810 to 462,400 (Table S1).
MR analysis 
A bidirectional MR approach was used to investigate the causal associations between sleep traits, sleep disorders, and GBM. 
Sensitivity analysis 
Sensitivity analysis was conducted to detect potential heterogeneity in MR studies. Cochran's Q test was used to assess heterogeneity among IVs, with P &gt; 0.05 indicating low heterogeneity, meaning the estimates among instrumental variables were randomly distributed and had little impact on IVW results. Considering the potential impact of genetic variation on the estimation of association effects, this study used the MR-Egger regression method to explore the presence of horizontal pleiotropy; when the intercept of MR-Egger regression approaches zero or is not statistically significant, it suggests the absence of pleiotropy. Additionally, the MR pleiotropy residual sum and outlier (MR-PRESSO) method was used to detect potential outliers (i.e., SNPs with P &lt; 0.05) and re-estimate causal associations after their removal to correct for horizontal pleiotropy. Leave-one-out analysis was employed to assess the robustness and consistency of the results.
RESULTS
The causal effects of sleep traits and sleep disorders on GBM 
First, we assessed the causal effects of sleep traits and sleep disorders on GBM risk. For sleep traits, 152 IVs were selected for chronotype, 73 for getting up in the morning, 84 for a midday nap, 68 for sleep duration, and 20 for sleep episodes. For sleep disorders, 39 IVs were selected for insomnia, 30 for narcolepsy, 16 for sleep apnea, and 27 for general sleep disorders. The F values are shown in Table S2. No associations were found between other sleep traits or any of the sleep disorders and GBM (all P &gt; 0.05) (Figure S1-4).
The causal effect of GBM on sleep traits and sleep disorders
The causal effects of GBM on sleep traits and sleep disorders were assessed. A total of 4 IVs were selected; the F values are shown in Table S2. No association was found when MR Egger and Weighted mode were applied (both P &gt; 0.05), while WM showed statistical significance (OR: 1.0085, 95% CI: 1.0011 - 1.0159, P = 0.024). There was no association between GBM and other sleep traits and sleep disorders (all P &gt; 0.05) (Figure S5-8).
DOI: 10.6084/m9.figshare.16695616.v3
title: CCFD_dataset

description: The dataset has been released by [1], which had been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of Université Libre de Bruxelles (ULB) on big data mining and fraud detection. <br>[1] Pozzolo, A. D., Caelan, O., Johnson, R. A., and Bontempi, G. (2015). Calibrating Probability with Undersampling for Unbalanced Classification. 2015 IEEE Symposium Series on Computational, pp. 159-166, doi: 10.1109/SSCI.2015.33 <br>open source kaggle : https://www.kaggle.com/mlg-ulb/creditcardfraud
DOI: 10.6084/m9.figshare.25526368.v2
title: 数据.dta

description: 这是论文的原始数据
DOI: 10.17632/r869b38ccm
title: Data for: What to teach? Towards an integrated approach for developing digital competency in preservice teacher training

description: The dataset includes raw association rules analysis results for a dataset including 931 Belgian preservice teachers' experiences developing digital competencies.
DOI: 10.6084/m9.figshare.24999317.v1
title: 240115 HRI7 UK 2022 HRI risk factors data

description: Dataset to support the manuscript: Incidence and risk factors for heat-related illness in dogs under UK emergency veterinary care in 2022
DOI: 10.17632/jgwx9vmnrp
title: Data for: Changes of organic matter composition in surface sediments from the Pearl River estuary to the coastal South China Sea revealed by rapid molecular screening using FTICR-MS

description: The dataset contains a list of identified molecular species and their intensities in 16 sediment samples collected along a transect in the Pearl River estuary, from January to May 2013, analyzed by APPI-P FTICR-MS
DOI: 10.17632/tw2h7bb9f7
title: Dataset of East China Sea cruise in August 2013

description: Data source: Zhang et al., 2021: Dissolved iron data and other auxiliary parameters from water samples during Dongfanghong-2 cruise in August 2013 in the East China Sea.

Method: The file contains dissolved iron (dFe) and inorganic nutrients concentrations. dFe concentration was processed with isotope dilution method (Lee et al., 2011, ACA) and measured on Neptune MC-ICP-MS at East China Normal University. Nutrients (NO2-, NO3-, NH4+, DIP, and DSi) were measured using a nutrient auto-analyzer (Skalar SANplus) at Ocean University of China (Liu et al., 2009, Biogeosciences; Dong et at., 2021, AOS, in press). The high resolution CTD data (temperature, salinity, density, turbidity, fluorescence) was also included in this dataset.

Supplement to: Jing Zhang, Xun Chi Zhu, Rui Feng Zhang, Jing Ling Ren, Ying Wu, Su Mei Liu and Da Ji Huang, Dissolved Fe in the East China Sea under the Influences of Land Sources and the Boundary Current with Implications for Global Marginal Seas.

Project(s): This study was funded by the Ministry of Science and Technology of China (MoST-China Ref.: 2011CB409801) and Natural Science Foundation of China (NSFC Ref.: 41876071).
DOI: 10.6084/m9.figshare.24631164.v3
title: soil organic carbon

description: 2019soil organic carbon
DOI: 10.17632/5n9vprvyhz
title: Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms

description: Abstract Reaction Mechanism Generator (RMG) constructs kinetic models composed of elementary chemical reaction steps using a general understanding of how molecules react. Species thermochemistry is estimated through Benson group additivity and reaction rate coefficients are estimated using a database of known rate rules and reaction templates. At its core, RMG relies on two fundamental data structures: graphs and trees. Graphs are used to represent chemical structures, and trees are used to represent ... Title of program: RMG Catalogue Id: AEZW_v1_0 Nature of problem Automatic generation of chemical kinetic mechanisms for molecules containing C, H, O, S, and N. Versions of this program held in the CPC repository in Mendeley Data AEZW_v1_0; RMG; 10.1016/j.cpc.2016.02.013 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)
DOI: 10.17632/9g9v8w3s4c
title: Zinc supplementation is associated with improvement in hand-foot skin reaction in patients on vascular endothelial growth factor receptor-tyrosine kinase inhibitors: a cohort study

description: Supplemental Material for the paper of "Zinc supplementation is associated with improvement in hand-foot skin reaction in patients on vascular endothelial growth factor receptor-tyrosine kinase inhibitors: a cohort study" in JAAD.
DOI: 10.17866/rd.salford.12248339.v1
title: Interview Transcripts

description: Interview transcripts, Add Energy Ltd, May 2020<br><br>Participants and all named persons in this transcript have signed consent forms for their information to be included.
DOI: 10.6084/m9.figshare.17707850.v1
title: mat_raw

description: The raw waveforms segmented from the DAS seismic data at the FORGE site.
DOI: 10.6084/m9.figshare.22793033.v1
title: Global soil antibiotics dataset (dry weight, ng/g)

description: We revisited previously published databases with records of the occurrence of antibiotics, and collected data on antibiotic concentrations in soils across the globe. Studies and datasets included in these databases were selected according to several criteria: (1) study was not duplicate, and duplicate records were checked and eliminated; (2) only soil-associated samples (within 0–20 cm of soil depth) were retained, and solid manure or waste samples were excluded; (3) soil samples that were not cultured or experimentally treated were included, and only the control soils were considered. However, soils treated by manure fertilization and wastewater irrigation, which corresponded to local land management practices, were not excluded; (4) geospatial information on sampling sites (longitude and latitude) was provided or could be extracted from maps after georeferencing; (5) antibiotic concentrations for each sampling sites were provided (or average values for replicates), and concentrations that were “not detected” or “below detection limit” were assigned zero values; (6) rather than all antibiotics, we mainly focused on environmental concentrations of tetracyclines, quinolones, and sulfonamides antibiotics in soils, as they were widely and frequently detected worldwide 51. In total, these criteria resulted in a final dataset of 659 locations, from which 1,351 soil samples were collected.
DOI: 10.6084/m9.figshare.1348220.v4
title: Nature Biotechnology 31, 720 - Silencing - Supplementary Software

description: Data and codes for Silencing algorithm. <strong>Supplementary Software 1:</strong> Silecing code (updated version) <strong>Supplementary Software 2:</strong> Silencing DREAM5 datasets <strong>Supplementary Software 3:</strong> Original silencing code (<strong>not updated</strong>, best to use SILENCING.m in Supplementary Software 1) <strong>Please cite:</strong> Baruch Barzel & Albert-László Barabási. Network link prediction by global silencing of indirect correlations. <em>Nature Biotechnology</em> <strong>31</strong>, 720–725 (2013)
DOI: 10.6084/m9.figshare.28837589.v1
title: <b>Greenhouse gas emissions evolution in South America study data</b>

description: Data for the study entitle <b>Greenhouse gas emissions evolution in South America: Identifying Profiles and Trends with Cluster Analysis during 1990-2021</b>
DOI: 10.17632/4gc8jmwsr2
title: Data for: The Stavelot-Venn Massif (Ardenne, Belgium), a rift shoulder basin ripped off the West African craton: cartography, stratigraphy, sedimentology, new U-Pb on zircon ages, geochemistry and Nd isotopes evidence
DOI: 10.17632/46k66mz9sz
title: UAV-derived waterfowl thermal imagery dataset

description: This dataset is the research data for: Accurately Wildlife Censusing by Using Deep Learning for UAV-Based Thermal Imageries
DOI: 10.6084/m9.figshare.13397651.v1
title: Expression of 97 surface markers and 462 mRNAs in 70017 cells from healthy young, healthy old and leukemic human bone marrow

description: Seurat v3 object <b><br></b><b>ASSAYS</b>:AB: Antibody expression dataRNA: mRNA expression dataintegrated: Antibody and mRNA expression matrices were concatenated and data was integrated across patients using scanorama<br><b>DIMENSIONALITY REDUCTION</b>MOFA: Multi-OMICS factor analysis to integrate AB and RNA data. MOFA was run on scanorama-integrated data and served as input for clustering and further dimensionality reduction.MOFAUMAP: UMAP performed on MOFA dimensions. Display used in the manuscript.<br>MOFATSNE: UMAP performed on MOFA dimensions.<br><br><b>METADATA</b>Batch: Source individual (BM1-3, Aged1-3, AML1, AML3 and Q59. Q59 is called AML2 in the manuscript).class: Type of individual (healthy young, healthy old or AML).