cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7488/ds/2925
title: Does Economic Optimisation Explain LAI and Leaf Trait Distributions Across an Amazon Soil Moisture Gradient?

description: Model outputs presented in Flack-Prain, S., Meir, P., Malhi, Y., Smallman, T. L., & Williams, M. (2020).Does Economic Optimisation Explain LAI and Leaf Trait Distributions Across an Amazon Soil Moisture Gradient?. Global Change Biology.
DOI: 10.7488/ds/2865
title: Cardboard lined informal settlement fire experiment (Test 5)

description: This dataset presents Test 5 (Cardboard lining experiment). Different from Baseline experiment, the interior walls are lined with cardboard, and the other conditions are totally the same with Baseline.
DOI: 10.7488/ds/2811
title: Dataset for Regular Waves Measured using a Single Photon Avalanche Diode Array

description: The data file comprise surface elevation data measured by a resistance-type wave gauge ("WG_Measured_Data") and by a SPAD sensor array ("SPAD_Measured_Data") in 12 different wave test cases. In addition,12 videos are included containing the processed surface elevation image and specific pixel histogram ("Video").
description: Please see in the "Data_description.pdf" file
DOI: 10.7488/ds/2942
title: Risk factors for RSV associated acute lower respiratory infection poor outcome and mortality in young children
title: Risk factors for RSV-ALRI poor outcome and mortality

description: Respiratory syncytial virus associated acute lower respiratory infection (RSV-ALRI) constitutes a substantial disease burden in young children. We aimed to identify all studies investigating the risk factors of RSV-ALRI poor outcome or mortality in young children. We carried out a systematic literature review across 7 databases with data from studies published from January 1995 to December 2019. Quality of all eligible studies was assessed according to modified GRADE criteria. We conducted meta–analyses to estimate odds ratio (OR) with 95% confidence intervals (CI) for individual risk factors. This study represents a comprehensive report of the association between various risk factors and RSV-ALRI poor outcome and mortality in young children. More research should be carried out to elucidate risk factors associated with poor outcome and mortality using multivariable analysis.
DOI: 10.7488/ds/2958
title: The impact of acute loud noise on the behaviour of laboratory birds - data

description: We examined whether exposure to a routine laboratory noise, a low-frequency fire alarm test, induced behavioural changes in laboratory zebra finches (Taeniopygia guttata). Twenty-four breeding pairs of zebra finches were randomly selected and exposed to the low-frequency fire alarm (sounding for 10-20 seconds) or no noise (control) on separate test days. All birds were filmed before and after the alarm sounded and on a subsequent control day (without the alarm). This data set contains all collected raw data from this study.
DOI: 10.7488/ds/2825
title: A General Workflow for Characterization of Nernstian Dyes and Their Effects on Bacterial Physiology

description: The electrical membrane potential (Vm) is one of the components of the electrochemical potential of protons across the biological membrane (proton motive force), which powers many vital cellular processes. Because Vm also plays a role in signal transduction, measuring it is of great interest. Over the years, a variety of techniques have been developed for the purpose. In bacteria, given their small size, Nernstian membrane voltage probes are arguably the favorite strategy, and their cytoplasmic accumulation depends on Vm according to the Nernst equation. However, a careful calibration of Nernstian probes that takes into account the tradeoffs between the ease with which the signal from the dye is observed and the dyes’ interactions with cellular physiology is rarely performed. Here, we use a mathematical model to understand such tradeoffs and apply the results to assess the applicability of the Thioflavin T dye as a Vm sensor in Escherichia coli. We identify the conditions in which the dye turns from a Vm probe into an actuator and, based on the model and experimental results, propose a general workflow for the characterization of Nernstian dye candidates.
description: * "Figures_data.xlsx" contains values of the data points plotted. * Zip files contain raw data (images and Excel spreadsheets) relating to figures 3,4,5,SI7 and SI10 respectively.
DOI: 10.7488/ds/2830
title: Face Coverings, Aerosol Dispersion and Mitigation of Virus Transmission Risk. Dataset 2: raw and processed data for tests 1 - 90
title: Mask Effectiveness As Part Of COVID-19 Response - BOS Airflow Tests - Dataset 2 of 4

description: "Face Coverings, Aerosol Dispersion and Mitigation of Virus Transmission Risk" Supplementary Information. The information is grouped into four, zipped, datasets, all of which contain the same ReadMe.txt and SupplementaryMaterialIndex.xlsx files: Dataset 1: videos, images and spirometry results (https://doi.org/10.7488/ds/2826); Dataset 2: raw and processed data for tests 1 - 90 (https://doi.org/10.7488/ds/2830); Dataset 3: raw and processed data for tests 97 - 174 (https://doi.org/10.7488/ds/2832); Dataset 4: raw and processed data for tests 175 - 280 (https://doi.org/10.7488/ds/2831). For further detail, please download the ReadMe.txt file from any dataset. The abstract of the pre-print can be found by navigating up one level to the Collection page.
DOI: 10.7488/ds/2831
title: Face Coverings, Aerosol Dispersion and Mitigation of Virus Transmission Risk. Dataset 4: raw and processed data for tests 175 - 280
title: Mask Effectiveness As Part Of COVID-19 Response - BOS Airflow Tests - Dataset 4 of 4

description: "Face Coverings, Aerosol Dispersion and Mitigation of Virus Transmission Risk" Supplementary Information. The information is grouped into four, zipped, datasets, all of which contain the same ReadMe.txt and SupplementaryMaterialIndex.xlsx files: Dataset 1: videos, images and spirometry results (https://doi.org/10.7488/ds/2826); Dataset 2: raw and processed data for tests 1 - 90 (https://doi.org/10.7488/ds/2830); Dataset 3: raw and processed data for tests 97 - 174 (https://doi.org/10.7488/ds/2832); Dataset 4: raw and processed data for tests 175 - 280 (https://doi.org/10.7488/ds/2831). For further detail, please download the ReadMe.txt file from any dataset. The abstract of the pre-print can be found by navigating up one level to the Collection page.
DOI: 10.7488/ds/2948
title: Summary statistics from epigenome-wide association studies and meta-analysis comparing APOE ε4 and APOE ε2 allele carriers in Generation Scotland

description: These datasets contain full summary statistics for epigenome-wide association studies (EWASs) carried out using two methylation samples from the Generation Scotland: Scottish Family Health Study to investigate associations between blood-based DNA methylation and APOE ε4 vs. ε2 carrier status. The two samples were termed the "discovery" and "replication" samples and a meta-analysis of the EWAS results from the two samples was performed. These datasets are described fully in the associated preprint, which is available on bioRxiv (https://www.biorxiv.org/content/10.1101/815035v2.full).
DOI: 10.7488/ds/2908
title: BioRock rare earth element leaching data
title: Data from the ESA BioRock experiment

description: Sets of data from the ESA BioRock experiment showing biomining/bioleaching of elements (rare earth elements). Experiment on board of the International Space Station to study microbe-mineral interactions in space. Data in support of the manuscript: Cockell et al. (2020) Space station biomining experiment demonstrates rare earth element extraction in microgravity and Mars gravity. Nature Communications (in review).
DOI: 10.7488/ds/2835
title: Dataset – Experimental Force Data of a Restrained ROV under Waves and Current

description: The experimental set-up includes a Remotely Operated (underwater) Vehicle (ROV), which was restrained with eight tethers to measure the hydrodynamic forces due to waves and current. Each tether was equipped with a load cell. The provided data are linked to an underwater motion capturing system, which observed the small response in six degrees of freedom. The provided load cases include different current speeds, irregular and regular waves as well as combined wave-current seas. In addition to the raw data, a processed dataset is provided which includes the forces in all three main coordinate directions for each mounting point on the ROV. This research was carried out at the FloWave Ocean Energy Research Facility of the Institute for Energy Systems, University of Edinburgh.
DOI: 10.7488/ds/2957
title: Synthetic IR data for the Attia et al. (2020) battery dataset

description: Data produced to accompany the paper Strange et al. (2021, Feb): Calum Strange, Shawn Li, Richard Gilchrist and Goncalo dos Reis, 2021 Feb, 'Elbows of Internal Resistance Rise Curves in Li-Ion Cells ' (https://www.mdpi.com/1996-1073/14/4/1206). The data completes the dataset of Attia et. al. (2020) (https://www.nature.com/articles/s41586-020-1994-5) of A123 APR18650M1A cylindrical cells cycled in a constant temperature environment under a variety of fast charging protocols, for which IR measurements were not provided. The predictor model for this data was trained on the dataset of Severson et. al. (2019) (https://www.nature.com/articles/s41560-019-0356-8), which contains data for the same type of battery cells (A123 APR18650M1A cylindrical cells) cycled in the same environment. Both datasets and corresponding descriptions can be found at https://data.matr.io/1/.
description: Provided file ('predicted_ir.pkl') is a python dictionary file exported/dumped with the python package 'pickle' version 4.0, containing the predicted data. The dictionary is sorted by cell each containing predicted values (column 0) and variance (column 1) of the prediction based on 20 independent model predictions (machine learning generated). Cell code Notation: across the 8 batches of cells in the two dataset, bXcY refers to cell Y of batch X. Batches 1-3 correspond to the three batches of cells provided by Severson et. al. (2019). Batches 4-8 correspond to the four batches provided by Attia et. al. (2020); concretely: batch 4 = Batch oed_0, batch 5 = Batch oed_1, batch 6 = Batch oed_2, batch 7 = Batch oed_3 and batch 8 = Batch9 (validation batch). This cell notation is used as the 'key' for each cell's data in our dictionary. Additional data description is provided in Section 2.1 of the associated publication, Strange et al. (2020, Nov).
DOI: 10.7488/ds/2963
title: The Impact of Climate Change and Climate Extremes on Sugarcane Production

description: We combine the Soil-Plant-Atmosphere model with detailed timeseries measurements from experimental sugarcane plots in Guangxi, China, and São Paulo State, Brazil. We first calibrated and validated modelled carbon and water cycling against field flux and biometric data. Second, we simulated sugarcane growth under the historical climate (1980-2018), and 6 future climate projections (2015-2100). From these simulations we retrieved modelled sugarcane yield, days to reach maturity, gross primary productivity (GPP), evapotranspiration, PAR incident on the canopy, leaf area index (LAI), and soil water potential (SWP). Next, to quantify the impact of individual climate drivers on sugarcane yield, we generated synthetic climate forcings. Within the historical climate dataset (1980-2018) we computed the mean value for each climate driver, for each year (i.e. temperature, CO2, precipitation, PAR and VPD). For each climate driver, we calculated the median of the annual means. We identified the annual timeseries corresponding to the median for each climate driver. For each climate projection, we substituted, one by one, the temperature, CO2, precipitation, PAR and VPD, with that of the median timeseries. We then ran the model with the new synthetic climate forcings and retrieved the yield, carbon, and water flux estimates for each alternation. For each year, under each climate projection, at each site, we calculated the difference between yield, carbon, and water flux estimates under the projected and alternated-synthetic climate to compute a ‘climate effect’ of each individual climate driver.
description: The dataset contains a file for each site (Guangxi, China and São Paulo Brazil). For each climate scenario, and each year we include model estimates of sugarcane yield, days taken to reach maturity, gross primary productivity (GPP), evapotranspiration, PAR incident on the canopy, leaf area index (LAI), and soil water potential (SWP). Estimates labelled nominal, are those simulated under the projected climate. Estimates labelled airt, CO2, precipitation, PAR and VPD, are those generated under synthetic climate forcings where the air temperature, atmospheric CO¬2, precipitation, photosynthetically active radiation, and vapor pressure deficit were alternated to the median of the historical timeseries.
DOI: 10.7488/ds/3258
title: Construction of a Computational Framework to Automatically Interpret Chest X-rays and Diagnose Pneumonia

description: This study aimed to systematically diagnose pneumonia directly from paediatric chest X-ray images using a computational framework. The project research goals were 1) to establish a high-quality dataset of pneumonia labelled X-ray images, 2) to extend existing deep learning architectures for pneumonia diagnoses, and 3) to construct a computational pipeline, enabling members of the broader community to interface with the computational models to diagnose pneumonia from X-ray images. The paediatric chest x-ray images were derived from 1) a WHO-supported surveillance study at Dhaka Shishu Hospital (DSH), from 2) a community site at Kumudini Women’s Medical College (KWMCH), and from 3) the WHO Chest Radiography in Epidemiological Studies (WHO CRES) working group. The images were interpreted by at least two trained clinicians / radiologists for the presence of 1) primary end-point pneumonia (PEP), 2) other lung infiltrates, and/or 3) pleural fluid in either the left or right lung, for a total of six possible binary outcomes, which will henceforth be called “labels”. A third reader resolved discordant PEP labels found between the first and second readers. An X-ray image were included in this study if 1) the age of the child for whom the X-ray was performed was ≤59 months, 2) the X-ray was performed in one of the two study hospitals or from the WHO CRES reference image set, and 3) the image captured the lung area. Any Image that was marked “uninterpretable” (features of the images were not interpretable with respect to presence or absence of PEP) by two readers were excluded. The deposited datasets contain the resulting labels from the multiple readers for each dataset described above.
DOI: 10.7488/ds/3275
title: The Influence of General Emotional Distress on FL Anxiety: a Study of First-year Chinese High School Students
title: The data of Depression, Anxiety, Stress Scale-21 and Foreign Language Classroom Anxiety Scale from the students of a Chinese High school

description: Foreign language anxiety has been receiving increasing attention in the literature of psychology and second language learning. And many focus on the relationship between FL anxiety and achievement. However, few studies have researched the relationship between general emotional distress and FL anxiety. The present study recruited 600 first-year Chinese high school students aged 14 to 16 to complete the foreign language classroom anxiety scale (FLCAS, Horwitz, Horwitz & Cope, 1986) and Depression, Anxiety, and Stress Scale-21 (S. H. Lovibond & P. F. Lovibond, 1995). FL anxiety and stress and anxiety were found higher, while depression was lower among the participants than previous studies regarding different age groups. Factor analysis was also conducted on FLCAS and found five components of FL anxiety: unconfidence of English learning, anxiety from teachers and peers, fear of proactive and passive speaking, negative self-consciousness of speaking ability, and attitudes towards language classroom. Correlation and regression analysis found an inverse relationship between some components of FL anxiety and achievement. Furthermore, the present study also revealed a linkage influence of factors of general emotional distress on components of FL anxiety and a linkage influence of factors of FL anxiety on FL achievement through these methods.
DOI: 10.7488/ds/3262
title: Additional Data pertaining to publication: Meta-GWAS Reveals Novel Genetic Variants Associated with Urinary Excretion of Uromodulin
title: Additional data: Meta_Analysis of Urinary Uromodulin

description: Additional Data pertaining to publication: Meta-GWAS Reveals Novel Genetic Variants Associated with Urinary Excretion of Uromodulin Uromodulin, the most abundant protein excreted in normal urine, plays major roles in kidney physiology and disease. The mechanisms regulating the urinary excretion of uromodulin remain essentially unknown. We conducted a meta-analysis of genome-wide association studies for raw (uUMOD) and indexed to creatinine (uUCR) urinary levels of uromodulin in 29,512 individuals of European ancestry from 13 cohorts. We tested whether variants in disease-causing genes associate with uromodulin levels and investigated the effects of keratin-40 (KRT40) on uromodulin processing. Results: Two genome-wide significant signals were identified for uUMOD: A novel locus (P 1.24E-08) over the KRT40 gene coding for KRT40, a type1 keratin expressed in the kidney, and the UMOD-PDILT locus (P 2.17E-88), which included two independent sets of SNPs spread over UMOD and PDILT. The meta-GWAS for uUCR yielded two genome-wide significant signals on the UMOD-PDILT locus and at the novel WDR72 locus previously associated with kidney function, urine pH and risk of kidney stones.
description: Additional Data pertaining to publication: Meta-GWAS Reveals Novel Genetic Variants Associated with Urinary Excretion of Uromodulin If you use this dataset, please cite the manuscript: "Meta-GWAS Reveals Novel Genetic Variants Associated with Urinary Excretion of Uromodulin". Joseph CB et al. Submitted to JASN. This dataset consists of additional summary statisics of the meta-analysis of genome-wide association studies (GWAS) of urinary Uromodulin (uUMOD) and urinary Uromodulin/Creatinine ratio from subsets of the cohorts analysed in the original dataset "MarkerName" "Allele1" "Allele2" "Freq1" "FreqSE" [6] "MinFreq" "MaxFreq" "Effect" "StdErr" "P" [11] "Direction" "HetISq" "HetChiSq" "HetDf" "HetPVal" [16] "CHR" "BP" "ID" Meta-analysis summary stat column descriptions MarkerName - Position of the SNP in chromosome:position format Allele1 - allele for which results are reported Allele2 - other allele Freq1 - Allele1 average frequency FreqSE - Standard error of Freq1 MinFreq - Minimum frequency Allele1 MaxFreq - Maximum frequency Allele1 Effect - association effect size StdErr - standard error of the effect size P - association p-value Direction - Direction of association for each cohort HetISq - Hardy-Weinberg deviation across cohorts HetChiSq - Hardy-Weinberg ChiSq HetDf - Hardy-Weinberg degrees of freedom HetPVal - Hardy-Weinberg equillibrium p-value CHR- chromosome of SNP (b37) BP- position of SNP (b37) ID - rsID of the SNP, where available
DOI: 10.7488/ds/3251
title: Data from "Conceptualisation of an efficient particle-based simulation of a twin-screw granulator"

description: Temporal and spatially averaged data from DEM simulations of a Twin-Screw Granulator. there are three subsets of data: - LargeFull_v4; - LargePeriodic_v4; - Periodic. LargeFull_v4 and LargePeriodic_v4 are the data from two different simulation types with the same input parameters. These are used in the creation of figures 8 & 9. 'Periodic' contains the results from the various parameters sets used in the sensitivity study using the periodic reduced-domain models.
description: Readme.md
DOI: 10.7488/ds/3270
title: Additional files for 'Genomic insights into the biosynthesis and physiology of the cyanobacterial neurotoxin 3-N-methyl-2,3-diaminopropanoic acid (BMAA)'

description: Supplementary data for Mantas MJQ, Nunn PB, Codd GA and Barker D 'Genomic insights into the biosynthesis and physiology of the cyanobacterial neurotoxin 3-N-methyl-2,3-diaminopropanoic acid (BMAA)' (2022, Phytochemistry 200:113198, doi:10.1016/j.phytochem.2022.113198). For further details, see 'README.pdf' within the zipfile.
description: Supplementary data for Mantas MJQ, Nunn PB, Codd GA and Barker D 'Genomic insights into the biosynthesis and physiology of the cyanobacterial neurotoxin 3-N-methyl-2,3-diaminopropanoic acid (BMAA)'. For further details, see 'README.pdf' within the zipfile.
DOI: 10.7488/ds/3261
title: DALEC-Grass : Model development, parameter calibration and validation
title: A model-data fusion approach to analyse carbon dynamics in managed grasslands: Data and code

description: Model inputs, model outputs and model code as used and presented in "Myrgiotis, V., Blei, E., Clement, R., Jones, S. K., Keane, B., Lee, M. A., Levy, P. E., Rees, R. M., Skiba, U. M., Smallman, T. L., Toet, S., & Williams, M. (2020). A model-data fusion approach to analyse carbon dynamics in managed grasslands. Agricultural Systems, 184, 102907" The submission includes the following : (1) The code (written in fortran) of the DALEC-Grass model used in the aforementioned study. (2) Model input data on climate and human-management variables for the two simulated sites (i.e. Easer Bush and Crichton). (3) Model output data for all variables presented in the published research article NOTES : (1) The model's fortran (.f90) code can be compiled into a python shared object (.so). This allows the model to be imported and called as a standard python function (2) In order to compile the .f90 into a python .so (using command line interface) navigate to the directory where the code is stored and run the following command "f2py -c DALEC_GRASS.f90 -m DALEC_GRASS" (3) For information on model input variables please read the first lines in DALEC-GRASS.f90 (4) The model is implemented on a daily time-step (daily inputs/outputs)
DOI: 10.7488/ds/3265
title: DALEC-GRASS: Inference of grassland vegetation management from earth observation data. Validation against ground data from the North Wyke Farm Platform

description: Model inputs, model outputs and model code as used for, and presented in, "Myrgiotis, V., Harris, P., Revill, A., Sint, H. & Williams, M. Inferring management and predicting sub-field scale C dynamics in UK grasslands using biogeochemical modelling and satellite-derived leaf area data. Agr Forest Meteorol 307, 108466 (2021)." The submission includes the following : (1) Model inputs for the three examined grassland fields (2) Model outputs for the three examined grassland fields (3) Results for the sensitivity-to-livestock-density analysis (4) The fortran code (.f90) of the DALEC-Grass model used in this study. NOTES : (1) The model's fortran (.f90) code can be compiled into a python shared object (.so). This allows the model to be imported and called as a standard python function (2) In order to compile the .f90 into a python .so (using command line interface) navigate to the directory where the code is stored and run the following command "f2py -c DALEC_GRASS.f90 -m DALEC_GRASS" (3) For information on model input variables please read the first lines in DALEC-GRASS.f90 (4) The model was implemented on a weekly time-step (weekly inputs/outputs) (5) Dairy Fields : NW013 , Burrow Fields : NW006 , Great Field : NW002
DOI: 10.7488/ds/3269
title: Autofluorescence lifetime endomicroscopic images on ex-vivo lung cancer

description: This dataset contains the data for the publication of "A Layer-Level Multi-Scale Architecture for Lung Cancer Classification with Fluorescence Lifetime Imaging Endomicroscopy". This was collected by a custom-built dual-band fluorescence lifetime imaging endomicroscopy (FLIM) on 21 pairs of ex-vivo cancer/non-cancer human lung tissue. There are 21 CSV files in the zip file and each CSV file contains the data from one patient. Detailed information can be found on https://github.com/qiangwang57/flim_cancer_ml.