cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3620748
title: OPEN-WINDOW: SOUND EVENT DATABASE FOR RESEARCH AND DEVELOPMENT

description: (1) Background: Situated in the domain of urban sound scene classification by humans and machines, the research in this project will be a first step towards mapping urban noise pollution experienced indoors and finding ways to reduce its negative impact in peoples' homes. The acoustic distinction between outdoor and indoor scenes is an active research field and can be automated with some success. A much subtler difference is the change in the indoor soundscape induced by an open window. Being able to determine this, however, would allow applications in warning systems and be a prerequisite for an app-based urban sound mapping project. Acoustic detection requires neither line of sight nor sensors at the window frame or knowledge of the number of windows or their size. The task, however, varies substantially in difficulty with the amount of sound inside and outside. From the point of machine classification the lack of specificity is the most problematic aspect: Very few sounds if any can be assumed to originate exclusively from outside <em>and</em> be present at all times to aid automatic detection. The required generalisation ability, however, can be assumed for humans, who might also use very subtle cues in the change of reverberations. (2) Aims The aims are (a) to determine the degree of reliability with which an open window can be recognised by humans and machines under varying circumstances based only on acoustic cues; (b) to investigate whether the findings for humans and machines can inform each other and can be used for further application-related research, e.g., window noise cancellation. (3) Method: (a) Dataset acquisition: A recording kit consisting of a dedicated laptop and microphone will be given to volunteers. Custom-programmed software will remind the user to specify the window state (establishing the so-called ground truth). (b) Perception experiments: Thirty participants will judge whether in the recorded clips a window is open or closed. After an extended familiarisation phase, they will proceed through two testing phases: In the first phase, all clips will originate from the recording locations with which the participants have already familiarised themselves, in the second they will judge clips from locations they haven't been exposed to before (partial data sets used for the familiar/unfamiliar conditions will be counterbalanced across participants). (c) Machine recognition: We will develop a machine learning system using state-of-the-art deep learning methods (artificial neural networks with multiple layers). To encourage other researchers to also take up this research, we will organise a machine learning challenge. In the challenge, a training data set including correct labels (ground truth) and a test set without the labels are provided. Researchers from academia and industry across the world will develop their own systems and send their classification results on the test set to the organisers to evaluate and publish online.
DOI: 10.15161/oar.it/30464
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.3911584
title: EMUE-D5-4-MethodComparisonWithCorrelation

description: Quantifying uncertainty when comparing measurement methods – Haemoglobin concentration as an example of correlation in straight-line regression In metrology, often two methods measuring the same quantity are to be judged whether or not they are in agreement. For measurements across a whole range of values, this can be done by comparing their straight-line fit to the identity line. Such a comparison is only meaningful, when uncertainties are available. Furthermore, the estimates of the straight-line fit and their uncertainties are only reliable when all sources of uncertainty have been accounted for. In particular, the measurements of both methods in a comparison are usually uncertain, and common instruments or standards cause correlation among or between them. When fitting a straight-line relation, the weighted total least-squares (WTLS) method accounts for correlation and uncertainties in both variables. This example focuses on WTLS and defines a measurement model from it to propagate all uncertainties and correlations through to the estimate of the slope and intercept, and associate uncertainties with them according to the GUM. Using the example of two high accuracy methods measuring the total haemoglobin concentration in blood, i.e. the cyanmethaemoglobin and alkaline haematin method, we indicate how correlations can be inferred, demonstrate how they can be accounted for and show their impact on the regression. The results are discussed and recommendations are given. Files contained in the dataset are: Example_A233.pdf: Report “Quantifying uncertainty when comparing measurement methods – Haemoglobin concentration as an example of correlation in straight-line regression”; Example_A233.tex: LaTeX file to be compiled in order to produce Example_A233.pdf; Compendium_A233.bib: bibliography file; Fig1a.pdf: PDF file needed to compile Example_A233.tex; Fig1b.pdf: PDF file needed to compile Example_A233.tex; Fig2a.pdf: PDF file needed to compile Example_A233.tex; Fig2b.pdf: PDF file needed to compile Example_A233.tex; data_A233.csv: data file containing the estimates and associated standard uncertainties for the measurements; Example_A233.Rmd: R Markdown file for quantifying uncertainty when comparing two measurement methods. As output an HTML file similar to Example_A233.pdf is created with results dynamically produced by underlying R code; aps.csl: CSL file needed to cite the reference in example_A233.Rmd according to the American Physical Society style.
DOI: 10.5281/zenodo.3911583
title: EMUE-D5-4-MethodComparisonWithCorrelation

description: Quantifying uncertainty when comparing measurement methods – Haemoglobin concentration as an example of correlation in straight-line regression In metrology, often two methods measuring the same quantity are to be judged whether or not they are in agreement. For measurements across a whole range of values, this can be done by comparing their straight-line fit to the identity line. Such a comparison is only meaningful, when uncertainties are available. Furthermore, the estimates of the straight-line fit and their uncertainties are only reliable when all sources of uncertainty have been accounted for. In particular, the measurements of both methods in a comparison are usually uncertain, and common instruments or standards cause correlation among or between them. When fitting a straight-line relation, the weighted total least-squares (WTLS) method accounts for correlation and uncertainties in both variables. This example focuses on WTLS and defines a measurement model from it to propagate all uncertainties and correlations through to the estimate of the slope and intercept, and associate uncertainties with them according to the GUM. Using the example of two high accuracy methods measuring the total haemoglobin concentration in blood, i.e. the cyanmethaemoglobin and alkaline haematin method, we indicate how correlations can be inferred, demonstrate how they can be accounted for and show their impact on the regression. The results are discussed and recommendations are given. Files contained in the dataset are: Example_A233.pdf: Report “Quantifying uncertainty when comparing measurement methods – Haemoglobin concentration as an example of correlation in straight-line regression”; Example_A233.tex: LaTeX file to be compiled in order to produce Example_A233.pdf; Compendium_A233.bib: bibliography file; Fig1a.pdf: PDF file needed to compile Example_A233.tex; Fig1b.pdf: PDF file needed to compile Example_A233.tex; Fig2a.pdf: PDF file needed to compile Example_A233.tex; Fig2b.pdf: PDF file needed to compile Example_A233.tex; data_A233.csv: data file containing the estimates and associated standard uncertainties for the measurements; Example_A233.Rmd: R Markdown file for quantifying uncertainty when comparing two measurement methods. As output an HTML file similar to Example_A233.pdf is created with results dynamically produced by underlying R code; aps.csl: CSL file needed to cite the reference in example_A233.Rmd according to the American Physical Society style.
DOI: 10.17632/rbtv7f5p68.1
title: Data for: Effectiveness of insecticides, physical barriers and size of planting stock against damage by the pine weevil (Hylobius abietis).

description: A data set for a series of trials testing barriers, insecticides, larger trees and feriliser application as a means of controlling Hylbius abietis damage on Sitka spruce (Picea sitchensis) in GB
DOI: 10.5281/zenodo.3911616
title: ITPC data for experiment 1 (audio, sentences, sense v nonsense) and experiment 2 (audio, phrases, AN, AV, MP, RR)

description: A matlab fieldtrip data structure containing all of the ITPC information for all conditions, all participants and all electrodes. Also has info about references etc. for both experiment 1 and experiment 2
DOI: 10.5281/zenodo.3911615
title: ITPC data for experiment 1 (audio, sentences, sense v nonsense) and experiment 2 (audio, phrases, AN, AV, MP, RR)

description: A matlab fieldtrip data structure containing all of the ITPC information for all conditions, all participants and all electrodes. Also has info about references etc. for both experiment 1 and experiment 2
DOI: 10.17632/sc64wp5c7m.3
title: Charles River Data Water Quality Sensor Data

description: Charles River Data Water Quality Sensor Data Descriptive Statistics data. This is year-wise data. And the descriptive statistical analysis is also given Year Wise. The dataset starts with the year 2015 and end with 2018 values. Based on this dataset, equation modelling and machine learning models can be built for water quality assessment.
DOI: 10.17632/ybjs4ppyzf.2
title: Wheat Water Stress detection using chlorophyll fluorescence image processing
DOI: 10.17632/47ypvbx9vm.1
title: PoroStrip_v1.0 – A code to compute anisotropic porosity fields for urban flood modelling

description: This repository contains the code "PoroStrip_v1.0". It allows obtaining the spatially-distributed porosity fields for anisotropic porosity models in differential form, that can be used for simulating large-scale urban floods. The method and its implementation are described in the paper: Ferrari, A., and Viero, D.P., 2020. Floodwater pathways in urban areas: a method to compute porosity fields for anisotropic subgrid models in differential form, Journal of Hydrology 589, 125193, https://doi.org/10.1016/j.jhydrol.2020.125193. The repository also contains the input (and some output) files for the three case studies described in the above paper. This repository contains the following folders: ⇒"Code_Porostrip_v1.0" with the code "PoroStrip_v1.0" available as both compiled and source code. The folder contains: •"Executable" with: -the Win86 executable file "PoroStrip_v1.0.exe"; -the "PoroStrip_v1.0_cfg.txt" configuration file, which determines the output files to be created; •"Source" with the source code (ASCII files), where some routines and intrinsic routine calls are specific for Intel Fortan; •the Intel Fortran project files. ⇒"CaseStudy1_Toce", "CaseStudy2_Spinea", and "CaseStudy3_Palmanova" folders, referring to the case study described in the companion paper. Each case study folder contains: •"input" with the files that have to be provided to the code as inputs: -the .bln file with the coordinates of the obstacle footprints; -the .asc files with the characteristics of the chosen (coarse) grid, structured as the header of ESRI ASCII grid files (xLLcenter, yLLcenter, cellsize, ncols, nrows, NODATA_VALUE); •"ouput" with some of the output files produced by the code for each tested grid size and strip width; •"visualization": the .dxf files produced by the code have been modified with AutoCAD Map3D to produce a .pdf file helpfull for the overall visualization of the results (e.g. location of the obstacles and resulting roseplots of conveyance porosity). The alternative .dxf version generated with AutoCAD is denoted by the "_prnt" suffix ("_prnt.dxf"). The .pdf files have been structured with the following switchable layers: -CELLS showing the (coarse) grid cells (pink-lined squares); -OBSTACLES representing the building footprint (gray areas); -PSI_L showing the L principal direction of maximum conveyance (blue segments); -PSI_T showing the T principal direction othogonal to L (red segments); -ROSEPLOTS representing the Psi roseplots (green lines). ⇒the "Layers_PorosityMaps.las" that can be used to assign proper styles to the AutoDesk AutoCAD layers of the dxf files produced by the "Porostrip_v1.0.exe" code. See also the "README.txt" file contained in this repository.
DOI: 10.17632/tmrs92j7pv.15
title: COVID-19: Time Series Datasets India versus World

description: This dataset consists of COVID-19 time series data of India since 24th March 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : nicresearchgroup@gmail.com) for more details. . [Dataset is updated Thrice a Week]
DOI: 10.17632/h25frb4wzc.1
title: Air pollution

description: These data are collected for the research of air pollution during COVID-19 in Nepal.
DOI: 10.17632/6z5zkzb3hv.1
title: Omics-driven systems interrogation of metabolic dysregulationin COVID-19 pathogenesis

description: Systematic analyses of metabolic alterations during COVID-19 pathogenesis will uncover candidate pathways implicated in disease progression. We utilize a combination of targeted and untargeted tandem mass spectrometry for quantitative profiling of plasma lipidome and metabolome in mild, moderate and severe COVID-19 patients and healthy controls. We found that the plasma lipidome of COVID-19 resembled that of monosialodihexosyl gangliosides (GM3)-enriched exosomes, with enhanced levels of sphingomyelins (SMs) and GM3s. We conducted systems evaluation of metabolic dysregulation in COVID-19 using multiscale embedded differential correlation networks, and uncovered two lipid modules implicating bis(monoacylglyero)phosphate (BMPs)-mediated lipid sorting in multivesicular bodies (MVBs), and the negative co-regulation between GM3s and phosphatidylserines (PSs) in COVID-19 pathogenesis. We validated our postulations drawn based on plasma lipid changes using exosomes isolated from the same cohort. We measured exosome lipids and demonstrated that GM3s were increasingly enriched in the exosomes of COVID-19 patients with elevating disease severity. The levels of lipids and metabolotes are presented in the attached excel file. The level of lipids was expressed as nmol/L, while the level of polar metabolites was expressed as peak area (intensity).
DOI: 10.17632/jpfjyycwck.2
title: RR intervals of 13 healthy subjects in rest and mental stress
DOI: 10.17632/jpfjyycwck.1
title: RR intervals of 13 healthy subjects in rest and mental stress

description: This database of RR intervals in healthy subjects at rest and mental stress was used in the article "Introduction of Application of Gini Coefficient to Heart Rate Variability Spectrum for Mental Stress Evaluation" https://doi.org/10.5935/abc.20190185 (https://www.researchgate.net/publication/335283138_Introduction_of_Application_of_Gini_Coefficient_to_Heart_Rate_Variability_Spectrum_for_Mental_Stress_Evaluation ) , is available to the scientific community for academic uses. In case of using the data provided you should cite this article. The authors
DOI: 10.17632/tw46ddmzgw.1
title: COMPARISON BETWEEN THE VENOARTERIAL CARBON DIOXIDE DIFFERENCE AND LACTATE AS A MARKER IN SEPTIC SHOCK

description: Background: The venoarterial carbon dioxide difference, or P(v-a)CO2, is a marker of tissue perfusion as well lactate. International guidelines use lactate as a target in management algorithms of patients with septic shock. Objective: Determine whether there is a difference in the behavior of P(v-a)CO2 and venous lactate in patients with septic shock during their evolution. Methods: A case-control study from a historical cohort was conducted. Results from 62 patients admitted to ICU in a third level hospital were included. They were divided into two groups based on their outcome (survivors or deaths). Lactate samples and P(v-a)CO2 calculation were collected every 24 hours from admission to discharge. The results were analyzed departing from the outcome. A Student’s T was performed on the samples of each day looking for a difference between both groups. A Pearson’s r was performed looking for a relationship between P(v-a)CO2 and lactate. Results: A significant elevation of lactate was found in deaths 2 days prior to the outcome (p.= 0.007). No significant difference in P(v-a)CO2 was found between the groups. No relationship between P(v-a)CO2 and lactate was found (r= 0.01). Conclusions: Lactate elevation before death may be an indicator of poor prognosis. Prior to outcome, P(v-a)CO2 does not act in the same way. Keywords: septic shock, venous-to-arterial carbon dioxide difference (P(v-a)CO2), lactate
DOI: 10.17632/cdfbftykp7.3
title: Dataset measuring organizational culture, motivation, commitmen and interpersonal communication based on the multicultural teacher performance

description: The data presented an examination of the multicultural teacher performance in Indonesia based on organizational culture, motivation, commitmen and interpersonal communication. The total number of participants was 100 multicultural teachers in Indonesia. A sample of participants from six school levels that were four multicultural elementary schools, one multicultural junior high school, and one multicultural high school. By documenting data information, the data article reveals the demographic characteristics of the participants, the reliability, and correlation of the actions involved. Data analysis can provide insight into the determining factors and predict the behavior of Islamic ethical work. Furthermore, the data will be useful for researchers and policymakers who are interested to know the organizational culture, motivation,commitmen interpersonal communication in today's multicultural schools in Indonesia. It can also be used as a reference to develop interventions, promoting, and facilitating performance in multicultural workplaces.
DOI: 10.17632/cdfbftykp7.4
title: Dataset on the performance of multicultural teachers with measures organizational culture, motivation, commitment and interpersonal communication

description: The data article investigates the performance of multicultural teachers with measures organizational culture, motivation, commitment and interpersonal communication. The total number of participants was one hundred 100 survey questionnaires from multicultural teachers in Yogyakarta, Indonesia. A sample of participants from six school levels that were four multicultural elementary schools, one multicultural junior high school, and one multicultural high school. By documenting data information, the data article This article includes information on reliability and factor loadings, as well as results of regression analyses.
DOI: 10.17632/cdfbftykp7.5
title: Dataset on the performance of multicultural teachers with measures organizational culture, motivation, commitment and interpersonal communication

description: The data article investigates the performance of multicultural teachers with measures organizational culture, motivation, commitment and interpersonal communication. The total number of participants was one hundred 100 survey questionnaires from multicultural teachers in Yogyakarta, Indonesia. A sample of participants from six school levels that were four multicultural elementary schools, one multicultural junior high school, and one multicultural high school. By documenting data information, the data article This article includes information on reliability and factor loadings, as well as results of regression analyses.
DOI: 10.17632/yxzzy26hbd.1
title: "Proline-Rich Motifs Control G2-CDK Target Phosphorylation and Priming an Anchoring Protein for Polo Kinase Localization". Örd et al.

description: Unprocessed autoradiographs, western blot images and confocal microscopy images from Örd et al "Proline-Rich Motifs Control G2-CDK Target Phosphorylation and Priming an Anchoring Protein for Polo Kinase Localization"
DOI: 10.17632/p4tx2k852r.1
title: SDCOR Synthetic Datasets

description: SDCOR: Scalable Density-based Clustering for Local Outlier Detection in Massive-Scale Datasets Link to arXiv e-print: https://arxiv.org/pdf/2006.07616.pdf This is a method for local outlier detection in massive-scale datasets, which is based on a batch-wise density-based clustering approach. SDCOR consists of three consecutive phases: 1) Sampling; 2) Scalable Clustering; and 3) Scoring. In the Sampling phase, a preliminary random sampling is conducted to obtain an abstraction of the entire data, named temporary clustering model; and also to acquire some information over the necessary parameters for the clustering procedures. Then, the Scalable Clustering phase will commence and the input data will be processed in chunks; as for each chunk, the temporary clustering model gets gradual updates, till it turns into the final clustering model after processing the last chunk. Ultimately, at the last phase of the algorithm, regarding the final clustering model attained through the batch-wise clustering, and also the Mahalanobis distance criterion, each object is given an outlying score called its local Mahalanobis distance. Each synthetic dataset in this repository is made of some Gaussian clusters with arbitrary mean vectors, far enough from each other, to impede probable overlappings among multidimensional clusters. For each of these artificial datasets, a specific amount of noises are added around every cluster in the corresponding data; and moreover, the outliers "truth" is available along with each synthetic data. For every artificial dataset, there is a n-by-p matrix of dataset X, along with the n-by-1 vector y of outlier labels, all together as a single binary MAT-file. We have implemented our code in MATLAB 9, which due to becoming reproducible, is accessible through our GitHub page (https://github.com/sana33/SDCOR).
DOI: 10.17632/8gb2625fvy.1
title: MATLAB script to generate gradient and radiofrequency waveforms for several 2D frequency-modulated pulses.

description: This is a simple MATLAB script to generate the gradient and RF waveforms for several 2D FM pulses. In MATLAB, type "help twoD_pulsegen" for an explanation of how to implement this script.
DOI: 10.7910/dvn/ojn6cq/jktdro
title: Supplementary figure 3.tiff

description: Subgroup analysis
DOI: 10.17632/wztrr8v7ps.1
title: GIT/PIX condensates are modular and ideal for distinct compartmentalized cell signaling

description: Original imaging data for the study entitled "GIT/PIX condensates are modular and ideal for distinct compartmentalized cell signaling"