cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3275151
title: Towards Symmetry Driven and Nature Inspired UVA Filter Design

description: In plants, sinapate esters offer crucial protection from the deleterious effects of ultraviolet radiation exposure. These esters are a promising foundation for designing UV filters, particularly for the UVA region (400 – 315 nm), where adequate photoprotection is currently lacking. Whilst sinapate esters are highly photostable due to a cis-trans (and vice versa) photoisomerization, the cis-isomer can display increased genotoxicity; an alarming concern for current cinnamate ester-based human sunscreens. To eliminate this potentiality, here we synthesize a sinapate ester with equivalent cis- and trans-isomers. We investigate its photostability through innovative ultrafast spectroscopy on a skin mimic, thus modelling the as close to true environment of sunscreen formulas. These studies are complemented by assessing endocrine disruption activity and antioxidant potential. We contest, from our results, that symmetrically functionalized sinapate esters may show exceptional promise as nature-inspired UV filters in next generation sunscreen formulations.
DOI: 10.21227/wj7z-a643
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras.
DOI: 10.21227/cme5-zm18
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras. When citing this dataset, please cite both the dataset and the article that introduces it: Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning. Instructions will be added here once the article is published.
DOI: 10.5281/zenodo.3506764
title: Bioprospecting for bioactive peptide production by lactic acid bacteria isolated from fermented dairy food

description: Table S1 of the review article "Bioprospecting for bioactive peptide production by lactic acid bacteria isolated from fermented dairy food" by Davide Tagliazucchi, Serena Martini and Lisa Solieri
DOI: 10.5281/zenodo.3506765
title: Bioprospecting for bioactive peptide production by lactic acid bacteria isolated from fermented dairy food

description: Table S1 of the review article "Bioprospecting for bioactive peptide production by lactic acid bacteria isolated from fermented dairy food" by Davide Tagliazucchi, Serena Martini and Lisa Solieri
DOI: 10.7910/dvn/eoyzkh/d0lirg
title: all_studies_game_data.tab

description: data file that includes the group level data (i.e., collective).
DOI: 10.7910/dvn/eoyzkh/ej4jmw
title: Mechanisms-fig5_table-S5.ipynb

description: :unav
DOI: 10.7910/dvn/eoyzkh/fql01c
title: Results-fig2-tables-S1-S4.ipynb

description: :unav
DOI: 10.7910/dvn/eoyzkh/hjyckp
title: Results_fig3.ipynb

description: :unav
DOI: 10.7910/dvn/eoyzkh/jksdvw
title: Results-fig4.ipynb

description: :unav
DOI: 10.7910/dvn/eoyzkh/m6zniw
title: tables-S1-S3.ipynb

description: :unav
DOI: 10.7910/dvn/eoyzkh/nmoe6w
title: all_studies_round_data.tab

description: data file at the individual level
DOI: 10.7910/dvn/eoyzkh/yaslvt
title: generate_group_data.ipynb

description: generates the "all_studies_game_data.csv" from the "all_studies_round_data.csv"
DOI: 10.3886/e114982v1
title: The Battle for the Public Perception

description: Abstract for project: The sudden increase in immigrants crossing the EU external borders in 2015 kicked off a political debate about the causes, consequences, and solutions to the following refugee crisis. The public perception of immigration being a major issue increased accordingly throughout the member states, even though some countries were only slightly affected. This study analyses newspaper content from eight EU countries from 2012- 2017 and combines ten waves of Eurobarometer surveys with information on the changes in asylum seekers, media agenda and framing of the immigration issue. The results suggest that both the media’s agenda and framing played a significant role in constructing the public perception of the immigration issue and preference for additional measures. Further, investigating the moderating effect of positioning on the left-right political spectrum reveals polarisation effects as citizens of the right-wing became increasingly more likely to support additional measures relative to the centre and left-wing citizens
DOI: 10.21227/ere6-p914
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras. When citing this dataset, please cite both the dataset and the article that introduces it: Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning. TODO: add instructions once the article is published.
DOI: 10.3886/e114982
title: The Battle for the Public Perception

description: Abstract for project: The sudden increase in immigrants crossing the EU external borders in 2015 kicked off a political debate about the causes, consequences, and solutions to the following refugee crisis. The public perception of immigration being a major issue increased accordingly throughout the member states, even though some countries were only slightly affected. This study analyses newspaper content from eight EU countries from 2012- 2017 and combines ten waves of Eurobarometer surveys with information on the changes in asylum seekers, media agenda and framing of the immigration issue. The results suggest that both the media’s agenda and framing played a significant role in constructing the public perception of the immigration issue and preference for additional measures. Further, investigating the moderating effect of positioning on the left-right political spectrum reveals polarisation effects as citizens of the right-wing became increasingly more likely to support additional measures relative to the centre and left-wing citizens
DOI: 10.21227/cy1m-2k75
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras. When citing this dataset, please cite both the dataset and the article that introduces it: Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning. Instructions will be added here once the article is published.
DOI: 10.25625/1p0qmn
title: Protokoll_Myokardbiopsien

description: Dieses Protokoll beschreibt die Gewinnung und Verarbeitung von Myokardbiopsien im Rahmen der Aortenstenosestudie.
DOI: 10.25625/1p0qmn/tqvlri
title: Protokoll_Myokardbiopsien.pdf

description: This protocol describes the collection and processing of left-ventricular myocard biopsies.
DOI: 10.24378/exe.1943
title: Biofilms facilitate cheating and social exploitation of β-lactam resistance in Escherichia coli (dataset)

description: Gram-negative bacteria such as Escherichia coli commonly resist β-lactam antibiotics using plasmid-encoded β-lactamase enzymes. Bacterial strains that express β-lactamases have been found to detoxify liquid cultures and thus to protect genetically susceptible strains, constituting a clear laboratory example of social protection. These results are not necessarily general; on solid media, for instance, the rapid bactericidal action of β-lactams largely prevents social protection. Here we tested the hypothesis that the greater tolerance of biofilm bacteria for β-lactams would facilitate social interactions. We used a recently isolated E. coli strain, capable of strong biofilm formation, to compare how cooperation and exploitation in colony biofilms and broth culture drives the dynamics of a non-conjugative plasmid encoding a clinically important β-lactamase. Susceptible cells in biofilms were tolerant of ampicillin - high doses and several days of exposure were required to kill them. In support of our hypothesis, we found robust social protection of susceptible E. coli in biofilms, despite fine-scale physical separation of resistant and susceptible cells and lower rates of production of extracellular β-lactamase. In contrast, social interactions in broth were restricted to a relatively narrow range of ampicillin doses. Our results show that β-lactam selection pressure on Gram-negative biofilms leads to cooperative resistance characterized by a low equilibrium frequency of resistance plasmids, sufficient to protect all cells.
description: This is the dataset used for the Amanatidou et al. (2019) article "Biofilms facilitate cheating and social exploitation of β-lactam resistance in Escherichia coli" published in npj Biofilms and Microbiomes.
description: Raw data for figures 1, and 3 through 6, for accepted MS. No special software is required to view this views. Data files are comprised of tab or comma delimited text.
DOI: 10.7910/dvn/9nx6ga/etfhi8
title: fullset_DEIDENTIFIED_2019_10_17.tab

description: :unav
DOI: 10.5066/p9l86og8
title: Water-quality and stream-habitat metrics calculated for the National Water-Quality Assessment Program's Regional Stream Quality Assessment conducted in the southeast United States in support of ecological and habitat stressor models, 2014

description: This data release includes metrics from the Regional Stream Quality Assessment (RSQA) from the Southeast Region for habitat stressors related to water-quality and habitat substrate. The goals of RSQA are to characterize multiple water-quality factors that are stressors to aquatic life -- contaminants, nutrients, sediment, and streamflow alteration -- and to develop a better understanding of the relation of these stressors to ecological conditions in streams throughout the region. In order to characterize water-quality variables and stream-habitat measurements as an aggregation of multiple measurements over a sampling period, and in support of ecological stressor modelling, metrics (summary statistics or indices) were computed from individual results by site using consistent methods over a consistent time frame. Water-quality metrics are based on discrete samples as well as long-term deployed passive samplers.
DOI: 10.25625/vp8dal
title: Blut und Urin Begleitschein

description: Auf diesem Begleitschein zur Blut- und Urinentnahme und -verarbeitung werde wichtige Metadaten zunächst händisch dokumentiert.
DOI: 10.25625/vp8dal/jkls1w
title: Blut und Urin Begleitschein.pdf

description: In this document important metadata can be documented along the collection and processing of blood and urine.
DOI: 10.5281/zenodo.3497461
title: The effect of spatial energy spread on sound image size and speech intelligibility [dataset]

description: This dataset contains the data and supplementary material for the publication titled "The effect of spatial energy spread on sound image size and speech intelligibility" published in the Journal of the Acoustical Society of America. This repository contains the results from the three experiments as well as a figure showing the adaptive tracks of the adaptive procedure and the estimated psychometric functions in experiment 3. The subject identifiers are common across the three experiments. <strong>Experiment 1: </strong> Factors: <em>Subject/Listener, ambisonics order, audio/stimulus type, spatial location of stimulus, room condition, repetition</em> Measured variables: <em>Source image size, source direction (azimuth), source distance</em> <strong>Experiment 2:</strong> Factors: <em>Subject/Listener, ambisonics order, interferer location, room condition</em> Measured variable: <em>Speech reception threshold (SRT)</em> <strong>Experiment 3:</strong> Factors: <em>Subject/Listener, ambisonics order, repetition</em> Measured variable: <em>Speech reception angle (SRA)</em> The figure (exp3_adaptiveTracks.png) shows the adaptive tracks in experiment 3. Each panel shows one of the ambisonics orders. The x-axis is the trial number and the y-axis the separation angle between target and interfering talkers. Each color indicates one subject. The figure (exp3_psychometricFcns.png) shows the estimated psychometric functions in experiment 3. Each panel shows one of the ambisonics orders. The x-axis is the separation angle between target and interfering talkers and the y-axis is the percent correct words. Each color indicates one subject. The black line indicates the median over the subjects and repetitions and the black cross the SRA predicted with this method.
DOI: 10.5281/zenodo.3497462
title: The effect of spatial energy spread on sound image size and speech intelligibility [dataset]

description: This dataset contains the data and supplementary material for the publication titled "The effect of spatial energy spread on sound image size and speech intelligibility" published in the Journal of the Acoustical Society of America. This repository contains the results from the three experiments as well as a figure showing the adaptive tracks of the adaptive procedure and the estimated psychometric functions in experiment 3. The subject identifiers are common across the three experiments. <strong>Experiment 1: </strong> Factors: <em>Subject/Listener, ambisonics order, audio/stimulus type, spatial location of stimulus, room condition, repetition</em> Measured variables: <em>Source image size, source direction (azimuth), source distance</em> <strong>Experiment 2:</strong> Factors: <em>Subject/Listener, ambisonics order, interferer location, room condition</em> Measured variable: <em>Speech reception threshold (SRT)</em> <strong>Experiment 3:</strong> Factors: <em>Subject/Listener, ambisonics order, repetition</em> Measured variable: <em>Speech reception angle (SRA)</em> The figure (exp3_adaptiveTracks.png) shows the adaptive tracks in experiment 3. Each panel shows one of the ambisonics orders. The x-axis is the trial number and the y-axis the separation angle between target and interfering talkers. Each color indicates one subject. The figure (exp3_psychometricFcns.png) shows the estimated psychometric functions in experiment 3. Each panel shows one of the ambisonics orders. The x-axis is the separation angle between target and interfering talkers and the y-axis is the percent correct words. Each color indicates one subject. The black line indicates the median over the subjects and repetitions and the black cross the SRA predicted with this method.
DOI: 10.5281/zenodo.3507827
title: Hui o ka Wai Ola Water Quality Data

description: This dataset is the quarterly report for the Hui o Ka Wai Ola citizen-science based water quality program. Included are physical and chemical data that were collected and analyzed every two or three weeks beginning from June 6, 2016 to September 24, 2019. New results will be posted as available. Please see huiokawaiola.com for more information. The goal of the Hui o Ka Wai Ola program is to increase the capacity for monitoring water quality in Maui coastal waters by generating reliable data to assess long-term water-quality conditions and detect temporal trends. These data augment the data produced by the Hawaii Department of Health Clean Water Branch beach monitoring program on Maui. Please use the most recent version, as it is the latest quality assured data available from the group.
description: Visit huiokawaiola.com for full methods and most recently data and analysis.
DOI: 10.5281/zenodo.3508505
title: Distinguishing mutations and null alleles from genotyping errors using mother progeny comparisons in Brazilian pine (Araucaria angustifolia)

description: the rate of null alleles, mutations and genotyping errors in microsatellite loci, using Araucaria angustifolia, a threatened species, as a case study. We estimated the rates of the different types of genotyping deviations using mother-progeny genotype comparison from 50 seed-trees and their respective progeny (seeds). A total of 2336 A. angustifolia samples were genotyped, and we found that the rate of null alleles was 0.045. From the 1972 mother-progeny comparisons, the overall genotype deviation rate was 1.58%, consisting of 145 inconsistences (mutations), 339 null alleles and 210 genotyping errors. In terms of seed numbers, 128 (6.5%) showed inconsistencies in at least one locus, 118 (6.0%) null alleles, and 321 (16.3%) genotyping errors. This is the first study to describe the inconsistences (mutations) between mother-progeny genotypes for A. angustifolia, and the outcome makes it clear that an understanding of these genotyping deviations must be considered in assessing the accuracy of inferences made based on population genetics analyses.