cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5535744
title: Crowd4SDG - Crowdsourced image classification and damage assessment

description: This data set contains crowdsourced classification and damage assessment of images of an earthquake extracted from social media. <br> <br> A data set of 907 images posted on Twitter related to the 2019 Albanian Earthquake, that are filtered and pre-classified using an automated technique is cross-validated for accuracy by two different crowds. One, digital humanitarian volunteers using the crowdsourcing platform CROWD4EMS and another, paid micro-taskers of the Amazon Mechanical Turk. In order to compare and evaluate the efficiency and accuracy of the volunteers and the paid micro taskers, ground truth is established with the help of a team of experts, who validated the same set of data. <br> <br> <strong>Parameters considered for volunteer contributions:</strong> The dataset was imported to the Crowd4EMS platform for Crowd contribution. In the forum, each volunteer will see the image to be validated along with the tweet text and the link to the original tweet. The user has to validate whether the given image is <em>relevant or</em> <em>irrelevant</em> to the disaster. In case of doubt, the user can refer to the tutorial explaining the relevance or skip the task. Once the image's relevance is validated, the user will be asked to label the <em>severity</em> of the impact, as seen in the image. The Automated algorithm has pre-classified the images as <em>severe </em>and <em>minimal </em>damage. The Crowd4EMS platform lets the volunteer label them as '<em>severe damage</em>,' <em>moderate damage'</em>,' <em>minimal damage', </em>and' <em>no damage'.</em> Each task has to be answered <em>at least three times</em>, and the final consensus is taken as per the<em> inter-rater agreement. </em><br> <br> <strong>Parameters considered for micro-taskers contribution:</strong> The dataset was imported to the <em>Amazon Mechanical Turk</em> platform for Crowd contribution. In the platform, each worker will see only the image that is to be categorised as follows: The user has to validate whether the given image depicts <em>severe damage, moderate damage, minimal damage, no damage </em>or <em>irrelevant</em> to the disaster. Each task has to be answered <em>at least ten times</em>, and the final consensus is taken as per the<em> inter-rater agreement. </em><br> <br> <strong>Acknowledgements:</strong> We want to thank Muhammad Imran of Qatar Computing Research Institute for sharing their pre-filtered social media imagery dataset on the Albanian earthquake from the Artificial Intelligence for Disaster Response (AIDR) Platform. We would also like to extend our gratitude to the volunteers for their contribution on the Crowd4EMS Platform.<br>
description: {"references": ["Ravi Shankar A, Fernandez-Marquez JL, Pernici B, Scalia G, Mondardini MR, Di Marzo Serugendo G. Crowd4Ems: A crowdsourcing platform for gathering and geolocating social media content in disaster response. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019 Aug 22;42:331-40."]}
DOI: 10.5281/zenodo.5535743
title: Crowd4SDG - Crowdsourced image classification and damage assessment

description: This data set contains crowdsourced classification and damage assessment of images of an earthquake extracted from social media. <br> <br> A data set of 907 images posted on Twitter related to the 2019 Albanian Earthquake, that are filtered and pre-classified using an automated technique is cross-validated for accuracy by two different crowds. One, digital humanitarian volunteers using the crowdsourcing platform CROWD4EMS and another, paid micro-taskers of the Amazon Mechanical Turk. In order to compare and evaluate the efficiency and accuracy of the volunteers and the paid micro taskers, ground truth is established with the help of a team of experts, who validated the same set of data. <br> <br> <strong>Parameters considered for volunteer contributions:</strong> The dataset was imported to the Crowd4EMS platform for Crowd contribution. In the forum, each volunteer will see the image to be validated along with the tweet text and the link to the original tweet. The user has to validate whether the given image is <em>relevant or</em> <em>irrelevant</em> to the disaster. In case of doubt, the user can refer to the tutorial explaining the relevance or skip the task. Once the image's relevance is validated, the user will be asked to label the <em>severity</em> of the impact, as seen in the image. The Automated algorithm has pre-classified the images as <em>severe </em>and <em>minimal </em>damage. The Crowd4EMS platform lets the volunteer label them as '<em>severe damage</em>,' <em>moderate damage'</em>,' <em>minimal damage', </em>and' <em>no damage'.</em> Each task has to be answered <em>at least three times</em>, and the final consensus is taken as per the<em> inter-rater agreement. </em><br> <br> <strong>Parameters considered for micro-taskers contribution:</strong> The dataset was imported to the <em>Amazon Mechanical Turk</em> platform for Crowd contribution. In the platform, each worker will see only the image that is to be categorised as follows: The user has to validate whether the given image depicts <em>severe damage, moderate damage, minimal damage, no damage </em>or <em>irrelevant</em> to the disaster. Each task has to be answered <em>at least ten times</em>, and the final consensus is taken as per the<em> inter-rater agreement. </em><br> <br> <strong>Acknowledgements:</strong> We want to thank Muhammad Imran of Qatar Computing Research Institute for sharing their pre-filtered social media imagery dataset on the Albanian earthquake from the Artificial Intelligence for Disaster Response (AIDR) Platform. We would also like to extend our gratitude to the volunteers for their contribution on the Crowd4EMS Platform.<br>
description: {"references": ["Ravi Shankar A, Fernandez-Marquez JL, Pernici B, Scalia G, Mondardini MR, Di Marzo Serugendo G. Crowd4Ems: A crowdsourcing platform for gathering and geolocating social media content in disaster response. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019 Aug 22;42:331-40."]}
DOI: 10.25405/data.ncl.16635694
title: Astrocytic changes in mitochondrial OXPHOS protein expression in Parkinson's disease

description: The astrocyte data uploaded includes the original dataset generated from imaging mass cytometry and its normalised z score data, alongside the area analysis data. The study has included a cohort of post-mortem human midbrain section from idiopathic PD cases (n=9) and individuals without neurological disease as controls (n=10). This dataset were used to investigate the expression change of mitochondrial OXPHOS proteins on Parkinson's astrocytes in the substantial nigra. The use of human tissue sections adhered to the Medical Research Council’s (MRC) guidelines on the use of human tissue in medical research. This work is currently under corrections with the journal, Movement Disorders.
DOI: 10.25405/data.ncl.16635694.v1
title: Astrocytic changes in mitochondrial OXPHOS protein expression in Parkinson's disease

description: The astrocyte data uploaded includes the original dataset generated from imaging mass cytometry and its normalised z score data, alongside the area analysis data. The study has included a cohort of post-mortem human midbrain section from idiopathic PD cases (n=9) and individuals without neurological disease as controls (n=10). This dataset were used to investigate the expression change of mitochondrial OXPHOS proteins on Parkinson's astrocytes in the substantial nigra. The use of human tissue sections adhered to the Medical Research Council’s (MRC) guidelines on the use of human tissue in medical research. This work is currently under corrections with the journal, Movement Disorders.
DOI: 10.5281/zenodo.5036070
title: Wave Parameters - North Atlantic Ocean - Period 2091-2100 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS

description: <strong>Wave Model:</strong> WAVEWATCH_III - version number 5.16 <strong>Global driver: </strong> ACCESS (Australian Community Climate and Earth System Simulator) <strong>Variables:</strong> Significant Wave Height Mean period, peak frequency Mean wave direction 0.25° x 0.25° horizontal resolution - 3h time resolution <strong>Region: </strong> southernmost latitude = 10° northernmost latitude = 42° westernmost longitude = -70° easternmost longitude = -5° <strong>360-day calendar</strong> <strong>NetCDF format</strong>
description: The data can also be downloaded from the SOCLIMPACT FTP Repository hosted at https://giotto.casaccia.enea.it/soclimpact/index.php/
DOI: 10.5281/zenodo.5036069
title: Wave Parameters - North Atlantic Ocean - Period 2091-2100 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS

description: <strong>Wave Model:</strong> WAVEWATCH_III - version number 5.16 <strong>Global driver: </strong> ACCESS (Australian Community Climate and Earth System Simulator) <strong>Variables:</strong> Significant Wave Height Mean period, peak frequency Mean wave direction 0.25° x 0.25° horizontal resolution - 3h time resolution <strong>Region: </strong> southernmost latitude = 10° northernmost latitude = 42° westernmost longitude = -70° easternmost longitude = -5° <strong>360-day calendar</strong> <strong>NetCDF format</strong>
description: The data can also be downloaded from the SOCLIMPACT FTP Repository hosted at https://giotto.casaccia.enea.it/soclimpact/index.php/
DOI: 10.5281/zenodo.4979539
title: Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS

description: <strong>Wave Model:</strong> WAVEWATCH_III - version number 5.16 <strong>Global driver: </strong> ACCESS (Australian Community Climate and Earth System Simulator) <strong>Variables:</strong> Significant Wave Height Mean period, peak frequency Mean wave direction 0.25° x 0.25° horizontal resolution - 3h time resolution <strong>Region: </strong> southernmost latitude = 10° northernmost latitude = 42° westernmost longitude = -70° easternmost longitude = -5° <strong>360-day calendar</strong> <strong>NetCDF format</strong>
description: The data can also be downloaded from the SOCLIMPACT FTP Repository hosted at https://giotto.casaccia.enea.it/soclimpact/index.php/
DOI: 10.5281/zenodo.4979538
title: Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS

description: <strong>Wave Model:</strong> WAVEWATCH_III - version number 5.16 <strong>Global driver: </strong> ACCESS (Australian Community Climate and Earth System Simulator) <strong>Variables:</strong> Significant Wave Height Mean period, peak frequency Mean wave direction 0.25° x 0.25° horizontal resolution - 3h time resolution <strong>Region: </strong> southernmost latitude = 10° northernmost latitude = 42° westernmost longitude = -70° easternmost longitude = -5° <strong>360-day calendar</strong> <strong>NetCDF format</strong>
description: The data can also be downloaded from the SOCLIMPACT FTP Repository hosted at https://giotto.casaccia.enea.it/soclimpact/index.php/
DOI: 10.5281/zenodo.5540556
title: Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: HadGEM

description: <strong>Wave Model:</strong> WAVEWATCH_III - version number 5.16 <strong>Global driver: </strong> HadGEM (Hadley Centre Global Environmental Model) <strong>Variables:</strong> Significant Wave Height Mean period, peak frequency Mean wave direction 0.25° x 0.25° horizontal resolution - 3h time resolution <strong>Region: </strong> southernmost latitude = 10. northernmost latitude = 42. westernmost longitude = -70. easternmost longitude = -5. <strong>360-day calendar</strong> <strong>NetCDF format</strong>
description: The data can also be downloaded from the SOCLIMPACT FTP Repository hosted at https://giotto.casaccia.enea.it/soclimpact/index.php/
DOI: 10.5281/zenodo.5540555
title: Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: HadGEM

description: <strong>Wave Model:</strong> WAVEWATCH_III - version number 5.16 <strong>Global driver: </strong> HadGEM (Hadley Centre Global Environmental Model) <strong>Variables:</strong> Significant Wave Height Mean period, peak frequency Mean wave direction 0.25° x 0.25° horizontal resolution - 3h time resolution <strong>Region: </strong> southernmost latitude = 10. northernmost latitude = 42. westernmost longitude = -70. easternmost longitude = -5. <strong>360-day calendar</strong> <strong>NetCDF format</strong>
description: The data can also be downloaded from the SOCLIMPACT FTP Repository hosted at https://giotto.casaccia.enea.it/soclimpact/index.php/
DOI: 10.17632/zhbpyvt4g9.1
title: A Dataset for Buffering Delays Due to the Interaction Between the Nagle Algorithm and the Delayed Acknowledgement Algorithm in Cyber-Physical Systems Communication

description: Here, we provide the research community with a data set for the buffering delays that data packets experience at TCP sending side in the realm of Cyber-Physical Systems (CPSs). We focus on the buffering that occurs at the sender side due to the the adverse interaction between the Nagle algorithm and the delayed acknowledgement algorithm, which both were originally introduced into TCP to prevent sending out many small-sized packets over the network.

The data set is collected using four real-life operating systems: Windows, Linux, FreeBSD, and QNX (a real-time operating system). In each scenario, there are three separate different (virtual) machines running various operating systems. One machine, or an end-host, acts a data source, another acts as a data sink, and a third acts a network emulator that introduces artificial propagation delays between the source and the destination.           

To measure buffering delay at the sender side, we record for each sent packet the two time instants: when the packet is first generated at the application layer, and when it is actually sent on the physical network. In each case, 10 different independent experiment replications/runs are executed. 

Here, we provide the full distribution of all delay samples represented by the cumulative distribution function (CDF).

The data exhibited here gives an impression of the amount and scale of the delay occurring at sender-side in TCP. More importantly, the data can be used investigate to what degree these delays affect the performance of cyber-physical systems or other real-time applications employing TCP.
DOI: 10.17632/494w7kjd56.1
title: Patients with alopecia areata are at risk of endothelial dysfunction

description: Comparison of patients with alopecia areata and healthy controls.
DOI: 10.5281/zenodo.4015100
title: Silicon-29 NMR Experimental Datasets used in Statistical Learning of NMR tensors from 2D Isotropic/Anisotropic Correlation Nuclear Magnetic Resonance Spectra

description: Processed silicon-29 Magic-Angle Flipping and Magic-Angle Turning Nuclear Magnetic Resonance spectra used as input to the smooth-LASSO linear inversion algorithm along with their corresponding NMR tensor parameter distributions as described in the paper "Statistical Learning of NMR tensors from 2D Isotropic/Anisotropic Correlation Nuclear Magnetic Resonance Spectra", by Srivastava and Grandinetti. Files with names containing "MAF" or "MAT" are the corresponding experimental Si-29 NMR MAF or MAT dataset on the composition given in the filename. Files with names containing "inverse" are the corresponding NMR tensor parameter distributions obtained from the inversion of the corresponding experimental MAF and MAT dataset with the composition given in the filename. <br> Details of the csdf dataset format are given in <em>PLOS ONE,</em> 15(1): e0225953 (2020), "Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data," D. Srivastava, T. Vosegaard, D. Massiot, and P.J. Grandinetti. The data within csdf files can be accessed with the Python package csdmpy, or other CSDM-compliant software.
DOI: 10.5281/zenodo.3964530
title: Silicon-29 NMR Experimental Datasets used in Statistical Learning of NMR tensors from 2D Isotropic/Anisotropic Correlation Nuclear Magnetic Resonance Spectra

description: Processed silicon-29 Magic-Angle Flipping and Magic-Angle Turning Nuclear Magnetic Resonance spectra used as input to the smooth-LASSO linear inversion algorithm along with their corresponding NMR tensor parameter distributions as described in the paper "Statistical Learning of NMR tensors from 2D Isotropic/Anisotropic Correlation Nuclear Magnetic Resonance Spectra", by Srivastava and Grandinetti. Files with names containing "MAF" or "MAT" are the corresponding experimental Si-29 NMR MAF or MAT dataset on the composition given in the filename. Files with names containing "inverse" are the corresponding NMR tensor parameter distributions obtained from the inversion of the corresponding experimental MAF and MAT dataset with the composition given in the filename. <br> Details of the csdf dataset format are given in <em>PLOS ONE,</em> 15(1): e0225953 (2020), "Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data," D. Srivastava, T. Vosegaard, D. Massiot, and P.J. Grandinetti. The data within csdf files can be accessed with the Python package csdmpy, or other CSDM-compliant software.
DOI: 10.5281/zenodo.5541663
title: Data from: Where are the beachmasters? Unexpectedly weak polygyny among southern elephant seals on a South Shetland Island

description: <strong>Abstract</strong> Intraspecific variation in animal mating systems can have important implications for ecological, evolutionary and demographic processes in wild populations. For example, patterns of mating can impact social structure, dispersal, effective population size and inbreeding. However, few species have been studied in sufficient detail to elucidate mating system plasticity and its dependence on ecological and demographic factors. Southern elephant seals (Mirounga leonina) have long been regarded as a textbook example of a polygynous mating system, with dominant ‘beachmaster’ males controlling harems of up to several hundred females. However, behavioural and genetic studies have uncovered appreciable geographic variation in the strength of polygyny among elephant seal populations. We therefore used molecular parentage analysis to investigate patterns of parentage in a small satellite colony of elephant seals at the South Shetland Islands. We hypothesised that dominant males would be able to successfully monopolise the relatively small numbers of females present in the colony, leading to relatively high levels of polygyny. A total of 424 individuals (comprising 33 adult males, 101 adult females and 290 pups) sampled over eight years were genotyped at 20 microsatellites and reproductive success was analysed by genetically assigning parents. Paternity could only be assigned to 31 pups (10.7%), despite our panel of genetic markers being highly informative and the genotyping error rate being very low. The strength of inferred polygyny was weak in comparison to previous genetic studies of the same species, with the most successful male fathering only seven pups over the entire course of the study. Our results show that, even in a species long regarded as a model for extreme polygyny, male reproductive skew can vary substantially among populations.
DOI: 10.5281/zenodo.5541662
title: Data from: Where are the beachmasters? Unexpectedly weak polygyny among southern elephant seals on a South Shetland Island

description: <strong>Abstract</strong> Intraspecific variation in animal mating systems can have important implications for ecological, evolutionary and demographic processes in wild populations. For example, patterns of mating can impact social structure, dispersal, effective population size and inbreeding. However, few species have been studied in sufficient detail to elucidate mating system plasticity and its dependence on ecological and demographic factors. Southern elephant seals (Mirounga leonina) have long been regarded as a textbook example of a polygynous mating system, with dominant ‘beachmaster’ males controlling harems of up to several hundred females. However, behavioural and genetic studies have uncovered appreciable geographic variation in the strength of polygyny among elephant seal populations. We therefore used molecular parentage analysis to investigate patterns of parentage in a small satellite colony of elephant seals at the South Shetland Islands. We hypothesised that dominant males would be able to successfully monopolise the relatively small numbers of females present in the colony, leading to relatively high levels of polygyny. A total of 424 individuals (comprising 33 adult males, 101 adult females and 290 pups) sampled over eight years were genotyped at 20 microsatellites and reproductive success was analysed by genetically assigning parents. Paternity could only be assigned to 31 pups (10.7%), despite our panel of genetic markers being highly informative and the genotyping error rate being very low. The strength of inferred polygyny was weak in comparison to previous genetic studies of the same species, with the most successful male fathering only seven pups over the entire course of the study. Our results show that, even in a species long regarded as a model for extreme polygyny, male reproductive skew can vary substantially among populations.
DOI: 10.5281/zenodo.5541642
title: Acanthamoeba castellanii genome assembly and infection by Legionella pneumophila

description: Data associated with the publication "<em>Regulation of the Acanthamoeba castellanii genome upon infection by Legionella pneumophila</em>". The record contains 4 archives, each associated with a github repository, and a "shared assets" archive, which contains processed files used by some repositories. The code from github repositories is embedded in each tarball, along with input and output data. Analyses are organized as independent snakemake pipelines for each part. For convenient reanalysis, genomes, annotations and merged contact maps used in the publication can be found in the `data/input` folder of Acastellanii_genome_analysis.tar.gz. The infection results are located in the `data/output` folder of Acastellanii_legionella_infection.tar.gz. <strong>Hybrid genome assembly:</strong> Genome assembly pipeline code and output data used for the assembly of 2 <em>A. castellanii</em> strains (Neff and C3) through a hybrid pipeline combining Illumina shotgun, Hi-C and Oxford Nanopore long reads. Github: https://github.com/cmdoret/Acastellanii_hybrid_assembly Archive: Acastellanii_hybrid_assembly.tar.gz <strong>Genome annotation:</strong> Genome annotation pipeline used for functional annotation of <em>A. castellanii</em> strains C3 and Neff, and associated output files. Github: https://github.com/cmdoret/Acastellanii_genome_annotation Archive: Acastellanii_genome_annotation.tar.gz <strong>Genome analyses:</strong> Code and data related to general analyses of genomic properties of <em>A. castellanii</em> strains C3 and Neff. Github: https://github.com/cmdoret/Acastellanii_genome_analysis Archive: Acastellanii_genome_analysis.tar.gz <strong>Infection analyses:</strong> Code and data related to the analysis of structural changes in the <em>A. castellanii</em> C3 genome during infection by <em>L. pneumophila</em>. Github: https://github.com/cmdoret/Acastellanii_legionella_infection Archive: Acastellanii_legionella_infection.tar.gz <br> <strong>Shared assets:</strong> This archive contains processed files (genomes, annotations, Hi-C matrices, differential expression results) which can be useful for reanalysis, and are automatically pulled when executing the pipeline of some repositories. Archive: shared_assets.tar.gz <br>