cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1285/i20356609v12i1p197
title: Populism and Euroscepticism in Podemos and in the Five Star Movement. Faraway, so close?

description: Since the Nineties the crisis of traditional political families has become particularly intense in the democracies of Southern Europe, with an increase in electoral volatility, the contraction of party membership, and crises of confidence as well as of electoral consensus. In these democracies, alongside the traditional parties of the twentieth century, a generation of new political parties was born that reject the right/left division, politicizing a new cleavage between the low and the high of society, or – better – between the people and the elite. In Spain and Italy, the anti-political-establishment supply side is provided expecially by two new movement-parties, Podemos and the Five Star Movement. This article focuses on the comparison between these two new political parties, highlighting differences and analogies in terms of policy, identity, and organization. The research hypothesis developed in this paper refers to the different form and identity in which populism is expressed in the two national cases. From the methodological point of view, the comparison between the two parties will be conducted through the analysis of the text of the electoral programs of the two different political organizations in the European elections in the period running from 2014 to 2017.
DOI: 10.25345/c52w84
title: MassIVE MSV000084059 - GNPS Standard Alpha-Punicalagin and Beta-Punicalagin
title: Standard Punicalagin (Alpha-Punicalagin and Beta-Punicalagin isomer)
DOI: 10.1285/i20356609v12i1p239
title: Vitz M. (2018), City on a Lake: Urban Political Ecology and the Growth of Mexico City, Duke University Press.
DOI: 10.1285/i20356609v12i1p250
title: Pasta S., Razzismi 2.0. Analisi socio-educativa dell'odio online, Brescia: Morcelliana, 2018, pp. 224.
DOI: 10.5281/zenodo.3268639
title: Microbial Proteomes with/without experimental optimal growth temperature

description: This repository contains proteomes of microorganisms with/without experimentally determined optimal growth temperature (OGT), used in the paper '<strong>Li G, Rabe KS, Nielsen J & Engqvist MKM (2019) Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. </strong><em>ACS Synth. Biol.</em><strong> 8: 1411–1420</strong>'. There are two .tar.gz files:

(1) classified.tar.gz. It contains 5761 proteomes with experimental OGT. The name format of each proteome is '{ogt}_{organism_name}_{organism domain}.fasta'. For example, '36_escherichia_coli_bacteria.fasta' for <em>Escherichia coli.</em>

(2) not_classified.tar.gz. It contains 1803 proteomes without experimental OGT. The name format is similar as in classified.tar.gz. The only different is to use  'tt' to represent the unknown OGT value. For example, 'tt_candidatus_azobacteroides_bacteria.fasta'.

All proteomes are in fasta format. 

If you used the dataset, please kindly cite the paper mentioned above.
DOI: 10.5281/zenodo.3268640
title: Microbial Proteomes with/without experimental optimal growth temperature

description: This repository contains proteomes of microorganisms with/without experimentally determined optimal growth temperature (OGT), used in the paper '<strong>Li G, Rabe KS, Nielsen J & Engqvist MKM (2019) Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. </strong><em>ACS Synth. Biol.</em><strong> 8: 1411–1420</strong>'. There are two .tar.gz files:

(1) classified.tar.gz. It contains 5761 proteomes with experimental OGT. The name format of each proteome is '{ogt}_{organism_name}_{organism domain}.fasta'. For example, '36_escherichia_coli_bacteria.fasta' for <em>Escherichia coli.</em>

(2) not_classified.tar.gz. It contains 1803 proteomes without experimental OGT. The name format is similar as in classified.tar.gz. The only different is to use  'tt' to represent the unknown OGT value. For example, 'tt_candidatus_azobacteroides_bacteria.fasta'.

All proteomes are in fasta format. 

If you used the dataset, please kindly cite the paper mentioned above.
DOI: 10.5281/zenodo.3268700
title: Excel mapping tool for 2019 avian influenza data reporting

description: Data collection is an important task of the European Food Safety Authority (EFSA) and a fundamental component of risk assessment (Articles 22 and 23 of Regulation (EC) No 178/2002). EFSA receives a large volume of data from Member States (MSs) that is used in support of its risk assessment mission.

Council Directive 2005/94/EC and Commission Decision 2010/367/EU lay down the guidelines for monitoring and reporting avian influenza surveillance data on poultry and wild birds by European Union (EU) MSs. EFSA has been assigned the task of collating, validating, analysing, and summarising in an annual report the results from the avian influenza surveillance programmes in poultry and wild birds. For the reporting of data, EFSA provides a Data Collection Framework that allows data providers to submit data in eXtensible Markup Language (XML) format through a web interface or a web service. Here, a data model describing the format, and the content requested when submitting data through the DCF, is provided.

The data model supports the reporting of laboratory testing results for avian influenza in both wild birds, and poultry. The use of this reporting standard ensures that reported results are comparable between reporting countries. Data reported in this format will be used to generate epidemiological updates on the avian influenza surveillance testing carry out in Europe, and to provide scientific advice to the European Commission.
description: EU; XLS; zoonoses_support@efsa.europa.eu
DOI: 10.17632/4khc6db5x6.1
title: Greek Implied Volatility Index (GRIV)

description: The dataset for Siriopoulos, C., & Fassas, A. (2012). An investor sentiment barometer—Greek implied volatility index (GRIV). Global Finance Journal, 23(2), 77-93.
DOI: 10.17632/s677jds6t2.2
title: Dataset related to the article "Effects of training, detraining, and retraining on strength, hypertrophy, and myonuclear number in human skeletal muscle"

description: This dataset is related to the figures presented in the paper "Effects of training, detraining, and retraining on strength, hypertrophy, and myonuclear number in human skeletal muscle" published in JAPPL 2019. The dataset is provided so that interested parties can do additional analysis and interpretations. Each individual row in the Excel spreadsheet corresponds to the same subject across the tabs. Training, biopsies, 1RM and muscle thickness measurements where performed at GIH by Niklas Psilander and colleagues. Please contact Niklas regarding questions related to that dataset. Histochemical analysis were performed at NIH by Kristoffer Toldnes Cumming and colleagues. Please contact Kristoffer regarding questions related to that dataset (nuclei per fiber cross section and fiber CSA) Single fiber analysis were performed at UiO by Einar Eftestøl and colleagues. Please contact Einar regarding questions related to that dataset (nuclei per sarcomere from single fibers, volume per fiber segment and myonuclear domain size)
DOI: 10.17632/2bwb7c9dph.2
title: Impact of Technology Use on Student Learning Outcomes: Data from Large Scale RCT Experiment

description: Data on school characteristics and results from pre and post tests conducted during the intervention. R codes for getting the results.
DOI: 10.17632/2bwb7c9dph.3
title: Impact of Technology Use on Student Learning Outcomes: Data from Large Scale RCT Experiment
DOI: 10.17632/2bwb7c9dph.1
title: Impact of Technology Use on Student Learning Outcomes: Data from Large Scale RCT Experiment
DOI: 10.17632/8dp58zkcv8.1
title: Butterflies do not alter oviposition or larval foraging in response to anthropogenic increases in sodium, Animal Behaviour (2019)

description: Raw data associated with Mitchell, T. S., et al., Butterflies do not alter oviposition or larval foraging in response to anthropogenic increases in sodium, Animal Behaviour (2019), https://doi.org/10.1016/j.anbehav.2019.06.015
DOI: 10.5281/zenodo.3256432
title: SLD: Sports Leagues Dataset

description: The Sports Leagues Dataset (SLD) contains statistical data of the major professional sports leagues in the United States: NFL (National Football League), NBA (National Basketball Association), NHL (National Hockey League) and MLB (Major League Baseball). One collect five topics (Player Expenses, Player Salaries, Players Performance, Team Salaries, Team Valuation) of two dimensions (Finance and Performance) in different seasons (2000-2007) from three data sources (Forbes, Spotrac and Sports Reference). <br> Please consider citing https://doi.org/10.5281/zenodo.3256432 if you found this dataset useful: [1] André Albino Bastos, Matheus de Oliveira Salim, Wladmir Cardoso Brandão. (2019). SLD: The Sports Leagues Dataset (Version 1.0) [Data set]. Zenodo.
DOI: 10.5281/zenodo.3268768
title: code_Fauvernier_et_al_2019_JRSSC

description: <pre># code_Fauvernier_et_al_2019_JRSSC

This repository contains the datasets used to perform the simulation study of the following article:

Fauvernier, M., Roche, L., Uhry, Z., Tron, L., Bossard, N., Remontet, L. and the CENSUR Working Survival Group (in press). Multidimensional penalized hazard model with continuous covariates: applications for studying trends and social inequalities in cancer survival, Journal of the Royal Statistical Society, series C. doi: 10.1111/rssc.12368

The rest of the code is available at:

https://rss.onlinelibrary.wiley.com/hub/journal/14679876/series-c-datasets
</pre>
DOI: 10.5281/zenodo.3268769
title: code_Fauvernier_et_al_2019_JRSSC

description: <pre># code_Fauvernier_et_al_2019_JRSSC

This repository contains the datasets used to perform the simulation study of the following article:

Fauvernier, M., Roche, L., Uhry, Z., Tron, L., Bossard, N., Remontet, L. and the CENSUR Working Survival Group (in press). Multidimensional penalized hazard model with continuous covariates: applications for studying trends and social inequalities in cancer survival, Journal of the Royal Statistical Society, series C. doi: 10.1111/rssc.12368

The rest of the code is available at:

https://rss.onlinelibrary.wiley.com/hub/journal/14679876/series-c-datasets
</pre>
DOI: 10.17632/hyxw2shy3z.1
title: Finite strain analysis - Samples IESP3SP31, IESP3SP34, IESP3SP36, IESP3SP37 and IESP3CH09 (Punta Bianca, La Spezia, Italy)

description: This dataseset contains finite strain analysis data for the samples IESP3SP31, IESP3SP34, IESP3SP36, IESP3SP37 and IESP3CH09 (see details on the SESAR geo samples repository). It includes the high-resolution scans of the polished slabs used for finite strain analysis and the data processed with EllipseFit (copyright: Vollmer, 2014). The samples are monogenic carbonate metabreccia (IESP3SP31 & IESP3SP34), polygenic metabreccia (IESP3SP36 & IESP3SP37) and a vesicled metavolcanite (IESP3CH09). The polished slabs, oriented parallel to the XZ and YZ sections of the finite strain ellipsoid, were processed with EllipseFit (Vollmer, 2015) to estimate the axial ratio Rf and the angle with the foliation (φ) of the deformed strain markers. See Dunnet (1969) and Lisle (1985) for details about the Rf/φ method. Samples were also processed using the Fry (1979) method. Fry graphs are provided for comparison.
DOI: 10.5281/zenodo.3256431
title: SLD: Sports Leagues Dataset

description: The Sports Leagues Dataset (SLD) contains statistical data of the major professional sports leagues in the United States: NFL (National Football League), NBA (National Basketball Association), NHL (National Hockey League) and MLB (Major League Baseball). One collect five topics (Player Expenses, Player Salaries, Players Performance, Team Salaries, Team Valuation) of two dimensions (Finance and Performance) in different seasons (2000-2007) from three data sources (Forbes, Spotrac and Sports Reference). <br> Please consider citing https://doi.org/10.5281/zenodo.3256432 if you found this dataset useful: [1] André Albino Bastos, Matheus de Oliveira Salim, Wladmir Cardoso Brandão. (2019). SLD: The Sports Leagues Dataset (Version 1.0) [Data set]. Zenodo.
DOI: 10.5519/qd.2esd7beo
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.11571/upei-roblib-data/researchdata:537
title: Brown seaweed (AquaArom) supplementation increases food intake and improves growth, antioxidant status and resistance to temperature stress in Atlantic salmon, Salmo salar
DOI: 10.17632/fsdyb4cw8k.1
title: PopulationProfiler

description: PopulationProfiler – is light-weight cross-platform open-source tool for data analysis in image-based screening experiments. The main idea is to reduce per-cell measurements to per-well distributions, each represented by a histogram. These can be optionally further reduced to sub-type counts based on gating (setting bin ranges) of known control distributions and local adjustments to histogram shape. Such analysis is necessary in a wide variety of applications, e.g. DNA damage assessment using foci intensity distributions, assessment of cell type specific markers, and cell cycle analysis. The software imports measurements from a simple text file, visualizes population distributions in a compact and comprehensive way, and can create gates for subpopulation classes based on control samples. The simple graphical user interface (GUI) allows selection of multiple csv files with image-based screening data. Each file is treated as a separate plate (i.e. independent experiment) with rows representing cell measurements. One measurement is processed at a time and cells are grouped based on well labels. The measurement is selected by the user from a drop-down list created from the csv file header (first row). The GUI also allows selection of control wells based on the treatment labels. If such labels are not available, the user can select control wells manually. The corresponding data is pooled and stored as a separate record in the output csv file. PopulationProfiler thereafter calculates and displays the distribution of the selected measurement as a histogram for each well. A vector representation of each well’s histogram is saved in the output file, and can be used as input for e.g., cluster analysis, elsewhere. The cell count for each well is also saved as a measure of statistical relevance of population effects. Cite this software as: Matuszewski, D.J., Wählby, C., Puigvert, J.C., Sintorn, I.-M. (2016) PopulationProfiler: A Tool for Population Analysis and Visualization of Image-Based Cell Screening Data. PloS one, 11(3), e0151554. The Python source code, sample data and user manual are available free of charge.
DOI: 10.5523/bris.3ma509dlakcf720aw8x82aq4tm
title: Lane_et_al_Benchmark_FUSE_GB
title: Benchmarking hydrological model performance across Great Britain

description: We evaluated 4 hydrological model structures and their parameters on over 1000 catchments across Great Britain, considering modelling uncertainties. This research provides a benchmark of national modelling capability as well as a library of the best performing parameter sets for the FUSE models. Here, we provide the data that may be most useful for future studies - a list of the best model parameters for each catchment, summaries of model scores for each catchment, and time-series of model output for each catchment from the 4 different model structures.
DOI: 10.17632/vj3c828cxs.2
title: MSD analysis with KymographDirect and @msdanalyzer

description: MATLAB code that loads particle tracking data (position vs time) for kymographs extracted using KymographDirect (https://sites.google.com/site/kymographanalysis/). MSD and other analyses are performed with a modified version of the package msdanalyzer (http://tinevez.github.io/msdanalyzer/).
DOI: 10.17632/g3cf5rcrzn.1
title: 10-year mortality in PAD patients after rehabilitation program
DOI: 10.17632/y7f6jbt6v7.2
title: Instantaneous velocity analysis of KymographDirect directed motion

description: MATLAB code that loads particle tracking data (position vs time) for kymographs extracted using KymographDirect (https://sites.google.com/site/kymographanalysis/). Calculates instantaneous velocities of smoothed trajectories.
DOI: 10.17632/br38t938c2.2
title: Code for Data-set investigation of a Gas Turbine for Electricity Generation

description: This contains the R markdown code used to perform the filtering and descriptive analysis on the raw turbine dataset found in http://dx.doi.org/10.17632/6w3vy3ybhg.2
DOI: 10.5281/zenodo.3268719
title: LIDAR - Wind profiler data of RACLETS field campaign

description: Please contact Maxime Hervo from MeteoSwiss for information about the data set.
DOI: 10.5519/qd.rrnkqhef
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.17632/6w3vy3ybhg.2
title: Data-set for Independent Gas turbine for Electricity Generation.

description: This dataset contains the raw, filtered and related parameters for the real-power measurements in an Independent 5.68 MW Gas turbine used for Electrical power generation. It also contains the correlation matrices of all 50 features of the complete dataset and that of the real power measurement related parameters.
DOI: 10.18112/openneuro.ds002013.v1.0.0
title: Visual Receptive Field Mapping Experiment
DOI: 10.18462/iir.cryo.2019.0019
title: Dynamic modelling of a liquid hydrogen loading cycle from onshore storage to a seaborne tanker.

description: For liquid hydrogen (LH2) to become a global, carbon-free energy commodity, scaling-up of liquefaction, storage and transport technologies requires extensive research and engineering. The present work analyses a large-scale LH2 transfer process from onshore storage to seaborne storage by dynamic simulations. Initial precooling of the transfer lines can be achieved by circulation of the naturally occurring boiloff gas from the onshore tank. In the subsequent LH2 transfer phase, the net vapour return flowrate to be handled in the hydrogen liquefier increases. An ejector is used to recompress the vapour return flow, the entrainment ratio of
which increases from 5.7 % to 7.7 % due to the additional boiloff during LH2 transfer. Simulation results for a preliminary ejector design show ample recompression capacity, which indicates that the increased vapour return can be re-liquefied at the cost of additional refrigeration load and power in the liquefier.
DOI: 10.5281/zenodo.3268718
title: LIDAR - Wind profiler data of RACLETS field campaign

description: Please contact Maxime Hervo from MeteoSwiss for information about the data set.
DOI: 10.18462/iir.cryo.2019.0021
title: Cryogenic tests of the main HTS current leads for the SIS100 heavy ion accelerator of the FAIR project.

description: The superconducting dipole and quadrupole magnets of SIS100 accelerator are powered by 14 kA HTS current leads. The current leads are manufactured by the M&W Company in Denmark. The accelerator and test facilities will be equipped with 18 pairs of main HTS current leads in total. During quality control at GSI each pair of current leads passed the cryogenic Site Acceptance Test (SAT) at the Serial Test Facility (STF). Such tests include the following: leak measurements, pressure test at 20 bar, 14 kA DC current lead ramps, 13.2 kA cycling at 1 Hz and 17 kA training ramps. Analysis of the test results are presented in this paper.
DOI: 10.5281/zenodo.3268511
title: SLDeep: Statement-Level Software Defect Prediction Using Deep-Learning Models on Static Code Features

description: Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus testing activities on more suspicious portions. Consequently, high-quality software is released with less time and effort. The current SDP techniques however work at coarse-grained units, such as a module or a class, putting some burden on the developers to locate the fault. To address this issue, we propose Statement-Level software defect prediction using Deep-learning model (SLDeep). To reify our proposal, we defined a suite of 32 statement-level metrics, such as the number of binary and unary operators used in a statement. Then, we applied as learning model, long short-term memory (LSTM). The significance of SLDeep for intelligent and expert systems is that it demonstrates a novel use of deep-learning models to the solution of a practical problem faced by software developers. We conducted experiments using more than 100,000 C/C++ programs within the Code4Bench. The programs total 2,356,458 lines of code with 292,064 faulty lines. The benchmark comprises diverse set of programs and versions, written by thousands of developers. Therefore, it tends to give a model that can be used for cross-project SDP. In the experiments, our trained model could successfully classify the unseen data with average performance measures 0.945, 0.971, and 0.976 in terms of recall, precision, and accuracy, respectively. These experimental results suggest that SLDeep is effective for statement-level SDP. The impact of this work is twofold. Working at statement-level further alleviates developer’s burden in pinpointing the fault locations. Second, cross-project feature of SLDeep helps defect prediction research become more industrially-viable

for more information visit https://github.com/sldeep/SLDeep
DOI: 10.5281/zenodo.3268512
title: SLDeep: Statement-Level Software Defect Prediction Using Deep-Learning Models on Static Code Features

description: Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus testing activities on more suspicious portions. Consequently, high-quality software is released with less time and effort. The current SDP techniques however work at coarse-grained units, such as a module or a class, putting some burden on the developers to locate the fault. To address this issue, we propose Statement-Level software defect prediction using Deep-learning model (SLDeep). To reify our proposal, we defined a suite of 32 statement-level metrics, such as the number of binary and unary operators used in a statement. Then, we applied as learning model, long short-term memory (LSTM). The significance of SLDeep for intelligent and expert systems is that it demonstrates a novel use of deep-learning models to the solution of a practical problem faced by software developers. We conducted experiments using more than 100,000 C/C++ programs within the Code4Bench. The programs total 2,356,458 lines of code with 292,064 faulty lines. The benchmark comprises diverse set of programs and versions, written by thousands of developers. Therefore, it tends to give a model that can be used for cross-project SDP. In the experiments, our trained model could successfully classify the unseen data with average performance measures 0.945, 0.971, and 0.976 in terms of recall, precision, and accuracy, respectively. These experimental results suggest that SLDeep is effective for statement-level SDP. The impact of this work is twofold. Working at statement-level further alleviates developer’s burden in pinpointing the fault locations. Second, cross-project feature of SLDeep helps defect prediction research become more industrially-viable

for more information visit https://github.com/sldeep/SLDeep
DOI: 10.5281/zenodo.3268727
title: Radar - wind profiler data of RACLETS field campaign

description: Please contact Maxime Hervo from MeteoSwiss for information about the data set.
DOI: 10.5281/zenodo.3268726
title: Radar - wind profiler data of RACLETS field campaign

description: Please contact Maxime Hervo from MeteoSwiss for information about the data set.