cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4659130
title: Supplementary Material for: Stability and Variability in Slovak Prosodic Boundaries

description: Background/Aim: Encoding intended meanings in the type and strength of prosodic boundaries and strategies for communicating these meanings in ambient noise use similar prosodic cues. We analyze how increasing the level of ambient noise affects the realization of Slovak prosodic boundaries. Methods: Five native speakers of Slovak read sentences, manipulating the boundary type (weak, rise, fall) and the location of pre-boundary pitch accent. Ambient noise of several levels was administered via headphones. Acoustic and articulatory data (electromagnetometry) were collected. Results: Under normal condition, boundary strength is signaled with longer pre-boundary rhymes, more frequent pauses, greater crossboundary f0 resets and jaw displacement. The strength of falls is realized in crossboundary features (pauses, f0 reset), and rises in pre-boundary features (rhyme duration, f0 range). Pitch-accented rhymes are strengthened in all features, but f0 range. In noise, the increase in boundary strength is weak, and falls strengthen more than rises. F0 targets for falls and rises are adjusted in addition to noiseinduced global f0 scaling and lengthening. Conclusion: Hyper-articulation of prosodic boundaries in ambient noise is not robust and uniform; rather, durational, f0 and jaw displacement features co-create complex prosodic patterns in a complementary and synergetic manner based on affordances in normal speech.
DOI: 10.6084/m9.figshare.4659130.v1
title: Supplementary Material for: Stability and Variability in Slovak Prosodic Boundaries

description: Background/Aim: Encoding intended meanings in the type and strength of prosodic boundaries and strategies for communicating these meanings in ambient noise use similar prosodic cues. We analyze how increasing the level of ambient noise affects the realization of Slovak prosodic boundaries. Methods: Five native speakers of Slovak read sentences, manipulating the boundary type (weak, rise, fall) and the location of pre-boundary pitch accent. Ambient noise of several levels was administered via headphones. Acoustic and articulatory data (electromagnetometry) were collected. Results: Under normal condition, boundary strength is signaled with longer pre-boundary rhymes, more frequent pauses, greater crossboundary f0 resets and jaw displacement. The strength of falls is realized in crossboundary features (pauses, f0 reset), and rises in pre-boundary features (rhyme duration, f0 range). Pitch-accented rhymes are strengthened in all features, but f0 range. In noise, the increase in boundary strength is weak, and falls strengthen more than rises. F0 targets for falls and rises are adjusted in addition to noiseinduced global f0 scaling and lengthening. Conclusion: Hyper-articulation of prosodic boundaries in ambient noise is not robust and uniform; rather, durational, f0 and jaw displacement features co-create complex prosodic patterns in a complementary and synergetic manner based on affordances in normal speech.
DOI: 10.6084/m9.figshare.4658986
title: Extended Data Table 1. from Capturing goats: documenting two hundred years of mitochondrial DNA diversity among goat populations from Britain and Ireland

description: Sample information for published whole mitochondrial genomes used in this study
DOI: 10.6084/m9.figshare.4658977
title: Extended Data Table 2. from Capturing goats: documenting two hundred years of mitochondrial DNA diversity among goat populations from Britain and Ireland

description: Sample information for published HV1 control regions used in this study
DOI: 10.6084/m9.figshare.4658986.v1
title: Extended Data Table 1. from Capturing goats: documenting two hundred years of mitochondrial DNA diversity among goat populations from Britain and Ireland

description: Sample information for published whole mitochondrial genomes used in this study
DOI: 10.6084/m9.figshare.4658977.v1
title: Extended Data Table 2. from Capturing goats: documenting two hundred years of mitochondrial DNA diversity among goat populations from Britain and Ireland

description: Sample information for published HV1 control regions used in this study
DOI: 10.6084/m9.figshare.4658947
title: Data and analysis scripts

description: This contains data and analysis scripts to process and visualise population-level RNA-seq (from Cembrowski et al., Neuron, 2016; Cembrowski et al., eLife, 2016) and single-cell RNA-seq (from Zeisel et al., Science, 2015). <br>Population-level RNA-seq data is found in the data/ directory, whereas single-cell RNA-seq can be downloaded from the Linnarsson laboratory following the link below.<br>Analysis scripts are found in the code/ directory.
DOI: 10.17632/87kwc65pyn.3
title: Supplemental Results

description: reporting rate
DOI: 10.6084/m9.figshare.4658815
title: Descriptive Statistic for 25 Portfolios' Negative and Positive realized jump measures

description: The excel contains two sheets. The first one is the original descriptive statistic for 25 Portfolios' negative and positive realized jump measures, and the second one is only the mean of negative and positive realized jump measures.
DOI: 10.6084/m9.figshare.4658806
title: File3. Fama-MacBeth first-stage time-series regression data

description: This file includes 3 excels on Fama-MacBeth first-stage time-series regression data. The first excel includes portfolios “s1b1”-“s1b5” and “s2b1”-“s2b5”; the second excel includes portfolios “s3b1”-“s3b5” and “s4b1”-“s4b5”, and the third one includes the portfolios “s5b1”-“s5b5”
DOI: 10.6084/m9.figshare.4658812
title: Final Fama-MacBeth regression data

description: The excel is Fama-MacBeth second-stage cross-sectional regression data. There are three sheets corresponding to data for three cross-sectional regression models the results of which are shown in table 5 in the context. The last sheet presents the regression results for the three cross-sectional regressions.
DOI: 10.17632/rxhpvwwmbz.1
title: Multi-Label Arabic Dataset

description: The dataset is a collection of hierarchical multi-label Arabic texts, related to the Islamic field. It consists of 26,470 instances distributed over 578 labels ordered in a hierarchy. After ranking the features using (BR-Chi-Square) feature selection method, a different number of the high-ranking features are selected for evaluation purposes which are 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000 features. The processed version of the dataset with all aforementioned features sets is available in the ARFF file format suitable for MULAN multi-label classification tool, along with the XML file format that defines the hierarchical structure of the labels.
DOI: 10.6084/m9.figshare.4658815.v1
title: Descriptive Statistic for 25 Portfolios' Negative and Positive realized jump measures

description: The excel contains two sheets. The first one is the original descriptive statistic for 25 Portfolios' negative and positive realized jump measures, and the second one is only the mean of negative and positive realized jump measures.
DOI: 10.7910/dvn/cal9uw
title: Credito para exportacao de bens e servicos de infraestrutura economica em paises estrangeiros

description: Este banco de dados e baseado nos dados originais do Banco Nacional de Desenvolvimento Economico e Social - BNDES - e retrata as operacoes referentes ao financiamento de exportacoes de bens e servicos de engenharia no periodo 1998-2015. O Banco de dados foi utilizado no estudo conjunto do Instituto de Pesquisa Economica Aplicada e a Agencia Francesa de Desenvolvimento em 2018 e apresentado no Seminario Cooperacao Internacional - Financiamento para o desenvolvimento, nos dias 12 e 13 de Dezembro de 2018, em Brasilia-DF.
DOI: 10.6084/m9.figshare.4658797
title: File2. Data for Time-series regression in 2007 to 2013

description: File 2 includes seven excels, named Data for Time-series regression in 2007 to Data for Time-series regression in 2013 respectively. In specific, we average all the 25 portfolios’ realized jump measures in 2007 as just one series from File 1 in the excel of “Data for Horse running regression in 2007”. The excels constitute jump variables and the other variables, among which value-weighted monthly returns of 25 portfolios are directly from the RESSET database.
DOI: 10.6084/m9.figshare.4658797.v1
title: File2. Data for Time-series regression in 2007 to 2013

description: File 2 includes seven excels, named Data for Time-series regression in 2007 to Data for Time-series regression in 2013 respectively. In specific, we average all the 25 portfolios’ realized jump measures in 2007 as just one series from File 1 in the excel of “Data for Horse running regression in 2007”. The excels constitute jump variables and the other variables, among which value-weighted monthly returns of 25 portfolios are directly from the RESSET database.
DOI: 10.6084/m9.figshare.4658794
title: File1. The original data and the data of constructed 25 portfolios

description: File 1 includes seven subsets respectively named according to the year as “2007” to “2013”. Specifically, in file “2007”, there are subsets of 25 texts and 50 files. According to our samples, we constructed 25 portfolios using all of the individual stocks, which are named “s1b1”, “s1b2”, “s1b3”, “s1b4”, “s1b5”, …, “s5b1”, “s5b2”, “s5b3”, “s5b4”, “s5b5”. And the 25 subset texts are the constituent stocks for each portfolio, named “s1b1_07” to “s5b5_07”. As for the 50 files, 25 of them are the original price data for 25 portfolios named “s1b1_07” to “s5b5_07”; the other 25 are the processed data for 25 portfolios named “s1b1_07result” to “s5b5_o7result”,in which realized jump measures data are calculated by the non-parametric method, for all constituent stocks of portfolio “s1b1” to portfolio “s5b5”, respectively.
DOI: 10.6084/m9.figshare.4658788
title: Anexo: Ludificación y sus posibilidades en el entorno blended learning

description: La presente investigación examina sistemáticamente la información presentada en bases de datos internacionales de alto impacto sobre ludificación en el entorno de aprendizaje semipresencial (blended). Para conseguir este objetivo, se analizaron 34 publicaciones entre 2012 y 2017*, ordenándose según cuatro criterios: público, tópico, diseño metodológico y conclusiones principales. Los resultados demostraron una inclinación por muestras de estudiantes de educación superior, especialmente de ingeniería, una configuración de prototipos y modelos, un diseño metodológico en el que predomina el marco teórico-conceptual, seguido de estudios empírico cuantitativos y unas conclusiones a favor de la ludificación en dicho entorno, dados sus efectos positivos en la motivación, retroalimentación y en la adquisición de competencias de los participantes.
DOI: 10.6084/m9.figshare.4658794.v1
title: File1. The original data and the data of constructed 25 portfolios

description: File 1 includes seven subsets respectively named according to the year as “2007” to “2013”. Specifically, in file “2007”, there are subsets of 25 texts and 50 files. According to our samples, we constructed 25 portfolios using all of the individual stocks, which are named “s1b1”, “s1b2”, “s1b3”, “s1b4”, “s1b5”, …, “s5b1”, “s5b2”, “s5b3”, “s5b4”, “s5b5”. And the 25 subset texts are the constituent stocks for each portfolio, named “s1b1_07” to “s5b5_07”. As for the 50 files, 25 of them are the original price data for 25 portfolios named “s1b1_07” to “s5b5_07”; the other 25 are the processed data for 25 portfolios named “s1b1_07result” to “s5b5_o7result”,in which realized jump measures data are calculated by the non-parametric method, for all constituent stocks of portfolio “s1b1” to portfolio “s5b5”, respectively.
DOI: 10.6084/m9.figshare.4658806.v1
title: Fama-MacBeth first-stage time-series regression data

description: This file includes 3 excels on Fama-MacBeth first-stage time-series regression data. The first excel includes portfolios “s1b1”-“s1b5” and “s2b1”-“s2b5”; the second excel includes portfolios “s3b1”-“s3b5” and “s4b1”-“s4b5”, and the third one includes the portfolios “s5b1”-“s5b5”
DOI: 10.6084/m9.figshare.4658779
title: FACS raw data Fig. 7

description: Here are the raw data of Fig. 7 as generated by the S3 Biorad flow cytometer using the ProSort software. The ROIs should be used according to the number of the experiment.
DOI: 10.6084/m9.figshare.4658776
title: MatchingLand - random disturbance data

description: This file is part of the MatchingLand testbed and contains the datasets of random disturbance group (point, line, and area). The random disturbance group is formed by synthetic datasets generated using a set of displacement vector fields applied over the initial datasets. The datasets are in Shapefile format and coordinate reference system EPSG:32628.
DOI: 10.6084/m9.figshare.4658773
title: MatchingLand - systematic disturbance line data

description: This file is part of the MatchingLand testbed and contains the datasets of systematic disturbance group (only line data). The systematic disturbance group is formed by synthetic datasets generated from a set of affine transformations over the initial datasets. The datasets are in Shapefile format and coordinate reference system EPSG:32628.
DOI: 10.6084/m9.figshare.4658773.v1
title: MatchingLand - systematic disturbance line data

description: This file is part of the MatchingLand testbed and contains the datasets of systematic disturbance group (only line data). The systematic disturbance group is formed by synthetic datasets generated from a set of affine transformations over the initial datasets. The datasets are in Shapefile format and coordinate reference system EPSG:32628.
DOI: 10.6084/m9.figshare.4658776.v1
title: MatchingLand - random disturbance data

description: This file is part of the MatchingLand testbed and contains the datasets of random disturbance group (point, line, and area). The random disturbance group is formed by synthetic datasets generated using a set of displacement vector fields applied over the initial datasets. The datasets are in Shapefile format and coordinate reference system EPSG:32628.
DOI: 10.6084/m9.figshare.4658788.v2
title: Anexo: Ludificación y sus posibilidades en el entorno blended learning

description: La presente investigación examina sistemáticamente la información presentada en bases de datos internacionales de alto impacto sobre ludificación en el entorno de aprendizaje semipresencial (blended). Para conseguir este objetivo, se analizaron 34 publicaciones entre 2012 y 2017*, ordenándose según cuatro criterios: público, tópico, diseño metodológico y conclusiones principales. Los resultados demostraron una inclinación por muestras de estudiantes de educación superior, especialmente de ingeniería, una configuración de prototipos y modelos, un diseño metodológico en el que predomina el marco teórico-conceptual, seguido de estudios empírico cuantitativos y unas conclusiones a favor de la ludificación en dicho entorno, dados sus efectos positivos en la motivación, retroalimentación y en la adquisición de competencias de los participantes.
DOI: 10.6084/m9.figshare.4658788.v1
title: Anexo: Ludificación y sus posibilidades en el entorno blended learning

description: La presente investigación examina sistemáticamente la información presentada en bases de datos internacionales de alto impacto sobre ludificación en el entorno de aprendizaje semipresencial (blended). Para conseguir este objetivo, se analizaron 34 publicaciones entre 2012 y 2017*, ordenándose según cuatro criterios: público, tópico, diseño metodológico y conclusiones principales. Los resultados demostraron una inclinación por muestras de estudiantes de educación superior, especialmente de ingeniería, una configuración de prototipos y modelos, un diseño metodológico en el que predomina el marco teórico-conceptual, seguido de estudios empírico cuantitativos y unas conclusiones a favor de la ludificación en dicho entorno, dados sus efectos positivos en la motivación, retroalimentación y en la adquisición de competencias de los participantes.
DOI: 10.6084/m9.figshare.4658770
title: MatchingLand - systematic disturbance point data

description: This file is part of the MatchingLand testbed and contains the datasets of systematic disturbance group (only point data). The systematic disturbance group is formed by synthetic datasets generated from a set of affine transformations over the initial datasets. The datasets are in Shapefile format and coordinate reference system EPSG:32628.
DOI: 10.6084/m9.figshare.4658764
title: MatchingLand - morphology modified data

description: This file is part of the MatchingLand testbed and contains the datasets of morphology modified group (line and area). The morphology modified group is composed of synthetic datasets with the predominance of some particular morphology class for lines or areas. The datasets are in Shapefile format and coordinate reference system EPSG:32628.
DOI: 10.6084/m9.figshare.4658767
title: MatchingLand - initial datasets

description: This file is part of the MatchingLand testbed and contains the datasets of initial group (point, line, and area) and the list of matching pairs in plain text. The initial group of datasets encompasses the data selected from original sources of Spanish mapping agencies at scales 1:25,000 and 1:10,000. The datasets are in Shapefile format and coordinate reference system EPSG:32628.
DOI: 10.6084/m9.figshare.4658770.v1
title: MatchingLand - systematic disturbance point data

description: This file is part of the MatchingLand testbed and contains the datasets of systematic disturbance group (only point data). The systematic disturbance group is formed by synthetic datasets generated from a set of affine transformations over the initial datasets. The datasets are in Shapefile format and coordinate reference system EPSG:32628.
DOI: 10.6084/m9.figshare.4658764.v1
title: MatchingLand - morphology modified data

description: This file is part of the MatchingLand testbed and contains the datasets of morphology modified group (line and area). The morphology modified group is composed of synthetic datasets with the predominance of some particular morphology class for lines or areas. The datasets are in Shapefile format and coordinate reference system EPSG:32628.
DOI: 10.6084/m9.figshare.4658761
title: MatchingLand - systematic disturbance area data

description: This file is part of the MatchingLand testbed and contains the datasets of systematic disturbance group (only area data). The systematic disturbance group is formed by synthetic datasets generated from a set of affine transformations over the initial datasets. The datasets are in Shapefile format and coordinate reference system EPSG:32628.
DOI: 10.6084/m9.figshare.4658734
title: Data Set 5: SNPs present in TF's regulatory regions

description: SNPs present in TF's regulatory regions. Each row of the table corresponds to a mutation found in a regulatory region of a TF of the regulatory network. Each row number agree with the number set in the Fig. S2C. Strain names came from a previous work. The genome position refers to the genomic position of the mutation. The column Gene refers to the annotation of the genomic position of the SNP (gene name or intergenic). The type of Transcription Start Site cames from a published work.
DOI: 10.6084/m9.figshare.4658737
title: Data Set 6: Regulatory influences predicted vs regulatory influences obtained from experimental data

description: This table shows the regulatory influence predicted by the computational models versus the regulatory influence derived from experimental data. A '+' symbol means positive regulation while a '-' symbol means negative regulation. Three experimental data sources where compared. In the first table, regulatory data from the H37Rv-ΔphoP strain obtained from microarray experiments. In the second table, regulatory data from the H37Rv-ΔphoP strain obtained from RNA-seq experiments. In the last table, regulatory data from the T83 strain obtained from RNA-seq experiments.
DOI: 10.6084/m9.figshare.4658734.v1
title: Data Set 5: SNPs present in TF's regulatory regions

description: SNPs present in TF's regulatory regions. Each row of the table corresponds to a mutation found in a regulatory region of a TF of the regulatory network. Each row number agree with the number set in the Fig. S2C. Strain names came from a previous work (1). The genome position refers to the genomic position of the mutation. The column Gene refers to the annotation of the genomic position of the SNP (gene name or intergenic). The type of Transcription Start Site cames from a published work (2).
DOI: 10.6084/m9.figshare.4658740
title: Data Set 4: Transcription factors from the network based on statistically validated interactions likely dysfunctional due to SNPs provoking stop-codons gain or loss.

description: Transcription factors from the network based on statistically validated interactions likely dysfunctional due to SNPs provoking stop-codons gain or loss. Each row of the table corresponds to a mutation found in a TF of the regulatory network. Each row number agree with the number set in the Fig. S2B. Strain names came from a previous work. The genome position refers to the genomic position of the mutation. The regulatory influence in the network refers to the number of genes regulated by the mutated TF in network derived from H37Rv network. The column principal GO terms overrepressented on the subnetwork came from the Gene Set Enrichment Analysis performed over the genes regulated by the TF.
DOI: 10.6084/m9.figshare.4658761.v1
title: MatchingLand - systematic disturbance area data

description: This file is part of the MatchingLand testbed and contains the datasets of systematic disturbance group (only area data). The systematic disturbance group is formed by synthetic datasets generated from a set of affine transformations over the initial datasets. The datasets are in Shapefile format and coordinate reference system EPSG:32628.
DOI: 10.6084/m9.figshare.4658737.v1
title: Data Set 6: Regulatory influences predicted vs regulatory influences obtained from experimental data

description: This table shows the regulatory influence predicted by the computational models versus the regulatory influence derived from experimental data. A '+' symbol means positive regulation while a '-' symbol means negative regulation. Three experimental data sources where compared. In the first table, regulatory data from the H37Rv-ΔphoP strain obtained from microarray experiments (1). In the second table, regulatory data from the H37Rv-ΔphoP strain obtained from RNA-seq experiments (2). In the last table, regulatory data from the T83 strain obtained from RNA-seq experiments (3).
DOI: 10.6084/m9.figshare.4658740.v1
title: Data Set 4: Transcription factors from the network based on statistically validated interactions likely dysfunctional due to SNPs provoking stop-codons gain or loss.

description: Transcription factors from the network based on statistically validated interactions likely dysfunctional due to SNPs provoking stop-codons gain or loss. Each row of the table corresponds to a mutation found in a TF of the regulatory network. Each row number agree with the number set in the Fig. S2B. Strain names came from a previous work (1). The genome position refers to the genomic position of the mutation. The regulatory influence in the network refers to the number of genes regulated by the mutated TF in network derived from H37Rv network. The column principal GO terms overrepressented on the subnetwork came from the Gene Set Enrichment Analysis performed over the genes regulated by the TF (see Material and Methods for details).