cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6480599.v1
title: Data from: "A rainfall-manipulation experiment with 517 Arabidopsis thaliana accessions"

description: The gold standard for studying natural selection and adaptation in the wild is to quantify lifetime fitness of individuals from natural populations that have been grown together in a common garden, or that have been reciprocally transplanted. By combining fitness values with species traits and genome sequences, one can infer selection coefficients at the genetic level. Here we present a rainfall-manipulation experiment with 517 whole-genome sequenced natural accessions of the plant Arabidopsis thaliana spanning the global distribution of the species. The experiments were conducted in two field stations in contrasting climates, in the Mediterranean and in Central Europe, where we built rainout shelters and simulated high and low rainfall. Using custom image analysis we quantified fitness- and phenology-related traits for 23,154 pots, which contained about 14,500 plants growing independently, and over 310,000 plants growing in small populations (max. 30 plants). This large field experiment dataset, which associates fitness and ecologically-relevant traits with genomes, will provide an important resource to test eco-evolutionary genetic theories and to understand the potential evolutionary impacts of future climates on an important plant model species.<br>
DOI: 10.6084/m9.figshare.6480554
title: Compositions: New insights into the building of the Variscan Belt in Eastern Europe (Romania, Serbia, Bulgaria)

description: The mineral chemical compositions of Corbu rocks.
DOI: 10.6084/m9.figshare.6480554.v1
title: Compositions: New insights into the building of the Variscan Belt in Eastern Europe (Romania, Serbia, Bulgaria)

description: The mineral chemical compositions of Corbu rocks.
DOI: 10.6084/m9.figshare.6480551
title: Calculations: New insights into the building of the Variscan Belt in Eastern Europe (Romania, Serbia, Bulgaria)

description: Calculations of chemical effective composition prevailing during garnet ultimate rim growth.
DOI: 10.6084/m9.figshare.6480551.v1
title: Calculations: New insights into the building of the Variscan Belt in Eastern Europe (Romania, Serbia, Bulgaria)

description: Calculations of chemical effective composition prevailing during garnet ultimate rim growth.
DOI: 10.6084/m9.figshare.6480527
title: Simulation of surface strain in tibiofemoral cartilage during walking for the prediction of collagen fibre orientation

description: The collagen fibres in the superficial layer of tibiofemoral articular cartilage exhibit distinct patterns in orientation revealed by split lines. In this study, we introduce a simulation framework to predict cartilage surface loading during walking to investigate if split line orientations correspond with principal strain directions in the cartilage surface. The two-step framework uses a multibody musculoskeletal model to predict tibiofemoral kinematics which are then imposed on a deformable model to predict surface strains. The deformable model uses absolute nodal coordinate formulation (ANCF) shell elements to represent the articular surface and a system of spring-dampers and internal pressure to represent the underlying cartilage. Simulations were performed to predict surface strains due to internal pressure, loading induced by walking, and the combination of both loading due to pressure and walking. Peak femoral and tibial cartilage deflections were slightly greater than 1 mm during simulated walking. First principal strain magnitudes within the cartilage surface ranged up to 3%. Time-averaged first principal strains agreed best with split line maps from the literature when surface loading due to internal cartilage pressure was included. This result suggests there may be a connection between pressure-induced surface strain patterns and the collagen fibre orientation patterns that emerge.
DOI: 10.6084/m9.figshare.6480527.v1
title: Simulation of surface strain in tibiofemoral cartilage during walking for the prediction of collagen fibre orientation

description: The collagen fibres in the superficial layer of tibiofemoral articular cartilage exhibit distinct patterns in orientation revealed by split lines. In this study, we introduce a simulation framework to predict cartilage surface loading during walking to investigate if split line orientations correspond with principal strain directions in the cartilage surface. The two-step framework uses a multibody musculoskeletal model to predict tibiofemoral kinematics which are then imposed on a deformable model to predict surface strains. The deformable model uses absolute nodal coordinate formulation (ANCF) shell elements to represent the articular surface and a system of spring-dampers and internal pressure to represent the underlying cartilage. Simulations were performed to predict surface strains due to internal pressure, loading induced by walking, and the combination of both loading due to pressure and walking. Peak femoral and tibial cartilage deflections were slightly greater than 1 mm during simulated walking. First principal strain magnitudes within the cartilage surface ranged up to 3%. Time-averaged first principal strains agreed best with split line maps from the literature when surface loading due to internal cartilage pressure was included. This result suggests there may be a connection between pressure-induced surface strain patterns and the collagen fibre orientation patterns that emerge.
DOI: 10.6084/m9.figshare.6480461
title: Elastic net regression training and testing (LENP)

description: For training and testing LENP models:Input: Expression matrix: Expression/filtered_lncRNA_z_EXP.csv Drug responses data: drug_res/drug_res_merge.csvOperation: (*lead to one or multi output) 1. Parameter Optimization* Output: optimized parameter for 265 drugs (IC50 and AUC) 2. Bootstrapping Using optimized parameters, compute the predictive scores for each drug (IC50 and AUC) 3. Get top predictive genes* Sort the genes by predictive scores and get top 20 genes. Output: predictive score of all genes for each drug (IC50 and AUC) 4. Final model construction*: i. Get coefficients Output: Average coefficiencts of every predictors in each model (IC50 and AUC) ii. Model performance assessments Pearson's correlation coefficients, kendall's tau
DOI: 10.6084/m9.figshare.6480461.v1
title: Elastic net regression training and testing (LENP)

description: For training and testing LENP models:Input: Expression matrix: Expression/filtered_lncRNA_z_EXP.csv Drug responses data: drug_res/drug_res_merge.csvOperation: (*lead to one or multi output) 1. Parameter Optimization* Output: optimized parameter for 265 drugs (IC50 and AUC) 2. Bootstrapping Using optimized parameters, compute the predictive scores for each drug (IC50 and AUC) 3. Get top predictive genes* Sort the genes by predictive scores and get top 20 genes. Output: predictive score of all genes for each drug (IC50 and AUC) 4. Final model construction*: i. Get coefficients Output: Average coefficiencts of every predictors in each model (IC50 and AUC) ii. Model performance assessments Pearson's correlation coefficients, kendall's tau
DOI: 10.6084/m9.figshare.6480380
title: In Search of the Golden Query: Dimensions and VIVO

description: Poster presentation at the 9th annual VIVO Conference, June 7, 2018, Durham, NC.
DOI: 10.6084/m9.figshare.6480380.v2
title: In Search of the Golden Query: Dimensions and VIVO

description: Poster presentation at the 9th annual VIVO Conference, June 7, 2018, Durham, NC.
DOI: 10.6084/m9.figshare.6480275
title: Radar altimetry data of Erebus volcano's active lava lake

description: Time series of lava lake level measurements made at Erebus volcano in December 2016 using version 1 of the Eredar radar system.<br>chirp_data.h5 : HDF5 file containing the raw chirp (ADC counts) data and the acquisition times.<br>normalised_lake_levels.txt : The lake levels (relative to the mean level) in metres, derived from the raw chirp data.<br>compute_lake_levels.py : A Python 2.7 script to compute the normalised lake levels from the raw chirp data. Requires NumPy, SciPy, and PyTables to run.
DOI: 10.6084/m9.figshare.6480266
title: Supporting data for 'High quality reference genomes for toxigenic and non-toxigenic Vibrio cholerae serogroup O139'

description: Toxigenic <i>Vibrio cholerae</i> of the O139 serogroup have been responsible for several large cholera epidemics in South Asia, and continue to be of clinical and historical significance today. This serogroup was initially feared to represent a new, emerging <i>V. cholerae</i> clone that would lead to an eighth cholera pandemic. However, these concerns were ultimately unfounded. The majority of clinically relevant <i>V. cholerae</i> O139 isolates are closely related to serogroup O1, biotype El Tor <i>V. cholerae</i>, and comprise a single sublineage of the seventh pandemic El Tor lineage. Although related, these <i>V. cholerae</i> serogroups differ in several fundamental ways, in terms of their O-antigen, capsulation phenotype, and the genomic islands found on their chromosomes. Here, we present four complete, high-quality genomes for <i>V. cholerae</i> O139, obtained using long-read sequencing. Three of these sequences are from toxigenic <i>V. cholerae</i>, and one is from a bacterium which, although classified serologically as <i>V. cholerae</i> O139, lacks the CTXφ bacteriophage and the ability to produce cholera toxin. We highlight fundamental genomic differences between these isolates, the <i>V. cholerae</i> O1 reference strain N16961, and the prototypical O139 strain MO10. These sequences are an important resource for the scientific community, and will improve greatly our ability to perform genomic analyses of non-O1 <i>V. cholerae</i> in the future. These genomes also offer new insights into the biology of a <i>V. cholerae</i> serogroup that, from a genomic perspective, is poorly understood.
DOI: 10.6084/m9.figshare.6480266.v1
title: Supporting data for 'High quality reference genomes for toxigenic and non-toxigenic Vibrio cholerae serogroup O139'

description: Toxigenic <i>Vibrio cholerae</i> of the O139 serogroup have been responsible for several large cholera epidemics in South Asia, and continue to be of clinical and historical significance today. This serogroup was initially feared to represent a new, emerging <i>V. cholerae</i> clone that would lead to an eighth cholera pandemic. However, these concerns were ultimately unfounded. The majority of clinically relevant <i>V. cholerae</i> O139 isolates are closely related to serogroup O1, biotype El Tor <i>V. cholerae</i>, and comprise a single sublineage of the seventh pandemic El Tor lineage. Although related, these <i>V. cholerae</i> serogroups differ in several fundamental ways, in terms of their O-antigen, capsulation phenotype, and the genomic islands found on their chromosomes. Here, we present four complete, high-quality genomes for <i>V. cholerae</i> O139, obtained using long-read sequencing. Three of these sequences are from toxigenic <i>V. cholerae</i>, and one is from a bacterium which, although classified serologically as <i>V. cholerae</i> O139, lacks the CTXφ bacteriophage and the ability to produce cholera toxin. We highlight fundamental genomic differences between these isolates, the <i>V. cholerae</i> O1 reference strain N16961, and the prototypical O139 strain MO10. These sequences are an important resource for the scientific community, and will improve greatly our ability to perform genomic analyses of non-O1 <i>V. cholerae</i> in the future. These genomes also offer new insights into the biology of a <i>V. cholerae</i> serogroup that, from a genomic perspective, is poorly understood.
DOI: 10.6084/m9.figshare.6478529
title: Evidências de uma divisa racial de riquezas no Brasil urbano

description: Programa de analise dos dados da PNAD para o artigo Evidências de uma divisa racial de riquezas no Brasil urbano a ser publicado na RBS
DOI: 10.6084/m9.figshare.6478529.v2
title: Evidências de uma divisa racial de riquezas no Brasil urbano

description: Programa de analise dos dados da PNAD para o artigo Evidências de uma divisa racial de riquezas no Brasil urbano a ser publicado na RBS
DOI: 10.6084/m9.figshare.6478529.v1
title: Evidências de uma divisa racial de riquezas no Brasil urbano

description: Programa de analise dos dados da PNAD para o artigo Evidências de uma divisa racial de riquezas no Brasil urbano a ser publicado na RBS
DOI: 10.6084/m9.figshare.6478148
title: Russian prepositions

description: The data shows comparative tables on different sources including Russian preposition lists. The sources are Russian grammars, scientific researches and defining dictionaries.
DOI: 10.6084/m9.figshare.6477845
title: Animal S1 - inverted DCMs for each time window

description: This dataset contains the inverted dynamic causal model summaries of seizure activity in the zebrafish brain used in the analysis described here: github.com/roschkoenig/Zebrafish_Seizures
DOI: 10.6084/m9.figshare.6477845.v1
title: Animal S1 - inverted DCMs for each time window

description: This dataset contains the inverted dynamic causal model summaries of seizure activity in the zebrafish brain used in the analysis described here: github.com/roschkoenig/Zebrafish_Seizures
DOI: 10.6084/m9.figshare.6477782
title: Twitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction

description: This dataset is created by leveraging the social media platforms such as twitter for developing corpus across multiple languages. The corpus creation methodology is applicable for resource-scarce languages provided the speakers of that particular language are active users on social media platforms. We present an approach to extract social media microblogs such as tweets (Twitter). We created corpus for multilingual sentiment analysis and emoji prediction in Hindi, Bengali and Telugu. Further, we perform and analyze multiple NLP tasks utilizing the corpus to get interesting observations.
DOI: 10.6084/m9.figshare.6477782.v1
title: Twitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction

description: This dataset is created by leveraging the social media platforms such as twitter for developing corpus across multiple languages. The corpus creation methodology is applicable for resource-scarce languages provided the speakers of that particular language are active users on social media platforms. We present an approach to extract social media microblogs such as tweets (Twitter). We created corpus for multilingual sentiment analysis and emoji prediction in Hindi, Bengali and Telugu. Further, we perform and analyze multiple NLP tasks utilizing the corpus to get interesting observations.
DOI: 10.6084/m9.figshare.6477782.v2
title: Twitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction

description: This dataset is created by leveraging the social media platforms such as twitter for developing corpus across multiple languages. The corpus creation methodology is applicable for resource-scarce languages provided the speakers of that particular language are active users on social media platforms. We present an approach to extract social media microblogs such as tweets (Twitter). We created corpus for multilingual sentiment analysis and emoji prediction in Hindi, Bengali and Telugu. Further, we perform and analyze multiple NLP tasks utilizing the corpus to get interesting observations.
DOI: 10.6084/m9.figshare.6477782.v3
title: Twitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction

description: This dataset is created by leveraging the social media platforms such as twitter for developing corpus across multiple languages. The corpus creation methodology is applicable for resource-scarce languages provided the speakers of that particular language are active users on social media platforms. We present an approach to extract social media microblogs such as tweets (Twitter). We created corpus for multilingual sentiment analysis and emoji prediction in Hindi, Bengali and Telugu. Further, we perform and analyze multiple NLP tasks utilizing the corpus to get interesting observations.
DOI: 10.6084/m9.figshare.6477782.v4
title: Twitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction

description: This dataset is created by leveraging the social media platforms such as twitter for developing corpus across multiple languages. The corpus creation methodology is applicable for resource-scarce languages provided the speakers of that particular language are active users on social media platforms. We present an approach to extract social media microblogs such as tweets (Twitter). We created corpus for multilingual sentiment analysis and emoji prediction in Hindi, Bengali and Telugu. Further, we perform and analyze multiple NLP tasks utilizing the corpus to get interesting observations.
DOI: 10.6084/m9.figshare.6477782.v5
title: Twitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction

description: This dataset is created by leveraging the social media platforms such as twitter for developing corpus across multiple languages. The corpus creation methodology is applicable for resource-scarce languages provided the speakers of that particular language are active users on social media platforms. We present an approach to extract social media microblogs such as tweets (Twitter). We created corpus for multilingual sentiment analysis and emoji prediction in Hindi, Bengali and Telugu. Further, we perform and analyze multiple NLP tasks utilizing the corpus to get interesting observations.
DOI: 10.6084/m9.figshare.6477743
title: Saved Models

description: Saved Models<br>Topics: Abortion, Evolution, Gay Marriage, and Gun Control
DOI: 10.6084/m9.figshare.6477743.v2
title: Saved Models

description: Saved Models<br>Topics: Abortion, Evolution, Gay Marriage, and Gun Control
DOI: 10.6084/m9.figshare.6477743.v1
title: Saved Model

description: Saved Model
DOI: 10.15161/oar.it/30653
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.18112/openneuro.ds003098.v1.0.0
title: ABCD protocol for BCBP
DOI: 10.6084/m9.figshare.6477494
title: Wnt evolution and function shuffling in liberal and conservative chordate genomes

description: Additional file 1 (pdf file; 11.8 MB) includes: <b>Figure S1</b>. Evolution of Wnt5 in ascidians. <b>Figure S2</b>. Expression of WntA in two ascidian species. <b>Figure S3</b>. Chordate Wnt expression. <b>Figure S4</b>. Wnt subfamilies in <i>A. lucayanum</i>, <i>P. marinus</i> and <i>C. milii</i>. <b>Table S1</b>. Non-vertebrate chordate Wnt genes. <b>Table S2</b>. <i>Branchiostoma lanceolatum</i> and <i>Halocynthia roretzi</i> primer and probe sequences. <b>Table S3</b>. Genomic coordinates (scaffold or chromosome) for cephalochordate, human and lamprey <i>Wnt</i> genes. <b>Text S1</b>. <i>Branchiostoma lanceolatum Wnt</i> expression as shown in Figure 2. <b>Text S2</b>. References for Figure S3. Additional file 2 (fasta file; 315 KB): Wnt sequence alignement for Figure 1. Additional file 3 (fasta file; 102 KB): Wnt sequence alignment for Figure S4.
DOI: 10.6084/m9.figshare.6477494.v1
title: Wnt evolution and function shuffling in liberal and conservative chordate genomes

description: Additional file 1 (pdf file; 11.8 MB) includes: <b>Figure S1</b>. Evolution of Wnt5 in ascidians. <b>Figure S2</b>. Expression of WntA in two ascidian species. <b>Figure S3</b>. Chordate Wnt expression. <b>Figure S4</b>. Wnt subfamilies in <i>A. lucayanum</i>, <i>P. marinus</i> and <i>C. milii</i>. <b>Table S1</b>. Non-vertebrate chordate Wnt genes. <b>Table S2</b>. <i>Branchiostoma lanceolatum</i> and <i>Halocynthia roretzi</i> primer and probe sequences. <b>Table S3</b>. Genomic coordinates (scaffold or chromosome) for cephalochordate, human and lamprey <i>Wnt</i> genes. <b>Text S1</b>. <i>Branchiostoma lanceolatum Wnt</i> expression as shown in Figure 2. <b>Text S2</b>. References for Figure S3. Additional file 2 (fasta file; 315 KB): Wnt sequence alignement for Figure 1. Additional file 3 (fasta file; 102 KB): Wnt sequence alignment for Figure S4.
DOI: 10.6084/m9.figshare.6477446
title: data.zip

description: Dataset
DOI: 10.6084/m9.figshare.6477446.v1
title: data.zip

description: Dataset
DOI: 10.5281/zenodo.3980355
title: MLED - Kenya Country-Study Input Data

description: <strong>M - LED: the Multi-Sectoral Latent Electricity Demand platform</strong> Input data for the Kenya country-study Download the MLED_database.zip file and unzip it to a path on your local machine. <strong>Ensure to unzip the database in a location where there are several gigabytes of space available, otherwise the process will file. This space will not be occupied permanently, but is required for the data processing to succeed.</strong> The folder contains data for replicating the Kenya country study, as well as an 'data_sources.txt' instructions file to support for retrieving data for other countries' data. Code and instructions for running the analysis: https://github.com/giacfalk/M-LED
DOI: 10.6084/m9.figshare.6477320
title: Multiple Sclerosis metabolomics data ver. april 2018

description: This dataset contains metabolomics information of five groups of samples: Control, Multiple Sclerosis, Neurodegeneration groups of dried plasma samples and two groups of dried blood QC samples (L1 and L2). Control and Multiple Sclerosis group are validated for data analysis, Neurodegeneration is still in progress. There are 43 metabolites (13 amino acids and 30 acylcarnitines) and their isotope-labeled internal standards and 5 parameters were measured: Component peak Area, Internal Standard Area, Component peak Height, Internal Standard Height, Signal-to-Noise Ratio. Data were obtained from Multiquant Software ver 2.0 (AB Sciex), Raw data were collected with Analyst ver. 1.6.2 (AB Sciex) and mass-spectrometer API 3200QTRAP in DIMS configuration (without column separation) in MRM mode. Chromsystems MassCheck 55000 test-system and protocol was used for sample preparation. Additional columns (Calculated concentration) was added in Excel (Peak Area parameter was used for calculation)<br>
DOI: 10.6084/m9.figshare.6477320.v1
title: Multiple Sclerosis metabolomics data ver. april 2018

description: This dataset contains metabolomics information of five groups of samples: Control, Multiple Sclerosis, Neurodegeneration groups of dried plasma samples and two groups of dried blood QC samples (L1 and L2). Control and Multiple Sclerosis group are validated for data analysis, Neurodegeneration is still in progress. There are 43 metabolites (13 amino acids and 30 acylcarnitines) and their isotope-labeled internal standards and 5 parameters were measured: Component peak Area, Internal Standard Area, Component peak Height, Internal Standard Height, Signal-to-Noise Ratio. Data were obtained from Multiquant Software ver 2.0 (AB Sciex), Raw data were collected with Analyst ver. 1.6.2 (AB Sciex) and mass-spectrometer API 3200QTRAP in DIMS configuration (without column separation) in MRM mode. Chromsystems MassCheck 55000 test-system and protocol was used for sample preparation. Additional columns (Calculated concentration) was added in Excel (Peak Area parameter was used for calculation)<br>