cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.9776075.v2
title: Data of: Differences in the structural chemical composition of the primary xylem of Cactaceae: a topochemical perspective

description: This data represent the gravimetric quantification of the structural chemical compounds of primary xylem of seedlings from Cactaceae, mean, standar deviation and coefficient of variation are part of the objectives. Ftir spectre of the eight seedling species are present too.
DOI: 10.6084/m9.figshare.9775955
title: Individual Learning Phenotypes Drive Collective Behavior Dataset

description: Variation in individual cognition affects how animals learn about and communicate information to others. We provide evidence that differences in how individual honey bees learn influences the collective foraging dynamics of a colony. By creating colonies of distinct learning phenotypes, we evaluated how bees make foraging choices in the field. Colonies containing individuals that learn to ignore unimportant information preferred familiar food locations; however, colonies of individuals that are unable to ignore familiar information visit novel and familiar feeders equally. Colonies with a 50/50 mix of these phenotypes prefer familiar food locations because individuals who learn the familiar location recruit nestmates by dancing more intensely. Our results reveal that cognitive variation among individuals nonlinearly shapes collective behavioral outcomes.
DOI: 10.6084/m9.figshare.9775835
title: Relationships between glucocorticoids and infection with Batrachochytrium dendrobatidis in three amphibian species

description: Pennsylvania Rana sylvatica, Rana catesbeiana, and Rana clamitans salivary corticosterone and Batrachochytrium dendrobatidis infection load data, paired with associated data (mass, SVL, location, date, etc.).
DOI: 10.6084/m9.figshare.9775955.v1
title: Individual Learning Phenotypes Drive Collective Behavior

description: Variation in individual cognition affects how animals learn about and communicate information to others. We provide evidence that differences in how individual honey bees learn influences the collective foraging dynamics of a colony. By creating colonies of distinct learning phenotypes, we evaluated how bees make foraging choices in the field. Colonies containing individuals that learn to ignore unimportant information preferred familiar food locations; however, colonies of individuals that are unable to ignore familiar information visit novel and familiar feeders equally. Colonies with a 50/50 mix of these phenotypes prefer familiar food locations because individuals who learn the familiar location recruit nestmates by dancing more intensely. Our results reveal that cognitive variation among individuals nonlinearly shapes collective behavioral outcomes.
DOI: 10.6084/m9.figshare.9775835.v1
title: Relationships between glucocorticoids and infection with Batrachochytrium dendrobatidis in three amphibian species

description: Pennsylvania Rana sylvatica, Rana catesbeiana, and Rana clamitans salivary corticosterone and Batrachochytrium dendrobatidis infection load data, paired with associated data (mass, SVL, location, date, etc.).
DOI: 10.6084/m9.figshare.9775955.v2
title: Individual Learning Phenotypes Drive Collective Behavior

description: Variation in individual cognition affects how animals learn about and communicate information to others. We provide evidence that differences in how individual honey bees learn influences the collective foraging dynamics of a colony. By creating colonies of distinct learning phenotypes, we evaluated how bees make foraging choices in the field. Colonies containing individuals that learn to ignore unimportant information preferred familiar food locations; however, colonies of individuals that are unable to ignore familiar information visit novel and familiar feeders equally. Colonies with a 50/50 mix of these phenotypes prefer familiar food locations because individuals who learn the familiar location recruit nestmates by dancing more intensely. Our results reveal that cognitive variation among individuals nonlinearly shapes collective behavioral outcomes.
DOI: 10.6084/m9.figshare.9775505
title: Mixing state evolution of agglomerating particles in an aerosol chamber: Comparison of measurements and particle-resolved simulations

description: This article presents a validation study of the stochastic particle-resolved aerosol model PartMC with experimental data from an aerosol chamber experiment. For the experiment, a scanning mobility particle sizer and a single-particle soot photometer were used to monitor the aerosol mixing state evolution of two initially externally mixed aerosol populations of ammonium sulfate and black carbon particles undergoing agglomeration. We applied an efficient optimization algorithm (ProSRS) to determine several unconstrained simulation parameters and were able to successfully reproduce number concentrations and size distributions of mixed particles that formed by agglomeration. The PartMC modeling approach in conjunction with the optimization procedure provides a tool for detailed comparisons of chamber experiments and modeling, where aerosol mixing state is the focus of investigation. Copyright © 2019 American Association for Aerosol Research
DOI: 10.6084/m9.figshare.9775496
title: California Vaccine Policy Data

description: Coverage and Exemption data used in California Vaccine Policy Manuscript by Lo, Karpel and Nyathi.
DOI: 10.6084/m9.figshare.9775496.v1
title: California Vaccine Policy Data

description: Coverage and Exemption data used in California Vaccine Policy Manuscript by Lo, Karpel and Nyathi.
DOI: 10.6084/m9.figshare.9775955.v3
title: Individual Learning Phenotypes Drive Collective Behavior

description: Variation in individual cognition affects how animals learn about and communicate information to others. We provide evidence that differences in how individual honey bees learn influences the collective foraging dynamics of a colony. By creating colonies of distinct learning phenotypes, we evaluated how bees make foraging choices in the field. Colonies containing individuals that learn to ignore unimportant information preferred familiar food locations; however, colonies of individuals that are unable to ignore familiar information visit novel and familiar feeders equally. Colonies with a 50/50 mix of these phenotypes prefer familiar food locations because individuals who learn the familiar location recruit nestmates by dancing more intensely. Our results reveal that cognitive variation among individuals nonlinearly shapes collective behavioral outcomes.
DOI: 10.6084/m9.figshare.9774809
title: Data sets - NAFLD

description: Clinical data related to the publication with PMID:31711876<br>
DOI: 10.6084/m9.figshare.9775505.v2
title: Mixing state evolution of agglomerating particles in an aerosol chamber: Comparison of measurements and particle-resolved simulations

description: This article presents a validation study of the stochastic particle-resolved aerosol model PartMC with experimental data from an aerosol chamber experiment. For the experiment, a scanning mobility particle sizer and a single-particle soot photometer were used to monitor the aerosol mixing state evolution of two initially externally mixed aerosol populations of ammonium sulfate and black carbon particles undergoing agglomeration. We applied an efficient optimization algorithm (ProSRS) to determine several unconstrained simulation parameters and were able to successfully reproduce number concentrations and size distributions of mixed particles that formed by agglomeration. The PartMC modeling approach in conjunction with the optimization procedure provides a tool for detailed comparisons of chamber experiments and modeling, where aerosol mixing state is the focus of investigation. Copyright © 2019 American Association for Aerosol Research
DOI: 10.6084/m9.figshare.9775955.v4
title: Individual Learning Phenotypes Drive Collective Behavior

description: Variation in individual cognition affects how animals learn about and communicate information to others. We provide evidence that differences in how individual honey bees learn influences the collective foraging dynamics of a colony. By creating colonies of distinct learning phenotypes, we evaluated how bees make foraging choices in the field. Colonies containing individuals that learn to ignore unimportant information preferred familiar food locations; however, colonies of individuals that are unable to ignore familiar information visit novel and familiar feeders equally. Colonies with a 50/50 mix of these phenotypes prefer familiar food locations because individuals who learn the familiar location recruit nestmates by dancing more intensely. Our results reveal that cognitive variation among individuals nonlinearly shapes collective behavioral outcomes.
DOI: 10.6084/m9.figshare.9775505.v1
title: Mixing state evolution of agglomerating particles in an aerosol chamber: Comparison of measurements and particle-resolved simulations

description: This article presents a validation study of the stochastic particle-resolved aerosol model PartMC with experimental data from an aerosol chamber experiment. For the experiment, a scanning mobility particle sizer and a single-particle soot photometer were used to monitor the aerosol mixing state evolution of two initially externally-mixed aerosol populations of ammonium sulfate and black carbon particles undergoing agglomeration. We applied an efficient optimization algorithm (ProSRS) to determine several unconstrained simulation parameters and were able to successfully reproduce number concentrations and size distributions of mixed particles that formed by agglomeration. The PartMC modeling approach in conjunction with the optimization procedure provides a tool for detailed comparisons of chamber experiments and modeling, where aerosol mixing state is the focus of investigation. Copyright © 2019 American Association for Aerosol Research
DOI: 10.6084/m9.figshare.9773843
title: R command history in MetaboanalystR - NAFLD

description: R history<br>
DOI: 10.6084/m9.figshare.9775955.v6
title: Individual Learning Phenotypes Drive Collective Behavior Dataset

description: Variation in individual cognition affects how animals learn about and communicate information to others. We provide evidence that differences in how individual honey bees learn influences the collective foraging dynamics of a colony. By creating colonies of distinct learning phenotypes, we evaluated how bees make foraging choices in the field. Colonies containing individuals that learn to ignore unimportant information preferred familiar food locations; however, colonies of individuals that are unable to ignore familiar information visit novel and familiar feeders equally. Colonies with a 50/50 mix of these phenotypes prefer familiar food locations because individuals who learn the familiar location recruit nestmates by dancing more intensely. Our results reveal that cognitive variation among individuals nonlinearly shapes collective behavioral outcomes.
DOI: 10.6084/m9.figshare.9775955.v5
title: Individual Learning Phenotypes Drive Collective Behavior

description: Variation in individual cognition affects how animals learn about and communicate information to others. We provide evidence that differences in how individual honey bees learn influences the collective foraging dynamics of a colony. By creating colonies of distinct learning phenotypes, we evaluated how bees make foraging choices in the field. Colonies containing individuals that learn to ignore unimportant information preferred familiar food locations; however, colonies of individuals that are unable to ignore familiar information visit novel and familiar feeders equally. Colonies with a 50/50 mix of these phenotypes prefer familiar food locations because individuals who learn the familiar location recruit nestmates by dancing more intensely. Our results reveal that cognitive variation among individuals nonlinearly shapes collective behavioral outcomes.
DOI: 10.6084/m9.figshare.9774809.v2
title: Data sets - NAFLD

description: Clinical data related to the publication with PMID:31711876<br>
DOI: 10.6084/m9.figshare.9773843.v1
title: R command history in MetaboanalystR - NAFLD

description: R history<br>
DOI: 10.6084/m9.figshare.9774809.v3
title: Data sets - NAFLD

description: Clinical data related to the publication with PMID:31711876<br>
DOI: 10.6084/m9.figshare.9774809.v4
title: Data sets - NAFLD

description: Clinical data related to the publication with PMID:31711876<br>
DOI: 10.6084/m9.figshare.9774809.v5
title: Data sets - NAFLD

description: Clinical data related to the publication with PMID:31711876<br>
DOI: 10.6084/m9.figshare.9773753
title: Simplifying the Assessment of Measurement Invariance over Multiple Background Variables: Using Regularized Moderated Nonlinear Factor Analysis to Detect Differential Item Functioning

description: Determining whether measures are equally valid for all individuals is a core component of psychometric analysis. Traditionally, the evaluation of measurement invariance (MI) involves comparing independent groups defined by a single categorical covariate (e.g., men and women) to determine if there are any items that display differential item functioning (DIF). More recently, Moderated Nonlinear Factor Analysis (MNLFA) has been advanced as an approach for evaluating MI/DIF simultaneously over multiple background variables, categorical and continuous. Unfortunately, conventional procedures for detecting DIF do not scale well to the more complex MNLFA. The current manuscript therefore proposes a regularization approach to MNLFA estimation that penalizes the likelihood for DIF parameters (i.e., rewarding sparse DIF). This procedure avoids the pitfalls of sequential inference tests, is automated for end users, and is shown to perform well in both a small-scale simulation and an empirical validation study.
DOI: 10.6084/m9.figshare.9773717
title: Tweets and Gab Postings for source classification

description: Data can be open using pickle with "rb" model.<br>LanguageExample object could be found here:https://gist.github.com/billweasley/b8e05969b5b2267473d13f263253812e<br><br><br>equally_split_examples.pickle:<br><br>- Hashtag Equally Devide Dataset [Ensure Hashtag (topic) distribution]- It is a dictionary containing two keys: "train" and "dev"<br>- The value in the dictionary is a list of <i>LanguageExample </i>object- 1168 Tweets, 1168 Gab Postings- Label: "0" indicates the sample from Gab, "1" indicates the sample from Twitter<br>random_split_examples.pickle:<br><br>- Random Devide Dataset [Ensure only the gab and tweet has a same number of samples, but do not ensure it has same distribution of hashtags for the different social network platforms]- It is a dictionary containing two keys: "train" and "dev"<br>- The value in the dictionary is a list of <i>LanguageExample </i>object- 2181 Tweets, 2181 Gab Postings- Label: "0" indicates the sample from Gab, "1" indicates the sample from Twitter<br>gabs_tags_information_maps.pickle- Original Gabs collected and pre-processed (URL removels, HTTP tag removel, HTTP decoding)- A map where the key is hashtags and the value is a list of gab language associated to the correspoding hashtag.<br><br>tweets_tags_information_maps.pickle- Original Tweets collected and pre-processed (URL removels, HTTP tag removel, HTTP decoding)- A map where the key is hashtags and the value is a list of tweets language associated to the correspoding hashtag.
DOI: 10.6084/m9.figshare.9773729
title: Design and validation of a robust multiplex polymerase chain reaction assay for <i>MAT</i> idiomorph within the <i>Fusarium fujikuroi</i> species complex

description: We discovered that published polymerase chain reaction (PCR) assays for determining mating type (<i>MAT</i>) idiomorph failed to genotype some of the <i>Fusarium fujikuroi</i> species complex (FFSC) isolates recovered from <i>Mangifera indica</i> (mango), <i>Swietenia macrophylla</i> (big-leaf mahogany), <i>Annona muricata</i> (soursop), <i>Bursera</i> sp., and <i>Tabebuia</i> sp. in Mexico. Thus, the primary objective of this study was to design and validate a robust multiplex PCR–based diagnostic for typing <i>MAT</i> within the FFSC. To accomplish this objective, we mined the <i>MAT1-1</i> or <i>MAT1-2</i> locus from the genomes of 60 FFSC isolates, representing 56 phylospecies, and from four species in its sister group, the <i>F. nisikadoi</i> species complex (FNSC). Bioinformatic searches were facilitated by targeting DNA lyase (<i>SLA2</i>) and apurinic endonuclease (<i>APN1</i>), the genes that flank the <i>MAT</i> locus in <i>Fusarium</i>. As expected, three genes were identified within <i>MAT1-1</i> (<i>MAT1-1-1, MAT1-1-2</i>, and <i>MAT1-1-3</i>) and two in <i>MAT1-2</i> (<i>MAT1-2-1</i> and <i>MAT1-2-9</i>), using the <i>ab initio</i> prediction tool AUGUSTUS. Of the three multiplex PCR assays we designed and tested, the one targeting <i>MAT1-1-2</i> and <i>MAT1-2-1</i> successfully genotyped the entire 71-isolate validation panel, which included 56 FFSC and 4 FNSC phylospecies. By contrast, the published PCR assays we tested produced positive genotypes for only 46.5–59% of the 71-isolate validation panel, but only when they were run as a uniplex assay. Although only one-fifth of the FFSC/FNSC are known to reproduce sexually, our results suggest that if they possess a sexual cycle, it is heterothallic (self-sterile).
DOI: 10.6084/m9.figshare.9773753.v1
title: Simplifying the Assessment of Measurement Invariance over Multiple Background Variables: Using Regularized Moderated Nonlinear Factor Analysis to Detect Differential Item Functioning

description: Determining whether measures are equally valid for all individuals is a core component of psychometric analysis. Traditionally, the evaluation of measurement invariance (MI) involves comparing independent groups defined by a single categorical covariate (e.g., men and women) to determine if there are any items that display differential item functioning (DIF). More recently, Moderated Nonlinear Factor Analysis (MNLFA) has been advanced as an approach for evaluating MI/DIF simultaneously over multiple background variables, categorical and continuous. Unfortunately, conventional procedures for detecting DIF do not scale well to the more complex MNLFA. The current manuscript therefore proposes a regularization approach to MNLFA estimation that penalizes the likelihood for DIF parameters (i.e., rewarding sparse DIF). This procedure avoids the pitfalls of sequential inference tests, is automated for end users, and is shown to perform well in both a small-scale simulation and an empirical validation study.
DOI: 10.6084/m9.figshare.9773729.v1
title: Design and validation of a robust multiplex polymerase chain reaction assay for <i>MAT</i> idiomorph within the <i>Fusarium fujikuroi</i> species complex

description: We discovered that published polymerase chain reaction (PCR) assays for determining mating type (<i>MAT</i>) idiomorph failed to genotype some of the <i>Fusarium fujikuroi</i> species complex (FFSC) isolates recovered from <i>Mangifera indica</i> (mango), <i>Swietenia macrophylla</i> (big-leaf mahogany), <i>Annona muricata</i> (soursop), <i>Bursera</i> sp., and <i>Tabebuia</i> sp. in Mexico. Thus, the primary objective of this study was to design and validate a robust multiplex PCR–based diagnostic for typing <i>MAT</i> within the FFSC. To accomplish this objective, we mined the <i>MAT1-1</i> or <i>MAT1-2</i> locus from the genomes of 60 FFSC isolates, representing 56 phylospecies, and from four species in its sister group, the <i>F. nisikadoi</i> species complex (FNSC). Bioinformatic searches were facilitated by targeting DNA lyase (<i>SLA2</i>) and apurinic endonuclease (<i>APN1</i>), the genes that flank the <i>MAT</i> locus in <i>Fusarium</i>. As expected, three genes were identified within <i>MAT1-1</i> (<i>MAT1-1-1, MAT1-1-2</i>, and <i>MAT1-1-3</i>) and two in <i>MAT1-2</i> (<i>MAT1-2-1</i> and <i>MAT1-2-9</i>), using the <i>ab initio</i> prediction tool AUGUSTUS. Of the three multiplex PCR assays we designed and tested, the one targeting <i>MAT1-1-2</i> and <i>MAT1-2-1</i> successfully genotyped the entire 71-isolate validation panel, which included 56 FFSC and 4 FNSC phylospecies. By contrast, the published PCR assays we tested produced positive genotypes for only 46.5–59% of the 71-isolate validation panel, but only when they were run as a uniplex assay. Although only one-fifth of the FFSC/FNSC are known to reproduce sexually, our results suggest that if they possess a sexual cycle, it is heterothallic (self-sterile).
DOI: 10.6084/m9.figshare.9773624
title: Metadata record for: STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse

description: This dataset contains key characteristics about the data described in the Data Descriptor STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.<br>
DOI: 10.6084/m9.figshare.9773717.v2
title: Tweets and Gab Postings for source classification

description: Data can be open using pickle with "rb" model.<br>LanguageExample object could be found here:https://gist.github.com/billweasley/b8e05969b5b2267473d13f263253812e<br><br><br>equally_split_examples.pickle:<br><br>- Hashtag Equally Devide Dataset [Ensure Hashtag (topic) distribution]- It is a dictionary containing two keys: "train" and "dev"<br>- The value in the dictionary is a list of <i>LanguageExample </i>object- 1168 Tweets, 1168 Gab Postings- Label: "0" indicates the sample from Gab, "1" indicates the sample from Twitter<br>random_split_examples.pickle:<br><br>- Random Devide Dataset [Ensure only the gab and tweet has a same number of samples, but do not ensure it has same distribution of hashtags for the different social network platforms]- It is a dictionary containing two keys: "train" and "dev"<br>- The value in the dictionary is a list of <i>LanguageExample </i>object- 2181 Tweets, 2181 Gab Postings- Label: "0" indicates the sample from Gab, "1" indicates the sample from Twitter<br>gabs_tags_information_maps.pickle- Original Gabs collected and pre-processed (URL removels, HTTP tag removel, HTTP decoding)- A map where the key is hashtags and the value is a list of gab language associated to the correspoding hashtag.<br><br>tweets_tags_information_maps.pickle- Original Tweets collected and pre-processed (URL removels, HTTP tag removel, HTTP decoding)- A map where the key is hashtags and the value is a list of tweets language associated to the correspoding hashtag.
DOI: 10.6084/m9.figshare.9773366
title: Bacterial diversity in Icelandic cold spring sources and in relation to the groundwater amphipod Crangonyx islandicus

description: The following files are data belonging to the manuscript: Bacterial diversity in Icelandic cold spring sources and in relation to the groundwater amphipod Crangonyx islandicus: <br>Ragnhildur Guðmundsdóttir, Agnes-Katharina Kreiling, Bjarni Kristófer Kristjánsson, Viggó Þór Marteinsson, Snæbjörn Pálsson 2019-08-05This describes the data files and supplementary files accompanying <br>the above publication.<br>For any further queries please contact rag41@hi.is or snaebj@hi.isThe following files are included: 1) Otu-table with non-rarefied read counts in a .csv file. Row names are sample names <br>and this is the same as in Table 1 in the publication. Column names are the names of the<br>closes relatives of the otu according to the SILVAngs database (release 128).2) Cell counts in a .csv file. The table contains three columns. The first column refers<br>to sample location and is the same as in Fig 1 in the publication above. Column two is the sample type and<br>the third column contains the number of cells per ml.