cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11704236.v7
title: Bayesian Inference for Regression Copulas

description: We propose a new semiparametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula is high-dimensional and constructed by inversion of a pseudo regression, where the conditional mean and variance are semiparametric functions of covariates modeled using regularized basis functions. By integrating out the basis coefficients, an implicit copula process on the covariate space is obtained, which we call a “regression copula.” We combine this with a nonparametric margin to define a copula model, where the entire distribution—including the mean and variance—of the response is a smooth semiparametric function of the covariates. The copula is estimated using both Hamiltonian Monte Carlo and variational Bayes; the latter of which is scalable to high dimensions. Using real data examples and a simulation study, we illustrate the efficacy of these estimators and the copula model. In a substantive example, we estimate the distribution of half-hourly electricity spot prices as a function of demand and two time covariates using radial bases and horseshoe regularization. The copula model produces distributional estimates that are locally adaptive with respect to the covariates, and predictions that are more accurate than those from benchmark models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11704236.v6
title: Bayesian Inference for Regression Copulas

description: We propose a new semiparametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula is high-dimensional and constructed by inversion of a pseudo regression, where the conditional mean and variance are semiparametric functions of covariates modeled using regularized basis functions. By integrating out the basis coefficients, an implicit copula process on the covariate space is obtained, which we call a “regression copula.” We combine this with a nonparametric margin to define a copula model, where the entire distribution—including the mean and variance—of the response is a smooth semiparametric function of the covariates. The copula is estimated using both Hamiltonian Monte Carlo and variational Bayes; the latter of which is scalable to high dimensions. Using real data examples and a simulation study, we illustrate the efficacy of these estimators and the copula model. In a substantive example, we estimate the distribution of half-hourly electricity spot prices as a function of demand and two time covariates using radial bases and horseshoe regularization. The copula model produces distributional estimates that are locally adaptive with respect to the covariates, and predictions that are more accurate than those from benchmark models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12851349.v4
title: Bias and High-Dimensional Adjustment in Observational Studies of Peer Effects

description: Peer effects, in which an individual’s behavior is affected by peers’ behavior, are posited by multiple theories in the social sciences. Randomized field experiments that identify peer effects, however, are often expensive or infeasible, so many studies of peer effects use observational data, which is expected to suffer from confounding. Here we show, in the context of information and media diffusion, that high-dimensional adjustment of a nonexperimental control group (660 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those using a large randomized experiment (215 million observations). Compared with the experiment, naive observational estimators overstate peer effects by over 300% and commonly available variables (e.g., demographics) offer little bias reduction. Adjusting for a measure of prior behaviors closely related to the focal behavior reduces this bias by 91%, while models adjusting for over 3700 past behaviors provide additional bias reduction, reducing bias by over 97%, which is statistically indistinguishable from unbiasedness. This demonstrates how detailed records of behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of peer effects in rare or new behaviors. More generally, these results show how large, high-dimensional datasets and statistical learning can be used to improve causal inference. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12844511
title: Marginally Calibrated Deep Distributional Regression

description: Deep neural network (DNN) regression models are widely used in applications requiring state-of-the-art predictive accuracy. However, until recently there has been little work on accurate uncertainty quantification for predictions from such models. We add to this literature by outlining an approach to constructing predictive distributions that are “marginally calibrated.” This is where the long run average of the predictive distributions of the response variable matches the observed empirical margin. Our approach considers a DNN regression with a conditionally Gaussian prior for the final layer weights, from which an implicit copula process on the feature space is extracted. This copula process is combined with a non-parametrically estimated marginal distribution for the response. The end result is a scalable distributional DNN regression method with marginally calibrated predictions, and our work complements existing methods for probability calibration. The approach is first illustrated using two applications of dense layer feed-forward neural networks. However, our main motivating applications are in likelihood-free inference, where distributional deep regression is used to estimate marginal posterior distributions. In two complex ecological time series examples, we employ the implicit copulas of convolutional networks, and show that marginal calibration results in improved uncertainty quantification. Our approach also avoids the need for manual specification of summary statistics, a requirement that is burdensome for users and typical of competing likelihood-free inference methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12844511.v3
title: Marginally Calibrated Deep Distributional Regression

description: Deep neural network (DNN) regression models are widely used in applications requiring state-of-the-art predictive accuracy. However, until recently there has been little work on accurate uncertainty quantification for predictions from such models. We add to this literature by outlining an approach to constructing predictive distributions that are “marginally calibrated.” This is where the long run average of the predictive distributions of the response variable matches the observed empirical margin. Our approach considers a DNN regression with a conditionally Gaussian prior for the final layer weights, from which an implicit copula process on the feature space is extracted. This copula process is combined with a non-parametrically estimated marginal distribution for the response. The end result is a scalable distributional DNN regression method with marginally calibrated predictions, and our work complements existing methods for probability calibration. The approach is first illustrated using two applications of dense layer feed-forward neural networks. However, our main motivating applications are in likelihood-free inference, where distributional deep regression is used to estimate marginal posterior distributions. In two complex ecological time series examples, we employ the implicit copulas of convolutional networks, and show that marginal calibration results in improved uncertainty quantification. Our approach also avoids the need for manual specification of summary statistics, a requirement that is burdensome for users and typical of competing likelihood-free inference methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12844511.v2
title: Marginally Calibrated Deep Distributional Regression

description: Deep neural network (DNN) regression models are widely used in applications requiring state-of-the-art predictive accuracy. However, until recently there has been little work on accurate uncertainty quantification for predictions from such models. We add to this literature by outlining an approach to constructing predictive distributions that are “marginally calibrated.” This is where the long run average of the predictive distributions of the response variable matches the observed empirical margin. Our approach considers a DNN regression with a conditionally Gaussian prior for the final layer weights, from which an implicit copula process on the feature space is extracted. This copula process is combined with a non-parametrically estimated marginal distribution for the response. The end result is a scalable distributional DNN regression method with marginally calibrated predictions, and our work complements existing methods for probability calibration. The approach is first illustrated using two applications of dense layer feed-forward neural networks. However, our main motivating applications are in likelihood-free inference, where distributional deep regression is used to estimate marginal posterior distributions. In two complex ecological time series examples, we employ the implicit copulas of convolutional networks, and show that marginal calibration results in improved uncertainty quantification. Our approach also avoids the need for manual specification of summary statistics, a requirement that is burdensome for users and typical of competing likelihood-free inference methods. Supplementary materials for this article are available online.
DOI: 10.15468/r7yccd
title: A description of the early juvenile colour patterns of eleven Lethrinus species (Pisces: Lethrinidae) from the Great Barrier Reef, Australia

description: This dataset contains the digitized treatments in Plazi based on the original journal article Wilson, G. G. (1998): A description of the early juvenile colour patterns of eleven Lethrinus species (Pisces: Lethrinidae) from the Great Barrier Reef, Australia. Records of the Australian Museum 50 (1): 55-83, DOI: 10.3853/j.0067-1975.50.1998.1274, URL: https://journals.australian.museum/wilson-1998-rec-aust-mus-501-5583/
DOI: 10.14457/cu.the.1981.1
title: การประเมินผลทางเศรษฐกิจของการทำงานของระบบพลังงานแสงอาทิตย์ ที่ใช้ปรับอากาศให้กับบ้านพักอาศัยในกรุงเทพฯ
DOI: 10.14457/cu.the.1981.2
title: ปัญญาสชาดก : ประวัติและความสำคัญที่มีต่อวรรณกรรมร้อยกรองของไทย
DOI: 10.14457/cu.the.1981.3
title: การใช้เวลาว่างของนักเรียนระดับมัธยมศึกษา ในเขตกรุงเทพมหานคร
DOI: 10.15129/a79e0465-457a-40cd-81f6-fd2b90df2a3d
title: Data for: "Expected and unexpected reactivities of homoleptic LiNacNac and heteroleptic NacNacMg(TMP) β-diketiminates towards various small unsaturated organic molecules"

description: The dataset contains four files: 1.NMR data (zip file) 2.Infrared data (zip file) 3.Crystallographic information file (X-ray crystallographic data) 4.CheckCIF (for X-ray crystallographic data) This data relates to the research reported in the manuscript "Expected and unexpected reactivities of homoleptic LiNacNac and heteroleptic NacNacMg(TMP) β-diketiminates towards various small unsaturated organic molecules", carried out in connection with the EPSRC DTP award "EP/T517938/1". Please note that the NMR data contained in file 1 requires a specialist program such as Topspin or Mestrelab and the CIF file (file 3) requires a program such as Diamond or Mercury to view.
DOI: 10.5281/zenodo.4656929
title: Gossypium hirsutum BBH (best bidirectional hit) and OMA homoeolog comparisons

description: This dataset contains the results for the paper "<strong>Homoeolog inference methods requiring bidirectional best hits or synteny miss many pairs."</strong> It contains the following data frames: hom_df.csv: All homoeolog pairs inferred from OMA (32426 pairs). Includes pairwise characteristics such as Evolutionary Distance, etc. hom_df_with_synteny_scores.csv: all homoeolog pairs for which synteny scores could be computed (31901 pairs). Includes pairwise characteristics such as BBH category, SyntenyScore, Evolutionary Distance, etc. gene_centric_df.csv: Nonredundant gene-centric dataframe with expression and other metrics tpm_count_goshi.csv: transcripts per kilobase million counts for each gene, using raw RNAseq data from Zhang et al 2015 Supplemental_Table_4_GO_df.csv: Gene Ontology enrichment for each BBH category, only significant results Agenes_vs_Ddb_maxtargetseqs1.blastp: BLASTP results of A subgenome genes as query and D subgenome as database Dgenes_vs_Adb_maxtargetseqs1.blastp: BLASTP results of D subgenome genes as query and A subgenome as database Homoeolog_BBH_vs_OMA_all_analyses_1Apr2021.ipynb: jupyter notebook containing all analyses (ipython notebook version) Homoeolog_BBH_vs_OMA_all_analyses_1Apr2021.html: jupyter notebook containing all analyses (html version)<br> <br>
DOI: 10.5281/zenodo.4656928
title: Gossypium hirsutum BBH (best bidirectional hit) and OMA homoeolog comparisons

description: This dataset contains the results for the paper "<strong>Homoeolog inference methods requiring bidirectional best hits or synteny miss many pairs."</strong> It contains the following data frames: hom_df.csv: All homoeolog pairs inferred from OMA (32426 pairs). Includes pairwise characteristics such as Evolutionary Distance, etc. hom_df_with_synteny_scores.csv: all homoeolog pairs for which synteny scores could be computed (31901 pairs). Includes pairwise characteristics such as BBH category, SyntenyScore, Evolutionary Distance, etc. gene_centric_df.csv: Nonredundant gene-centric dataframe with expression and other metrics tpm_count_goshi.csv: transcripts per kilobase million counts for each gene, using raw RNAseq data from Zhang et al 2015 Supplemental_Table_4_GO_df.csv: Gene Ontology enrichment for each BBH category, only significant results Agenes_vs_Ddb_maxtargetseqs1.blastp: BLASTP results of A subgenome genes as query and D subgenome as database Dgenes_vs_Adb_maxtargetseqs1.blastp: BLASTP results of D subgenome genes as query and A subgenome as database Homoeolog_BBH_vs_OMA_all_analyses_1Apr2021.ipynb: jupyter notebook containing all analyses (ipython notebook version) Homoeolog_BBH_vs_OMA_all_analyses_1Apr2021.html: jupyter notebook containing all analyses (html version)<br> <br>
DOI: 10.21227/t5h9-j127
title: February-3-Basu-Sukanta

description: February-3-Basu-Sukanta
DOI: 10.5281/zenodo.4645907
title: Stratospheric inorganic fluorine data from ground-based FTIR, BASCOE CTM and TOMCAT

description: These datasets are fully described in: M. Prignon, S. Chabrillat, M. Friedrich, D. Smale, S. E. Straha, P. F. Bernath, M. P. Chipperfield, S. S. Dhomse, W. Feng, D. Minganti, C. Servais, and E. Mahieu.(2021). Stratospheric fluorine as a tracer of circulation changes: comparison between infrared remote-sensing observations and simulations with five modern reanalyses. <em>Journal of Geophysical Research: Atmospheres</em> (Submitted)<em>.</em> Please read the REAMDE.txt file for more information on each file. The data published here are monthly means. BASCOE CTM is extracted at pixels of Jungfraujoch and Lauder NDACC stations. Zonal averages are also given. TOMCAT is extracted at pixels of Jungfraujoch and Lauder NDACC stations. The three inorganic fluorine reservoirs are available for TOMCAT (i.e., HF, COF<sub>2 </sub>and COClF). Ground-based FTIR data are monthly total columns (molec. cm<sup>-2</sup>) of the sum of HF and 2COF<sub>2</sub> (i.e., *Fy, see paper for more information). If you need any other data formats feel free to contact Maxime Prignon. Please contact the authors (mandatory) and cite the paper if you use one of these datasets. M.P Chipperfield, S.S Dhomse and W. Feng are in charge of the TOMCAT simulations S. Chabrillat and M. Prignon, with the support of Yves Christophe (BIRA-IASB, Belgium), are in charge of the BASCOE CTM simulations published here. E. Mahieu, M. Prignon and C. Servais, with the support of Olivier Flock (Uliège, Belgium), are in charge of the Jungfraujoch FTIR data published here. D. Smale is in charge of the Lauder FTIR data published here. We are grateful to the International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG, Bern) for supporting the facilities needed to perform the FTIR observations at Jungfraujoch. Measurements at Lauder are core-funded by NIWA (programme CAAC_2101) through New Zealand’s Ministry of Business, Innovation and Employment Strategic Science Investment Fund. The TOMCAT modelling work was supported by the NERC SISLAC project (NE/R001782/1) and used the national Archer and Leeds ARC HPC facilities. We thank the reanalysis centres (ECMWF, NASA GSFC and JMA) for providing their support and data products.
DOI: 10.5281/zenodo.4645906
title: Stratospheric inorganic fluorine data from ground-based FTIR, BASCOE CTM and TOMCAT

description: These datasets are fully described in: M. Prignon, S. Chabrillat, M. Friedrich, D. Smale, S. E. Straha, P. F. Bernath, M. P. Chipperfield, S. S. Dhomse, W. Feng, D. Minganti, C. Servais, and E. Mahieu.(2021). Stratospheric fluorine as a tracer of circulation changes: comparison between infrared remote-sensing observations and simulations with five modern reanalyses. <em>Journal of Geophysical Research: Atmospheres</em> (Submitted)<em>.</em> Please read the REAMDE.txt file for more information on each file. The data published here are monthly means. BASCOE CTM is extracted at pixels of Jungfraujoch and Lauder NDACC stations. Zonal averages are also given. TOMCAT is extracted at pixels of Jungfraujoch and Lauder NDACC stations. The three inorganic fluorine reservoirs are available for TOMCAT (i.e., HF, COF<sub>2 </sub>and COClF). Ground-based FTIR data are monthly total columns (molec. cm<sup>-2</sup>) of the sum of HF and 2COF<sub>2</sub> (i.e., *Fy, see paper for more information). If you need any other data formats feel free to contact Maxime Prignon. Please contact the authors (mandatory) and cite the paper if you use one of these datasets. M.P Chipperfield, S.S Dhomse and W. Feng are in charge of the TOMCAT simulations S. Chabrillat and M. Prignon, with the support of Yves Christophe (BIRA-IASB, Belgium), are in charge of the BASCOE CTM simulations published here. E. Mahieu, M. Prignon and C. Servais, with the support of Olivier Flock (Uliège, Belgium), are in charge of the Jungfraujoch FTIR data published here. D. Smale is in charge of the Lauder FTIR data published here. We are grateful to the International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG, Bern) for supporting the facilities needed to perform the FTIR observations at Jungfraujoch. Measurements at Lauder are core-funded by NIWA (programme CAAC_2101) through New Zealand’s Ministry of Business, Innovation and Employment Strategic Science Investment Fund. The TOMCAT modelling work was supported by the NERC SISLAC project (NE/R001782/1) and used the national Archer and Leeds ARC HPC facilities. We thank the reanalysis centres (ECMWF, NASA GSFC and JMA) for providing their support and data products.
DOI: 10.6084/m9.figshare.14355601.v1
title: Additional file 10 of Coupled microbiome analyses highlights relative functional roles of bacteria in a bivalve hatchery

description: Additional file 10. Results from the MetaGOmics analysis. Each tab contains the results for a different pairwise comparison either between days at a single pH or between pH on a single day.
DOI: 10.6084/m9.figshare.14355827.v1
title: Suppl_Table_2_Core microbiota.xlsx

description: <b>Supplementary Table 2. Core microbiota at Family and Genus level in traditionally and alternatively fed water buffaloes. </b>List of the Families and Genera shared by at least 80% of samples for each GI site.
DOI: 10.6084/m9.figshare.14355827
title: Suppl_Table_2_Core microbiota.xlsx

description: <b>Supplementary Table 2. Core microbiota at Family and Genus level in traditionally and alternatively fed water buffaloes. </b>List of the Families and Genera shared by at least 80% of samples for each GI site.
DOI: 10.6084/m9.figshare.14355698
title: Suppl_Table_1_negative controls.xlsx

description: Supplementary Table 1. Features present in the negative controls included in the study. Feature ID, Frequency, Frequency in study samples, Number of samples and Taxonomic classifications of the features found in the negative controls included in the present study.
DOI: 10.6084/m9.figshare.14355698.v1
title: Suppl_Table_1_negative controls.xlsx

description: Supplementary Table 1. Features present in the negative controls included in the study. Feature ID, Frequency, Frequency in study samples, Number of samples and Taxonomic classifications of the features found in the negative controls included in the present study.
DOI: 10.6084/m9.figshare.14355863.v1
title: Untitled Item

description: Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data
DOI: 10.5519/qd.o44kg8mf
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (8937 records)
DOI: 10.6084/m9.figshare.14355821
title: BUPT_ICNCAD_Harmonization System Code Datasets.rar

description: Harmonization System Code Datasets includes HS_ Dataset1 and HS_ Dataset2, the former collected from some cooperative companies and website which contains 226528 samples, and the latter gathered from a third party company which contains 8899 samples. The datasets contain the commodity name, declaration elements and corresponding harmonization system code.
DOI: 10.6084/m9.figshare.14174042
title: Deconstructing the attractiveness of biocluster imaginaries

description: In this paper, we investigate the promises that are employed within and around clusters that were formed in the evolving bioeconomy: bioclusters for short. Our paper aims to provide a conceptual clarification of the biocluster concept. To that effect, we employ the prism of sociotechnical imaginaries. We argue that both industrial clusters and the bioeconomy constitute separate, but partly overlapping sociotechnical imaginaries that shape stakeholder attitudes towards bioclusters. We applied a Q-methodology study in two bioeconomy clusters, one in Germany and one in The Netherlands, to investigate the resonance of different imaginaries in the cluster regions. Five distinct narratives, combining specific elements of cluster and bioeconomy imaginaries, are shared by different stakeholder groups. We revealed bioeconomy imaginaries at large to be far more contested than different cluster imaginaries. The latter mobilise overwhelmingly positive associations across diverse stakeholder groups. From this perspective, the popularity of biocluster promotional policies can be explained as they support some of the contested elements of bioeconomy imaginaries in gaining traction.