cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3531536.v1
title: Supplement 1. FORTRAN source code and dynamic link library files for implementing the Bayesian Markov Chain Monte Carlo (MCMC) bandwidth selection procedure used in this study.

description: File List hCalc_MCMC_dll.f90 -- Program source code hCalc_MCMC_dll.dll -- Compiled dll file hCalc_MCMC_dll.lib -- Compiled library file Description Program hCalc_MCMC_dll, written by KAK, generates a dynamic link library (dll) that calculates multivariate bandwidths (h-values) for input into a product kernel estimator. Calculations use the Bayesian Markov Chain Monte Carlo (MCMC) procedure of Zhang et al. (2006; X. Zhang, M. L. King, and R. J. Hyndman. A Bayesian approach to bandwidth selection for multivariate kernel density estimation. Computational Statistics and Data Analysis 50:3009–3031). Arguments and other variables are detailed in the source code file hCalc_MCMC_dll.f90.
DOI: 10.6084/m9.figshare.3531533
title: Appendix A. Variation in the number of interactions observed at different timescale resolutions.

description: Variation in the number of interactions observed at different timescale resolutions.
DOI: 10.6084/m9.figshare.3531533.v1
title: Appendix A. Variation in the number of interactions observed at different timescale resolutions.

description: Variation in the number of interactions observed at different timescale resolutions.
DOI: 10.6084/m9.figshare.3531530
title: Appendix B. Switching behavior classification.

description: Switching behavior classification.
DOI: 10.6084/m9.figshare.3531530.v1
title: Appendix B. Switching behavior classification.

description: Switching behavior classification.
DOI: 10.6084/m9.figshare.3531524
title: Appendix C. Temporal changes in network structure during 1981–1983.

description: Temporal changes in network structure during 1981–1983.
DOI: 10.6084/m9.figshare.3531524.v1
title: Appendix C. Temporal changes in network structure during 1981–1983.

description: Temporal changes in network structure during 1981–1983.
DOI: 10.6084/m9.figshare.3531521
title: Appendix D. Test effects for nine morphological variables predicting the variation in the proportion of fruits in the diet.

description: Test effects for nine morphological variables predicting the variation in the proportion of fruits in the diet.
DOI: 10.6084/m9.figshare.3531521.v1
title: Appendix D. Test effects for nine morphological variables predicting the variation in the proportion of fruits in the diet.

description: Test effects for nine morphological variables predicting the variation in the proportion of fruits in the diet.
DOI: 10.6084/m9.figshare.3531518
title: Appendix E. Values and significance of the statistic K for the variables under study.

description: Values and significance of the statistic K for the variables under study.
DOI: 10.6084/m9.figshare.3531518.v1
title: Appendix E. Values and significance of the statistic K for the variables under study.

description: Values and significance of the statistic K for the variables under study.
DOI: 10.6084/m9.figshare.3531515
title: Supplement 1. MATLAB code used in the calculation of asymmetry values.

description: File List Asymmetry.m -- main script <br> corr.m -- supplementary script<br> intimp.m -- supplementary script <br> quant2bin.m -- supplementary script<br> sortmat.m -- supplementary script Description This Supplement contains a Matlab script used to calculate asymmetry values. Asymmetry.m needs four supplementary scripts ( corr.m, intimp.m, quant2bin.m, sortmat.m). Asymmetry.m generates the following outputs: total degree each row (1st column); frequency of interactions each row (2nd column); sum dependences each row (3rd column); asymmetry value for each row (4th column). Finally, it gives the correlation values between abundance and asymmetry.
DOI: 10.6084/m9.figshare.3531515.v1
title: Supplement 1. MATLAB code used in the calculation of asymmetry values.

description: File List Asymmetry.m -- main script <br> corr.m -- supplementary script<br> intimp.m -- supplementary script <br> quant2bin.m -- supplementary script<br> sortmat.m -- supplementary script Description This Supplement contains a Matlab script used to calculate asymmetry values. Asymmetry.m needs four supplementary scripts ( corr.m, intimp.m, quant2bin.m, sortmat.m). Asymmetry.m generates the following outputs: total degree each row (1st column); frequency of interactions each row (2nd column); sum dependences each row (3rd column); asymmetry value for each row (4th column). Finally, it gives the correlation values between abundance and asymmetry.
DOI: 10.6084/m9.figshare.3531512
title: Appendix A. Three-way ANOVAs testing linkages among larval release, transport, and settlement.

description: Three-way ANOVAs testing linkages among larval release, transport, and settlement.
DOI: 10.6084/m9.figshare.3531512.v1
title: Appendix A. Three-way ANOVAs testing linkages among larval release, transport, and settlement.

description: Three-way ANOVAs testing linkages among larval release, transport, and settlement.
DOI: 10.6084/m9.figshare.3531509
title: Appendix B. Regression equations of the field strip experiments.

description: Regression equations of the field strip experiments.
DOI: 10.6084/m9.figshare.3531509.v1
title: Appendix B. Regression equations of the field strip experiments.

description: Regression equations of the field strip experiments.
DOI: 10.6084/m9.figshare.3531506
title: Appendix A. Calculations of production used in the study.

description: Calculations of production used in the study.
DOI: 10.6084/m9.figshare.3531506.v1
title: Appendix A. Calculations of production used in the study.

description: Calculations of production used in the study.
DOI: 10.6084/m9.figshare.3531503
title: Appendix B. A figure showing the raw data for the lakes on a seasonal scale.

description: A figure showing the raw data for the lakes on a seasonal scale.
DOI: 10.6084/m9.figshare.3531503.v1
title: Appendix B. A figure showing the raw data for the lakes on a seasonal scale.

description: A figure showing the raw data for the lakes on a seasonal scale.
DOI: 10.6084/m9.figshare.3531500
title: Appendix A. Calculated changes in alkalinity and ionic strength in response to short-term and long-term experiments.

description: Calculated changes in alkalinity and ionic strength in response to short-term and long-term experiments.
DOI: 10.6084/m9.figshare.3531500.v1
title: Appendix A. Calculated changes in alkalinity and ionic strength in response to short-term and long-term experiments.

description: Calculated changes in alkalinity and ionic strength in response to short-term and long-term experiments.
DOI: 10.6084/m9.figshare.3531497
title: Appendix B. Additional methodological details.

description: Additional methodological details.
DOI: 10.6084/m9.figshare.3531494
title: Appendix C. Assessment of the effect of increased ionic strength and alkalinity, and raising pH through NaOH addition vs. dissolved CO2 depletion.

description: Assessment of the effect of increased ionic strength and alkalinity, and raising pH through NaOH addition vs. dissolved CO2 depletion.
DOI: 10.6084/m9.figshare.3531494.v1
title: Appendix C. Assessment of the effect of increased ionic strength and alkalinity, and raising pH through NaOH addition vs. dissolved CO2 depletion.

description: Assessment of the effect of increased ionic strength and alkalinity, and raising pH through NaOH addition vs. dissolved CO2 depletion.
DOI: 10.6084/m9.figshare.3531491
title: Appendix D. Effect of elevated pH on specific bacterial production and respiration in short-term incubations.

description: Effect of elevated pH on specific bacterial production and respiration in short-term incubations.
DOI: 10.6084/m9.figshare.3531491.v1
title: Appendix D. Effect of elevated pH on specific bacterial production and respiration in short-term incubations.

description: Effect of elevated pH on specific bacterial production and respiration in short-term incubations.
DOI: 10.6084/m9.figshare.3531488
title: Appendix A. General characteristics of the experimental blocks in the Lauwersmeer.

description: General characteristics of the experimental blocks in the Lauwersmeer.
DOI: 10.6084/m9.figshare.3531485
title: Appendix B. Photograph of four out of eight experimental blocks in the shallow lake Lauwersmeer shortly after summer exclosure establishment in March 2005.

description: Photograph of four out of eight experimental blocks in the shallow lake Lauwersmeer shortly after summer exclosure establishment in March 2005.
DOI: 10.6084/m9.figshare.3531482
title: Appendix A. Matrix model of Acyrthosiphon pisum.

description: Matrix model of Acyrthosiphon pisum.
DOI: 10.6084/m9.figshare.3531482.v1
title: Appendix A. Matrix model of Acyrthosiphon pisum.

description: Matrix model of Acyrthosiphon pisum.
DOI: 10.6084/m9.figshare.3531479
title: Appendix B. Effect of initial conditions on transient amplification after 20 days.

description: Effect of initial conditions on transient amplification after 20 days.
DOI: 10.6084/m9.figshare.3531479.v1
title: Appendix B. Effect of initial conditions on transient amplification after 20 days.

description: Effect of initial conditions on transient amplification after 20 days.
DOI: 10.6084/m9.figshare.3531476
title: Appendix C. Perturbation analysis of the Acyrthosiphon pisum model that includes larval pseudo-stages.

description: Perturbation analysis of the Acyrthosiphon pisum model that includes larval pseudo-stages.
DOI: 10.6084/m9.figshare.3531476.v1
title: Appendix C. Perturbation analysis of the Acyrthosiphon pisum model that includes larval pseudo-stages.

description: Perturbation analysis of the Acyrthosiphon pisum model that includes larval pseudo-stages.
DOI: 10.6084/m9.figshare.3531473
title: Appendix D. Transient amplification (T20) as a function of asymptotic population growth rate (λ).

description: Transient amplification (T20) as a function of asymptotic population growth rate (λ).
DOI: 10.6084/m9.figshare.3531473.v1
title: Appendix D. Transient amplification (T20) as a function of asymptotic population growth rate (λ).

description: Transient amplification (T20) as a function of asymptotic population growth rate (λ).
DOI: 10.6084/m9.figshare.3531470
title: Appendix A. Pearson's correlation matrix for covariation among the abundance of herbivore species on field plots of Brassica oleracea cultivars.

description: Pearson's correlation matrix for covariation among the abundance of herbivore species on field plots of Brassica oleracea cultivars.
DOI: 10.6084/m9.figshare.3531470.v1
title: Appendix A. Pearson's correlation matrix for covariation among the abundance of herbivore species on field plots of Brassica oleracea cultivars.

description: Pearson's correlation matrix for covariation among the abundance of herbivore species on field plots of Brassica oleracea cultivars.
DOI: 10.6084/m9.figshare.3531467
title: Appendix A. Map showing locations and identification numbers of the 16 Hesperolinen californicum study populations in California, USA.

description: Map showing locations and identification numbers of the 16 Hesperolinen californicum study populations in California, USA.
DOI: 10.6084/m9.figshare.3531467.v1
title: Appendix A. Map showing locations and identification numbers of the 16 Hesperolinen californicum study populations in California, USA.

description: Map showing locations and identification numbers of the 16 Hesperolinen californicum study populations in California, USA.
DOI: 10.6084/m9.figshare.3531464
title: Appendix B. Geographic and demographic data for host and pathogen by population and survey year.

description: Geographic and demographic data for host and pathogen by population and survey year.
DOI: 10.6084/m9.figshare.3531461
title: Appendix C. Results of NMDS analysis of all soil samples from the 16 study populations and bubble plots showing the influence of calcium and magnesium on NMDS resolution.

description: Results of NMDS analysis of all soil samples from the 16 study populations and bubble plots showing the influence of calcium and magnesium on NMDS resolution.
DOI: 10.6084/m9.figshare.3531461.v1
title: Appendix C. Results of NMDS analysis of all soil samples from the 16 study populations and bubble plots showing the influence of calcium and magnesium on NMDS resolution.

description: Results of NMDS analysis of all soil samples from the 16 study populations and bubble plots showing the influence of calcium and magnesium on NMDS resolution.
DOI: 10.6084/m9.figshare.3531458
title: Appendix D. Kaplan Meyer survival analysis of seedling survivorship probabilities relative to severity of infection at time of initial survey.

description: Kaplan Meyer survival analysis of seedling survivorship probabilities relative to severity of infection at time of initial survey.
DOI: 10.6084/m9.figshare.3531458.v1
title: Appendix D. Kaplan Meyer survival analysis of seedling survivorship probabilities relative to severity of infection at time of initial survey.

description: Kaplan Meyer survival analysis of seedling survivorship probabilities relative to severity of infection at time of initial survey.
DOI: 10.6084/m9.figshare.3531455
title: Appendix A. Biomass detection model.

description: Biomass detection model.
DOI: 10.6084/m9.figshare.3531455.v1
title: Appendix A. Biomass detection model.

description: Biomass detection model.
DOI: 10.6084/m9.figshare.3531452
title: Appendix B. Percent cover detection model.

description: Percent cover detection model.
DOI: 10.6084/m9.figshare.3531452.v1
title: Appendix B. Percent cover detection model.

description: Percent cover detection model.
DOI: 10.6084/m9.figshare.3531449
title: Discovery of potential novel microsomal triglyceride transfer protein inhibitors via virtual screening of pharmacophore modelling and molecular docking

description: In order to identify potential natural inhibitors against the microsomal triglyceride transfer protein (MTP), HipHop models were generated using 20 known inhibitors from the Binding Database. Using evaluation indicators, the best hypothesis model, Hypo1, was selected and utilised to screen the Traditional Chinese Medicine Database, which resulted in a hit list of 58 drug-like compounds. A homology model of MTP was built by MODELLER and was minimised by CHARMm force field. It was then validated by Ramachandran plot and Verify-3D so as to obtain a stable structure, which was further used to refine the 58 hits using molecular docking studies. Then, five compounds with higher docking scores which satisfied the docking requirements were discovered. Among them, Ginkgetin and Dauricine were most likely to be candidates that exhibition inhibiting effect on MTP. The screening strategy in this study is relatively new to the discovery of MTP inhibitors in medicinal chemistry. Moreover, it is important to note that, lomitapide, an approved MTP inhibitor, fits well with Hypo1 as well as our homology model of MTP, which confirmed the rationality of our studies. The results indicated the applicability of molecular modeling for the discovery of potential natural MTP inhibitors from traditional Chinese herbs.
DOI: 10.6084/m9.figshare.3531449.v1
title: Discovery of potential novel microsomal triglyceride transfer protein inhibitors via virtual screening of pharmacophore modelling and molecular docking

description: In order to identify potential natural inhibitors against the microsomal triglyceride transfer protein (MTP), HipHop models were generated using 20 known inhibitors from the Binding Database. Using evaluation indicators, the best hypothesis model, Hypo1, was selected and utilised to screen the Traditional Chinese Medicine Database, which resulted in a hit list of 58 drug-like compounds. A homology model of MTP was built by MODELLER and was minimised by CHARMm force field. It was then validated by Ramachandran plot and Verify-3D so as to obtain a stable structure, which was further used to refine the 58 hits using molecular docking studies. Then, five compounds with higher docking scores which satisfied the docking requirements were discovered. Among them, Ginkgetin and Dauricine were most likely to be candidates that exhibition inhibiting effect on MTP. The screening strategy in this study is relatively new to the discovery of MTP inhibitors in medicinal chemistry. Moreover, it is important to note that, lomitapide, an approved MTP inhibitor, fits well with Hypo1 as well as our homology model of MTP, which confirmed the rationality of our studies. The results indicated the applicability of molecular modeling for the discovery of potential natural MTP inhibitors from traditional Chinese herbs.
DOI: 10.6084/m9.figshare.3531446
title: Appendix C. Bayesian model fitting.

description: Bayesian model fitting.
DOI: 10.6084/m9.figshare.3531446.v1
title: Appendix C. Bayesian model fitting.

description: Bayesian model fitting.
DOI: 10.6084/m9.figshare.3531443
title: Appendix D. Model fit.

description: Model fit.
DOI: 10.6084/m9.figshare.3531443.v1
title: Appendix D. Model fit.

description: Model fit.
DOI: 10.6084/m9.figshare.3531440
title: Appendix A. Dispersal mode of plants found in Antietam National Battlefield.

description: Dispersal mode of plants found in Antietam National Battlefield.
DOI: 10.6084/m9.figshare.3531440.v1
title: Appendix A. Dispersal mode of plants found in Antietam National Battlefield.

description: Dispersal mode of plants found in Antietam National Battlefield.
DOI: 10.6084/m9.figshare.3531437
title: Appendix A. A climate graph showing mean monthly temperature and total monthly rainfall for the study period, plotted onto 30-yr averages at the field site.

description: A climate graph showing mean monthly temperature and total monthly rainfall for the study period, plotted onto 30-yr averages at the field site.
DOI: 10.6084/m9.figshare.3531437.v1
title: Appendix A. A climate graph showing mean monthly temperature and total monthly rainfall for the study period, plotted onto 30-yr averages at the field site.

description: A climate graph showing mean monthly temperature and total monthly rainfall for the study period, plotted onto 30-yr averages at the field site.
DOI: 10.6084/m9.figshare.3531434
title: Appendix B. Survival analysis of fine roots from different tree species from each seasonal cohort.

description: Survival analysis of fine roots from different tree species from each seasonal cohort.
DOI: 10.6084/m9.figshare.3531434.v1
title: Appendix B. Survival analysis of fine roots from different tree species from each seasonal cohort.

description: Survival analysis of fine roots from different tree species from each seasonal cohort.
DOI: 10.6084/m9.figshare.3531431
title: Optimal Forecasts from Markov Switching Models

description: We derive forecasts for Markov switching models that are optimal in the mean square forecast error (MSFE) sense by means of weighting observations. We provide analytic expressions of the weights conditional on the Markov states and conditional on state probabilities. This allows us to study the effect of uncertainty around states on forecasts. It emerges that, even in large samples, forecasting performance increases substantially when the construction of optimal weights takes uncertainty around states into account. Performance of the optimal weights is shown through simulations and an application to U.S. GNP, where using optimal weights leads to significant reductions in MSFE. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3531431.v3
title: Optimal Forecasts from Markov Switching Models

description: We derive forecasts for Markov switching models that are optimal in the mean square forecast error (MSFE) sense by means of weighting observations. We provide analytic expressions of the weights conditional on the Markov states and conditional on state probabilities. This allows us to study the effect of uncertainty around states on forecasts. It emerges that, even in large samples, forecasting performance increases substantially when the construction of optimal weights takes uncertainty around states into account. Performance of the optimal weights is shown through simulations and an application to U.S. GNP, where using optimal weights leads to significant reductions in MSFE. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3531431.v2
title: Optimal forecasts from Markov switching models

description: We derive forecasts for Markov switching models that are optimal in the MSFE sense by means of weighting observations. We provide analytic expressions of the weights conditional on the Markov states and conditional on state probabilities. This allows us to study the effect of uncertainty around states on forecasts. It emerges that, even in large samples, forecasting performance increases substantially when the construction of optimal weights takes uncertainty around states into account. Performance of the optimal weights is shown through simulations and an application to US GNP, where using optimal weights leads to significant reductions in MSFE.
DOI: 10.6084/m9.figshare.3531428
title: Appendix C. A table showing fine-root demography (production, death, and mortality) based on root numbers.

description: A table showing fine-root demography (production, death, and mortality) based on root numbers.
DOI: 10.6084/m9.figshare.3531425
title: Appendix D. A table showing fine-root demography (production, death, and mortality) based on root lengths.

description: A table showing fine-root demography (production, death, and mortality) based on root lengths.
DOI: 10.6084/m9.figshare.3531425.v1
title: Appendix D. A table showing fine-root demography (production, death, and mortality) based on root lengths.

description: A table showing fine-root demography (production, death, and mortality) based on root lengths.
DOI: 10.6084/m9.figshare.3531422
title: Appendix E. A table comparing three different estimates of fine-root turnover rates across tree species.

description: A table comparing three different estimates of fine-root turnover rates across tree species.
DOI: 10.6084/m9.figshare.3531422.v1
title: Appendix E. A table comparing three different estimates of fine-root turnover rates across tree species.

description: A table comparing three different estimates of fine-root turnover rates across tree species.
DOI: 10.6084/m9.figshare.3531419
title: Appendix F. Survival analysis of different root branching orders from each tree species.

description: Survival analysis of different root branching orders from each tree species.
DOI: 10.6084/m9.figshare.3531419.v1
title: Appendix F. Survival analysis of different root branching orders from each tree species.

description: Survival analysis of different root branching orders from each tree species.
DOI: 10.6084/m9.figshare.3531416
title: Appendix G. Results of the proportional hazards regression for root survivorship data (species and root branching order effects).

description: Results of the proportional hazards regression for root survivorship data (species and root branching order effects).
DOI: 10.6084/m9.figshare.3531416.v1
title: Appendix G. Results of the proportional hazards regression for root survivorship data (species and root branching order effects).

description: Results of the proportional hazards regression for root survivorship data (species and root branching order effects).
DOI: 10.6084/m9.figshare.3531413
title: Appendix H. A figure showing the species-specific time courses of the number of living roots pertaining to each seasonal cohort.

description: A figure showing the species-specific time courses of the number of living roots pertaining to each seasonal cohort.
DOI: 10.6084/m9.figshare.3531413.v1
title: Appendix H. A figure showing the species-specific time courses of the number of living roots pertaining to each seasonal cohort.

description: A figure showing the species-specific time courses of the number of living roots pertaining to each seasonal cohort.