cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3561411
title: Appendix D. Patterns of density dependence of tick abundance in weakly and highly infested lizards.

description: Patterns of density dependence of tick abundance in weakly and highly infested lizards.
DOI: 10.6084/m9.figshare.3561411.v1
title: Appendix D. Patterns of density dependence of tick abundance in weakly and highly infested lizards.

description: Patterns of density dependence of tick abundance in weakly and highly infested lizards.
DOI: 10.6084/m9.figshare.3561408
title: Appendix A. Supplemental descriptions of Costus allenii, C. villosissimus, and their hybrids.

description: Supplemental descriptions of Costus allenii, C. villosissimus, and their hybrids.
DOI: 10.6084/m9.figshare.3561408.v1
title: Appendix A. Supplemental descriptions of Costus allenii, C. villosissimus, and their hybrids.

description: Supplemental descriptions of Costus allenii, C. villosissimus, and their hybrids.
DOI: 10.6084/m9.figshare.3561405
title: Appendix B. Detailed descriptions of the transplant sites in central Panama.

description: Detailed descriptions of the transplant sites in central Panama.
DOI: 10.6084/m9.figshare.3561405.v1
title: Appendix B. Detailed descriptions of the transplant sites in central Panama.

description: Detailed descriptions of the transplant sites in central Panama.
DOI: 10.6084/m9.figshare.3561402
title: Appendix C. Detailed description of methods for cloning juvenile Costus plants.

description: Detailed description of methods for cloning juvenile Costus plants.
DOI: 10.6084/m9.figshare.3561402.v1
title: Appendix C. Detailed description of methods for cloning juvenile Costus plants.

description: Detailed description of methods for cloning juvenile Costus plants.
DOI: 10.6084/m9.figshare.3561399
title: Appendix D. Supplementary results: statistical analysis of reciprocal transplant experiments conducted with Costus allenii and C. villosissimus in Central Panama.

description: Supplementary results: statistical analysis of reciprocal transplant experiments conducted with Costus allenii and C. villosissimus in Central Panama.
DOI: 10.6084/m9.figshare.3561399.v1
title: Appendix D. Supplementary results: statistical analysis of reciprocal transplant experiments conducted with Costus allenii and C. villosissimus in Central Panama.

description: Supplementary results: statistical analysis of reciprocal transplant experiments conducted with Costus allenii and C. villosissimus in Central Panama.
DOI: 10.6084/m9.figshare.3561396
title: Appendix A. Figuresand tables showing the absolute biomass of organisms, the effect of latitudinal location, the relative biomass of predator species, habitat complexity, listing all species and statistical results.

description: Figuresand tables showing the absolute biomass of organisms, the effect of latitudinal location, the relative biomass of predator species, habitat complexity, listing all species and statistical results.
DOI: 10.6084/m9.figshare.3561393
title: Appendix A. Coefficient estimates, example derivation of the "dive residual", and distribution plots showing how the dive variables alter between states.

description: Coefficient estimates, example derivation of the "dive residual", and distribution plots showing how the dive variables alter between states.
DOI: 10.6084/m9.figshare.3561393.v1
title: Appendix A. Coefficient estimates, example derivation of the "dive residual", and distribution plots showing how the dive variables alter between states.

description: Coefficient estimates, example derivation of the "dive residual", and distribution plots showing how the dive variables alter between states.
DOI: 10.6084/m9.figshare.3561390
title: Appendix B. Model fit and diagnostics.

description: Model fit and diagnostics.
DOI: 10.6084/m9.figshare.3561387
title: Supplement 1. R and WinBUGS code to facilitate running of the covariate state-space model.

description: File List argos.csv (MD5: 231845256e0e3e2780b89b1979b11593) dive.csv (MD5: 0637c54ff8df8166d7f07b80295eaa5a) example.R (MD5: )5c05f59f227390015949a4789d2feef9 dat4bugsCOV.R (MD5: e1648cc3144721f82d351922cd28f7e5) ssmMeta.R (MD5: 63baa0688eb6061afb2f8d7a80a6d2f4) DCRWSmeta_cov1oneway.txt (MD5: 4d1eadc5bc3c88f9d3ac1534c71ab5ba) Description Supplement contains sample data, functions and scripts to prepare the data and fit the covariate state-space model presented in this paper. Two raw data files contain samples of Argos location data (argos.csv) and individual dive data (dive.csv) for a subset of Southern elephant seals tagged with CTD-SRDLs at Davis Station, Antarctica in 2011 under the Australian Integrated Marine Observing System. Following the worked example given in the example.R file should enable readers to run the covariate state-space model and/or fit their own Argos and dive data using the R and WinBUGS codes provided (dat4bugsCOV.R, ssmMeta.R, and DCRWSmeta_cov1oneway.txt). The argos.csv file is a comma-separated file containing the Southern elephant seal raw Argos tracking data. Column definitions "id" - is a unique identifier for the seal from which the tracking data set came. "time" - is the GMT date-time of each observation with the following format: "2001-11-13 07:59:59". "lc" is the Argos location quality class of each observation. Values in ascending order of quality are: "Z", "B", "A", "0", "1", "2", "3". "lon" is the observed longitude in decimal degrees. "lat" is the observed latitude in decimal degress. The dive.csv file is a comma-separated file containing the Southern elephant seal individual dive data. Column definitions "id" - is a unique identifier for the seal from which the dive dataset came (same as for the argos.csv file). "time" - is the GMT date-time of each observation with the following format: "2001-11-13 07:59:59". "MAX_DEP" – the dive covariate used here is the logarithm of the maximum depth in meters recorded for the dive. The reader may use any dive covariate, raw or pre-processed (with any name), here. example.R contains R code for the worked Southern elephant seal example showing model implementation. dat4bugsCOV.R is an R function which prepares and writes input data for WinBUGS model. ssmMeta.R is an R function which fits covariate state-space model to Argos tracking data. DCRWSmeta_cov1oneway.txt contains WinBUGS code for the covariate state-space model.
DOI: 10.6084/m9.figshare.3561384
title: Appendix A. Daily estimates of gross primary production and ecosystem respiration in the upper and lower reaches of French Creek.

description: Daily estimates of gross primary production and ecosystem respiration in the upper and lower reaches of French Creek.
DOI: 10.6084/m9.figshare.3561384.v1
title: Appendix A. Daily estimates of gross primary production and ecosystem respiration in the upper and lower reaches of French Creek.

description: Daily estimates of gross primary production and ecosystem respiration in the upper and lower reaches of French Creek.
DOI: 10.6084/m9.figshare.3561381
title: Appendix B. Measured 13CDIC (dissolved inorganic carbon) before, during, and after 2 4-h daytime drip additions of 13C-NaH13CO3 in French Creek, Wyoming, USA.

description: Measured 13CDIC (dissolved inorganic carbon) before, during, and after 2 4-h daytime drip additions of 13C-NaH13CO3 in French Creek, Wyoming, USA.
DOI: 10.6084/m9.figshare.3561381.v1
title: Appendix B. Measured 13CDIC (dissolved inorganic carbon) before, during, and after 2 4-h daytime drip additions of 13C-NaH13CO3 in French Creek, Wyoming, USA.

description: Measured 13CDIC (dissolved inorganic carbon) before, during, and after 2 4-h daytime drip additions of 13C-NaH13CO3 in French Creek, Wyoming, USA.
DOI: 10.6084/m9.figshare.3561378
title: Appendix C. Fate of 13C tracer in benthic organic C pools from 1–44 d after the 13CDIC tracer addition.

description: Fate of 13C tracer in benthic organic C pools from 1–44 d after the 13CDIC tracer addition.
DOI: 10.6084/m9.figshare.3561378.v1
title: Appendix C. Fate of 13C tracer in benthic organic C pools from 1–44 d after the 13CDIC tracer addition.

description: Fate of 13C tracer in benthic organic C pools from 1–44 d after the 13CDIC tracer addition.
DOI: 10.6084/m9.figshare.3561375
title: Appendix A. More detailed information about the cyanobacteria found in the reservoirs and additional figures with phytoplankton community and nutrient trends in the lake with the agricultural watershed.

description: More detailed information about the cyanobacteria found in the reservoirs and additional figures with phytoplankton community and nutrient trends in the lake with the agricultural watershed.
DOI: 10.6084/m9.figshare.3561375.v1
title: Appendix A. More detailed information about the cyanobacteria found in the reservoirs and additional figures with phytoplankton community and nutrient trends in the lake with the agricultural watershed.

description: More detailed information about the cyanobacteria found in the reservoirs and additional figures with phytoplankton community and nutrient trends in the lake with the agricultural watershed.
DOI: 10.6084/m9.figshare.3561372
title: Appendix A. Tests for riparian habitat breadth.

description: Tests for riparian habitat breadth.
DOI: 10.6084/m9.figshare.3561372.v1
title: Appendix A. Tests for riparian habitat breadth.

description: Tests for riparian habitat breadth.
DOI: 10.6084/m9.figshare.3561369
title: Appendix B. Visual representation of the nestedness analysis.

description: Visual representation of the nestedness analysis.
DOI: 10.6084/m9.figshare.3561369.v1
title: Appendix B. Visual representation of the nestedness analysis.

description: Visual representation of the nestedness analysis.
DOI: 10.6084/m9.figshare.3561366
title: Appendix C. Relationship between stream size and plant growth forms.

description: Relationship between stream size and plant growth forms.
DOI: 10.6084/m9.figshare.3561366.v1
title: Appendix C. Relationship between stream size and plant growth forms.

description: Relationship between stream size and plant growth forms.
DOI: 10.6084/m9.figshare.3561363
title: Full Archive

description: Full Archive
DOI: 10.6084/m9.figshare.3561360
title: Appendix A. The data, data sources, and details of the methods used in the meta-analyses of fatty acid content, egg production, and egg mortality rates.

description: The data, data sources, and details of the methods used in the meta-analyses of fatty acid content, egg production, and egg mortality rates.
DOI: 10.6084/m9.figshare.3561357
title: Appendix B. A video of one species of fish spawning and another species of fish immediately feeding on the egg boon produced by the spawning fishes.

description: A video of one species of fish spawning and another species of fish immediately feeding on the egg boon produced by the spawning fishes.
DOI: 10.6084/m9.figshare.3561357.v1
title: Appendix B. A video of one species of fish spawning and another species of fish immediately feeding on the egg boon produced by the spawning fishes.

description: A video of one species of fish spawning and another species of fish immediately feeding on the egg boon produced by the spawning fishes.
DOI: 10.6084/m9.figshare.3561354
title: Appendix A. Species list and flowering time data used in analyses.

description: Species list and flowering time data used in analyses.
DOI: 10.6084/m9.figshare.3561354.v1
title: Appendix A. Species list and flowering time data used in analyses.

description: Species list and flowering time data used in analyses.
DOI: 10.6084/m9.figshare.3561351
title: Appendix B. Phenological shifts and phenological sensitivity to snowmelt date and summer temperature data used in analyses.

description: Phenological shifts and phenological sensitivity to snowmelt date and summer temperature data used in analyses.
DOI: 10.6084/m9.figshare.3561348
title: Appendix C. Strength and significance of phylogenetic signal in mean flowering time, 1974–2012.

description: Strength and significance of phylogenetic signal in mean flowering time, 1974–2012.
DOI: 10.6084/m9.figshare.3561345
title: Appendix D. Sensitivity test of phylogenetic signal results.

description: Sensitivity test of phylogenetic signal results.
DOI: 10.6084/m9.figshare.3561342
title: Supplement 1. Data files for the community phylogeny used in analyses.

description: File List caradonna_supplement_gothic_community_phylogeny.txt (MD5: 360de557e32e0f214e4cd03c0160cb59) Description The caradonna_supplement_gothic_community_phylogeny.txt file is a text file that contains the raw data for the community phylogeny used in analyses. Each name within the file corresponds to a species within the plant community, and numbers correspond to branch lengths.
DOI: 10.6084/m9.figshare.3561339
title: Appendix A. Effects of rhizobia on Phaseolus lunatus aboveground and belowground biomass.

description: Effects of rhizobia on Phaseolus lunatus aboveground and belowground biomass.
DOI: 10.6084/m9.figshare.3561339.v1
title: Appendix A. Effects of rhizobia on Phaseolus lunatus aboveground and belowground biomass.

description: Effects of rhizobia on Phaseolus lunatus aboveground and belowground biomass.
DOI: 10.6084/m9.figshare.3561336
title: Appendix B. Initial conceptual “saturated” model of the relationship between rhizobia, aboveground plant traits, number of ants attracted and the model selection procedure.

description: Initial conceptual “saturated” model of the relationship between rhizobia, aboveground plant traits, number of ants attracted and the model selection procedure.
DOI: 10.6084/m9.figshare.3561333
title: Appendix C. Results of structural equation models.

description: Results of structural equation models.
DOI: 10.6084/m9.figshare.3561336.v1
title: Appendix B. Initial conceptual “saturated” model of the relationship between rhizobia, aboveground plant traits, number of ants attracted and the model selection procedure.

description: Initial conceptual “saturated” model of the relationship between rhizobia, aboveground plant traits, number of ants attracted and the model selection procedure.
DOI: 10.6084/m9.figshare.3561333.v1
title: Appendix C. Results of structural equation models.

description: Results of structural equation models.
DOI: 10.6084/m9.figshare.3561330
title: Appendix D. Bivariate correlations between response variables in the structural equation model.

description: Bivariate correlations between response variables in the structural equation model.
DOI: 10.6084/m9.figshare.3561330.v1
title: Appendix D. Bivariate correlations between response variables in the structural equation model.

description: Bivariate correlations between response variables in the structural equation model.
DOI: 10.6084/m9.figshare.3561327
title: Appendix A. More information regarding the application of portfolio theory to river networks, a figure illustrating the vast dendritic network of the Fraser River study system, a figure showing wavelet analyses, and a table that highlights previous research on stability in river processes.

description: More information regarding the application of portfolio theory to river networks, a figure illustrating the vast dendritic network of the Fraser River study system, a figure showing wavelet analyses, and a table that highlights previous research on stability in river processes.
DOI: 10.6084/m9.figshare.3561324
title: Appendix A. Simulation study.

description: Simulation study.
DOI: 10.6084/m9.figshare.3561324.v1
title: Appendix A. Simulation study.

description: Simulation study.
DOI: 10.6084/m9.figshare.3561321
title: Appendix B. Modeling of the multiple detection methods design.

description: Modeling of the multiple detection methods design.
DOI: 10.6084/m9.figshare.3561321.v1
title: Appendix B. Modeling of the multiple detection methods design.

description: Modeling of the multiple detection methods design.
DOI: 10.6084/m9.figshare.3561318
title: Supplement 1. R and BUGS code to fit all the models presented.

description: File List BUGS-Models.bug (MD5: bbc0c6df0dc6e68ff25a3172ad349f07) FormatData_forBUGS.r (MD5: cb618b5c9dae1c6a5c19730541f8a74a) Likelihoods.r (MD5: 0e56760d67549e4e6efc587d94097907) SimulateData_Calibration.r (MD5: cf2a083f6a73bdb71db9d6c4966d8976) SimulateData_ObservationConf.r (MD5: a08687fcc8a826dbebb64b7e57647b78) SimulateData_SiteConf-MM.r (MD5: 885ee8cb22dea9bee08ea53d7cacaede) SimulateData_SiteConf-MS.r (MD5: f826d94e8c110481c544696e94d5cf60 ) Description The Four files named as SimulateData_xxx.r are used to simulate data for each of the four corresponding study design described in the text. Note that the site confirmation design has two variants: (1) the Multiple-Method design (MM) and (2) the Multiple-State design (MS). The file Likelihoods.r contains the likelihoods for the four models, and it can be run in R, as is. To run the Bayesian models in OpenBUGS (or WinBUGS), run the script FormatData_forBUGS first. It will transform the simulated data in the appropriate from for BUGS. Then, you can use the code in BUGS-Models.bug to run each of the four models presented in the paper.
DOI: 10.6084/m9.figshare.3561315
title: Supplement 2. R code of the simulation study presented in Appendix A.

description: File List ConvergenceIssues.r (MD5: c579e3efb69ab080a2263e919563c7a3) SimulationStudy.r (MD5: 0389b50a5ef371d0e9e6957357656345) Description The file SimulationStudy.r contains all the necessary code to run the simulation study in R. The different parmaters and variables are explained and defined in the script. The file ConvergenceIssues.r can be used as a demonstration of how, in presence of data from the Observation Confirmation Design, estimates from the Site Confirmation model do not always converge to the true (known) values. Convergence from the Observation Confirmation is much better. You can change the simulated ('true') values to assess how the convergence properties change.
DOI: 10.6084/m9.figshare.3561312
title: Appendix A. Simulation results: log-linear trend data and log-linear trend model.

description: Simulation results: log-linear trend data and log-linear trend model.
DOI: 10.6084/m9.figshare.3561315.v1
title: Supplement 2. R code of the simulation study presented in Appendix A.

description: File List ConvergenceIssues.r (MD5: c579e3efb69ab080a2263e919563c7a3) SimulationStudy.r (MD5: 0389b50a5ef371d0e9e6957357656345) Description The file SimulationStudy.r contains all the necessary code to run the simulation study in R. The different parmaters and variables are explained and defined in the script. The file ConvergenceIssues.r can be used as a demonstration of how, in presence of data from the Observation Confirmation Design, estimates from the Site Confirmation model do not always converge to the true (known) values. Convergence from the Observation Confirmation is much better. You can change the simulated ('true') values to assess how the convergence properties change.
DOI: 10.6084/m9.figshare.3561312.v1
title: Appendix A. Simulation results: log-linear trend data and log-linear trend model.

description: Simulation results: log-linear trend data and log-linear trend model.
DOI: 10.6084/m9.figshare.3561309
title: Appendix B. Simulation results: log-linear trend data analyzed with the Markov and generalized Markov models.

description: Simulation results: log-linear trend data analyzed with the Markov and generalized Markov models.
DOI: 10.6084/m9.figshare.3561306.v2
title: Solving the stochastic Burgers equation with a sensitivity derivative-driven Monte Carlo method

description: Code to solve a 1D stochastic viscous Burgers equation using a sensitivity derivative-driven Monte Carlo method. A Docker image is provided that can run all of the included code.<br>This code generates the figures shown in the paper: Accelerating Monte Carlo estimation with derivatives of high-level finite element models, P. Hauseux, J.S. Hale, S.P.A. Bordas. 1 May 2017. 318, pp. 917-936. Computer Methods in Applied Mechanics and Engineering.http://dx.doi.org/10.1016/j.cma.2017.01.041http://hdl.handle.net/10993/28618This figshare repository is for archival purposes. It is easiest to use the code and image directly from the Bitbucket and Dockerhub repositories shown in the References section below. Full instructions are given in the README.md file inside the code archive file.<br><br>The code is licensed under the LGPL v3.0.<br><br>The Docker image contains binaries for a variety of open source software. All software binaries in the container were compiled from unmodified sources from the project originators.
DOI: 10.6084/m9.figshare.3561306.v1
title: Solving the stochastic Burgers equation with a sensitivity derivative-driven Monte-Carlo method

description: Code to solve a 1D stochastic viscous Burgers equation using a sensitivity derivative-driven Monte-Carlo method. A Docker image is provided that can run all of the included code.<br>This code generates the figures shown in the paper: Accelerating Monte-Carlo estimation with derivatives of high-level finite element models, P. Hauseux, J.S. Hale, S.P.A. Bordas.This figshare repository is for archival purposes. It is easiest to use the code and image directly from the Bitbucket and Dockerhub repositories shown in the References section below. Full instructions are given in the README.md file inside the code achive file.<br>The code is licensed under the LGPL v3.0.<br><br>The Docker image contains binaries for a variety of open source software. All software binaries in the container were compiled from unmodified sources from the project originators.
DOI: 10.6084/m9.figshare.3561303
title: Appendix C. Simulation results for parameters associated with abundance and detection from Markov data analyzed with the Markov and log-linear trend models.

description: Simulation results for parameters associated with abundance and detection from Markov data analyzed with the Markov and log-linear trend models.
DOI: 10.6084/m9.figshare.3561303.v1
title: Appendix C. Simulation results for parameters associated with abundance and detection from Markov data analyzed with the Markov and log-linear trend models.

description: Simulation results for parameters associated with abundance and detection from Markov data analyzed with the Markov and log-linear trend models.
DOI: 10.6084/m9.figshare.3561300
title: Supplement 1. Data, R code and JAGS model description to repeat simulation study investigating power to detect population trends with correlated abundances between years, using the Markov and the log-linear trend model.

description: File List data.100m.R: (MD5: 25cca5e0b23be3915b197a50bcd1fb18) R data object containing data used in the hierarchical distance sampling analysis of island scrub-jay abundance by Sillett et al. (2012); loads the following objects covs: data frame with survey point covariates <br> Xall.100m: matrix with detections from fall survey, survey site by 100-m distance band<br> Xall.spring.100m: matrix with detections from spring survey, survey site by 100-m distance band Sollmann_et_al_R_script_Markovian_model.txt: (MD5: ) R script file with code to simulate, analyze, and summarize data under a Markovian abundance model where <i>N</i> at time <i>t</i> depends on <i>N</i> at time <i>t</i>-1, using the open-population hierarchical distance sampling model. Sollmann_et_al_Markovian_model_JAGS_code.txt: (MD5: ) JAGS model description of the open-population hierarchical distance sampling model used in the R script above. Sollmann_et_al_R_script_Markovian_data_log-linear_trend_model.txt: (MD5: ) R script file with code to simulate data under a Markovian abundance model (see above), and analyze and summarize it under a hierarchical distance sampling model with a log-linear time effect on abundance. Sollmann_et_al_Independent_years_model_JAGS_code.txt: (MD5: ) JAGS model description of the hierarchical distance sampling model with linear time effect used in the R script above. Description Data files and code are set up to repeat the simulation study investigating power to detect population trends with independent and correlated abundances between years, as presented in the Simulation section in the manuscript.