cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4005735
title: Research data: preparation of nanocellulose using non-aligned hemp bast fibers

description: The datafiles and archives contain the experimental results and the Optical, FESEM and TEM micrographs collected and analysed for the article (submitted to Cellulose). The files are named following this convention: Hemp_A_B.ext where A is the type of material: PT = fibers after all pretreatment steps PT(xx) = fibers after the pretreatment step indicated in the parenthesis PT_NC = fibers after all pretreatment steps and nanocellulose NC = nanocellulose and B is the type of measurement done: TEM = Transmission Electron Microscope FESEM = Field Emission Scanning Electron Microscope Dimensions = lengths and diameters of the fibers, as analysed from the Optical, FEEM and TEM micrographs with the software ImageJ XRD_bkgsub = X-ray diffraction measurements, with background subtracted FTIR = Fourier Transform Infrared Spectroscopy (in ATR mode) TGA = Thermogravimetric analysis The data will be made open as soon as possible depending on the publishing process of the article
DOI: 10.5281/zenodo.4010434
title: Automated Aural Device 15 and 20 Alpha Index Datasets

description: The datasets are 45 day time series of data collected by automated aural devices (Wildlife Acoustics SM4 recorders) in Bochum Germany at a mixed deciduous and evergreen forest location (AAD 20) and a mixed use urban location (AAD 15).
DOI: 10.6084/m9.figshare.5417863
title: Data for Shields stress 4.1

description: small and large grain number ratio time-series for Shields stress 4.1. Concentration map of large grains can be made using the following contour plot commands in MATLAB or other data analysis packages:<br>%%%%nlratio_tot = nlg./(nsm+nlg);for i=1:458time_ts(i,:)=time;endy_pos = (1:1:458)*(25/458);for i=1:length(time)y_mesh(:,i)=y_pos;end[xq,yq] = meshgrid(0:0.0005:1,0:0.175:25);vq = griddata(time_ts(1:458,1:length(time)),25-y_mesh(1:458,1:length(time)),nlratio_tot(1:458,1:length(time)),xq,yq);figure; [C,h] = contourf(xq,yq,vq,'LineStyle', 'none','LevelStep', 0.01);colormap parula;%%%%
DOI: 10.6084/m9.figshare.5417863.v1
title: Data for Shields stress 4.1

description: small and large grain number ratio time-series for Shields stress 4.1. Concentration map of large grains can be made using the following contour plot commands in MATLAB or other data analysis packages:<br>%%%%nlratio_tot = nlg./(nsm+nlg);for i=1:458time_ts(i,:)=time;endy_pos = (1:1:458)*(25/458);for i=1:length(time)y_mesh(:,i)=y_pos;end[xq,yq] = meshgrid(0:0.0005:1,0:0.175:25);vq = griddata(time_ts(1:458,1:length(time)),25-y_mesh(1:458,1:length(time)),nlratio_tot(1:458,1:length(time)),xq,yq);figure; [C,h] = contourf(xq,yq,vq,'LineStyle', 'none','LevelStep', 0.01);colormap parula;%%%%
DOI: 10.6084/m9.figshare.5417764
title: Data for Shields stress 2.7

description: small and large grain number ratio time-series for Shields stress 2.7. Concentration map of large grains can be made using the following contour plot commands in MATLAB or other data analysis packages:<br>%%%%nlratio_tot = nlg./(nsm+nlg);for i=1:458time_ts(i,:)=time;endy_pos = (1:1:458)*(25/458);for i=1:length(time)y_mesh(:,i)=y_pos;end[xq,yq] = meshgrid(0:0.0005:1,0:0.175:25);vq = griddata(time_ts(1:458,1:length(time)),25-y_mesh(1:458,1:length(time)),nlratio_tot(1:458,1:length(time)),xq,yq);figure; [C,h] = contourf(xq,yq,vq,'LineStyle', 'none','LevelStep', 0.01);colormap parula;%%%%
DOI: 10.6084/m9.figshare.5417761
title: Data for Shields stress 3.8

description: small and large grains number ratio time-series for Shields stress 3.8. Concentration map of large grains can be made using the following contour plot commands in MATLAB or other data analysis packages:<br>%%%%nlratio_tot = nlg./(nsm+nlg);for i=1:458time_ts(i,:)=time;endy_pos = (1:1:458)*(25/458);for i=1:length(time)y_mesh(:,i)=y_pos;end[xq,yq] = meshgrid(0:0.0005:1,0:0.175:25);vq = griddata(time_ts(1:458,1:length(time)),25-y_mesh(1:458,1:length(time)),nlratio_tot(1:458,1:length(time)),xq,yq);figure; [C,h] = contourf(xq,yq,vq,'LineStyle', 'none','LevelStep', 0.01);colormap parula;%%%%
DOI: 10.6084/m9.figshare.5417764.v1
title: Data for Shields stress 2.7

description: small and large grain number ratio time-series for Shields stress 2.7. Concentration map of large grains can be made using the following contour plot commands in MATLAB or other data analysis packages:<br>%%%%nlratio_tot = nlg./(nsm+nlg);for i=1:458time_ts(i,:)=time;endy_pos = (1:1:458)*(25/458);for i=1:length(time)y_mesh(:,i)=y_pos;end[xq,yq] = meshgrid(0:0.0005:1,0:0.175:25);vq = griddata(time_ts(1:458,1:length(time)),25-y_mesh(1:458,1:length(time)),nlratio_tot(1:458,1:length(time)),xq,yq);figure; [C,h] = contourf(xq,yq,vq,'LineStyle', 'none','LevelStep', 0.01);colormap parula;%%%%
DOI: 10.6084/m9.figshare.5417761.v1
title: Data for Shields stress 3.8

description: small and large grains number ratio time-series for Shields stress 3.8. Concentration map of large grains can be made using the following contour plot commands in MATLAB or other data analysis packages:<br>%%%%nlratio_tot = nlg./(nsm+nlg);for i=1:458time_ts(i,:)=time;endy_pos = (1:1:458)*(25/458);for i=1:length(time)y_mesh(:,i)=y_pos;end[xq,yq] = meshgrid(0:0.0005:1,0:0.175:25);vq = griddata(time_ts(1:458,1:length(time)),25-y_mesh(1:458,1:length(time)),nlratio_tot(1:458,1:length(time)),xq,yq);figure; [C,h] = contourf(xq,yq,vq,'LineStyle', 'none','LevelStep', 0.01);colormap parula;%%%%
DOI: 10.17608/k6.auckland.5417746
title: Bay of Islands Bottlenose Dolphin Catalogue photos

description: This catalogue is a collection of uniquely identified bottlenose dolphin dorsal fin photos from the Bay of Islands, New Zealand 1993-2013. Where possible there is a right and left side image of each dorsal fin.<br>The photograph codes are as follows:<br>BOI = Bay of IslandsXXX = unique number for each dolphinRS/LS = right side/ left side<br><br>All work was conducted by researchers at the University of Auckland and the catalogue is curated by Dr Rochelle Constantine, University of Auckland (r.constantine@auckland.ac.nz). Please contact Rochelle if you have any queries.
DOI: 10.17608/k6.auckland.5417746.v1
title: Bay of Islands Bottlenose Dolphin Catalogue photos

description: This catalogue is a collection of uniquely identified bottlenose dolphin dorsal fin photos from the Bay of Islands, New Zealand 1993-2013. Where possible there is a right and left side image of each dorsal fin.<br>The photograph codes are as follows:<br>BOI = Bay of IslandsXXX = unique number for each dolphinRS/LS = right side/ left side<br><br>All work was conducted by researchers at the University of Auckland and the catalogue is curated by Dr Rochelle Constantine, University of Auckland (r.constantine@auckland.ac.nz). Please contact Rochelle if you have any queries.
DOI: 10.6084/m9.figshare.5417629
title: Do host-associated gut microbiota mediate the effect of an herbicide on disease risk in frogs?

description: Here, we test whether early-life exposure to the estimated environmental concentration of atrazine affects the gut bacterial composition of Cuban tree frog tadpoles and adults and whether any atrazine-induced change in community composition might affect host defenses against <i>Bd</i>. The files include all data that were used in the analyses.
DOI: 10.6084/m9.figshare.5417629.v1
title: Do host-associated gut microbiota mediate the effect of an herbicide on disease risk in frogs?

description: Here, we test whether early-life exposure to the estimated environmental concentration of atrazine affects the gut bacterial composition of Cuban tree frog tadpoles and adults and whether any atrazine-induced change in community composition might affect host defenses against <i>Bd</i>. The files include all data that were used in the analyses.
DOI: 10.6084/m9.figshare.5417593
title: Datasets for Loffler & Hoey Journal of Ecology

description: Data describing changes in Sargassum density and height after manipulations mimicking storm damage.<br><br>Also includes data on changes in number of holdfasts on rocks caged and exposed to herbivory over 16 weeks. <br>
DOI: 10.6084/m9.figshare.5417596
title: Data for Shields stress 4.7

description: small and large grains number ratio time-series for Shields stress 4.7. Concentration map of large grains can be made using the following contour plot commands in MATLAB or other data analysis packages:<br>%%%%nlratio_tot = nlg./(nsm+nlg);for i=1:458time_ts(i,:)=time;endy_pos = (1:1:458)*(25/458);for i=1:length(time)y_mesh(:,i)=y_pos;end[xq,yq] = meshgrid(0:0.0005:1,0:0.175:25);vq = griddata(time_ts(1:458,1:length(time)),25-y_mesh(1:458,1:length(time)),nlratio_tot(1:458,1:length(time)),xq,yq);figure; [C,h] = contourf(xq,yq,vq,'LineStyle', 'none','LevelStep', 0.01);colormap parula;%%%%
DOI: 10.6084/m9.figshare.5417587
title: Multiset Model Selection

description: Model selection algorithms are required to efficiently traverse the space of models. In problems with high-dimensional and possibly correlated covariates, efficient exploration of the model space becomes a challenge. To overcome this, a multiset is placed on the model space to enable efficient exploration of multiple model modes with minimal tuning. The multiset model selection (MSMS) framework is based on independent priors for the parameters and model indicators on variables. Posterior model probabilities can be easily obtained from multiset averaged posterior model probabilities in MSMS. The effectiveness of MSMS is demonstrated for linear and generalized linear models. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.5417590
title: functional test case with one resistor

description: Simple functional product data model encoded in ISO 10303-21 concrete syntax.
DOI: 10.6084/m9.figshare.5417587.v1
title: Multiset Model Selection

description: Model selection algorithms are required to efficiently traverse the space of models. In problems with high-dimensional and possibly correlated covariates, efficient exploration of the model space becomes a challenge. To overcome this, a multiset is placed on the model space to enable efficient exploration of multiple model modes with minimal tuning. The multiset model selection (MSMS) framework is based on independent priors for the parameters and model indicators on variables. Posterior model probabilities can be easily obtained from multiset averaged posterior model probabilities in MSMS. The effectiveness of MSMS is demonstrated for linear and generalized linear models. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.5417590.v1
title: functional test case with one resistor

description: Simple functional product data model encoded in ISO 10303-21 concrete syntax.
DOI: 10.6084/m9.figshare.5417596.v1
title: Data for Shields stress 4.7

description: small and large grains number ratio time-series for Shields stress 4.7. Concentration map of large grains can be made using the following contour plot commands in MATLAB or other data analysis packages:<br>%%%%nlratio_tot = nlg./(nsm+nlg);for i=1:458time_ts(i,:)=time;endy_pos = (1:1:458)*(25/458);for i=1:length(time)y_mesh(:,i)=y_pos;end[xq,yq] = meshgrid(0:0.0005:1,0:0.175:25);vq = griddata(time_ts(1:458,1:length(time)),25-y_mesh(1:458,1:length(time)),nlratio_tot(1:458,1:length(time)),xq,yq);figure; [C,h] = contourf(xq,yq,vq,'LineStyle', 'none','LevelStep', 0.01);colormap parula;%%%%
DOI: 10.6084/m9.figshare.5417557
title: Welfare Changes from Agricultural Yield Shocks for 1-3 Degrees of Global Warming (Meta-Analysis)

description: Shape file with 140 GTAP regions giving welfare changes for 1-3 degrees of warming. Three values are given for each level of warming - the central estimate (50th percentile) and the upper (97.5th percentile) and lower (2.5th percentile) bounds of the 95% confidence interval. The column "VOM" gives the total value of the shocked sectors for that region, used to normalize the welfare changes to give % change, as reported in Moore et al (in press).<br>Welfare changes are equivalent variation and are based on yield shocks derived from a meta-analysis from the yield impacts database reported in Challinor et al. (2014). Gridded yield shocks used as input for the simulation are available at 10.6084/m9.figshare.5417548<br>Moore, F. C., Baldos, U., Hertel, T. W. & Diaz, D. New Science of Climate Change Impacts on Agriculture Implies Higher Social Cost of Carbon. Nat. Commun.<br><br>Challinor, A. J., Watson, J., Lobell, D. B., Howden, S. M., Smith, D. R. & Chhetri, N. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang. 4, 287–291 (2014).<br>
DOI: 10.6084/m9.figshare.5417560
title: Welfare Changes from Agricultural Yield Shocks for 1-3 Degrees of Global Warming (AgMIP, Preferred Ensemble)

description: Shape file with 140 GTAP regions giving welfare changes for 1-3 degrees of warming. Three values are given for each level of warming - the central estimate (50th percentile) and the upper (97.5th percentile) and lower (2.5th percentile) bounds of the 95% confidence interval. The column "VOM" gives the total value of the shocked sectors for that region, used to normalize the welfare changes to give % change, as reported in Moore et al (in press).<br>Welfare changes are equivalent variation and are based on yield shocks derived from averaging AgMIP Global Gridded Crop Model Intercomparison results (Rosenzweig et al 2014). Data are available from the ISIMIP Fast Trak project page: https://esg.pik-potsdam.de/projects/isimip-ft/ and were analyzed according to methods reported in Moore et al (in press). The results here are for a preferred ensemble of crop models, excluding the ones that do not explicitly represent nitrogen stress. <br>Moore, F. C., Baldos, U., Hertel, T. W. & Diaz, D. New Science of Climate Change Impacts on Agriculture Implies Higher Social Cost of Carbon. Nat. Commun.<br><br>Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N., Neumann, K., Piontek, F., Pugh, T. A. M., Schmid, E., Stehfest, E., Yang, H. & Jones, J. W. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl. Acad. Sci. U. S. A. 111, 3268–73 (2014).<br><br><br>
DOI: 10.6084/m9.figshare.5417545
title: Autophagy-mediated regulation of phytohormone metabolism during rice anther development

description: Autophagy has recently been shown to be required for postmeiotic anther development including anther dehiscence, programmed cell death-mediated degradation of the tapetum and pollen maturation in rice. Several phytohormones are known to play essential roles during male reproductive development including pollen maturation. However, the relationship between phytohormone metabolism and autophagy in plant reproductive development is unknown. We here comprehensively analyzed the effect of autophagy disruption on phytohormone contents in rice anthers at the flowering stage, and found that endogenous levels of active-forms of gibberellins (GAs) and cytokinin, trans-zeatin, were significantly lower in the autophagy-defective mutant, Os<i>atg7–1</i>, than in the wild type. Treatment with GA<sub>4</sub> partially recovered maturation of the mutant pollens, but did not recover the limited anther dehiscence as well as sterility phenotype. These results suggest that autophagy affects metabolism and endogenous levels of GAs and cytokinin in rice anthers. Reduction in bioactive GAs in the autophagy-deficient mutant may partially explain the defects in pollen maturation of the autophagy-deficient mutant, but tapetal autophagy also plays other specific roles in fertilization.
DOI: 10.6084/m9.figshare.5417545.v1
title: Autophagy-mediated regulation of phytohormone metabolism during rice anther development

description: Autophagy has recently been shown to be required for postmeiotic anther development including anther dehiscence, programmed cell death-mediated degradation of the tapetum and pollen maturation in rice. Several phytohormones are known to play essential roles during male reproductive development including pollen maturation. However, the relationship between phytohormone metabolism and autophagy in plant reproductive development is unknown. We here comprehensively analyzed the effect of autophagy disruption on phytohormone contents in rice anthers at the flowering stage, and found that endogenous levels of active-forms of gibberellins (GAs) and cytokinin, trans-zeatin, were significantly lower in the autophagy-defective mutant, Os<i>atg7–1</i>, than in the wild type. Treatment with GA<sub>4</sub> partially recovered maturation of the mutant pollens, but did not recover the limited anther dehiscence as well as sterility phenotype. These results suggest that autophagy affects metabolism and endogenous levels of GAs and cytokinin in rice anthers. Reduction in bioactive GAs in the autophagy-deficient mutant may partially explain the defects in pollen maturation of the autophagy-deficient mutant, but tapetal autophagy also plays other specific roles in fertilization.
DOI: 10.6084/m9.figshare.5417548
title: Gridded Yield Changes 1-3 Degrees Global Warming with Uncertainty

description: These are gridded yield shocks based on the meta-analysis described in Moore et al (in press) derived from a re-analysis of the database of yield studies reported in Challinor et al (2015).<br>Files are provided for maize, rice, soy and wheat. Grids are R rasters (requires the installation of the raster library in R) compiled into a list and are provided for the main effect, and upper and lower bounds of the 95% confidence intervals.<br>Each file contains a list of length 3 named "yieldchange_errorbars" where each element of the list is also a list of length 3. Data are organized first by quantile and then by temperature change. For example yieldchange_errorbars[[1]][[3]] would be the lower bound (2.5th quantile) of the yield changes as three degrees of warming. yieldchange_errorbars[[2]][[1]] would be the central estimate (50th quantile) of yield changes at one degree of warming.<br>In addition, the Excel file "Response Function Coefficients" gives the coefficients of the temperature response functions used to calculate these yield changes and the variance-covariance matrix. Use this if you want to use our estimated yield response functions with your own climate forcing.<br>
DOI: 10.6084/m9.figshare.5417536
title: Geary_etal_Pelecanus_occidentalis_figshare.txt

description: Microsatellite allele calls for 540 brown pelican nestlings, sampled from 2007-2011 on barrier islands throughout the northern Gulf of Mexico (Texas to Florida), as well as one Atlantic colony in Florida.<br>First two digits of field numbers represent the year each sample was taken. Field site abbreviations correspond to those in the manuscript. Zeroes in allele calls represent missing data.
DOI: 10.6084/m9.figshare.5417536.v1
title: Geary_etal_Pelecanus_occidentalis_figshare.txt

description: Microsatellite allele calls for 540 brown pelican nestlings, sampled from 2007-2011 on barrier islands throughout the northern Gulf of Mexico (Texas to Florida), as well as one Atlantic colony in Florida.<br>First two digits of field numbers represent the year each sample was taken. Field site abbreviations correspond to those in the manuscript. Zeroes in allele calls represent missing data.
DOI: 10.6084/m9.figshare.5417290
title: Relevance of genetic relationship in GWAS and genomic prediction

description: Data files and description file.<br>
DOI: 10.6084/m9.figshare.5417353
title: Wheeler et al EJN 2017 Data.xls

description: Title: Corticosterone regulates both naturally occurring and cocaine-induced dopamine signaling by selectively decreasing dopamine uptake <br><br>Authors: Daniel S. Wheeler, Amanda L. Ebben, Beliz Kurtoglu, Marissa E. Lovell, Austin T. Bohn, Isabella A. Jasek, David A. Baker, John R. Mantsch, Paul J. Gasser*, Robert A. Wheeler*
DOI: 10.6084/m9.figshare.5417419
title: Example Pieman data

description: Example data from 5 subjects listening to the Pieman stimulus
DOI: 10.6084/m9.figshare.5417290.v1
title: Relevance of genetic relationship in GWAS and genomic prediction

description: Data files and description file.<br>
DOI: 10.6084/m9.figshare.5417353.v1
title: Wheeler et al EJN 2017 Data.xls

description: Title: Corticosterone regulates both naturally occurring and cocaine-induced dopamine signaling by selectively decreasing dopamine uptake <br><br>Authors: Daniel S. Wheeler, Amanda L. Ebben, Beliz Kurtoglu, Marissa E. Lovell, Austin T. Bohn, Isabella A. Jasek, David A. Baker, John R. Mantsch, Paul J. Gasser*, Robert A. Wheeler*
DOI: 10.6084/m9.figshare.5415283.v1
title: Applications of emerging transmission electron microscopy technology in PCD research and diagnosis

description: Primary Ciliary Dyskinesia (PCD) is a heterogeneous genetic condition characterized by dysfunction of motile cilia. Patients suffer from chronic infection and inflammation of the upper and lower respiratory tract. Diagnosis of PCD is confirmed by identification of a hallmark defect of ciliary ultrastructure or by identification of biallelic pathogenic mutations in a known PCD gene. Since the first description of PCD in 1976, assessment of ciliary ultrastructure by transmission electron microscopy (TEM) has been central to diagnosis and research. Electron tomography is a technique whereby a series of transmission electron micrographs are collected at different angles and reconstructed into a single 3D model of a specimen. Electron tomography provides improved spatial information and resolution compared to a single micrograph. Research by electron tomography has revealed new insight into ciliary ultrastructure and consequently ciliary function at a molecular and cellular level. Gene discovery studies in PCD have utilized electron tomography to define the structural consequences of variants in cilia genes. Modern transmission electron microscopes capable of electron tomography are increasingly being installed in clinical laboratories. This presents the possibility for the use of tomography technique in a diagnostic setting. This review describes the electron tomography technique, the contribution tomography has made to the understanding of basic cilia structure and function and finally the potential of the technique for use in PCD diagnosis.
DOI: 10.6084/m9.figshare.5415201
title: Evaluation trees

description: Concatenated evaluation trees from https://figshare.com/projects/Evaluating_fast_maximum_likelihood-based_phylogenetic_programs_using_empirical_phylogenomic_data_sets/22040
DOI: 10.6084/m9.figshare.5415211
title: Histograms

description: Histograms of evaluation trees
DOI: 10.6084/m9.figshare.5415201.v1
title: Evaluation trees

description: Concatenated evaluation trees from https://figshare.com/projects/Evaluating_fast_maximum_likelihood-based_phylogenetic_programs_using_empirical_phylogenomic_data_sets/22040
DOI: 10.6084/m9.figshare.5415211.v1
title: Histograms

description: Histograms of evaluation trees
DOI: 10.6084/m9.figshare.5415193
title: Species tree

description: Data set of species trees from: https://figshare.com/projects/Evaluating_fast_maximum_likelihood-based_phylogenetic_programs_using_empirical_phylogenomic_data_sets/22040