Find research datasets worth reusing

Data files and computer code scripts for reproducing the results of a manuscript on the measurement and ranking of cotton drought tolerance capacity

<p>This upload contains the data files and computer code scripts for reproducing the main results, esp. figures,  of the manuscript entitled<br>"Rapid measurement and statistical ranking of leaf drought tolerance capacity in cotton," by X. Dong, D. A. Mott, J. Garg, Q. Zhou, J. Sunoj V. S., and B. M. McKnight. The manuscriptt is currently under peer review.</p>

Open Datasets for the Manuscript Submitted to Geophysical Research Letters

<ol> <li>Hourly land surface temperature data derived from GOES-R</li> <li>Hourly land surface temperature data derived from Himawari-8</li> <li>MODIS land surface temperature</li> <li>Urban-rural delineation</li> <li>Annual temperature cycle modeling results</li> </ol>

R codes prepared for the manuscript, entitled "The roles of Y chromosomal genes in mouse sex spectrum phenotypes"

<p>R codes for the manuscript, entitled "The roles of Y chromosomal genes in mouse sex spectrum phenotypes" </p>

Data used in a manuscript: High sensitivity of cloud formation to aerosol changes

<p>Data used in a manuscript entitled: Revealing uncertainties in defining the sensitivity of cloud formation to aerosol changes</p> <p><strong>Puijo station datafiles (all data 1 hour averages)</strong></p> <p>Puijo_meteorological_data_1h_ave.csv</p> <ul> <li>Meteorological data measured in Puijo station</li> <li>Columns: Time, Visibility in m, Rain Intensity in mm/h, Temperature in C</li> </ul> <p>Puijo_DMPS_total_inlet_data_1h_ave.csv</p> <ul> <li>Particle size distributions measured via total inlet at Puijo station with DMPS system </li> <li>Columns: Time, total particle number concentration in #/cm3, particle concentrations at different size bins in #/cm3</li> <li>Size bin diameters in a Puijo_DMPS_Dp_nm_values.csv file    </li> </ul> <p>Puijo_DMPS_interstitial_inlet_data_1h_ave.csv  </p> <ul> <li>Particle size distributions measuresed via interstitia inlet at Puijo station with DMPS system </li> <li>Columns: Time, total particle number concentration in #/cm3, particle concentrations at different size bins in #/cm3</li> <li>Size bin diameters in a Puijo_DMPS_Dp_nm_values.csv file  </li> </ul> <p>Puijo_DMPS_Dp_nm_values.csv</p> <ul> <li>Particle diameters for certain size bins in files  Puijo_DMPS_total_inlet_data_1h_ave.csv and Puijo_DMPS_interstitial_inlet_data_1h_ave.csv</li> </ul> <p>Puijo_Forces2020_updraft_temp_data_1h_ave.csv</p> <ul> <li>updraft velocity and temperature data measured during Forces 2020 campaign</li> <li>Columns: time, updraft velocity in m/s (wwind), sigma wwind in m/s, r2 value, temperature in C</li> </ul> <p><strong>Pallas station datafiles (all data 1 hour averages)</strong></p> <p>Pallas_meteorological_data_1h_ave.csv</p> <ul> <li>Meteorological data measured in Pallas station</li> <li>Columns: Time, Visibility in m, Rain Intensity in mm/h, Temperature in C</li> </ul> <p>Pallas_DMPS_total_inlet_data_1h_ave.csv</p> <ul> <li>Particle size distributions measured via total inlet at Pallasstation with DMPS system </li> <li>Columns: Time, total particle number concentration in #/cm3, particle concentrations at different size bins in #/cm3</li> <li>Size bin diameters in a Puijo_DMPS_Dp_nm_values.csv file    </li> </ul> <p>Pallas_DMPS_Dp_nm_values.csv</p> <ul> <li>Particle diameters for certain size bins in a file Pallas_DMPS_total_inlet_data_1h_ave.csv</li> </ul> <p><strong>Zeppelin station datafiles (all data 1 hour averages)</strong></p> <p>Zeppelin_updraft_temp_data_1h_ave.csv</p> <ul> <li>Anemometer measured in Zeppelin stations</li> <li>Columns: Time, wind speed in m/s (svel), temperature in C, sigma svel in m/s</li> </ul>

Data repository for manuscript "A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi"

<p>Dataset to accompany the publication <em>“A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi”</em> by Margherita Molaro, Sakshi Mohan, Bingling She, Martin Chalkley, Tim Colbourn, Joseph H. Collins, Emilia Connolly, Matthew M. Graham, Eva Janou&scaron;ková, Ines Li Lin, Gerald Manthalu, Emmanuel Mnjowe, Dominic Nkhoma, Pakwanja D. Twea, Andrew N. Phillips, Paul Revill, Asif U. Tamuri, Joseph Mfutso-Bengo, Tara Mangal, and Timothy B. Hallett.</p> <p>The Thanzi La Onse (TLO) model used to produce this data is open source and available for review and usage at<a href="https://github.com/UCL/TLOmodel"> https://github.com/UCL/TLOmodel</a>. In particular, the outputs analysed in this study can be reproduced from model tag "Molaro_et_al_2024_HBP_design" (accessible at https://github.com/UCL/TLOmodel/tags) using the scenario file src/scripts/healthsystem/impact_of_policy/scenario_impact_of_policy.py. All analysis scripts used to generate the plots in the manuscript are located in the same directory and have filenames beginning with "analysis_impact_of_policy_".</p> <p>This repository contains post-processed simulation outputs, which were generated using the script src/scripts/healthsystem/impact_of_policy/analysis_extract_data.py (available from the same tag). The data included have the following structure:</p> <p>"Draw": Represents a specific prioritisation-policy, identified by the acronyms listed in Table 1 of the publication.</p> <p>"Run": Represents a single simulation instance of a draw. Each draw was simulated 10 times, each with independent random sampling, resulting in 10 "runs" per draw.</p> <p>The data files included in this repository are:</p> <p><strong>DALYS_by_cause_with_time.csv</strong>: DALYs (as defined in the publication) incurred on a given year due to each of the causes of DALYs considered.</p> <p><strong>HSIs_requested_by_type_and_facility_level_with_time.csv</strong>: total number of requested HSIs on a given year, broken down by HSI type and the facility level at which they were requested.</p> <p><strong>HSIs_delivered_by_type_and_facility_level_with_time.csv</strong>:total number of HSIs delivered on a given year broken down by HSI type and the facility level at which they were delivered.</p> <p><strong>Population_with_time.csv</strong>:total population size on a given year. </p> <p> </p> <p> </p>

Code for Manuscript - Near-term lake water temperature forecasts can be used to anticipate the ecological dynamics of freshwater species -

<p>Code for Manuscript - Near-term lake water temperature forecasts can be used to anticipate the ecological dynamics of freshwater species -</p>

Data for manuscript "Evidence for large-scale climate forcing of dense shelf water variability in the Ross Sea"

<p>Relevant data shown in figures of the manuscript "Evidence for large-scale climate forcing of dense shelf water variability in the Ross Sea"</p>

opencc-by-4.0Nov 2023View details →

Data associated with the manuscript "Complex epistatic interactions between ELF3, PRR9, and PRR7 regulates the circadian clock and plant physiology"

<p>Datasets associated with the figures in the paper entilted: "<strong>Complex epistatic interactions between ELF3, PRR9, and PRR7 regulates the circadian clock and plant physiology"</strong></p>

Gene Expression Raw Data - Manuscript 1 CAREQiPSC

<p>This Excel file contains the delta delta Ct (cycle threshold) values from gene expression experiments conducted during the generation and characterisation of equine induced pluripotent stem cells, as part of the project CAREQiPSC.</p> <p>Data is grouped as the figures presented in a manuscript pending of publication in a peer-reviewed journal, which will be linked to this dataset.</p>

Data underlying the manuscript: "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020".

<p>This is the research data for the manuscript "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020".<br>The following is the original abstract: Introduction: Sharing research data on climate change would facilitate the development of solutions to curb its impact, for this, data needs to be shared in an optimal way. General objective: To identify how many Spanish scientific articles on climate change published during 2020 share their research data in some way. Specific objectives: a) Identify the attributes of shared research data b) Describe the characteristics of the case studies found on how research data are shared. Methodology: Qualitative and descriptive study analyzing nine attributes: availability (1), accessibility (2), format (3), license (4), linkage (5), funding (6), editorial policy (7), content (8), statistics (9). Results: We analyzed 2212 articles were analyzed, 1867 (84%) articles had no associated research data. The remaining 16% have associated research data: 152 (7%) articles deposited their data in repositories, 42 (2%) submitted their data as supplementary material, 136 (6%) will share their data upon request to the author and 15 (1%) do not have publication permissions. Conclusions: Researchers are willing to share their research data, but under different conditions. Researchers who reused research data did not share the new data they generated. There is a lack of training among researchers on how to manage their research data. There is information on the web on this topic, but it is not just a matter of publishing manuals, but also of creating training spaces within universities, institutes and research centers to build a community of researchers committed to Open Science.</p>

Raw ptychographic synthetic data for manuscript with title 'Purity-based self-calibration in ptychography'

<ul> <li>Here given are the dataset for purity scan, numerically created based on the synthetic setup in the manuscript titled "Purity-based self-calibration in ptychography". The dataset includes raw diffraction patterns, preprocessed diffraction patterns for reconstruction, as well as the preprocessed script.</li> <li>For the two experimental verificatoin cases, the preprocessed diffraction patterns are provided, where the scanning grid is included.</li> <li>The reconstruction, calculation of purity, and zPIE were conducted at open-source PtyLab framework.</li> <li>Experimental data were measured at the Institute of Applied Physics in Jena using a Fiber Laser driven High-order harmonic source, which can be referenced in </li> </ul> <p>Please contact me (liu.chang@uni-jena.de) for additional support.</p>

Raw data for the submitted manuscript: The Effect of Clay Type on the Toxicity of Carbendazim and Imidacloprid to the Earthworm Eisenia andrei in Artificial Soils

<p>Raw data obtained from toxicity tests with the earthworm <em>Eisenia </em><em>andrei </em>exposed for 56 days to carbendazim and imidacloprid in artificial soils created with kaolin and bentonite clay. Tests were performed following OECD guideline 222. The file includes data on earthworm starting and ending weights, survival, and reproduction.</p>

Dataset for manuscript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model"

<p>Datasets and Jupyterlab python script for plotting all figures relevant to the mansucript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model" by Herbert et al.</p> <p>https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1538/</p> <p>Data needs to be unzipped and paths (input and output) updated in the jupyterlab python script.</p> <p> </p>

Raw images, video, and data file for the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials"

<p>This dataset includes the raw images and data file in the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials", specifically:</p> <ul> <li>Raw images for the optimized print with the PEGDA-glycerol-water resin (Figure 2 & Figure S2)</li> <li>Raw images for the optimized print with the PEGDA-glycerol-LB resin (Figure 2)</li> <li>Raw images for the optimized print with the BSA-PEGDA-water resin (Figure 3)</li> <li>Raw images and video for the spontaneous capillary flow of LB media in a PEGDA-glycerol-LB microfluidic chip (Figure 4)</li> <li>Raw data for the UV-vis spectrum of LB media (Figure S4)</li> </ul>

Data for manuscript: The structural influence of the oncogenic driver mutation N642H in the STAT5B SH2 domain

<p>The data is provided as a part of the manuscript "<strong>The structural influence of the oncogenic driver mutation N642H in the STAT5B SH2 domain</strong>". </p> <p>This repository includes an archive with folders:</p> <div> </div> <div>MD_Data</div> <div>-- Contains shortened versions of the MD trajectories, and initial structure files used to run simulations</div> <div> </div> <div>FigureData </div> <div>-- Contains comma separated value files for each figure</div> <p> </p>

opencc-by-4.0May 2025View details →

Processed data supporting the manuscript "Cutting the sap: first molecular phylogeny of twig-girdler longhorn beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) suggests shifts in host plant attack behaviors contributed to morphological evolution"

<div><strong>Processed data supporting the manuscript: </strong>Cutting the sap: first molecular phylogeny of twig-girdler longhorn beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) suggests shifts in host plant attack behaviors contributed to morphological evolution</div> <div> </div> <div><strong>By:</strong> Diego de S. Souza 1, 2, Rowan L. K. French 3, José O. Silva Júnior 4, Eugenio H. Nearns 5, Luciane Marinoni 4, Ian P. Swift 6, Kelly B. Miller 7, Felix A. H. Sperling 2 & Marcela L. Monné 1</div> <div> </div> <div>1 Department of Entomology, National Museum, Federal University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.</div> <div>2 Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.</div> <div>3 Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada.</div> <div>4 Department of Zoology, Federal University of Paraná, Curitiba, Paraná, Brazil.</div> <div>5 National Museum of Natural History, Smithsonian Institution, Washington, DC, USA.</div> <div>6 California State Collection of Arthropods, Sacramento, California, USA.</div> <div>7 Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, New Mexico, USA.</div> <div> </div> <div>Corresponding author: Diego de S. Souza, dsouza@fieldmuseum.org. Current affiliation: Field Museum of Natural History, Chicago, Illinois, USA.</div> <div> </div> <div> </div> <div><strong>List of Contents: </strong></div> <div> </div> <div><strong>Onciderini_concat_matrix.phy</strong></div> <div>Concatenated matrix (cox1, Wg and CPS) used for the phylogenetic analyses of Onciderini (Coleoptera: Cerambycidae: Lamiinae: Onciderini). </div> <div> </div> <div><strong>PartitionFinder_AICc_best_scheme.txt</strong></div> <div>Results from PartitionFinder v2.1.1, containing the best partitioning scheme for the concatenated matrix of Onciderini, identified using the corrected Akaike Information Criterion (AICc), with model definitions for use in the phylogenetic analyses.</div> <div> </div> <div><strong>RAxML_Onciderini_concat_matrix (zip file)</strong></div> <div>- Onciderini_concat_matrix.phy: concatenated matrix (cox1, Wg and CPS) used in the RAxML phylogenetic analyses of Onciderini (Coleoptera: Cerambycidae: Lamiinae: Onciderini).</div> <div>- Partitions_AICc_RAxML.txt: partitioning scheme used in the RAxML analysis as predefined by PartitionFinder v2.1.1 using the corrected Akaike Information Criterion (AICc).</div> <div>- RAxML_bestTree.Onciderini_concat_matrix_ML: best-scoring maximum likelihood tree inferred by RAxML for the concatenated matrix of Onciderini.</div> <div>- RAxML_bipartitions.Onciderini_concat_matrix_final: bipartitions (clades) of the maximum likelihood tree inferred by RAxML with support values estimated from 1,000 pseudoreplicates.</div> <div>- RAxML_bipartitionsBranchLabels.Onciderini_concat_matrix_final: final maximum likelihood tree inferred by RAxML for the concatenated matrix of Onciderini, with labeled branches showing bootstrap support values.</div> <div>- RAxML_bootstrap.Onciderini_concat_matrix_bootstrap: bootstrap trees generated from a non-parametric bootstrap analysis in RAxML based on 1,000 pseudoreplicates.</div> <div>- RAxML_info.Onciderini_concat_matrix_bootstrap: log file containing details of the bootstrap analysis, including the settings and parameters used in the non-parametric bootstrap runs in RAxML.</div> <div>- RAxML_info.Onciderini_concat_matrix_final: log file summarizing the RAxML analysis, including settings and convergence statistics for the final maximum likelihood tree.</div> <div>- RAxML_info.Onciderini_concat_matrix_ML: log file containing details of the maximum likelihood tree search, including the parameters and models applied during the maximum likelihood analysis conducted by RAxML.</div> <div>- RAxML_log.Onciderini_concat_matrix_ML: log file of the maximum likelihood tree search for the concatenated matrix of Onciderini.</div> <div>- RAxML_parsimonyTree.Onciderini_concat_matrix_ML: parsimony starting tree used by RAxML during the maximum likelihood analysis for the concatenated matrix of Onciderini.</div> <div>- RAxML_result.Onciderini_concat_matrix_ML: maximum likelihood tree inferred by RAxML from the concatenated matrix of Onciderini, summarizing the tree topology and likelihood score for the best tree obtained.</div> <div> </div> <div><strong>BI_AICc_Onciderini_concat_matrix (zip file)</strong></div> <div>- BI_AICc_Onciderini_concat_matrix.nex: nexus file containing the concatenated matrix of Onciderini used for Bayesian Inference (BI), including the best-fit model scheme identified by PartitionFinder and MCMC parameters for running the analysis in MrBayes.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex_r1_r2_combined_consensus.tree: consensus tree from two combined independent Bayesian Inference (BI) runs based on the concatenated matrix of Onciderini, after discarding the first 25% of initial generations as burn-in.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex.run1.p: log file containing parameter values and likelihood scores from the first run of the Bayesian Inference (BI) based on the concatenated matrix of Onciderini.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex.run2.p: log file containing parameter values and likelihood scores from the second run of the Bayesian Inference (BI) based on the concatenated matrix of Onciderini.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_lognormal (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_lognormal.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- BEAST2_Onciderini_BD_lognormal_run[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_exponential (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_exponential.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- BEAST2_Onciderini_BD_exponential_[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- TreeAnnotator_Onciderini_BD_exponential_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- TreeAnnotator_Onciderini_BD_exponential_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_uniform (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_uniform.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- BEAST2_Onciderini_BD_uniform_[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- TreeAnnotator_Onciderini_BD_uniform_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- TreeAnnotator_Onciderini_BD_uniform_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>Comparative_analyses (zip file)</strong></div> <div><strong>RawData (folder):</strong> raw morphometric and girdling data, plus tree that was later pruned for downstream comparative analyses; these data were used as input for the OncidHeadDimorphism-DatasetPREP-FINAL.R data cleaning script. </div> <div>- Onciderini_BD_lognormal_run1-run8_consensus.nwk: newick version of TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.tre (outputted as a newick file by importing the .tre file into FigTree and exporting in newick format).</div> <div>- Measurements_Onciderini_Raw_Final.csv: individual-level raw morphometric data for Onciderini.</div> <div>- Behav_Matrix_2_states_trimmed_2022-12-15.csv: species-level data on girdling status for Onciderini species, with all Lochmaeocles species classified as girdlers (2 behavioral states across Onciderini species). </div> <div>- Matrix_3_states_trimmed_final.csv: species-level data on girdling status for Onciderini species, with all Lochmaeocles species classified as facultative girdlers (3 behavioral states across Onciderini species). </div> <div>- Matrix_2_states_trimmed_1LochGirdler_Final.csv: species-level data on girdling status for Onciderini species, with only one Lochmaeocles species (L. tessellatus) classified as a girdler (2 behavioral states across Onciderini species). </div> <div> </div> <div><strong>ProcessedData (folder): </strong>filtered data and pruned trees outputted by the OncidHeadDimorphism-DatasetPREP-FINAL.R script</div> <div>- oncid_f_36spp_clean.csv: dataset of species means and log-ratios for morphometric traits in females, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_m_42spp_clean.csv: dataset of species means and log-ratios for morphometric traits in males, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_sd_35spp_clean.csv: dataset of species means for sexual dimorphism in morphometric traits, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_girdlingbehav_allingroupspp_clean.csv: full dataset of girdling behavior for 56 Onciderini species that are in the phylogenetic tree; includes separate columns for the three alternative girdling classification schemes</div> <div>- oncid_tree_behavfull_56spp.nwk: pruned phylogenetic tree for the full girdling dataset (56 species)</div> <div>- oncid_tree_f_36spp.nwk: pruned phylogenetic tree for the female morphometric dataset (36 species)</div> <div>- oncid_tree_m_42spp.nwk: pruned phylogenetic tree for the male morphometric dataset (42 species)</div> <div>- oncid_tree_mf_43spp.nwk: pruned phylogenetic tree for all species with morphometric data for males or females; used for the stochastic character map next to the heatmap plot (Fig 4)</div> <div>- oncid_tree_sd_35spp.nwk: pruned phylogenetic tree for the sexual dimorphism dataset (35 species)</div> <div> </div> <div><strong>FittedModels (folder): </strong>fitted models (mvgls, model comparison analyses, OUM models, simmaps) outputted by the OncidHeadDimorphism-Analysis-FINAL.R script </div> <div>- MacroModelFits_logRtraits_f_36spp-2024-10-12.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to female morphometric data across 100 stochastic character maps of girdling behavior</div> <div>- MacroModelFits_logRtraits_m_42spp-2024-10-12.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to male morphometric data across 100 stochastic character maps of girdling behavior</div> <div>- MacroModelFits-SDDI-35spp_2024-10-11.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to sexual dimorphism data across 100 stochastic character maps of girdling behavior</div> <div>- mvgls-results-headsize-mf-2024-10-12.Rdata: fitted mvgls regression models for male and female traits (analyzed separately)</div> <div>- mvgls-results-sddi-2024-10-12.Rdata: fitted mvgls regression models for sexual dimorphism</div> <div>- OUM_headtraits_f_36spp-2024-10-12.Rdata: fitted OUM models and summary statistics for female head traits</div> <div>- OUM_headtraits_m_42spp-2024-10-12.Rdata: fitted OUM models and summary statistics for male head traits</div> <div>- OUM-SDDI-35spp-2024-10-12.Rdata: fitted OUM models and summary statistics for sexual dimorphism in head traits</div> <div>- simmaps_ard_full_2state.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with two behavioral states (girdling or non-girdling) - all Lochmaeocles species are classified as girdlers</div> <div>- simmaps_ard_full_3state.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with three behavioral states (girdling, non-girdling, or facultative girdling)</div> <div>- simmaps_ard_full_1Loch.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with two behavioral states (girdling or non-girdling) - only one Lochmaeocles species (L. tessellatus) is classified as a girdler</div> <div>- simmaps_ard_m.RDS: stochastic character map for the 42 species used in the analyses of male morphometric traits</div> <div>- simmaps_ard_f.RDS: stochastic character map for the 36 species used in the analyses of female morphometric traits</div> <div>- simmaps_ard.RDS: stochastic character map for the 35 species used in the sexual dimorphism analyses; 2 behavioral states.</div> <div> </div> <div><strong>Rscripts (folder): </strong>R scripts used to process data and run phylogenetic comparative analyses of head size and girdling behavior.</div> <div>- OncidHeadDimorphism-DatasetPREP-FINAL.R: R script used to filter data and prune trees from the RawData folder for downstream phylogenetic comparative analyses; outputs of this script are in the ProcessedData folder.</div> <div>- OncidHeadDimorphism-Analysis-FINAL.R: R script used to analyze data in the ProcessedData folder to answer questions about the origin and evolution of girdling behavior and the relationship between girdling and head size or head size sexual dimorphism; fitted models outputted by this script are in the FittedModels folder</div> <div>- OncidHeadDimorphism-Plots-FINAL.R: R script used to generate plots for the manuscript<br><br></div>

Fault Traces Dataset for Zou and Fialko Earth and Space Sciences Manuscript

<p>The 'xx_fault.dat' contains the linked fault traces of different regions (nz: Northern New Zealand; nv: Basin and Range Province; ca: Ventura County, California; np: Pennsylvania and Northern New Jersey); The 1st and 2nd columns are UTM coordinates; The 3rd column is the random number assigned for distinguishing each fault traces.</p> <p>The 'xx_len.dat' contains the length of each fault trace in the corresponding regions, in km.</p> <p>The other '.dat' files and the 'SunData.xls' contain the length of fractures from outcrop and lab data. All of them are frequency distribution, except the 'LaHouve_Villemin.dat' which is already in cumulative distribution. The unit of 'SunData.xls' is mm; for the two 'Bahat' datasets is cm; for the rest of the outcrop data is m.</p> <p>The .m files are the codes for calculating cumulative length distribution, frequency density distribution, and fault connection.</p> <p> </p> <p> </p> <p>For any questions please contact Xiaoyu Zou via x3zou@ucsd.edu</p>

Dataset for the manuscript "Assessing the Role of Hydrodynamics in Enhancing HAND-Derived Synthetic Rating Curves: A Comparative Study in the Wu River Basin, Taiwan"

<p>This is the dataset for developing the HAND-hd workflow mentione in the manuscript "Assessing the Role of Hydrodynamics in Enhancing HAND-Derived Synthetic Rating Curves: A Comparative Study in the Wu River Basin, Taiwan". In the manuscript, we used the topographical cross-sectional survey data to generate topographical synthetic rating curves (RCb) to validate the performance of the HAND method based synthetic rating curves (HAND-SRC) produced by the HAND-hd workflow. Due to regulatory restrictions under Taiwanese law, public sharing of 5-meter (or finer) DEM data is prohibited. However, researchers interested in accessing processed HAND raster data for research purposes may contact the corresponding author.</p>

Primary data for Manuscript provisionally titled Stereoselective access to bioactive cyclopropanes (+)-PPCC and (1R,2S)-2-aminomethyl-1-arylcyclopropane-1-carboxamides from (−)-levoglucosenone

<p><span>Contains HRMS and FID data for compounds described in the manuscript titled, "<span>Stereoselective access to bioactive cyclopropanes (+)-PPCC and (1<em><span>R</span></em>,2<em><span>S</span></em>)-2-aminomethyl-1-arylcyclopropane-1-carboxamides from (−)-levoglucosenone"</span></span></p> <p><span>FIDs can be opened using SpinWorks or Topspin programs.</span></p> <p> </p>