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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>

opencc-by-4.0Oct 2024View details →

Geophysical Signals from Magma Propagation: Experimental and Processed Data

<p>This repository contains the experimental data analyzed and interpreted in the manuscript titled "<em>Geophysical signals induced by magma propagation: Insights from analog experiments</em>" by S. Furst, J. Vandemeulebrouck, and V. Pinel. It includes video recordings, timelapse photos, accelerometer data, and deformation data. Additionally, there are three MATLAB scripts for post-processing the timelapse photos following the approach described in the manuscript. The results of the MFP analysis on the accelerometer data, as well as the outcomes from the COMSOL Multiphysics simulations, are also included in the repository.</p>

opencc-by-4.0Jun 2023View details →

BioValue´s Sensory Evaluation Workshop of Novel Processed Foods

<p>The Workshop for the sensory evaluation of novel processed foods was organised with great success in May 2023 at CIHEAM MAICh. Nine newly developed novel processed foods containing at least one of the underutilized selected crops, i.e., Lens culinaris, Lathyrus spp., Fagopyron spp., Tomato ideotypes, Eggplant landraces, Cucumis melo var. flexuosus and Sonchus oleraceus, were prepared in the experimental kitchen of MAICh. A group of about 30 tasters consisting of professionals, i.e., nutritionists, food chemists, doctors, chefs, as well as consumers from a range of societal groups participated in the sensory evaluation process that included blind-tasting and filling in a specific questionnaire prepared for the evaluation of the appearance, aroma, taste, and texture of the new products. For the selection of the participants, gender equality and age variation were taken into consideration. In accordance with the feedback obtained from this evaluation, the improvement of the recipe design will be discussed. The BioValue team of CIHEAM MAICh would like to thank all the participants for their time and willingness to take part in this workshop.</p>

opencc-by-4.0Aug 2024View details →

Rapid Karstification Process with Evaporite-Driven Sinkholes in Southern Kohat Basin, NW Pakistan

<p>This file consists of datasets (GIS files, InSAR Velocities, Electrical resistivity data, and profiles) used in our study. </p>

Processed species-level data for "Global Biodiversity Loss from Outsourced Deforestation"

<p>This repository holds processed species-level data for the following paper:</p> <blockquote> <p>Wiebe, R.A.* and Wilcove, D.S. <em>2025</em>. Global Biodiversity Loss from Outsourced Deforestation.</p> </blockquote> <p>*<a href="mailto:rwiebe@princeton.edu">rwiebe@princeton.edu</a>, Guyot Hall, Princeton University, Princeton, NJ</p> <p>In this analysis, we quantified the range loss to forest-dwelling vertebrates that is attributable to demand for agricultural and forestry products by developed countries in 2001-2015.</p> <p>We used several datasets in this analysis. Data on forest cover and forest loss were sourced from <em>Hansen et al.</em> and publicly available to download online: <a href="https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html" rel="nofollow">https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html</a>. Data on range maps for species were sourced from the IUCN and downloaded from the IUCN’s API (<a href="https://www.iucnredlist.org/resources/spatial-data-download" rel="nofollow">https://www.iucnredlist.org/resources/spatial-data-download</a>) and its partner BirdLife International’s API (<a href="http://datazone.birdlife.org/species/requestdis" rel="nofollow">http://datazone.birdlife.org/species/requestdis</a>). These datasets are made available for academic research on request, and an API key is necessary for their downloads. We sourced data on land use attribution from Nguyen Tien Hoang and Keiichiro Kanemoto, from their 2021 publication “Mapping the deforestation footprint of nations reveals growing threat to tropical forests.” These data are not publicly available, but the authors kindly provided the data on request.</p> <p>Code necessary to replicate the analysis can be found in a public repository at: https://github.com/AlexWiebe/Outsourced-Biodiversity-Loss.</p>

Data for the publication "Evaluation of climatic impacts of ice nucleating particles through precipitation process with a GCM"

<p>These data are a set of annual-mean values for 5yr simulations using the MIROC6-SPRINTARS global aerosol-climate model with different treatments of precipitation (i.e., diagnostic and prognostic). The outputs include diagnostics from the satellite simulator COSP2.<br>The data are used in the manuscript entitled "Evaluation of climatic impacts of ice nucleating particles through precipitation process with a GCM". All data used in this study are available from the corresponding author upon request.</p>

Processed data for Evidence of horizontal gene transfer and environmental selection impacting antibiotic resistance evolution in soil-dwelling Listeria

<p>Processed/source data for the manuscript Evidence of horizontal gene transfer and environmental selection impacting antibiotic resistance evolution in soil-dwelling <em>Listeria</em>.</p>

(Un)Fair Process Mining Event Logs (Converted to OCEL)

<p><strong>Converted to OCEL 1.0 JSONOCEL and OCEL 2.0 XML from traditional event logs available at: </strong><a href="https://zenodo.org/records/8059489" target="_new" rel="noopener">Zenodo - Record 8059489</a>.</p> <p><strong>Object Types:</strong> <code>{ Person }</code></p> <p><strong>Person-level Attributes:</strong></p> <ul> <li><strong>(int) overallProtected:</strong> An attribute (0/1) indicating whether the person has experienced discrimination. <em>(Note: If you're developing a fairness assessment algorithm, only use this attribute in the testing phase!)</em></li> <li><strong>(int) sumBoolDiscrFactors:</strong> Counts the number of possible discrimination factors that apply to the person.</li> <li><strong>(int) reworkedActivities:</strong> The total amount of rework involved in the person’s processing.</li> <li><strong>(float) throughputTime:</strong> The total processing time for a person.</li> <li><strong>(int) numOcc_ACTIVITY:</strong> Counts the number of times an activity occurs in the person’s lifecycle.</li> </ul> <p><strong>Event-level Attributes:</strong></p> <ul> <li><strong>resource:</strong> The resource involved in processing a given person.</li> </ul> <p> </p> <p><strong>* Hiring</strong></p> <p>The data describes a multifaceted recruitment process with diverse application pathways ranging from minimal processing to extensive multi-step procedures. The variability of these routes, largely dependent on numerous determinants, yields a spectrum of outcomes from instant rejection to successful job offers.</p> <p>The logs include attributes such as age, citizenship, German proficiency, gender, religion, and years of education. While these attributes may inform candidate profiles, their misuse could engender discrimination. Variables like age and education may signify experience and skills, citizenship and German language may address job logistics, but these should not unjustly eliminate applicants. Gender and religion, unrelated to job performance, must not sway hiring. Therefore, the use of these attributes must uphold fairness, avoiding any potential bias.</p> <p><strong>* Hospital</strong></p> <p>The data depicts a hospital treatment process that commences with registration at an Emergency Room or Family Department and advances through stages of examination, diagnosis, and treatment. Notably, unsuccessful treatments often entail repetitive diagnostic and treatment cycles, underscoring the iterative nature of healthcare provision.</p> <p>The logs incorporate patient attributes such as age, underlying condition, citizenship, German language proficiency, gender, and private insurance. These attributes, influencing the treatment process, may unveil potential discrimination. Factors like age and condition might affect case complexity and treatment path, while citizenship may highlight healthcare access disparities. German proficiency can impact provider-patient communication, thus affecting care quality. Gender could spotlight potential health disparities, while insurance status might indicate socio-economic influences on care quality or timeliness. Therefore, a comprehensive examination of these attributes vis-a-vis the treatment process could shed light on potential biases or disparities, fostering fairness in healthcare delivery.</p> <p><strong>* Lending</strong></p> <p>This data illustrates the steps within a loan application process. From an initial appointment request, the process navigates various stages, including information verification and underwriting, culminating in loan approval or denial. Additional steps may be required, such as co-signer enlistment or collateral assessment. Some cases experience outright appointment denial, indicating the process's variability, reflecting applicants' differing credit situations.</p> <p>The logs' attributes can aid in identifying influences on outcomes and detecting discrimination. Personal characteristics ('age', 'citizen', 'German speaking', and 'gender') and socio-economic indicators ('YearsOfEducation' and 'CreditScore') can impact the process. While 'yearsOfEducation' and 'CreditScore' can validly inform creditworthiness, 'age', 'citizen', 'language ability', and 'gender' should not bias loan decisions, ensuring these attributes are used responsibly fosters equitable loan processes.</p> <p><strong>* Renting</strong></p> <p>The data represents a rental process. It begins with a prospective tenant applying to view a property. Subsequent steps include an initial screening phase, viewing, decision-making, and a potential extensive screening. The process ends with the acceptance or rejection of the prospective tenant. In some cases, a tenant may apply for viewing but be rejected without the viewing occurring.</p> <p>The logs contain attributes that can shed light on potential biases in the process. 'Age', 'citizen', 'German speaking', 'gender', 'religious affiliation', and 'yearsOfEducation' might influence the rental process, leading to potential discrimination. While some attributes may provide useful insights into a potential tenant's reliability, misuse could result in discrimination. Thus, fairness must be observed in utilizing these attributes to avoid potential biases and ensure equitable treatment.</p>

Processed extreme composite cyclone datasets from X-SHiELD simulations.

<p>Processed extreme cyclones composites datasets during 2020-2022 from X-SHiELD simulations (see Cheng et al. (2022) and Harris et al. (2023) for details on the X-SHiELD model and experiment setups), including total column water vapour (TCWV in the paper, but here shortened as iwp), total precipitation (pr), and 10-m wind speeds for both the current climate and a warming scenario with a uniform 4-K increase in sea surface temperature.</p>

opencc-zeroDec 2015View details →

Data from: Developmental lead exposure has mixed effects on butterfly cognitive processes

While the effects of lead pollution have been well studied in vertebrates, it is unclear to what extent lead may negatively affect insect cognition. Lead pollution in soils can elevate lead in plant tissues, suggesting it could negatively affect neural development of insect herbivores. We used the cabbage white butterfly (Pieris rapae) as a model system to study the effect of lead pollution on insect cognitive processes, which play an important role in how insects locate and handle resources. Cabbage white butterfly larvae were reared on a 4-ppm lead diet, a concentration representative of vegetation in polluted sites; we measured eye size and performance on a foraging assay in adults. Relative to controls, lead-reared butterflies did not differ in time or ability to search for a food reward associated with a less preferred color. Indeed, lead-treated butterflies were more likely to participate in the behavioral assay itself. Lead exposure did not negatively affect survival or body size, and it actually sped up development time. The effects of lead on relative eye size varied with sex: lead tended to reduce eye size in males, but increase eye size in females. These results suggest that low levels of lead pollution may have mixed effects on butterfly vision, but only minimal impacts on performance in foraging tasks, although follow-up work is needed to test whether this result is specific to cabbage whites, which are often associated with disturbed areas.

opencc-zeroApr 2020View details →

Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data

The evolutionary dynamics of complex ecological traits – including multistate representations of diet, habitat, and behavior – remain poorly understood. Reconstructing the tempo, mode, and historical sequence of transitions involving such traits poses many challenges for comparative biologists, owing to their multidimensional nature. Continuous-time Markov chains (CTMC) are commonly used to model ecological niche evolution on phylogenetic trees but are limited by the assumption that taxa are monomorphic and that states are univariate categorical variables. A necessary first step in the analysis of many complex traits is therefore to categorize species into a pre-determined number of univariate ecological states, but this procedure can lead to distortion and loss of information. This approach also confounds interpretation of state assignments with effects of sampling variation because it does not directly incorporate empirical observations for individual species into the statistical inference model. In this study, we develop a Dirichlet-multinomial framework to model resource use evolution on phylogenetic trees. Our approach is expressly designed to model ecological traits that are multidimensional and to account for uncertainty in state assignments of terminal taxa arising from effects of sampling variation. The method uses multivariate count data for individual species to simultaneously infer the number of ecological states, the proportional utilization of different resources by different states, and the phylogenetic distribution of ecological states among living species and their ancestors. The method is general and may be applied to any data expressible as a set of observational counts from different categories.

opencc-zeroMay 2020View details →

Data from: The roles of non-production vegetation in agroecosystems: a research framework for filling process knowledge gaps in a social-ecological context

<p>1. An ever-expanding human population, climatic changes, and the spread of intensive farming practices is putting increasing pressure on agroecosystems and their inherent biodiversity. Non-production vegetation elements, such as woody patches, riparian margins, and restoration plantings, are vital for conserving agroecosystem biodiversity. Further, such elements are key building blocks that are manipulated via land management, thereby influencing the biotic and abiotic processes that underpin functioning agroecosystems.</p> <p>2. Despite this critical role, there has been a lack of synthesis on which types of vegetation elements drive and/or support ecological processes, and the mechanisms by which this occurs. Using a systematic, quantitative literature review of 342 articles, we asked: what are the effects of non-production vegetation on agroecosystem processes and how are these processes measured within global agroecosystems?</p> <p>3. Woody patches, hedgerows and borders, riparian margins, and shelterbelts were the most studied types of non-production vegetation. The majority (61%) of studies showed positive effects of non-production vegetation on ecological processes, where the presence, level or rate of the studied process was increased or enhanced.</p> <p>4. However, four key research gaps were revealed: (1) most studies (83%) used proxies for, instead of direct measurements of, ecosystem processes related to non-production vegetation; (2) study designs used to investigate non-production vegetation effects on ecosystem processes directly were largely limited to observational comparisons of non-production vegetation types, farm-scale vegetation configurations, and different proximities to vegetation in terms of the effect on ecological processes; relatively few studies used manipulative experiments (3) the relatively few studies directly measuring ecosystem processes were dominated by four process categories: invertebrate biocontrol, predator and natural enemy spillover, animal movement, and ecosystem cycling, and (4) the methods used to directly measure non-production vegetation effects comprised a surprisingly limited set of approaches.</p> <p>5. To fill key research gaps that will inform the use of non-production vegetation to enhance agroecosystem processes, we present a framework for future research that emphasises the need to combine an understanding of human decision making with carefully-designed and targeted investigations into the roles of taxa, ecosystem processes, and landscape heterogeneity related to non-production vegetation, at multiple spatial scales within agroecosystems.</p>

Modality-specific dysfunctional neural processing of social-abstract and non-social-concrete information in schizophrenia

<p>Schizophrenia is characterized by marked communication dysfunctions encompassing potential impairments in the processing of social-abstract and non-social-concrete information, especially in everyday situations where multiple modalities are present in the form of speech and gesture. To date, the neurobiological basis of these deficits remains elusive. In a functional magnetic resonance imaging (fMRI) study, 17 patients with schizophrenia or schizoaffective disorder, and 18 matched controls watched videos of an actor speaking, gesturing (unimodal), and both speaking and gesturing (bimodal) about social or non-social events in a naturalistic way. Participants were asked to judge whether each video contains person-related (social) or object-related (non-social) information. When processing social-abstract content, patients showed reduced activation in the medial prefrontal cortex (mPFC) only in the gesture but not in the speech condition. For non-social-concrete content, remarkably, reduced neural activation for patients in the left postcentral gyrus and the right insula was observed only in the speech condition. Moreover, in the bimodal conditions, patients displayed improved task performance and comparable activation to controls in both social and non-social content. To conclude, patients with schizophrenia displayed modality-specific aberrant neural processing of social and non-social information, which is not present for the bimodal conditions. This finding provides novel insights into dysfunctional multimodal communication in schizophrenia, and may have potential therapeutic implications.</p>

Sample Raw (PPG, Accel, and Gyro) and Processed (steps, calories, sleep, HR, HRV, SPO2, Respiratory Rate, R-R) data over 24 hours

<p>Over the course of 24 hours, we collected raw (Photoplethysmography (PPG), Acceleration, and Gyro) and processed (steps, calories, sleep, HR, HRV, SPO2, Respiratory Rate, R-R) data samples. Biostrap approaches health insights from a data-driven perspective. Our clinical-grade hardware enables users to accurately track SpO2, HRV, RHR, and a variety of other biometrics with confidence.</p>

Tutorial output for Tourmaline amplicon sequence processing workflow

<p>Tutorial output for the <a href="https://github.com/aomlomics/tourmaline">Tourmaline</a> amplicon sequence processing workflow.</p> <p>Tourmaline was run on the test data provided in the directory <a href="https://github.com/aomlomics/tourmaline/tree/master/00-data">00-data</a>, which were downloaded along with the rest of the repository using this command:</p> <pre><code>git clone https://github.com/aomlomics/tourmaline</code></pre> <p>Reference data were downloaded and symlinked using these commands:</p> <pre><code>cd tourmaline/01-imported wget https://data.qiime2.org/2021.2/common/silva-138-99-seqs-515-806.qza wget https://data.qiime2.org/2021.2/common/silva-138-99-tax-515-806.qza ln -s silva-138-99-seqs-515-806.qza refseqs.qza ln -s silva-138-99-tax-515-806.qza reftax.qza</code></pre> <p>Paths in 00-data/manifest_pe.csv and 00-data/manifest_se.csv were edited to match the local paths.</p> <p>Output for all modes of the workflow were then generated in series:</p> <pre><code>conda activate qiime2-2021.2 snakemake dada2_pe_report_unfiltered snakemake dada2_pe_report_filtered snakemake dada2_se_report_unfiltered snakemake dada2_se_report_filtered snakemake deblur_se_report_unfiltered snakemake deblur_se_report_filtered</code></pre> <p> </p>

Dataset: Process Mining for Reliability Modeling of Smart Manufacturing Systems with Reduced Data Requirements

<p>Operational state logs from the Industry 4.0 Lab, University of Southern Denmark.</p> <p>"I4.0Lab_state_log.csv" -> without failures</p> <p>I4.0Lab_state_log_failures.csv -> with failures</p>

opencc-by-4.0Feb 2017View details →

Business Process Intelligence Challenge 2017 - Processed

<p>This dataset is the pre-processed version of the BPIC17 dataset. It contains potential confounding features, the treatment indicator, and the cycle time.</p>

opencc-by-4.0Sep 2020View details →

Code and data for publication "pyGRETA, pyCLARA, pyPRIMA: A pre-processing suite to generate flexible model regions for energy system models"

<p>This dataset contains the code of the three pre-processing tools <a href="https://github.com/tum-ens/pyGRETA">pyGRETA</a>,  <a href="https://github.com/tum-ens/pyPRIMA">pyPRIMA</a> and <a href="https://github.com/tum-ens/pyCLARA">pyCLARA</a> and an examplary database for the scope of Austria.</p> <p>To run the code with full functionality additional data is needed. Check the documentation of the tools for further information.</p> <p> </p> <p>Sources for data can be found here: </p> <p>pyGRETA: https://pygreta.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyPRIMA: https://pyprima.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyCLARA: https://pyclara.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p>

opencc-zeroMar 2020View details →

Data from: Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics

Time-lapse cameras facilitate remote and high-resolution monitoring of wild animal and plant communities, but the image data produced require further processing to be useful. Here we publish pipelines to process raw time-lapse imagery, resulting in count data (number of penguins per image) and 'nearest neighbour distance' measurements. The latter provide useful summaries of colony spatial structure (which can indicate phenological stage) and can be used to detect movement – metrics which could be valuable for a number of different monitoring scenarios, including image capture during aerial surveys. We present two alternative pathways for producing counts: 1) via the Zooniverse citizen science project Penguin Watch and 2) via a computer vision algorithm (Pengbot), and share a comparison of citizen science-, machine learning-, and expert- derived counts. We provide example files for 14 Penguin Watch cameras, generated from 63,070 raw images annotated by 50,445 volunteers. We encourage the use of this large open-source dataset, and the associated processing methodologies, for both ecological studies and continued machine learning and computer vision development.

DEVELOPMENT OF AN INNOVATIVE PROCESS INVOLVING THE USE IONIC LIQUIDS FOR THE RECOVERY AND PURIFICATION OF RARE EARTH FROM PERMANENT MAGNETS AND NiMH BATTERIES

<p>Experimental data</p>