Find research datasets worth reusing

Predicting Nanoparticle Uptake by Biological Membranes: Theory and Simulation - data

Simulation data described in paper entitled "Predicting Nanoparticle Uptake by Biological Membranes: Theory and Simulation"

The encoding of stochastic regularities is facilitated by action-effect predictions

The data represent the raw EEG datasets generated and analysed during the current study, along with the Principal Component Analysis (PCA) solutions computed for the active and passive tasks, respectively. For details regarding the EEG preprocessing, statistical analyses, and results, please refer to the main manuscript body.

opencc-zeroNov 2020View details →

Quantifying thermal exposure for migratory riverine species: phenology of Chinook salmon populations predicts thermal stress

Migratory species are particularly vulnerable to climate change because habitat throughout their entire migration cycle must be suitable for the species to persist. For migratory species in rivers, predicting climate change impacts is especially difficult because there is a lack of spatially-continuous and seasonally-varying stream temperature data, habitat conditions can vary for an individual throughout its life cycle, and vulnerability can vary by life stage and season. To predict thermal impacts on migratory riverine populations, we first expanded a spatial stream network model to predict mean monthly temperature for 465,775 river km in the western U.S., and then applied simple yet plausible future stream-temperature change scenarios. We then joined stream temperature predictions to 44,396 spatial observations and life stage-specific phenology (timing) for 26 ecotypes (i.e. geographically distinct population groups expressing one of four distinct seasonal migration patterns) of Chinook salmon (Oncorhynchus tshawytscha), a phenotypically diverse anadromous salmonid that is ecologically and economically important but declining throughout its range. Thermal stress, assessed for each life stage and ecotype based on federal criteria, was influenced by migration timing rather than latitude, elevation, or migration distance, such that sympatric ecotypes often showed differential thermal exposure. Early-migration phenotypes were especially vulnerable due to prolonged residency in inland streams during the summer. We evaluated the thermal suitability of 31,699 stream km which are currently blocked by dams to explore reintroduction above dams as an option to mitigate the negative effects of our warmer stream temperature scenarios. Our results showed that negative impacts of stream temperature warming can be offset for almost all ecotypes if formerly occupied habitat above dams is made available. Our approach of combining spatial distribution and phenology data with spatially- and temporally-explicit temperature predictions enables researchers to examine thermal exposure of migrating populations that use seasonally-varying habitats.

opencc-zeroNov 2020View details →

Predicting amphibian intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data

The growing availability of genetic datasets, in combination with machine learning frameworks, offer great potential to answer long-standing questions in ecology and evolution. One such question has intrigued population geneticists, biogeographers, and conservation biologists: What factors determine intraspecific genetic diversity? This question is challenging to answer because many factors may influence genetic variation, including life history traits, historical influences, and geography, and the relative importance of these factors varies across taxonomic and geographic scales. Furthermore, interpreting the influence of numerous, potentially correlated variables is difficult with traditional statistical approaches. To address these challenges, we analyzed repurposed data using machine learning and investigated predictors of genetic diversity, focusing on Nearctic amphibians as a case study. We aggregated species traits, range characteristics, and >42,000 genetic sequences for 299 species using open-access scripts and various databases. After identifying important predictors of nucleotide diversity with random forest regression, we conducted follow-up analyses to examine the roles of phylogenetic history, geography, and demographic processes on intraspecific diversity. Although life history traits were not important predictors for this dataset, we found significant phylogenetic signal in genetic diversity within amphibians. We also found that salamander species at northern latitudes contain lower genetic diversity. Data repurposing and machine learning provide valuable tools for detecting patterns with relevance for conservation, but concerted efforts are needed to compile meaningful datasets with greater utility for understanding global biodiversity.

Systematic reanalysis of co-fractionation mass spectrometry data: predicted interactomes

This upload contains predicted interactomes for 27 species or clades: 17 individual organisms with at least three published CF-MS experiments, and 9 phylogenetic groupings of those organisms.  The following individual organisms are represented: <ul> <li>Arabidopsis thaliana</li> <li>Brassica oleracea</li> <li>Caenorhabditis elegans</li> <li>Chaetomium thermophilum</li> <li>Chlamydomonas reinhardtii</li> <li>Dictyostelium discoideum</li> <li>Drosophila melanogaster</li> <li>Nematostella vectensis</li> <li>Homo sapiens</li> <li>Mus musculus</li> <li>Oryza sativa</li> <li>Plasmodium berghei</li> <li>Plasmodium falciparum</li> <li>Plasmodium knowlesi</li> <li>Strongylocentrotus purpuratus</li> <li>Triticum aestivum</li> <li>Trypanosoma brucei</li> <li>Xenopus laevis</li> </ul> The following clades are also represented: <ul> <li>BOP clade</li> <li>Deuterostomia</li> <li>Eucharontoglires</li> <li>Eukaryota</li> <li>Mesangiospermae</li> <li>Opiskothonta</li> <li>Plasmodium</li> <li>Tetrapoda</li> <li>Viridaplantae</li> </ul> The interactomes are provided in two forms. Files in the 'All interactions' directory include the complete classifier scores for every possible protein pair (sorted in descending order). Files in the '50% precision' directory include only those interactions identified above 50% precision, for convenience.  Proteins were mapped to orthogroups using eggNOG. Maps from eggNOG orthogroups to UniProt accessions are available from https://github.com/skinnider/CF-MS-analysis/tree/master/data/resources/eggNOG. The phylogenetic tree used to group species into clades is also available from https://github.com/skinnider/CF-MS-analysis/tree/master/data/resources/TimeTree/species.nwk. The third and final directory, 'Human', contains the consensus CF-MS human interactome, in which proteins were merged across 46 human experiments by their gene names, rather than based on eggNOG orthogroups. The directory contains both complete classifier scores (file 'classifier-scores.tsv.gz') and the consensus CF-MS interactome, at 50% precision (file 'CF-MS-interactome.tsv').

opencc-by-4.0Aug 2020View details →

Can we predict global patterns of long-term climate change from short-term simulations?

Post-processed data from "Can we predict global patterns of long-term climate change from short-term simulations?". Also includes python dictionary for translating file code into the name of the run. For use with code publicly available on github.com/lm2612/Ridge_3 and github.com/lm2612/GPRegression.

Experimental and numerical data of an article on the prediction of wind load on pylons for a neutral atmospheric boundary layer ow over two successive hills

his deposit contains experimental and numerical data related to the article entitled "Towards prediction of wind load on pylons for a neutral atmospheric boundary layer flow over two successive hills" that will be publised in Journal of Wind Engineering & Industrial Aerodynamics. It consists in post-processed experimental data (turbulence statistics) for flows around 2D hills, measured by PIV, and numerical date of k-epsilon simulations of the same configirations. All data are provided in matlab metadata format. Matlab functions are given to plot all the figures of the article.

opencc-zeroDec 2020View details →

Hemipteran defensive odors trigger predictable color biases in jumping spider predators

Multimodal warning displays often pair one signal modality (odor) with a second modality (color) to avoid predation. Experiments with bird predators suggest these signal components interact synergistically, with aversive odors triggering otherwise hidden aversions to particular prey colors. In a recent study, this phenomenon was found in a jumping spider (Habronattus trimaculatus), with the defensive odor from a coreid bug (Acanthocephala femorata) triggering an aversion to red. Here, we explore how generalizable this phenomenon is by giving H. trimaculatus the choice between red or black prey in the presence or absence of defensive odors secreted from (1) eastern leaf-footed bugs (Leptoglossus phyllopus, Hemiptera), (2) grass stinkbugs (Mormidea pama, Hemiptera), (3) Asian ladybird beetles (Harmonia axyridis, Coleoptera), and (4) eastern lubber grasshoppers (Romalea microptera, Orthoptera). As expected, in the presence of the hemipteran odors, spiders were less likely to attack red prey (compared to no odor). Unexpectedly, the beetle and grasshopper odors did not bias spiders away from red. Our results with the hemipteran odors were unique to red; follow-up experiments indicated that these odors did not affect biases for/against green prey. We discuss our findings in the context of generalized predator foraging behavior and the functions of multimodal warning displays.

opencc-zeroDec 2020View details →

Age predicts risky investment better than residual reproductive value

Life-history theory predicts that investment into reproduction should increase as future reproductive opportunities (i.e., residual reproductive value, RRV) decrease. Researchers have thus intuitively used age as a proxy for RRV and assume RRV decreases with age when interpreting age-specific investment. Yet, age is an imperfect proxy for RRV and may even be a poor correlate in some systems. We used a 30-year study of the nesting ecology of painted turtles ( Chrysemys picta ) to assess how age and RRV compare in explaining variation in a risky investment behavior. We predicted that RRV would be a better predictor of risky investment than age because RRV accounts for variation in future reproductive potential across life. We found that RRV was high in early life, slowly decreased until midlife, and then steadily decreased to terminal reproduction. However, age predicted risky behavior better than RRV. This finding suggests stronger correlates of age (e.g., size) may be more responsible for this behavior in turtles. This study highlights that researchers should not assume that age-specific investment is driven by RRV and that future work should quantify RRV to more directly test this key element of life-history theory.

opencc-by-4.0Jun 2019View details →

Wind farm power short-term prediction using WRF model and Kalman filtering

This repository contains the data used and generated in the paper: Mamani, R., & Hendrick, P. (2019). Wind farm power short-term prediction using WRF model and Kalman filtering. ECOS 2019

opencc-zeroDec 2020View details →

Repeatable differences in exploratory behaviour predict tick infestation probability in wild great tits

Abstract Ecological factors and individual-specific traits affect parasite infestation in wild animals. Ixodid ticks are important ectoparasites of various vertebrate hosts, which include passerine bird species such as the great tit (Parus major). We studied various key ecological variables (breeding density, human disturbance) and phenotypic traits (exploratory behaviour, body condition) proposed to predict tick infestation probability and burden in great tits. Our study spanned 3 years and 12 nest box plots located in southern Germany. Breeding, adult great tits were assessed for exploration behaviour, body condition, and tick burden. Plots were open to human recreation; human disturbance was quantified in each plot as a recreation pressure index from biweekly nest box inspections. Infested individuals were repeatable in tick burden across years. These repeatable among-individual differences in tick burden were not attributable to exploration behaviour. However, faster explorers did have a higher infestation probability. Furthermore, body condition was negatively correlated to tick burden. Recreation pressure was correlated to increased infestation probability, although this relationship was just above the threshold of statistical significance. Our study implies that avian infestation probability and tick burden are each determined by distinct phenotypic traits and ecological factors. Our findings highlight the importance of animal behaviour and human disturbance in understanding variation in tick burden among avian hosts. Significance statement Various abiotic and biotic factors, including personality type, influence tick parasitism in birds, but exactly how all these factors interplay remains unclear. We studied a wild population of great tits over a 3-year period and assessed birds for their exploration behaviour and tick infestation. We found that more explorative great tits were more likely to be infested with ticks. By contrast, faster explorers did not have higher tick burdens. Tick burden was nevertheless moderately repeatable among individuals. Our results imply that animal personality influences the probability of parasite infestation, and that infestation likelihood versus intensity are determined by distinct mechanisms. Our work highlights the importance of animal behaviour to understand parasite infestation in wild populations.

opencc-zeroMar 2021View details →

Data from: Seed size predicts global effects of small mammal seed predation on plant recruitment

We conducted a global literature review and meta-analysis to evaluate whether seed size could predict post-dispersal seed predator effects on seed removal and plant recruitment, respectively. Datasets were built using data extracted from published studies focusing on seed predation by small mammals (see Methods for criteria and data extraction protocol). We found that seed size predicted small mammal seed removal rates and their impacts on plant recruitment consistent with optimal foraging theory, with intermediate seed sizes most strongly impacted globally - for both native and exotic plants. However, differences in seed size distributions among ecosystems conditioned seed predation patterns, with relatively larger-seeded species most strongly affected in grasslands (smallest seeds), and relatively smaller-seeded species most strongly affected in tropical forests (largest seeds). Such size-dependent seed predation has profound implications for coexistence among plants because it may enhance or weaken opposing life-history tradeoffs in an ecosystem-specific manner. Our results suggest that seed size may serve as a key life-history trait that can integrate consumer effects to improve understandings of plant coexistence.

opencc-by-4.0Dec 2020View details →

Status quo in data availability and predictive models of nano-mixture toxicity

Supplementary materials for manuscript: Status quo in data availability and predictive models of nano-mixture toxicity. This table contains the list of 183 curated literature used in this study.

Data for "Potential of mid-tropospheric water vapor isotopes to improve large-scale circulation and weather predictability"

This contains simulation results of "Potential of mid-tropospheric water vapor isotopes to improve large-scale circulation and weather predictability".

Supplementary Data for Secondary structure and DNA binding domain prediction

This project contains the following extended data: <ul> <li>Supplementary Table 1: Summary table of DNA binding domains (DBD), the counts of target regions within the genome and statistical analysis. (DNA_BINDING_DOMAINS_ID.tsv)</li> <li>Sequence: ecCEBPα secondary structure prediction with RNAplfold at a pairing probability cut off of 0.1. N represents all sequences with pairing probability greater than 0.1.  (predicted_secondary_structure_of_ecCEBPA.fa)</li> </ul>

Dataset for testing the calibration of the variant prediction methods

Dataset used for testing the calibration of 6 methods for predicting the impact of single nucleotide variants in the publication:  Benevenuta S, Capriotti E, Fariselli P. (2021). Calibrating variant-scoring methods for clinical decision making. Bioinformatics. DOI: <a href="http://doi.org/10.1093/bioinformatics/btaa943">10.1093/bioinformatics/btaa943</a>. The file contains the following columns: <ol> <li>CHROM: Chromosome number  </li> <li>POS: Chromosome coordinate</li> <li>REF: Reference Allele     </li> <li>ALT: Alternative Allele     </li> <li>Coding: Coding Variants (Yes/No) </li> <li>Effect: Variant impact (Pathogenic/Benign) </li> <li>DANN: DANN output   </li> <li>PhDSNP: PhD-SNPg output </li> <li>FATHMM: FATHMM output </li> <li>CADD: CADD output   </li> <li>DeepSea: DeepSea output</li> <li>Eigen: Eigen output</li> </ol>

opencc-by-4.0Oct 2020View details →

"A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level" by Blanco et al. dataset

Raw, original data and fits data set for "A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level" by Blanco et al. in EXCEL and GraphPad Prism file formats and FORTRAN code.

opencc-byJan 2021View details →

Establishing diversity in synthetic time series for prediction performance evaluation

This dataset enables practitioners to evaluate their time series prediction algorithms on various types of time series

Commonalities and differences in predictive neural processing of discrete vs continuous action feedback

Dataset relative to the following publication: Schmitter, C.V., Steinsträter, O., Kircher, T., van Kemenade, B.M., Straube, B. (2021). Commonalities and differences in predictive neural processing of discrete vs continuous action feedback. NeuroImage. DOI: <a href="https://doi.org/10.1016/j.neuroimage.2021.117745">10.1016/j.neuroimage.2021.117745</a>   Details can be found in the readme file.

Predicting Photosynthetic Capacity in Tobacco (Nicotiana tabacum) Using Shortwave Infrared Spectral Reflectance

These files contains the data used for analysis in the (currently under-review) publication "Predicting Photosynthetic Capacity in Tobacco (Nicotiana tabacum) Using Shortwave Infrared Spectral Reflectance"  in the Journal of Experimental Botany.