Find research datasets worth reusing

opencc-zeroDec 2016View details →

Data from: Multimodal in situ datalogging quantifies inter-individual variation in thermal experience and persistent origin effects on gaping behavior among intertidal mussels (Mytilus californianus)

In complex habitats, environmental variation over small spatial scales can equal or exceed larger-scale gradients. This small-scale variation may allow motile organisms to mitigate stressful conditions by choosing benign microhabitats, whereas sessile organisms may rely on other behaviors to cope with environmental stresses in these variable environments. We developed a monitoring system to track body temperature, valve gaping behavior, and posture of individual mussels (Mytilus californianus) in field conditions in the rocky intertidal zone. Neighboring mussels' body temperatures varied by up to 14°C during low tides. Valve gaping during low tide and postural adjustments, which could theoretically lower body temperature, were not commonly observed. Rather, gaping behavior followed a tidal rhythm at a warm, high intertidal site; this rhythm shifted to a circadian period at a low intertidal site and for mussels continuously submerged in a tidepool. However, individuals within a site varied considerably in time spent gaping when submerged. This behavioral variation could be attributed in part to persistent effects of mussels' developmental environment. Mussels originating from a wave-protected, warm site gaped more widely, and they remained open for longer periods during high tide than mussels from a wave-exposed, cool site. Variation in behavior was modulated further by recent wave heights and body temperatures during the preceding low tide. These large ranges in body temperatures and durations of valve closure events - which coincide with anaerobic metabolism - support the conclusion that individuals experience "homogeneous" aggregations such as mussel beds in dramatically different fashion, ultimately contributing to physiological variation among neighbors.

opencc-zeroMay 2022View details →

Niche overlap in rodents increases with competition but not ecological opportunity: A role of inter-individual difference

<div> Niche variation at population level mediates niche packing (i.e., patterns of species' spread within the niche space) and species coexistence at community level. Competition and ecological opportunity (resource diversity) are two of the main mechanisms underlying niche variation. Dense niche packing could occur through increased niche partitioning or increased niche overlap. In this study we used stable carbon and nitrogen isotope data of 635 individual rodents from 4 species across 9 sites in the montane region of a subtropical island to test the effects of competition and ecological opportunity on population isotope niche size, inter-individual niche difference within population, and inter-specific niche overlap within community. We used the Bayesian Standard Ellipse Area (SEAB, the ellipse area enclosed by carbon and nitrogen isotope values of organisms on a bi-plot) to estimate population niche size and inter-specific niche overlap. Inter-individual niche difference within population was quantified as isotopic divergence and isotopic uniqueness. We used rodent abundance (the number of unique individuals captured) to measure competition and plant isotope niche size (plant SEAB) to measure ecological opportunity. The rodents experienced competition as evidenced by a negative relationship between population change rate and conspecific abundance. Rodent population niche size increased with ecological opportunity but not competition. The inter-individual niche difference (isotopic uniqueness) increased with competition (inter-specific competition only) but not ecological opportunity. At community level, inter-specific niche overlap (herbivore—omnivore pair only) increased with competition (the combined abundance of the pair) but not ecological opportunity. This study demonstrated that isotope niche variation of the rodents could be hierarchically influenced by ecological opportunity and competition, with the former setting the limit of population niche size across communities and the latter shaping inter-individual niche difference and inter-specific niche overlap within communities. Under strong intra-specific competition and limited ecological opportunity for niche expansion, individuals may choose to increase their isotopic uniqueness from conspecifics at the cost of overlapping with heterospecifics of different trophic roles within the community niche space as overall competition increases. Denser niche packing of these rodent communities might be achieved through increased niche overlap. </div>

publicAug 2017View details →

Data from: Multimodal in situ datalogging quantifies inter-individual variation in thermal experience and persistent origin effects on gaping behavior among intertidal mussels (Mytilus californianus)

Open the record for dataset details and reuse information.

publicJun 2022View details →

Niche overlap in rodents increases with competition but not ecological opportunity: A role of inter-individual difference

Open the record for dataset details and reuse information.

opencc-zeroSep 2019View details →

Data from: Inter-individual spacing affects the finder's share in ring-tailed coatis (Nasua nasua)

Social foraging models are often used to explain how group size can affect an individual's food intake rate and foraging strategies. The proportion of food eaten before the arrival of conspecifics, the finder's share, is hypothesized to play a major role in shaping group geometry, foraging strategy, and feeding competition. The variables which affect the finder's share in ring-tailed coatis were tested using a series of food trials. The number of grapes in the food trials had a strong negative effect on the finder's share and the probability that the finder was joined. The effect of group size on the finder's share and foraging success was not straightforward, and was mediated by socio-spatial factors. The finder's share increased when the time to arrival of the next individual was longer, the group was more spread out, and the finder was in the back of the group. Similarly, the total amount of food eaten at a trial was higher when more grapes were placed, arrival time was longer, and number of joiners was smaller. Individuals at the front edge of the group found far more food trials, but foraging success was higher at the back of the group where there were fewer conspecifics to join them. This study highlights the importance of social spacing strategies and group geometry on animal foraging tactics and the costs and benefits of sociality.

zenodo36/100

Intra and Inter-Individual Variability in Functional Connectomes of Patients with First Episode of Psychosis

Test-retest functional connectomes for 32 Healthy Controls and 30 First Episode of Psychosis patients. This dataset was originally used in the following article: (Preprint) Tepper, Ángeles and Núñez, Javiera Vásquez and Ramirez-Mahaluf, Juan Pablo and Aguirre, Juan Manuel and Barbagelata, Daniella and Maldonado, Elisa and Dellarossa, Camila Díaz and Nachar, Ruben and Gonzalez-Valderrama, Alfonso and Undurraga, Juan and Goñi, Joaquín and Crossley, Nicolas, Intra and Inter-Individual Variability in Functional Connectomes of Patients with First Episode of Psychosis. Available at SSRN: <a href="https://ssrn.com/abstract=4241607">https://ssrn.com/abstract=4241607</a> or <a href="http://dx.doi.org/10.2139/ssrn.4241607">http://dx.doi.org/10.2139/ssrn.4241607</a> More details and python code used for analyses can be found in this <a href="https://github.com/angietep/Inter-and-Intra-Indiv-Variability">GitHub repository</a>

opencc-by-4.0Jan 2023View details →

opencc-zeroSep 2023View details →

Data from: A hierarchical model for external electrical control of an insect, accounting for inter-individual variation of muscle force properties

Cyborg control of insect movement is promising for developing miniature, high-mobility, and efficient biohybrid robots. However, considering the inter-individual variation of the insect neuromuscular apparatus and its neural control is challenging. We propose a hierarchical model including inter-individual variation of muscle properties of three leg muscles 14 involved in propulsion (retractor coxae), joint stiffness (pro- and retractor coxae), and stance-swing transition (protractor coxae and levator trochanteris) in the stick insect Carausius morosus. To estimate mechanical effects induced by external muscle stimulation, the model is based on the systematic evaluation of joint torques as functions of electrical stimulation parameters. A nearly linear relationship between the stimulus burst duration and generated torque was observed. This stimulus-torque characteristic holds for burst durations of up to 500ms, corresponding to the stance and swing phase durations of medium to fast walking stick insects. Hierarchical Bayesian modeling revealed that linearity of the stimulus-torque characteristic was invariant, with individually varying slopes. Individual prediction of joint torques provides significant benefits for precise cyborg control.

publicMay 2025View details →

Consistent inter-individual variability in movement traits shapes the wild boar movement syndrome

Open the record for dataset details and reuse information.

publicSep 2019View details →

Data from: Inter-individual spacing affects the finder’s share in ring-tailed coatis (Nasua nasua)

Open the record for dataset details and reuse information.

publicSep 2023View details →

Data from: A hierarchical model for external electrical control of an insect, accounting for inter-individual variation of muscle force properties

Open the record for dataset details and reuse information.

dryad32/100

Data from: Selective increases in inter-individual variability in response to environmental enrichment in female mice

One manifestation of individualization is a progressively differential response of individuals to the non-shared components of the same environment. Individualization has practical implications in the clinical setting, where subtle differences between patients are often decisive for the success of an intervention, yet there has been no suitable animal model to study its underlying biological mechanisms. Here we show that enriched environment (ENR) can serve as a model of brain individualization. We kept 40 isogenic female C57BL/6JRj mice for 3 months in ENR and compared these mice to an equally sized group of standard-housed control animals, looking at the effects on a wide range of phenotypes in terms of both means and variances. Although ENR influenced multiple parameters and restructured correlation patterns between them, it only increased differences among individuals in traits related to brain and behavior (adult hippocampal neurogenesis, motor cortex thickness, open field and object exploration), in agreement with the hypothesis of a specific activity-dependent development of brain individuality.

opencc-zeroDec 2017View details →

opencc-by-nc-nd-4.0Mar 2024View details →

Supplementary Materials for the manuscript "Inter-individual speed variation of bacteria dispersing on fungal highways"

This repository includes all datasets used and created for the manuscript "Inter-individual speed variation of bacteria dispersal on fungal highways".  ----5 videos were used for producing the dispersal speed distribution of the RFP labelled Pseudomonas putida UWC1 cells: 100x_rfp_2days***.gif ----5 videos were used for producing the dispersal speed distribution of the GFP labelled Pseudomonas putida KT2440 cells: 100x_gfp_2days***.gif ----Data used for location tracking of the two videos shown in Figure 1: 100x_rfp_2days028.nd2 TrackMate capture of 100x_rfp_2days028.tif 100x_gfp_2days005.nd2 TrackMate capture of 100x_gfp_2days005.tif ----5 Speed distribution raw data files for the RFP labelled cells: rfp_2days***_11fps_2_2movement.csv ----5 Speed distribution raw data files for the GFP labelled cells: gfp_2days***_11fps_2_movement.csv ----R script for calculating speed distribution of cells: speed calculation.R ----Mathematica Notebook file for data visulization: HistCompare.nb ----10 best fits of probability distribution functions and goodness-of-fit criteria analysis of the speed of RFP labelled cells: rfp-FitTable.csv ----10 best fits of probability distribution functions and goodness-of-fit criteria analysis of the speed of GFP labelled cells: gfp-FitTable.csv

opencc-by-4.0Oct 2024View details →

Co-GWAS unveils the genetic architecture of inter-individual epistasis affecting biomass and disease severity in wheat binary mixtures

The repository contains all the data and scripts required to perform the analysis and generate the figures and tables presented in the article: "Co-GWAS unveils the genetic architecture of inter-individual epistasis affecting biomass and disease severity in wheat binary mixtures". 0. DATA FOLDER: 0_Data This folder contains the raw phenotypic and genotypic data. I. FILE PREPARATION FOLDER: 1_FilePrep_Design_Fig The R script prepares the phenotypic and genotypic data for analysis and generates the kinship matrix. It also creates a figure illustrating the pairs of phenotyped genotypes. II. PHENOTYPIC ANALYSIS  FOLDER: 2_GeneticEffects_PhenoCorr_Residus The R script tests the significance of genetic effects, calculates the proportion of phenotypic variance explained for each phenotype, and checks for correlations between phenotypes. It also controls the residuals in the models. III. A. DGE-BASED GWAS FOLDER: 3_DGE_GWAS This folder contains three subfolders for each phenotype (GWAS_DGE_B, GWAS_DGE_N, GWAS_DGE_P). For example, the pycnidia folder contains the script 3.A_GWAS_DGE_P_AsREML_cluster.R, which performs DGE-based GWAS for the pycnidia phenotype. III. B. Analysis of GWAS Results for DGE FOLDER: 3_DGE_GWAS Within the same folder, the script 3.B_Results_GWAS_DGE_P.Rmd combines the result files and produces Manhattan plots and plots of significant SNPs. IV. A. IGE-BASED GWAS FOLDER: 4_IGE_GWAS This folder contains three subfolders for each phenotype (GWAS_IGE_B, GWAS_IGE_N, GWAS_IGE_P). For example, the pycnidia folder contains the script 4.A_GWAS_IGE_P_AsREML_cluster.R, which performs IGE-based GWAS for the pycnidia phenotype. IV. B. Analysis of GWAS Results for IGE FOLDER: 4_IGE_GWAS Within the same folder, the script 4.B_Results_GWAS_IGE_P.Rmd combines the result files and produces Manhattan plots and plots of significant SNPs. V. PREPARATION OF FILES FOR CO-GWAS FOLDER: 5_FilePrep_coGWAS The script 5.A_FilePrep_coGWAS.Rmd prepares the phenotypic and genotypic data for analysis after SNP pruning. The script 5.B_FilePrep_SNP_Pruning.R performs SNP pruning. The script 5.C_Plot_SNP_Pruning_position.R plots the positions of SNPs before and after pruning. VI. CO-GWAS FOLDER: 6_coGWAS This folder contains three subfolders for each phenotype (coGWAS_B, coGWAS_N, and coGWAS_P).  For example, the pycnidia folder contains the script 6.A_coGWAS_DGEIGE_P_Sommer_cluster.R, which performs the co-GWAS for the pycnidia phenotype. The other scripts in this folder combine the result files. VII. HEATMAPS AND QQPLOT OF CO-GWAS RESULTS FOLDER: 7_Heatmaps_qqplots_coGWAS This folder contains three subfolders for each phenotype (Heatmaps_qqplots_B, Heatmaps_qqplots_N, and Heatmaps_qqplots_P). For example, the pycnidia subfolder includes two scripts: 7.A_coGWAS_P_Heatmaps.R generates heatmaps for the pycnidia phenotype and 7.B_coGWAS_P_qqplots.R produces the QQ plot. VIII. BOXPLOTS - 3D PLOTS - PHYSICAL MAPS FOLDER: 8_3Dplots_PhysicalMaps_Boxplots_coGWAS This folder contains three subfolders for each phenotype (Boxplots_PhysicalMaps_3Dplots_B, Boxplots_PhysicalMaps_3Dplots_N, and Boxplots_PhysicalMaps_3Dplots_P). For example, the pycnidia folder contains the script 8_coGWAS_P_Boxplots_PhysicalMaps_3Dplots.R, which generates 3D plots, boxplots and physical maps for the significant interactions.  IX. CIRCULAR PLOTS   FOLDER: 9_CircularPlots_coGWAS This folder contains three subfolders for each phenotype (CircularPlots_B, CircularPlots_N, and CircularPlots_P). For example, the pycnidia folder contains the script 9_coGWAS_P_CircularPlot.R, which generates the necessary files to create the circular plot.  The script circos.conf creates the circular plot.  X. GO ENRICHMENT ANALYSIS  FOLDER: 10_GOterms_coGWAS This folder contains three subfolders for each phenotype (GOenrichments_B, GOenrichments_N, and GOenrichments_P). For example, the pycnidia folder contains the script 10_coGWAS_P_GOenrichments.Rmd, which generates GO enrichment plots.

opencc-by-4.0Nov 2024View details →

Inter-individual gene expression variability implies stable regulation of brain-biased genes across organs

<div> <div> <h2>Abstract</h2> <a href="https://github.com/christabel-bucao/fish-variability-across-organs/#abstract"></a></div> Phenotypic variation among individuals plays a key role in evolution, since variation provides the material on which natural selection can act. One important link between genetic and phenotypic variation is gene expression. As for other phenotypes, the range of accessible expression variation is limited and biased by different evolutionary and developmental constraints. Gene expression variability broadly refers to the tendency of a gene to vary in expression (i.e., between individuals or cells) due to stochastic fluctuations or differences in genetic, epigenetic, or environmental factors, separately from the differences between e.g. organs. Variability due to biomolecular stochasticity (transcriptional ‘noise’) and cell-to-cell heterogeneity has been well-studied in isogenic populations of unicellular organisms such as bacteria and yeasts. However, for more complex organisms with multiple cells, tissues, and organs sharing the same genetic background, the interplay between inter-individual expression variability, gene and organ function, and gene regulation remains an open question. In this study, we used highly multiplexed 3’-end Bulk RNA Barcoding and sequencing (BRB-seq) to generate transcriptome profiles spanning at least nine organs in outbred individuals of three ray-finned fish species: zebrafish, Northern pike, and spotted gar. For each condition, we measured expression variation per gene independent of mean expression level. We observed that lowly variable genes are enriched in cellular housekeeping functions whereas highly variable genes are enriched in stimulus-response functions. Furthermore, genes with highly variable expression between individuals evolve under weaker purifying selection at the coding sequence level, indicating that intra-species gene expression variability predicts inter-species protein sequence divergence. Genes that are broadly expressed across organs tend to be both highly expressed and lowly variable between individuals, whereas organ-biased genes are typically highly variable within their top organ of expression. For genes with organ-biased expression profiles, we inferred differences in selective pressure on gene regulation depending on their top organ. We found that genes with peak expression in the brain have low inter-individual expression variability across non-nervous organs, suggesting stabilizing selection on regulatory evolution of brain-biased genes. Conversely, liver-biased genes have highly variable expression across organs, implying weaker regulatory constraints. These patterns show that gene regulatory mechanisms evolved differently based on constraints on the primary organ. <h2>Directory Structure</h2> </div> <ul> <li> <code>config/</code>: Contains YAML file indicating package versions for conda environment </li> <li> <code>data/</code>: Contains input data <ul> <li><code>counts/</code>: Contains counts and UMI-deduplicated counts. Currently under embargo and will be made available upon acceptance for publication.</li> <li><code>gene_metadata/</code>: Contains gene biotype information from Ensembl</li> <li><code>sample_metadata/</code>: Contains sample metadata files for each species</li> <li><code>selectome/</code>: Contains selection statistics from the <a href="https://selectome.org/" rel="nofollow">Selectome</a> database </li> </ul> </li> <li> <code>results/</code>: Contains output files sorted by subfolders labeled after each step of the analysis pipeline. Only R notebook HTML files are available on the Git repository, please check Zenodo for R data files. <ul> <li><code>run_pipeline.Rdata</code>: Contains all parameters used for each step of the analysis pipeline </li> </ul> </li> <li> <code>workflow/</code>: Contains scripts used for the analysis pipeline <ul> <li><code>analysis/</code>: Contains all steps of the analysis pipeline, available as .Rmd files</li> <li><code>functions/</code>: Contains all functions used for analysis/</li> <li><code>renv/</code>: Used for package management in R</li> <li><code>run_pipeline.R</code>: Runs all the steps under analysis/</li> <li><code>run_go_figure.sh</code>: Runs <a href="https://gitlab.com/evogenlab/GO-Figure" rel="nofollow">GO-Figure!</a> 1.0.0 (downloaded separately)</li> <li><code>demultiplex_brbseq_fastq.sh</code>: Used for demultiplexing BRB-seq fastq files using <a href="https://github.com/DeplanckeLab/BRB-seqTools">BRB-seqTools</a> 1.6.1 (downloaded separately) for uploading to NCBI SRA</li> <li><code>rename_fastq_files.sh</code>: Used for renaming demultiplexed fastq files by mapping each barcode to their corresponding sample name</li> <li><code>renv.lock</code>: Lockfile for managing R package versions. Run <code>renv::restore()</code> to set up the R environment based on packages specified in the lockfile. All package versions used are also specified in the output HTML files under results/.</li> </ul> </li> </ul> <div> <h2>Species Codes</h2> </div> <ul> <li>LOC: Lepisosteus oculatus (spotted gar)</li> <li>ELU: Esox lucius (Northern pike)</li> <li>DRE: Danio rerio (zebrafish)</li> </ul>

opennotspecifiedOct 2009View details →

FIGURE 4 in An essay on precision in morphometric measurements in anurans: inter-individual, intra-individual and temporal comparisons

FIGURE 4. Projection of morphometric measurements of the three study species on the first two canonical variables from a multiple discriminant function analysis, according to the temporal comparison obtained by the five people. A: living animals; B: freshly preserved specimens; C: specimens after five months of preservation.

opennotspecifiedOct 2009View details →

FIGURE 3 in An essay on precision in morphometric measurements in anurans: inter-individual, intra-individual and temporal comparisons

FIGURE 3. Projection of inter-individual morphometric measurements of Dendropsophus microcephalus on the first two canonical variables from a multiple discriminant function analysis. A: living animals; B: freshly preserved specimens; C: specimens after five months of preservation.