Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
184
datasets available to search
ShareScore release 0.9.0
Dataset results
184 results for “variability analysis”
Supplementary tables for: Dependent variable selection in phylogenetic generalized least squares regression analysis under Pagel's lambda model
<p class="MsoNormal"><span>Phylogenetic generalized least squares (PGLS) regression is widely used to detect evolutionary correlations. In contrast to the equal treatment of analyzed traits in conventional correlation methods such as Pearson and Spearman's rank tests, we must designate one trait as the independent variable and the other as the dependent variable. However, in our PGLS regression analyses (using Pagel's <em>λ</em> model) of both empirical and simulated datasets, switching independent and dependent variables yielded many conflicting results. A serious problem with PGLS regression that has not been noticed before is that selecting an inappropriate trait as the dependent variable will often result in an error. To assess correlations in simulated data, we established a gold standard by analyzing changes in traits along phylogenetic branches. Next, we tested seven potential criteria for dependent variable selection: log-likelihood, Akaike information criterion, <em>R</em><sup>2</sup>, <em>p</em>-value, Pagel's <em>λ</em>, Blomberg et al.'s <em>K</em>, and the estimated <em>λ</em> in <a name="_Hlk136010442"></a>Pagel's <em>λ</em> model. We determined that the last three criteria performed equally well in selecting the dependent variable and were superior to the other four. For practicality, we suggest using the trait with a higher <em>λ</em></span><span> or <em>K</em> </span><span>value as the dependent variable in future PGLS regressions. In analyzing the evolutionary relationship between two traits, we should designate the trait with a stronger phylogenetic signal as the dependent variable even if it could logically assume the cause in the relationship.</span></p>
Exploring variability in the diet of depredating sperm whales in the Gulf of Alaska through stable isotope analysis
Open the record for dataset details and reuse information.
Data and analyses from: Context matters: A meta-analysis of the variable impacts of transgenerational and developmental plasticity on responses to stress
Open the record for dataset details and reuse information.
Supplementary tables for: Dependent variable selection in phylogenetic generalized least squares regression analysis under Pagel’s lambda model
Open the record for dataset details and reuse information.
Meta-analysis suggests variable, but pCO2-specific, effects of ocean acidification on crustacean biomaterials
Open the record for dataset details and reuse information.
Dataset to ACM MMSys'20 paper entitled "Comparing Fixed and Variable Segment Durations for Adaptive Video Streaming – A Holistic Analysis"
<p>Dataset for the ACM MMSys'20 paper entitled "Comparing Fixed and Variable Segment Durations for Adaptive<br> Video Streaming – A Holistic Analysis".<br> The dataset includes</p> <ul> <li>Results from video encoding (using variable and fixed segment durations)</li> <li>Video sequences used for streaming evaluations</li> </ul>
Exascale Potholes for HPC: Execution Performance and Variability Analysis of the Flagship Application Code HemeLB
<p>Performance measurement and analysis of parallel applications is often challenging, despite many excellent commercial and open-source tools being available. Currently envisaged exascale computer systems exacerbate matters by requiring extremely high scalability to effectively exploit millions of processor cores. Unfortunately, significant application execution performance variability arising from increasingly complex interactions between hardware and system software makes this situation much more difficult for application developers and performance analysts alike.<br> </p> <p>This work considers the performance assessment of the HemeLB exascale flagship application code from the EU HPC Centre of Excellence (CoE) for Computational Biomedicine (CompBioMed) running on the SuperMUC-NG Tier-0 leadership system, using the methodology of the Performance Optimisation and Productivity (POP) CoE. Although 80% scaling efficiency is maintained to over 100,000 MPI processes, disappointing initial performance with more processes and corresponding poor strong scaling was identified to originate from the same few compute nodes in multiple runs, which later system diagnostic checks found had faulty DIMMs and lacklustre performance. Excluding these compute nodes from subsequent runs improved performance of executions with over 300,000 MPI processes by a factor of five, resulting in 190x speed-up compared to 864 MPI processes. While communication efficiency remains very good up to the largest scale, parallel efficiency is primarily limited by load balance found to be largely due to core-to-core and run-to-run variability from excessive stalls for memory accesses, that affect many HPC systems with Intel Xeon Scalable processors. The POP methodology for this performance diagnosis is demonstrated via a detailed exposition with widely deployed `standard' measurement and analysis tools.</p>
Data in the analysis of Comparative Study of the Impact of Dummy Variables on Regression Coefficients and Canonical Correlation Indices: an Empirical perspective
<p>This data is used in the analysis related to the article Comparative Study of the Impact of Dummy Variables on Regression Coefficients and Canonical Correlation Indices: an Empirical perspective. The data contain Crude Oil Prices, Exchange Rates, and Dummy variables coded 1 for the era before the Covid-19 outbreak and 0 for the era of Covid-19.</p> <p> </p>
Spin-up time and internal variability analysis for overlapping time slices in a regional climate model
<p>In order to increase computational efficiency, several long-term regional climate simulations were split into overlappings time slices. These overlappings slices were used to explore the relative role of spin-up time and internal variability in the discontinuities that are produced once the slices are joined.</p> <p>This dataset was generated using the Weather Research and Forecasting (WRF) model and includes two sets of time slices for the periods 2002-2006 and 2006-2010 over the CORDEX South American domain at 0.44º horizontal resolution (SAM-44), regular on a rotated latitude-longitude projection. The data was forced by the scenario RCP 8.5 and driven by the Canadian Earth System model (CanESM2). The model configuration files are also attached.</p>
Dataset for article: Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Biological Psychology, 82(1), pp. 82-88
<p>Dataset for article:</p> <p>Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Biological Psychology, 82(1), pp. 82-88</p>
Breathing frequency bias in fractal analysis of heart rate variability (datasets)
<p>data form the article:</p> <p>Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Bi- ological Psychology, 82(1), pp. 82-88 </p>
(variable) Frequency-based Layer Identification Procedure (FLIP and vFLIP) for spectrolaminar analysis
<p class="MsoNormal">The mammalian cerebral cortex is anatomically organized into a six-layer motif. In this dataset and associated paper (Mendoza-Halliday et al., 2023) we show that a corresponding laminar motif of neuronal activity patterns exists across the cortex in the power of local field potentials (LFP). Using laminar probes, we recorded LFPs in five macaque monkeys in 14 cortical areas across the cortical hierarchy. The laminar locations of recordings were histologically identified via electrolytic lesions. Across all areas, we found a ubiquitous spectrolaminar pattern characterized by an increasing deep-to-superficial layer gradient of high-frequency power peaking in layers 2/3 and an increasing superficial-to-deep gradient of alpha-beta power peaking in layers 5/6. Our results suggest the existence of a canonical layer- and frequency-based mechanism for cortical computation.</p> <p class="MsoNormal">To facilitate the detection of these spectrolaminar patterns, we are releasing data associated with "Study 1" and "Study 2" as reported in Mendoza-Halliday et al., 2023. This comprises relative power of LFPs recorded in 4 macaque monkeys from areas V4, 7A, MT, MST, LIP, and LPFC. The FLIP and vFLIP algorithms were created to facilitate electrophysiological characterization of cortical layers based on power analysis of the local field potential recordings (LFP) that are provided in these datasets. The algorithms can also be used to perform the same analysis on any other multi-channel LFP dataset. We have verified that the spectrolaminar pattern is highly preserved in macaque monkeys, marmosets, and humans, but is more dissimilar in mouse. </p>
A seasonal analysis of sea spray aerosol variability across the Southern Ocean
<p>This dataset includes filter based aerosol observations of atmospheric sodium concentration from the Southern Ocean marine boundary layer in summer (2018/19), winter (2019) and spring (2019). Environmental data such as sea surface temperature and wind speed are also included.</p>
Exploring Cellular Dynamics: A Comprehensive Analysis of SH-SY5Y Cell Growth in Variable Polycarbonate Chambers and Comparative Insights with Standard Petri Dishes
<div> <div> <div> <div> <div> <div> <p>The provided illustrations delineate the proliferation of SH-SY5Y cells within polycarbonate chambers with varying diameters, ranging from 3 mm to 50 mm. HBE and HeLaGFP cells serve as points of comparison. Five microscopic images were captured for each culture plate, one at the central location and four at random sites. To trace the growth trajectory across different chamber sizes, images from various culture days underwent manual binarization using GIMP and/or ImageJ software. The resulting binarized images can be accessed in the 'masks' directories within the zipped files, corresponding to the attached images. Comparative analysis of cell growth within these chambers was conducted against growth observed in standard 60 mm Petri dishes from Corning.</p> <p>For monitoring cell growth, we employed an Olympus phase-contrast microscope (CKX53) equipped with a DLT camera (DLT-Cam PRO 1080 HDMI USB).</p> <div> <div> <div> <p>The file nomenclature employed herein serves to represent the primary experimental conditions under consideration. It comprises a hierarchical structure, encompassing various parameters as follows: cellsName_passageNumber_cellSuspension_measurementDayNumber_chamberDiameter_regionNumber_hOfFluids_number.</p> <p>The cellsNames parameter encapsulates the complete nomenclature of the cells utilized in the experiment.</p> <p>The passageNumber parameter represents the passage number and is encoded as 'pNumber.'</p> <p>The cellSuspension parameter is denoted by distinct codes:</p> <ul> <li>'c1' designates a cell suspension with a concentration of 5x10^5 cells/ml.</li> <li>'c3' signifies a cell suspension with a concentration of 1x10^5 cells/ml.</li> <li>'c4' denotes a cell suspension with a concentration of 5x10^4 cells/ml.</li> </ul> <p>The measurementDayNumber parameter is encoded as 'afterXXXdays,' signifying the number of culture days.</p> <p>The 'd' followed by a number (e.g., d6, d8, d10, d12, d15) indicates the diameter of the chamber in millimeters. In a given series of files (e.g., 1day_d6_1, 1day_d6_2, 1day_d6_3, 1day_d6_4, 1day_d6_5), the trailing numeral (_1, _2, _3, _4, _5) corresponds to a photograph captured in a distinct region of the sample.</p> <p>The hOfFluids parameter is employed to signify the height ('h') of the fluid above the cells in the chamber, expressed in millimeters.</p> <div> <div> <div> <p>In adherence to the aforementioned conventions, every file name serves to distinctly characterize a particular collection of experimental conditions. This practice enhances both the comprehensibility and systematic organization of the data. It is noteworthy that the complete set of parameters may not always be provided; for instance, the height of the column of medium is assumed to be 2 mm unless otherwise specified.</p> </div> </div> </div> </div> </div> </div> <br> <p> </p> </div> </div> </div> </div> </div> </div>
Data on soil variables (with plot IDs) and grassland species traits used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. Journal of Vegetation Science, 35, e13259. Available from: https://doi.org/10.1111/jvs.13259
<p>File <a href="../api/records/10983049/draft/files/Plot_IDs_990ua.txt/content" target="_blank" rel="noopener noreferrer">Plot_IDs_990ua.txt</a> contains the IDs of the 1-m2 plots used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. The plot data are stored in the sPlot database (PPBio South Brazilian Grassland Database).</p> <p>File <a href="../api/records/10983049/draft/files/E_990ua_21SoilVar.txt/content" target="_blank" rel="noopener noreferrer">E_990ua_21SoilVar.txt</a> contains data on soil variables evaluated in the 250 m transects, but here expanded to the 990 1-m2 plots (each transect was sampled using 10 1-m2 pots).</p> <p>File <a href="../api/records/10983049/draft/files/B_769spp_4t.txt/content" target="_blank" rel="noopener noreferrer">B_769spp_4t.txt</a> is the species trait database collected in the framework of several research projects in the Quantitative Ecology Lab (EcoQua) and Grassland Vegetation Studies Lab (LevCamp) of Universidade Federal do Rio Grande do Sul (UFRGS). Data gaps were filled by compiled from the TRY database and data imputation.</p> <p> </p> <p> </p>
Variables of importance in the projection (VIP) list with values > 1 from OPLS-DA analysis of Citrus samples inoculated with Xanthomonas citri subs. citri
<p>Features filtered containing Variables of importance in the projection (VIP) values > 1 from OPLS-DA analysis (SIMCA) of Citrus sinensis samples inoculated with Xanthomonas citri subs. citri after 24 and 48 hours post inoculation.</p>
Dispersal increases spatial synchrony of populations but has weak effects on population variability: a meta-analysis
<p><span>The effects of dispersal on spatial synchrony and population variability have been well documented in theoretical research, and a growing number of empirical tests have been performed. Yet a synthesis is still lacking. Here, we conducted a meta-analysis of relevant experiments and examined how dispersal affected spatial synchrony and temporal population variability across scales. Our analyses showed that dispersal generally promoted spatial synchrony, and such effects </span><span>increased with dispersal rate and decreased with environmental correlation among patches. The synchronizing effect of dispersal, however, was only detected when spatial synchrony was measured using the correlation-based index, but not for the covariance-based index. In contrast to theoretical predictions, the effect of dispersal on local population variability was generally non-significant, except when environment correlation among patch was negative and/or experimental period was long. At the regional scale, while low dispersal stabilized metapopulation dynamics, high dispersal led to destabilization. </span><span>Overall, the sign and strength of dispersal effects on spatial synchrony and population variability were modulated by taxa, environmental heterogeneity, </span><span><span>type of perturbations, patch number, and experimental length. </span>Our synthesis demonstrates that dispersal can substantially affect the dynamics of spatially distributed populations, but its effects are context dependent on abiotic and biotic factors. </span></p>
Code for data analysis - intra-community variability of leaf-out in temperate tree canopies
<p>The code and data were used for producing the results of "Phenology across scales: an intercontinental analysis of leaf-out dates in temperate deciduous tree communities", by Delpierre et al.</p>
Data underlying the article: "Excuse me, there is a mutant in my bioactivity soup! A comprehensive analysis of the genetic variability landscape of bioactivity databases and its effect on activity modelling"
<p>This repository contains the data underlying the article: “Excuse me, there is a mutant in my bioactivity soup! A comprehensive analysis of the genetic variability landscape of bioactivity databases and its effect on activity modelling” available as a preprint on ChemRxiv.</p> <p>Main authors: Marina Gorostiola González & Olivier J.M. Béquignon (Leiden University)</p> <p>Senior author: Gerard J.P. van Westen (Leiden University)</p> <p>This analysis was performed using the code available at <a href="https://github.com/CDDLeiden/chembl_variants" target="_blank" rel="noopener">https://github.com/CDDLeiden/chembl_variants</a></p>
Data from: Billeci et al. "Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis"
<p>Dataset of electrocardiogram and electroencephalogram signals (.edf) acquired in epileptic patients (N=15).</p> <p>All the patients were long-term monitored with a Video-EEG, with electrodes arranged on the basis of the international 10-20 system, and with ECG. ECG was measured simultaneously with a sampling rate of 512 Hz.</p> <p>Each data include a descriptor file (.txt) containing all the information related to the acquisition: data, registration start (time), registration end (time), seizure/s start, seizure/s end and the electrodes involved at the seizure onset.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.