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Dataset results
311 results for “Empirical data”
Empirical Data from a Longitudinal Software Maintenance Experiment
<p>This dataset contains IDE interaction traces (logs) and screen videos obtained from a comprehensive study conducted to understand in detail how software professionals plan and execute maintenance activities on real-life software.</p>
Empirical Analysis of Performance Changes at Code Level - Analysis and Measurement Data
<p>This dataset contains the information of the paper 'Empirical Analysis of Performance Changes at Code Level'. In particular, it contains 4 folders:<br> (1) the versions where performance could have changed based on regression test selection (dependencies)<br> (2) the measurement data and the derived performance changes (measurement)<br> (3) the tracediffs (tracediffs) and<br> (4) the properties of the changes and the manual classification of the changes (properties).<br> The folders contain READMEs with detailed information about each folder.</p>
Data accompanying "An empirical pipeline for choosing the optimal clustering threshold in RADseq studies"
<p>Data files accompanying "An empirical pipeline for choosing the optimal clustering threshold in RADseq studies"</p>
waveform data of the Empirical Green's Functions (EGFs) and earthquakes for the manuscript "Unraveling the Mantle Dynamics in Central Asia with Full Waveform Inversion Tomography"
<p>The dataset uploaded contains the original ASDF files and the SAC files (with '_sac') converted from ASDF</p> <p>For the ASDF files:</p> <p>The ASDF data file could be read through python module pyasdf and obspy after decompressing. </p> <p>The earthquake information could be accessed by the following command</p> <p>import pyasdf</p> <p>ds=pyasdf.ASDFDataSet(ASDFfile, mode='r')</p> <p>event=ds.events[0].preferred_origin()</p> <p>print(event)</p> <p>the waveforms coul dbe accessed through</p> <p>stream=ds.waveforms[net.station].raw_recording</p>
Simulation and empirical data for "Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations"
<p>Simulation data and empirical data used to generate figures from "Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations". Can be used with code provided on the associated github to regenerate the figures. </p>
Replication Package for "A Call for Critically Rethinking and Reforming Data Analysis in Empirical Software Engineer"
<p><strong>Content Overview</strong></p> <p>This replication package contains the following materials:</p> <p>- Data: RAW Focus Group Datasets and Aggregated Results</p> <p>For any issues, questions, or further assistance, please do not hesitate to contact the paper's authors. We are here to help!</p>
Data from: An empirical and mechanistic explanation of abundance-occupancy relationships for a critically endangered nomadic migrant
The positive abundance-occupancy relationship (AOR) is a pervasive pattern in macroecology. Similarly, the association between occupancy (or probability of occurrence) and abundance is also usually assumed to be positive and in most cases constant. Examples of AORs for nomadic species with variable distributions are extremely rare. Here we examined temporal and spatial trends in the AOR over seven years for a critically endangered nomadic migrant which relies on dynamic pulses in food availability to breed. We predicted a negative temporal relationship, where local mean abundances increase when the number of occupied sites decreases, and a positive relationship between local abundances and the probability of occurrence. We also predicted that these patterns are largely attributable to spatiotemporal variation in food abundance. The temporal AOR was significantly negative and annual food availability was significantly positively correlated with the number of occupied sites, but negatively correlated with abundance. Thus, as food availability decreased, local densities of birds increased, and vice-versa. The abundance - probability of occurrence relationship was positive and non-linear, but varied between years due to differing degrees of spatial aggregation caused by changing food availability. Importantly, high abundance (or occupancy) did not necessarily equate to high quality habitat and may be indicative of resource bottlenecks or exposure to other processes affecting vital rates. Our results provide a rare empirical example that highlights the complexity of AORs for species that target aggregated food resources in dynamic environments.
Data from: Dissimilarity of individual microsatellite profiles under different mutation models: empirical approach
Microsatellites (simple sequence repeats, SSRs) still remain popular molecular markers for studying neutral genetic variation. Two alternative models outline how new microsatellite alleles evolve. Infinite alleles model (IAM) assumes that all possible alleles are equally likely to result from a mutation, while stepwise mutation model (SMM) describes microsatellite evolution as stepwise adding or subtracting single repeat units. Genetic relationships between individuals can be analyzed in higher precision when assuming the SMM scenario with allele size differences as a proxy of genetic distance. If population structure is not predetermined in advance, an empirical data analysis usually includes (a) estimating proximity between individual SSR profiles with a selected dissimilarity measure and (b) determining putative genetic structure of a given set of individuals using methods of clustering and/or ordination for the obtained dissimilarity matrix. We developed new dissimilarity indices between SSR profiles of haploid, diploid, or polyploid organisms assuming different mutation models and compared the performance of these indices for determining genetic structure with population data and with simulations. More specifically, we compared SMM with a constant or variable mutation rate at different SSR loci to IAM using data from natural populations of a freshwater bryozoan Cristatella mucedo (diploid), wheat leaf rust Puccinia triticina (dikaryon), and wheat powdery mildew Blumeria graminis (monokaryon). We show that inferences about population genetic structure are sensitive to the assumed mutation model. With simulations, we found that Bruvo's distance performs generally poorly, while the new metrics are capturing the differences in the genetic structure of the populations.
Data from: Species delimitation with ABC and other coalescent-based methods: a test of accuracy with simulations and an empirical example with lizards of the Liolaemus darwinii complex (Squamata: Liolaemidae)
Species delimitation is a major research focus in evolutionary biology because accurate species boundaries are a prerequisite for the study of speciation. New species delimitation methods (SDMs) can accommodate non-monophyletic species and gene tree discordance as a result of incomplete lineage sorting via the coalescent model, but do not explicitly accommodate gene flow after divergence. Approximate Bayesian Computation (ABC) can incorporate gene flow and estimate other relevant parameters of the speciation process while testing alternative species delimitation hypotheses. We evaluated the accuracy of BPP, SpeDeSTEM, and ABC for delimiting species using simulated data and applied these methods to empirical data from lizards of the Liolaemus darwinii complex. Overall, BPP was the most accurate, ABC showed an intermediate accuracy, and SpeDeSTEM was the least accurate under most simulated conditions. All three SDMs showed lower accuracy when speciation occurred despite gene flow, as found in previous studies, but ABC was the method with the smallest decrease in accuracy. All three SDMs consistently supported the distinctness of southern and northern lineages within L. darwinii. These SDMs based on genetic data should be complemented with novel SDMs based on morphological and ecological data to achieve truly integrative and statistically robust approaches to species discovery.
Data Set: Software-Related Fatal Failures: An Empirical Exploration of RISKS Reports
<p>The data set used in the empirical exploration of RISKS reports. This data set acts as a basis for further research and investigation to categorize and analyze software-related fatal failures spanning more than 30 years.</p>
Data set for Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services' trajectories
<p>Data set for the paper: Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services’ trajectories</p> <p>Based on: Marin-Garcia, J. A., Ruiz, A., Julien, M., & Garcia-Sabater, J. P. (2021). A data generator for covid-19 patients’ care requirements inside hospitals. WPOM-Working Papers on Operations Management, 12(1), 76-115. https://doi.org/10.4995/wpom.15332</p> <p> </p>
Dataset for How open is innovation research?–An empirical analysis of data sharing among innovation scholars
<p>This dataset comprises responses from 241 innovation researchers on their personal data sharing behavior as well as their perceptions of and attitudes towards open research data. This dataset is the supplementary material to Barczak et al. (2021) (<a href="https://doi.org/10.1080/13662716.2021.1967727" target="_blank" rel="noopener">https://doi.org/10.1080/13662716.2021.1967727</a>).</p>
Data set for EMPIR project MeDDII Paper bmt-2022-0040
<p>Data set for EMPIR project MeDDII Paper</p> <p>https://doi.org/10.1515/bmt-2022-0040</p>
Data set for EMPIR project MeDDII Report A2.3.4 D4
<p>Data set for EMPIR project MeDDII Report A2.3.4 D4</p> <p>Report on the use of a calibrated microfluidic multi-parameter chip for the in-line measurement of pressure, viscosity and temperature</p>
Data set for EMPIR project MeDDII Report A2.1.4
<p>Data set for EMPIR project MeDDII Report A2.1.4</p> <p>Validation of the pipe viscometers (primary standards) for the in-line measurement of the dynamic viscosity of Newtonian liquids</p>
Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID90
<p>Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID90</p> <p>Dynamic vs constant liquid flow calibrations down to 20 nL/min</p>
Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID88
<p>Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID88</p> <p>First comparison of inline measurements of dynamic viscosity</p>
Data set for EMPIR project MeDDII Report A2.3.3
<p>Data set for EMPIR project MeDDII Report A2.3.3</p> <p>Characterization of devices for the in-line measurement of the dynamic viscosity and the density of Newtonian liquids</p>
Data set for "Spatially explicit ecological modeling improves empirical characterization of dispersal"
<p>Data set used and created in the simulations, analysis and figures of the associated paper.</p>
Data from: Exploring the impact of read clustering thresholds on RADseq-based systematics: an empirical example from European amphibians.
<p>This repository contains genetic sequences obtained from Hybrid-Enrichment and RAD sequencing protocols of the amphibian genera <em>Discoglossus</em>, <em>Lissotriton</em>, <em>Rana </em>and <em>Triturus, </em>as well as phylogenetic trees inferred from the RADseq data. This data was generated for the manuscript "Exploring the impact of read clustering thresholds on RADseq-based systematics: an empirical example from European amphibians.", in which we tested the influence of the clustering threshold used to assemble RADseq data on downstream phylogenetic inferences. Details on the data generation and analyses can be found in the manuscript and related supplementary materials.</p> <p>The repository is organised as follow:</p> <p>-> Hybrid-Enrichment: alignments of the Hybrid-Enrichment markers in phylip/fasta format (with one subdirectory for each of the four datasets assembled: Discoglossus, Lissotriton, Rana, Triturus)</p> <p>--> RADseq: Assemblies and phylogenetic trees obtained from a RADseq protocol</p> <p> --> Assemblies: RADseq assemblies (complete loci sequences and SNP matrices, spreadsheets with assembly metrics). Divided into "iCT" (assemblies produced with 23 different intra-sample Clustering Threshold [iCT] and a fixed between-samples Clustering Threshold [bCT]) and "bCT" (assemblies produced with a fixed iCT and 23 different bCT). Both iCT and bCT are further divided in four sub-directories corresponding to the four datasets: Discoglossus, Lissotriton, Rana, Triturus)</p> <p> --> Trees: Phylogenetic trees inferred from the aforementionned assemblies. Divided into "iCT" (RAxML concatenation trees inferred from the assemblies with different iCTs) and "bCT" (RAxML concatenation trees and Tetrad species trees inferred from the assemblies with different bCTs).</p>
ScienceDex guides
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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.