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.
13
datasets available to search
ShareScore release 0.9.0
Dataset results
13 results for “evolutionary coupling”
Evolutionary coupling range varies widely among enzymes: data and code
<p>Data and code needed to reproduce result of the paper "Evolutionary coupling range varies widely among enzymes", by J. Echave.</p> <p>biorxiv: https://doi.org/10.1101/2020.12.19.423588</p>
A Genome-Wide Evolutionary Simulation of the Transcription-Supercoiling Coupling: extended version
<p>Data set used for the Artificial Life journal paper <a href="https://direct.mit.edu/artl/article-abstract/28/4/440/112557/A-Genome-Wide-Evolutionary-Simulation-of-the"><em>A Genome-Wide Evolutionary Simulation of the Transcription-Supercoiling Coupling: extended version</em></a>. A preprint of the paper is also available <a href="https://hal.archives-ouvertes.fr/hal-03667822">here</a>.</p> <p>This data is also used in Chapter 4 of my <a href="https://gitlab.inria.fr/tgrohens/phd">PhD thesis</a>.</p>
Dataset for: "Phylogenetic weighting does little to improve the accuracy of evolutionary coupling analyses"
<p>This is the dataset and results folders for the manuscript titled "Phylogenetic weighting does little to improve the accuracy of evolutionary coupling analyses". The dataset is necessary to run the code that can be found at <a href="https://github.com/adamhockenberry/dca-weighting">https://github.com/adamhockenberry/dca-weighting</a> </p>
Dataset for: "Evolutionary couplings detect side-chain interactions"
<p>This is the pre-publication dataset for "Evolutionary couplings detect side-chain interactions", a manuscript currently undergoing the review process and posted at bioRxiv (doi.org/10.1101/447409)</p>
Data from: A test for rate-coupling of trophic and cranial evolutionary dynamics in New World bats
<p>Data supporting article Shi et al. 2021, Evolution: <a href="https://doi.org/10.1111/evo.14188">https://doi.org/10.1111/evo.14188</a></p> <p>Morphological evolution is often assumed to be causally related to underlying patterns of ecological trait evolution. However, few studies have directly tested whether evolutionary dynamics of and major shifts in ecological resource use are coupled with morphological shifts that may facilitate trophic innovation. Using diet and multivariate cranial (microCT) data, we tested whether rates of trophic and cranial evolution are coupled in the radiation of New World bats. <span>We developed a generalizable information-theoretic method for describing evolutionary rate heterogeneity across large candidate sets of multi-rate models of evolution, without relying on a single best-fitting model</span>. We found considerable variation in trophic evolutionary dynamics, in sharp contrast to a largely homogeneous cranial evolutionary process. This dichotomy is surprising given documented functional associations between overall skull morphology and trophic ecology. We suggest that assigning discrete trophic states may underestimate trophic generalism, and that this radiation could be characterized by generally labile crania that result in a homogeneous dynamic of overall high morphological rates. Overall, we discuss how trophic classification could substantively impact our interpretation of how these dynamics covary in adaptive radiations.</p>
Data from: Experimental evidence for an eco-evolutionary coupling between local adaptation and intraspecific competition
Determining how adaptive evolution can be coupled to ecological processes is key for developing a more integrative understanding of the demographic factors that regulate populations. Intraspecific competition is an especially important ecological process because it generates negative density dependence in demographic rates. Although ecological factors are most often investigated to determine the strength of density dependence, evolutionary processes such as local adaptation could also feed back to shape variation in the strength of density dependence among populations. Using an experimental approach with damselflies, a predaceous aquatic insect, we find evidence that both density-dependent intraspecific competition and local adaptation can reduce per capita growth rates. In some cases, the effects of local adaptation on reducing per capita growth rates exceeded the ecological competitive effects of a doubling of density. However, we also found that these ecological and evolutionary properties of populations are coupled, and we offer two interpretations of the causes underlying this pattern: (1) the strength of density-dependent competition depends on the extent of local adaptation, or (2) the extent of local adaptation is shaped by the strength of density-dependent competition. Regardless of the underlying causal pathway, these results show how eco-evolutionary dynamics can affect a key demographic process regulating populations.
Data from: Environmental coupling of heritability and selection is rare and of minor evolutionary significance in wild populations
Predicting the rate of adaptation to environmental change in wild populations is important for understanding evolutionary change. However, predictions may be unreliable if the two key variables affecting the rate of evolutionary change, heritability and selection, are both affected by the same environmental variable. To determine how general such an environmentally induced coupling of heritability and selection is, and how this may influence the rate of adaptation, we made use of freely accessible, open data on pedigreed wild populations to answer this question at the broadest possible scale. Using 16 populations from 10 vertebrate species, which provided data on 50 traits (body mass, morphology, physiology, behaviour and life history), we found evidence for an environmentally induced relationship between heritability and selection in only 6 cases, with weak evidence that this resulted in an increase or decrease in expected selection response. We conclude that such a coupling of heritability and selection is unlikely to strongly affect evolutionary change even though both heritability and selection are commonly postulated to be environment dependent.
Enhancing Change Impact Prediction by Integrating Evolutionary Coupling with Software Change Relationships
<div> <div><strong>Replication Package For Paper: Enhancing Change Impact Prediction by Integrating Evolutionary Coupling with Software Change Relationships</strong></div> <br> <div>IMPER is a change <strong>IM</strong>pact <strong>P</strong>rediction method that integrates <strong>E</strong>volutionary coupling with software change <strong>R</strong>elationships. The approach consists of following four main steps:</div> <br> <div><em>Step 1</em>: Relevant Transaction Filtering. Filtering the relevant transaction set based on the given query $<em>Q</em>$.</div> <div><em>Step 2</em>: Association Rule Generation. Generating association rules and obtaining the impact candidate set.</div> <div><em>Step 3</em>: Spatial-Temporal Window Processing. Considering the temporal and spatial change relationships to obtain the impact candidate set.</div> <div><em>Step 4</em>: Candidates Integrating and Ranking. Making a union of these two sets to find the final impact set.</div> <br> <div>The repo provides files for the paper replication. It includes::</div> </div> <div> </div> <div><strong>git_repo.zi</strong>p: Git repository used in the study.</div> <div><em><strong>***-rec-database-1107.tar.gz</strong>:</em> Neo4j dataset containing entity-level software relationship graphs and change information (changed classes, member member methods, member member variables, and files) from over 19,003 versions of six well-maintained projects.</div> <div><strong><em>code_entiy_genealogy</em></strong>: Data for code entity genealogy.<br> <div><em><strong>imper_jar.zip</strong>:</em> Tool for the study, including: 1) Generating qualified queries; 2) Change prediction using four approaches (ROSE, TARMAQ, IMPER, IMPER-ST); 3) Evaluation. The tool outputs evaluation results for each query and average metrics (MAP, Precision, Recall, and F1-score).</div> <div><strong><em>evaluation_metrics_details.zip</em></strong>: Detailed evaluation metric results for different projects.</div> <div><strong><em>Imper_evaluation_results.xlsx</em></strong>: Summary of evaluation metric results for all projects.</div> <div> </div> <br> <div>Users are suggested to refer to the research paper for more detials.</div> <br> <div>This project is still in an early stage of development, and we are improving the implementation and documentation. Please feel free to contact ******(author) for the replication package.</div> <br><strong>Environment</strong><br> <div>- java 11+</div> <div>- <a href="https://neo4j.com/download-center/#community">neo4j 4.2</a> </div> <div>- memory 64G+</div> <br><strong>Quick Start</strong>:<br>To begin our study, follow these steps. Note that only one project can be analyzed at a time.</div> <div><br><strong><em>Step 1: Database Setup</em></strong></div> <div> </div> <div>Copy the relevant database to the folder `data` (in the root path of the Neo4j database), copy the plugins to the plugin folder, and open the database using Neo4j 4.2 (Recommended).</div> <div><br><strong><em>Step 2: Configure application-dev.yml</em></strong></div> <div> </div> <div>Adjust the application-dev.yml file in the folder `imper_jar` to match your configuration. If you're using an existing database, simply configure the database address, username, and password. <div> <p> </p> </div> </div> <div><strong><em>Step 3: Run imper-study.jar</em></strong></div> <div><br>Command: `<em>java -jar imper-study.jar -imp -config ./application-dev.yml -rd /home/user/code_rec -pj commons-io -fi commons-io_file(Map)_1031.txt -mi commons-io_functionInfo(Map).txt -tw 35 -mc 20 -od /home/user/code_rec</em>`.</div> <div><br> <div>-imp: Used to run IMPER</div> <div>-config: Path of `<em>application-dev.yml</em>`.</div> <div>-rd: Root path of code entiy genealogy, e.g., `<em>/home/user/code_rec</em>`.</div> <div>-pj: Project name, e.g., `<em>commons-io</em>`.</div> <div>-fi: the code entiy genealogy (file), e.g., `<em>commons-io_file(Map)_1031.txt</em>` (the absolute path is `<em>/home/user/code_rec/commons-io/commons-io_file(Map)_1031.txt</em>`).</div> <div>-mi: the code entiy genealogy (method), e.g., `<em>commons-io_functionInfo(Map).txt</em>` (the absolute path is `<em>/home/user/code_rec/commons-io/commons-io_functionInfo(Map).txt</em>`).</div> <div>-tw: Time window (half), e.g., <em>-tw 35 (the time window size is 70).</em></div> <div>-mc: Maxium commit size in the time window.</div> <div>-od: Output root path for evaluation results (MAP, Precison, Recall and F1-score), e.g., `<em>/home/user/code_rec</em>`.</div> </div>
Data from: Environmental coupling of heritability and selection is rare and of minor evolutionary significance in wild populations
Open the record for dataset details and reuse information.
Data from: Experimental evidence for an eco-evolutionary coupling between local adaptation and intraspecific competition
Open the record for dataset details and reuse information.
Data from: A test for rate-coupling of trophic and cranial evolutionary dynamics in New World bats
Open the record for dataset details and reuse information.
Data for the manuscript of The Evolutionary Coupling between Detachment Faulting and Non-transform Discontinuities, Insights from Near-bottom Surveys along Ultraslow Ridges
<p><span>High-resolution bathymetry and magnetics data collected by the <em>Qianlong II</em> and <em>III </em>AUVs</span></p>
Data from: Closely coupled evolutionary history of ecto- and endosymbionts from two distantly related animal phyla
Open the record for dataset details and reuse information.
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.