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285 results for “evolution models”
Data from: Evolution of social versus individual learning in an infinite island model
We model the evolution of learning in a population composed of infinitely many, finite-sized islands connected by migration. We assume that there are two discrete strategies, social and individual learning, and that the environment is spatially homogeneous but varies temporally in a periodic or stochastic manner. Using a population-genetic approximation technique, we derive a mathematical condition for the two strategies to coexist stably and the equilibrium frequency of social learners under stable coexistence. Analytical and numerical results both reveal that social learners are favored when island size is large or migration rate between islands is high, suggesting that spatial subdivision disfavors social learners. We also show that the average fecundity of the population under stable coexistence of the two strategies is in general lower than that in the absence of social learners and is minimized at an intermediate migration rate.
The input files and associated data products for "Modeling High Mass X-ray Binaries to Double Neutron Stars through Common Envelope Evolution"
<p>Simulations were made using the version 12115 of the MESA code together with the x86_64-linux-20190830 MESA SDK. "template.zip" provides the MESA inlist files to reproduce our simulations. "CE_1.zip" provides our simulated results for a grid of binary systems with common envelope ejection efficiencies set to be 1.0. Different folders indicate the binary systems with different initial parameters. Inside each folder information can be found for binary properties in the "history.data" file. Each folder also contains the "result.txt" file with the terminal output of the simulation. "CE_3.zip", "CE_0.3.zip" and "CE_0.1.zip" are the same as "CE_1.zip" but with common envelope ejection efficiencies set to be 3.0, 0.3 and 0.1, respectively.</p>
SCORE D3.9 - Models for the long-term evolution of the coastline (package)
<p>SCORE project TD3.9 datasets and scripts:</p> <p>Modelling tools and local datasets produced in the SCORE Task 3.5, for the long-term evolution of the coastline and uncertainties, estimated by the study of local morphodynamic processes and climatic projections. It contains a hybrid 2D/3D modelling chain, for sediment transport processes connected to hydrodynamic models at the storm scale. </p>
Supplementary Information for 'Evolution of resistance under alternative models of selective interference' (2021, JEB)
<p>The Supplementary Information folder contains all the files needed to reproduce the data and figures in our paper, as well as containing both supplementary figures and the means of reproducing those supplementary figures. The data was produced via simulation using R (v4.0.3) using .R files. The data is available in .csv files. The figures are .pdf files and their legends are in the .docx file. An additional .xlsx file describes which .R and .csv files link to which .pdf files. </p>
Risk Assessment and Syndrome Evolution Models for Chronic Atrophic Gastritis Malignant Transformation
ClinicalTrials.gov study NCT03261934. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation and Modeling of the G-CSF Effect on the Evolution of Neutrophils During Chemotherapy Based on Eribulin
ClinicalTrials.gov study NCT02841722. IPD Sharing: NO. Countries: 1. Publications: 0.
Multiomics Tumor Evolution Model of NSCLC
ClinicalTrials.gov study NCT05352035. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Evaluation and Modeling of the Effect of G-CSF on the Evolution of Polynuclear Neutrophils During Dense Dose Epirubicin-Cyclophosphamide Regeneration
ClinicalTrials.gov study NCT05296317. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Advanced Modeling of the Evolution of the Epidemiological Outbreak of SARS-CoV-2 Pandemic
ClinicalTrials.gov study NCT06070896. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Genetic evolution, plasticity and bet-hedging as adaptive responses to temporally autocorrelated fluctuating selection: a quantitative genetic model
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Data from: Evolution of social versus individual learning in an infinite island model
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Murine models of IDH-wild-type glioblastoma exhibit spatial segregation of tumor initiation and manifestation during evolution
GEO Series GSE152071. Mus musculus. 40 samples. Type: Expression profiling by array.
Statistical and Dynamic Model of Surface Morphology Evolution during Polishing in Additive Manufacturing
<p>This repository maintains data and code associated with our accepted paper in IISE Transactions titled "Statistical and Dynamical Model of Surface Morphology Evolution during Polishing in Additive Manufacturing". To briefly summarize,</p><p><strong>1. Polishing_stagewise_data.zip</strong> - Contains height values measured at 32 different locations on the 3D printed sample using an optical profilometer prior to polishing (Stage 0) and post every stage of polishing (Stages 1 to 6). Please refer to the following paper for experimentation details and process parameters: "<i>Jin, S., A. Iquebal, S. Bukkapatnam, A. Gaynor, and Y. Ding (2019, 10). A Gaussian process model-guided surface polishing process in additive manufacturing. Journal of Manufacturing Science and Engineering 142, 1–17.</i>"</p><p><strong>2. Initial_surface_generation.m</strong> - Script containing the Initial surface generation algorithm using the random circle packing algorithm. This file generates the surface asperity distribution and their graph connectivity of a 3D printed sample prior to polishing (Figure 4(b) in paper). One such realization is stored and compared with experimental data (Refer #3).</p><p><strong>3. Stage0_fitted_data.mat</strong> - .mat file containing data pertaining to height measures of the 3D printed sample prior to polishing and generated initial surface (simulation) which is statistically similar to the actual data.</p><p><strong>4. Parameter_fitting_Polishing.m</strong> - Script containing the model capturing polishing dynamics with network formation, evaluated at each stage of polishing. This file generates the Bearing Area Curves of the initial surface simulated after each stage of polishing and compares them with experimental data (Figures 3, 5, 6, 7, and 8 in paper). (The script makes use of other functions defined in #5).</p><p><strong>5. surface_roughness.m, graph_evolution.m, solve_for_d.m, KLDiv.m</strong> and <strong>Gen_hurst.m</strong> - Matlab scripts containing functions that are called within the main script (Parameter_fitting_Polishing.m)</p><p><strong>6. Simulated_Annealing.zip</strong> - A zip file containing files related to Simulated Annealing Algorithm. Please read the <strong>README_Simulated_Annealing.txt</strong> for instructions to reproduce the optimized parameter solutions.</p><p><strong>7. pub_fig.m</strong> - Script containing the formatting options for plots and figures.</p>
On the deep carbon cycle in numerical modelling of mantle convection: Implications for the long-term climate evolution
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Spatio-temporal Evolution of Near-field Deformation in Analogue Strike-slip Fault Models with Various Locked Segments Length
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Figure 8 in A dynamic model for the evolution of sabrecat predatory bite mechanics
Figure 8. Estimated relative force outputs [combined rotational torque of ten regularly spaced intervals of the M. temporalis (T1–T10), at five regularly spaced intervals of the M. masseter profunda + M. zygomaticomandibularis (M1–M5) and anterior-most insertion of the M. masseter superficialis] from occlusion to maximal inferred gape. A, estimated total rotational torque about the temporomandibular joint (TMJ) [I(Tf(cos Q)) where I, inlever moment arm; Tf, theoretical force output from the muscle fibre; Q, angle between the effective (rotational) torque about the temporomandibular joint and T]; B, estimated total outforce at the canines [(I(T(cos Q))/O) where O, outlever moment arm to the centre of C1]; C, f f ca ca estimated total outforce at the carnassial [(I(T(cos Q))/O) where O, outlever moment arm to the carnassial (P4) paracone f c c apex]. All species are scaled to a uniform condylobasal length.
Recurrent rearrangement during adaptive evolution in an interspecific yeast hybrid suggests a model for rapid introgression
GEO Series GSE18060. Saccharomyces bayanus; Saccharomyces cerevisiae. 12 samples. Type: Genome variation profiling by array.
Mapping the Evolution of Acinar cell derived Pancreatic Preneoplastic Lesions in a Mouse Model: A Detailed Transcriptomic and Pathological Characterization
GEO Series GSE279507. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing; Other.
Modeling clonal evolution and oncogenic dependency in vivo in the context of hematopoietic transformation
GEO Series GSE203454. Mus musculus. 50 samples. Type: Expression profiling by high throughput sequencing.
A revised model of clonal evolution of intraductal papillary mucinous neoplasm-related pancreatic carcinogenesis
GEO Series GSE114163. Homo sapiens. 5 samples. Type: Methylation profiling by genome tiling array.
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.