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285 results for “evolution models”
Results of ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century", published in The Cryosphere, <a href="https://tc.copernicus.org/articles/14/3033/2020/">https://tc.copernicus.org/articles/14/3033/2020/</a></p> <p>Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020.</p> <p>Contact: Helene Seroussi, Helene.seroussi@jpl.nasa.gov</p> <p>Further information on ISMIP6 and ISMIP6 Antarctica Projections can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica</p> <p>Users should cite the original publication when using all or part of the data. <br> In order to document CMIP6’s scientific impact and enable ongoing support of CMIP, users are also obligated to acknowledge CMIP6, ISMIP6 and the participating modeling groups.</p> <p>About the dataset:</p> <p>- The results are based on model output computed from the ISMIP6 native grids that vary between models. <br> - The results are calculated over the ice-covered area of Antarctica, corrected for map projection errors, ice sheet model specific densities taken into account.<br> - Results for the experiments 'exp*' are provided both as raw results and calculated as differences to the control experiment (ctrl_proj_open or ctrl_proj_std depending on the experiment). The later files are named with "minus_ctrl_proj" to indicate that the control run is substracted.<br> - Results for ctrl_proj_open, ctrl_proj_std, hist_open and hist_std are not corrected to remove the control run.</p> <p><br> ------------------------------------------------</p> <p>Directory structure:</p> <p>groupname1<br> modelname1<br> expid<br> computed_iareafl_AIS_groupname1_modelname1_expid.nc<br> computed_iareafl_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_iareagr_AIS_groupname1_modelname1_expid.nc<br> computed_iareagr_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_icearea_AIS_groupname1_modelname1_expid.nc<br> computed_icearea_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_ivol_AIS_groupname1_modelname1_expid.nc<br> computed_ivol_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_ivaf_AIS_groupname1_modelname1_expid.nc<br> computed_ivaf_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_smb_AIS_groupname1_modelname1_expid.nc<br> computed_smb_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_smbgr_AIS_groupname1_modelname1_expid.nc<br> computed_smbgr_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_bmbfl_AIS_groupname1_modelname1_expid.nc<br> computed_bmbfl_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> ...</p> <p>-------------------------------------------------</p> <p><br> Description of variables:</p> <p>icearea - ice area [m^2]<br> iareafl - floating ice area [m^2]<br> iareagr - grounded ice area [m^2]<br> ivol - ice volume [m^3]<br> ivaf - ice volume above floatation [m^3]<br> smb - spatially integrated surface mass balance [kg/s]<br> smbgr - spatially integrated surface mass balance over grounded ice [kg/s]<br> bmbfl - spatially integrated basal melt rate under floating ice (negative for melting ice) [kg/s]</p> <p>Variables per file:</p> <p>rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, in years</p> <p>[variable] - global variable integrated over the Antarctica ice sheet<br> [variable]_region_1 - variable integrated over West Antarctica<br> [variable]_region_2 - variable integrated over East Antarctica<br> [variable]_region_3 - variable integrated over the Antarctic Peninsula<br> [variable]_sector_X - variable integrated over the X sector of the Antarctic ice sheet (18 sectors, from 1 to 18)</p> <p>--------------------------------------------------</p> <p><br> Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below.</p> <p>"We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020.</p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A., Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M., Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C. M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R. S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.: Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models, The Cryosphere, 14, 2331–2368, https://doi.org/10.5194/tc-14-2331-2020, 2020.</p>
Quality-quantity tradeoffs drive functional trait evolution in a model microalgal "climate change winner"
<p>Phytoplankton are the unicellular photosynthetic microbes that form the base of aquatic ecosystems, and their responses to global change will impact everything from food web dynamics to global nutrient cycles. Some taxa respond to environmental change by increasing population growth rates in the short-term, and are projected to increase in frequency over decades. To gain insight into how these projected "climate change winners" evolve, we grew populations of microalgae in ameliorated environments for several hundred generations. Most populations evolved to allocate a smaller proportion of carbon to growth while increasing their ability to tolerate and metabolise reactive oxygen species (ROS). This tradeoff drives the evolution of traits that underlie the ecological and biogeochemical roles of phytoplankton. This offers evolutionary and a metabolic frameworks for understanding trait evolution in projected "climate change winners", and suggests that short-term population booms have the potential to be dampened or reversed when environmental amelioration persists.</p>
Results of ISMIP6 CMIP6 forced simulations: a multi-model ensemble of the Greenland and Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "Payne et al. 2021 Future sea level change under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets", published in GRL</p> <p>Contact: Tony Payne a.j.payne@bristol.ac.uk, Sophie Nowicki sophien@buffalo.edu, ismip6@gmail.com </p> <p><br> Further information on ISMIP6 can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Greenland</p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in the process producing this data set. Acknowledgements should have language similar to the below (if you only use CMIP5 forcing, remove CMIP6 and vice versa).</p> <p>“We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>For Greenland datasets </p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., Fettweis, X., and Nowicki, S.: Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution , The Cryosphere, 14, 985–1008, https://doi.org/10.5194/tc-14-985-2020, 2020.</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: <br> Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p>For Antarctica datasets</p> <p>Seroussi, H., Nowicki, S., Simon, E., Abe-Ouchi, A., Albrecht, T., Brondex, J., Cornford, S., Dumas, C., Gillet-Chaulet, F., Goelzer, H., Golledge, N. R., Gregory, J. M., Greve, R., Hoffman, M. J., Humbert, A., Huybrechts, P., Kleiner, T., Larour, E., Leguy, G., Lipscomb, W. H., Lowry, D., Mengel, M., Morlighem, M., Pattyn, F., Payne, A. J., Pollard, D., Price, S. F., Quiquet, A., Reerink, T. J., Reese, R., Rodehacke, C. B., Schlegel, N.-J., Shepherd, A., Sun, S., Sutter, J., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., and Zhang, T.: initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6, The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019, 2019.</p> <p>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p><br> Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p> </p>
Data and R code from: Modelling the evolution of cognitive styles
Background <p>Individuals consistently differ in behaviour, exhibiting so-called personalities. In many species, individuals differ also in their cognitive abilities. When personalities and cognitive abilities occur in distinct combinations, they can be described as 'cognitive styles'. Both empirical and theoretical investigations produced contradicting or mixed results regarding the complex interplay between cognitive styles and environmental conditions.</p> Results <p>Here we use individual-based simulations to show that, under just slightly different environmental conditions, different cognitive styles exist and under a variety of conditions, can also co-exist. Co-existences are based on individual specialization on different resources, or, more generally speaking, on individuals adopting different niches or microhabitats.</p> Conclusions <p>The results presented here suggest that in many species, individuals of the same population may adopt different cognitive styles. Thereby the present study may help to explain the variety of styles described in previous studies and why different, sometimes contradicting, results have been found under similar conditions.</p>
Model inputs and results from FastScape landscape evolution model runs for southern Africa
<p>These are the input files and results for the models presented in the revised version of the paper "Constraining plateau uplift in southern Africa by combining thermochronology, sediment flux, topography, and landscape evolution modeling" submitted to JGR:Solid Earth in October 2020 and revised in May 2017. The corresponding code needed to run the inputs can be found here: http://doi.org/10.5281/zenodo.4150333. The readme.txt file contained here explains the included data and results, as well as simple instructions for how to run the models.</p>
Bayesian inference of ancestral host-parasite interactions under a phylogenetic model of host repertoire evolution
<p>Intimate ecological interactions, such as those between parasites and their hosts, may persist over long time spans, coupling the evolutionary histories of the lineages involved. Most methods that reconstruct the coevolutionary history of such interactions make the simplifying assumption that parasites have a single host. Many methods also focus on congruence between host and parasite phylogenies, using cospeciation as the null model. However, there is an increasing body of evidence suggesting that the host ranges of parasites are more complex: that host ranges often include more than one host and evolve via gains and losses of hosts rather than through cospeciation alone. Here, we develop a Bayesian approach for inferring coevolutionary history based on a model accommodating these complexities. Specifically, a parasite is assumed to have a host repertoire, which includes both potential hosts and one or more actual hosts. Over time, potential hosts can be added or lost, and potential hosts can develop into actual hosts or vice versa. Thus, host colonization is modeled as a two-step process that may potentially be influenced by host relatedness. We first explore the statistical behavior of our model by simulating evolution of host-parasite interactions under a range of parameter values. We then use our approach, implemented in the program RevBayes, to infer the coevolutionary history between 34 Nymphalini butterfly species and 25 angiosperm families. Our analysis suggests that host relatedness among angiosperm families influences how easily Nymphalini lineages gain new hosts.</p>
Model, configuration, data, and analysis scripts for The Evolution of Cooperation by the Hankshaw Effect
<p>Computational model, configuration files, result data, and analysis scripts for The Evolution of Cooperation by the Hankshaw Effect as published in Evolution (doi: 10.1111/evo.12928)</p>
Long read genome assembly of Automeris io (Lepidoptera: Saturniidae) an emerging model for the evolution of deimatic displays
<p>Automeris moths are a morphologically diverse group with 145 described species that have a geographic range that spans from the New World temperate zone to the Neotropics. Many Automeris have hindwing eyespots that are thought to deter or disrupt the attack of potential predators, allowing the moth time to escape. Some species in the genus have vestigial eyespots or lack them completely, suggesting that this trait may provide a selective benefit. The Io moth (Automeris io), known for its striking eyespots, is the most widely studied species within the genus and is an emerging model system to study the evolution of deimatism, a predatory defense that combines visual stimuli and movement. Here we present a high-quality, PacBio HiFi genome assembly for Io moth to aid existing research on the molecular development of eyespots. Genomic research is needed to address questions involving antipredatory defenses and eyespot pattern development. BUSCO analysis for this genome shows a completeness of 98.4%, and N50 of 15.</p>
An ulvophycean marine green alga produces large parthenogenetic isogametes as predicted by the gamete dynamics model for the evolution of anisogamy
<p>In eukaryotes, the gamete size difference between the two sexes (anisogamy) evolved from gametes of equal size in both mating types (isogamy) and is plausibly claimed to generate sexual selection in morphology and behaviour. The gamete dynamics (GD) model for anisogamy evolution combines gamete limitation and competition and predicts that, if gametes of both mating types can develop parthenogenetically (i.e. without fusing with the opposite mating type), large isogamy can evolve under gamete-limited conditions. Ulvophycean marine green algae that exhibit various gametic systems from isogamy to anisogamy are important models for testing such theories. However, in most previous papers, whether a species is isogamous or anisogamous has not been examined statistically, which leaves the above theoretical prediction untested. We reveal (i) that the gametic system of <em>Struvea okamurae</em> is large isogamy using a generalized linear mixed model (GLMM), which accounted for the variation of gamete size among individual gametophytes, and (ii) that gametes of this alga can actually develop parthenogenetically, contrary to a previous report. Habitat environments and gametic behaviour suggest that this alga might experience gamete-limited conditions. <em>S. okamurae</em> seems to produce large parthenogenetic isogametes following GD model predictions, as an adaptation to deep waters.</p>
Evolution towards increasing complexity through functional diversification in a protocell model of the RNA world
<p>The encapsulation of genetic material inside compartments together with the creation and sustenance of functionally diverse internal components are likely to have been key steps in the formation of 'live', replicating protocells in an RNA world. Several experiments have shown that RNA encapsulated inside lipid vesicles can lead to vesicular growth and division through physical processes alone. Replication of RNA inside such vesicles can produce a large number of RNA strands. Yet, the impact of such replication processes on the emergence of the first ribozymes inside such protocells and on the subsequent evolution of the protocell population remains an open question. In this paper, we present a model for the evolution of protocells with functionally diverse ribozymes. Distinct ribozymes can be created with small probabilities during the error-prone RNA replication process via the rolling circle mechanism. We identify the conditions that can synergistically enhance the number of different ribozymes inside a protocell and allow functionally diverse protocells containing multiple ribozymes to dominate the population. Our work demonstrates the existence of an effective pathway towards increasing complexity of protocells that might have eventually led to the origin of life in an RNA world.</p>
High-quality video files for Hermsen, R, "Emergent multilevel selection in a simple spatial model of the evolution of altruism" (2021)
<p>The supplementary movies published with the article<br> <br> R. Hermsen<em>, Emergent multilevel selection in a simple spatial model of the evolution of altruism</em><br> <br> have a relatively low resolution. Here, the same three movies are provided at a higher resolution.</p> <p>Note: In Version 1 of this deposit, Movie 1 was incorrect: it visualized a different simulation run than intended. This is corrected in Version 2.</p>
Impact of model assumptions on the inference of the evolution of ectomycorrhizal symbiosis in fungi
<p>Ectomycorrhiza (ECM) is a symbiotic relation between plant and fungi that is essential for nutrient uptake of many stand forming trees. There are two conflicting views about the evolution of ECM in fungi suggesting (1) relatively few transitions to ECM followed by reversals to non-ECM, or (2) many independent origins of ECM and no reversals. In this study, we compare these, and other, hypotheses and test the impact of different models on inference. We assembled a dataset of five marker gene sequences (nuc58, nucLSU, nucSSU, rpb1, and rpb2) and 2,174 fungal taxa covering the three subphyla: Agaricomycotina, Mucoromycotina and Pezizomycotina. The fit of different models, including models with variable rates in clades or through time, to the pattern of ECM fungal taxa was tested in a Bayesian framework, and using AIC and simulations. We find that models implementing variable rates are a better fit than models without rate shift, and that the conclusion about the relative rate between ECM and non-ECM depend largely on whether rate shifts are allowed or not. We conclude that standard constant-rate ancestral state reconstruction models are not adequate for the analysis of the evolution of ECM fungi, and may give contradictory results to more extensive analyses. </p>
Stratigraphic and Isotopic Evolution of the Martian Polar Caps from Paleo-Climate Models
<p>The data in this folder is a collection of outputs or manipulated variables from the LMD-MGCM simulations presented in figures in the JGR article "Stratigraphic and Isotopic Evolution of the Martian Polar Caps from Paleo-Climate Models."<br> </p>
Finite element models from: Mechanical compensation in the evolution of the early hominin feeding apparatus
<p>Australopiths, a group of hominins from the Plio-Pleistocene of Africa, are characterized by derived traits in their crania hypothesized to strengthen the facial skeleton against feeding loads and increase the efficiency of bite force production. The crania of robust australopiths are further thought to be stronger and more efficient than those of gracile australopiths. Results of prior mechanical analyses have been broadly consistent with this hypothesis, but here we show that the predictions of the hypothesis with respect to mechanical strength are not met: some gracile australopith crania are as strong as that of a robust australopith, and the strength of gracile australopith crania overlaps substantially with that of chimpanzee crania. We hypothesize that the evolution of cranial traits that increased the efficiency of bite force production in australopiths may have simultaneously weakened the face, leading to the compensatory evolution of additional traits that reinforced the facial skeleton. The evolution of facial form in early hominins can therefore be thought of as a trade-off between the need to increase the efficiency of bite force production and the need to maintain the structural integrity of the face. This may have implications for interpreting cranial form in other vertebrates.</p>
Supplementary data and code of the manuscript: "Strong evidence for the adaptive walk model of gene evolution in Drosophila and Arabidopsis"
<p>This repository contains all data tables ad code to reproduce the analysis performed in "Strong evidence for the adaptive walk model of gene evolution in Drosophila and Arabidopsis". </p>
Synthetic Microstructure Evolution in SLM Processes: 2D Slices from Potts Model Simulations in SPPARKS
<p>The dataset comprises 2D slices of synthetic microstructures, which were simulated under various Selective Laser Melting (SLM) processing conditions.</p> <p>We employed the <em>Potts kinetic Monte Carlo model</em>, which is integrated within the open-source simulation tool, <a href="https://spparks.github.io/">SPPARKS</a>. </p> <p>For the base microstructural information, we utilized a 3D Electron Backscatter Diffraction (EBSD) dataset—specifically using Inconel 100 for simplicity—to generate a representative volume element (<a href="https://www.mdpi.com/2073-4352/10/10/944">RVE</a>). This RVE serves as the initial structure from which we evolve the microstructure across different processing conditions that are pertinent to SLM techniques. The description of the parameters is provided in the <a href="https://spparks.github.io/doc/app_am_ellipsoid.html">SPPARKS Docs</a>.</p> <p>The outcome is a dataset of 2D slices that reflect the potential microstructural variations resulting from specific manufacturing scenarios.</p> <p>Please follow the instructions in the <a href="https://github.com/sara-nl/spparks_hpc">SPPARKS_HPC Repo</a> to reproduce the results.</p>
A New Model and Dating for the Evolution of Complex Plastids of Red Alga Origin
<p>The zip files includes alignments of protein sequences in fasta format (SequenceAlignments.zip) and their concatenated sets used in our research project (ConcatenatedAlignments.zip). Additionally, we included raw (unaligned) protein sequences in fasta format (RawSequences.zip). In total, we employed 97 amino acid sequences of conserved plastid-encoded proteins, carefully selected from the NCBI reference sequence database (<a href="https://www.ncbi.nlm.nih.gov/refseq/">https://www.ncbi.nlm.nih.gov/refseq/</a>), and GenBank (<a href="https://www.ncbi.nlm.nih.gov/genbank/">https://www.ncbi.nlm.nih.gov/genbank/</a>), representing 112 organisms. Our dataset included 111 eukaryotes carrying red-alga derived plastids and the closest plastid cyanobacterial relative <i>Gloeomargarita lithophora</i> Alchichica D10. We performed independent alignments of each homologous protein group using a slow and accurate L-INS-i algorithm implemented in MAFFT v7.429 (<a href="https://doi.org/10.1093/molbev/mst010">https://doi.org/10.1093/molbev/mst010</a>). The resulting multiple sequence alignments were carefully assessed using AliView (<a href="http://dx.doi.org/10.1093/bioinformatics/btu531">http://dx.doi.org/10.1093/bioinformatics/btu531</a>), and phylogenetically informative sites were selected through trimAl <a href="https://doi.org/10.1093/bioinformatics/btp348">https://doi.org/10.1093/bioinformatics/btp348</a>and ClipKIT (<a href="https://doi.org/10.1371/journal.pbio.3001007">https://doi.org/10.1371/journal.pbio.3001007</a>). The trimmed alignments were concatenated into supermatrices using SequenceMatrix 1.8 (<a href="https://doi.org/10.1111/j.1096-0031.2010.00329.x">https://doi.org/10.1111/j.1096-0031.2010.00329.x</a>)to generate comprehensive datasets for phylogenetic and molecular clock analyses. We also generated a supermatrix composed of untrimmed alignments.</p>
Replication data for "Gekko gecko as a model organism for understanding aspects of laryngeal vocal evolution"
<p>This dataset contains raw data and analysis code used in the preparation of the manuscript “<em>Gekko gecko</em> as a model organism for understanding aspects of laryngeal vocal evolution”.</p>
Geomorphic expressions of active rifting reflect the role of structural inheritance: A new model for the evolution of the Shanxi Rift, North China
<p>A geopackage and associated raster files for the geomorphic indices that were used in this study. Can be opened with the freely available QGIS software. Excel table of fault values used for generation of violin plots in R</p>
Evolution models of helium white dwarf-main-sequence star merger remnants: the mass distribution of single low-mass white dwarfs
<p>Inlists and data for "<a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.474..427Z/abstract">Evolution models of helium white dwarf-main-sequence star merger remnants: the mass distribution of single low-mass white dwarfs</a>"</p>
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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.