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14,965 results for “evolution”
Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (<a href="https://doi.org/10.1034/j.1600-0870.2001.00241.x" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the article "<em>The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example</em>", published by Chaos (<a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a>.</p> <p>All files uploaded were generated from simulations run by the authors.</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p> <p><strong>Note:</strong> This version (v1.1) is the same version as v1.0 but with the correct README file.</p>
Data and code from "Black-throated blue warblers (Setophaga caerulescens) exhibit diet flexibility and track seasonal changes in insect availability" Kaiser et al. 2024 Ecology and Evolution
Changes in leaf phenology from warming spring and autumn temperatures have lengthened the temperate zone growing ‘green’ season and breeding window for migratory birds in North America. However, the fitness benefits of an extended breeding season will depend, in part, on whether species have sufficient dietary flexibility to accommodate seasonal changes in prey availability. We used fecal DNA metabarcoding to test the hypothesis that seasonal changes in the diets of the insectivorous, migratory black-throated blue warbler (Setophaga caerulescens) track changes in the availability of arthropod prey at the Hubbard Brook Experimental Forest, New Hampshire, USA. We examined changes across the breeding season and along an elevation gradient encompassing a two-week difference in green season length. From 98 fecal samples, we identified 395 taxa from 17 arthropod orders; 242 were identified to species, with Cecrita guttivitta (saddled prominent moth), Theridion frondeum (eastern long-legged cobweaver), and Philodromus rufus (white-striped running crab spider) occurring at the highest frequency. We found significant differences in diet composition between survey periods and weak differences among elevation zones. Variance in diet composition was highest late in the season, and diet richness and diversity were highest early in the season. Diet composition was associated with changes in prey availability surveyed over the green season. However, several taxa occurred in diets more or less than expected relative to their frequency of occurrence from survey data, suggesting that prey selection or avoidance sometimes accompanies opportunistic foraging. This study demonstrates that black-throated blue warblers exhibit diet flexibility and track seasonal changes in prey availability, which has implications for migratory bird responses to climate-induced changes in insect communities with longer green seasons. These data were gathered as part of the Hubbard Brook Ecosystem Study (HB
Constraints on mantle viscosity and Laurentide ice sheet evolution from pluvial paleolake shorelines in the western United States: Datasets
<p>*********** Please view the README.txt file for detailed documentation of data. ***********</p> <p><strong>Title:</strong> Constraints on mantle viscosity and Laurentide ice sheet evolution from pluvial paleolake shorelines in the western United States: Datasets</p> <p><strong>Version: </strong>1.0</p> <p><strong>Date of Release: </strong>2019/12/16</p> <p><strong>Identifier: </strong>10.5281/zenodo.3576251</p> <p><strong>Associated publication:</strong> Austermann, J., Chen, C.Y., Lau, H.C.P., Maloof, A.C., and Latychev, K. (2019) Constraints on mantle viscosity and Laurentide ice sheet evolution from pluvial paleolake shorelines in the western United States. <em>Earth and Planetary Science Letters</em>. doi: 10.1016/j.epsl.2019.116006</p> <p><strong>Link to publication: </strong><a href="https://doi.org/10.1016/j.epsl.2019.116006">https://doi.org/10.1016/j.epsl.2019.116006</a></p> <p><strong>Suggested citation: </strong>Please reference the associated publication above when using any datasets or materials described in the README file.</p> <p><strong>Contact information:</strong> Jacky Austermann (jackya@ldeo.columbia.edu) and Christine Y. Chen (cychen.earth@gmail.com)</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>This directory contains the following datasets:</p> <p>SHORELINE FEATURE ELEVATION DATA</p> <ul> <li><strong>Bonneville_Provo_Sehoo_shoreline_feature_elev_Austermann2019_EPSL.xlsx</strong>: shoreline feature elevation measurements of the Bonneville, Provo, and Sehoo lake stages of Lake Bonneville and Lake Lahontan; original measurements were made by Adams et al. (1999), Chen and Maloof (2017), and Currey (1982)</li> </ul> <p>MODELED RECONSTRUCTIONS OF LAKE VOLUME AND PALEOTOPOGRAPHY</p> <ul> <li><strong>LakeBonneville_NAICE_l20.ump02p25.lmp5VM5.mat:</strong> model output for Lake Bonneville, including reconstructions of lake volume and paleotopography</li> <li><strong>LakeLahontan_NAICE_l20.ump02p25.lmp5VM5.mat</strong>: model output for Lake Lahontan, including reconstructions of lake volume and paleotopography</li> </ul>
Sample of facial mask N95 and FFP2 pricing on retail webs and time evolution per country
<p>We've gathered - for a Data Science educational project - the pricing of several face mask for breathing protection in a given period of time.</p> <p>Countries : Spain', 'USA', 'France', 'UK', 'Germany', 'Italy', 'Netherlands', 'Australia'</p> <p> </p> <p>'asin' type: STRING "Código de identifícación único de product equivalente de AMAZON"</p> <p>'description' type: STRING 'Texto descriptivo del producto'</p> <p>'dateTime' type: TIMESTAMP 'Cadena de carateres que contiene fecha y hora GMT'</p> <p>'date' Type: DATETIME ' Formato diferente de la misma fecha / hora de captura '</p> <p>'country' type: STRING 'Pais al que pertenece a distribución del producto 'Valores posibles: '</p>
Data from: "Rapid molecular evolution of Spiroplasma symbionts of Drosophila"
<p>This repository contains data and information to reproduce the findings reported in the paper.</p> <p>File descriptions:</p> <ul> <li>OTU_sequences.fasta – all <em>Spiroplasma</em> sequences that contained an <a href="https://pfam.xfam.org/family/OTU">OTU domain</a> as predicted by <a href="https://www.ebi.ac.uk/Tools/pfa/pfamscan/">PfamScan</a></li> <li>OTU_alignments.fasta – alignment of OTU domains performed using <a href="https://mafft.cbrc.jp/alignment/software/">Mafft</a></li> <li>RIP_sequences.fasta – all <em>Spiroplasma</em> sequences that contained an <a href="https://pfam.xfam.org/family/RIP">RIP domain</a> as predicted by <a href="https://www.ebi.ac.uk/Tools/pfa/pfamscan/">PfamScan</a></li> <li>RIP_alignments.fasta – alignment of RIP domains performed using the <a href="http://hmmer.org/">HMMER package</a></li> <li>Spiroplasma_supermatrix.fasta – Fasta alignment of concatenated single copy <em>Spiroplasma</em> loci conserved across the investigated strains. Loci that showed signs of recombination were not included</li> <li>Spiroplasma_partitions.txt – Lists the loci that make up the <em>Spiroplasma</em> supermatrix</li> <li>Spiroplasma_partitioning.scheme.txt – Partitioning scheme employed in our Maximum Likelihood analysis of the supermatrix. This was the best fitting partitioning scheme as determined with <a href="http://www.iqtree.org/">IQ-TREE</a></li> <li>Protocol_1.pdf – Chloroform–Ethanol protocol used for extracting <em>Spiroplasma</em> DNA for <em>s</em>Hy-Tx</li> </ul>
Evolution of binary stars on the HR diagram
<p>We studied the evolution of different classes of binary objects that can be observed in a typical stellar cluster, using a grid of detailed massive binary evolution models (Wang et al. 2020) with an initial metallicity of that of the Small Magellanic Cloud (SMC). To compute the models, we use the 1D stellar evolution code MESA (Modules for Experiments in Stellar Astrophysics, Paxton et al. 2011, 2013, 2015, 2018, version 8845).</p> <p>Our grid consists of 2078 binary models with initial primary masses greater than 5 MSun. This translates to a total cluster mass of ∼10^5 MSun in stars between 0.1 to 100 MSun (assuming a binary fraction of 1. The grid covers an initial mass ratio (mass of secondary over the mass of primary, hence always less than 1) range of 0.3-0.95 and orbital periods of 1 day to 8.6 yrs. In this range of masses, mass ratios, and orbital periods, a Monte Carlo method was used to sample initial binary model parameters assuming a Saltpeter initial mass function (IMF) (Salpeter 1955), a flat distribution of mass ratios and logarithm of initial orbital periods.</p> <p>Translucent grey circles indicate pre-interaction binaries - binaries that have not yet undergone a mass transfer phase via Roche Lobe overflow. Hence, the grey line traced on the HRD by the collection of pre-interaction binaries together essentially denotes the Single Star Isochrone (SSI). Grey squares indicate the merger product when we expect a binary to merge during the Case A mass transfer phase. We note that we only model and follow the evolution of Main Sequence mergers and not the mergers coming from the Case B channel. As such, the number of mergers in each frame is likely to be the lower limit to the number of merger products. Single star tracks at SMC metallicity are also plotted in the background from 5-100 MSun. When any component of a binary system completes core carbon burning at a certain cluster age (or helium-burning for the most massive stars), we mark the occurrence of a supernova by putting an ‘*’ symbol in the HRD, that fades over three time steps in the animation.</p> <p>Mass donors and accretors are shown with triangles and diamonds respectively. The binaries that are interacting or have interacted during their Main Sequence lifetime (i.e. the Case A models) are shown in colour, with the colour coding describing the rotation of the component stars (v rot /v crit ) of the binary. All donors and accretors of binaries that have interacted via Case B/C are shown in greyscale. A black frame around the triangles for the mass donor indicates that the surface Hydrogen mass fraction is less than 0.1. Similarly, a black frame around the diamonds for the mass accretors indicates that the surface Helium mass fraction is greater than 0.3. The current age of the cluster is displayed in the center bottom with a time bar that fills up as the animation moves forward in time.</p> <p>In the table above the legend, (from top) we indicate the number of Algol systems i.e. in the nuclear timescale slow Case A mass transfer phase, the number of Main Sequence merger products that are still burning hydrogen at the core, and the number of cool red supergiants (log T e f f < 3.7) at the respective time frames. Moreover, in the next two rows, we denote the number of OB stars that has a neutron star or black hole companion, arising from Case A and Case B evolution channels, at that cluster age. In the next row, we indicate the number of supernovae that have already happened until the current cluster age of the animation. We report the numbers of supernovae occurring from Case A and Case B donors separately from the other progenitors as we expect that the donor stars that have interacted via the Case A or Case B channels will be highly stripped of their envelopes and will likely be progenitors to stripped-envelope supernova (of type Ib and IIb) while the remaining will be progenitors to type IIp/n. The last line gives the number of pre-interacting binaries having luminosity lesser than the brightest non-interacted binary component by up to 1.5 dex.</p> <p>The individual components of the binaries that are in the semi-detached configuration and are interacting via the nuclear timescale slow Case A phase are joined together with solid black lines with an arrow indicating the direction of mass transfer (donor to the accretor). On the other hand, the individual components that have interacted in the past via Case A or B mass transfer are connected to each other with grey dotted lines. These are usually the systems where the donor star is a post-Main Sequence helium star and the accretor is a rejuvenated star still burning hydrogen at the core. The black and white dots over the Case A and B accretors denote that the donors of those binaries have imploded/exploded to form a black hole or neutron star respectively.</p>
The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering - Supporting Data
<p>Supporting code and data for the paper: </p> <p><em>The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering</em></p> <p>Raen (2020)</p> <p><strong>Supporting code</strong> includes `run_star_extras.f`, inlist templates, and our dark matter module (to be used in conjunction with MESA: <a href="http://mesa.sourceforge.net/index.html">Modules for Experiments in Stellar Astrophysics</a>). The full source code used in the production of this paper is available at <a href="https://github.com/troyraen/DM-in-Stars/">github.com/troyraen/DM-in-Stars</a> in the Raen2020 branch. (The master branch is intended for use by those wishing to use our module to explore DM effects beyond the scope of this paper.) We used MESA version 12115, and MESA SDK version 20190830.</p> <p><strong>Model data</strong> includes MESA history and profile data for the models highlighted in the paper (<span class="math-tex">\(1.0\ \mathrm{M}_\odot\)</span> and <span class="math-tex">\(3.5\ \mathrm{M}_\odot\)</span> models with <span class="math-tex">\(\Gamma_B = 0\)</span> (no dark matter), <span class="math-tex">\(\Gamma_B = 10^4\)</span>, and <span class="math-tex">\(\Gamma_B = 10^6\)</span>). The specific inlists used to generate the models are also included. Additional data will be shared on reasonable request to the paper's corresponding author.</p>
Kin selection explains the evolution of cooperation in the gut microbiota, by Simonet & McNally, 2020, Dataset S1 and codes for statistical analysis and figures production
<p>Dataset S1 contains all raw and processed material referred to in the published article "Kin selection explains the evolution of cooperation in the gut microbiota". R codes files provide all codes to replicate the analysis. Please refer to the README file for a description of all code files. The manifest files are those obtained by accessing the HMP portal on April 2020 under Project > HMP, Body Site > feces, Studies>WGS-PP1, File Type > WGS raw sequences set, File format > FASTQ.</p> <p>We also provide access to these data and codes at our GitHub (https://github.com/CamilleAnna/HamiltonRuleMicrobiome gitRepos.git) which can be cloned to directly re-run this analysis. </p> <p><strong>Legends for Dataset S1:</strong></p> <ul> <li>Sheet 1: Metagenomic samples used and access links.</li> <li>Sheet 2: Reference on bacterial cooperation retrieved from Web of Science search: TI¯((microb* OR bacter* OR microorganis* OR micro-organis*) AND (coop* OR social*)</li> <li>Sheet 3: Retained bacteria cooperation keywords</li> <li>Sheet 4: GOs identified by annotating all MIDAS database genomes (5944 genomes) with PANNZER2.</li> <li>Sheet 5: Full list of potential bacterial cooperation GO terms and description of manual curation decisions.</li> <li>Sheet 6: Final list of bacterial cooperation GO used for the analysis</li> <li>Sheet 7: Genomic diversity of the bacterial population within and across host. Computed from MIDAS snp_diversity.py pipeline.</li> <li>Sheet 8: final dataset for statistical analysis.</li> <li>Sheet 9: per-gene annotation of cooperation.</li> </ul>
An Empirical Characterization of Event Sourced Systems and Their Schema Evolution - Lessons from Industry - Accompanying Anonymized Transcripts
<p>Anonymized interviews with 25 engineers on their experience applying Event Sourcing, with accompanying classifications. These transcripts are used in our publication "An Empirical Characterization of Event Sourced Systems and Their Schema Evolution - Lessons from Industry".</p>
Mining API Interactions to Analyze SoftwareRevisions for the Evolution of Energy Consumption (MSR'2021 Dataset)
<p><strong>Motivation</strong></p> <p>This repository contains the data-set used as a basis for our MSR'2021 paper <em>Mining API Interactions to Analyze Software Revisions for the Evolution of Energy Consumption</em>.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset is stored in a file <em>msr_2021_dataset.csv</em> and contains the following data:</p> <ul> <li>id - an individual identifier</li> <li>sampleNr - a number identifying the group this sample relates to</li> <li>name - the name of the library examined</li> <li>className - the class name as an abbreviation</li> <li>method - the name of the executed method</li> <li>duration - duration of method execution</li> <li>durationAdjusted - duration after alignment between method trace and energy profile</li> <li>energyConsumption - computed energy consumption</li> <li>watts - recorded wattage</li> <li>`package-names` - per package uAPI profile</li> <li>uApi - the computed uAPI profile value</li> </ul> <p>The files <em>joule_anova_posthoc_result.csv</em> and <em>uAPI_anova_posthoc_result.csv</em> contain the results of the ANOVA and Tukey HSD posthoc analysis to determine accuracy and F1-score of the presented approach.</p> <p> </p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p>
Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation
<p>Big, time-scaled phylogenies are fundamental to connecting evolutionary processes to modern biodiversity patterns. Yet inferring reliable phylogenetic trees for thousands of species involves numerous trade-offs that have limited their utility to comparative biologists. To establish a robust evolutionary timescale for all ~6000 living species of mammals, we developed credible sets of trees that capture root-to-tip uncertainty in topology and divergence times. Our 'backbone-and-patch' approach to tree-building applies a newly assembled 31-gene supermatrix to two levels of Bayesian inference: (i) backbone relationships and ages among major lineages, using fossil node- or tip-dating; and (ii) species-level 'patch' phylogenies with non-overlapping in-groups that each correspond to one representative lineage in the backbone. Species unsampled for DNA are either excluded ('DNA-only' trees) or imputed within taxonomic constraints using branch lengths drawn from local birth-death models ('completed' trees). Joining time-scaled patches to backbones results in species-level trees of extant Mammalia with all branches estimated under the same modeling framework, thereby facilitating rate comparisons among lineages as disparate as marsupials and placentals. We compare our phylogenetic trees to previous estimates of mammal-wide phylogeny and divergence times, finding that (i) node ages are broadly concordant among studies, and (ii) recent (tip-level) rates of speciation are estimated more accurately in our study than in previous 'supertree' approaches where unresolved nodes led to branch length artifacts. Credible sets of mammalian phylogenetic history are now available for download at <a href="http://vertlife.org/phylosubsets">http://vertlife.org/phylosubsets</a>, enabling investigations of long-standing questions in comparative biology.</p>
A 31-Year Bibliometric Review of Team Effectiveness Research: Evolution, Trends, and Future Directions (DATA).
<p>This dataset is derived from a comprehensive study of team effectiveness over three decades, utilizing bibliometric techniques to examine scholarly publications across multiple databases. The repository includes bibliometric data from 6,051 publications related to team effectiveness, featuring metadata such as authors, titles, publication years, citations, and keywords.</p> <h3>Repository Structure:</h3> <p>The data shared in this repository is organized as follows:</p> <ul> <li><strong>Bibliometrix database.xlsx:</strong> This excel file contains information on 6051 publications in the field of Team Effectiveness downloaded from the Scopus and Web of Science databases using specific search terms and inclusion criteria detailed in our associated paper (see our publication for more information on the methodology). The file contains the necessary headers for direct use in the Biblioshiny interface of the bibliometrix library for R.</li> <li><strong>List of stop words.txt:</strong> This file contains keywords, separated by commas, that we have decided to eliminate from our analyses due to their potential to introduce bias in the results.</li> <li><strong>List of synonyms.txt:</strong> This file contains groups of semantically synonymous keywords, separated by semicolons. Each line represents a group of synonyms, with the first keyword being the one that Bibliometrix will use to replace all other keywords in that line.</li> <li><strong>Appendix [A-H].pdf:</strong> List of appendices that complement the results of the study carried out.</li> </ul> <h3>Recommended Usage:</h3> <div> <div> <div> <div> <p>This dataset is ideal for researchers interested in conducting new analyses of research trends, co-authorship network analysis, and thematic evolution in the field of team effectiveness. For example, researchers can narrow the scope to focus exclusively on team effectiveness in educational contexts. Our publication provides all the necessary details to replicate or extend our methodology. For any queries related to the data, please contact <strong>Yeray Barrios-Fleitas</strong> at <em><a rel="noreferrer">y.d.c.barriosfleitas@utwente.nl</a></em>.</p> </div> </div> </div> </div> <div> </div> <h3>License and Citation:</h3> <p>The data are distributed under the CC BY license. Please cite any use of this dataset using the following format:<br>Barrios Fleitas, Y., Marcella A.M.G., Eysink, T.H., & Rensink , A. (2025). A 31-Year Bibliometric Review of Team Effectiveness Research: Evolution, Trends, and Future Directions. ZENODO, <a href="https://doi.org/10.5281/zenodo.12082529" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12082529</a></p>
Phlorest phylogeny derived from Lee & Hasegawa 2013 'Evolution of the Ainu Language in Space and Time'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee S, Hasegawa T (2013) Evolution of the Ainu Language in Space and Time. PLoS ONE 8(4): e62243. doi: 10.1371/journal.pone.0062243</p> </blockquote>
Phlorest phylogeny derived from Hruschka et al. 2015 'Detecting regular sound changes in linguistics as events of concerted evolution'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hruschka, D. J., Branford, S., Smith, E. D., Wilkins, J., Meade, A., Pagel, M., & Bhattacharya, T. (2015). Detecting regular sound changes in linguistics as events of concerted evolution. Current Biology, 25(1), 1-9.</p> </blockquote>
Data Tables for the CARMA Extragalactic Database for Galaxy Evolution (CARMA EDGE)
<p>Data tables accompanying the Python package <a href="https://github.com/tonywong94/edge_pydb">edge_pydb</a> which provides access to spatially resolved measurements of CO and optical emission from the CARMA EDGE sample of 125 nearby galaxies. A full description can be found in the article by <a href="https://doi.org/10.3847/1538-4365/ad20c9" target="_blank" rel="noopener">Wong et al. (2024)</a>.</p>
The evolution and future of research on Nature-based Solutions to address societal challenges
<p>This dataset comprises the bibliographic text files used to analyse the Nature-based Solutions research landscape as presented in:</p> <ul> <li>Dunlop, T., Khojasteh, D., Cohen-Shacham, E., Glamore, W., Haghani, M., van den Bosch, M., Rizzi, D., Greve, P., Felder, S. The Evolution and Future of Research on Nature-based Solutions to Address Societal Challenges. <em>Communications Earth & Environment</em>. 2024.</li> </ul> <p>Excel spreadsheets containing data for the Global Water Security Index (Gain et al., 2016) presented in Figure 2 and the data required to reproduce Figures 1 and 2 in the paper above are also shared.</p>
Range expansion is slower and more variable with rapid evolution across a spatial gradient in temperature
<p><span>Rapid evolution in colonizing populations can alter our ability to predict future range expansions. Recent theory suggests that the dynamics of replicate range expansions are less variable, and hence more predictable, with increased selection at the expanding range front. Here, we test whether selection from environmental gradients across space produces more consistent range expansion speeds, using the experimental evolution of replicate duckweed populations colonizing landscapes with and without a temperature gradient. We found that range expansion across a temperature gradient was slower on average, with range-front populations displaying higher population densities, and genetic signatures and trait changes consistent with directional selection. Despite this, we found that with a spatial gradient range expansion speed became more variable and less consistent among replicates over time. Our results therefore challenge current theory, highlighting that chance can still shape the genetic response to selection to influence our ability to predict range expansion speeds.</span></p>
Evolution-Guided Engineering of Trans-Acyltransferase Polyketide Synthases
<p>Data underlying the manuscript 'Evolution-Guided Engineering of <em>Trans</em>-Acyltransferase Polyketide Synthases' by Mabesoone, Leopold-Messer and Minas et al.</p> <p>The repository contains:</p> <p>Sequencing data - Genbanks files of construct designs, ab1 and fasta files for Sanger sequencing. For whole plasmid sequencing, fasta, annotated GenBank files, overviews of sequencing statistics and fastq files for selected plasmids, for which fastq files were provided by the sequencing service, are provided.</p> <p>NMR data - raw data and MestReNova files. Also includes HPLC-MS traces of isolated compounds.</p> <p>HPLC-MS data - Raw data collected on Thermo-Fisher instruments. This data can be analyzed with the Xcalibur software suite. The mzXML data can be analyzed with the Python scripts provided in the scripts folder to generate the images shown in the SI. The data collected for Bacillus and Serratia is MS1 data. The data collected for Gynuella also contains MS-MS data.</p> <p>SCA data - Python scripts, GenBank files and produced data underlying the SCA.</p> <p>Bioactivity data - Data underlying the toxicity assays in Figure S122.</p> <p> </p>
Evolution of Surface Tension and Hansen Parameters of Homologous Series of Imidazolium-Based Ionic Liquids
<p>This is the raw data for the manuscript:</p> <p>Evolution of Surface Tension and Hansen Parameters of Homologous Series of Imidazolium-Based Ionic Liquids</p> <p> </p> <p>All data are sorted according to their appearance in the figures of the main manuscript. All data is stored as .xslx format, the content of the colums can be found in the headlines and is following the nomenclature of the manuscript.</p> <p>doi journal article: <a title="DOI URL" href="https://doi.org/10.1021/acs.langmuir.4c00094">https://doi.org/10.1021/acs.langmuir.4c00094</a></p>
Multiple Evolution Modes of Megaripples in the Qaidam Basin and Implications for Ripple-Like Aeolian Landforms on Mars
<p>The dataset includes wind regime data for Golmud, Sebei, and the west bank of the Narin Gol River in the Qaidam Basin, as well as sediment grain size and morphological parameters of the megaripples. In addition, we provide R language source code for data processing and visualization.</p><p>The primary directory contains the data and the source code in the R language. The data includes sediment grain size, morphological parameters and wind regime analysis data of megaripples. Modifying the working path and installation package is necessary to call the R source code for data loading.</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.