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1,819 results for “Experimental data”
Luquillo Experimental Forest Canopy Trimming Experiment CTE2 2015-2020 30-minute abiotic data
The data archive is here: https://doi.org/10.2737/RDS-2021-0028 please use this DOI when citing this dataset. This data publication contains 30-minute values for abiotic field data from 3 treated and 3 control plots from the Canopy Trimming Experiment (CTE) located near El Verde Field Station in the Luquillo Experimental Forest (El Yunque National Forest), Puerto Rico collected from 2015 through 2020. In December of 2014 (CTE2), in 0.09 hectare (ha) square plots near the El Verde Field Station the forest canopy was trimmed and the canopy debris was littered to the forest floor. The plot size and trim amounts were based on the patch disturbance after the two most recent hurricanes before 2017, both category 3 hurricanes at the location of El Verde: Hugo in September 1989, and Georges in September 1998. Data were collected in the inner 0.04 ha quadrants of the 0.09 ha trimmed plots to minimize edge effects. Each plot was made up of 16 subplots with different data types collected in each subplot. There were 3 sets of control and treated plots, with each set near El Verde field station. Field data include 30-minute: solar radiation, soil profile volumetric water content, shallow soil volumetric water content, canopy leaf saturation, litter leaf saturation, air temperature, soil temperature, air relative humidity, vapor pressure, and throughfall. Field data were collected by one automatic sensor in each plot inside a designated subplot, except: soil profile volumetric water content, canopy leaf saturation, and litter leaf saturation; each were collected in three subplots. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical
MCR LTER: Coral Reef: Data to support manuscript: Experimental Support for Alternative Attractors on Coral Reefs
Ecological theory predicts that ecosystems with multiple basins of attraction can get locked in an undesired state, which has profound ecological and management implications. Despite their significance, alternative attractors have proven to be challenging to detect and characterize in natural communities. On coral reefs, it has been hypothesized that persistent coral-to-macroalgae ‘phase shifts’ that can result from overfishing of herbivores and/or nutrient enrichment may reflect a regime shift to an alternate attractor, but to date the evidence has been equivocal. Our field experiments in Moorea, French Polynesia, revealed: (1) hysteresis in the herbivory - macroalgae relationship, creating the potential for coral - macroalgae bistability at some levels of herbivory, and (2) that macroalgae were an alternative attractor under prevailing conditions in the lagoon but not on the fore reef where ambient herbivory fell outside the experimentally delineated region of hysteresis. These findings help explain the different community responses to disturbances between lagoon and fore reef habitats of Moorea over the past several decades, and reinforce the idea that reversing an undesired shift on coral reefs can be difficult. Our experimental framework represents a powerful diagnostic tool to probe for multiple attractors in ecological systems, and as such, can inform management strategies needed to maintain critical ecosystem functions in the face of escalating stresses. These data are associated with a manuscript currently in review: Schmitt, R. J., Holbrook, S. J., Davis, S. L., Brooks, A. J., Adam, T. C. Experimental support for alternative attractors on coral reefs. The dataset includes data from two different experimental tests of alternate attractors.
Virtual memory on a many-core NoC: experimental data
<p>Experimental data that accompanies the thesis "Virtual Memory on a Many-Core NoC" (http://etheses.whiterose.ac.uk/25675/). The data is textual and compressed.</p>
"The Veiled Virgin illustrates visual segmentation of shape by cause": Stimuli and Experimental Data
<p>Stimuli and raw experimental data from the experiments reported in PNAS article "The Veiled Virgin illustrates visual segmentation of shape by cause"</p>
Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack: Experimental Data
<p>Full experimental dataset for the publication "Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack". The archive `application_motivated_benchmarks.zip` contains the following files and directories:</p> <p>- uncompiled_log.csv</p> <p>Gives IDs for the uncompiled circuits initially generated for use in our<br> experiments, along with the properties of the circuits.</p> <p>- properties_log.csv</p> <p>Gives IDs for device property files, along with the device and the time at which<br> they were collected.</p> <p>- compiled_log.csv</p> <p>Gives the calculated figures of merits for the compiled and run circuits.<br> Compiled circuits are identified by the ID of the uncompiled circuit, the<br> compilation strategy used, and the device compiled onto. Device property IDs at<br> the time of compilation and run are given.</p> <p>- circuits/</p> <p>Contains a subdirectory for each uncompiled circuit. Each subdirectory has files<br> of 2 forms.<br> <br> - uncompiled.qasm is the uncompiled circuit.<br> - files of the form 'strategy'_'device'.qasm are the compiled circuits.</p> <p>- data/</p> <p>Contains a subdirectory for each uncompiled circuit. Each subdirectory has files<br> of 3 forms.</p> <p> - prob_vector.csv contains the ideal output probability distribution.<br> - files of the form 'strategy'_'device'.csv contain the shot counts for<br> each compiled circuit when run on the real device.<br> - files of the form 'strategy'_'device'_simulated.csv contain the shot<br> counts for each compiled circuit when run using a classical simulator<br> with noise model build from device properties at the time of the real<br> run.</p> <p>- device_properties/</p> <p>Contains json files detailing device properties for each device property ID.</p> <p> </p>
The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"
<p>The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"</p> <p><strong>1. Introduction</strong></p> <p>Frequency Fitness Assignment (FFA) replaces the objective value in the selection step of an optimization method with its encounter frequency in any selection step so far. It turns static problems into dynamic ones. Here we experimentally investigated this approach in two important contexts: First, we integrated it into a basic (1+1)-EA, obtaining the (1+1)-FEA. We applied both algorithms to several well-known benchmark problems with bit-string based search spaces, including the OneMax, LeadingOnes, TwoMax, Jump, Plateau, and W-Model functions. We also applied them to the Max-3-Sat instances from SATLib. We then also integrated FFA into a Memetic Algorithm for the Job Shop Problem.</p> <p><strong>2. Paper</strong></p> <p>This data is used as the basis for the following article: Thomas Weise, Zhize Wu, Xinlu Li, and Yan Chen. Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value, originally submitted to <a href="https://arxiv.org/abs/2001.01416">arxiv</a> on 2020-01-06 (under the title Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function), updated with the new data in June 2020, and submitted to the IEEE Transactions on Evolutionary Computation.</p> <p><strong>3. Data</strong></p> <p>This data set contains all the results of these experiments, the source codes used in the experiments (i.e., the algorithm implementations), as well as the scripts used for evaluating the results.</p> <p><strong>4. Version History</strong></p> <p>This is the second version of the data set, including extended experiments and more evaluation results. Most importantly, data for larger scales of OneMax and LeadingOnes has been added. The original version is at <a href="http://dx.doi.org/10.5281/zenodo.3598172">10.5281/zenodo.3598172</a>.</p> <p><strong>5. Contact</strong></p> <p>If you have any questions or suggestions, please contact <a href="http://iao.hfuu.edu.cn/team/director">Prof. Dr. Thomas Weise</a> of the <a href="http://iao.hfuu.edu.cn/">Institute of Applied Optimization</a> at <a href="http://www.hfuu.edu.cn">Hefei University</a> in Hefei, Anhui, China via email to <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a> with CC to <a href="mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a>.</p>
Experimental data to the publication "Genetic-optimised aperiodic code for distributed optical fibre sensors"
<p>The source data underlying Figs. 3-5 and Supplementary Figs. 6, 8-14 are provided as a Source Data file.</p>
Raman spectroscopic data derived from Calluna vulgaris charcoals, experimentally generated across a range of natural wildfire temperatures
<p>This data has been derived from deconvolved Raman spectra, utilising two first order bands - D (Disordered) and G (Graphitic). Spectra were collected from experimentally pyrolysed charcoals, made from Calluna vulgaris (Ling Heather) separated into three main components; stem, root and flower. For each component at 250, 400, 600 and 800 degrees centigrade respectively, 5 charcoal samples (A, B, C, D, E) were analysed. Following deconvolution, median values for each spectra were produced. These correspond to parameters derived from the Raman data, including D- and G-band width (FWHM), intensity (ID/IG or 'R1') and area (AD/AG) ratios, band separation (G-D or 'RBS'), and band width ratios (D-FWHM/G-FWHM). All parameters have been compiled for each component material, and displayed graphically within this dataset.</p>
Data and analysis scripts associated with the paper 'Long-term experimental evolution of HIV-1 reveals effects of environment and mutational history''
<p><em>Eva Bons, Christine Leemann, Karin J. Metzner, Roland R. Regoes</em></p> <p>This repository contains all the data and analysis scripts associated with the paper 'Long-term experimental evolution of HIV-1 reveals effects of environment and mutational history'</p> <p>See the readme after unpacking the .zip for a description of the files</p>
Quasi-static cyclic tests on masonry spandrels - Experimental data
<p>This data set is the experimental data underlying the publication:</p> <p>Beyer K, Dazio A (2012) Quasi-static cyclic tests on masonry spandrels, Earthquake Spectra 28(3): 907-929. http://dx.doi.org/10.1193/1.4000063</p> <p>Abstract of publication:</p> <p>This paper presents the results of an experimental campaign on masonry spandrels. Within this campaign, four masonry spandrels were subjected to quasi-static cyclic loading. Two different spandrel configurations were tested. The first configuration comprised a masonry spandrel with a timber lintel, and the second configuration, a masonry spandrel on a shallow masonry arch. For each configuration, two specimens were tested. The first was tested with a constant axial load in the spandrel, while for the second specimen, the axial load in the spandrel depended on the axial elongation of the spandrel. This paper summarizes the properties of the four test units, the test setup, and the most important results from the experiments, documenting the failure mechanisms that developed and the force-deformation hysteresis of the spandrel elements. The paper also presents a mechanical model for estimating the peak strength of masonry spandrels.</p>
Peri Lake Experimental Catchment Data Set
<p>This is the Peri Lake Experimental Catchment data set (PLEC) developed by the Hydrology Laboratory - LabHidro group at the Federal University of Santa Catarina, Florianópolis - Brazil.<br> PLEC data set provides:<br> (i) meteorological data for the Peri Lake catchment (solar radiation, relative humidity, air temperature, wind velocity and wind direction data);<br> (ii) rainfall data (rain gauges located at the Peri Lake park headquarters (HQ) and Retiro meteorological station);<br> (iii) experimental interception data (23 and 24 throughfall gauges and 18 and 20 stemflow gauges in the HQ and Retiro plots, respectively);<br> (iv) overland flow data measured in Retiro headwater catchment (23 Overland Flow Detectors (OFD));<br> (v) streamflow and flow velocity data measured in 31 cross sections of the Peri Lake watershed;<br> (vi) groundwater levels mesured in 10 wells installed close to the flow lines in Retiro headwater catchment;<br> (vii) soil characteristics estimated in the Retiro headwater catchment (hydraulic conductivity and infiltration rate);<br> (viii) geographic information system (GIS) data for the Peri lakecatchment (digital elevation model (DEM) of Peri Lake Watershed, delimitation of the Peri Lake watershed, delimitation of Peri Lake, stream network of Peri Lake Watershed, information of streamflow and flow velocity, land cover of Peri Lake Watershed, location of Water Level Gauge of Ribeirao Grande Watershed, delimitation of the Ribeirao Grande watershed, location of Interception gauge, delimitation of the Retiro Headwater, drainage network lines, intermittent flow lines, location of Hydraulic Conductivity test of soil, locations of the overland flow detectors, locations of the wells to monitor the groudwater level).<br> More details on each data can be found in the readme.txt files available within each subdirectory of this data set. </p> <p>For more information on the available data and collaborations, please communicate with the lead author pedro.chaffe@ufsc.br (www.labhidro.ufsc.br).</p>
Experimental Data on Pragmatic, Constructive and Reconstructive Memory Influences on the Hindsight Bias
<p>After knowing how events turned out, we are quick to say ‘we knew it all along.’ Decades of research on hindsight bias have shown that outcome information biases what we later present as our original judgments. This experiment combined established between- and within-participant designs in a longitudinal study.</p>
Experimental data in support of manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</p>
Experimental data, Periodic responses of a structure with 3:1 internal resonance
<p>Dataset for</p> <p>Alexander D Shaw; Thomas L Hill; Simon A Neild; Michael I Friswell</p> <p>Periodic responses of a structure with 3:1 internal resonance</p> <p>MSSP, in press (as of 18/3/2016)</p> <p>10.1016/j.ymssp.2016.03.008</p> <p> </p>
Experimental data - project: strategic risk seeking - instructions
<p>Experimental data - wheels of fortune scenario - instructions to further sample-size based riks taking</p>
Gilbert delta laboratory experimental data
<p>The .mat file contains the data of three laboratory experiments on Gilbert delta progradation and stratification subject to base level change. The .txt file explains how the data is organized. </p>
Single molecule experimental data for intensity histograms in Gilburt et al, Angewandte 2017
<p>Raw and partially processed single molecule intensity histogram data for the following publication:</p> <p>James A H Gilburt, Hajrah Sarkar, Peter Sheldrake, Julian Blagg, Liming Ying, Charlotte A Dodson (2017) Dynamic equilibrium of the Aurora-A kinase activation loop revealed by single molecule spectroscopy. <em>Angewandte Chemie</em></p> <p><strong><em>Please cite our publication in any use of this data.</em></strong></p>
Single molecule experimental data for dwell time histogram in Gilburt et al, Angewandte Chemie 2017
<p>Raw and partially processed data for the dwell time histogram in the following publication:</p> <p>James A H Gilburt, Hajrah Sarkar, Peter Sheldrake, Julian Blagg, Liming Ying, Charlotte A Dodson (2017) Dynamic equilibrium of the Aurora-A kinase activation loop revealed by single molecule spectroscopy. <em>Angewandte Chemie</em></p> <p><strong><em>Please cite our publication in any use of this data</em></strong></p>
Data from: Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.
<p><span lang="EN">This BBC_2025__README.txt file was generated on 2025-09-24 by Beatriz Baselga Cervera</span></p> <p><span lang="EN">GENERAL INFORMATION</span></p> <ol> <li><span lang="EN">Title of Dataset and code: Data from: Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.</span></li> </ol> <p><span lang="EN"> </span></p> <p><span lang="EN">2. Author Information</span></p> <p><span lang="EN"> Corresponding Investigator</span></p> <p><span lang="EN"> Name: Ph.D. Beatriz Baselga-Cervera</span></p> <p><span lang="EN"> Institution: University of Minnesota Twin cities, Minnesota, US.</span></p> <p><span lang="EN"> Email: <a href="mailto:bbaselga@umn.edu"><span>bbaselga@umn.edu</span></a>; beabaselga@gmail.com</span></p> <p><span lang="EN"> Co-investigator 1</span></p> <p><span lang="EN"> Name: Ph.D. Nahui <span>Olin Medina-Chávez</span></span></p> <p><span lang="EN"> Institution: University of Minnesota Twin cities, Minnesota, US.</span></p> <p><span lang="EN"> Email: nmedinac@umn.edu</span></p> <p><span lang="EN"> Co-investigator 2</span></p> <p><span lang="EN"> Name: Ph.D. Noah Gettle</span></p> <p><span lang="EN"> Institution: Wellcome Sanger Institute, Hinxton, UK.</span></p> <p><span lang="EN"> Email: nbgettle@gmail.com </span></p> <p><span lang="EN">Co-investigator 3</span></p> <p><span lang="EN"> Name: Ph.D. Michael Travisano</span></p> <p><span lang="EN"> Institution: University of Minnesota Twin cities, Minnesota, US.</span></p> <p><span lang="EN"> Email: travisan@umn.edu</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">3. Data collectors: Ph.D. Beatriz Baselga-Cervera, Ph.D. Nahui Olin Medina-Chávez & Ph.D. Noah Gettle.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">4. Date of data collection: 2022-2024</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">5. Geographic location of data collection: Saint Paul, US</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">6. Funding sources that supported the collection of the data: Fundación Alfonso Martín Escudero, Madrid, Spain (BBC).</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">7. Recommended citation for this dataset: Baselga-Cervera et al. (2024), Data from: Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">DATA & FILE OVERVIEW</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">8. Description of dataset</span></p> <p><span lang="EN">In this study, we address whether stochastic phenotypic switching can shape biological diversity contributing to evolutionary change across the transition from singles cells to multicellular clutters in <em>Saccharomyces cerevisiae </em>multicellular yeast system. Populations characterization was conducted with a Coulter Counter multisize 4, a FlowCam 3, under the optic microscope, via ACE2 gene sequencing and RNA sequencing and mathematical modeling. The populations studied were the genetically uniform diploid wild-type <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>). </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">9. File list:</span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Coulter Counter size distribution data: </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 1 name: File_1_Coulter_Counter_Counts_20h.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 1 description: Size distributions of <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 20-hours growth. Data for: Fig. 1A, Fig. 3A and Fig. S2, Table S2 and Table S3.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 2 name: File_2_Coulter_Counter_Counts_24h.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 2 description: Size distributions of <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 24-hours growth. Data for: Fig. 1A, Fig. 3A, Fig. S2, Table S2 and Table S3. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 3 name: File_3_Coulter_Counter_Counts_48h.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 3 description: Size distributions of <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 48-hours growth. Data for: Fig. 1, Fig. 3A, Fig. S2, Table S2 and Table S3.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File name: File_4_Coulter_Counter_Counts_Constructed_strains_diversity.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 4 description: Size distributions of the constructed ACE2 knockout and a strain containing the homozygous missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 24h growth. Size distributions were obtained from populations before (initial) and five resuspended colonies obtained from small-size particles by plating the top fraction of the population after gravitational selection from three isolates per strain. Data for: Fig. 1, Fig. S2, Table S2 and Table S3.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 5 name: File_5_Coulter_Counter_Counts_Selection_Experiment.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 5 description: Size distributions of C1W8.1 and C1W8.2 multicellular evolved strains in YPD at 24h growth. Size distributions from the selection experiment for small-size particles by plating the top fraction of the population after gravitational selection over three cycles of selection. Data for: Fig. 2B and Fig. S6.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 6 name: File_6_Coulter_Counter_Counts_12h.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 6 description: Size distributions of C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 12-hours growth. Data for: Fig. 3A and Fig. S3. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">FlowCam data:</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 7 name: File_7_Rawdata_FlowCam_all.csv </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 7 description: FlowCam data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 24h growth. Data for: Fig. S4. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Data generated statistically:</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 8 name: File_8_C1W8.2_overlapPairs_Selection_Experiment.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 8 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data of the C1W8.2 derived strain over the selection experiment. Data for: Fig. S6D.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 9 name: File_9_C1W8.1_overlapPairs_Selection_Experiment.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 9 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">of the C1W8.1 derived strain over the selection experiment. Data for: Fig. S6C.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 10 name: File_10_ overlapPairs_Constructed_strains_diversity.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 10 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data</span></p> <p><span lang="EN">of the constructed ACE2 knockout and a strain containing the homozygous missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 24h growth. Size distributions were obtained from populations before (initial) and after gravitational selection of five resuspended colonies from three isolates per strain. Data for: Fig. S7.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Data from ImageJ:</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 11 name: File_11_ImageJ_analyses.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 11 description: ImageJ analyses of the microphotographs from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>). Cultures were grown in culture tubes with 10 ml of media, 50 mL Erlenmeyer flasks with 10 mL and 30 mL of media, in YPD under non-shaking and shaking at 250 rpm. YPD media was used across all conditions. Cultures were assessed after 24 hours growth at 30°C.<span> </span>Microphotographs of each condition and strain were obtained with a Nikon TE2000 microscope using 10x objective.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Pictures:</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 12 name: File_12_ FlowCam_Pictures.zip</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 12 description FlowCam IMAGES from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 24h growth. Data for: Fig. 1B. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 13 name: File_13_Microphotography_controled_experimental_conditions.zip</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 13 description: Microphotographs<em> </em>from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>). Cultures were grown in culture tubes with 10 mL of media, 50 mL Erlenmeyer flasks with 10 mL and 30 mL of media, in YPD under non-shaking and shaking at 250 rpm. YPD media was used across all conditions. Cultures were assessed after 24 hours of growth at 30°C. Pictures were obtained with a Nikon TE2000 microscope using 10x objective.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Mathematical Model</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 14 name: File_14_Mathematical_model.zip</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 14 description: Mathematical model R code and generated values. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">ARN data</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 15 name: File_15_rnaseq-final-results-Top_v_Bottom.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 15 description: RNA analyses final results Top vs Bottom phenotypic subdistributions. Top is used as control. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 16 name: File_16_Variant_Call_format_file.vcf</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 16 description: Variant Calling analyses of the sample ARN sample <em>Top 1. </em>Adhesion number: SRR32105384. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Time-lapse videos</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 17 name: Supp. Video 1. C1W8.1 from 17 to 22 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 17 description: Supplementary Video 1. Experimentally evolved multicellular yeast video between 17 and 22 hours of growth (C1W8.1-derived strain) — time-lapse video of the formation of a single-cell propagule from a multicellular cluster<strong>. </strong></span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 18 name: Supp. Video 2. Ace2x2KO over 26 hours growth.</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 18 description: Supplementary Video 2. <em>ace2Δ knockout</em> constructed strain growth — time-lapse video of a single large multicellular cluster over 26 hours. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 19 name: Supp. Video 3. C1W8.1 over 6 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 19 description: Supplementary Video 3. Experimentally evolved multicellular yeast growth between 6 and 12 hours of growth (C1W8.1-derived strain). </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 20 name: Supp. Video 4. C1W8.1 over 24 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 20 description: Supplementary Video 4. Experimentally evolved multicellular yeast growth over 24 hours (C1W8.1-derived strain) — cell division stops in small ancestral-like phenotypes. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 21 name: Supp. Video 5. Ace2x2KO over 24 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 21 description: Supplementary Video 5. <em>ace2Δ knockout</em> constructed strain growth — time-lapse video of multiple large multicellular clusters over 24 hours. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 22 name: Supp. Video 6. Ace2x2missense from 0 to 3h45m hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 22 description: Supplementary Video 6. <em>ace2Δ missense</em> constructed strain growth — time-lapse video of multiple large multicellular clusters up to 3 hours 45 min. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">METHODOLOGICAL INFORMATION</span></p> <p><span lang="EN">Strains: ancestral wildtype (Y55 strains), C1W8.1 and C1W8.2 multicellular derived strains isolated after 60 days of selection in YPD media, constructed ACE2 gene knockouts, and strains containing the ACE2 missense mutation (ACE2 <sup>c.1934 A>T</sup>).</span></p> <p><span lang="EN">Media: Growth media used in this study were Yeast Peptone Dextrose media (YPD; 1% (v/w) yeast extract, 2% (v/w) peptone, 2% (v/w) D-glucose, pH 5.8).</span></p> <p><span lang="EN">Phenotypic characterization of the different strains was conducted in a Coulter Counter Multisizer 4 and FlowCam® 3.0 Fluid Imaging Technologies, optic microscopy and a mathematical model. Replicate populations of different individual isolates per strain were analyzed to obtain the population distributions in YPD media.</span></p> <p><span lang="EN">RNA was extracted using an Invitrogen® PureLink RNA Mini Kit. Three out of four extracted samples per treatment with the highest RNA integrity score were submitted for TrueSeq Stranded RNA-Seq. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">10. Detailed description</span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Coulter Counter size distribution data of all the populations: </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 1 name: File_1_Coulter_Counter_Counts_20h.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 1 description: strains naming convention; strain_Isolate_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout; C1W8.1= C1W8.1 evolved multicellular strain; C1W8.2= C1W8.2 evolved multicellular strain; Y55= ancestral strain.</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Volume (um3)</span></p> <p><span lang="EN">Column 2: Diameter (um2)</span></p> <p><span lang="EN">Columns 3 to the last column: strains counts.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 2 name: File_2_Coulter_Counter_Counts_24h.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 2 description: strains naming convention; strain_Isolate_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout; C1W8.1= C1W8.1 evolved multicellular strain; C1W8.2= C1W8.2 evolved multicellular strain; Y55= ancestral strain.</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Volume (um3)</span></p> <p><span lang="EN">Column 2: Diameter (um2)</span></p> <p><span lang="EN">Columns 3 to the last column: strains counts.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 3 name: File_3_Coulter_Counter_Counts_48h.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 3 description: strains naming convention; strain_Isolate_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout; C1W8.1= C1W8.1 evolved multicellular strain; C1W8.2= C1W8.2 evolved multicellular strain; Y55= ancestral strain.</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Volume (um3)</span></p> <p><span lang="EN">Column 2: Diameter (um2)</span></p> <p><span lang="EN">Column 3 to the last column: strains counts.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File_4_Coulter_Counter_Counts_Constructed_strains_diversity.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 4 description: strains naming convention; strain_Isolate_colony_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout.</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Volume (um3)</span></p> <p><span lang="EN">Column 2: Diameter (um2)</span></p> <p><span lang="EN">Column 3 to the last column: strains counts.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File_5_Coulter_Counter_Counts_Selection_Experiment.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 5 description: strains naming convention; strain_colony.phenotype_selection.cycle_run.pseudoreplicate. Strains: C1W8.2= C1W8.2 evolved multicellular strain and C1W8.1= C1W8.1 evolved multicellular strain.</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Volume (um3)</span></p> <p><span lang="EN">Column 2: Diameter (um2)</span></p> <p><span lang="EN">Column 3 to the last column: strains counts.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 6 name: File_6_Coulter_Counter_Counts_12h.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 6 description: Size distributions of C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 12-hours growth. Data for: Fig. 3A and Fig. S3. </span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Volume (um3)</span></p> <p><span lang="EN">Column 2: Diameter (um2)</span></p> <p><span lang="EN">Column 3: Time</span></p> <p><span lang="EN">Column 4: replicate</span></p> <p><span lang="EN">Column 5: Strain name (strain_f)</span></p> <p><span lang="EN">Column 6: Isolate (isolate_f)</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 7 name: File_3_Rawdata_Flowcam_all.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 7 description: strains naming convention; ace2_isolate= ACE2 knockout;</span></p> <p><span lang="EN">Ace2m_isolate= strain containing the ACE2 missense mutation (ACE2 <em>c.1934 A>T</em>); c1w82_isolate=C1W8.2 evolved multicellular strain; C1W81_isoalte C1W8.1 evolved multicellular strain; Y55_isolate=ancestral strain. </span></p> <p><span lang="EN">§ Page 1:</span></p> <p><span lang="EN"> Column 1: Particle ID</span></p> <p><span lang="EN"> Column 2: Area ABD</span></p> <p><span lang="EN"> Column 3: Aspect Ratio (Width/Length)</span></p> <p><span lang="EN"> Column 4: Circle Fit</span></p> <p><span lang="EN"> Column 5: Area base Diameter (ABD)</span></p> <p><span lang="EN"> Column 6: Equivalent Spherical Diameter (ESD)</span></p> <p><span lang="EN"> Column 7: Elongation</span></p> <p><span lang="EN"> Column 8: Perimeter</span></p> <p><span lang="EN"> Column 9: Roughness</span></p> <p><span lang="EN"><span> </span><span> </span>Column 10: Volume ABD-based</span></p> <p><span lang="EN"> Column 11: Volume ESD-based</span></p> <p><span lang="EN"> Column 12: Width</span></p> <p><span lang="EN"> Column 13: Source. Name of the sample.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 8 name: File_8_C1W8.2_overlapPairs_Selection_Experiment.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 8 description: C1W8.2 _lineage_selection.cycle= C1W8.2 evolved multicellular strain, lineage (A=ancestral, M1= lineage 1,<span> </span>M2= lineage 2 , M3= lineage 3) and selection cycle<span> </span>(0, 1, 2 and 3).</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Var1= strain 1</span></p> <p><span lang="EN">Column 2: Var2= strain 2</span></p> <p><span lang="EN">Column 3: overlap value of both strains compared.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 9 name: File_9_C1W8.1_overlapPairs_Selection_Experiment.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 9 description: C1W8.1 _lineage_selection.cycle =C1W8.1 evolved multicellular strain, lineage (A=ancestral, M1= lineage 1,<span> </span>M2= lineage 2 , M3= lineage 3) and selection cycle<span> </span>(0, 1, 2 and 3).</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Var1= strain 1</span></p> <p><span lang="EN">Column 2: Var2= strain 2</span></p> <p><span lang="EN">Column 3: overlap value of both strains compared.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 10 name: File_10_overlapPairs_Constructed_strains_diversity.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 10 description: variables naming convention; strain _isolate_colony.number. Strains; ace2x2m=strains containing the ACE2 missense mutation (ACE2 <sup>c.1934 A>T</sup>); ace2x2= ACE2 knockout. Isolate; 1,2 and 3. Colony.number; Initial=initial population and colony number (1,2,3,4 and 5).</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Var1= strain 1</span></p> <p><span lang="EN">Column 2: Var2= strain 2</span></p> <p><span lang="EN">Column 3: overlap value of both strains compared.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 11 name: File_11_ ImageJ _analyses.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 11 description: ImageJ analyses of the microphotographs from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>). Cultures were grown in culture tubes with 10 mL of media, 50 mL Erlenmeyer flasks with 10 mL and 30 mL of media, in YPD under non-shaking and shaking at 250 rpm. YPD media was used across all conditions. Cultures were assessed after 24 hours growth at 30°C.<span> </span>Microphotographs of each condition and strain were obtained with a Nikon TE2000 microscope using 10x objective.</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Var1= strain 1</span></p> <p><span lang="EN">Column 2: </span><span lang="EN">Var2 =<span> strain 2</span></span></p> <p><span lang="EN">Column 3: overlap value of both strains compared.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 12 name: File_12_ FlowCam_Pictures.zip</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 12 description: FlowCam runs, images, and raw data of <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 24h growth. Data for: Fig. 1B. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 13 name: File_13_Microphotography_controled_experimental_conditions.zip</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 13 description: 149 microphotographs. </span></p> <p><span lang="EN">§ Folder 1:<span> </span>Images </span><span lang="EN">of Erlenmeyer flasks<span> with 30ml of YPD</span></span></p> <p><span lang="EN">§ Folder 2:<span> </span>Images </span><span lang="EN">of <span>Erlenmeyer’s and tubes with 10ml of YPD</span></span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 14 name: File_14_Mathematical_model.zip</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 14 description: Mathematical model, R code, and generated values. </span></p> <p><span lang="EN">§ Document 1:<span> </span>R code of the model</span></p> <p><span lang="EN">§ Document 2:<span> </span>Resulted data from </span><span lang="EN">the <span>mathematical model with different inset</span> <span>values of <em>k</em>, alpha</span>,<span> and beta. </span></span></p> <p><span lang="EN">§ Document 2:<span> </span>Resulted data from the mathematical model with different inset values of <em>k</em>, alpha, gamma, and beta. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 15 name: File_15_rnaseq-final-results-Top_v_Bottom.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 15 description: </span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: number</span></p> <p><span lang="EN">Column 2: ID</span></p> <p><span lang="EN">Column 3: protID</span></p> <p><span lang="EN"><span> </span>Column 4: gene_symbol<span> </span></span></p> <p><span lang="EN"><span> </span>Column 5: chr</span></p> <p><span lang="EN"><span> </span>Column 6: chr_latin</span></p> <p><span lang="EN">Column 7: location </span></p> <p><span lang="EN">Column 8: baseMean</span></p> <p><span lang="EN"><span> </span>Column 9: log2FoldChange</span></p> <p><span lang="EN">Column 10: lfcSE</span></p> <p><span lang="EN">Column 11: stat</span></p> <p><span lang="EN"><span> </span>Column 12: pvalue<span> </span>padj</span></p> <p><span lang="EN">Column 13: test</span></p> <p><span lang="EN">Column 14: log10padj</span></p> <p><span lang="EN"><span> </span>Column 15: log10baseMean</span></p> <p><span lang="EN">Column 16: blast_pident</span></p> <p><span lang="EN">Column 17: transcript_length</span></p> <p><span lang="EN"><span> </span>Column 18: blast_evalue</span></p> <p><span lang="EN">Column 19: blast_bitscore</span></p> <p><span lang="EN">Column 20: rnaID</span></p> <p><span lang="EN"><span> </span>Column 21: feature</span></p> <p><span lang="EN">Column 22: accession</span></p> <p><span lang="EN">Column 23: strain</span></p> <p><span lang="EN"><span> </span>Column 24: gene_accession</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 16 name: File_16_Variant_Call_format_file.vcf</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 16 description: Variant Calling analyses of the<span> </span>ARN sample <em>Top 1. </em>Adhesion number: SRR32105384. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 17 name: Supp. Video 1. C1W8.1 from 17 to 22 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 17 description: <strong>Supplementary Video 1. Experimentally evolved multicellular yeast video between 17 and 22 hours of growth (C1W8.1-derived strain) — time-lapse video of the formation of a single-cell propagule from a multicellular cluster. </strong>The time-lapse video captures growth dynamics over this period, highlighting the formation of a single-cell propagule from a multicellular cluster on two occasions (visible in the lower left region of the frame). Images were acquired every 15 minutes using a 10x objective lens. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 18 name: Supp. Video 2. Ace2x2KO over 26 hours </span><span lang="EN">of <span>growth.</span></span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 18 description: <strong>Supplementary Video 2. <em>ace2Δ knockout</em></strong> <strong>constructed strain growth</strong> <strong>— time-lapse video of a single large multicellular cluster over 26 hours.</strong> The video captures large, multicellular clusters that produce both large, multicellular and small, ancestral-like clusters. The video shows a single large multicellular cluster fragmenting into two large multicellular clusters at ~ 13 hours of growth (from 02:09 to 02:10 minutes in the time-lapse) and generating two small ancestral-like propagules at ~19 hours of growth (from 03:07 to 03:09 minutes in the time-lapse). Microphotographs were obtained at 3-minute intervals under a 10x objective over 26 hours. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 19 name: Supp. Video 3. C1W8.1 over 6 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 19 description: <strong>Supplementary Video 3. Experimentally evolved multicellular yeast growth between 6 and 12 hours of growth (C1W8.1-derived strain). </strong>The time-lapse video captures large, multicellular clusters of the C1W8.1 strains, which produce both large, multicellular and small, ancestral-like clusters. Additionally, small ancestral-like clusters are observed undergoing cellular division <strong>—</strong>no separation is observed<strong>—</strong> during the first 2 to 3 hours, followed by a cessation of division for the remainder of the time-lapse. Images were acquired every 30 seconds using a 10x objective lens.</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 20 name: Supp. Video 4. C1W8.1 over 24 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 20 description: <strong>Supplementary Video 4. Experimentally evolved multicellular yeast growth over 24 hours (C1W8.1-derived strain) — cell division stops in small ancestral-like phenotypes. </strong>The footage captures multiple large multicellular clusters undergoing fragmentation into propagules. Additionally, a small ancestral-like cluster is observed undergoing division during the first 2 to 3 hours, followed by a cessation of division for the remainder of the time-lapse (visible in the lower left region of the frame). This early division phase is evident during the first 10 seconds of the video. Images were acquired every 5 minutes using a 10x objective lens. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 21 name: Supp. Video 5. Ace2x2KO over 24 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 21 description: <strong>Supplementary Video 5. <em>ace2Δ knockout</em> constructed strain growth</strong> <strong>— time-lapse video of multiple large multicellular clusters over 24 hours.</strong> The video shows multiple large multicellular clusters fragmenting into large clusters and several small ancestral-like clusters being dragged by Brownian motion and evaporation of the sample. Microphotographs were obtained at fixed intervals of 3 minutes under the 10x objective over 24 hours. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 22 name: Supp. Video 6. Ace2x2missense from 0 to 3h45m hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 22 description: <strong>Supplementary Video 6. <em>ace2Δ missense</em> constructed strain growth</strong> <strong>— time-lapse video of multiple large multicellular clusters up to 3 hours 45 min.</strong> The video shows multiple large multicellular clusters fragmenting into large clusters</span><span lang="EN">,<span> generating two small ancestral-like propagules before being dragged by Brownian motion and evaporation of the sample. Microphotographs were obtained at </span>3-minute intervals <span>under the 10x objective. </span></span></p> <p><span lang="EN"> </span></p> <p> </p>
Data for: Drivers of wood decay in tropical ecosystems: Termites vs. microbes along spatial, temporal and experimental precipitation gradients
<ol> <li>Models estimating decomposition rates of dead wood across space and time are mainly based on studies carried out in temperate zones where microbes are dominant drivers of decomposition. However, most dead wood biomass is found in tropical ecosystems, where termites are also important wood consumers. Given the dependence of microbial decomposition on moisture with termite decomposition thought to be more resilient to dry conditions, the relative importance of these decomposition agents is expected to shift along gradients in precipitation that affect wood moisture.</li> <li>Here, we investigated the relative roles of microbes and termites in wood decomposition across precipitation gradients in space, time and with a simulated drought experiment in tropical Australia. We deployed mesh bags with non-native pine wood blocks, allowing termite access to half the bags. Bags were collected every six months (end of wet and dry seasons) over a four-year period across 5 sites along a rainfall gradient (ranging from savanna to wet sclerophyll to rainforest) and within a simulated drought experiment at the wettest site. We expected microbial decomposition to proceed faster in wet conditions with greater relative influence of termites in dry conditions.</li> <li>Consistent with expectations, microbial-mediated wood decomposition was slowest in dry savanna sites, dry seasons, and simulated drought conditions. Wood blocks discovered by termites decomposed 16% to 36% faster than blocks undiscovered by termites regardless of precipitation levels. Concurrently, termites were 10 times more likely to discover wood in dry savanna compared with wet rainforest sites, compensating for slow microbial decomposition in savannas. For wood discovered by termites, seasonality and drought did not significantly affect decomposition rates.</li> <li>Taken together, we found that spatial and seasonal variation in precipitation are important in shaping wood decomposition rates as driven by termites and microbes, although these different gradients do not equally impact decomposition agents. As we better understand how climate change will affect precipitation regimes across the tropics, our results can improve predictions of how wood decomposition agents will shift with potential for altering carbon fluxes.</li> </ol>
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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.