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373 results for “stochastic”
Data archive for "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network"
<p>This datasets supports the paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network" submitted to IEEE Transactions in Geoscience and Remote Sensing. A preprint of the paper can be found here: <a href="https://arxiv.org/abs/2005.10374">https://arxiv.org/abs/2005.10374</a>. The code that uses these data is available at <a href="https://github.com/jleinonen/downscaling-rnn-gan">https://github.com/jleinonen/downscaling-rnn-gan</a>.</p> <p>The file "goes-samples-2019-128x128.nc" contains the training dataset called "GOES-COT" in the paper, consisting of cloud optical depth measurements from the GOES-16 satellite. The files "gen_weights*.nc" contain the generator weights saved at different time steps during training for the two different datasets described in the paper.<br> </p>
Dataset for: Associating Mechano-electrochemical Phenomena to Stochastic Current Events in Micro-Electrochemical Cells Containing TiNb2O7 Particles
<p>This is the raw data used in a manuscript that will be submitted to ChemElectroChem. If you have any questions, please email the creators.</p>
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Data to support "Stochastic density effects on adult fish survival and implications for population fluctuations"
Data on stage-specific abundance of black surfperch (Embiotoca jacksoni), the amount of foraging habitat and the availability of surfperch prey (crustaceans) were collected at fixed sites on the north shore of Santa Cruz Island, California annually (autumn) from 1993-2009. Data are grouped into four regions. Counts of fish distinguished among young-of-year, juveniles (1 year old) and adults (>= 2 years old). These data have been presented in Okamoto, D. K., R. J. Schmitt and S. J. Holbrook. 2016. Sochastic density effects on adult fish survival and implications for population fluctuations. Ecology Letters, 19:153-162. doi: 10.1111/ele.12547.
Stochastic accretion of the Earth
<p>Raw output data associated with the publication 'Stochastic accretion of the Earth' in Nature Astronomy. </p>
Dataset: Stochastic data-driven parameterization of unresolved eddy effects in a baroclinic quasi-geostrophic model
<p>This dataset is used to reproduce the figures in the manuscript "Stochastic data-driven parameterization of unresolved eddy effects in a baroclinic quasi-geostrophic model"</p> <p>The figures can be created with the following Python scripts:</p> <p>.</p> <p> </p>
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes: Dataset
<p>Three occupancy grid map (OGM) datasets for the paper titled "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes" by Zhanteng Xie and Philip Dames</p> <p>1. OGM-Turtlebot2: collected by a simulated Turtlebot2 with a maximum speed of 0.8 m/s navigates around a lobby Gazebo environment with 34 moving pedestrians using random start points and goal points</p> <p>2. OGM-Jackal: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Jackal robot with a maximum speed of 2.0 m/s at the outdoor environment of the UT Austin</p> <p>3. OGM-Spot: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Spot robot with a maximum speed of 1.6 m/s at the Union Building of the UT Austin</p> <p>The relevant code is available at: <br> OGM prediction: https://github.com/TempleRAIL/SOGMP<br> OGM mapping with GPU: https://github.com/TempleRAIL/occupancy_grid_mapping_torch</p>
Comparing recent PTA results on the nanohertz stochastic gravitational wave background - full noise and GWB parameter comparison plots
<p>A full collection of plots comparing the noise properties of individual pulsars and gravitational wave background parameters discussed in the companion paper <em>Comparing recent PTA results on the nanohertz stochastic gravitational wave background</em> (IPTA 2024).</p> <p><code>Section4_GWB_comparison.zip</code> supplements and expands section 4.1, "Comparing the published GWB measurements," of IPTA (2024). It contains parameter difference distributions for GWB model parameters. There are four different models included. The HD correlated powerlaw (PL) model make up the basis for Figure 2. Additionally, there are three comparisons not included in IPTA (2024). First, comparisons the common uncorrelated red noise (CURN) PL model are included. Finally, comparisons of two free spectral (FS) models (HD and CURN) are included. These comparisons fit the HD and CURN FS posteriors using the <code>ceffyl</code> software package, and then compare the parameters of the resulting powerlaw fits.</p> <p><code>Section5_Noise_comparison.zip</code> supplements section 5, "Comparing Pulsar Noice Properties," of IPTA (2024). It contains plots for 27 pulsars timed by more than one PTA collaboration, including the plots for PSR J1012+5307, which are presented in Figure 7. The plots include noise parameter posteriors, time domain GP realizations, TOA residuals, and TOA radio frequency.</p>
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models
<p><strong>Baum_et_al_2019_Supplementary_Figures.pdf: </strong>Supplementary Figures S1-S4. Legends are included under each figure.</p> <p><strong>sbm-for-correlation-based-networks-master.zip: </strong>Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.</p>
WP5.1 Stochastic and Empirical Multi-Hazard Event Sets for Europe: Winterstorm Stochastic Event Set for Europe (ISO3166 code)
<p>Stochastic event set of winter storms based on PRIMAVERA European winter windstorm event set: https://zenodo.org/record/6492182<br> Footprints for each model provided as *.csv file in xyz-format ("_footprints" suffix) and resampled as annualy maximum windspeeds for a 10.000-year stochastic run ("_10kyears" suffix). Winterstorm windspeed return period maps provided as *.tif at 5, 10, 20, 50 and 100-year periods. Windspeeds given as maximum surface windspeeds per event in "km/h".</p>
Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars
<p>Supporting data for peer-reviewed publication entitled: 'Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars', published in A&A. For the purpose of open access, the authors have applied a CC BY licence to the author accepted manuscript version and made it publicly available: <a href="https://arxiv.org/abs/2410.12726">https://arxiv.org/abs/2410.12726</a></p> <p>Evolutionary models and stability window calculations courtesy of Jermyn et al. 2022 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ac4e89">10.3847/1538-4357/ac4e89</a>) are publicly available via: <a href="https://github.com/adamjermyn/conv_trends">https://github.com/adamjermyn/conv_trends</a></p> <p>TESS full-frame image data are publicly available from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute (STScI): <a href="https://archive.stsci.edu/missions-and-data/tess">https://archive.stsci.edu/missions-and-data/tess</a></p> <p>TESS light curves (provided in this repository) were extracted using the publicly available tglc (Han & Brandt 2023; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/acaaa7">10.3847/1538-3881/acaaa7</a>) software package: <a href="https://github.com/TeHanHunter/TESS_Gaia_Light_Curve">https://github.com/TeHanHunter/TESS_Gaia_Light_Curve </a></p> <p>SLF variability parameters (provided in this repository; cf. Tables 1 and 2 of the paper) were obtained using GP regression with the publicly available celerite2 (Foreman-Mackey et al. 2017; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/aa9332">10.3847/1538-3881/aa9332</a>) software package: <a href="https://celerite2.readthedocs.io/en/latest/">https://celerite2.readthedocs.io/en/latest/</a> and confidence intervals were obtained using the publicly available pymc3 (Salvatier et al. 2016; <a href="https://doi.org/10.7717/peerj-cs.55">https://doi.org/10.7717/peerj-cs.55</a>) software package: <a href="https://github.com/pymc-devs/pymc">https://github.com/pymc-devs/pymc</a></p> <p>This research was supported in part by the National Science Foundation (NSF) under Grant Number NSF PHY-1748958; the Research Foundation Flanders (FWO) with grant agreement numbers 1286521N, 11F7120N, and V411621N; UK Research and Innovation (UKRI) in the form of a Frontier Research grant under the UK government's ERC Horizon Europe funding guarantee (SYMPHONY; grant number: EP/Y031059/1); a Royal Society University Research Fellowship (URF; grant number: URF\R1\231631); and the KU Leuven Research Council (grant number C16/18/005: PARADISE).</p>
PRIMAVERA_UOXF_Stochastic_Experiments
<p>Data from two climate model ensemble experiments ran by Kristian Strommen as part of Oxford's contribution to the Horizon 2020 Project "PRIMAVERA". Each ensemble consists of 3 simulations covering 1950-2015 with historical forcing, following the hist-1950 protocol of PRIMAVERA. The model used is the PRIMAVERA configuration of the coupled EC-Earth3 model, EC-Earth3P. </p> <p>One ensemble uses the default EC-Earth3P set-up (experiment id's dtc1, dtc2 and dtc3), while the other is run with an additional suite of stochastic schemes in the sea ice and ocean components of the model (experiment id's otc1, otc2 and otc3).</p> <p>The data here is daily sea ice concentration (siconc) and daily geopotential height at 500hPa (zg500), regridded onto a regular 2.5 degree grid. This data forms the basis of work done on Arctic-midlatitude teleconnections by Kristian Strommen and Stephan Juricke (2021).</p>
Raw Data: Timing of Calanus finmarchicus diapause in stochastic environments
<p><strong>This dataset contains the raw (unprocessed) model outputs of the Individual-Based Model used to generate the research article: </strong></p> <p>Bandara, K., Varpe, Ø., Maps, F., Ji, R., Eiane, K., & Tverberg, V. (2021). Timing of Calanus finmarchicus diapause in stochastic environments. <em>Ecological Modelling</em>, <em>460</em>, 109739.</p> <p><strong>Description</strong></p> <p>ITEM DESCRIPTION<br> ----------------<br> CONTENTS : RAW DATA FILES<br> UNCOMPRESSED SIZE : 19.6 GB <br> COMPRESSION : YES<br> COMPRESSED SIZE : 3.74 GB<br> COMPRESSION FORMAT : .ZIO<br> ENCRYPTION : NO<br> ENCRYPT METHOD : N/A<br> INTERNAL FILE TYPE(S) : .CSV<br> SOURCE : PASCAL <br> ID : v3.x (2020-2022)<br> DECRYPTION PASSWD : N/A<br> DOWNLOADABLE FROM : 10.5281/zenodo.7646734</p> <p>DATA DESCRIPTION<br> ----------------<br> THIS ARCHIVE CONTAINS FIVE (05) DIRECTORIES. EACH FROM A SINGLE SIMULATION EXPERIMENT, INDICATED BY THE FILE NAMES. <br> + DeterministicSetting : SIMULATION RAN IN DETERMINISTIC MODEL ENVIRONMENT (FIG. 2A-C)<br> + STStochasticSetting : SIMULATION RAN IN SHORTER-TERM (6-H) STOCHASTIC ENVIRONMENT (FIG. 2D-F)<br> + LTStochasticSetting-TFI : SIMULATION RAN IN LONGER-TERM (INTERANNUAL) STOCHASTIC ENVIRONMENT WITH GROWTH POTENTIAL VARIABILITY ONLY (FIG. 2G)<br> + LTStochasticSetting-VPR : SIMULATION RAN IN LONGER-TERM (INTERANNUAL) STOCHASTIC ENVIRONMENT WITH GROWTH POTENTIAL + VISUAL PREDATION RISK VARIABILITY (FIG. 2G)<br> + LTStochasticSetting-NVPR : SIMULATION RAN IN LONGER-TERM (INTERANNUAL) STOCHASTIC ENVIRONMENT WITH GROWTH POTENTIAL VARIABILITY + NON-VISUAL PREDATION RISK VARIABILITY (FIG. 2G)</p> <p>KEY<br> ---<br> + AnnualLog : DOUBLE VALUES OF LOW-RESOLUTION TRACKERS <br> + DailyLog_I : INTEGER VALUES OF HIGH-RESOLUTION TRACKERS<br> + DailyLog_R : DOUBLE VALUES OF HIGH-RESOLUTION TRACKERS<br> + VerticalPosition_N : INTEGER VALUES OF POPULATION VERTICAL DISTRIBUTION (NUMBER OF INDIVIDUALS IN EACH 1M x 6H BIN)<br> + VerticalPosition_CW : DOUBLE VALUES OF AVERAGE BODYMASS VERTICAL DISTRIBUTION (AVERAGE INDIVIDUAL BODYMASS IN EACH 1M x 6H BIN) :: UNUSED IN ANALYSES<br> + VerticalPosition_SW : DOUBLE VALUES OF AVERAGE ENERGY RESERVE VERTICAL DISTRIBUTION (AVERAGE INDIVIDUAL ENERGY RESERVES [LIPIDSCAPE] IN EACH 1M x 6H BIN) :: UNUSED IN ANALYSES<br> + FoodConcentration : DOUBLE VALUES OF 1D FOOD ENVIRONMENT<br> + Irradiance : DOUBLE VALUES OF 1D SUB-SURFACE IRRADIANCE ENVIRONMENT<br> + Temperature : DOUBLE VALUES OF 1D TEMPERATURE ENVIRONMENT<br> + VisualPredationRisk : DOUBLE VALUES OF 1D UNSCALED VISUAL PREDATION ENVIRONMENT</p> <p>NOTES<br> -----<br> The above attributes (excl. AnnualLog) are recorded for 100 calendar year in the LTStochasticSetting-TFI, LTStochasticSetting-VPR & LTStochasticSetting-NVPR simulations.<br> These are named after the year number (e.g. DailyLog_I_1, DailyLog_I_2....)</p> <p>ADDITIONAL FILES<br> ----------------<br> SOME DIRECTORIES MAY CONTAIN ADDITIONAL FILES, SUCH AS THOSE USED AS PCA INPUTS. THESE ARE PROCESSED FILES.<br> AVERAGE ANNUAL ENVIRONMENTAL VARIABLES TRACED IN THE "ENV" DIRECTORY (ONLY FOR LONGER-TERM STOCHASTIC SIMULATIONS).</p> <p>FURTHER QUESTIONS?<br> ------------------<br> Direct further question, either to: info@kanchanabandara.com (personal) or kba@akvaplan.niva.no (official)</p>
Dataset for "Separate measurement- and feedback-driven entanglement transitions in the stochastic control of chaos"
<p>Raw datasets used in the paper "Separate measurement- and feedback-driven entanglement transitions in the stochastic control of chaos". Use with the GitHub repository to reproduce results and figures from the referenced paper (https://github.com/clema12/CliffordBernoulli)</p>
Slip model and dataset - A stochastic view of the 2020 Elazig Mw 6.8 earthquake (Turkey)
<p>This repository contains files describing the slip model and data published in "A stochastic view of the 2020 Elazig Mw 6.8 earthquake (Turkey)" by T. Ragon et al.</p> <p>The slip model has been inferred with a Bayesian approach (AlTar), assuming a complex fault geometry with triangular subfaults and layered crustal structure, and accounting for epistemic uncertainties.<br> The slip model and dataset are extensively described in the publication.</p> <p>Description of the files:<br> > Slip model<br> - elazig_sigma_dip.dat : Standard deviation of the slip in the along-strike direction<br> - elazig_sigma_stk.dat : Standard deviation of the slip in the along-dio direction<br> - elazig_slip.dat : Total slip amplitude <br> - elazig_slip_dip.dat : Slip amplitude in the along-dip direction<br> - elazig_slip_stk.dat : Slip amplitude in the along-strike direction <br> - elazig_slipdir.dat : Vectors for the rake<br> > Data<br> - elazig_A116_data_rect.dat : downsampled surface displacement for the Sentinel 1A asc. interferogram<br> - elazig_A182_data_rect.dat : downsampled surface displacement for the ALOS 2 asc. interferogram<br> - elazig_A182_po_data_rect.dat : downsampled surface displacement for the ALOS 2 pixel offset ascending track<br> - elazig_D077_data_rect.dat : downsampled surface displacement for the ALOS 2 dsc. interferogram<br> - elazig_D077_po_data_rect.dat : downsampled surface displacement for the ALOS 2 pixel offset descending track<br> - elazig_D123_data_rect.dat : downsampled surface displacement for the Sentinel 1A dsc. interferogram<br> - elazig_gps_data.dat : GPS data</p> <p>The format of all *slip* and *sigma* files at the exception of 'elazig_slipcenterll.dat' and 'elazig_slipdir.dat' is as follow:<br> > -Z[ slip amplitude ] # [subault index 1] [subfault index 2] 9999999 # [ strike slip amplitude] [dip slip amplitude] 0.0 <br> [longitude] [latitude] [depth of point 1]<br> [longitude] [latitude] [depth of point 2]<br> [longitude] [latitude] [depth of point 3]</p> <p>The format of all inteferograms and PO files is as follow:<br> > -Z[surface displacement in the LOS or azimuth direction]<br> [lon] [lat for NW corner]<br> [lon] [lat for NE corner]<br> [lon] [lat for SE corner]<br> [lon] [lat for SW corner]<br> [lon] [lat for NW corner]</p>
Test Collection Reliability: A Study of Bias and Robustness to Statistical Assumptions via Stochastic Simulation
<p>This archive contains the simulated collections, their diagnosis data, and the estimates of accuracy. For the full code and description, please refer to https://github.com/julian-urbano/irj2015-reliability</p>
Stochastic modeling of sediment connectivity for reconstructing sand fluxes and origins in the unmonitored Se Kong, Se San, and Sre Pok tributaries of the Mekong River
<p>Sediment supply to rivers, subsequent fluvial transport, and the resulting connectivity on network-scales are often sparsely monitored or subject to major uncertainty. Hence, we propose to adopt stochastic modeling approaches for studying network sediment connectivity. We demonstrate such a stochastic approach for modeling sand connectivity in the major, poorly monitored Se Kong, Se San, and Sre Pok (3S) tributaries of the Mekong River. Specifically, we run many random initializations of the CASCADE modeling framework for sediment connectivity in a Monte Carlo approach in order to quantify how unknown properties of sediment sources translate into uncertainty regarding network sediment connectivity. We identify a reduced ensemble of model realizations that reproduces downstream observations of sediment transport. This ensemble presents an inverse stochastic approximation of the spatial distribution, magnitude, and variability of transport capacity, sediment flux, and bed material grain size in the entire network (i.e., upscaling point observations to the entire network). The approximated magnitude of sediment flux in each tributary is controlled by reaches of low transport capacity (“bottlenecks”). These “bottlenecks” limit the ability of the inverse stochastic approximation to predict sediment transport in the upper parts of the catchment but they allow a clear partitioning of sand deliveries from the 3S to the Mekong, with the Se Kong delivering less (1.9 Mt/yr) and coarser (median grain size: 0.4 mm) sand than the Se San (5.3 Mt/yr, 0.22 mm) and Sre Pok (11 Mt/yr, 0.19 mm).</p>
Code outputs and figures from "Efficient high-resolution refinement in cryo-EM with stochastic gradient descent"
<p>Code outputs and figures for the numerical experiments on preconditioned SGD for cryo-EM reconstruction for reproducing the results in the article:</p> <blockquote> <p><a title="doi" href="https://doi.org/10.1107/S205979832500511X" target="_blank" rel="noopener"><code><em>Efficient high-resolution refinement in cryo-EM with stochastic gradient descent</em>.</code></a></p> <p><em>Bogdan Toader, Marcus A. Brubaker, Roy R. Lederman</em></p> <pre>Acta Crystallographica Section D, 2025</pre> </blockquote> <p>The outputs are obtained by running the Jupyter notebooks in the <em>notebooks/preconditioned_sgd</em> directory in the GitHub repository (release v0.2):</p> <blockquote> <p><a title="github link" href="https://github.com/bogdantoader/simplecryoem">https://github.com/bogdantoader/simplecryoem</a></p> </blockquote> <div>The particle images used for these experiments can be downloaded from <a title="empiar-10076 link" href="https://www.ebi.ac.uk/empiar/EMPIAR-10076">EMPIAR-10076</a> and require inverting the contrast. The file containing the pose variables and CTF parameters is the <em>particles_file/my_particles_8.star </em>file in the attached archive.</div>
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>
Supplemental Movie 1: Stochastic Ca2+ transients in ICC-DMP.
<p><strong><span>Supplemental Movie 1: S</span><span>tochastic Ca</span><sup><span>2+</span></sup><span><span> </span></span><span>transients in</span></strong><span> <strong>ICC-DMP.<span> </span></strong>Movie of intracellular Ca</span><sup><span>2+</span></sup><span><span> </span></span><span>transients in ICC-DMP labelled with the genetically encoded Ca</span><sup><span>2+</span></sup><span><span> </span></span><span>indicator GCaMP3. The top left FOV shows elongated ICC-DMP at 60x and the top right FOV shows ICC-DMP at 100x magnification. Note the lack of coincidence of Ca</span><sup><span>2+</span></sup><span><span> </span></span><span>transients between the blue bit-masked cell and the non-bit-masked cell in the 100x FOV. The blue bit-masked ICC-DMP in the 100x FOV was used to construct a spatio-temporal map of Ca</span><sup><span>2+</span></sup><span>-induced fluorescence intensity along the length of the cell (lower panel).<span> </span>Note the stochastic firing of spontaneous Ca</span><sup><span>2+</span></sup><span><span> </span></span><span>transients in ICC-DMP.<span> </span>Copied with permission from reference </span><span><span>(127)</span></span><span>.<span> </span></span></p> <p> </p> <p><span><span><a href="https://doi.org/10.1152/physrev.00036.2022">https://doi.org/10.1152/physrev.00036.2022</a></span></span></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.