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1,079 results for “source data”
Supplementary Source Data
<p>Supplementary Main Data.</p>
qPCR data for "An innovative passive sampling approach for the detection of cyanobacterial gene targets in freshwater sources"
<p>qPCR data to accompany the manuscript "An innovative passive sampling approach for the detection of cyanobacterial gene targets in freshwater sources"</p>
Data for "Revealing the sources and sinks of negative cluster ions in an urban environment through quantitative analysis"
<p>This file consists of the detection efficiency of APi-TOF, the time series of total negative cluster ions and CS in urban Beijing, and the average spectrums of negative clusters ions measured by APi-TOF during haze and clean periods in urban Beijing, which have been analyzed in the manuscript "Revealing the sources and sinks of negative cluster ions in an urban environment through quantitative analysis". For more details, please contact the author (rujing.yin@helsinki.fi).</p>
Orthogonal light-activated DNA for patterned biocomputing within synthetic cells (Source Data)
<p>Source data for the published version of "Orthogonal light-activated DNA for patterned biocomputing within synthetic cells": Preprint (https://chemrxiv.org/engage/chemrxiv/article-details/63b55bb6ff4651ef52429534)</p>
Data source and projections of maintenance energy gaps for "Caloric reductions needed to achieve obesity goals by 2030 and 2040: A modeling study"
<p><strong>Variables in "data_ENSANUT_waves.xlsx"</strong></p> <table> <thead> <tr> <th scope="col">Name</th> <th scope="col">Variable</th> </tr> </thead> <tbody> <tr> <td><em>id</em></td> <td>Identifier for each individual in the data.</td> </tr> <tr> <td><em>est_var</em></td> <td>Strata for the estimation of variances, accounting for survey design.</td> </tr> <tr> <td><em>svy_weights</em></td> <td>Complex survey weight.</td> </tr> <tr> <td>code_upm</td> <td>Identifier of the primary sampling unit.</td> </tr> <tr> <td>sex</td> <td>Sex of the individual (``male'' or ``female'').</td> </tr> <tr> <td>age</td> <td>Age (yrs).</td> </tr> <tr> <td>body_weight</td> <td>Measured body weight (kg).</td> </tr> <tr> <td>height</td> <td>Measured height (cm).</td> </tr> <tr> <td>bmi</td> <td>Body mass index, estimated before the simulation process (kg/m<sup>2</sup>).</td> </tr> <tr> <td>SES</td> <td>Socioeconomic level, divided in tertiles. This variable was constructed using Principal Components Analysis.</td> </tr> <tr> <td>year</td> <td>Indicator for each ENSANUT wave (2000, 2006, 2012, 2016, 2018).</td> </tr> <tr> <td>svy_weights_raking_2030</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2030.</td> </tr> <tr> <td>svy_weights_raking_2040</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2040.</td> </tr> <tr> <td>body_weight_final_2030_Nordpred</td> <td>Simulated body weight by 2030 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Nordpred</td> <td>Simulated body weight by 2040 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Nordpred</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Nordpred</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Nordpred</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Nordpred</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_Gompertz</td> <td>Simulated body weight by 2030 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Gompertz</td> <td>Simulated body weight by 2040 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Gompertz</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Gompertz</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Gompertz</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Gompertz</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_linear</td> <td>Simulated body weight by 2030 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_linear</td> <td>Simulated body weight by 2040 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_linear</td> <td>Simulated body mass index by 2030 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_linear</td> <td>Simulated body mass index by 2040 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_linear</td> <td>Indicator of obesity by 2030, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_linear</td> <td>Indicator of obesity by 2040, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_rootSquare</td> <td>Simulated body weight by 2030 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_rootSquare</td> <td>Simulated body weight by 2040 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_rootSquare</td> <td>Simulated body mass index by 2030 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_rootSquare</td> <td>Simulated body mass index by 2040 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_rootSquare</td> <td>Indicator of obesity by 2030, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_rootSquare</td> <td>Indicator of obesity by 2040, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> </tbody> </table>
Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees: Source Data
<p>Source Data file for the article "Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees"</p>
Source data belonged to "Establishing structure-property linkages for wicking time predictions in porous polymeric membranes using a data-driven approach"
<p>This record contains all the necessary data to obtain the results of the study "Establishing structure-property linkages for wicking time predictions in porous polymeric membranes using a data-driven approach"</p>
Data source
<p>The data source used for the article "Generic and vague uses of a second-person singular pronoun in an open-class person-reference system and speaker creativity in reported speech: The case of anata in Japanese" in <em>Linguistics</em> (forthcoming).</p>
Source data for paper "Operando electron microscopy investigation of polar domain dynamics in twisted van der Waals homobilayers"
<p>Source data for paper "Operando electron microscopy investigation of polar domain dynamics in twisted van der Waals homobilayers"</p>
Data and code for "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model"
<p>Original data and code associated with the paper "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model".<br> <br> Further details on the data are available in the readme.txt files.</p>
Figure source data for 'Ultrafast activation of the double-exchange interaction in antiferromagnetic manganites'
<p>This repository contains figure source data for 'Ultrafast activation of the double-exchange interaction in antiferromagnetic manganites'</p>
Source data for the manuscript "CCR7 acts as both a sensor and a sink for CCL19 to coordinate collective leukocyte migration"
<p>The zip file includes source data used in the manuscript "CCR7 acts as both a sensor and a sink for CCL19 to coordinate collective leukocyte migration", as well as a representative Jupyter notebook to reproduce the main figures. Please see the <a href="https://www.biorxiv.org/content/10.1101/2022.02.22.481445v1">preprint on bioRxiv</a> and the DOI link there to access the final published version. Note the title change between the preprint and the published manuscript.</p> <p>A sample script for particle-based simulations of collective chemotaxis by self-generated gradients is also included (see Self-generated_chemotaxis_sample_script.ipynb) to generate exemplary cell trajectories. A detailed description of the simulation setup is provided in the supplementary information of the manuscipt.</p>
Source Data for Blinova et al., Observation of an Alice Ring in a Bose-Einstein Condensate (2023).
<p>Source data.</p>
Stellar Sources data for the Weltgeist code
<p>Dataset for stellar feedback in the Weltgeist code for simulations of stellar feedback in 1D spherical coordinates. Further information about the software, including source code and installation instructions can be found here: https://github.com/samgeen/Weltgeist</p>
Data and code for: "An open-source GIS approach to understanding dunefield morphologic variability at Kati Thanda (Lake Eyre), central Australia"
<p>Data and reproducabel code</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part II: Main runs)
<p>This is Part II of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for every simulations used in the paper.</p> <p>Each zip file corresponds to a model run. </p> <p>TIGER_XX.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 0-100.<br>TIGER_XX_100.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 100-200.<br>TIGER_XX_HYYY.zip: Scenario XX, hydro-morphodynamics only, year YYY.</p> <p>Main scenarios:<br>- 01: Spartina (Figures 1-5, S3-S10)<br>- 02: Salicornia (Figures 1-5, S3-S10)<br>- 83: No vegetation (Figures 1-5, S3, S8-S10)</p> <p>Additional scenarios:<br>- 146: Spartina, low bulk drag coefficient (Figure S3)<br>- 147: Spartina, very low bulk drag coefficient (Figure S3)<br>- 148: Salicornia, low bulk drag coefficient (Figure S3)<br>- 149: Salicornia, very low bulk drag coefficient (Figure S3)<br>- 122: Spartina, low settling velocity (Figure S8)<br>- 123: Spartina, high settling velocity (Figure S8)<br>- 124: Salicornia, low settling velocity (Figure S8)<br>- 125: Salicornia, high settling velocity (Figure S8)<br>- 126: No vegetation, low settling velocity (Figure S8)<br>- 127: No vegetation, high settling velocity (Figure S8)<br>- 128: Spartina, low critical bed erosion shear stress (Figure S8)<br>- 129: Spartina, high critical bed erosion shear stress (Figure S8)<br>- 130: Salicornia, low critical bed erosion shear stress (Figure S8)<br>- 131: Salicornia, high critical bed erosion shear stress (Figure S8)<br>- 132: No vegetation, low critical bed erosion shear stress (Figure S8)<br>- 133: No vegetation, high critical bed erosion shear stress (Figure S8)<br>- 134: Spartina, low Partheniades constant (Figure S8)<br>- 143: Spartina, high Partheniades constant (Figure S8)<br>- 136: Salicornia, low Partheniades constant (Figure S8)<br>- 144: Salicornia, high Partheniades constant (Figure S8)<br>- 138: No vegetation, low Partheniades constant (Figure S8)<br>- 145: No vegetation, high Partheniades constant (Figure S8)<br>- 150: Spartina, low sediment dry bulk density (Figure S8)<br>- 151: Spartina, high sediment dry bulk density (Figure S8)<br>- 152: Salicornia, low sediment dry bulk density (Figure S8)<br>- 153: Salicornia, high sediment dry bulk density (Figure S8)<br>- 154: No vegetation, low sediment dry bulk density (Figure S8)<br>- 155: No vegetation, high sediment dry bulk density (Figure S8)<br>- 76: Spartina, replicate #1 (Figures S9-S10)<br>- 77: Spartina, replicate #2 (Figures S9-S10)<br>- 78: Spartina, replicate #3 (Figures S9-S10)<br>- 88: Spartina, replicate #4 (Figures S9-S10)<br>- 80: Salicornia, replicate #1 (Figures S9-S10)<br>- 81: Salicornia, replicate #2 (Figures S9-S10)<br>- 82: Salicornia, replicate #3 (Figures S9-S10)<br>- 89: Salicornia, replicate #4 (Figures S9-S10)<br>- 85: No vegetation, replicate #1 (Figures S9-S10)<br>- 86: No vegetation, replicate #2 (Figures S9-S10)<br>- 87: No vegetation, replicate #3 (Figures S9-S10)<br>- 90: No vegetation, replicate #4 (Figures S9-S10)</p>
Source data for Ordouie, E. et al. Differential phase-diversity electrooptic modulator for cancellation of fiber dispersion and laser noise.
<p>The experimental data and primary simulation results.</p>
The variable source of the plasma sheet during a geomagnetic storm: data
<p>This dataset contains the files supporting the paper "The variable source of the plasma sheet during a geomagnetic storm" published in Nature Communications. Included are the following:</p> <p>1) LEPI-HE2 contains cdf files of the He++ data of the LEPI instrument in the same format as the other species files available from the ERG science center (https://ergsc.isee.nagoya-u.ac.jp/)</p> <p>2) WIND_SWE contains idl save sets with the results of the fits to the WIND/SWE data using two proton peaks and an alpha peak. The data is in the structure ppa.fits, with variable descriptions in ppa.pnames and ppa.pdesc.</p> <p>3) The data that is plotted in Figures 3, 4, and 6. These are in tplot save formats that can be read with the spedas software (<a>http://themis.ssl.berkeley.edu/software.shtml</a>). IDL spedas programs (.pro) to read the data files and recreate the figures are also included.</p>
Data and Code for: Plasticity and not adaptation is the primary source of temperature-mediated variation in flowering phenology in North America
<p>This submission contains all the code and data necessary for reproducing 1) the dataset, 2) the main results, and 3) all supplemental analyses appearing in the manuscript titled: <em>Plasticity and not adaptation is the primary source of temperature-mediated variation in flowering phenology in North America</em> (Ramirez-Parada, Park, Record, Davis, Ellison, and Mazer, 2023). A preprint of this manuscript can be accessed at: https://doi.org/10.21203/rs.3.rs-3131821/v1.</p> <p> </p> <p>Extracting the compressed file will generate a folder titled "Project folder", containing sub-folders named "Data" and "R code". In order for the code to work, users need to preserve the folder structure of the code and data, as the R Markdown files in the "R code" folder have relative file paths that read and write data within the "Data" folder. Moving either would require re-writing the filepaths across Rmds for the code to run.</p> <p><br> To replicate the results, the following R Markdowns must be run in sequence (once they have been run, the Rmds for supplemental analyses can be used in any order):</p> <p><br> <em>"1. Subsetting Dataset.Rmd"</em></p> <p>This file processes a specimen dataset of ca. 2.3 million specimens that we assembled for this project (publicly available on Dryad: <a href="https://doi.org/10.25349/D9WP6S">https://doi.org/10.25349/D9WP6S</a>), filtering out duplicates, specimens out of the spatial scope of the PRISM data used for all analyses, and subsetting to only those species represented by a minimum of 300 specimens. This filtering yields a dataset of 1,038,047 specimens in flower across 1,605 species.</p> <p>For an in-depth description of the starting dataset, please refer to the "READ ME.txt" file within the "Project folder", and visit its corresponding Dryad repository (linked above).</p> <p><br> <em>"2. Main Analysis - Estimating S_space, S_time, and S_diff.Rmd"</em></p> <p>This file uses the subset dataset produced by the previous Rmd to fit the varying-intercepts, varying-slopes model that produced the estimates of apparent plasticity and apparent adaptation underlying all main analyses. This Rmd exports a dataset of species-specific estimates of S<sub>space</sub>, S<sub>time</sub>, and S<sub>space</sub> - S<sub>time</sub> that is used to recreate Figures 2, 3, and 4 of the main text in the next step. This is the most time consuming R Markdown file to run, as each MCMC chain used to fit the model in Stan must be run on a dedicated processor (limiting the usefulness of parallel computation). Fitting the model using 3 MCMC chains, 1000 iterations for warmup, and 4000 iterations for sampling, took approximately 24 hours using an Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz processor. </p> <p> </p> <p><em>"3. Main Analysis - Figures 2, 3, and 4.Rmd"</em></p> <p>Finally, this Rmd uses the dataset of species-specific estimates to conduct all analyses underlying Figures 2, 3, and 4, recreating each of these figures.</p> <p><strong><em>For detailed descriptions of all materials (code and data) and instructions for using them, please refer to the "READ ME.txt" file within "Project folder". </em></strong></p> <p> </p>
Figure 1c source data
<p>Haplotype <em>variations</em> data used to produce Figure 1c.</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.