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1,079 results for “source data”
Source data for manuscript "Polygenic burden of short tandem repeat expansions promote risk of Alzheimer's disease"
<p>Included are the source data and scripts used to make all plots for the mansucript "Polygenic burden of short tandem repeat expansions promote risk of Alzheimer's disease"</p>
Source Data
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Data for "Magnetic Characterization of Sediment Source-to-Sink Processes in the Bengal Fan since 45 ka"
<p>The file is the dataset for the manuscript entitled 'Magnetic Characterization of Sediment Source-to-Sink Processes in the Bengal Fan since 45 ka' by Huang et al., including age, original and smoothed low frequency magnetic susceptibility, anhysteretic remanence magnetization (ARM) data, and bulk mean grain size data of five gravity cores in the manuscript. Hysteresis loop data is also included.</p>
Source data for "Submesoscales are a significant turbulence source in global ocean surface boundary layer"
<p>The files here provide the data and codes for reproducing figures from the paper titled "Submesoscales are a significant turbulence source in global ocean surface boundary layer" by Dong et al.</p> <p>It should be clarified that Fig.1 is originally generated by Python and then produced in Illustrator, and Fig.5 is completely produced by Illustrator. All other figures are produced by MATLAB.</p>
Source Data for the manuscript: General-purpose machine-learned potential for 16 elemental metals and their alloys
<p>***Source Data***<br>This folder contains multiple .txt files that provide the source data for the figures and tables presented in the paper: "General-purpose machine-learned potential for 16 elemental metals and their alloys."</p> <p>The source data are organized in the following folders and files:</p> <p>1) Fig2<br>2) Fig3<br>3) Fig4<br>4) Fig5<br>5) Fig6<br>6) FigS1<br>7) FigS2<br>8) FigS3<br>9) FigS4-6-pure<br>10) FigS7-9-binary<br>11) FigS10-12-ternany<br>12) FigS13-15-quaternary<br>13) FigS16-17-quinary<br>14) FigS18-20<br>15) FigS21<br>16) FigS22<br>17) FigS23<br>18) FigS26<br>19) Table1-Element-atoms-GPU-Speed.txt<br>20) Table-S1-DFT-EAM-UNEP-Elastic.txt<br>21) Table-S2-DFT-EAM-UNEP-Mono-vacanc.txt<br>22) Table-s3-Surface-100-110-111-DFT-EAM-UNEP.txt<br>23) Table-S4-Melting-EAM-UNEP-Exp.txt</p>
The usage of transcriptomics datasets as sources of Real-World Data for clinical trialling -- Supplementary Data
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MD source and data of Zebhb-PHZ
Python Codes, shell scripts and GROMACS data files of molecular dynamics simulations of Zebrafish Hemoglobin protein (Zebhb) and Phenylhydrazine (PHZ) Ligand
Source data for Fig. S1F associated with "Endo-IP and Lyso-IP Toolkit for Endolysosomal Profiling of Human Induced Neurons"
<p>This entry contains source data and statistical test for plots in Fig. S1F associated with: https://doi.org/10.1101/2024.09.24.614704</p>
Meteorological data of Ali_Tazhong_Minfeng and Potential Source Analysis Code_FEAST
<p>Meteorological data of Ali_Tazhong_Minfeng and Potential Source Analysis Code_FEAST</p>
An Integrated Approach for enhanced SMAP Soil Moisture Retrieval: Multi-Source Data Fusion and Data-Driven Machine Learning
<p><span>Accurate satellite-based soil moisture (SM) retrieval is essential for hydrometeorological and agroecological applications, yet traditional physical models for L-band SM retrieval are hindered by uncertainties stemming from inaccuracies in prior parameters. This work combines multi-source data fusion and a physically-guided machine learning framework to develop a Soil Moisture Active Passive (SMAP) SM retrieval model (Fusion-LightGBM, F-LGB) that bypasses the need for static prior parameters, resulting in a new SM product. The retrieval benchmark is a new seamless SM data constructed by combining Triple Collection correlation coefficients (TC-R) and the Maximized-R method, which demonstrates superior temporal correlation on 20 International Soil Moisture Network (ISMN) <em>in-situ</em> networks compared to existing SM data, including ECMWF Reanalysis v5-Land (ERA5-Land), SMAP Level 4 (SMAP L4), and Global Land Data Assimilation System (GLDAS) Noah. The machine learning model incorporates input variables that represent the Tau-Omega model’s radiative transfer process, including brightness temperature, vegetation optical depth, soil temperature, and an external variable for precipitation. In the 2015-2020 validation set, F-LGB demonstrated the highest correlation (mean R = 0.72, significantly surpassing the second-best SMAP-INRAE-BORDEAUX (SMAP-IB) SM and deep neural network (DNN) SM at 0.67) and the lowest ubRMSE (mean value of 0.052 m<sup>3</sup>/m<sup>3</sup>, better than 0.055 m<sup>3</sup>/m<sup>3</sup> for both DNN and SMAP-IB). F-LGB performed well across diverse land covers, vegetation densities, and climates, with SHAP analysis showing H-polarized brightness temperature as crucial, especially in areas with low to moderate vegetation. This new machine learning-based SMAP SM product may improve global satellite-based SM estimation capabilities.</span></p>
Data from: Influence of natural and novel organic carbon sources on denitrification in forest, degraded urban, and restored streams
Organic carbon is important in regulating ecosystem function, and its source and abundance may be altered by urbanization. We investigated shifts in organic carbon quantity and quality associated with urbanization and ecosystem restoration, and its potential effects on denitrification at the riparian–stream interface. Field measurements of streamwater chemistry, organic carbon characterization, and laboratory-based denitrification experiments were completed at two forested, two restored, and two unrestored urban streams at the Baltimore Long-Term Ecological Research site, Maryland, USA. Dissolved organic carbon (DOC) and nitrate loads increased with runoff according to a power-law function that varied across sites. Stable isotopes and molar C:N ratios suggested that stream particulate organic matter (POM) was a mixture of periphyton, leaves, and grass that varied across site types. Stable-isotope signatures and lipid biomarker analyses of sediments showed that terrestrial organic carbon sources in streams varied as a result of riparian vegetation. Laboratory experiments indicated that organic carbon amendments significantly increased rates of denitrification (35.1 ± 9.4 ng N·[g dry sediment]−1·h−1; mean ± SE) more than nitrate amendments (10.4 ± 4.0 ng N·[g dry sediment]−1·h−1) across streamflow conditions and sites. Denitrification experiments with naturally occurring carbon sources showed that denitrification was significantly higher with grass clippings from home lawns (1244 ± 331 ng N·g dry sediment−1·h−1), and overall unrestored urban sites showed significantly higher denitrification rates than restored and forest sites. We found that urbanization influences organic carbon sources and quality in streams, which can have substantial downstream impacts on ecosystem services such as denitrification.
Data from: Phylogenetic diversity reveals hidden patterns related to population source and species pools during restoration
A phylogenetic perspective of community assembly can reveal new insights into how variation within dominant species interacts with the local species pool to influence the structure of restored plant communities. Many studies have examined the effect of dominant species in structuring plant communities, but few have investigated their effect on phylogenetic diversity (PD). We established grassland in a post-agricultural field using two population sources (cultivars and local ecotypes) of three dominant grasses (Sorghastrum nutans, Andropogon gerardii and Schizachyrium scoparium) with three unique pools of subordinate species that varied in PD but not taxonomic or life-form diversity. We tested the effect of the population source treatment on two metrics of community PD (net relatedness index [NRI] and nearest taxon index [NTI]) during the first 4 years of restoration. The NRI measures the overall pairwise phylogenetic distance between all pairs of taxa in a community. By contrast, NTI measures the pairwise distance between closely related taxa in a community. Population sources had a transitory effect on community phylogenetic structure over time. Local ecotypes decreased the abundance of closely related eudicots, monocots (low +NRI and +NTI values) and volunteer species (−NTI) more than cultivars. However, population sources did not affect ecologically conservative species (i.e. species with intermediate-to-poor ecological tolerance and a high degree of fidelity to prairie habitats). Thus, cultivars might have a positive effect on community phylogenetic diversity more than local ecotypes by decreasing the abundance of a phylogenetically diverse community of less closely related volunteer species. Differences in PD of seed mixes were maintained in the community of high-fidelity species, but did not affect PD of the unsown (volunteer) species in the assembling community. Synthesis and applications. This is the first experiment to show consequences of using different seed sources on phylogenetic diversity (PD) in grassland restoration. Phylogenetics can reveal the effects of population sources on the abundance of volunteer species not evident through traditional analyses of species diversity. The PD of seed mixes or establishing communities, or other assessments of phylogenetic relationships, by restoration practitioners is recommended as a metric to allow consequences of the evolutionary patterns among species to be included in conservation planning. Increased accessibility of phylogenetic tools will allow the application of PD in restoration monitoring.
Data from: A sink host allows a specialist herbivore to persist in a seasonal source
<p><strong>Filename: </strong>1_Population_growth_rate.xlsx</p> <p>Variables:</p> <p>1. Source host - the plant species from which experimental females were transferred <br> 2. Target host - the plant species to which experimental females were transferred<br> 3. Generations - tested time period, in generations<br> 4. N<sub>0</sub> - the number of females placed at the beginning of the experiment<br> 5. N - the number of mites (being a progeny of N<sub>0</sub> females) counted after each tested time period</p> <p><strong>Filename: </strong>2_Emigration_dispersal.xlsx</p> <p>Variables:</p> <p>1. Source host - the plant species infested by mites and exposed to wind<br> 2. Target host - the plant species toward which mites could disperse<br> 3. N - population size on the source host<br> 4. D - the number of individuals that dispersed from the source host</p> <p><strong>Filename: </strong>3_Emigration_acceptance.xlsx</p> <p>Variables:</p> <p>1. Source host - the plant species from which experimental females were transferred <br> 2. Target host - the plant species to which experimental females were transferred<br> 3. N - the number of females placed on the experimental arena<br> 4. R - the number of females that stayed on the experimental arena after incubation </p> <p><strong>Filename: </strong>4_Experimental_evolution.xlsx</p> <p>Variables:</p> <p>1. Regime - host selection regime (W - wheat; B - brome; WB - wheat-brome alternating each three generations on each host species)<br> 2. Generations - the number of generations the population survived<br> 3. Status - 0 - censored observation; 1 - observed event of extinction</p> <p><strong>Filename: </strong>5_Field_database.xlsx</p> <p>Variables:</p> <p>1. Sampling date - date of plant collection in the field<br> 2. Day - the day of the year when the sample was collected<br> 3. Year - the year of the sample collection<br> 4. Database - N: the sample collected during 2012-2014 surveys; O: the sample collected during 2007-2014 surveys<br> 5. Host - the plant species collected<br> 6. GPS lat. [N] - the latitude in the northern hemisphere<br> 7. GPS long. [E] - the longitude in the eastern hemisphere<br> 8. n - the number of all plant shoots examined<br> 9. k - the number of plant shoots infested</p>
Google Scholar as a Data Source for Research Assessment in the Social Sciences
<p>Column 1</p> <p>Source</p> <p>Data sources that the publications retrieved. Values for this column are “Google Scholar”, “Scopus”, and “Web of Science”.</p> <p>Column 2</p> <p>Authors</p> <p>The authors of the publications. This column is kept as additional information for verification of data. Not used in the analysis, it has not been standardized.</p> <p>Column 3</p> <p>Title</p> <p>Titles of the publications. For non-English publications, English titles, if available, are kept in this column. Otherwise, the original titles have been entered. The headings were checked and errors and omissions were corrected. Corrected titles are marked in red.</p> <p>Column 4</p> <p>Title translated with Google Translate</p> <p>In this Column, the English translated titles of the publications that do not have English titles are kept. Google Translate is used for detecting the language and translation. For publications with an English title, the expression [Title in English] has been entered. The translations of the original titles kept in this field were used in the analysis made through VOSviewer. It is marked in red as it is newly added data.</p> <p>Column 5</p> <p>Language</p> <p>Language of the publications. The languages of all publications were checked, missing data were completed and errors were corrected. If the language of the publication could not be determined, the value is [Not found]. The cells with addition or correction are marked in red.</p> <p>Column 6</p> <p>Document type</p> <p>Types of the documents. For all publications, publication type information was checked, missing ones were completed and corrections were made. All intervened cells are marked in red. Article and Review types are referred to as “Article” in the text.</p> <p>Column 7</p> <p>Full-text available</p> <p>Values for this column are “Yes” and “No”. The values for this column are Yes and No. If there is access to the full text of the publication via the web, "Yes", otherwise the "No" value has been entered.</p> <p>Column 8</p> <p>On research evaluation</p> <p>Values for this column are “Yes” and “No”. Using the title and/or abstract information, it was tried to determine whether the publications were related to the research evaluation. “Yes”, if found relevant, and “No” if not. It is marked in red as it is newly added data.</p> <p>Column 9</p> <p>Publication year</p> <p>The publication years of the documents. If the publication years are missing, they have been completed. The current publication years have been checked and corrected if necessary. If the year of publication could not be found, it is indicated as [Not found].</p> <p>Column 10</p> <p>English abstract</p> <p>Abstracts of the publications. If there is an accessible/available English abstract for the publication, it is kept in this column. [Not found/Not available] for missing values. Abstracts that were added, changed, corrected, or completed are marked in red.</p>
Source code and GCM data for 'Direct radiative effects of airborne microplastics'
<p>Source code and GCM data for 'Direct radiative effects of airborne microplastics' by Revell et al., (2021)</p>
Source Data for "Imaging biological tissue with high-throughput single-pixel compressive holography"
<p>This file contains five subfolders, which are archived with relevant data that are necessary for reconstructing the holographic images of biological samples and resolution targets, respectively. <br> Here we introduce in order:<br> 1. 'dataset 1' is prepared for holographic reconstruction of stained tissue from mouse tails;<br> 2. 'dataset 2' is provided for holographic reconstruction of 80-um unstained tissue from mouse brains.<br> 3. 'dataset 3' is provided for verification of amplitude resolution in large-FOV mode;<br> 4. 'dataset 4' is provided for verification of amplitude resolution in high-resolution mode;<br> 5. 'dataset 5' is provided for verification of phase resolution in high-resolution mode;<br> 6. 'additional dataset 1' is prepared for additional holographic reconstruction of another stained tissue from mouse tails;<br> 7. 'additional dataset 2' is prepared for additional holographic reconstruction of 100-um unstained tissue from mouse brains;<br> 8. 'additional dataset 3' is prepared for additional holographic reconstruction of 120-um unstained tissue from mouse brains;<br> 9. 'additional dataset 4' is prepared for additional holographic reconstruction of 10-um unstained tissue from mouse brains;</p> <p>Both subfolders have the same structures, including the MATLAB data and raw data collected from the data acquisition card, which are necessary for holographic imaging reconstruction.<br> Here we introduce in order:<br> *) biological_sample.mat: The raw data of imaging biological sample. The format of the data has been converted from .tdms to .mat file.</p> <p>*) target_sample.mat: The raw data of imaging resolution target. The format of the data has been converted from .tdms to .mat file.</p> <p>*) background_curvature.mat: The raw data used to correct for phase contaminations from system aberrations. The format of the data has been converted from .tdms to .mat file.</p> <p>*) biological_sample_rawdata.tdms: The raw data of imaging biological sample. The data was collected through DAC and was in the format of TDMS.</p> <p>*) target_sample_rawdata.tdms: The raw data of imaging resolution target. The data was collected through DAC and was in the format of TDMS.</p> <p>*) background_curvature_rawdata.tdms: The raw data used to correct for phase contaminations from system aberrations. The data was collected through DAC and was in the format of TDMS.<br> </p>
Figure source data for 'Coherent spin-wave transport in an antiferromagnet'
<p>This repository contains figure source data for 'Coherent spin-wave transport in an antiferromagnet'</p> <p> </p> <p><strong>Contents</strong></p> <p><em>Figure 1:</em></p> <p>(a) optical absorption DyFeO3 for different photon energies: 1a_absorption.txt</p> <p>(b) Penetration depth for different photon energies: 1a_penetetrationdepth.txt</p> <p>(c) Time-resolved polarization rotation for transmission and reflection geometries: 1c_time-resolved.txt</p> <p>Fourier amplitude spectra for different geometrie: 1c_FourierInsets</p> <p><em>Figure 2:</em></p> <p>(b) Extracted oscillation frequencies as function of temperature: 2b_frequencies.txt</p> <p><em>Figure 3</em></p> <p>(a) Time-resolved polarization rotation for different photon energies (folder figure3a)</p> <p>(b) Amplitude spectra of the time-resolved polarization rotation for different photon energies (folder figure3b)</p> <p>(c) Amplitude vs penetration depth: 3c.txt</p> <p><em>Figure 4</em></p> <p>(a) Time- and space dependent amplitude: 4a.txt</p> <p>(b) Fourier spectra for different incidence angles + data-inset: 4b.txt</p> <p>(c) Fourier spectra for different probe wavelengths: 4c.txt</p> <p>(d) Extracted central frequencies and calculated group velocity for different wavenumbers: 4de.txt</p> <p> </p> <p>. </p>
Figure source data
<p>Source data for 3 figures in the main text and 3 in Supplementary Information.</p> <p>Reference:</p> <p>Correia, A.L., Sena, E.T., Silva Dias, M.A.F., Koren, I. Preconditioning, aerosols, and radiation control the temperature of glaciation in Amazonian clouds. <em>Communications Earth & Environment</em> <strong>2</strong>, 168 (2021). <a href="https://doi.org/10.1038/s43247-021-00250-3">https://doi.org/10.1038/s43247-021-00250-3</a></p>
GPUSAT3 Benchmark Data and Source Code
<p>Benchmark data and source code for GPUSAT3.</p> <p>This dataset contains:</p> <p>* decompositions_mccext.zip: Tree decompositions by htd, flowcutter and tamaki for the GPUSAT3 extended benchmark set.</p> <p>* track12.zip: Benchmark instances for the GPUSAT3 extended benchmark.</p> <p>* mccuda-source.zip: GPUSAT3 source code at the time of submission.</p> <p>* experiments.zip: Benchmark results for GPUSAT3 and compared solvers.</p> <p>* </p>
Source data for Incarbone et al (2021) - "Immunocapture of dsRNA-bound proteins provides insight into tobacco rattle virus replication complexes and reveals Arabidopsis DRB2 to be a wide-spectrum antiviral effector"
<p>Source data for Incarbone et al (2021) - "Immunocapture of dsRNA-bound proteins provides insight into tobacco rattle virus replication complexes and reveals Arabidopsis DRB2 to be a wide-spectrum antiviral effector"</p> <p>Includes full scans of blots mounted in figures and additional microscopy acquisitions, including brightfield channel</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.