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Data from: A nuclear DNA barcode for eastern North American oaks and application to a study of hybridization in an Arboretum setting
DNA barcoding has proved difficult in a number of woody plant genera, including the ecologically important oak genus Quercus. In this study, we utilized restriction-site associated DNA sequencing (RAD-seq) to develop an economical single-nucleotide polymorphism (SNP) DNA barcoding system that suffices to distinguish eight common, sympatric eastern North American white oak species. Two de novo clustering pipelines, PyRAD and Stacks, were used in combination with post-clustering bioinformatic tools to generate a list of 291 potential SNPs, 80 of which were included in a barcoding toolkit that is easily implemented using MassARRAY mass spectrometry technology. As a proof-of-concept, we used the genotyping toolkit to infer potential hybridization between North American white oaks transplanted outside of their native range (Q. michauxii, Q. montana, Q muehlenbergii/ Q. prinoides and Q. stellata) among natural forests of locally native trees (Q. alba and Q. macrocarpa) in the living collection at The Morton Arboretum (Lisle, IL, USA). Phylogenetic and clustering analyses suggested low rates of hybridization between cultivated and native species, with the exception of one Q. michauxii mother tree, the acorns of which exhibited high admixture from either Q. alba or Q. stellata and Q. macrocarpa; and a hybrid between Q. stellata that appears to have backcrossed almost exclusively to Q. alba. Together, RAD-seq and MassARRAY technologies allow for efficient development and implementation of a multispecies barcode for one of the more challenging forest tree genera.
Data set for ISOP paper
<p>Data to reproduce results in the ISOP paper</p>
FEM data sets for heat generation due to plastic deformation and the and associated contact temperature during a normal impact between an elastic-perfectly-plastic particle and a rigid surface.
<p>This dataset contains essential data from the Finite Element Method (FEM) model predicting heat generation due to plastic deformation during the normal impact of a deformable spherical particle and a rigid flat substrate. Part of this data was processed and featured in a publication of a journal article (https://doi.org/10.1016/j.ijimpeng.2023.104831). The following is the description of the data files and the associated figures in the original paper. </p><p>'Energy_Vy200_1200 .xlsx' and 'Temp_Vy200_1200 .xlsx' - data for the evolution of heat and nodal temperature, respectively for varying impact velocities. Data was used for Figs.5 -7 in the published paper.</p><p>'Energy_YM_5_1000.xlsx' and 'Temp_YM_5_1000.xlsx' - data for the evolution of heat and nodal temperature, respectively for varying Young moduli. Data was used for Figs. 8 and 9 in the published paper.</p><p>'Energy_YS_50_800.xlsx' , 'Temp_YS_50_800.xlsx' - data for the evolution of heat and nodal temperature, respectively for varying yield strengths. Data was used for Figs. 10 and 11 in the published paper.</p><p>'Energy_Den_500_8000.xlsx' , 'TempC_Den_500_8000.xlsx'- data for the evolution of heat and nodal temperature, respectively for varying densities. Data was used for Figs. 12 and 13 in the published paper.</p><p>'Temp_TC.xlsx' , 'Temp_HC.xlsx' - data for the evolution of nodal temperatures for varying thermal conductivity and specific heat capacities, respectively. Data was used for Figs. 14 -17 in the published paper.</p><p> </p><p> </p><p> </p><p> </p><p> </p>
Complete Data Set for the Master Thesis: Acceptance of Robotics in Tourism
<p>Data Set for the Master Thesis <i>Acceptance of Robotics</i> reseached and written by Elena Schiffmann. </p><p>Use only with permission. </p>
Augment Single-cell RNA-seq data with Surface Protein Levels using Gene set-based Deep Learning and Transfer Learning Methods
<p><span>Necessary data, scripts and saved models for "Augment Single-cell RNA-seq data with Surface Protein Levels using Gene set-based Deep Learning and Transfer Learning Methods" manuscript.</span></p>
Data Set of paper_photoelectrocatalytic disinfection of water using titania nanotube photoanodes with carbon cathodes and determination of the radicals produced
Open the record for dataset details and reuse information.
Selected data sets for Marsh et al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'
<p>Data sets used in the for the manuscript <strong>Marsh<em> </em>et<em> </em>al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'</strong>. The DOIs that link to all other data sets used in the publication are available in Tables S2-5 of the supplementary information. The z-score standardised data, and outputs of RMarkdown documents outline all the steps in the processing and analysis of the data are available at https://zenodo.org/uploads/13161799.</p> <p> </p> <p>This repository contains data used for:</p> <h3><strong><em>Mean canopy height</em></strong></h3> <p>Canopy height and vertical profiles of forest structure were compiled using airborne remote sensing with LiDAR collected by NERC’s Airborne Research Facility (ARF) in November 2014, using a Leica ALS50-II LiDAR. A Beer-Lambert approximation was used to convert point clouds to plant area density (PAD) distributions, a similar measure to leaf-area index, but where methods do not distinguish between leaves and branches or trunks. LiDAR measurements for the carbon plots were converted to rasters with 0.5 × 0.5 m cell size. Plots were rotated to a North-South axis if necessary</p> <h3><br><em><strong>Spectral diversity</strong></em></h3> <p>Spectral measurements were made on five leaves attached to tree branches used to measure leaf chemical traits. Leaves were randomly selected but we avoided damaged and young plant material to avoid potential confounding factors. Reflectance spectra (350–2500 nm) were acquired using a FieldSpec 4, produced by Analytical Spectral Devices (ASD, Boulder, Colorado, USA). The spectroradiometer's contact probe was mounted on a clamp and firmly pushed down onto the sample against a black background so that no extraneous light was included in the measurement. Spectral measurements were taken halfway between the petiole and leaf tip, and between the main vein and the leaf edge, with the abaxial surface pointing towards the probe. The readings were calibrated against a Spectralon white reference panel every five samples. Leaf reflectance measured at 430 nm, 660 nm, 1450, 1980 nm and 2350 nm align closely with absorption features for pigments, water content, proteins and cellulose. Spectral diversity calculated from these absorption features can provide an integrated measure of the functional trait variability within plant communities and may be used as a proxy for functional diversity.</p> <p> </p> <h3><em><strong>Liana abundance</strong></em></h3> <p>Percentage liana cover for large canopy and emergent trees. The four quadrants of the canopy were scored as 0 (no lianas), 1 (1-20%), 2 (20-40%), 3 (40-60%), 4 (60-80%) and 5 (80-100%).</p> <p> </p> <h3><em><strong>Leaf-area index<br></strong></em></h3> <p>Leaf area index (LAI) for carbon plots was derived from hemispherical photos (Sigma 8mm SRL fish eye lens and Canon EOS 600D digital camera, mounted on a tripod at 1 m height). Between 5-27 photos were taken over time in each subplot. Images were processed with Hemisfer® software (www.wsl.ch/dienstleistungen/produkte/software/hemisfer/index_EN). LAI was calculated with the method by Thimonier et <em>al</em>. (2010) <em>European Journal of Forest Research</em> 129, 543–562 (2010), with a canopy clumping correction applied from Chen & Cihlar (1995) <em>IEEE Transactions on Geoscience and Remote Sensing</em> 33, 777–787.</p> <p> </p> <h2>Funding</h2> <p>Analyses were carried out, and data were collected, as part of the BALI (Biodiversity And Land-use Impacts on tropical ecosystem function) using the following funding:</p> <ul> <li>NERC's Human Modified Tropical Forests research programme (grant number NE/K016377/1 awarded to the BALI consortium)</li> <li>MHN was supported by a PhD scholarship from the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, grant No. 201516/2014-4) from Brazil</li> </ul>
Data Set_Exploring the Molecular Mechanisms of Endothelial Dysfunction Affecting Myocardial Infarction by Integrating Multiple Datasets with In Vivo Experimental Validation
Open the record for dataset details and reuse information.
Ozone data set
<p>Ground-level O<sub>3</sub> observation data during summertime (June–August) of 2014–2019 were retrieved from Beijing Municipal Ecological and Environmental Monitoring Center, ultimately comprising 31 air quality stations.</p>
data_set_for_revision_of_manuscript_contrail_formation_within_cirrus_Verma_Burkhardt_26112021
<p>This data set has new figures used in the revision of the manuscript 'contrail formation within cirrus'. and data relevent to paper is in the below given DOI.</p> <pre><strong>https://doi.org/10.5281/zenodo.5744985</strong></pre> <p> </p>
data set for Magnon heat transport in a 2D Mott insulator
<p>data set for the paper Magnon heat transport in a 2D Mott insulator</p> <p>source code for generating the data:</p> <p><a href="https://github.com/wenowang96/Determinant-QMC">https://github.com/wenowang96/Determinant-QMC</a></p> <p>Publication: Phys. Rev. B <strong>105</strong>, L161103</p> <p> </p> <p>refer to <a href="https://github.com/wenowang96/DQMC_analysis_data">https://github.com/wenowang96/DQMC_analysis_data</a> (the folder ''magnon-heat-transport'') for follow up updates.</p>
Supporting data set for: Three-dimensional Configuration of Induced Magnetic Fields around Mars
<p>Supplementary data to reproduce figures for: Three-dimensional Configuration of Induced Magnetic Fields around Mars</p>
Data Sets for "Online Charge Measurement for Petawatt Laser-Driven Ion Acceleration" (submitted manuscript)
<p>The data presented in the paper titled "Online Charge Measurement for Petawatt Laser-Driven Ion Acceleration" is provided in this data repository.</p> <p>The data is sorted into subfolders according to their presentation in the figures of the paper.</p>
Data set for phase-field studies of K-feldspar dissolution through etch-pit formation
<p>The numerical data in this repository consists of the simulation data of K-feldspar dissolution through etch-pit formation. The simulations were performed using the software package named "Pace3D".</p> <p>The data is organized, the way it appears in the figures (only, those figures, where simulation data is rquired) in the manuscript and the folders are named accordingly.</p> <ul> <li>The intermediate growth stage simulation data were translated from Pace3D output data format to VTK data format. Using open-source software tools such as Paraview, the VTK files can be examined. Each subfolder's data files are likewise compressed (*.gz file extension). Data must be decompressed for visualization (e.g. with gzip, 7zip).</li> </ul>
Grey-brick buildings, an open data set of calibrated RC models of Dutch residential building heat dynamics
<p>Building thermal modeling is the founding stone upon which numerous carbon reduction strategies in the building sector are built. Yet, as of today, little to no interpretable and calibrated models founded on real-world measurements have been open-sourced. This work attempts to remedy this deficiency and renders public improved results of a recently published stochastic model identification of building heat dynamics study evaluated over 225 Dutch residential buildings. Calibrated lumped resistance-capacity models are made available, along with thermal characterizations of the buildings and reported meta-data. The paper discusses how open-access building thermal models support a collection of building service applications such as building performance benchmarks, model-based control, demand-side management, policy impact assessment, and data augmentation. Insights provided present a starting point for open access benchmarks of building thermal dynamics, paving the way toward new scientific discoveries from common standards.</p>
Data sets collected as part of the workshops conducted with secondary school teachers and TEL experts.
<p>This dataset contains all the material collected during the workshops with teachers and TEL experts. It consists of activities designed by teachers, canva indicating relevant information and platform designs by means of charts and illustrations. It is part of two studies published by Calvera-Isabal M. referenced below (one pending publication). </p> <p>This work has been funded by PID2020-112584RB-C33 funded by MCIN/AEI/10.13039/501100011033, the CS Track project, EU Horizon 2020 programme [grant agreement No 872522], grant for activities to increase the social impact of research in 2021 from Universitat Pompeu Fabra (UPF) and H2O Learn project PID2020-112584RB-C33 funded by MCIN/ AEI / 10.13039/501100011033.</p> <p>Please contact miriam.calvera@upf.edu for data availability.</p>
Q Score Segmented FAST5 Evaluation Data Set
<p>Q Score segmented raw FAST5 data set used for accuracy characterization of the novel Alignment Matrix soft decoding algorithm (<a href="https://doi.org/10.5281/zenodo.11454877">https://doi.org/10.5281/zenodo.11454877</a>) applied to the HEDGES DNA-information storage code. Implementation of the HEDGES code used for accuracy assessment of our algorithm is based on the publication of Press et al. (<a href="https://doi.org/10.1073/pnas.2004821117">https://doi.org/10.1073/pnas.2004821117</a>).</p> <p><strong> </strong></p> <p>Each archive in this data set generally corresponds to a certain design length and HEDGES rate. For example, 1250, 1667, 3333, and 5000 correspond to HEDGES rates of 0.125, 0.167, 0.33, and 0.5 respectively. Additionally, archives labeled with "half" and "quarter" indicate DNA molecule designs that are approximately half and quarter the length of archives labeled "full". Archives labeled with "s1" or "s2" correpsond to data for strands indexed as 1 and 2 for the 0.167 hedges "full" design. Within each archive are FAST5 directories that each correspond to Q Score segment ranges that were used to evaluate the impact of Q Score on soft decoding byte error rate. Each FAST5 directory is clearly labeled with the start and end Q Score value that was used to construct the data set. </p> <p> </p>
Spike-in and real-world proteomics data sets used in publication of PRONE
<p>Spike-in and real-world data sets used in the evaluation study by Arend et al. (see reference), and some utilized in the vignettes of PRONE, an R package designed for preprocessing, normalization, and performance evaluation of normalization methods of proteomics data. </p> <h3>Overview of the Data Sets</h3> <p>Due to the unavailability of proteomics quantification data from all original publications, data were extracted from alternative sources, which are also listed in the table below. Please refer to the paper's supplementary material and GitHub repository (https://github.com/lisiarend/PRONE.Evaluation) for more comprehensive information on the data sets. A processed metadata file and the protein quantification data file are provided for all data sets. The original quantification data used to generate these two files for each data set are consistently provided in the `original_data` directory of each data set.</p> <p> </p> <div> <table> <tbody> <tr> <td> <p>Data Set</p> </td> <td> <p>Type</p> </td> <td> <p>Quantification Type</p> </td> <td> <p>Raw Data (ID)</p> </td> <td> <p>Quantification Data</p> </td> </tr> <tr> <td> <p>dS1</p> </td> <td> <p>UPS1 spike-in </p> <p>(4 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Tabb et al. <a href="https://www.zotero.org/google-docs/?gkmf6j">[1]</a></p> </td> <td> <p>Välikangas et al. <a href="https://www.zotero.org/google-docs/?Tt81W8">[10]</a> </p> </td> </tr> <tr> <td> <p>dS2</p> </td> <td> <p>UPS1 spike-in </p> <p>(6 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Ramus et al. <a href="https://www.zotero.org/google-docs/?HP8b9X">[2]</a> (PXD001819)</p> </td> <td> <p>Graw et al. <a href="https://www.zotero.org/google-docs/?aiEVfL">[11]</a></p> </td> </tr> <tr> <td> <p>dS3</p> </td> <td> <p>E.coli spike-in </p> <p>(5 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Shen et al. <a href="https://www.zotero.org/google-docs/?zHIDZy">[3]</a></p> <p>(PXD003881)</p> </td> <td> <p>Sticker et al. <a href="https://www.zotero.org/google-docs/?aZ7oZu">[12]</a></p> </td> </tr> <tr> <td> <p>dS4</p> </td> <td> <p>E.coli spike-in </p> <p>(2 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Cox et al. <a href="https://www.zotero.org/google-docs/?scM19M">[4]</a></p> <p>(PXD00279)</p> </td> <td> </td> </tr> <tr> <td> <p>dS5</p> </td> <td> <p>E.coli spike-in </p> <p>(3 levels)</p> </td> <td> <p>TMT 10-plex (1)</p> </td> <td> <p>Zhu et al. <a href="https://www.zotero.org/google-docs/?qAz08E">[5]</a></p> <p>(PXD013277)</p> </td> <td> <p>Phil Wilmarth <a href="https://www.zotero.org/google-docs/?i7xmgP">[13]</a></p> </td> </tr> <tr> <td> <p>dS6</p> </td> <td> <p>yeast spike-in </p> <p>(3 levels)</p> </td> <td> <p>TMT 11-plex (1)</p> </td> <td> <p>O’Connell et al. <a href="https://www.zotero.org/google-docs/?C2XoaJ">[6]</a></p> <p>(PXD007683)</p> </td> <td> <p>Ammar et al. <a href="https://www.zotero.org/google-docs/?UWcyly">[14]</a></p> </td> </tr> <tr> <td> <p>dR1</p> </td> <td> <p>Osteogenic differentiation of hPCLSCs (4 time points)</p> </td> <td> <p>TMT 6-plex (3)</p> </td> <td> <p>Li et al. <a href="https://www.zotero.org/google-docs/?BouGSs">[8]</a> (PXD020908)</p> </td> <td> <p>MaxQuant executed in-house</p> </td> </tr> <tr> <td> <p>dR2</p> </td> <td> <p>Prospective Ovarian JHU Proteome</p> </td> <td> <p>TMT 10-plex (13)</p> </td> <td> <p>Hu et al. <a href="https://www.zotero.org/google-docs/?kVn4pf">[9]</a></p> <p>(PDC000110)</p> </td> <td> <p>MaxQuant executed in-house</p> </td> </tr> <tr> <td> <p>dR3</p> </td> <td> <p>AROM+ transgenic vs. wild-type mice</p> </td> <td> <p>LFQ</p> </td> <td> <p>Vehmas et al. <a href="https://www.zotero.org/google-docs/?qwYM2A">[7]</a></p> <p>(PXD002025)</p> </td> <td> </td> </tr> <tr> <td> <p>dR4</p> </td> <td> <p>Mycobacterium tuberculosis</p> <p>(healthy, disease vs. treated)</p> </td> <td> <p>TMT 10-plex (2)</p> </td> <td> <p>Schmidt et al. <a href="https://www.zotero.org/google-docs/?m1VHIG">[15]</a></p> <p>(PXD030883)</p> </td> <td> </td> </tr> </tbody> </table> </div> <h3>References</h3> <p>[1] D. L. Tabb et al., ‘Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography−Tandem Mass Spectrometry’, J. Proteome Res., vol. 9, no. 2, pp. 761–776, Feb. 2010, doi: 10.1021/pr9006365.<br>[2] C. Ramus et al., ‘Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods’, Data Brief, vol. 6, pp. 286–294, Mar. 2016, doi: 10.1016/j.dib.2015.11.063.<br>[3] X. Shen et al., ‘IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts’, Proc. Natl. Acad. Sci., vol. 115, no. 21, pp. E4767–E4776, May 2018, doi: 10.1073/pnas.1800541115.<br>[4] J. Cox, M. Y. Hein, C. A. Luber, I. Paron, N. Nagaraj, and M. Mann, ‘Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ *’, Mol. Cell. Proteomics, vol. 13, no. 9, pp. 2513–2526, Sep. 2014, doi: 10.1074/mcp.M113.031591.<br>[5] Y. Zhu et al., ‘DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis *’, Mol. Cell. Proteomics, vol. 19, no. 6, pp. 1047–1057, Jun. 2020, doi: 10.1074/mcp.TIR119.001646.<br>[6] J. D. O’Connell, J. A. Paulo, J. J. O’Brien, and S. P. Gygi, ‘Proteome-Wide Evaluation of Two Common Protein Quantification Methods’, J. Proteome Res., vol. 17, no. 5, pp. 1934–1942, May 2018, doi: 10.1021/acs.jproteome.8b00016.<br>[7] A. P. Vehmas et al., ‘Liver lipid metabolism is altered by increased circulating estrogen to androgen ratio in male mouse’, J. Proteomics, vol. 133, pp. 66–75, Feb. 2016, doi: 10.1016/j.jprot.2015.12.009.<br>[8] J. Li et al., ‘Dynamic proteomic profiling of human periodontal ligament stem cells during osteogenic differentiation’, Stem Cell Res. Ther., vol. 12, no. 1, p. 98, Feb. 2021, doi: 10.1186/s13287-020-02123-6.<br>[9] Y. Hu et al., ‘Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma’, Cell Rep., vol. 33, no. 3, p. 108276, Oct. 2020, doi: 10.1016/j.celrep.2020.108276.<br>[10] T. Välikangas, T. Suomi, and L. L. Elo, ‘A systematic evaluation of normalization methods in quantitative label-free proteomics’, Brief. Bioinform., vol. 19, no. 1, pp. 1–11, Jan. 2018, doi: 10.1093/bib/bbw095.<br>[11] S. Graw et al., ‘proteiNorm – A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification’, ACS Omega, vol. 5, no. 40, pp. 25625–25633, Oct. 2020, doi: 10.1021/acsomega.0c02564.<br>[12] A. Sticker, L. Goeminne, L. Martens, and L. Clement, ‘Robust Summarization and Inference in Proteome-wide Label-free Quantification’, Mol. Cell. Proteomics, vol. 19, no. 7, pp. 1209–1219, Jul. 2020, doi: 10.1074/mcp.RA119.001624.<br>[13] ‘understanding_IRS’. Accessed: Mar. 07, 2024. [Online]. Available: https://pwilmart.github.io/IRS_normalization/understanding_IRS.html<br>[14] C. Ammar, M. Gruber, G. Csaba, and R. Zimmer, ‘MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins[S]’, Mol. Cell. Proteomics, vol. 18, no. 9, pp. 1880–1892, Sep. 2019, doi: 10.1074/mcp.RA119.001509.<br>[15] F. Biadglegne et al., ‘Mycobacterium tuberculosis Affects Protein and Lipid Content of Circulating Exosomes in Infected Patients Depending on Tuberculosis Disease State’, Biomedicines, vol. 10, no. 4, p. 783, Mar. 2022, doi: 10.3390/biomedicines10040783.</p>
Bruker NMR data set for journal article: 3.4. Understanding the Microstructure Connectivity in Photopolymerizable Aluminum-Phosphate-Silicate Sol−Gel Hybrid Materials for Additive Manufacturing
<p>Solid state fast MAS 1H data for hybrid polymerizable compounds. </p>
Data Set for Manuscript: Citrullination, a novel post-translational modification of elastin, is involved in COPD Pathogenesis
<p>Elastin is an extracellular matrix protein (ECM) that supports elasticity of the lung, and in patients with chronic obstructive pulmonary disease (COPD) and emphysema, the structural changes that reduce the amount of elastic recoil, lead to loss of pulmonary function. We recently demonstrated that elastin is a target of peptidyl arginine deiminase (PAD) enzyme-induced citrullination, thereby leading to enhanced susceptibility of this ECM protein to proteolysis. The current study aimed to investigate the impact of PAD activity in vivo and furthermore assessed whether pharmacological inhibition of PAD activity protects against pulmonary emphysema. </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.