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1,773 results for “Supplementary data,”
Supplementary material for "Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences"
<p>This material supplements the following conference publication:</p> <p>Winkler, Rosenthal, Strecker (2024). "Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences". Modellierung 2024.</p>
Supplementary Data for: A time-calibrated 'Tree of Life' of aquatic insects for knitting historical patterns of evolution and measuring extant phylogenetic biodiversity across the world
<p>This compendium of files includes the dated phylogenetic tree in Newick format (<strong>Data S1</strong>), the list of statistical routines used for the three empirical case studies (<strong>Data S2</strong>), and the high-resolution version of the figures in the supplementary materials and main text (<strong>Data S3</strong>) for the <em>Earth-Science Reviews</em> paper "A time-calibrated ‘Tree of Life’ of aquatic insects for knitting historical patterns of evolution and measuring extant phylogenetic biodiversity across the world", which is under consideration. The best-scoring molecular tree (<strong>Data S1</strong>) can be opened using freely available programs like R (R Development Core Team, 2021), Dendroscope (Huson and Scornavacca, 2012), and FigTree (Rambaut, 2018).</p> <p>Please, feel free to send an email to the maintainer Dr. Jorge García Girón (jogarg@unileon.es OR Jorge.Garcia-Giron@oulu.fi) if you face any trouble downloading, opening, or using these files.</p> <ul> <li>Huson, D. H., & Scornavacca, C. (2012). Dendroscope 3: An interactive tool for rooted phylogenetic trees and networks. <em>Systematic Biology</em>, <em>61(6)</em>, 1061–1067.</li> <li>R Development Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/</li> <li>Rambaut, A. (2018). FigTree. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK. http://tree.bio.ed.ac.uk/software/figtree/</li> </ul>
Supplementary data and code for: Non-Abelian Hyperbolic Band Theory from Supercells
<p>This data and code collection contains all necessary code and data required to reproduce the results of the accompanied publication. To run the code contained in this collection GAP, the HyperCells package, Mathematica, the NCAlgebra (version >= 6.0) and the HyperBloch package are required. Please refer to the included Readme file and the links therein for more details.</p> <p>Changes: add NCAlgebra to list of dependencies.</p>
Supplementary data and scripts for the publication "Discovery of numerous novel Helitron-like elements in eukaryote genomes using HELIANO"
<p>##Information of directories. Each directory contains necessary data and scripts to do the corresponding analysis</p> <p>1. Benchmarking/: benchmarking analysis for heliano. (For Figure 3, Supplementary Table S2)<br>2. GeneCapture/: analysis for captured genes within HLEs. (For Figure 6, Supplementary Table S11)<br>3. HLE_model_species/: analysis for heliano results on three model species. (For Table 1, Figure 4, Supplementary Table S5-9, Supplementary Figure 4)<br>4. HLE_sampled_species/: analysis for heliano results on 404 sampled species. (For Figure 5, Supplementary Table S10, Supplementary Figure 5)<br>5. hmm_model/: build hmm model for Hel and Rep domains. (For RepHel.hmm that is important to detect and classify HLEs)<br>6. Phylogenetical_tree/: making phylogenetical trees. (For Figure 1, Supplementary Figure 2-3)<br>7. Sample_species/: about how to sample species from various genome assemblies. (Supplementary Table S3)<br>8. HLE_feature/: information about the position of RepHel domain and the length of HLE sequences. (Supplementary Figure 1)</p>
Supplementary Data for "Streamlining Vocabulary Conversion to SKOS: A YAML-based Approach to Facilitate Participation in the Semantic Web"
<p>This dataset contains quality assessment results for 26 vocabularies. The assessment was conducted using the <a href="https://skos-play.sparna.fr/skos-testing-tool/">qSKOS vocabulary quality assessment tool</a>.</p> <p>The 26 assessed vocabularies were converted from their original formats into the Simple Knowledge Organization System (SKOS) data model using the approach described in our paper titled <a href="https://doi.org/10.1007/978-3-031-62362-2_9">"Streamlining Vocabulary Conversion to SKOS: A YAML-based Approach to Facilitate Participation in the Semantic Web"</a>, presented at the <a href="https://doi.org/10.1007/978-3-031-62362-2">24th International Conference on Web Engineering (ICWE 2024)</a>.</p> <p>The dataset contains a quality assessment for the following vocabularies:</p> <ol> <li>A Taxonomy of Evaluation Towards Standards</li> <li>Cross-Device Taxonomy</li> <li>What Makes a Data-driven Business Model? A Consolidated Taxonomy</li> <li>DDI Aggregation Method</li> <li>DDI Mode of Collection</li> <li>Building a New Taxonomy for Data Discretization Techniques</li> <li>Demopaedia</li> <li>Data Science Glossary</li> <li>A Taxonomy of Evaluation Approaches in Software Engineering</li> <li>Evaluation Thesaurus</li> <li>The Glossary of Human Computer Interaction</li> <li>Human-Factors Taxonomy</li> <li>A Taxonomy to Structure and Analyze Human–Robot Interaction</li> <li>A Taxonomy of Interaction for Instructional Multimedia</li> <li>A Taxonomy of Interrogation Methods</li> <li>Design Vocabulary for Human–IoT Systems Communication</li> <li>Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors</li> <li>Thesaurus Mass Communication</li> <li>Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey</li> <li>A Taxonomy of Quality of Service and Quality of Experience of Multimodal Human-Machine Interaction</li> <li>A Human-Centered Taxonomy of Interaction Modalities and Devices</li> <li>A Taxonomy of Spatial Interaction Patterns and Techniques</li> <li>A Taxonomy of Social Errors in Human-Robot Interaction</li> <li>Taxonomy of Digital Research Activities in the Humanities</li> <li>Virtual Reality and the CAVE: Taxonomy, Interaction Challenges and Research Directions </li> <li>Cross-Device Interaction</li> </ol>
Overcoming confusion and stigma in habitat fragmentation research - supplementary data and R code
<p>Data and code necessary to produce results and figures for the manuscript:</p> <p>Riva, Koper and Fahrig (2024). "Overcoming confusion and stigma in habitat fragmentation research". Biol Rev. Accepted conditional on minor revisions. </p>
Supplementary data to "Radiative forcing and equivalent effective chlorine due to hydrochlorofluorocarbons peaked in 2021"
<p><span>README for Supplementary data to “</span><span>Radiative forcing and equivalent effective chlorine due to hydrochlorofluorocarbons peaked in 2021”</span></p> <p> </p> <p><span>This repository contains 4 folders:</span></p> <p><span>1) agage: contains the inputs to the 12-box model and the derived monthly and annual mole fractions (global and semi-hemispheric) using measurements from the AGAGE network.</span></p> <p><span>2) noaa: contains the inputs to the 12-box model and the derived monthly and annual mole fractions (global and semi-hemispheric) using measurements from the NOAA network.</span></p> <p><span>3) vollmer: contains the inputs to the 12-box model and the derived monthly and annual mole fractions (global and semi-hemispheric) using measurements from the measurements published in Vollmer et al. (2021).</span></p> <p><span>4) Projections: contains a csv file with the merged mole fractions (i.e., mean) from the various networks and the projected quantities.</span></p> <p> </p> <p><span>The 12-box model and the method used to quantify global mean mole fractions are available via GitHub (https://github.com/mrghg/py12box (last accessed 5 March 2024) and https://github.com/mrghg/py12box_invert (last accessed 5 March 2024)) and Zenodo (https://doi.org/10.5281/zenodo.6857447 and https://doi.org/10.5281/zenodo.6857794).</span></p> <p> </p> <p><span>AGAGE data are also available at http://agage.mit.edu/data/agage-data (last accessed 5 March 2024) and https://data.ess-dive.lbl.gov/ (current dataset <a href="https://doi.org/10.15485/1998580"><span>https://doi.org/10.15485/1998580</span></a>) and newer data can be made available upon request. The most recent NOAA atmospheric observations are available at https://gml.noaa.gov/aftp/data/hats/hcfcs/ (last accessed 5 March 2023). </span></p> <p> </p> <p><span>References:</span></p> <p><span>Vollmer, M. K. et al. Unexpected nascent atmospheric emissions of three ozone-depleting hydrochlorofluorocarbons. Proc Natl Acad Sci USA 118, e2010914118 (2021).</span></p>
Rules of River Avulsion Supplementary Data Files
<p>This dataset contains the data gathered and analyzed for the rules of river avulsion manuscript.</p> <p>CHANGELOG:</p> <p>0.0.2: BUG FIX: Updated a few avulsion foldernames/filenames that didn't match the supplementary tables (eg. "BETSI" instead of "BETSIBOKA_2004"), which threw errors in the visualization routines</p> <p>0.0.3: Changed plotting code for better visualization, added the correct examples for Fig. 4 as some were changed during revision. updated data names and measurements for A018, A002, B4, G9 (n=4, 1 datapoint each, cross sections were extended further away from the channel to capture the full range of near-channel topography)</p>
Supplementary Data for ZeroPM D2.3 - Lessons learnt from previous applications of the essential-use concept
<p>This dataset contains the data collected for all the studies which are mentioned in the ZeroPM deliverable D2.3 titled "Lessons learnt from previous applications of the essential-use concept".</p>
Data, code, and supplementary information for Hyracoid locus identification
<p>Serially homologous elements pose an identification problem in fragmentary records, particularly those of vertebrate fossils. Examples include individual vertebrae in the vertebral column and teeth in a tooth row. Until an isolated element can be accurately attributed to a specific position within its series, multiple lines of ecological and evolutionary research cannot be conducted. However, varying levels of differentiability between loci, and varying patterns of differentiation across clades, make it impossible to develop a single set of diagnostic traits for any particular set of serial homologs, particularly mammalian molars. Here, we test the utility of a set of classification criteria for distinguishing molar tooth positions of hyraxes (Mammalia, Afrotheria, Hyracoidea), which have been considered indistinguishable in previous taxonomic studies. As part of the test, we evaluate the degree to which between-locus variation is conservative in this taxon, which would strengthen the predictive power of proposed traits even in cases where species identity is unknown. Suitable tests for hypotheses of conservatism in categorical traits did not exist, to our knowledge, and we, therefore, explored the behavior of previously developed metrics, Borges et al.'s δ, to assess conservatism in contrast to the phylogenetic signal produced by Brownian motion. This metric shows some promise but the nature of resulting distributions makes tests difficult to interpret, indicating a line of potential future methods improvement. We used a linear morphometric characterization of shape to validate the candidate traits. In the case of hyracoid molars, relatively simple ratios of linear measurements have strong discriminatory power despite evolutionary variation in between-locus differences. Overall, new or understudied taxa are likely to have lower molar loci differentiable by their relative length and talonid vs. trigonid width.</p>
Supplementary data of article "Domestication has altered gene expression and secondary metabolites in pea seed coat".
<p><strong>Table S1.</strong> Excel- GO_terms_MF_selected_WGCNA_modules.</p> <p><strong>Table S2.</strong> Excel- GO_terms_MF_DEGs_UP_and_DOWN.</p> <p><strong>Table S3.</strong> Excel- GO_terms_MF_DEGs_summary.</p> <p><strong>Table S4.</strong> Excel- List of DEGs involved in flavonoid pathway found in WILD gene set.</p> <p><strong>Table S5.</strong> Protein recoveries calculated for individual pea protein samples. Numbers 1, 2, 3 denote treatment groups corresponding to seed developmental stages (D1, D2 and mature seeds, respectively). Letters a–d denote biological replicates within the treatment groups.</p> <p><strong>Table S6.</strong> Excel- Annotation of proteins differentially expressed in wild and domesticated pea seed coat samples.</p> <p><strong>Table S7.</strong> Primary metabolites identified by spectral similarity library search and/or co-elution with authentic standards in pea seed coats aqua methanolic extracts. Metabolite analysis relied on GC-EI-Q-MS analysis after derivatization of the lyophilized extracts with methoxamine hydrochloride (MOA) and <em>N</em>-methyl-<em>N</em>-(trimethylsilyl)trifluoroacetamide (MSTFA).</p> <p><strong>Table S8.</strong> Primary metabolites detected in the aq. methanolic extracts of mature Cameor seed coats demonstrating statistically significant up- and down-regulation in comparison to those of wild JI261.</p> <p><strong>Table S9.</strong> Primary metabolites of mature JI92 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of wild JI261.</p> <p><strong>Table S10.</strong> Primary metabolites of mature JI1794 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261.</p> <p><strong>Table S11.</strong> Primary metabolites of mature JI64 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261.</p> <p><strong>Table S12.</strong> Mass analyzer settings applied for QqTOF-MS experiments in analysis of seed coat (cell wall) hydrolyzates and reference authentic standards.</p> <p><strong>Table S13.</strong> Cell wall-bound metabolites extracted from the seed coats of wild (JI64, JI1794, JI261) and domesticated (Cameor, JI92) peas upon alkali hydrolysis of corresponding isolated and purified cell wall material.</p> <p><strong>Table S14.</strong> Excel- Coordinates of markers in S-plot obtained from OPLS-DA analysis (FIA-ESI-HRTMS, negative ionization, lock mass uncorrected).</p> <p><strong>Table S15.</strong> List of identified significantly differential metabolites rising during seed coat development.</p> <p><strong>Table S16.</strong> List of identified significantly differential metabolites decreasing during seed coat development (positive ionization mode).</p> <p><strong>Table S17.</strong> List of identified metabolites with significantly higher content in wild compared cultivated genotypes in older developmental stages (D5-6).</p> <p><strong>Table S18.</strong> Excel- Expression of genes encoding enzymes of monolignol pathway in seed coats (SC) and embryos (E) of domesticated (Cameor, JI92 and <em>Pisum abyssinicum</em> PI358617) and wild (JI64, JI1794, JI261) peas over five seed developmental stages (13, 17, 20, 23, 28 DAP, labelled as 1-5). PAL: phenylalanine ammonia-lyase, C4H: cinnamate-4-hydroxylase, 4CL: 4-coumaroyl: CoA ligase, HCT: hydroxycinnamoyl CoA:shikimate hydroxycinnamoyltransferase, COMT: caffeic acid O-methyltransferase, CSE: caffeoyl shikimate esterase, CAD: cinnamyl alcohol dehydrogenase, CCR: cinnamoyl CoA reductase, CCoAMT: caffeoyl CoA-3-methyltransferase, F5H: ferulate-5-hydroxylase</p> <p><strong>Table S19.</strong> Studied metabolites of phenylpropanoid pathway.</p> <p><strong>Table S20.</strong> Instrument settings used in the proteomics LIT-Orbitrap-MS and -MS/MS experiments.</p> <p><strong>Table S21.</strong> Procedures and specific settings for data processing and post-processing of the proteomics data.</p> <p><strong>Table S22.</strong> Gas chromatographic (GC) separation conditions and electron ionization-quadrupole-mass spectrometry (EI-Q-MS) settings for GC-EI-Q-MS analysis of the primary metabolites in pea seed coats.</p> <p><strong>Table S23.</strong> Chromatographic conditions used for UHPLC separation of seed coat (cell wall) hydrolyzates and reference authentic standards.</p> <p><strong>Table S24.</strong> Variable parameters of MS/cIMS/MS measurements.</p> <p><strong>Figure S1.</strong> The dynamics of gene expression between studied developmental stages within all genotypes (a) or among genotypes in particular developmental stages (b).</p> <p><strong>Figure S2.</strong> Twelve representative groups of transcription factors described within 20 gene modules of pea SC. Visualized by Cytoscape 3.9.0.</p> <p><strong>Figure S3.</strong> SDS-PAGE electropherograms of the total protein fractions isolated from the seed coats of JI92 (a, c, e) and JI64 (b, d, f) seeds before and after tryptic hydrolysis. Numbers 1, 2, 3 denote seed developmental stages: DS1, DS2 and mature seeds, respectively. Letters a-d denote biological replicates. The aliquots (10 μg) of samples before hydrolysis (a, b), the incompletely digested aliquots left on filter unit after peptide elution (c, d) and aliquots of tryptic hydrolysates (corresponding to 5 μg of protein), (e, f) were loaded on gels. Inter-gel normalization relied on the total density of the Protein Ladder (PageRuler™ Prestained Protein Ladder #26616, 10–180 kDa) lane (St); the ND (non-digested) sample represents a reference protein not subjected to hydrolysis.</p> <p><strong>Figure S4.</strong> The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups) (c) identified in domesticated JI92 seed coats at developmental stages D1, D2 and D6. The tryptic digests (<em>n</em> =&thinsp;3) obtained from seed coats were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode.</p> <p><strong>Figure S5.</strong> The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups, c) identified in wild pea JI64 seed coats at D1, D2 and D6 stages. The tryptic digests (<em>n</em> =&thinsp;3), obtained from pea seedlings, were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode.</p> <p><strong>Figure S6.</strong> Principal component analysis (PCA) with score plot representation (a) accomplished for seed coat proteins differentially expressed at developmental stages D1 and D2 and in the mature state (D6) and hierarchical clustering with a heatmap representation (b).</p> <p><strong>Figure S7.</strong> Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D1. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of domesticated JI92 and wild JI64, respectively.</p> <p><strong>Figure S8.</strong> Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D2. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of the domesticated JI92 and wild JI64, respectively.</p> <p><strong>Figure S9.</strong> Prediction of sub-cellular localization of the proteins more expressed in the developing seeds of JI92 and JI64 with the BUSCA prediction tool.</p> <p><strong>Figure S10.</strong> Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated Cameor by principal component analysis (PCA).</p> <p><strong>Figure S11.</strong> Representation of the differences in the metabolic profiles of the mature seed coats obtained from the wild JI261 and domesticated Cameor by the t-test with Volcano plot representation (a) and the top 30 differentially abundant metabolites demonstrating the most pronounced differences of corresponding GC-MS signals associated with seed dormancy (b).</p> <p><strong>Figure S12.</strong> Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated JI92 by principal component analysis (PCA) with score plot representation (a) and hierarchical clustering with heatmap representation (b).</p> <p><strong>Figure S13.</strong> Principal component analysis (PCA) illustrating distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261.</p> <p><strong>Figure S14.</strong> Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild pea genotypes, JI64 and JI261(control).</p> <p><strong>Figure S15.</strong> Evaluation of the differences in the patterns of the cell wall-bound metabolites obtained from mature seed coats of wild JI261 and domesticated Cameor: principal component analysis (PCA) with score plot representation (a), hierarchical clustering with heatmap representation (b) and <em>t</em>-test analysis with the Volcano-plot representation (c).</p> <p><strong>Figure S16.</strong> Statistical analysis (<em>t</em>-test with Volcano plot representation) characterizing the differences between the levels of mature seed coat cell wall-bound metabolites of Cameor compared with those of wild JI261.</p> <p><strong>Figure S17.</strong> Principal component analysis (PCA) illustrates the distribution of mature seed coat metabolic profiles of domesticated JI92 and wild JI261.</p> <p><strong>Figure S18.</strong> Principal component analysis (PCA) shows the distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261, control.</p> <p><strong>Figure S19.</strong> Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild genotypes, JI64 and control JI261.</p> <p><strong>Figure S20.</strong> Annotated cell wall-bound metabolites extracted from the seed coats of the dormant wild pea genotype JI261 and the seed coats from several pea genotypes varying in their dormancy (Cameor, JI92, JI64, and JI1794) upon alkali hydrolysis of corresponding isolated and purified cell wall material. </p> <p><strong>Figure S21.</strong> Ion mobility separation of <em>m/z</em> 299.0841.</p> <p><strong>Figure S22.</strong> Ion mobility separation of <em>m/z</em> 701.1907. </p> <p><strong>Figure S23.</strong> Ion mobility separation of <em>m/z</em> 619.1041.</p> <p><strong>Figure S24.</strong> Ion mobility separation of <em>m/z</em> 631.1017.</p> <p><strong>Figure S25.</strong> Ion mobility separation of <em>m/z</em> 641.1139.</p> <p><strong>Figure S26.</strong> Ion mobility separation of <em>m/z</em> 771.1346. </p> <p><strong>Figure S27.</strong> Reconstructed chromatograms of p-hydroxybenzoic and salicylic acids in DS5 of dormant JI64 and domesticated landraces JI92 (LC/HRTMS, negative ionization mode).</p> <p><strong>Figure S28.</strong> Module-trait relationship depiction showing the correlation between expression of the gene modules and the abundance of identified metabolites of the monolignol pathway.</p>
Supplementary data for the paper: "Enhancing concrete durability in chloride-rich environments through manual application of healing agents"
<p>Supplementary data for the paper: “Enhancing concrete durability in chloride-rich environments through manual application of healing agents”.<br><br>Open data concerning experimental work. The paperinvestigates the use of three potential healing agents: water-repellent agent (WRA), sodium silicate (SS), and polyurethane (PU). The agents are tested separately under two conditions: exposure to a 3.3% NaCl concentration at 20°C and exposure to cyclic freeze-thaw conditions. The agents are manually injected into the cracks and evaluated for their healing efficiency using the capillary absorption test and microscopy analysis. Additionally, their resistance to freeze-thaw cycles by monitoring mass loss and chloride bulk diffusion is measured using sprayed silver nitrate, titration, and EDX mapping. </p>
Supplementary Data for 'Transformers Significantly Improve Splice Site Prediction'
<p><strong>Description:</strong></p> <p>This repository contains supplementary data accompanying the manuscript <strong>"Transformers Significantly Improve Splice Site Prediction"</strong>. The data includes annotations used for training our splice site prediction models and the predictions made by our model and SpliceAI 10k. These datasets are provided to facilitate replication of our results and to support further research in RNA splicing and machine learning applications in genomics.</p> <p><strong>Contents:</strong></p> <ol> <li> <p><strong>Annotations Used for Model Training:</strong></p> <ul> <li> <p><strong>clinvar_splice_variants.tsv</strong></p> <ul> <li><em>Description:</em> Contains detailed information about ClinVar splice variants used in our study.</li> <li><em>Contents Include:</em> Variant identifiers, genomic coordinates, associated clinical significance, and relevant annotations.</li> </ul> </li> <li> <p><strong>splice_site_annotation_gtex.tsv</strong></p> <ul> <li><em>Description:</em> Splice site annotations derived from all tissues in GTEx V8.</li> <li><em>Contents Include:</em> Coordinates of splice sites and transcript information.</li> </ul> </li> <li> <p><strong>splice_site_annotation_icelandic_whole_blood_plus_gtex.tsv</strong></p> <ul> <li><em>Description:</em> Splice site annotations derived from a combination of Icelandic whole blood samples and samples from all tissues in GTEx V8.</li> <li><em>Contents Include:</em> Coordinates of splice sites and transcript information.</li> </ul> </li> </ul> </li> <li> <p><strong>Model Predictions:</strong></p> <p><strong>a. SpliceAI 10k Model Predictions:</strong></p> <ul> <li> <p><strong>spliceai_10k_clinvar_delta.vcf</strong></p> <ul> <li><em>Description:</em> SpliceAI 10k delta scores for ClinVar splice variants.</li> <li><em>Contents Include:</em> Variant Call Format (VCF) file containing delta scores that indicate the predicted impact on splicing for ClinVar variants.</li> </ul> </li> <li> <p><strong>spliceai_10k_no_sqtl_delta.vcf</strong></p> <ul> <li><em>Description:</em> SpliceAI 10k delta scores for variants unlikely to be splicing quantitative trait loci (sQTLs) in Icelandic whole blood.</li> <li><em>Contents Include:</em> VCF file with delta scores for variants not associated with sQTLs.</li> </ul> </li> <li> <p><strong>spliceai_10k_sqtl_delta.vcf</strong></p> <ul> <li><em>Description:</em> SpliceAI 10k delta scores for sQTLs detected in Icelandic whole blood.</li> <li><em>Contents Include:</em> VCF file with delta scores for variants identified as sQTLs.</li> </ul> </li> </ul> <p><strong>b. Transformer 45k Model Predictions:</strong></p> <ul> <li> <p><strong>transformer_45k_clinvar_delta.vcf</strong></p> <ul> <li><em>Description:</em> Transformer 45k delta scores for ClinVar splice variants.</li> <li><em>Contents Include:</em> VCF file with delta scores from our Transformer model, indicating the predicted impact on splicing.</li> </ul> </li> <li> <p><strong>transformer_45k_no_sqtl_delta.vcf</strong></p> <ul> <li><em>Description:</em> Transformer 45k delta scores for variants unlikely to be sQTLs in Icelandic whole blood.</li> <li><em>Contents Include:</em> VCF file with delta scores for variants not associated with sQTLs, as predicted by our model.</li> </ul> </li> <li> <p><strong>transformer_45k_sqtl_delta.vcf</strong></p> <ul> <li><em>Description:</em> Transformer 45k delta scores for sQTLs detected in Icelandic whole blood.</li> <li><em>Contents Include:</em> VCF file with delta scores for variants identified as sQTLs, based on our Transformer model predictions.</li> </ul> </li> </ul> </li> </ol> <p><strong>Additional Information:</strong></p> <ul> <li> <p><strong>Delta Scores and Their Interpretation:</strong></p> <ul> <li>Each variant is assessed for its potential impact on splicing through four delta scores: <ul> <li><strong>Acceptor Site Creation (<code>top_a_creation_delta</code>):</strong> Predicts the likelihood of creating a new acceptor site.</li> <li><strong>Acceptor Site Disruption (<code>top_a_disruption_delta</code>):</strong> Predicts the likelihood of disrupting an existing acceptor site.</li> <li><strong>Donor Site Creation (<code>top_d_creation_delta</code>):</strong> Predicts the likelihood of creating a new donor site.</li> <li><strong>Donor Site Disruption (<code>top_d_disruption_delta</code>):</strong> Predicts the likelihood of disrupting an existing donor site.</li> </ul> </li> <li><strong>Final Delta Score Calculation:</strong> <ul> <li>The overall impact of a variant is determined by taking the maximum of these four delta scores: <div> <div><code>final_delta_score = <span>max</span>(top_a_creation_delta, top_a_disruption_delta, top_d_creation_delta, top_d_disruption_delta) </code></div> </div> </li> <li>A higher final delta score indicates a greater predicted impact on splicing.</li> </ul> </li> <li><strong>Positions:</strong> <ul> <li>The positions (<code>*_pos</code>) indicate the genomic coordinates where the predicted splicing events occur, providing insight into the specific locations affected by the variant.</li> </ul> </li> </ul> </li> </ul> <ul> <li> <ul> <li><strong>Interpreting Delta Scores:</strong> <ul> <li>Delta scores range from 0 to 1.</li> <li>Scores closer to 1 suggest a higher probability of the variant affecting splicing.</li> </ul> </li> </ul> </li> <li> <p><strong>Purpose:</strong></p> <ul> <li>These datasets support the findings reported in our manuscript by providing the raw data used for model training and evaluation.</li> <li>Researchers can use these datasets to replicate our experiments, compare model performances, or conduct further studies on splice site prediction.</li> </ul> </li> </ul> <p> </p> <p><strong>Citation:</strong></p> <p>Please cite this dataset as:</p> <blockquote> <p>B.A. Jónsson, G.H. Halldórsson, S. Árdal, S. Rögnvaldsson, E. Einarsson, P. Sulem, D.F. Guðbjartsson, P. Melsted, K. Stefánsson, M.Ö. Úlfarsson (2023). Supplementary Data for "Transformers Significantly Improve Splice Site Prediction". Zenodo. <a target="_new" rel="noopener">https://doi.org/10.5281/zenodo.14109868</a></p> </blockquote> <p> </p> <p><strong>Contact Information:</strong></p> <p>For questions or further information, please contact:</p> <ul> <li><strong>Benedikt A. Jónsson</strong></li> <li><strong>Affiliation:</strong> deCODE Genetics/Amgen, Inc., Reykjavik, Iceland</li> <li><strong>Email: </strong>benediktj@decode.is</li> </ul>
SUPPLEMENTARY DATA TO: Using a citizen science approach to assess nanoplastics pollution in remote high-altitude glaciers
<p>This is the repository of the supplementary data, and it contains the following files: </p> <p>Raw data files as the original output of TD-PTR-ToF-MS for all the samples, all the blanks, all the spikes and all the calibration runs (.h5 files in three zip arcives)</p> <p>Polymer library files (a zip archive including csv files.</p> <p>A data analysis file including raw data, blank subtraction and LOD correction of all measurements (xlsx file).</p> <p>A fingerprinting result file for each plastic type (xlsx file)</p> <p>A data analysis file after plastic fingerprinting (xlsx file). </p> <p> </p> <p> </p>
Supplementary data frames, AlphaFold models, Normal Mode Analysis (NMA) Data, and NMA of Corresponding NMR Ensembles in the S2RCI, MD, and S2 Datasets for "Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics"
<h1><strong>Changes applied to V2</strong></h1> <p>In addition to the supplementary dataframes and AlphaFold models from each dataset in V1, V2 includes the additional data outlined below.</p> <p>The <strong>S2RCI</strong> and <strong>MD</strong> datasets include comprehensive analyses of AlphaFold2 models (both before and after truncation). These datasets feature: </p> <ul> <li><strong>AlphaFold2 Models</strong>: Both original and truncated structures. </li> <li><strong>WEBnma Modes</strong>: `modes.txt` files generated from WEBnma analysis, available for both non-truncated and truncated AF2 models. </li> <li><strong>Root-Mean-Square-Fluctuations (RMSF)</strong>: Profiles calculated before and after truncation of AF2 models. </li> <li><strong>NMR Data: Normal Mode Analysis (NMA)</strong>: Performed on corresponding NMR ensembles (see below). </li> </ul> <p> </p> <p>The <strong>NMR Data</strong> of NMA in these datasets includes: </p> <ul> <li>NMR ensembles </li> <li>Individual NMR models extracted from each ensemble </li> <li>STRIDE secondary structure calculations per-individual NMR models</li> <li>RMSF profiles per-individual NMR models</li> </ul> <p>For detailed information, please refer to the `Readme.txt` file within each corresponding folder. </p> <p>The <strong>S2 dataset</strong> includes all the features listed above, except for the NMR analysis.</p>
Supplementary data to "The effects of small-scale heterogeneity on biomonitoring of desmid phytobenthos in Central European temperate mountain peatlands"
<p>The supplementary data consist of the files including the species-in-samples data and their associated NCV scores used for the analyses described in the manuscript submitted to hydrobiologia. In addition, two R scripts used for the analyses are included, too.</p> <p> </p>
Supplementary data to "GD-1 STELLAR STREAM AND COCOON IN THE DESI EARLY DATA RELEASE"
<p>Supplementary material to DESI's publication "GD-1 STELLAR STREAM AND COCOON IN THE DESI EARLY DATA RELEASE" to comply with the data management plan. Data associated with revised version of paper submitted to The Astrophysical Journal. Directory contains data points for all figures with file names indicating the figure to which the data corresponds as well as a FITS file for Table 4. Data for 13 figures and one table (46 FITS files) included in .zip directory</p>
Supplementary data for "Mechanism of surface solar irradiance variability under broken cloud cover"
<p>Open Data for manuscript to be submitted in ACP: "Mechanisms of surface solar irradiance variability under broken clouds". Refer to the README for details. Most (larger) files within the .zip archives are gzipped. Use `gunzip` to decompress.</p>
Supplementary data for: Chromosome-scale genome assemblies of aphids reveal extensively rearranged autosomes and long-term conservation of the X chromosome
<p><strong><em>Myzus persicae </em>clone O v2 frozen release</strong></p> <p>Genome assembly: Myzus_persicae_O_v2.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3</p> <p>List of gene models containing internal stop codons (removed from the protein and cds fasta files): Myzus_persicae_O_v2.0.scaffolds.braker2.bad_genes.lst</p> <p>BRAKER2 protein sequences: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.cds.fa</p> <p>BRAKER2 coding sequences (longest transcript per gene only): Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.cds.LTPG.fa</p> <p><em>De novo </em>repeat library (ReapeatModeler merged with repbase insecta): Myzus_persicae_O_v2.0_repeat_lib.repeatmodeler_merged_repbase_insecta.fa</p> <p>RepeatMasker transposable element annotation using the <em>M. persicae de novo</em> repeat library: Myzus_persicae_O_v2.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff.out</p> <p>RepeatMasker transposable element annotation using the <em>M. persicae</em> <em>de novo r</em>epeat library (gff format): Myzus_persicae_O_v2.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff</p> <p><strong><em>Acyrthosiphon pisum</em> clone JIC1 v1 frozen release</strong></p> <p>Genome assembly: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff</p> <p>List of gene models containing internal stop codons (removed from the protein and cds fasta files): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.bad_genes.lst</p> <p>BRAKER2 protein sequences: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.cds.fa</p> <p>BRAKER2 coding sequences (longest transcript per gene only): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.cds.LTPG.fa</p> <p><em>De novo </em>repeat library (ReapeatModeler merged with repbase insecta): Acyrthosiphon_pisum_JIC1_repeat_lib.repeatmodeler_merged_repbase_insecta.fa</p> <p>RepeatMasker transposable element annotation using the <em>A. pisum</em> <em>de novo</em> repeat library: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.out</p> <p>RepeatMasker transposable element annotation using the <em>A. pisum de novo</em> repeat library (gff format): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff</p> <p><strong><em>Rhodnius prolixus</em> DNA zoo chromosome-scale genome assembly annotation</strong></p> <p><em>R. prolixus </em>chromosome-scale genome assembly was obtained here: <a href="https://www.dnazoo.org/assemblies/Rhodnius_prolixus">https://www.dnazoo.org/assemblies/Rhodnius_prolixus</a>.</p> <p>Genome assembly: Rhodnius_prolixus-3.0.3_HiC.fasta</p> <p>BRAKER2 gene models: Rhodnius_prolixus-3.0.3_HiC.braker2.gff</p> <p>BRAKER2 protein sequences: Rhodnius_prolixus-3.0.3_HiC.braker2.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Rhodnius_prolixus-3.0.3_HiC.braker2.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Rhodnius_prolixus-3.0.3_HiC.braker2.gff.cds.fa</p> <p><strong><em>Triatoma rubrofasciata</em> chromosome-scale genome assembly annotation</strong></p> <p><em>T. rubrofasciata </em>chromosome-scale genome assembly was obtained here: <a href="http://dx.doi.org/10.5524/100614">http://dx.doi.org/10.5524/100614</a></p> <p>Genome assembly: zhuichun_assembly.fasta</p> <p>BRAKER2 gene models: zhuichun_assembly.braker2.gff</p> <p>BRAKER2 protein sequences: zhuichun_assembly.braker2.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): zhuichun_assembly.braker2.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: zhuichun_assembly.braker2.gff.cds.fa</p> <p><strong>Hemiptera orthogroups and species tree</strong></p> <p>OrthoFinder was used to cluster proteomes of 14 Hemiptera into orthogroups for phylogenomic analysis. All proteomes were reduced to the longest transcript per gene. See here for full details:</p> <p>Species included, taxon IDs and data source:</p> <p>Mcer = Myzus cerasi v1.1 (<a href="https://bipaa.genouest.org/sp/myzus_cerasi/">https://bipaa.genouest.org/sp/myzus_cerasi/</a>)</p> <p>MperO = Myzus persicae clone O v2 (This study)</p> <p>Dnox = Diuraphis noxia Thorpe et. al. gene predictions (<a href="https://bipaa.genouest.org/sp/diuraphis_noxia/">https://bipaa.genouest.org/sp/diuraphis_noxia/</a>)</p> <p>Apis = Acyrthosiphon pisum JIC1 v1 (This study)</p> <p>Pnig = Pentalonia nigronervosa (This study)</p> <p>Rmai = Rhopalosiphum maidis v0.1 (<a href="http://gigadb.org/dataset/100572">http://gigadb.org/dataset/100572</a>)</p> <p>Rpad = Rhopalosiphum padi v1.0 (<a href="https://bipaa.genouest.org/sp/rhopalosiphum_padi/">https://bipaa.genouest.org/sp/rhopalosiphum_padi/</a>)</p> <p>Agly = Aphis glycines biotype 4 v2.1 (<a href="https://zenodo.org/record/3453468#.XnpL5JOgLRY">https://zenodo.org/record/3453468#.XnpL5JOgLRY</a>)</p> <p>BtabMEAM1 = Bemissia tabacci MEAM1 v1.2 (<a href="http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi">http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi</a>)</p> <p>Trub = Triatoma rubrofasciata (This study)</p> <p>Rpro = Rhodnius prolixus (This study)</p> <p>Ofas = Oncopeltus fasciatus OGS v1.0 (<a href="https://i5k.nal.usda.gov/Oncopeltus_fasciatus">https://i5k.nal.usda.gov/Oncopeltus_fasciatus</a>)</p> <p>Sfuc = Sogatella furcifera v1 (<a href="http://dx.doi.org/10.5524/100255">http://dx.doi.org/10.5524/100255</a>)</p> <p>Nlug = Nilaparvata lugens (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0521-0#Sec42">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0521-0#Sec42</a>)</p> <p>Files:</p> <p>Proteomes included in the analysis: proteomes.tar.gz</p> <p>Orthogroups: Orthogroups.txt</p> <p>Gene counts per orthogroup, per species: Orthogroups.GeneCount.csv</p> <p>Single copy conserved orthogroups used for species tree: SingleCopyOrthogroups.txt</p> <p>Species tree alignment: SpeciesTreeAlignment.fa</p> <p>r8s configuration file (includes time calibrations and OrthoFinder ML species tree with branch lengths): species_tree_rooted.r8s.nex</p> <p>r8s time calibrated species tree: r8s_tree.nwk</p>
FIG. 1. — A in From fin rays to DNA: supplementary morphological and molecular data to identify Mormyrus subundulatus Roberts, 1989 (Pisces: Mormyridae) from the Bandama River in Côte d'Ivoire
FIG. 1. — A, radiography of the paratype SU 63507 Mormyrus subundulatus Roberts, 1989 from the Tano River (© California Academy of Sciences, Dept. of Ichthyology); B, specimen number MNHN-IC-2018-0558 caught in the Bandama River near the type locality; C, specimen number MNHN-IC-2018-0559. Scale bar: A, 10 cm.
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.