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397 results for “Supplementary table”
Supplementary table 1 for 'Imperial timber? Dendrochronological evidence for large-scale road building along the Roman limes in the Netherlands' (2015)
<p>This supplementary table to Visser(2015) was not openly available. This dataset provides the supplementary table in the open ODS-format and also as XLS and CSV.</p> <div> <div>Publication: Visser, RM. 2015 Imperial timber? Dendrochronological evidence for large-scale road building along the Roman limes in the Netherlands. <em>Journal of Archaeological Science</em> 53: 243–254. DOI: <a href="https://doi.org/10.1016/j.jas.2014.10.017">https://doi.org/10.1016/j.jas.2014.10.017</a>.</div> </div>
Supplementary Table S27.1: Animal species native to South Africa that have invasive populations elsewhere.
<p>Animal species native to South Africa that have invasive populations elsewhere. Sorted by expected chronological appearance in the first place they were recorded as alien species. Notes are made on whether the introduction is known to be (Y) or not (N) from South Africa (or unknown U). Pathways are according to the CBD pathway classification scheme (Harrower et al. 2017), along with an indication of whether the introduction was intentional or accidental. Species that have multi-continental distributions, and which may in addition have some introduced populations are shown at the end of the table.</p>
Supplementary Table 1 and data from the workshop on Digital Building Logbooks and Permit Processes for Sustainability in Sustainable Places 24.9.2024 in Luxembourg
<p>This repository contains the supplementary Table 1 and data collected during a workshop on Digital Building Logbooks and Permit Processes for Sustainability. The workshop was held in Sustainable Places on the 24th of September 2024 in Luxembourg. </p>
Perception and evaluation of (modified) wood by older adults from Slovenia and Norway (Datasets, R analysis code, and supplementary tables)
<p>This entry contains datasets, R analysis code, and supplementary tables for the article <em>Perception and evaluation of (modified) wood by older adults from Slovenia and Norway.</em></p> <p>The article investigates human perception and evaluation of handrails made of different materials. Our goal was to identify how older adults perceive handrails made of unmodified wood, modified wood, and steel. We examined if certain materials are more preferred than others, which material properties might be associated with differences in human preference, and what are the roles of tactile and tactile-visual domains in material perception. Our analysis is based on the results from an 11-item rating scale and a ranking task.</p>
Expanded Supplementary Table
<p>This file is an expanded version of TableS1 of Coyne et al. 2022. It includes full annotation of all citations contributing to Figures 2 and 3. Detail included about thresholding for Algicidal Activity.</p>
Thesis: Supplementary Tables and reports
<p>Contains Supplementary information for my Thesis</p> <p>Files:</p> <p>model_perfs_and_motifs_top5_annot.mht: MHTML table containing top5 motifs, B1H alignments and dataset quality annotations along with other information. Download and open in Google Chrome browser</p> <p>all_reports.tar.gz : All TF Modisco reports bundled together which are linked in the Table as a standalone resource. Contains every motif, submotif , motif hit distribution with respect to the peak summits and alignments to B1H recognition-code</p> <p>model_archive.tar.gz : All models</p> <p>models_info.tsv: Maps model name to ENCODE ids and dataset related information.</p> <p>Datasets used other than ENCODE use these keys: </p> <p>HughesNB:</p> <p>Najafabadi, Hamed S., et al. "C2H2 zinc finger proteins greatly expand the human regulatory lexicon." <em>Nature biotechnology</em> 33.5 (2015): 555-562.</p> <p> </p> <p>HughesGR:</p> <p>Schmitges, Frank W., et al. "Multiparameter functional diversity of human C2H2 zinc finger proteins." <em>Genome research</em> 26.12 (2016): 1742-1752.</p> <p>ChipExo:</p> <p>Imbeault, Michaël, Pierre-Yves Helleboid, and Didier Trono. "KRAB zinc-finger proteins contribute to the evolution of gene regulatory networks." <em>Nature</em> 543.7646 (2017): 550-554.</p> <p> </p> <p> </p> <p> </p>
IODP Expedition 382: Supplementary Tables for "Episodes of early Pleistocene West Antarctic Ice Sheet retreat recorded by Iceberg Alley sediments"
<p>IODP Expedition 382: Supplementary Tables for "Episodes of early Pleistocene West Antarctic Ice Sheet retreat recorded by Iceberg Alley sediments"</p> <p>Includes SEM QEMSCAN® and <sup>40</sup>Ar/<sup>39</sup>Ar data for International Ocean Discovery Program (IODP) Expedition 382 Site U1538. Also includes a movie of a 3D-volume realization of an iceberg-rafted sedimentary layer from this site based on non-destructive X-ray microtomography imaging.</p> <p> </p> <p><strong>Data Set Captions:</strong></p> <p> </p> <p><strong>Data Set S1. </strong>Modal mineralogy data based on QEMSCAN® analyses, which infer minerals from chemistry. The mineral name assignations for each chemistry-based category stated in this table are aided by visual (microscope-based) inspection of the raw sieved samples.</p> <p><strong>Data Set S2. </strong>Mineral association data based on QEMSCAN® analyses. Please read data in columns, mineral against mineral (down then across left). These data define what touches what in the sample and is displayed as a percentage. Association refers to adjacency. Two minerals are “associated” if a pixel of one of the minerals occurs adjacent to a pixel of the other mineral. iExplorer software used scans the measured particles horizontally, from left to right, counting the associations that occur in the images (so the more pixels/closer the x-ray spacing the more accurate the data). Each column is independent. That is, it is split into a percentage of what touches what, so it is not expected that any two minerals’ data are reciprocal. The background category primarily reflects the free boundaries of ‘grains’ rather than liberated grains/particles. While it may provide an indicator of liberation, it does not represent liberation since it does not describe ‘particles’ which are made up of mineral grains. Inclusions and composite particles are therefore not described. Please consider the modal mineralogy (Tab. S1) when examining these mineral association data.</p> <p><strong>Data Set S3. </strong>Lithotyping data based on QEMSCAN® analyses. Particles have been digitally filtered using a set of lithotype rules (also displayed in this data set). These rules are based on the mineral grains in the particles themselves and use their area percent within each particle and their size in microns. The lithotype names stated here are largely assigned based on the dominant mineral grain in each category.</p> <p><strong>Data Set S4. </strong>40Ar/39Ar ages of individual sand-sized hornblende and mica. See main text for method used to generate these ages.</p> <p><strong>Data Set S5.</strong> Ties to place Hole U1538A NGR data on Dove Basin Stack (Reilly et al., 2021) depths.</p> <p><strong>Movie S1. </strong>3D-volume realization based on non-destructive X-ray microtomography imaging of a centimeter-scale iceberg-rafted debris-rich layer in Hole U1538A-36X-3W. 3D images were generated using a helical scanning trajectory that allows for long scan sequences and fast acquisition time. Based on the sample geometry, a voxel (pixel) resolution of ~14-μm was achieved. The 7000+ projection images were reconstructed to produce a 3D volume of image intensities (where higher values indicate greater x-ray attenuation). Avizo software was used for 3D segmentation and volume rendering to visualize gravel and sand to create this animation. The different colors assigned to each clast were chosen arbitrary.</p>
Functional genomics analysis to disentangle the role of genetic variants in major depression - Supplementary Tables
<p>This entry contains the data generated by the study "Functional genomics analysis to disentangle the role of genetic variants in major depression" that are part of the Supplementary information of the article describing the study.</p> <p>The entry contains the following data:</p> <p><strong>Supplementary Tables S1-S7</strong></p> <p>Supplementary Table S1. Summary of resources.</p> <p>Supplementary Table S2. Causal GVs for MD.</p> <p>Supplementary Table S3. pGenes functional and disease enrichment analysis.</p> <p>Supplementary Table S4. Fine-mapped MD causal GVs disease enrichment analysis.</p> <p>Supplementary Table S5. Colocalizing GWAS-eQTLs association to disease.</p> <p>Supplementary Table S6. TFBS analysis.</p> <p>Supplementary Table S7. GVs state annotation. </p>
USGS Table AHG Parameters And Supplementary Data
<p>Password Key: 69262qRead ; For more information please email: <strong>sha17hab.afshari@gmail.com</strong> / <strong>safshar00@citymail.cuny.edu</strong> </p> <p>Simplified hydraulic geometry relationships representing the average conditions over longer reaches could reduce the need for detailed field surveys and minimize the computational burden while studying river channel flow dynamics.</p> <p> Natural streams are characterized by changes in cross-section geometry and geophysical properties (e.g., bed-roughness, channel slope, channel planform, sediment load, etc.) along their reaches. Variations in the shape of the channel bed geometry are affected by several interacting features including the effect of different flow regimes, channel slope, sediment load, etc. Simplifying the river bed geometries will reduce the burden of assembling the required data and computational burden. “At-A-Station” Hydraulic Geometry (or AHG) relations are power-law functions that relate key hydraulic variables (i.e., velocity, depth, width, and flow area) to discharge at a river monitoring station (Dingman 2007; Dingman and Afshari 2018).</p> <p> The AHG relations have been introduced and discussed among researchers, engineers, and geomorphologists since the '50s based upon a limited number of observations made over a few flow monitoring stations across the United States. Afshari et. al., 2017 introduced a data filtering procedure that was trained and tested over both synthetic and realistic data followed by being applied over ~4000 U.S. Geological Survey’s river monitoring stations to compute AHG parameters based upon robust hydraulic vs. discharge measures. Estimated AHG parameters are combined with basic statistics (mean, minimum, maximum, and standard deviation) of key morphological and geophysical features at all USGS river monitoring sites, e.g. stream (Stahler) order, channel pattern (channel sinuosity), channel bed-slope, and channel lateral [or overbank] slope. The fundamental hydraulics, geographical, and geophysical data sources (websites) applied for making the "USGS Table AHG Parameters And Supplementary Data" table are</p> <ul> <li>USGS National Water Information System (<a href="https://waterdata.usgs.gov/nwis/sw">USGS-NWIS</a>)</li> <li>USGS Staged Product Directory (<a href="https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/1/ArcGrid/">The National Map</a>)</li> <li>National Hydrography Dataset Plus V2 (<a href="http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.php">Horizon System Corporation</a>)</li> </ul> <p> In doing so, potential interrelation among independent and dependent variables will be highlighted. Accordingly, given some assumptions, it is verified how well channel morphology and hydraulic components are intertwined and combined with AHG parameters and how categorizing river monitoring stations according to these characteristics will be practical and useful for further studies.</p> <p><strong>References:</strong></p> <ol> <li>Afshari, S., B.M. Fekete, S.L. Dingman, N. Devineni, D.M. Bjerklie, and R.M. Khanbilvardi. 2017. "Statistical filtering of river survey and streamflow data for improving At-A-Station hydraulic geometry relations." J. Hydrol. 547: 443–454. doi:10.1016/j.jhydrol.2017.01.038 </li> <li>Dingman, S.L., and S. Afshari. 2018. "Field verification of analytical at-a-station hydraulic- geometry relations." J. Hydrol. 564: 859-872. doi:10.1016/j.jhydrol.2018.07.020</li> <li>Dingman, S.L. 2007. "Analytical derivation of at-a-station hydraulic geometry relations." J. Hydrol. 334: 17–27</li> </ol>
USGS Table AHG Parameters And Supplementary Data MannN
<p>For more information please send me an email to either: <strong>sha17hab.afshari@gmail.com</strong> or <strong>safshar00@citymail.cuny.edu</strong></p> <p>Simplified hydraulic geometry relationships representing the average conditions over longer reaches could reduce the need for detailed field surveys and minimize the computational burden while studying or carrying out numerical analyses and of the flow dynamics.</p> <p> Natural streams are characterized by changes in cross-section geometry, slope, and geophysical properties (bed-roughness, channel slope, etc.) along with their reaches. Variations in the shape and size of the channel bed geometry result from several interacting features of the river system including the effect of different flow regimes, slope, sediment load, etc. Simplifying the river bed geometries could reduce the burden of assembling the required data, so implementing less detailed routing procedures could lower the computational burden. “At-A-Station” Hydraulic Geometry (or AHG) relations are power-law functions which relate river key the hydraulics (i.e., velocity, depth, width, and flow area) to discharge at a river monitoring station (Dingman 2007; Dingman and Afshari 2018).</p> <p> The AHG relations have been introduced and discussed among researchers, engineers, and geomorphologist since the '50s based upon a limited number of observations made over a few flow monitoring stations across the United States. Afshari et. al., 2017 introduced a data filtering procedure which was trained and tested over both synthetic and realistic data followed by being applied over ~4000 U.S. Geological Survey’s river monitoring stations to compute AHG parameters based upon robust hydraulic vs. discharge measures. Given “refined” dataset, estimated AHG parameters are combined with basic statistics (mean, minimum, maximum, and standard deviation) of key morphological and geophysical features at all USGS river monitoring sites, e.g. stream (Stahler) order, channel pattern (channel sinuosity), channel bed-slope, and channel lateral [or overbank] slope. The fundamental hydraulics, geographical, and geophysical data sources (websites) applied for making the "USGS Table AHG Parameters And Supplementary Data" table are</p> <ul> <li>USGS National Water Information System (<a href="https://waterdata.usgs.gov/nwis/sw">USGS-NWIS</a>)</li> <li>USGS Staged Product Directory (<a href="https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/1/ArcGrid/">The National Map</a>)</li> <li>National Hydrography Dataset Plus V2 (<a href="http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.php">Horizon System Corporation</a>)</li> </ul> <p> Doing so, potential interrelation among independent and dependent variables will be highlighted. Accordingly, given some assumptions, it is verified how well channel morphology and hydraulic components are intertwined and combined with AHG parameters and how categorizing river monitoring stations according to these characteristics will be practical and useful for further studies.</p> <p><strong>References:</strong></p> <ol> <li>Afshari, S. 2019. USGS Table AHG Parameters And Supplementary Data (Version v1.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2563440</li> <li>Afshari, S., B.M. Fekete, S.L. Dingman, N. Devineni, D.M. Bjerklie, and R.M. Khanbilvardi. 2017. "Statistical filtering of river survey and streamflow data for improving At-A-Station hydraulic geometry relations." J. Hydrol. 547: 443–454. doi:10.1016/j.jhydrol.2017.01.038 </li> <li>Dingman, S.L., and S. Afshari. 2018. "Field verification of analytical at-a-station hydraulic- geometry relations." J. Hydrol. 564: 859-872. doi:10.1016/j.jhydrol.2018.07.020</li> <li>Dingman, S.L. 2007. "Analytical derivation of at-a-station hydraulic geometry relations." J. Hydrol. 334: 17–27</li> </ol>
Supplementary table
<h2><span>Supplementary table showing f</span><span>actors associated with ANC visits (≥4) in Kenya (2022), South Africa (2016) and Nigeria (2018) and factors associated with the timing of the first ANC visits in Kenya (2022), South Africa (2016) and Nigeria (2018).</span></h2>
Supplementary tables
<p>Dataset for manuscript </p> <p><span>Evaluation of Stem Rust Tolerance of Kazakhstan and Russian Commercial Wheat Cultivars</span></p>
Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Supplementary Tables
<p>This repository contains the Supplementary Tables for Suriyalaksh et al. Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes.</p> <p>The list of table files can be found in <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/Supplementary%20table%20guide.pdf">Supplementary Tables guide.pdf</a></p> <p>Tables S1, S2 and S3 corresponding to physical gene-gene interaction data are in a separate repository doi:10.5281/zenodo.4382337</p> <p>Details about some of the Supplementary tables:</p> <p>TableS4_inferred_networks.csv - list of inferred GRNs for specified input combinations (set of input regulators, length of the time sequence, NI tool and prior used).</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS5_consensus_network_member.xlsx">TableS5_consensus_network_member.xlsx</a> - list of groups of topologically similar GRNs (from Table S4)</p> <p>Table S6: edge lists (source,target) for each one of the three consensus networks selected according to the GS validation metrics: middle PFE/AUFE, max AUFE, max PFE.<br> TableS6a_max_AUFE_GRN.txt - max AUFE; largest network - this is the one we used in the main analysis and discussion<br> TableS6b_max_PFE_GRN.xt - max PFE<br> TableS6c_middle_AUFE_PFE_GRN.txt - middle PFE/AUFE</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS7_qRTPCR_ddCt_network_accuracy.csv">TableS7_qRTPCR_ddCt_network_accuracy.csv</a> - gene expression count differences for RNAi knockdown GRN validation experiments. </p> <p>Table S8: Group membership for each one of the nodes in each one of the selected networks according to the SBM that best describes the observed network topology. Each column shows the group membership for each level in a SBM block hierarchy. Our analysis is in the second most coarse-grained level (level 1).</p> <p>TableS8a_max_AUFE_SBM.csv<br> TableS8b_max_PFE_SBM.csv<br> TableS8c_middle_AUFE_PFE_SBM.csv</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS9_glp_gs_datasets.pdf">TableS9_glp_gs_datasets.pdf</a> - list of datasets used for defining functional clusters.</p> <p>TableS14a_glp_l1_vs_fem_l1_lifespan_assay.xlsx - Day13 survival of fem-3(q20)ts vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1</p> <p>TableS14b_glp_l1_vs_glp_l4_lifespan_assay.xlsx - Day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L4 vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1</p> <p>TableS15a_glp1_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of glp-1(e2144)ts;rrf-3(pk1426)</p> <p>TableS15b_fem3_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of fem-3(q20)ts</p> <p>TableS17_input_regulators_annotated.csv - list input regulators used as input for Network Inference Tools annotated by source type (2nd column): GenAge, known transcription factors (TF) and gene with high variability in the gene expression time series (HV). The third column lists whether that regulator has an orthologue in human (y) according to WormBase (v 278).</p> <p>TableS20_epistasis_lifespan_data.xlsx - Epistasis lifespan data of glp-1(e2144)ts</p> <p>All the image (TIF) files represent representative images in the following genetic backgrounds (below) that have been treated </p> <p>with empty vector (EV) or RNAi against the gene highlighted in the title of the image. See methods section for details. </p> <p><strong>femliu1: </strong></p> <p><em>fem-3(q20)ts.; dhs-3p::dhs-3::gfp</em></p> <p><strong>femsod3:</strong></p> <p><em>fem-3(q20)ts.; sod-3p::gfp</em></p> <p><strong>glp1lgg1:</strong></p> <p><em>glp-1(e2144); lgg-1p:lgg-1:gfp</em></p>
Supplementary Tables for Expression of cell-wall related genes is highly variable and correlates with sepal morphology
<p>Supplementary Information and script for "Expression of cell-wall related genes is highly variable and correlates with sepal morphology"</p> <p>R scripts for analysis</p> <p>Data necessary to run analyses</p> <p>Generated data</p> <p>Supplementary Tables</p> <p> </p>
Supplementary table 5.1
<p>The table includes 192,744 branch point sequence positions predicted from the Susscrofa11.1 assembly. The table lists the gene name, strand, transcript, intron number, chromosome, intron start and end positions, branch point sequence (heptamer with the branch point at position 6), distance from the 3’ splice site, and prediction score from the BPP tool.</p>
CoBaHMA domains: supplementary Table and Data
<p>Supplementary Table and Supplementary Data 1 to 4 to the article "<strong>AlphaFold2-guided description of CoBaHMA, a novel family of bacterial domains within the Heavy-Metal Associated superfamily"</strong></p> <p> </p> <p><strong>Supplementary Table 1 </strong>(.xlsx file):</p> <ol> <li><strong>Structural and functional annotations of the large communities of CobaHMA domain proteins.</strong> The number of community sequences matching a given profile (IPR or ECOD) is indicated. The color code corresponds to the following functional categories. For each IPR accession, the type of profile is indicated between brackets (D for domain, F for family, H for superfamily).</li> <li><strong>Structural and functional annotations of small communities.</strong> Only the 20 small communities with at least one new annotation (e.g. not shared with the large communities, in grey and black) are detailed here. For these 20 communities, some annotations are shared with the large communities, corresponding only to P-type (cyan) and HMA (yellow). For a complete view of the annotations of the small communties, see <strong>Supplementary Figure 5</strong>. The number of community sequences matching a given profile (IPR or ECOD) is indicated. For each IPR accession, the type of profile is indicated between brackets (D for domain, F for family, H for superfamily).</li> </ol> <p> </p> <p><strong>Supplementary Data 1 </strong>(.zip file):</p> <p>Multiple sequence alignments (MSA) of the sequences of the most populated communities, build using MAFFT and rendered using EspPript3. The first two lines report the 2D structures of the AF2 model of the representative sequence of the community, and its amino acid sequence. The initial MSA (fasta format) is also provided, together with the coordinates of the 3D structure model (pdb format) and the HCA plot (postcript file) of the representative sequence of the considered community.</p> <p> </p> <p><strong>Supplementary Data 2 </strong>(.zip file):</p> <p>Annotations by communities: Modular organization of the full-length CoBaHMA domain proteins within the 48 large community (Functional annotations are indicated along the sequence by shaded areas. Each functional category of domain is highlighted by the following color code: CoBaHMA (green), HMA_2 (lime), HMA (yellow), P-type (cyan) and Serca (darkblue), ABC (orange), Calcyanin Gly-Zip (red), PAP2 (magenta). Membrane regions as identified by deepTMHMM are indicated by gray areas.</p> <p> </p> <p><strong>Supplementary Data 3 </strong>(.tsv file)</p> <p>Main features of communities (e.g. representative sequence, community size, mean amino acid length, annotations, …).</p> <p> </p> <p><strong>Supplementary Data 4 </strong>(.tsv file)</p> <p>Mean features of sequences possessing CoBaHMA domain(s).</p>
Thermal Comfort and Perception of Different Materials for Tabletops: Datasets, Processing Code, and Supplementary Tables.
<p>In this data repository, data related to the study <em>Thermal Comfort and Perception of Different Materials for Tabletops </em> is deposited, and includes datasets, processing code, and supplementary tables.</p> <p> </p>
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
IODP Expedition 382: Supplementary Tables for "New magnetostratigraphic insights from Iceberg Alley on the rhythms of Antarctic climate during the Plio-Pleistocene"
<p>Supplementary tables for "New magnetostratigraphic insights from Iceberg Alley on the rhythms of Antarctic climate during the Plio-Pleistocene"</p> <p>Includes stratigraphic data for International Ocean Discovery Program (IODP) Expedition 382 Sites U1536 and U1537.</p> <p> </p> <p><strong>Table Captions:</strong></p> <p><strong>Table S1.</strong> Splice table and additional appended cores for Site U1536 used in this study. </p> <p><strong>Table S2.</strong> Splice table and additional appended cores for Site U1537 used in this study. </p> <p><strong>Table S3.</strong> Correlation table for creation of correlated equivalent depth (ced) scale between Sites U1536 and U1537.</p> <p><strong>Table S4.</strong> Uncertainty estimates for Site U1536 natural gamma radiation (NGR) correlation to Site U1537 on mcd depth scale using Undatable (Lougheed & Obrochta, 2019). </p> <p><strong>Table S5.</strong> Site U1536 inclination, natural gamma radiation (NGR), gamma ray attenuation (GRA), and b* data used in this study.</p> <p><strong>Table S6.</strong> Site U1537 inclination, natural gamma radiation (NGR), gamma ray attenuation (GRA), and b* data used in this study.</p> <p><strong>Table S7.</strong> Meters below sea floor (mbsf) depths of magnetic reversals at Site U1536. Reversal ages are those used in this study’s age models (see Methods; Channell et al., 2016; Lisiecki & Raymo, 2005).</p> <p><strong>Table S8.</strong> Meters composite depth (mcd) splice depths of magnetic reversals at Site U1536. Reversal ages are those used in this study’s age models (see Methods; Channell et al., 2016; Lisiecki & Raymo, 2005).</p> <p><strong>Table S9.</strong> Meters below sea floor (mbsf) depths of magnetic reversals at Site U1537. Reversal ages are those used in this study’s age models (see Methods; Channell et al., 2016; Lisiecki & Raymo, 2005).</p> <p><strong>Table S10.</strong> Meters composite depth (mcd) splice depths of magnetic reversals at Site U1537. Reversal ages are those used in this study’s age models (see Methods; Channell et al., 2016; Lisiecki & Raymo, 2005).</p> <p><strong>Table S11.</strong> Magnetostratigraphic age model for Site U1536 generated with Undatable (Lougheed & Obrochta, 2019).</p> <p><strong>Table S12.</strong> Magnetostratigraphic age model for Site U1537 generated with Undatable (Lougheed & Obrochta, 2019).</p> <p><strong>Table S13.</strong> Dove Bain data stacks used in this study. </p> <p><strong>Table S14.</strong> Stratigraphic summary of magnetic reversals discussed in this study. U1308 ages from Channell et al., 2016. In relation to benthic δ<sup>18</sup>O, warm intervals are intervals with more positive values. In relation to Dove Basin facies, warm intervals are intervals with high higher b*, lower NGR, and lower GRA.</p>
Supplementary tables for publication "A reference-free algorithm discovers regulation in the plant transcriptome"
<p>Supplementary tables for publication "A reference-free algorithm discovers regulation in the plant transcriptome" (doi: https://doi.org/10.1101/2024.05.23.595613)</p> <p>Table A: complete list of significant anchors and associated genes from analysis of sorghum dataset</p> <p>Table B: complete list of significant anchors and associated genes from analysis of maize dataset</p> <p>Table C: complete list of significant anchors and associated genes from analysis of Arabidopsis P/Fe dataset</p> <p>Table D: complete list of significant anchors and associated genes from analysis of Arabidopsis FLOE1 dataset</p> <p>arabidopsis_floe1_ALL_anchors_satc_truncated.txt: data from the Arabidopsis FLOE1 dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>arabidopsis_pfe_ALL_anchors_satc_truncated.txt: data from the Arabidopsis P/Fe dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>maize_pollen_ALL_anchors_satc_truncated.txt: data from the maize dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>sorghum_drought_ALL_anchors_satc_truncated.txt: data from the sorghum dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample.</p> <p>cryptic_splicing_anchors.tsv: list of anchors described in Supplementary Information section of the article that are examples of cryptic splicing. Columns are dataset name, gene name/ID, anchor sequence, target 1 sequence, and target 2 sequence. </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.