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3,101 results for “tables”
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>
Navigating the complex policy landscape for carbon farming in The Netherlands and the EU -- Open Research Europe Extended Data-- Tables 1-6, Figures 1-2
<p>This is extended data for the article entitle 'Navigating the complex policy landscape for carbon farming in The Netherlands and the EU' submitted to Open Research Europe by Eise Spijker. </p>
TABLE 2 in First evidence of cannibalism in Crassimurex (s. s.) calcitrapa (Lamarck, 1803) (Gastropoda, Muricidae) from the Lutetian of the Paris Basin (France)
<p>TABLE 2. — Morphological and functional patterns of holes associated with gastropod and <i>Octopus</i> predation (modified from Gordillo <i>et al.</i> 2022).</p><table><thead><tr><th><b>Characteristic</b></th><th><b>Pattern A</b></th><th><b>Pattern B</b></th><th><b>Pattern C</b></th></tr></thead><tbody><tr><th>Shape</th><td>A hole, round to oval</td><td>Paired breaks</td><td>A hole, rounded to irregular</td></tr><tr><th>Outline character</th><td>Regular outline</td><td>Irregular breakage</td><td>Regular to irregular</td></tr><tr><th>Profile cross section</th><td>Straight or sloping sides or parabolic outline</td><td>Random breakage</td><td>Width and direction of hole vary with depth</td></tr><tr><th>Drill hole location</th><td>Primarily Abapertural, dorsal to ventral</td><td>Unspecified</td><td>Apertural, ventral to dorsal, parietal (left)</td></tr><tr><th>Produced by</th><td>Secretions of the ABO and rasping by the radula</td><td>Possible chemical softening and biting marks of upper and lower beaks</td><td>Secretion of salivary glands and rasping by radula and teeth of the papillary shield and terminal process</td></tr><tr><th>Potential predator</th><td>Drilling gastropods</td><td><i>Octopus</i></td><td><i>Octopus</i></td></tr><tr><th>Present in this sampling</th><td>Yes</td><td>No</td><td>No</td></tr></tbody></table>
Shakespeare: Julius Caesar 1.2.30-187, sentences with Maximum Similarty Score. Appendix Table for "Innovation and Repetiton in Dramatic Texts"
<p>This is an illustrative table for the study "Innovation and Repetition in Dramatic Texts", published in the journal JCLS by Botond Szemes and Mihály Nagy. The table contains a scene from <em>Julius Caesar</em> 1.2.30-187' , assigning to each utterance the most similar sentence and the degree of similarity based on an S-BERT model.</p>
An example RNA-seq count table using nf-core/rnaseq
<p>An example RNA-seq count table generated from data deposited under GSE40419 (lung adenocarcinoma study) and nf-core/rnaseq for testing downstream RNA-seq analyses.</p>
Supplementary tables
<p>Dataset for manuscript </p> <p><span>Evaluation of Stem Rust Tolerance of Kazakhstan and Russian Commercial Wheat Cultivars</span></p>
Dataset for FLEXCHX Article BCAB2021 Table 1
<p>Dataset for FLEXCHX Article: Pilot-scale development of pressurized fixed-bed gasification for synthesis gas production from biomass residues / Table 1<br> https://doi.org/10.1007/s13399-021-01554-2</p> <p>Biomass Conversion and Biorefinery</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>
Throwbot - Dataset and evaluation of a thrown table tennis egg
<p>Dataset, videos and MATLAB live script for the evaluation of the trajectory of a thrown table tennis egg</p>
M. Kelly PhD thesis; Chapter 4 - Supplemental Table S1
<p>Supplemental Table for my PhD thesis (Chapter 4). Supplemental Table S1 contains all data relevant to the predator choice tests.</p>
Lake Cadagno sediment core hyperspectral imaging and pigment data tables
<p>Data Tables related to the manuscript "Hyperspectral imaging sediment core scanning tracks high-resolution Holocene variations in (an)oxygenic phototrophic communities at Lake Cadagno, Swiss Alps" in submission. </p>
MammoTab 22: a giant and comprehensive dataset for Semantic Table Interpretation
<p>MammoTab is a dataset designed to evaluate semantic table annotation approaches.</p> <p>It includes two types of annotation:</p> <ol> <li>cell/mentions to Knowledge Graph (KG) entity matching (CEA task) and;</li> <li>column to KG class matching (CTA task).</li> </ol> <p>It is composed of 980254 tables extracted from 21149260 Wikipedia pages and annotated through Wikidata v. 20220708. The dataset is compliant with the data format used in <a href="https://www.cs.ox.ac.uk/isg/challenges/sem-tab/2019/index.html">SemTab2019</a>.</p>
Data tables associated with manuscript 'Grant et al., Regional amplified warming in the Southwest Pacific during the mid-Pliocene (3.3-3.0 Ma)'
<p>This repository holds the data files associated with manuscript Grant et al., Regional amplified warming in the Southwest Pacific during the mid-Pliocene (3.3-3.0 Ma), submitted to Climate of the Past. https://doi.org/10.5194/egusphere-2023-108 </p> <p> The R Script and R Data used to analyse the data and produce the figures can be found in GitHub repository https://github.com/GRG-GNS/Pliocene-SST-Southwest-Pacific.git</p> <p>###########Terms and units</p> <p>Sea Surface Temperatures (SSTs) are in degrees Celsius. Latitude are in degrees north. Longitude in degrees east.</p> <p> </p> <p>NZESM (New Zealand Earth system model; Williams et al., 2016), UKESM (United Kingdom Earth System model; Sellar et al., 2019), HadISST 1870-1879 AD (HadleyCentre Sea Ice and Sea Surface Temperature; NCAR, 2022), mPWP (mid-Pliocene Warm Period 3.3 - 3.0 Ma), MIS5e (Marine Isotope Stage 5e; 125 ka). SSP1 /2/ 3 (Socio-economic Pathways; IPCC). UK'37 - alkenone biomarker SST proxy TEX - TEX86 biomarker SST proxy</p> <p>Two periods were extracted from NZESM and UKESM i) 2036-2040 AD for SSP 2, and ii) 2090-2099 AD for SSP1, SSP 2, SSP 3. These are provided as stand alone values and in reference to HadiIST (e.g. NZESM - HadISST or NZESM.HadISST) </p> <p><strong>Table 2.</strong> <strong>Statistical distribution of mid-Pliocene Warm Period (3.3-3.0 Ma) Sea Surface Temperatures anomalies (SST; °C) relative to HadiSST (1870-1879 AD) using </strong> <strong>BAYSPLINE calibration (Tierney and Tingley, 2018). The total range is calculated as the difference between maximum and minimum temperature and represents glacial to interglacial extremes. </strong></p> <p><strong>Table 3. Site annual mean Sea Surface Temperature anomalies (SST; °C) for UKESM and NZESM with respect to HadISST (1870-1879 AD) for SSP2-4.5 2040 AD (2036–2045 AD). </strong></p> <p><strong>Table 4. Site annual mean Sea Surface Temperature anomalies (SST; °C)for UKESM and NZESM with respect to HadISST (1870-1879 AD) for SSP1-2.6, SSP2-4.5, SSP3-7.0 at 2095 AD (2090–2099 AD). </strong></p> <p><strong>Table A1. Comparison between </strong> <strong> derived SST using BAYSPLINE with TEX<sub>86</sub> -index SST calibrations of Schouten <em>et al</em>. (2002), Kim <em>et al</em>, (2010), OPTIMAL (Dunkley Jones <em>et al</em>., 2020) and BAYSPAR (Tierney and Tingley, 2015). </strong></p> <p><strong>Table S1. All site sea surface temperature (SST; °C) data used in results with index and calibrations of Müller98 (Müller <em>et al</em>., 1998) and BAYSPLINE (Tierney and Tingley, 2018). The proxy type and references are also provided.</strong></p> <p><strong>Table S2. Site sample data for analyses undertaken this study, including all and TEX<sub>86 </sub>index calculations and calibrations. References for calibrations are contained within column headers.</strong></p> <p><strong>Table S3. Seasonal and annual mean sea surface temperature (SST; °C) model outputs of HadISST (NCAR, 2022), UKESM (Sellar <em>et al</em>., 2019), NZESM (Williams <em>et al</em>., 2016) at the seven Southwest Pacific sites (DSDP 594, ODP 1172, ODP 1168, ODP 1125, ODP 1123, DSDP 593, DSDP 590) for SSP2 2040 AD (2036-2045 AD), and SSP1, 2, and 3 2095 AD (2090-2099 AD). Including UKESM and NZESM with respect to HadISST.</strong></p> <p><strong>Table S4. Site sea surface temperature (SST; °C) annual means and seasonal range for UKESM and NZESM SSP2-4.5 2036-2045 AD, with MPWP interglacial modal means and total glacial range (maximum to minimum SST). </strong></p> <p><strong>Table S5. Compiled sea surface temperature (SST; °C) interglacial means for MIS 5e (125kyr; Cortese et al., 2013) and mPWP (3.3-3.0 Ma) and model annual means for HadISST (1870-1879 AD), and SSP2-4.5 2090-2099 AD for UKESM, NZESM.</strong></p> <p> </p>
Supplemental tables for a study of the seasonal Impacts of the Physical Environment on Biogeochemical Cycles in Arctic Lakes of the Mackenzie River Delta
<p>submitted abstract</p> <p>We conducted two- and six-year-long deployments of continuous water samplers (OsmoSamplers) and sensors (Temperature, pressure, light level, dissolved oxygen (DO) and conductivity) in nine lakes within the mid- to outer-delta region of the Mackenzie River and documented biogeochemical fluctuations (Mn, Fe, sulfate, and DO), defined physical processes that that drive such fluctuations, and constrained the impact of lake solutes on annual riverine fluxes. Five lakes were in the mid-delta region near Inuvik, NT, two lakes were in the outer delta, and two lakes were on the Arctic coastal plain and were not impacted by the Mackenzie River. In general, temperature minima occurred in September/October, indicative of ice formation, and distinct hydrostatic pressure (water level) anomalies occurred in May/June associated with ice breakup, lasting for days to months and impacting lake levels up to 4.2 m higher than “normal”. Such anomalies coincide with a dramatic change in solute concentrations. Systematic changes in solute concentrations indicate redox-driven biogeochemical reactions, salt exclusion during ice formation, and continuous to sporadic exchange of river water. Redox reactions were regulated by DO inputs stemming from atmospheric, photosynthetic, and riverine sources. During ice-covered periods dissolved sulfate may be conservative but was generally removed. Manganese and iron concentrations showed phases of production and removal during ice-covered periods, but both were produced overall. Calculated solute fluxes from lake waters alone to the Arctic Ocean may only impact yearly riverine fluxes for solutes that exceed ten times the river concentration prior to ice breakup (e.g., Mn and Fe).</p>
MAGIC performances 2015 data tables
<p>An extraction of data tables from the MAGIC performance paper of 2015: <em>The major upgrade of the MAGIC telescopes, Part II: A performance study using observations of the Crab Nebula</em>, J. Aleksíc et al, 2015, <a href="https://doi.org/10.1016/j.astropartphys.2015.02.005">https://doi.org/10.1016/j.astropartphys.2015.02.005</a></p> <p>The arXiv version: <a href="https://arxiv.org/abs/1409.5594">https://arxiv.org/abs/1409.5594</a></p>
uCARe taxonomy alliance code lookup table
<p>uCARe project (https://doi.org/10.5281/zenodo.3669985)</p> <p>Alliance code lookup spreadsheet, as referenced in Deliverable 1.2</p> <p> </p> <p> </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>
Reads-per-UMI tables across single-cell RNA sequencing protocols
<p>Data analyzed in <a href="https://www.biorxiv.org/content/10.1101/2023.08.02.551637v1">Lause, Ziegenhain et al. (2023)</a>.</p> <p>Code to obtain these tables from public data sources is available on <a href="https://github.com/berenslab/read-normalization">github</a>.</p> <p> </p> <p>Each row in the table is a UMI-tag detected in a certain cell (column RG) attached to a molecule from a specific gene (column GE) with a certain barcode (column UB). Column N gives the number of times the UMI was detected for that gene and cell.</p> <p>Data sources and protocols are given with the respective file names below.</p> <p><strong>Johnsson2022_Smartseq3_PE.hd1.txt.gz</strong>: Mouse fibroblasts profiled with <strong>Smart-seq3</strong> paired-end; accession E-MTAB-10148, sample plate2,<br> <a href="https://doi.org/10.1038/s41588-022-01014-1">Paper</a><br> <br> <strong>Hagemann-Jensen2020_Smartseq3_SE.hd1.txt.gz: </strong>Mouse fibroblasts profiled with <strong>Smart-seq3</strong> single-end; accession E-MTAB-8735, sample Smartseq3.Fibroblasts.smFISH<br> <a href="https://doi.org/10.1038/s41587-020-0497-0">Paper</a><br> <br> <strong>Hagemann-Jensen2022_Smartseq3xpress.hd1.txt.gz: </strong>HEK293 cells profiled with <strong>Smart-seq3Xpress</strong>; accession E-MTAB-11467.<br> <a href="https://www.biorxiv.org/content/10.1101/2021.07.10.451889v1">Paper</a><br> <br> <strong>Ziegenhain2017.hd1.txt.gz: </strong>Mouse embryonic stem cells profiled by <strong>CEL-seq2, Drop-seq, MARS-seq, </strong>and<strong> SCRB-seq</strong>; GEO accession GSE75790<br> <a href="https://doi.org/10.1016/j.molcel.2017.01.023">Paper</a></p>
Tables for: The Panchromatic Hubble Andromeda Treasury XXI. The Legacy Resolved Stellar Photometry Catalog
<p>This deposit contains the full machine-readable tables for the accepted version of the manuscript, "<em>The Panchromatic Hubble Andromeda Treasury XXI. The Legacy Resolved Stellar Photometry Catalog</em>", submitted and accepted to the Astrophysical Journal Supplments. </p> <p>The specific files included in this deposit are the full version of Tables 1-3 in this manuscript:</p> <ul> <li>Table 1: Simplified table of PHAT photometry for easy use;</li> <li>Table 2: Simplified table of artificial star test results for easy use;</li> <li>Table 3: Summary of artificial star statistics as a function of brightness and stellar density.</li> </ul> <p>The *.txt files are formatted according to the machine-readable standards adopted by the AAS Journals and CDS/Vizier. Documentation of this format can be found at these links:</p> <ul> <li><a href="https://journals.aas.org/mrt-overview/">AAS Journals MRT overview</a></li> <li><a href="http://vizier.u-strasbg.fr/doc/catstd.htx">CDS/Vizier standards</a></li> </ul> <p>These files can be read in python using the astropy package or with the most recent version of <a href="https://www.star.bris.ac.uk/~mbt/topcat/">TOPCAT</a> (> Version 4.8). An example script for reading these files in astropy is given here:</p> <pre><code class="language-python"> from astropy.table import Table data = Table.read("datafile3.txt", format="ascii.cds") </code></pre> <p>In addition, a headerless, compressed TeX (&-delimited; "full_table.tex.xz") version of Table 1, and a header-only, dataless version of Table 1 ("datafile1_headeronly.txt") are provided to give users additional tools for dealing with this large dataset. </p> <p>The compression routine was applied using xz <https://tukaani.org/xz/> with the encodings</p> <ul> <li> <p><code>xz -z -7 -T 0 full_table.tex</code></p> </li> <li> <p><code>xz -z -9 -T 0 datafile1.txt</code></p> </li> </ul> <p>The Table 1 files on Zenodo were then split into smaller chunks to upload them to Zenodo using the GNU <a href="https://www.gnu.org/software/coreutils/manual/html_node/split-invocation.html"><code>split</code></a> routine. The full files can be recovered by recombining them before decompressing them. The list of related commands and the expected md5 hexidecimal checksums are given here:</p> <ul> <li> <p><code>split --bytes=512M ../full_table.tex.xz full_table.tex.xz.</code></p> </li> <li> <p><code>split --bytes=512M ../datafile1.txt.xz datafile1.txt.xz.</code></p> </li> <li> <p><code>cat full_table.tex.xz.a* > full_table.tex.xz</code></p> </li> <li> <p><code>cat datafile1.txt.xz.a* > datafile1.txt.xz</code></p> </li> <li> <p><code>MD5 (full_table.tex.xz) = 7f9195210bec61c08d04926ada4a021a</code></p> </li> <li> <p><code>MD5 (datafile1.txt.xz) = 63205fe1051e97f4e04380dd23469893</code></p> </li> </ul> <p>Finally, those interested in generating their own cuts from the DOLPHOT quality parameters can access the full photometry tables, available at MAST as a High Level Science Product via <a href="https://doi.org/10.17909/T91S30">doi.org/10.17909/T91S30</a></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.