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3,101 results for “tables”
TABLE 1 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies
<p>TABLE 1 — Estimated values of temperature and precipitation for the lower Río Turbio Formation member.</p><table><thead><tr><th><b>Lower RTF</b></th><th></th><th><b>Equation</b></th><th><b>Dataset</b></th><th><b>R</b> <b>2</b></th><th><b>Error</b></th><th><b>Source</b></th></tr></thead><tbody><tr><th>Temperature (°C)</th><td>16.5</td><td>MAT = 3.25 + 0.24*% non-tooth</td><td>CLAMP 3B SA</td><td>0.9</td><td>2.1°C</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Temperature (°C)</th><td>15.7</td><td>MAT = 26.03pE + 1.31</td><td>SA</td><td>0.82</td><td>2.8°C</td><td>Hinojosa <i>et al.</i> 2011</td></tr><tr><th>Temperature (°C)</th><td>15.6</td><td>MAT = 0.204E + 4.6</td><td>LMA</td><td>0.58</td><td>4.8°C</td><td>Peppe <i>et al.</i> 2011</td></tr><tr><th>Temperature (°C)</th><td>16.9</td><td>See manuscript (1)</td><td>DiLP</td><td>0.7</td><td>4°C</td><td>Peppe <i>et al.</i> 2011</td></tr><tr><th>Precipitation (mm)</th><td>1764</td><td>Ln(MAP) = 1.63 + 0.49*MLnA</td><td>CLAMP 3B SA</td><td>0.6</td><td>Ln(0.5) cm</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Precipitation (mm)</th><td>1435</td><td>lnMAP = 0.283(MlnA) + 2.92</td><td>LAA</td><td>0.23</td><td>0.61 cm</td><td>Peppe <i>et al.</i> 2011</td></tr><tr><th>Precipitation (mm)</th><td>1303</td><td>See manuscript (2)</td><td>DiLP</td><td>0.27</td><td>0.6 cm</td><td>Peppe <i>et al.</i> 2011</td></tr></tbody></table>
TABLE 2 in Finite element analysis of the proximal phalanx of the thumb in Hominoidea during simulated stone tool use
<p>TABLE 2. — Percentage of main locomotor behavoir of the non-human sample, according to Hunt (2004).</p><table><tbody><tr><th><b>Taxon</b></th><th><b>Climb</b></th><th><b>Braquiate</b></th><th><b>Clamber</b></th><th><b>Walk</b></th></tr></tbody><tbody><tr><th>Chimpanzee</th><td>6.5</td><td>0.8</td><td>0.0</td><td>89.9</td></tr><tr><th>Gorilla</th><td>19.7</td><td>3.6</td><td>0.0</td><td>64.4</td></tr><tr><th>Orangutan</th><td>31.3</td><td>15.5</td><td>40.7</td><td>12.0</td></tr><tr><th>Gibbon</th><td>34.2</td><td>51.2</td><td>0.0</td><td>0.0</td></tr></tbody></table>
Dissertation Chapter 1 Supplemental Tables
<p>Supplementary tables for first chapter of dissertation: "<span>A haplotype-resolved chromatin landscape connects cis-regulatory variants to trait variation in Citrus"</span></p>
Curated new database list from the 2024 NAR Database issue - Table 1
<p>This is a curated list of 90 new databases recently published in the <a href="https://doi.org/10.1093/nar/gkad1173">2024 Nucleic Acids Research (NAR) Database issue</a>. These databases were specifically categorized as the "new databases" (not previously published) in Table 1 of the <a href="https://doi.org/10.1093/nar/gkad1173">2024 compilation paper</a>. In this dataset, we curated a set of data columns to facilitate knowledge modeling and database integration efforts:</p> <ul> <li>"has API": does the database provide API access, Yes or No.</li> <li>"Primary Entity Type": the primary biomedical entity types captured in the database.</li> <li>"Downloadable": does the database provide direct file downloads, Yes or No.</li> <li>"license specified?": does the database specify a data license explicitly, Yes or No.</li> <li>"knowledge level": the level of knowledge expressed in the database, example values are specified in <a href="https://biolink.github.io/biolink-model/knowledge_level/">the BioLink model</a>.</li> <li>"agent type": the high-level category of agent who originally generated a statement of knowledge or other type of information, example values are specified in <a href="https://biolink.github.io/biolink-model/agent_type/">the BioLink model</a>.</li> <li>"notes": optional extra notes</li> </ul>
Supplementary Table for Earth observation data-driven cropland soil monitoring: A review
<p>Table including 46 manuscripts written in English referring to topsoil monitoring related to Earth observation data-driven cropland soil monitoring: A review paper.</p>
Table S1. Uncorrected p distance matrix
<p>Uncorrected p distance values for combined COI and 16S sequences from phylogenetic analysis of <em>Helminthoglypta</em> land snails (Mollusca: Gastropoda: Helminthoglyptidae) from northern California and southern Oregon, USA.</p> <p>Note: Specimen numbers marked with * were missing COI sequences.</p> <p>Table in both Excel (*.xlsx) and comma-separated value (*.csv) formats.</p> <p> </p>
Which journal characteristics are crucial for scientists when selecting journals for their publications? Results tables of an online survey
<p>As part of the BMBF-funded project "B!SON - Bibliometric and Semantic Open Access Recommender Network", an online survey was conducted among scientists using SoSci Survey (Leiner, 2019). The aim of the survey was to determine the importance of various characteristics of scientific journals in the decision for a publication venue by scientists. The characteristics were determined by analysis of other recommender systems, literature research and discussion with scientists. The data published here are based on 884 completed questionnaires (only questionnaires in which at least 90% of the questions had been answered were included in the analysis).</p> <p>In the questionnaire, a distinction was made between those characteristics that scientists would like to use to limit the selection of eligible journals from the outset (table "B!SON_Survey_Filter_Criteria_EN") and those that scientists need for their final decision from a list of recommended journals ("B!SON_Survey_Journal_Selection_EN"). For each journal property, the respondents could choose between the categories of a 5-point Likert scale: "not at all important - not very important - somewhat important - very important - extremely important". If the scientists were not able to evaluate a characteristic, they could also select "I can't say". </p> <p>In the two tables of results, the approval percentage and the rank based on it are listed for all journal properties queried in the respective part of the survey. The approval percentage is calculated from the percentage of people who rated the respective property as "very important" or "extremely important". Approval rate and rank are presented across all respondents (overall column), as well as within the 4 science disciplines by DFG (Natural Sciences, Engineering Sciences, Life Sciences, Humanities and Social Sciences) and the category "Other Sciences".</p> <p>Both tables can be sorted by approval rate and rank per scientific discipline or across disciplines.</p> <p>Note on the use of the HTML files: These can be downloaded via the download button and then opened and viewed with any web browser.</p> <p>A German version of the results tables is available under <a href="https://doi.org/10.5281/zenodo.5412197">https://doi.org/10.5281/zenodo.5412197</a>.</p>
Table S1. Geographical localization of Pinus pseudostrobus Lindley. specimens used in the study and concordance of morphological identification with real-time PCR-HRM assay for Pinus pseudostrobus varieties pseudostrobus, apulcensis, oaxacana and coatepecensis, based on the cluster pattern.
<p>File encloses geographical location of collected Pinus pseudostrobus samples in México, as well as haplotype grouping obtaines from HRM analysis</p>
Tables and figures complementing the European Union OneHealth 2020 Zoonoses Report
<p>All summary tables and figures produced for the European Union One Health 2020 Zoonoses Report are provided as archives containing Excel files for tables, and as PDF or PNG files for figures.</p> <p><strong>All country data connected to this Report are published SEPARATELY on Knowledge Junction - see related identifiers. This is because DATA OWNERSHIP for country data stays with the organisation(s) of the country submitting the data - for further reference see doi:10.2903/sp.efsa.2019.EN-1544.</strong></p>
Interpolated depth to water table (groundwater) maps for the continental United States
<p><strong>DATA:</strong></p> <p>This is a collection of depth to water table maps with uncertainty estimates for the continental United States for years 1989 and 2019. Data used to create these maps were obtained from the National Ground-Water Monitoring Network (NGWMN). Data included 14,351 sites and 17,632,047 observations for the years 1989-2019. To improve our inference a set of auxiliary variables proven to have a relation with depth to water table were included. We paired point estimates of depth to water table data with environmental data, as well as terrain variables derived out of a base digital elevation model (DEM) created by NASA at a 1x1km resolution. Climatic layers (temperature, precipitation, and snow melt equivalent) for 1989-2019 were obtained from Daymet (Version 4), which provides a continuous grid of historical monthly and annual weather data, with a 1x1km spatial resolution (Thornton et al., 2020). Out of the DEM, primary (slope, aspect) and secondary terrain attributes (curvatures, upslope contributing areas) were used to calculate a compound topographic index (CTI). </p> <p> </p> <p><strong>MODELING FRAMEWORK:</strong></p> <p>Water table depth analyses were conducted using a three-step interpolation approach: 1) we utilized gradient boosted regression trees (GBRT) to make predictions, 2) we used kriging interpolation on GBRT residuals to reduce bias from spatial autocorrelation, to incorporate a spatial correlation structure and to create uncertainty maps, and 3) we then combined the GBRT and kriging predictions for the final map. This method is equivalent to a Universal Kriging, where in our case, we evaluated the trend using GBRT. Model metrics were calculated for the training (80% of the data) and validation (20% of the data) datasets to evaluate overall performance.</p> <p>*** Uncertainty is greater surrounding the 1989 interpolations due to a lower number of observations.</p>
Glyco Atlas Supplemental Tables (Leo Alexander Dworkin) PhD Thesis
<p>The complex multi-step process of glycosylation occurs in a single cell, yet current analytics generally cannot measure the output (the glycome) of a single cell. Here, we addressed this discordance by testing usage of single cell transcriptomics to break this barrier. We investigated how single cell RNA-seq data can be used to characterise the state of the glycosylation machinery and metabolic network in single cell. The metabolic network involves 214 glycosylation and modification enzymes with their contributions to the glycome outlined in our previously built atlas of cellular glycosylation pathways. We studied differential mRNA regulation of enzymes at the organ and single cell level, finding that most of the general protein and lipid oligosaccharide scaffolds are produced by enzymes exhibiting limited transcriptional regulation among cells. We predict key enzymes within different glycosylation pathways to be highly transcriptionally regulated as regulatable hotspots of the cellular glycome. We designed the Glycopacity software that enables investigators to extract and interpret glycosylation information from transcriptome data and define hotspots of regulation.</p>
FastNLO interpolation tables for NLO pQCD predictions of Inclusive Jet Production in $pp$ collisions at $\sqrt{s}=200$ and $510$~GeV
<p>This is generated using the NLOJet++ interface using jet pT for renormalization and factorization scale.</p> <p><strong>run12pp200_R050.tab:</strong></p> <p>\sqrt{s} = 200 GeV<br> anti-kT R=0.5<br> |eta| bins: 0.0 0.5 0.9<br> pT bins (GeV): 6.9 8.2 9.7 11.5 13.6 16.1 19.0 22.5 26.6 31.4 37.2 44.0 52.0</p> <p><strong>run12pp510.tab:</strong></p> <p>\sqrt{s} = 510 GeV<br> anti-kT R=0.5<br> |eta| bins: 0.0 0.5 0.9<br> pT bins (GeV): 8.0 10.0 13.0 17.0 21.0 26.0 32.0 39.0 47.0 57.0 68.0 80.0</p> <p><em>This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute.</em></p> <p>If you use this, please cite: D. Britzger, T. Kluge, K. Rabbertz, F. Stober, M. Wobisch, arXiv:1109.1310</p>
Fig. 2 and table 3 in Phenotypic Diversities Of Four Populations Of Clarias Gariepinus (Siluriformes, Clariidae) Obtained From Ogun And Ondo State Waterbodies In South-Western Nigeria
Fig. 2 and table 3 illustrate the values of PCA loadings for the morphometrics of Clarias gariepinus obtained from the four Rivers showing Pre-ventral distance (PVD) as the trait with the highest variation.
Table of Individual MMT Hectochelle Observations of the Triangulum II Field
<p>Complete table of observations of the Triangulum II dwarf galaxy from MMT Hectochelle used in Buttry et. al 2022</p>
Protostars and Planets VII -- Figures and Table for "The Origin and Evolution of Multiple Star Systems"
<p>Figures 1-9 and Table 1 for "The Origin and Evolution of Multiple Star Systems" chapter to appear in Protostars and Planet VII.</p>
The creative, collaborative lab. A round table (June 01, 2022. 1400-1600 UTC)
<p>The purpose of this roundtable is to gain an interdisciplinary understanding of the lab as a collaborative and creative space. Our panel are distinguished scientists at the University of Lethbridge who have experience running and/or working in a variety of lab contexts. In this session they will discuss their experiences, provide tips, and generally share their knowledge and experience with lab science.</p>
Table of contents for Alpenwort corpus
<p>The file contains the Table of contents for the Alpenwort corpus with additional links to the PDFs and wikidata aswell as gazetteer IDs and Keywords for each article, aswell as the gazeteer and and RDF (https://www.w3.org/RDF/) of the table of contents.</p>
The impact of low input DNA on the reliability of DNA methylation as measured by the Illumina Infinium MethylationEPIC BeadChip, supplementary table 3
<p>Supplementary table 3: Summary statistics from an EWAS assessing the relationship between variance in DNA methylation value and DNA input level.</p>
Tables and data for "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data"
<p>These are tables and data files for the paper "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data" submitted to the Journal of Atmospheric Research: Atmospheres</p>
Annex 2: Supplementary Tables to 'Understanding Chinese hamster translation at sub-codon resolution'
<p>Annex 2 to PhD thesis titled 'Understanding Chinese hamster ovary cell translation at sub-codon resolution'. </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.