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3,101 results for “tables”
Summary table of museum specimens Overview of the museum acronyms in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
<p><i>Summary table of museum specimens</i> Overview of the museum acronyms</p><table><thead><tr><th>Acronym</th><th><b>Name + Contact name + Database website</b></th></tr></thead><tbody><tr><th>AMNH</th><td>American Museum of Natural History, New York, USA; Nancy B. Simmons http://sci-web-001.amnh.org/db/emuwebamnh/index.php</td></tr><tr><th>BMNH</th><td>The Natural History Museum (formerly British Museum of Natural History), London, UK; Paulina Jenkins http://data.nhm.ac.uk/</td></tr><tr><th>CAS</th><td>California Academy of Sciences, San Francisco, USA; Maureen Flannery http://researcharchive.calacademy.org/research/bmammals/MamColl/index.asp</td></tr><tr><th>CM</th><td>Carnegie Museum, Pittsburgh, USA; Suzanne B. McLaren</td></tr><tr><th>DNSM</th><td>Durban Natural Science Museum, Durban, South Africa; Leigh Richards</td></tr><tr><th>FMNH</th><td>Field Museum of Natural History, Chicago, USA; Bruce D. Patterson</td></tr><tr><th></th><td>http://emuweb.fieldmuseum.org/mammals/taxonomic.php?_ga=1.142122738.1331713266.1463234212</td></tr><tr><th>HNHM</th><td>Hungarian Natural History Museum, Budapest, Hungary; Gábor Csorba</td></tr><tr><th>HZM</th><td>Harrison Institute (formerly Harrison Zoological Museum), Sevenoaks, UK; Paul Bates</td></tr><tr><th>INPBC</th><td>Institute of the National Parks of Belgian Congo, Brussels, Belgium</td></tr><tr><th>KU</th><td>University of Kansas, Natural History Museum, Lawrence, USA; Maria Eiffler http://collections.biodiversity.ku.edu/KUMammals/</td></tr><tr><th>MCZ</th><td>Museum of Comparative Zoology, Harvard University, Cambridge, USA; Judith Chupasko http://mczbase.mcz.harvard.edu/SpecimenSearch.cfm</td></tr><tr><th>MHNG</th><td>Muséum d’Histoire Naturelle, Genève, Switzerland; Manuel Ruedi</td></tr><tr><th>MNHN</th><td>Muséum national d’Histoire naturelle, Paris, France; Jean Marc Pons https://science.mnhn.fr/institution/mnhn/collection/zm/item/search</td></tr><tr><th>MSB</th><td>Museum of Southwestern Biology, Albuquerque, USA; Joseph Cook http://arctos.database.museum/SpecimenSearch.cfm</td></tr><tr><th>NMBA</th><td>Naturhistorisches Museum, Basel, Switzerland; Denis Vallan, Raffael Winkler</td></tr><tr><th>NMP</th><td>National Museum (Natural History), Prague, Czech Republic; Petr Benda</td></tr><tr><th>NMW</th><td>Naturhistorisches Museum, Vienna, Austria; Frank Emmanuel Zachos</td></tr><tr><th>NMZL</th><td>National Museum of Zambia, Livingstone, Zambia; Clare Mateke</td></tr><tr><th>OSU</th><td>Oklahoma State University, Stillwater, USA; Karen McBee</td></tr><tr><th>RBINS</th><td>Royal Belgian Institute for Natural Sciences, Brussels, Belgium; Georges Lenglet, Tom Geerinckx, Olivier Pauwels https://darwin.naturalsciences.be/</td></tr><tr><th>RMCA</th><td>Royal Museum of Central Africa, Tervuren, Belgium; Wim Wendelen, Emmanuel Gilissen</td></tr><tr><th>RMNH</th><td>Naturalis - National Natuurhistorish Museum (formerly Rijksmuseum voor Natuurlijke Historie), Leiden, the Netherlands; Pepijn Kamminga, Steven van der Mije http://bioportal.naturalis.nl/</td></tr></tbody></table>
TABLE 1 in Contributions to the knowledge of Elanela: Elanela jordi sp. nov., from Amazonas, Brazil (Hemiptera: Heteroptera: Pentatomidae)
<p><b><b>TABLE</b> 1.</b> Measurements of <i>Elanelajordi</i> <b>sp. nov.</b> (Max = maximum measurement; Min = minimum measurement).</p><table><thead><tr><th colspan="2" rowspan="2"></th><th colspan="2">Males</th><th colspan="2"><b>Females</b></th></tr></thead><tbody><tr><td>Mean ± standard deviation</td><td>Max-Min</td><td>Mean ± standard deviation</td><td>Max-Min</td></tr><tr><th colspan="2">Total length</th><td>5.1 ± 0.26</td><td>5.3 - 4.8</td><td>5.9 ± 0.13</td><td>6.1 - 5.8</td></tr><tr><th colspan="2">Abdominal width</th><td>3.3 ± 0.18</td><td>3.6 - 3.1</td><td>3.7 ± 0.08</td><td>3.8 - 3.6</td></tr><tr><th colspan="2">Interocular distance</th><td>0.9 ± 0.04</td><td>0.9 - 0.8</td><td>0.9 ± 0.02</td><td>1.0 - 0.9</td></tr><tr><th colspan="2">Head width</th><td>1.1 ± 0.07</td><td>1.2 - 1.1</td><td>1.2 ± 0.06</td><td>1.2 - 1.1</td></tr><tr><th colspan="2">Head length</th><td>1.0 ± 0.07</td><td>1.1 - 0.9</td><td>0.9 ± 0.02</td><td>1.0 - 0.9</td></tr><tr><th rowspan="5">Antenal segments length</th><td>I</td><td>0.3 ± 0.02</td><td>0.3 - 0.3</td><td>0.3 ± 0.02</td><td>0.3 - 0.2</td></tr><tr><td>II</td><td>0.7 ± 0.04</td><td>0.7 - 0.6</td><td>0.7 ± 0.10</td><td>0.8 - 0.6</td></tr><tr><td>III</td><td>0.6 ± 0.06</td><td>0.7 - 0.6</td><td>0.7 ± 0.04</td><td>0.7 - 0.6</td></tr><tr><td>IV</td><td>1.1 ± 0.03</td><td>1.1 - 1.1</td><td>1.2 ± 0.03</td><td>1.2 - 1.2</td></tr><tr><td>V</td><td>1.2 ± 0.10</td><td>1.2 - 1.1</td><td>0.7 ± 0.76</td><td>1.4 - 1.2</td></tr><tr><th rowspan="4">Rostral segments length</th><td>RI</td><td>0.4 ± 0.03</td><td>0.4 - 0.4</td><td>0.5 ± 0.05</td><td>0.5 - 0.4</td></tr><tr><td>RII</td><td>0.8 ± 0.04</td><td>0.8 - 0.8</td><td>0.8 ± 0.03</td><td>0.9 - 0.8</td></tr><tr><td>RIII</td><td>0.5 ± 0.03</td><td>0.5 - 0.5</td><td>0.6 ± 0.03</td><td>0.6 - 0.5</td></tr><tr><td>RIV</td><td>0.5 ± 0.01</td><td>0.5 - 0.4</td><td>0.5 ± 0.02</td><td>0.5 - 0.5</td></tr><tr><th colspan="2">Pronotum length</th><td>1.3 ± 0.04</td><td>1.3 - 1.2</td><td>1.4 ± 0.04</td><td>1.4 - 1.3</td></tr><tr><th colspan="2">Pronotum width</th><td>3.4 ± 0.08</td><td>3.5 - 3.3</td><td>3.7 ± 0.09</td><td>3.8 - 3.6</td></tr><tr><th colspan="2">Scutellum length</th><td>2.2 ± 0.13</td><td>2.4 - 2.1</td><td>2.5 ± 0.07</td><td>2.6 - 2.4</td></tr><tr><th colspan="2">Scutellum width</th><td>2.2 ± 0.13</td><td>2.3 - 2.0</td><td>2.4 ± 0.06</td><td>2.5 - 2.4</td></tr><tr><th colspan="2">Corium width</th><td>2.9 ± 0.21</td><td>3.1 - 2.6</td><td>3.4 ± 0.02</td><td>3.4 - 3.4</td></tr></tbody></table>
ID linking table for all samples that have been sequenced in the CRUK-ICGC Prostate Group projects
<p>Prostate cancer somatic genomic sequencing data generated from 2011 onwards under auspices of the <a href="https://icgc.org/icgc/cgp/70/508/71331">International Cancer Genome Consortium Prostate Cancer UK consortium</a> (CRUK-ICGC Prostate Group), co-led by Colin Cooper and Ros Eeles, with other Principal Investigators (Brewer, Neal, Bova, McDermott, Wedge, Lynch, Massie, and Foster) and others as listed in study publications. Funded by Cancer Research UK and other funders as listed in study publications. Contents and publications related to each dataset are described with each dataset (EGAD) entry.</p> <p>Our study was funded with the ambition of collecting Whole Genome DNA sequence data from 250 prostate cancers, with matching transcriptome and methylome data.</p> <p>The original aims of the project were:</p> <p>1. To understand the significance of multifocal prostate cancer. </p> <p>2. To understand the clinical heterogeneity of prostate cancer to devise markers for predicting outcome and for drug targeting.</p> <p>3. To understand the molecular basis of development and spread of castration-resistant and metastatic disease </p> <p>4. To understand the aetiology of prostate cancer particularly the large variation in incidences that occur in different populations and ethnic groups. </p> <p>This table contains links between all the IDs, in particular between those that have been published in research articles and the The European Genome-phenome Archive (EGA) where the raw sequencing data is held (https://ega-archive.org/studies/EGAS00001000262).</p>
Table S1.
<p>Table S1. Data corresponding to ADMET information of the virtual library.</p>
CO2 absorption coefficients (ABSCO) tables for the 4700 to 5100 cm-1 region using Voigt profile accounting for the line-mixing
<p>The attached files include the calculated absorption coefficient (in cm-1) for the 4700 to 5100 wavenumber region when using the Voigt profile accounting for the line-mixing parameter. The structure of the tables is as follows:</p> <p>Col 1- Wavenumber (in cm-1)</p> <p>Col 2- Absorption coefficient using the Voigt profile</p> <p>Col 3- Absorption coefficient using the Voigt profile including the first-order line-mixing</p> <p>Col 4- Absorption coefficient using the Voigt profile including the full line-mixing</p> <p>The JSON files contain the calculation conditions such as pressure, temperature, and mixing ratios of CO2 with a unique ID referring to the specific table.</p> <p>We note that for the Q branch lines and denser regions, the use of the first-order line-mixing is not effective in obtaining the absorption coefficient and the use of full line-mixing is recommended.</p> <p>For more information on how the calculation is performed, see ref. [JQSRT 151, 88 (2015)].</p>
dataset for paper Vanhaebost J, Faouzi M, Mangin P, Michaud K: New reference tables and user-friendly Internet application for predicted heart weights. Int J Legal Med 2014, 128(4):615-620.
<p>The heart weight is the most important parameter in the determination of cardiac hypertrophy. The obtained heart weight value should be compared against tables of normal weights by age, gender and body weight and height</p> <p>In the study by Vanhaebost<em> et al</em>. has been shown in the Swiss population that the heart weight increases along with the increase of the body weight, body height, BMI and body surface area (BSA). The mean heart weight is greater in men than in women at a similar body weight. The reference tables for predicted heart weights obtained from this study are presented as an user-friendly internet application (<a href="http://calc.chuv.ch/Heartweight">http://calc.chuv.ch/Heartweight</a>) enabling the comparison of heart weights observed at autopsy with the reference values.</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>
θ-Mθe Lookup Table (ERA-Interim)
<p>θ-Mθe Lookup Table computed from ERA-Interim with daily resolution and 1K interval.</p>
TABLE 2 in Two new species of the similis-subgroup of Triconia Böttger- Schnack, 1999 (Copepoda, Oncaeidae) and a redescription of T. denticula Wi, Shin & Soh, 2011 from the northeastern equatorial Pacific
TABLE 2. — Morphometric variation among individuals of four species of the Triconia similis-subgroup from the northeastern equatorial Pacific: comparison of the length of spines on P2-P4 enp-3 of Triconia komo n. sp., Triconia onnuri n. sp. and Triconia denticula Wi, Shin & Soh, 2011. Abbreviations see material and methods. Symbol: ×, damaged; unit: µm.
Colophons and Scholars website source tables
<p>These excel tables are the source files for the projec site <a href="https://colophons-and-scholars.com">https://colophons-and-scholars.com</a>.</p>
Figure 1 in Biology and life-table of Typhlodromus (Anthoseius) athenas (Acari: Phytoseiidae) fed with the Old World Date Mite, Oligonychus afrasiaticus (Acari: Tetranychidae)
Figure 1 Population dynamics ofO. afrasiaticus andT. (A.) athenas on date of 'Alig' cultivar at Segdoud, South of Tunisia in 2005 and 2006.
Replication Data for: Continuously moving table MRI with golden angle radial sampling
<p>Continuously moving table (CMT) MRI is a high throughput technique that has multiple applications in whole-body imaging. In this work, CMT MRI based on golden angle (GA, 111.246° azimuthal step) radial sampling is developed at 3 Tesla (T), with the goal of increased flexibility in image reconstruction using arbitrary profile groupings.</p>
X-ray Reflection Table Models
<p>Xspec Table Models of Monte Carlo X-ray reflection simulations from Giant Molecular Clouds.</p> <p>See, http://arxiv.org/abs/1609.00175, for description of the models.</p> <p>The number on the end of each file name represents the iron abundance relative to solar.</p>
A join of the Kepler DR24 injections table with the robovetter table
<p>A join of the injection and robovetter results for Kepler DR24. The references for these data are:</p> <ul> <li>Christiansen et al. (2016): http://adsabs.harvard.edu/abs/2016ApJ...828...99C</li> <li>Coughlin et al. (2016): http://adsabs.harvard.edu/abs/2016ApJS..224...12C</li> <li>Mullally et al. (2016): http://adsabs.harvard.edu/abs/2016PASP..128g4502M</li> </ul>
Table of relative abundance (L3) - Halocline - Overwinting expedition in Amundsen Gulf
<p>This is a text file representing relative abundance of taxa (L3) from surface water (Fungi and Metazoa filtered out).</p> <p>This table had been used to produced Figure S1A from the paper "Seasonal patterns in Arctic prasinophytes and inferred ecology of <em>Bathycoccus </em>unveiled in an Arctic winter metagenome".</p>
Table of relative abundance (L3) - Surface - Overwinting expedition in Amundsen Gulf
<p>This is a text file representing relative abundance of taxa (L3) from surface water (Fungi and Metazoa filtered out).</p> <p>This table had been used to produced Figure 2 from the paper "Seasonal patterns in Arctic prasinophytes and inferred ecology of <em>Bathycoccus </em>unveiled in an Arctic winter metagenome".</p>
ASV Tables inferred by DADA2 from the TARA Oceans v9 metabarcoding dataset
<p>Tables of exact amplicon sequence variants (ASVs) were generated from the TARA Oceans metabarcoding data (~766 million reads from 334 plankton samples, V9 region of the 18S rRNA gene, DOI: 10.5281/zenodo.15600) by DADA2 on a 2016 Macbook Pro. The R script used to process the data is included, alongside 3 ASV tables: the observed ASVs before chimera removal (sta), after consensus chimera removal (st.consensus, recommended) and after pooled chimera removal (st.nochim).</p> <p>The ASV tables are available in two formats. The first format is as matrices (rows named by sample, columns named by sequence variant) stored in RDS format -- these can be read back into R with the readRDS command. The second format is as biom-format files (json).</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>
WHY Toolkit plug-in tables for PRIMES and TIAM-ECN
<p>Plug-in tables for the PRIMES and TIAM-ECN models, generated using the WHY Toolkit. These data represent estimates of energy consumption subdivided by energy service, energy carrier, country, building type, building age, renovation status and temporal unit. The outputs of the WHY toolkit are expressed as energy per building. For each service-carrier combination, the data delivered by the WHY toolkit represent the amount of carrier (in terms of kWh input) that would be needed if this carrier were to fulfill the whole service demand for each combination of building type, construction period and renovation status. A detailed description of the data and how they can be used in energy system models is available in deliverable D4.2 of the WHY project, which can be found in the following webpage: <a href="https://www.why-h2020.eu/materials/articles-and-reports">Articles and reports: WHY H2020 Project (why-h2020.eu</a> </p>
IPBES Invasive Alien Species Assessment: Summary for Policymakers. Figures, tables and captions
<p>Figures, tables and their captions from the Summary for Policymakers of the Thematic Assessment Report on Invasive Alien Species and their Control of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
ScienceDex guides
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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.