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7,370 results for “supplement”
F I G U R E 5 in Effects of dietary hydrolysate supplementation on growth, body composition, hematological responses, and liver histology of juvenile giant trevally (Caranx ignobilis Forsskal, 1775)
F I G U R E 5 Liver microscopy of giant trevally fed fish protein hydrolysate (FPH) for 8 weeks (scale bar = 50 μm, 400 magnification). Stained with hematoxylin and eosin.
F I G U R E 3 in Effects of dietary hydrolysate supplementation on growth, body composition, hematological responses, and liver histology of juvenile giant trevally (Caranx ignobilis Forsskal, 1775)
F I G U R E 3 Proximate composition in the whole body of juvenile giant trevally fed tested diets for 8 weeks. ns, non-significant. Different subscript letters indicate differences among treatments.
F I G U R E 4 in Effects of dietary hydrolysate supplementation on growth, body composition, hematological responses, and liver histology of juvenile giant trevally (Caranx ignobilis Forsskal, 1775)
F I G U R E 4 Hematological and serum biochemical parameters of giant trevally fed experimental diets for 8 weeks. ns, non-significant. Different subscript letters indicate differences among treatments.
Data supplementing Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. Scientific Reports, 14, 8858.
<p>These files supplement the publication <br>Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. <em>Scientific Reports, </em>14, 8858. https://doi.org/10.1038/s41598-024-58953-4</p> <p>The files data_expX.mat, where X is the experiment number (1-4), contain the data as described below. </p> <p>The files dataTraining_expX.mat contain the data of the first (training) block of each experiment. They are needed only for the supplemental material. </p> <p>To exemplify the usage, the functions figure2and3.m, figure4.m, figure5.m, figure6.m and Table1.m output the paper's figures and the data of Table 1, respectively; figureS2.m, figureS3.m, figureS4.m and figureS5.m output the figures of the supplemental material (figure S1 needs substantial amounts of external source code to compute the salience maps and is therefore not included).</p> <p><br>data_exp1.mat contains the following variables<br>For alert trials, variable of dimensions subjects x blocks x alert trials (20x10x64); note that only used participants and blocks with alert trials (2 through 11) are included in the data set:<br>alert_aud - the salience level of the alert tone (1-8, corresponding to 54dB(A) through 89 dB(A))<br>alert_vis - the salience level of the alert frame (1-8, corresponding to 0.10 to 8.50 Weber contrasts in logarithmic steps)<br>alert_side - the side on which the alert frame and the tone were presented (1-left, 2-right)<br>alert_fixOk - derived from eye movement data, was the first fixation closer to the alert square than to the center?<br>alert_primaryRT - primary-task reaction time (for alert trials)<br>alert_alertRT - alert-task reaction time <br>alert_correctAlert - was the response (up/down) to the alert correct?<br>alert_intrusionAlert - was there an intrusion (left/right pressed before up or down)?<br>alert_correctPrimary - was the primary task conducted correctly?<br>alert_intrusionPrimary - was there an intrusion for the primary task?<br>alert_timeToFixation - time to first fixation on alert square <br>alert_fixationToResp - time from beginning of fixation to response to the alert <br>alert_fixDur - duration of first fixation after trial onset</p> <p>For no-alert trials, variable of dimensions subjects x blocks x no-alert trials (20x10x448):<br>noalert_correctPrimary - was the primary task conducted correctly?<br>noalert_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)?</p> <p>For all trials, variable of dimensions subjects x blocks x no-alert trials (20x10x512):<br>all_correctPrimary - was the primary task conducted correctly?<br>all_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)?<br>all_RT - reaction time in the primary task<br>all_isAlertTrial - was the trial an alert trial? (useful to map no-alert trials and alert trials on all trials)</p> <p>In addition, there are some raw eye movement data for the alert blocks:<br>alert_eyeX, alert_eyeY - dimension 20 x 10 x 64 x 6000; x and y position in pixel coordinates relative to trial (and alert) onset, 1ms/sample, ends at conclusion of trials, filled up with NaN if duration was less than 6000ms <br>alert_eyeFixX, alert_eyeFixY, alert_eyeFixTon, alert_eyeFixDur - 20 x 10 x 64 x 15; x and y position, onset (in ms relative to trial onset) and duration of fixations during the trial (from onset to primary-task response), filled with NaN when less than 15 fixations were made. Note that the first entry of alert_eyeFixDur along the forth dimension will usually equal the alert_fixDur</p> <p><br>data_exp2.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_vis - contains only two levels (1 and 2) corresponding to Weber contrasts of 0.10 and 2.39, respectively<br>alert_dur - the level of duration of the alert frame (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms)<br>alert_aud is not included (all tones were at 54 dB(A))<br>there are only 19 participants; hence the variables are of size 19 x ...<br>note: block 8 for subject 6 contains only 450 trials (57 alert trials), the remainder is filled with NaN.</p> <p><br>data_exp3.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively<br>alert_dur - the level of duration of the alert tone (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms)<br>alert_vis is not included (all alert frames were at 0.10 contrast)</p> <p> </p> <p>data_exp4.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively<br>alert_vis is not included and replaced by<br>alert_condBefore - alert frame contrast level before the saccade (1 - 0.10 contrast, 2 - 2.39 contrast)<br>alert_condAfter - alert frame contrast level after the saccade (1 - 0.10 contrast, 2 - 2.39 contrast)</p> <p><br>dataTraining_expX.mat contains for the first (training) block of experiment X (X being 1, 2, 3 or 4) the following variables of size 20x512 (participant x trial) [19x512 in case of Experiment 2]:<br>all_correctPrimary - was the primary task conducted correctly?<br>all_RT - reaction time in the primary task<br>[Note that there are no alert trials in this block and these data are only used in the supplementary material (part 4)]</p>
Supplemental data for: A scoping review of studies on ruscism
<p><span>The supplementary materials accompanying the paper: "A scoping review of studies on ruscism". These materials include: raw data from citation databases, table for quantitative analysis of imported bibliographic data, key topics and content analysis of publications, python code. These supplementary materials are intended to enhance the usability of the research data, allowing for in-depth secondary analyses and aiding scholars in conducting related studies.</span></p>
An 8-(Diazomethyl) Quinoline Derivatized Acyl-CoA In Silico Mass Spectral Library Reveals the Landscape of Acyl-CoA in the Aging Mouse Organs (Data Supplement)
<p>Data supplement for publication "An 8-(Diazomethyl) Quinoline Derivatized Acyl-CoA In Silico Mass Spectral Library Reveals the Landscape of Acyl-CoA in the Aging Mouse Organs (Data Supplement)" (2024)</p> <p>Jinhui Yu<sup>1†</sup>, Menghao Guo<sup>1,3†</sup>, Sha Li<sup>5</sup>, Jian Ni<sup>2</sup>, Yu-Qi Feng<sup>4,5</sup>*, Jun Ding<sup>1,2</sup>*</p> <p>1. CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, PR China.</p> <p>2. Renmin Hospital of Wuhan University, Wuhan University, 430072, Wuhan, P. R. China.</p> <p>3. College of Life Sciences, Wuhan University, Wuhan 430072, China.</p> <p>4. School of Bioengineering and Health, Wuhan Textile University, Wuhan 430200, China.</p> <p>5. Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan, 430071, China.</p> <p> </p> <p>†The authors contribute equally to this work.</p> <p>* Corresponding author Email: <a href="mailto:dingjun@wbgcas.cn">dingjun@wbgcas.cn</a>, <a href="mailto:yqfeng@whu.edu.cn">yqfeng@whu.edu.cn</a></p> <p>----<br>Content:<br>1) Developement: XLS template sheet for development (can be used to adjust or create new library)<br>2) MSP library: spectra in NIST MSP format (use with MS-Dial or NIST MS Search)<br>3) NIST library: NIST23 compatible 8-DMQ-acyl-CoA library (use with NIST MS Search)<br>4) Reference spectra msp: 8-DMQ-acyl-CoA authentic MS/MS spectrum in NIST MSP format (generated by Thermo QE HF-X MS (HCD), for searching NIST MS-Search)</p> <p>Version 1.0<br>April 22 2024</p>
Data Supplement for "Phase diagram of compressible and paired states in the quarter-filled Landau level"
<p><strong>Description:</strong> This dataset provides supplemental data for the paper <em>Arxiv 2408.08354</em>, specifically the Density Correlation Function (DCF) and Harmonic Coefficients <span><span>GkG_k</span><span><span><span><span>G</span><span><span><span><span><span><span>k</span></span></span><span></span></span></span></span></span></span></span></span> (Pol) for various fractional quantum Hall wave functions, as outlined in the paper.</p> <h3>Contents:</h3> <ul> <li><strong>Density Correlation Function (DCF)</strong>: Corresponds to Eq. (4) in the paper, capturing correlation behavior in fractional quantum Hall systems.</li> <li><strong>Harmonic Coefficients <span><span>Gk</span></span> (Pol)</strong>: Corresponds to Eq. (5), representing harmonics relevant to polarization effects.</li> </ul> <h3>File Structure:</h3> <p>The data files follow the structure:</p> <ul> <li><strong>DCF[WF_name, Ne, Nc]</strong> and <strong>Pol[WF_name, Ne, Nc]</strong>: <ul> <li><code>WF_name</code>: Identifier for the wave function (details below).</li> <li><code>Ne</code>: Number of particles.</li> <li><code>Nc</code>: Lowest Landau level projection cutoff used in Eq. (C5), if projection is needed.</li> </ul> </li> </ul> <h3>Data Format:</h3> <p>The data is formatted for Mathematica, with each entry stored as a comma-separated list enclosed in curly braces <code>{}</code>. Each function entry is structured as:</p> <ul> <li><strong>DCF[WF_name, Ne, Nc] = {value, ...}</strong> and <strong>Pol[WF_name, Ne, Nc] = {value, ...}</strong></li> </ul> <h3>Error Estimation:</h3> <p>For error assessment, each wave function's data is divided into 20 batches. Each batch is averaged separately:</p> <ul> <li><strong>DCFbatch[WF_name, Ne, Nc, id]</strong> and <strong>Polbatch[WF_name, Ne, Nc, id]</strong>: <ul> <li><code>id</code>: Batch number ranging from 1 to 20.</li> </ul> </li> </ul> <h3>Wave Function Naming Conventions:</h3> <p>The naming convention used in <strong>WF_name</strong> designates the state as follows:</p> <ul> <li> <p><strong>First Letter</strong>: Filling factor</p> <ul> <li><code>H</code>: Half-filled</li> <li><code>Q</code>: Quarter-filled</li> </ul> </li> <li> <p><strong>Second and Third Letters</strong>: Pairing channel (shift)</p> <ul> <li><code>AP</code>: Anti-Pfaffian (<span><span>l=−3</span></span>)</li> <li><code>PH</code>: PH-Pfaffian (<span><span>l=−1</span></span>)</li> <li><code>MR</code>: Moore-Read (<span><span>l=1</span></span>)</li> <li><code>FW</code>: f-wave (<span><span>l=3</span></span>)</li> <li><code>Q0</code>: CFL only (<span><span>l=0</span></span>)</li> </ul> </li> <li> <p><strong>Ending Type</strong>: Wave function type</p> <ul> <li><code>S</code>: Paired state with single-particle projection.</li> <li><code>CFL</code>: Composite Fermi liquid.</li> <li><code>E</code>: Exact wave function for Moore-Read.</li> </ul> </li> </ul> <p><strong>Examples:</strong></p> <ul> <li><strong>QAPS</strong>: Quarter-filled Anti-Pfaffian wave function.</li> <li><strong>HPHCFL</strong>: Half-filled Composite Fermi liquid at PH-Pfaffian shift.</li> <li><strong>QMRE</strong>: Quarter-filled Moore-Read exact wave function without expansion of pairing function.</li> </ul> <h3>Naming Exceptions:</h3> <ul> <li><code>La</code>: Laughlin state at <span><span>ν=1/3.</span></span></li> <li><code>QSU2</code>: SU(2)<span><span>2_2</span><span><span><span><span><span><span><span></span></span></span></span></span></span></span></span> wave function, Eq. (20) with + sign at <span><span>ν=1/4</span></span> (p=2).</li> <li><code>HASU2</code> and <code>QASU2</code>: SU(2)<span><span>2_</span><span><span><span><span><span><span><span><span><span>2</span></span></span><span></span></span></span></span></span></span></span></span> wave function, Eq. (20) with - sign at <span><span>ν=1/2</span></span> and <span><span>ν=1/4</span></span> (p=1 and 2).</li> </ul>
Supplemental data for: Structural polymorphism and diversity of human segmental duplications
<p>Data used for figure generation and analysis in: Structural polymorphism and diversity of human segmental duplications</p> <p> </p> <p>Code used for data analysis is on https://github.com/hrrsjeong/pangenome_SD</p>
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>
Supplement for "Tactic Script Optimisation for Aesop"
<p>This is the supplement for the paper "Tactic Script Optimisation for Aesop", to be published at CPP 2025. Please refer to the enclosed README for further information.</p>
Supplemental material to the journal article "The complete mitogenome of an unidentified Oikopleura species"
<ul> <li>Wibisana2024)_rev2.tar.gz: aligned sequence files, command-line notes and figures related to the phylogenetic tree in the journal article “The complete mitogenome of an unidentified Oikopleura species”, revision 2.</li> <li>Wibisana2024_rDNA_PacBio_read.fa: a sequence read from the same run used to assemble the mitogenome, that contains a copy of the rDNA locus of the nuclear genome.</li> <li>Wibisana2024_supplementary_rev2.pdf: supplementary figures and tables for the article, revision 2.</li> </ul>
Electronic Supplement / Data Archive for "Comparison of a Neutral Density Model With the SET HASDM Density Database"
<p>These files provide supplemental data to accompany the paper "Comparison of a Neutral Density Model With the SET HASDM Density Database,” submitted to <em>Space Weather, </em>with manuscript number 2021SW002888. Details are provided in the file DataArchiveDocumentation.pdf.</p>
Data supplement for "Topological magnon band structure of emergent Landau levels in a skyrmion lattice"
<p>Collection of the data sets for our paper, <a href="https://doi.org/10.1126/science.abe4441"><em>Topological magnon band structure of emergent Landau levels in a skyrmion lattice</em></a>. (The source code supplement can be found <a href="https://doi.org/10.5281/zenodo.5718363">here</a>.)</p> <p> </p> <p><strong>Contents</strong></p> <table> <caption>Data files used for the paper's figures.</caption> <thead> <tr> <th scope="col">Scan</th> <th scope="col">Figure</th> <th scope="col">File(s)</th> </tr> </thead> <tbody> <tr> <td>(i)</td> <td>2</td> <td>ill_thales/exp_4-01-1621/rawdata/025280<br> ill_thales/exp_4-01-1621/rawdata/025281</td> </tr> <tr> <td>(ii)</td> <td>S17</td> <td>ill_thales/exp_INTER-436/rawdata/022169</td> </tr> <tr> <td>(iii)</td> <td>2</td> <td>ill_thales/exp_4-01-1597/rawdata/023454</td> </tr> <tr> <td>(iv)</td> <td>3</td> <td>mlz_reseda/*</td> </tr> <tr> <td>(v)</td> <td>4</td> <td>ill_thales/exp_INTER-413/rawdata/020778<br> ill_thales/exp_INTER-413/rawdata/020779</td> </tr> <tr> <td>(vi)</td> <td>4</td> <td>ill_thales/exp_INTER-413/rawdata/020777</td> </tr> <tr> <td>(vii)</td> <td>S16</td> <td>ill_thales/exp_INTER-436/rawdata/022168</td> </tr> <tr> <td>(viii)</td> <td>S16</td> <td>ill_thales/exp_INTER-413/rawdata/020793</td> </tr> <tr> <td> </td> <td>S10</td> <td>ill_thales/exp_4-01-1597/rawdata/023488</td> </tr> <tr> <td> </td> <td>S10</td> <td>ill_thales/exp_4-01-1597/rawdata/023489</td> </tr> <tr> <td> </td> <td>S11</td> <td>ill_thales/exp_4-01-1597/rawdata/023453</td> </tr> <tr> <td> </td> <td>S11</td> <td>ill_thales/exp_4-01-1597/rawdata/023553<br> ill_thales/exp_4-01-1597/rawdata/023559</td> </tr> <tr> <td> </td> <td>S12</td> <td>ill_thales/exp_INTER-436/rawdata/022213<br> ill_thales/exp_INTER-436/rawdata/022216<br> ill_thales/exp_INTER-436/rawdata/022217</td> </tr> </tbody> </table> <p> </p> <table> <caption>Overview of experimental data sets.</caption> <thead> <tr> <th scope="col">Instrument</th> <th scope="col">Proposal</th> <th scope="col">Directory</th> </tr> </thead> <tbody> <tr> <td><a href="http://doi.org/10.1080/10448632.2015.1057050">THALES (ILL)</a></td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.INTER-413">INTER-413</a></td> <td>ill_thales/exp_INTER-413/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.INTER-436">INTER-436</a></td> <td>ill_thales/exp_INTER-436/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.4-01-1597">4-01-1597</a></td> <td>ill_thales/exp_4-01-1597/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.INTER-477">INTER-477</a></td> <td>ill_thales/exp_INTER-477/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.4-01-1621">4-01-1621</a></td> <td>ill_thales/exp_4-01-1621/</td> </tr> <tr> <td><a href="http://doi.org/10.1016/j.nima.2011.01.173">LET (RAL)</a></td> <td><a href="http://dx.doi.org/10.5286/ISIS.E.RB1620412">RB1620412</a></td> <td><em>Impossible to include in archive due to size.</em></td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5286/ISIS.E.RB1720033">RB1720033</a></td> <td><em>Impossible to include in archive due to size.</em></td> </tr> <tr> <td><a href="https://www.psi.ch/en/sinq/tasp">TASP (PSI)</a></td> <td>20181324 (part 1)</td> <td>psi_tasp/exp_20181324_1/</td> </tr> <tr> <td> </td> <td>20181324 (part 2)</td> <td>psi_tasp/exp_20181324_2/</td> </tr> <tr> <td> </td> <td>20151888</td> <td>psi_tasp/exp_20151888/</td> </tr> <tr> <td><a href="http://doi.org/10.1016/j.nima.2017.09.063">MIRA (MLZ)</a></td> <td>13511</td> <td>mlz_mira/exp_13511</td> </tr> <tr> <td> </td> <td>15633</td> <td>mlz_mira/exp_15633</td> </tr> <tr> <td><a href="http://doi.org/10.1016/j.nima.2019.05.056">RESEDA (MLZ)</a></td> <td>P00745-01</td> <td>mlz_reseda/</td> </tr> </tbody> </table> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank E. Villard and P. Chevalier for technical support and J. Locatelli for IT support during the <em>THALES</em> experiments; and J. Frank for technical support during the <em>MIRA</em> experiments. We thank J. K. Jochum for support with the <em>RESEDA</em> experiment. We thank M. Kugler for his early experiments on skyrmion dynamics in MnSi.</p> <p> </p> <p>► Please see the <strong>readme.txt</strong> file in the archive for details.</p> <p> </p>
Benchmarking eliminative radiomic feature selection for head and neck lymph node classification - Supplemental data
<p>Supplementary files for the publication "Benchmarking eliminative radiomic feature selection for head and neck lymph node classification"</p>
Supplemental image data for "Mechanical annealing and memories in a disordered solid"
<p>This archive contains raw and subtracted experimental images of a 2D colloidal disordered solid, sufficient to verify the major qualitative results of the paper "Mechanical annealing and memories in a disordered solid" by Nathan C. Keim and Dani Medina. Descriptions of the images may be found in the main text and Supplementary Information for that paper, a preprint of which is available at arXiv.org.</p>
Supplemental Material Surveillance Improves Outcomes for Carriers of SDHB Pathogenic Variants
<p><strong>Supplemental table for Surveillance improves outcomes for carriers of <em>SDHB</em> pathogenic variants: a multi-center study</strong></p>
Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives (Supplemental material)
<p>This data upload provides a detailed account of indicators that can be used to measure RRI progress around the world against the UNESCO Recommendation for Science and Scientific Researchers at the level of individual researchers and public opinion. This data upload provides supplemental material for an article in Open Research Europe entitled, 'Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives'.</p> <p>Abstract for main article:</p> <p>As calls for more socially responsible research and innovation (RRI) policies and practices grow more insistent, the need for high quality indicators that can be used to evaluate progress is becoming increasingly important. Given the global nature of science, such indicators need to be relevant to countries across all world regions. Moreover, the methodological quality of indicators is critical to provide a strong foundation for long-term comparative measurement of the impacts of different kinds of policy intervention. There is a practical challenge in this effort relating to the uneven mechanisms for data collection and analysis available in different countries. There is also a geopolitical challenge in gaining buy-in from countries with very different, and sometimes competing, agendas. Here, the 2017 UNESCO-led Recommendation on Science and Scientific Researchers is highlighted as an existing vehicle that can enable cooperation on globally comparative measurement of socially responsible research and innovation. In particular, the quadrennial monitoring of the implementation of this wide-ranging global policy instrument that has been ratified by 195 countries affords a unique opportunity to add value for these countries by linking RRI to the 2017 Recommendation while establishing benchmark indicators for RRI more generally. As a practical and methodological contribution to the global community of science and innovation policymakers, researchers and research and innovation stakeholders committed to socially responsible research, this report contains specific, detailed survey questions and response options focusing on the public opinion and individual researchers’ level of measurement. It provides details of sources of benchmark survey data that have readily available open data that can be used to benchmark the development of socially responsible research and innovation over time from the vantage points of the public and researchers around the world. The aim of this kind of indicators framework is to enable evidence-based practice in socially responsible research and innovation. Robust and practical RRI measurement on a global scale can reduce the risk that well-intentioned but ill-conceived RRI policy and practice interventions create undetected, unchecked, and unreformed negative impacts in practice.</p>
Supplement to "On the accuracy of RTTOV-SCATT for radiative transfer at all-sky microwave and submillimeter frequencies"
<p>This supplement contains a set of realistic cloudy and precipitating conditions from the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the corresponding simulated brightness temperatures for the journal article “On the accuracy of RTTOV-SCATT for radiative transfer at all-sky microwave and submillimeter frequencies”, which is under review at the Journal of Quantitative Spectroscopy and Radiative Transfer. These data are available as benchmark for model developers.</p> <p>The IFS profiles correspond to model data interpolated (using the IFS observation operator) to the locations of the Atmospheric Infrared Sounder (AIRS) for a 12 h period centred on 03 UTC on 1 November 2018. Note that only a subset of the full set of AIRS locations are supplied here. The IFS model version cycle 46r1 was used, in a research experiment, to generate these profiles. The model was configured to T1279co resolution (about 8-9 km) with 137 levels in the vertical. The forecast was initialized from operational analyses at 18 UTC on 31 October 2018, and hence corresponds to the 12 h forecast "background" used in the data assimilation cycle.</p> <p>The following information concerns only the IFS model data:</p> <ul> <li>Copyright statement: Copyright "© 2022 European Centre for Medium-Range Weather Forecasts (ECMWF)".</li> <li>Source: www.ecmwf.int.</li> <li>Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.</li> <li>Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0), https://creativecommons.org/licenses/by/4.0/.</li> <li>Further information on the ECMWF data policy, and guidance on how to comply with it, is at https://apps.ecmwf.int/datasets/licences/general/.</li> </ul> <p>The simulated brightness temperatures are also distributed under Creative Commons Attribution 4.0 International (CC BY 4.0).</p> <p>The full journal description of this dataset is currently under review, with the current citation:</p> <p>Barlakas, V., Galligani, V. S., Geer, A. J., Eriksson, P., 2022. On the accuracy of RTTOV-SCATT for radiative transfer at all-sky microwave and submillimeter frequencies. Submitted to Journal of Quantitative Spectroscopy and Radiative Transfer.</p> <p>The work of Vasileios Barlakas at Chalmers University of Technology is funded by a EUMETSAT fellowship program.</p>
Medical Education Journal Data and Supplemental Files (2000 - 2020)
<p>This is the supplemental data, figures, and tables for <em>The Voices of Medical Education Science: Describing the Landscape</em>. This also includes the author thesaurus and institution thesaurus with supporting read me files. </p> <p><strong>Abstract</strong> </p> <p>Introduction</p> <p>Medical education has been described as a dynamic and growing field, driven in part by its unique body of scholarship. The voices of authors who publish medical education literature have a powerful impact on the discourses of the community. While there have been numerous studies looking at aspects of this literature, there has been no comprehensive view of recent publications.</p> <p>Method</p> <p>The authors conducted a bibliometric analysis of all articles published in 24 medical education journals published between 2000-2020 to identify article characteristics, with an emphasis on author gender, geographic location, and institutional affiliation. This study replicates and greatly expands on two previous investigations by examining all articles published in these core medical education journals. </p> <p>Results </p> <p>The journals published 37,263 articles with the most articles published in 2020 (n=3,957, 10.7%) and least in 2000 (n=711, 1.9%) representing a 456.5% increase. The articles were authored by 139,325 authors of which 62,708 were unique. Men were more prevalent across all authorship positions (n=62,828; 55.7%) than women (n=49,975; 44.3%). Authors listed 154 country affiliations with the United States (n=42,236, 40.4%), United Kingdom (n=12,967, 12.4%), and Canada (n=10,481, 10.0%) most represented. Ninety-three countries (60.4%) were low- or middle-income countries accounting for 9,684 (9.3%) author positions. Few articles were written by multinational teams (n=3,765; 16.2%). Authors listed affiliations with 4,372 unique institutions. Across all author positions, 48,189 authors (46.1%) were affiliated with institutions ranked globally as Top 200 institutions by the Times Higher Education ranking. </p> <p>Discussion </p> <p>There is a relative imbalance of author voices in medical education. If the field values a diversity of perspectives, there is considerable opportunity for improvement.</p>
Data supplement to: Quality control of image sensors using gaseous tritium light sources
<p>In the article "Quality Control of Image Sensors using Gaseous Tritium Light Sources" (<a href="https://doi.org/10.1098/rsta.2021.0130)">https://doi.org/10.1098/rsta.2021.0130)</a> we propose a practical method for radiometrically calibrating cameras using widely available gaseous tritium light sources (<em>betalights</em>). This dataset includes all the recorded data along with the scripts necessary to reproduce the results and figures.</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.