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FIGURE 3. Bayesian consensus tree for Neoplecostomus species obtained from 5 million generations. Numbers after branches are posterior probabilities. Values below 0.95 in Neoplecostomus canastra, a new catfish (Teleostei: Siluriformes) species from upper Rio Paraná basin
FIGURE 3. Bayesian consensus tree for Neoplecostomus species obtained from 5 million generations. Numbers after branches are posterior probabilities. Values below 0.95 are not shown. Results from different methods of species delimitation are shown with bars colored at right side of the phylogeny. Pink bars represent morphospecies, yellow bars GMYC model and blue bars 2% genetic distance between clusters. The upper-left graphic is the Lineage-through-time plot representing the threshold time at -0.007195772 (yellow line).
FIGURE 5 in Moss diversity: New look at old numbers
FIGURE 5. Current country distribution for encoded moss collections held in TROPICOS. Darker country colors indicates more captured specimens. Data for approximately half of the 500,000 specimens in the collection at MO have been captured and supplemented by data from several partners (including AAU, GB, HUA, LPB, USZ), but with an obvious new collection and project bias.
FIGURE 2 in Moss diversity: New look at old numbers
FIGURE 2. Chart of all validly published moss names, 1801-2009. The large center spike in the 1890's are primarily names described by Carl Müller (3426 in the 1890's, 4368 over all) with additional names from Bescherelle, Brotherus, Kindberg, Paris, and Renauld & Cardot, each contributing over 500 new names during this period.
FIGURE 3 in Moss diversity: New look at old numbers
FIGURE 3. Valid moss names published in the years between 1801 and 2009 and the accumulated total names in current use – 13,053.
FIGURE 2 in Pattern of stigma numbers as a taxonomic character in some didemnid ascidians (Aplousobranchia: Didemnidae)
FIGURE 2. Component ratio of stigma patterns in the five morphotypes of Didemnum molle. Black indicates the eight stigma patterns found in all morphotypes; gray indicates the patterns shared in two to four morphotypes; white indicates the patterns unique to one morphotype.
FIGURE 1 in Pattern of stigma numbers as a taxonomic character in some didemnid ascidians (Aplousobranchia: Didemnidae)
FIGURE 1. Collection sites of didemnid ascidians in the Ryukyu Archipelago–Taiwan. The site symbols (A–O) correspond to those in TABLE 1. A: Bise, Okinawajima Island (26°42΄40"N, 127°52΄30"E); B: Seragaki, Okinawajima Island (26°30΄30"N, 127°51΄37"E); C: Manza, Okinawajima Island (26°30΄10"N, 127°50΄34"E); D: Zanpa, Okinawajima Island (26°26΄20"N, 127°42΄45"E); E: Nagahama, Okinawajima Island (26°25΄20"N, 127°44΄10"E); F: Odo, Okinawajima Island (26°5΄25"N, 127°42΄30"E); G:Nishizaki, Iejima Island, Okinawajima Islands (26°42΄30"N, 127°45΄40"E); H: Sesoko,Sesokojima Island, Okinawajima Islands (26°38΄50"N, 127°52΄25"E); I: Shinri-hama, Kumejima Island, Okinawajima Islands (26°20΄57"N, 126°42΄50"E); J: Ara-hama, Kumejima Island (26°18΄55"N, 126°46΄25"E); K: Nagama-hama, Kurimajima Island, Miyako Islands (24°43΄40"N, 125°14΄25"E); L: Kaiji-hama, Taketomi Island, Yaeyama Islands (24°18΄50"N, 124°4΄50"E); M: Nakamoto, Kuroshima Island, Yaeyama Islands (24°13΄50"N, 123°59΄50"E); N: Gueiwan, Lyudao, Taiwan (22°38΄45"N, 121°28΄32"E); and O: Nanwan, Kenting, Taiwan (21°57΄30"N, 120°45΄50"E).
FIGURE 3 in Pattern of stigma numbers as a taxonomic character in some didemnid ascidians (Aplousobranchia: Didemnidae)
FIGURE 3. Thoraxes of Trididemnum clinides (A, right side view) and Trididemnum nubilum (B, left side view). The stigma patterns are <9, 8, 8> in A and <5, 5, 4> in B. Numbers indicate the stigmata in the first rows. en, endostyle; es, esophagus. Scale bars, 0.1 mm.
Number of bovine animals tested by age group, reporting country and target group, 2021
<p>The tables contain the number of bovine animals tested by age group, reporting country and target group, 2021.</p>
Performance of high-order Godunov-type methods in simulations of astrophysical low Mach number flows
<p>Supplementary materials for the paper "Performance of high-order Godunov-type methods in simulations of astrophysical low Mach number flows". The supplementary materials are split into two .zip archives (KH2D-plots.zip and 3D-TURBULENCE-WAVES-plots.zip) containing figures not shown in the paper.</p>
Towing car BT-1, number G-051
Apart from using vehicles intended for carrying passengers, transportation companies worldwide use technical support vehicles to enable their enterprises to operate effectively. This was also the case in pre-war Breslau. Between WWI and WWII the engine was designated as BT1 – Betriebs Triebwagen (service/freight car). The model presented is probably the only vehicle from Wroclaw of this type to survive WWII. After the war it took part in the campaign of clearing the post-war rubble in Wroclaw. It has been part of the Museum of Urban Engineering's collection since 2001. The tram was operated in Lower Silesia until the 1970's. In 2016, thanks to subsidies obtained by the Museum, a restoration of the car was carried out in collaboration with MPK SA. Manufacturer: Städtische Strassenbahn Breslau; Linke-Hofman-Werke AG, Breslau 1920. Inv. No.: MIM346/I-6 Model prepared on the basis of photogrammetric and ToF measurements Licence: CC BY-NC-SA Source: Objaverse 1.0 / Sketchfab
Qi vs photon number Ta resonators
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Data from: The twenty amino acids are identified by unique numbers assigned to the uracil, cytosine, adenine, and guanine found in the three base positions of the sixty-four messenger RNA genetic codons
<p>A codon's three bases consist of any combination of uracil, cytosine, adenine, or guanine and these encode the twenty amino acids. When the codon's first two bases are given specific values, and those values are multiplied, then the third base of the codon is used during translation only when the product is greater than three. Here we show that those values plus more variables within the ribosomal decoding site results in specific flow values for each of the twenty amino acid groups. These results are demonstrated in a flow chart showing the unidirectional flow which is expected during the translation process. All twenty amino acids can be represented by numbers that describe their relationship to each other and to the decoding site. We anticipate our findings will increase discussion about using a number system to better understand the translation process.</p>
Dataset for "Controlling the photon number coherence of solid-state quantum light sources for quantum cryptography"
<p>Dataset for <strong>"Controlling the Photon Number Coherence of Solid-state Quantum Light Sources for Quantum Cryptography"</strong></p> <p>This dataset contains data for</p> <p>journal website: <a href="https://www.nature.com/articles/s41534-024-00811-2">https://www.nature.com/articles/s41534-024-00811-2 </a></p> <p>DOI: <a href="https://doi.org/10.1038/s41534-024-00811-2">https://doi.org/10.1038/s41534-024-00811-2</a></p> <p> </p> <p>The data is in either <strong>.txt or .csv f</strong>ormat. The zip file <strong>"PNCDataSet"</strong> includes the following folders and data:</p> <ul> <li>Dataset for Indistinguishability: <ul> <li>Folder <strong>.\HOM</strong> <ul> <li><strong>"HOM_Distinguishable.csv"</strong> as HOM data of Distinguishable case</li> <li><strong> "HOM_stim.csv"</strong> as HOM data of stiX.</li> <li><strong>"HOM_TPE.csv"</strong> as HOM data of reX.</li> </ul> </li> </ul> </li> <li>Dataset for g2 <ul> <li> folder <strong>.\g2</strong> <ul> <li><strong>"Stim_G2.csv"</strong> as g2 data of stiX.</li> <li><strong>"TPE_G2.csv"</strong> as g2 datra of reX.</li> </ul> </li> </ul> </li> <li>Dateset for Spectra <ul> <li>folder <strong>.\Spectra</strong> <ul> <li><strong>"TPE_SPECTRA.csv"</strong> as the spectra of reX.</li> <li><strong>"Stim_SPECTRA.csv" </strong>as the spectra of stiX</li> </ul> </li> </ul> </li> <li>Dataset for reX PNC <ul> <li>folder <strong>.\TPEPhaseScan</strong> <ul> <li>The folder contains 49 CSV files in alphabetical order. Each CSV file contains a 30-second count trace of 2 detectors for different TPE powers specified in the paper.</li> </ul> </li> </ul> </li> <li>Dataset for stiX PNC <ul> <li>folder <strong>.\StimTPE-PhaseScan</strong> <ul> <li>The folder contains 50 CSV files in alphabetical order. Each CSV file contains a 30-second count trace of 2 detectors for different TPE powers and a stimulation pulse power specified in the paper.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Additional data for the supplementary materials is available upon a reasonable request.</p> <p> </p> <p>How to extract data:</p> <p><strong>Windows:</strong></p> <ol> <li> <p>Locate the .zip file on your computer. In this case, the zip file is named "SUPERDataset".</p> </li> <li> <p>Right-click on the .zip file and select "Extract All" from the context menu. This will open the extraction wizard.</p> </li> </ol> <p><strong>macOS:</strong></p> <ol> <li> <p>Locate the .zip file on your computer. In this case, the zip file is named "SUPERDataset".</p> </li> <li> <p>Double-click on the .zip file. macOS will automatically extract the contents of the .zip file to the same location.</p> </li> </ol> <p><strong>Linux:</strong></p> <ol> <li> <p>Open a terminal window.</p> </li> <li> <p>Navigate to the directory where the .zip file is located using the <code>cd</code> command.</p> </li> <li> <p>Run the following command to unzip the file:<br> </p> <pre><code>unzip PNCDataSet.zip</code></pre> <p> </p> </li> </ol> <p> </p> <h2>Acknowledgements</h2> <p>Y.K., F.K., R.S., V.R. and G.W. acknowledge financial support through the Austrian Science Fund FWF projects W1259 (DK-ALM Atoms, Light, and Molecules), FG 5, TAI-556N (DarkEneT), F 7114 (BeyondC) and I4380 (AEQuDot). DAV and TH acknowledge financial support by the German Federal Ministry of Education and Research (BMBF) via projects 13N14876 (‘QuSecure’) and 16KISQ087K (tubLAN Q.0). TKB and DER acknowledge financial support from the German Research Foundation DFG through project 428026575 (AEQuDot). A.R. and SFCdS acknowledge the FWF projects FG 5, P 30459, I 4320, the Linz Institute of Technology (LIT) and the European Union’s Horizon 2020 research, and innovation program under Grant Agreement Nos. 899814 (Qurope), 871130 (ASCENT+) and the QauntERA II Program (project QD-E-QKD). L.M.H., P.W. and J.C.L. acknowledge financial support from the European Union’s Horizon 2020 and Horizon Europe research and innovation program under grant agreement No 899368 (EPIQUS), the Marie Skłodowska-Curie grant agreement No 956071 (AppQInfo), and the QuantERA II Program under Grant Agreement No 101017733 (PhoMemtor); FWF through F7113 (BeyondC), and FG5 (Research Group 5); from the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.</p>
Number of Joint Precipitation and either Wind or Storm Surge Extremes Based on CMIP6 Simulations
<p>This data accompanies the manuscript ‘Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models’, Hermans et al. (2024). The dataset consists of the number of joint wind speed & precipitation or storm surge &precipitation extremes derived from CMIP6 models, for different time periods in the 20th and 21st centuries. </p> <p>The code used to produce this dataset can be accessed here: https://github.com/Timh37/CMIP6cex. </p> <p><em>For the production of this dataset, we acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid </em><em>Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.580. We also acknowledge the computing and storage resources provided by the ‘NSF Science and Technology Center (STC) Learning the Earth with Artificial intelligence and Physics (LEAP)‘ (Award #2019625).</em></p>
Optimised geometries of a number of protonation models.
<p>Optimised geometries in pdb format presented in Fig.S12 of "A novel subgroup of light-driven sodium pumps with an additional Schiff base counterion" paper (https://doi.org/10.1101/2023.10.11.561842).</p>
Dataset of stream hydrochemistry at Fushan Experimental Forest watershed number 1
<h3><span>2013-2017 is Typhoon period and 2018-2022 is a No-typhoon period</span><span><br><span><span>Here includes monthly and annual streamflow (discharges) and fluxes of major ions.</span></span></span></h3>
Data for "Misestimation of forest soil carbon and nitrogen stocks due to rock fragments: A case study of large number samples in a boreal forest watershed ecosystem of northeast China"
<p>Here are the data for "<span>Misestimation of forest soil carbon and nitrogen stocks due to rock fragments: A case study of large number samples in a boreal forest watershed ecosystem of northeast China</span>", using the format of"excel".</p>
Particle Number Size Distributions from Rochester NY 2002-2019
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A left-to-right bias in number-space mapping across ages and cultures
<p>Datasets and scripts related to the paper 'A universal left-to-right bias in number-space mapping across ages and cultures' </p> <p>Below is a description of the elements contained in each folder.</p> <p><strong>Code<br></strong>This folder contains all the scripts used to perform the analyses reported in the paper. It also includes the script used to run the numerosity comparison experiment (please notice it does not run as a stand-alone file, it is just for example purposes)</p> <p><strong>Data:<br></strong>This folder includes all the necessary to replicate the analyses cited in the manuscript. Both aggregated and individual raw files are available. Specifically within this folder, you'll find:</p> <ul> <li>subfolder with individual raw data for the numerosity comparison task for each population</li> <li>CSV file with aggregate percentage of card configuration shape (used for chi-square analysis for experiment 1)</li> <li>CSV file with the individual cards correlation score (used for testing distribution against chance level for experiment 1)</li> <li>CSV file with individual Inverse Efficiency Scores for all participants (used for Mixed Model Analysis)</li> <li>CSV file with the Congruency Effect already computed for each participant (used in the ANOVA analysis for experiment 2)</li> <li>CSV file with the combined datasets (used to test the correlation between experiment 1 and experiment 2 measures)</li> <li>CSV files with participants' anonymized information (.e.g. age, gender, literacy level)</li> </ul> <p><strong>JASP<br></strong>This folder contains all the output files from the JASP software used to compute Bayesian analysis supporting the null results.</p>
a price list from Macau's Number One Sauna,
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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)
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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.