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Physiological parameters for three farm animal species (cattle, sheep, and swine) as the basis for the development of generic physiologically based kinetic models
<p><strong>IMPORTANT : PLEASE DISREGARD VERSION 1 OF THIS UPLOAD SINCE IT INCLUDES ERRONEOUS INFORMATION.</strong></p> <p>This excel file (DOI: 10.5281/zenodo.3433224) provides physiological parameters and their inter-individual variability (mean, coefficient of variation, sample size) for three farm animal species: cattle (<em>Bos taurus</em>), sheep (<em>Ovis aries</em>), and swine (<em>Sus scrofa domesticus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020). This file is associated with R codes (DOI: 10.5281/zenodo.3432796) for generic PBK models, partition coefficient Quantitative Structure Activity Relationship (QSAR) models for each farm animal species and parameterisation of the model.</p> <p>The full data collection and implementation of the models using case studies are described in Lautz et al., 2020 (10.1016/j.toxlet.2019.10.008).</p>
Three-Enzyme Phosphorylase Cascade Immobilized on Solid Support for Biocatalytic Synthesis of Cello-oligosaccharides
<p>We provide here the underlying data of the scientific publication "Three-Enzyme Phosphorylase Cascade Immobilized on Solid Support for Biocatalytic Synthesis of Cello-oligosaccharides". Please find the abstract below:</p> <p>Enzyme cascades are promising for multistep biocatalytic synthesis, but their effective use beyond the proof-of-concept stage is challenging. Strategies to recycle the individual enzymes are critical for the applicability of such cascades. Immobilization on solid support is well developed for single enzymes but remains difficult for enzyme ensembles. Here, we show a controlled co-immobilization of three glycoside phosphorylases to establish a highly active and recyclable biocatalyst for the conversion of sucrose and glucose into soluble (short-chain) cello-oligosaccharides. We use protein fusion with the binding module Z<sub>basic2 </sub>to enable non-covalent surface tethering of all enzymes according to a uniform principle and in a programmable fashion. We thus achieve loading of the phosphorylases in an activity ratio optimal for the overall conversion rate and for controlling the cello-oligosaccharide chain length (≤ 6), hence the solubility, in the reaction. We demonstrate efficient production of ~12 g/L cello-oligosaccharides in 5 reaction cycles with integrated enzyme re-use. This study presents a major advance toward the practical use of systems bio-catalysis on solid support.</p>
Operation of a three terminal solar cell for children
<p>The video describes in a simple manner the operation of a three terminal solar cell. Blue arrows represent the high energy photons. Red arrows represent the low energy photons, that are absorbed deeper in the cell. Each colored square represents a semiconductor layer. White balls, represent electrons. Black ball represent holes. For a more detailed description of a particular kind of three terminal solar cell, see for example:</p> <p> A. Martí and A. Luque, “Three-terminal heterojunction bipolar transistor solar cell for high-efficiency photovoltaic conversion,” <em>Nat. Commun.</em>, vol. 6, pp. 6902–6902, Apr. 2015. (<a href="https://doi.org/10.1038/ncomms7902">https://doi.org/10.1038/ncomms7902</a>)</p> <p>The video has been created in Blender 2.82. (<a href="https://www.blender.org/">https://www.blender.org/</a>). Both the video and the source file with which we created it are attached</p>
Replication package of "Good Things Come In Threes: Improving Search-based Crash Reproduction With Helper Objectives"
<p>The replication package for the study about using new helper objectives (MOHO) for crash reproduction. This study has been accepted at ASE 2020.</p> <p> </p> <p>Abstract:</p> <p>Evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called <em>Crash Distance</em> to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called <em>MO-HO</em>, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-Objectives for crash reproduction, namely <em>test length</em> (to minimize) and <em>method sequence diversity</em> (to maximize), in addition to <em>Crash Distance</em>.</p> <p>We assessed <em>MO-HO</em> using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 hard-to-reproduce crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for <em>MO-HO</em>.</p> <p>We evaluated this best-performing algorithm for <em>MO-HO</em> against the state-of-the-art: single-objective approach (Single-Objective Search) and decomposition-based multi-objectivization approach (<em>De-MO</em>). Our results show that <em>MO-HO</em> reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, <em>MO-HO</em> improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-Objective Search and <em>De-MO</em>, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time. </p>
JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.
<p>The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that <strong>(1)</strong> gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute <strong>(2)</strong> theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes</p> <p>We deliver (1) data collection and (2) numerical codes in the following files attached:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-GRL.pdf</strong> that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li><strong>JUMP-JetsInTheLab.zip</strong></li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter<strong> </strong> <ul> <li><strong>uvData-JupObs-istep-0-nstep-4-niz-1.nc</strong></li> <li><strong>StatisticalData-JupObs.nc</strong></li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li><strong>IPV-QGPV-Jupiter-Saturn.zip</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul> <p> </p> <p>The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver <strong>(1)</strong> velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and <strong>(2)</strong> guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.</p>
Surface electromyogram (sEMG) dataset recorded from forearm for 9 hand movements and three electrode array positions
<p>This repository contains raw surface Electromyography signals termed surface Electromyograms (<a href="https://en.wikipedia.org/wiki/Electromyography">sEMG</a>) recorded with 8 circular surface Ag/AgCl pairs of electrodes placed circumferentially around the forearm of the dominant arm in 10 able-bodied individuals (5 Females and 5 Males). The proposed method for processing sEMG data with subjects' characteristics and protocol can be found in <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>.</p> <p>For each subject, sEMG was recorded from <strong>three recording electrode array positions</strong> termed P1, P2, and P3 for 9 hand movements. We provide a compressed .7z folder with 10 sub-folders for each subject named by <strong>subject ID</strong> (ID1, ID2, ... ID10). Each sub-folder contains 27 .txt data files (for 9 movements × 3 electrode array positions), except for subject ID7 (there are 24 .txt records, since three records for wrist extension EX in P1, P2, and P3 positions got corrupted in subject ID7). Average size of 10 sub-folders is 167.50 ± 27.02 MB with maximum of 194 MB and minimum of 117 MB.</p> <p>The subjects performed following hand movements from the reference resting position –relaxation, R (explained in-detail in <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>): (1) spherical power grasp, PS, (2) three finger sphere grasp, 3F, (3) two finger prismatic grasp, PP, (4) wrist flexion, FL, (5) wrist extension, EX, (6) radial deviation, RD, (7) ulnar deviation, UD, and then forearm rotation i.e. (8) pronation, PR, and (9) supination, SU. PS, 3F, PP, FL, EX, RD, UD, PR, and SU correspond to <strong>type of hand movement</strong> in naming convention for .txt data files.</p> <p><a href="https://www.youtube.com/playlist?list=PLI3SYeiSufnBo6UDAZt9NJO9ecb-InJqb">Hand movements YoutTube playlist</a> contains explanatory videos for 9 hand movements recorded in this study, and we also provide corresponding .wmv here in the "movies hand movements.7z". Naming convention for .wmv files is <strong>type of hand movement</strong> with both full name and abbreviation for the movement (for example "radialDeviation-RD.wmv").</p> <p>Naming convention for .txt data files within 10 sub-folders is: <strong>subjects ID _ type of hand movement _ recording electrode array position</strong> (for example: "ID1_3F_P1.txt" in sub-folder ID1, "ID9_RD_P3.txt" in sub-folder ID9).</p> <p><strong>Dataset contents</strong></p> <ol> <li><a href="https://zenodo.org/record/4039550/files/EMG%20dataset.7z?download=1">EMG dataset.7z</a>, 267 .txt data files, text format</li> <li><a href="https://zenodo.org/record/4039550/files/movies%20hand%20movements.7z?download=1">movies hand movements.7z</a>, 9 .wmv files, explanatory hand movement videos (also available on <a href="https://www.youtube.com/playlist?list=PLI3SYeiSufnBo6UDAZt9NJO9ecb-InJqb">YouTube</a>)</li> <li><a href="https://zenodo.org/record/4039550/files/README.txt?download=1">README.txt</a>, metadata for data files, text format</li> </ol> <p><strong>Data files contain numerical values with decimal point* according to the following structure</strong></p> <ol> <li>column - CH1** (recorded samples from channel 1)</li> <li>column - CH2** (recorded samples from channel 2)</li> <li>column - CH3** (recorded samples from channel 3)</li> <li>column - CH4** (recorded samples from channel 4)</li> <li>column - CH5** (recorded samples from channel 5)</li> <li>column - CH6** (recorded samples from channel 6)</li> <li>column - CH7** (recorded samples from channel 7)</li> <li>column - CH8** (recorded samples from channel 8)</li> </ol> <p>* For subjects ID1 and ID2 three decimal places are provided, while for other subjects 6 decimal places in .txt data files are provided.</p> <p>** Each data file contains at least 10 repetitions of the corresponding movement. In cases where file contains >10 repetitions (overall 162 .txt data files), we used the first or the last ten for the analysis (except for two files where short and strong artifact appeared during the measurement procedure, and corresponding movement repetitions were discarded) presented in <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>.</p> <p>Sample rate was set at 1000 Hz and <a href="https://en.wikipedia.org/wiki/Analog-to-digital_converter">A/D card</a> had 16 bits resolution. Gain of the amplifier was set at 1000. For more in-detail explanations of electrode array assemble and positioning for sEMG channels CH1, CH2, ... CH8, please refer to <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>.</p> <p>If you find these signals useful for your own research or teaching class, please cite both relevant preprint and dataset as:</p> <ol> <li> <p>Miljković, N. and Isaković, M.S., 2021. Effect of the sEMG electrode (re) placement and feature set size on the hand movement recognition. <em>Biomedical signal processing and control</em>, 64:102292. <em><a href="https://doi.org/10.1016/j.bspc.2020.102292">10.1016/j.bspc.2020.102292</a></em></p> </li> <li> <p>Miljković, N. and Isaković, M.S., 2020. Surface electromyogram (sEMG) dataset recorded from forearm for 9 hand movements and three electrode array positions. [Data set]. <em>Zenodo</em> <em><a href="https://zenodo.org/record/4039550">10.5281/zenodo.4039550</a></em>.</p> </li> </ol> <p><strong>ACKNOWLEDGEMENTS</strong> (from <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>): "Special appreciation the authors owe to Professor Mirjana B. Popović from the University of Belgrade for her kind support,precious guidance, and advice regarding this research which significantly improved the manuscript. Also, the authors would like to thank Dr Matija Štrbac from Tecnalia Serbia Ltd. for providing advice throughout the study.The authors thank all volunteers for their participation."</p>
Annotated checklist of the beetles of three beech forests in Gran Sasso National Park, Central Italy
<p>The checklist contains 163 species of beetles which belong to 36 families. The species were collected during a field study carried out in the years 2013 and 2016 and aimed at describing the community of beetles. The collection methods consisted of window flight traps and emergence traps. The study area included 3 beech forest sites, named Prati di Tivo (42.5096 N, 13.5679 E), Venacquaro (42.4988 N, 13.5139 E) and Incodara (42.5123 N, 13.4735 E) located in the Italian Central Apennines. The sites are included in the Natura 2000 site IT7110202 “Gran Sasso”.</p> <p>The checklist is annotated with information on the taxonomy of the species (order and family), number of individuals, locality, habitat type (following EUNIS habitat classification 2017), sampling protocol, collector name, specialist name, IUCN Red List categories of the saproxylic species (Carpaneto et al. 2015). </p> <p>The terms used for the dataset fields follows the Darwin Core Maintenance Group. 2020. List of Darwin Core terms. Biodiversity Information Standards (TDWG). <a href="https://dwc.tdwg.org/list/">https://dwc.tdwg.org/list/</a></p> <p>Investigations on stand structure and forest biodiversity (Sabatini et al. 2016) and faunistic analysis (Zanetti and Parisi 2019) have been based on this dataset.</p> <p>The harmonization of the dataset to the point of view of taxa, authorship, LSID and the massive upgrading of the related identifiers in Zenodo record was performed by the use of R script using respectively dplyr, taxize (Chamberlain and Szöcs, 2013) and zen4r (Blondel and Barde, 2020) packages.</p>
Red-optical spectra of three nearby M dwarfs with SALT HRS
<p>We conducted observations of three nearby mid-M dwarfs with the High-Resolution Spectrograph (HRS) at the Southern African Large Telescope (SALT, DDT proposal code: 2019-2-DDT-006). We obtained spectra in its red arm over a wavelength range of 5,500-8,900 Angström with a spectral resolution of about 40,000 in medium-resolution mode. The observations were carried out on February 08 and February 09, 2020. The data were reduced with the PEPSI data reduction software (Strassmeier et al. 2018). The reduction followed the standard steps of bias overscan detection and subtraction, scattered light extraction from the inter-order space and subtraction, definition of échelle orders, optimal extraction of spectral orders, wavelength calibration, and a self-consistent continuum fit to the full two-dimensional (2D) image of extracted orders.</p> <p><strong>Files in this dataset</strong></p> <p>salt1.txt: TIC 44984200 (2MASS J08380224-5855583)<br> salt2.txt: TIC 277539431 (2MASS J10551532-7356091)<br> salt3.txt: TIC 300741820 (2MASS J07404497-6648318)</p> <p><strong>Columns in each file from left to right:</strong></p> <p>1 Wavelength in Angstrom<br> 2 Normalized flux<br> 3 Uncertainty on normalized flux</p> <p><strong>Corresponding author</strong></p> <p>Ekaterina Ilin, eilin@aip.de, Leibniz Institute for Astrophysics Potsdam (AIP)</p>
Three-dimensional magnetic reconnection in particle-in-cell simulations of anisotropic plasma turbulence (Simulation Data)
<p>This folder contains the output of the following simulation: </p> <p>We use the explicit Plasma Simulation Code (PSC, Germaschewski et al.2016) to simulate eight anisotropic counter-propagating Alfvén waves in an ion-electron plasma. The anisotropy of the initial fluctuation is set up according to the theory of critical balance by Sridhar & Goldreich (1994) and Goldreich & Sridhar (1995) at the small scale end of the inertial range: <span class="math-tex">\(k_{\parallel} d_{i} = C (|k_{\perp}|d_{i})^{2/3}\)</span>, where <span class="math-tex">\(C= 10^{-4/3}\)</span>. The normalization parameters are the speed of light <span class="math-tex">\(c = 1\)</span>, the vacuum permittivity <span class="math-tex">\(\epsilon_{0} = 1\)</span>, the magnetic permeability <span class="math-tex">\(\mu_{0} = 1\)</span>, the Boltzmann constant <span class="math-tex">\(k_{b}=1\)</span>, the elementary charge <span class="math-tex">\(q=1\)</span>, the ion mass <span class="math-tex">\(m_{i}=1\)</span>, the density of ions and electrons <span class="math-tex">\(n_{i}=n_{e}=1\)</span> and the ion inertial length <span class="math-tex">\(d_{i}=c/\omega_{pi}\)</span> where <span class="math-tex">\(\omega_{pi}=\sqrt{n_{i}q^{2}/m_{i}\epsilon_{0}}\)</span> is the ion plasma frequency. We set <span class="math-tex">\(\beta_{s,\parallel}=1\)</span> and <span class="math-tex">\(T_{s,\parallel}/T_{s,\perp}=1\)</span>, where <span class="math-tex">\(\beta_{s,\parallel}=2 n_s \mu_{0} k_{B}T_{s,\parallel}/B_{0}^{2}\)</span> is the ratio between the plasma pressure parallel to the background magnetic field <span class="math-tex">\(\mathbf{B}_{0}\)</span> and the magnetic pressure and $T_{s,\parallel}$ is the parallel temperature. The magnetic field is normalised to <span class="math-tex">\(B_{0}=V_{A}/c\)</span>, where <span class="math-tex">\(V_{A}=B_{0} / \sqrt{\mu_{0}n_{i}m_{i}}\)</span> is the ion Alfvén speed. We use 100 particles per cell (100 ions and 100 electrons), a mass ratio of <span class="math-tex">\(m_{i}/m_{e} = 100\)</span> so that <span class="math-tex">\(d_e = 0.1 d_{i}\)</span> where <span class="math-tex">\(m_{e}\)</span> is the electron mass and <span class="math-tex">\(d_{e}\)</span> is the electron inertial length. The simulation box size is <span class="math-tex">\(L_{x} \times L_{y} \times L_{z} = 24d_{i}\times24d_{i}\times125d_{i}\)</span> and the spatial resolution is <span class="math-tex">\(\Delta x =\Delta y = \Delta z = 0.06d_{i}\)</span>. We use a time step <span class="math-tex">\(\Delta t =0.06/ \omega_{pi}\)</span>. In our normalisation, the Debye length <span class="math-tex">\(\lambda_{D}=d_{i}\sqrt{\beta_{i}/2}V_{A}/c\)</span> defines the minimum spatial distance that needs to be resolve in the simulation and <span class="math-tex">\(\lambda_D=0.07d_i\)</span>.</p> <p>This output corresponds to <span class="math-tex">\(t=120 \omega_{pi}\)</span>. </p> <p>These data were produced using the Data Intensive at Leicester (DIaL) facility provided by the DiRAC project<br> dp126 "Identifying and Quantifying the Role of Magnetic Reconnection in Space Plasma Turbulence".</p>
GUV total ozone column and effective cloud transmittance from three Norwegian sites 1995-2019
<p>Total ozone column (TOC) and effective cloud transmittance (eCLT) from GUV-511 in Oslo (Norway), GUV-541 from Andøya/Tromsø (Norway), and GUV-541 from Ny-Ålesund (Svalbard, Norway).</p> <p>Responsible institute: NILU - Norwegian Institute for Air Research<br> Collaborative institute: Norwegian Radiation and Nuclear Safety Authority, DSA</p> <p>Method described in: Dahlback, A. (1996), Measurements of biologically effective UV doses, total ozone abundances, and cloud effects with multichannel, moderate bandwidth filter instruments, Appl. Opt. 35, 6514–6521</p> <p>1h average noon-time values, based on data with 1-minute time resolution.</p> <p>TOC retrievals from 305/320 nm channel ratio<br> eCLT retrievals from 340 nm channel </p> <p>Calibrations based on the FARIN2005-campaign and annual site visits with a travelling reference GUV instrument from DSA (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007JD009731)</p> <p>Funding: NILU - Norwegian Institute for Air Research, Norwegian Environment Agency, Norwegian Ministry of health and Care Services</p>
Interlaboratory study for the evaluation of three microtiter plate-based biofilm quantification methods
<p>Data collected in the Print-aid interlaboratory study (ring trial) to evaluate the repeatability and reproducibility of three microtiter plate based methods: crystal violet, resazurin and plate counts. The files contain all the raw data collected for each laboratory as well as the tranformed data. Analysis and protocol details can be found in the following publication https://www.nature.com/articles/s41598-021-93115-w </p>
Dataset - Survey results - Applying Model-based Requirements Engineering in Three Large European Collaborative Projects
<p>This dataset and its associated report contain the results of an online survey on using a model-based requirements engineering approach in three European projects. </p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - volume filtering verification
<p>For each of the four ROIs (R1 to R4 in file names) we pick few sub-regions (annotated in R?_legend.png) and show the changes between sections (temporally encoded) from initial (registered) input, marked "1" in video to the final result of volume filtering, marked "5" in video.</p> <p>Subregion number j in ROI number i is bears the video file name "R<i>_verification_volume_-_region_<j>_FullHD.mov".</p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - registered sections (input data)
<p>These are the 3500x3500 ROI data, selected from complete scans.</p> <p>Following procedure was applied:</p> <p>- Coarse registration (Ulrich et al, 2014)<br> - ROI selection, 4k x 4k regions<br> - Normalisation<br> - Fine-grain registration (Lobachev et al, 2016)<br> - Crop to the center to obtain 3500x3500 size.</p> <p>We estimated the slice thickness to be 7 µm.</p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - overview videos
<p>We reconstruct the 3D shape of the micro-vasculature in human bone marrow from serial sections.</p> <p>This upload shows three kinds of overview data.</p> <p>- R[n]_overview_... shows an overview of the mesh from ROI n as video<br> - R[n]_input_animated_... shows a thumb cinema overlay of initial input data<br> - R[n]_both.png shows a still with a frame from overview video (blue) and diameter measurement video (red).</p>
Ground beetle (Coleoptera:Carabidae) species composition of three forests in the Netherlands
<p>During this research the carabid fauna assemblage of two forests, the Amsterdamse Bos and Purmerendse Bos, was determined. Throughout the forests series (locations within the forests) were chosen to place pitfall traps. Each series consisted of five plastic cups that were dug into the soil in such a way that they were flush with the surface. Each cup was located five meters away from the subsequent one. The cups were filled with formaldehyde (diluted water 1: 10) as conservative and a small amount of soap in order to decrease water tension and thus let the organisms submerge. Afterwards the trap was covered with a wooden plate attached onto the soil with nails to protect it from rain and damage. The plates were covered up with plant material as camouflage. A small opening in between the soil and the plate was left so there was space for soil fauna to crawl into the cup (Picture 1). Because carabids are often dispersed throughout an area in small populations instead of being homologous spread (Raino & Niemelä, 2003) a diversity of locations was chosen. Therefore biotic and abiotic conditions were recorded (soil, light invasion, litter and dominant vegetation) to select the most diverse sites. </p> <p>Nine series in the Amsterdamse Bos and eleven series in the Purmerendse Bos were placed. These forests were sampled for a time span of 63 days. When traps were emptied the formaldehyde was refreshed. After the third time all of the traps were removed and holes filled up with soil. </p> <p>The content of emptied traps was washed with water and afterwards the ground beetles were selected and preserved in 95% ethanol. The ground beetles found were identified by making use of “De Loopkevers van Nederland & Vlaanderen” by Boeken, Desender, Drost, van Gijzen, Koese, Muilwijk, Turin & Vermeulen (2002. </p> <p>For each series the quantity of caught individuals was recorded. From the Eyserbos, data collected by supervisor B. Brugge in the years from 2012 to 2014 was used for analysis; during this research the same catching methods were used but the time scale was different. For four years, five series of pitfall traps were placed for one week halfway of June thus for a total of 28 days. Data of the species composition and the ecological characteristics and classification of the three forests was collected. Following classifications and ecological characters of the species that were used for analysis were documented: the status of the species in the Netherlands, Belgium, Denmark and Luxemburg, the status of the species in the Netherlands, the distribution in the Netherlands, how important the species’ population in the Netherlands is in its distribution in Europe (so called I-species), which type of habitats a species can migrate through to spread to other habitat patches, the classification of the species by Lindroth (1969), the degree of eurytopicity of the species and the flight capabilities of the species. </p> <p>Data obtained can be found in the file:</p> <p><strong>201703-05_groundbeetle_species_composition_Eyserbos_AmsterdamseBos_and_PurmerendseBos.txt</strong></p> <p>The possible inputs for characteristics can be found in the file</p> <p><strong>201706_Legenda_data_groundbeetles_species_composition_Amsterdamse_and_Purmerendse_bos.txt</strong></p>
Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories
<p>These files provide useful data and supplementary material associated with the publication 'Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories' (MNT information, atmospheric forcings, R scripts used to process and draw the graphs from the EcH2O-iso simulations, and observed water discharges).</p>
Eleven years of training data for south foehn for three regions of Western Austria
<p>This south foehn training data is suited for machine learning purposes. </p> <p>It was created by applying objective foehn classification (OFC, Vergeiner 2004) on hourly data of various stations in Western Austria. Three regions (Vorarlberg, Tiroler Unterland, Tiroler Oberland) and two intensities are available, where</p> <ul> <li>0.0 means no foehn on that day,</li> <li>0.5 means localised foehn on that day (up to half the stations in the region responded to OFC),</li> <li>1.0 means widespread foehn on that day (more than half the stations in the region responded to OFC),</li> </ul> <p>provided for each region individually.</p> <p>A paper, where the process of creation is described, is in preperation and will be linked as soon as it is reviewed. </p> <p> </p>
Aligned DNA sequence matrix for phylogenetic analyses in the article "Three new species of Torrent Treefrogs (Anura: Hylidae) of the Hyloscirtus bogotensis group from the eastern Andean slopes and the biogeographic history of the genus"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "Three new species of Torrent Treefrogs (Anura: Hylidae) of the Hyloscirtus bogotensis group from the Amazon foothills and the biogeographic history of the genus"</p> <p>The matrix is in NEXUS format and has 3259 bp and 25 terminals.</p> <p>Partitions are as follows:</p> <div>charset 12S = 1-955;</div> <div>charset ND1_nonCoding1 = 956-1279;</div> <div>charset ND1_Pos1 = 1280-2240\3;</div> <div>charset ND1_Pos2 = 1281-2241\3;</div> <div>charset ND1_Pos3 = 1282-2242\3;</div> <div>charset ND1_nonCoding2 = 2243-2361;</div> <div>charset cmyc_Pos1 = 2362-2779\3;</div> <div>charset cmyc_Pos2 = 2363-2780\3;</div> <div>charset cmyc_Pos3 = 2364-2781\3;</div> <div>charset Rag1_Pos1 = 2782-3415\3;</div> <div>charset Rag1_Pos2 = 2783-3416\3;</div> <div>charset Rag1_Pos3 = 2784-3417\3;</div>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the region of the Yucatán Peninsula
<p>The ensemble provides future projections of key marine variables under climate change for the region of the Yucatán Peninsula. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).<br> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the Chilean coast, see “Related identifiers”.</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></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.