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15,733 results for “article”
Water surface occurrence and recurrence from the article "Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction"
<p>This data package contains the 10 m spatial resolution occurrence and recurrence water surface masks from the article "Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction" . These maps have been produced with Sentinel-1 images (10 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. Water surface occurrence is computed for the period 2022-2023 and indicates the percentage of time that a pixel is classified as water (100%: always water, 0%: never water, and values between 0 and 100 indicate seasonality). Water surface recurrence is computed for the period 2022-2023 and indicates the number of times that a pixel was classified as a water surface, i.e., 35 indicates that the pixel was classified 35 times as a water surface during the 2022-2023 period. The total size of the dataset is 158 Mo and is distributed in two Geotiffs, one for the water surface occurence and one for the water surface. When using this dataset, please cite the original article <a href="https://doi.org/10.3390/rs16061056">https://doi.org/10.3390/rs16061056</a></p>
Data for article: Microplastics in Arctic Waters of the Finnish Sámi Area
<p><strong>Description of the dataset:</strong></p> <p>This dataset was created in 2021 and 2023. It was first made open in 2023 and later updated in 2024. </p> <p>Lapland_Sampling.xlsx contains all sampling dates and coordinates alongside with sample names, volumes, area descriptions and codes used to describe the samples in the article. </p> <p>Lapland_RawData.xlsx contains all the raw data for all the samples. This includes polymer types, particle sizes and an estimation of particle mass. Furthermore, the file contains a table with all the polymer types and their counts from all samples. </p> <p> </p> <p><strong>Data creation and processing:</strong></p> <p>The raw data was created by measuring filtered water samples with FPA-FTIR . The spectral analysis was done with siMPle (Primpke et al. 2020, <em>Applied Spectroscopy</em>, 74, 1127-1138, <a href="https://doi.org/10.1177/0003702820917760">https://doi.org/10.1177/0003702820917760</a>).</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>
Dataset for the article "Frequency regulation with storage: On losses and profits"
<p>This dataset complements the article <em>"Frequency regulation with storage: On losses and profits"</em> by Dirk Lauinger, François Vuille, and Daniel Kuhn, available at <a href="https://doi.org/10.1016/j.ejor.2024.03.022">https://doi.org/10.1016/j.ejor.2024.03.022</a> and at <a href="https://arxiv.org/pdf/2306.02987v2.pdf">https://arxiv.org/pdf/2306.02987v2.pdf</a>. </p> <p>The dataset contains the following files:</p> <p>1. <strong>Case_study.ipynb</strong>, which relies on the datafiles <strong>delta_10s.h5</strong>, <strong>pa.h5</strong>, <strong>pb.h5</strong>, <strong>pd.h5</strong>, and on the excel files <strong>conso_mix_RTE_2019.xls</strong> and <strong>ReserveAjustement_2019.xlsx</strong> to construct all figures in the article. The jupyter notebook is also available at <a href="https://github.com/lauinger/cost-of-frequency-regulation-through-electricity-storage">https://github.com/lauinger/cost-of-frequency-regulation-through-electricity-storage</a>.</p> <p>2. <strong>delta_10s.h5</strong>, which contains normalized frequency deviations with a 10s resolution from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw frequency measurement data in <strong>build_delta_10s.rar</strong>. The frequency measurements are taken from the website of the French transmission system operator RTE: <a href="https://www.services-rte.com/en/download-data-published-by-rte.html?category=public_transmission_system&type=network_frequencies">https://www.services-rte.com/en/download-data-published-by-rte.html?category=public_transmission_system&type=network_frequencies</a> (link live as of 7 June 2023).</p> <p>3. <strong>pa.h5</strong>, which contains availability prices for delivering frequency regulation to RTE from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw price data in <strong>build_price_data.rar</strong>. The availability price data are taken from the website of the French transmission system operator RTE:<br><a href="https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=procured_reserves">https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=procured_reserves</a> (link live as of 7 June 2023).</p> <p>4. <strong>pd.h5</strong>, which contains delivery prices for delivering frequency regulation to RTE from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw price data in <strong>build_price_data.rar</strong>. The delivery price data are taken from the website of the French transmission system operator RTE:<br><a href="https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=actived_offers">https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=actived_offers</a> (link live as of 7 June 2023).</p> <p>5. <strong>pb.h5</strong>, which contains utility prices for a subscribed apparent power of 9kVA from the state regulated <em>"tarif bleu"</em> of EDF, the largest electricity provider in France. This dataset is constructed from the raw price data in <strong>build_price_data.rar</strong>. Current price data (including taxes and transportation fees) is available at <a href="https://particulier.edf.fr/content/dam/2-Actifs/Documents/Offres/Grille_prix_Tarif_Bleu.pdf">https://particulier.edf.fr/content/dam/2-Actifs/Documents/Offres/Grille_prix_Tarif_Bleu.pdf</a>. The French Energy Department publishes the electricity prices in the official French Government journal. The corresponding legal texts are accessible under the following links:<br>01/11/2014 - 31/07/2015: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000033172637">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000033172637</a><br>01/08/2015 - 31/07/2016: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000030954456">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000030954456</a><br>01/08/2016 - 31/07/2017: <a href="https://www.edf.fr/sites/default/files/contrib/collectivite/electricite-et-gaz/CGV%2018avril/jo_du_29_juillet_2016_trv.pdf">https://www.edf.fr/sites/default/files/contrib/collectivite/electricite-et-gaz/CGV%2018avril/jo_du_29_juillet_2016_trv.pdf</a><br>01/08/2017 - 31/01/2018: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000035297675">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000035297675</a><br>01/02/2018 - 31/07/2018: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000036559814">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000036559814</a><br>01/08/2018 - 31/05/2019: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000037262170">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000037262170</a><br>01/06/2019 - 01/08/2019: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038528381">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038528381</a><br>01/08/2019 - 31/12/2019: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038850867">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038850867</a><br>These prices exclude taxes and transportation fees. The <em>"Option Base"</em> offers a flat price throughout the day. The <em>"Option Heures Creuses"</em> has a higher price for peak (6:00-22:00) than off-peak (22:00-6:00) hours. EDF refers to peak hours as <em>"Heures Pleines (HP)"</em> and to off-peak hours as <em>"Heures Creuses (HC)"</em>. The prices of these options only change, when EDF changes its electricity tariffs, which is up to three times per year. Conversely, the <em>"Option Tempo"</em> is a pricing scheme in which each day is either a high-price (<em>"Rouge"</em>), medium-price (<em>"Blanc"</em>) or low-price (<em>"Bleu"</em>) day. The price level of each day is announced by 10:30 am on the previous day. RTE, the French transmission system operator, keeps track of the daily price levels (<a href="https://www.services-rte.com/en/view-data-published-by-rte/schedule-of-Tempo-type-supply-offerings.html">https://www.services-rte.com/en/view-data-published-by-rte/schedule-of-Tempo-type-supply-offerings.html</a>). The price data can be downloaded from RTE's eco2mix platform (<a href="https://www.rte-france.com/eco2mix/telecharger-les-indicateurs">https://www.rte-france.com/eco2mix/telecharger-les-indicateurs</a>).</p> <p>6. <strong>conso_mix_RTE_2019.xls</strong>, which contains total French electricity demand throughout the year 2019 with 15 minute resolution. This data is provided by RTE, the French electricity system operator, at <a href="https://www.services-rte.com/en/download-data-published-by-rte.html?category=consumption&type=short_term">https://www.services-rte.com/en/download-data-published-by-rte.html?category=consumption&type=short_term</a> (link live as of 27 March 2024).</p> <p>7. <strong>ReserveAjustement_2019.xlsx</strong>, which contains the quantities and availability price for four reserve products: "réserve primaire" (primary frequency regulation, which we use in this article), "réserve secondaire", "réserve rapide", and "réserve complémentaire", with 30 minute resolution throughout the year 2019. This data is provided RTE, the French electricity system operator, at <a href="https://www.services-rte.com/en/download-data-published-by-rte.html?category=market&type=balancing_capacity&subType=procured_reserves">https://www.services-rte.com/en/download-data-published-by-rte.html?category=market&type=balancing_capacity&subType=procured_reserves</a> (link live as of 27 March 2024).</p>
Data related to the article "Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes<br>(Giovanni Pireddu, Connie J. Fairchild, Samuel P. Niblett, Stephen J. Cox and Benjamin Rotenberg)</p> <p>ChemRxiv: https://doi.org/10.26434/chemrxiv-2023-2ccrw</p> <p>Published version: to be inserted upon publication</p> <p>The folder EXAMPLE_INPUT_FILES contains typical [MetalWalls](https://doi.org/10.21105/joss.02373) ([repository](https://gitlab.com/ampere2/metalwalls)) and [LAMMPS]([repository](https://github.com/lammps/lammps)) input files used to perform the molecular simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper (see below).</p> <p><br>Notes: <br>1) In the file names, the notation 'M01', 'M05', 'M10' and 'M15' refers to the salt concentration in each system (0.1, 0.5, 1.0 and 1.5, respectively). 'W' refers to pure water (0 M) systems.<br>2) In the file names, the notation 'd1', 'd2', 'd3', 'd4', refers to different interelectrode distances (d1= 2.56 nm; d2= 5.07 nm; d3= 9.80 nm; d4= 19.84 nm) <br>3) The files containing the polarization cross-correlation are marked with 'AxB' indicating the cross-correlation between the contributions A and B. Specifically A and B can be: <br> - T = total<br> - I = ion<br> - W = water</p> <p><br>Figure 1:<br>- Panel B<br> - 'Fig1_CapConcentration': Differential capacitance scaled by electrode area as a function of NaCl concentration<br>- Panel C<br> - 'Fig1_QACF_*': Electrode charge autocorrelation function<br>- Panel D<br> - 'Fig1_Norm_QACF_*': Normalized electrode charge autocorrelation function<br> - 'Fig1_NormChar_*': Normalized non-equilibrium charge response</p> <p>Figure 2:<br>- Panel A: <br> - 'Fig2_ReZ_*': Real part of impedance<br>- Panel B:<br> - 'Fig2_nImZ_*': Negative imaginary part of impedance<br>- Panel C:<br> - 'Fig2_ReZint_*': Real part of interfacial impedance<br> - 'Fig2_Resistivities.dat': Resistivity as a function of NaCl concentration (bulk, confined, Nernst-Einstein)<br>- Panel D:<br> - 'Fig2_nImZint_*': Negative imaginary part of interfacial impedance<br> - 'Fig2_ECM*': Capacitor contributions to the imaginary part of interfacial impedance (finite concentrations)<br> - 'Fig2_ECW1.dat': Capacitor contributions to the imaginary part of interfacial impedance (pure water). Full cell capacitance taken into account<br> - 'Fig2_ECW2.dat': Capacitor contributions to the imaginary part of interfacial impedance (pure water). Interfacial capacitance taken into account </p> <p>Figure 3:<br>- Panel A:<br> - 'Fig3_ReCond_Peyman_M10.dat': Real part of conductivity (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))<br> - 'Fig3_ReCond_Querry_M10.dat': Real part of conductivity (data from: MR Querry, RC Waring, WE Holland, GM Hale, W Nijm, Optical Constants in the Infrared for Aqueous Solutions of NaClt. J. Opt. Soc. Am. 62 (1972)) <br> - 'Fig3_ReCond_Vinh_M10.dat': Real part of conductivity (data from: NQ Vinh, et al., High-precision gigahertz-to-terahertz spectroscopy of aqueous salt solutions as a probe of the femtosecond-to-picosecond dynamics of liquid water. The J.<br>Chem. Phys. 142, 164502 (2015).)<br> - 'Fig3_ReCond_M10.dat': Real part of conductivity from MD simulations<br>- Panel B:<br> - 'Fig3_ReCond_M*/W.dat': Real part of conductivity from MD simulations<br> - 'Fig3_ReCond_Peyman_M*': Real part of conductivity (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))<br>- Panel C:<br> - 'Fig3_Cond0.dat': Static conductivity as a function of concentration (MD data)<br> - 'Fig3_Cond0_Buchner.dat': Static conductivity as a function of concentration (data from: R Buchner, GT Hefter, PM May, Dielectric relaxation of aqueous nacl solutions. The J. Phys. Chem. A 103, 1–9 (1999))<br> - 'Fig3_Cond0_Peyman.dat': Static conductivity as a function of concentration (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))</p> <p>Figure 4:<br>- Panel A: <br> - 'Fig4_ReZ_d*': Real part of impedance (MD simulations)<br> - 'Fig4_ReZEC_d*': Real part of impedance (equivalent circuit model)<br>- Panel B:<br> - 'Fig4_nImZ_d*': Negative imaginary part of impedance (MD simulations)<br> - 'Fig4_nImZEC_d*': Negative imaginary part of impedance (equivalent circuit model)</p> <p>Figure 5:<br>- 'Fig5_TauQ.dat': timescales from the total charge autocorrelation functions<br>- 'Fig5_iontot.dat': timescales from the TxI autocorrelation function<br>- 'Fig5_RC.dat': timescales from the RC estimates<br>- 'Fig5_RbulkC.dat': timescales from the RbulkC estimates<br>- 'Fig5_Taudiff.dat': timescales from the difference between electrolyte and pure water QACFs<br>- 'Fig5_taud.dat': tau_d analytical timescales<br>- 'Fig5_tauDebye.dat': tau_Debye analytical timescales<br>- 'Fig5_taumix.dat': tau_mix analytical timescales</p> <p>Figure 6:<br>- Panel A:<br> - 'Fig6_Static_*: Static correlation between polarization contributions as a function of salt concentration<br>- Panel B:<br> - 'Fig6_Dynamic_EQ_*_M01' Dynamical correlations between polarization contributions (equilibrium MD results)<br> - 'Fig6_Dynamic_NEQ_*_M01' Dynamical correlations between polarization contributions (non-equilibrium MD results)<br>- Panel C:<br> - 'Fig6_Dynamic_EQ_*_M10' Dynamical correlations between polarization contributions (equilibrium MD results)<br> - 'Fig6_Dynamic_NEQ_*_M10' Dynamical correlations between polarization contributions (non-equilibrium MD results)</p> <p> </p> <p> </p>
Supp. Data for the article From raw microalgae to bioplastics: conversion of Chlorella vulgaris starch granules into thermoplastic starch
<p>Supplementaty Data (Videos) for the article From raw microalgae to bioplastics: conversion of Chlorella vulgaris starch granules into thermoplastic starch</p>
Dataset for article - Dislocation dynamics in Ni: Parameterising dislocation trajectories from atomistic simulations
<p>Data supporting findings in "Dislocation dynamics in Ni: Parameterising dislocation trajectories from atomistic simulations". In this work, 8 identical Molecular Dynamics (MD) simulations of edge dislocations in pure Nickel were run and analysed, where dislocation positions were extracted and each dislocation is tracked for further analysis. Each simulation only differs in the random seed used for setting the initial atom velocities. Files needed to reproduce a specific simulation can be found in the directories provided in <em>data.zip</em>:</p> <ul> <li>fr_18430</li> <li>fr_27743</li> <li>fr_44345</li> <li>fr_46165</li> <li>fr_54382</li> <li>fr_54992</li> <li>fr_97181</li> <li>fr_98232</li> </ul> <p>The random seed needed to set the initial velocities of the atoms are set in the LAMMPS script. The log files generated by LAMMPS are provided for the different stages of a given simulation in the <em>LAMMPS_logs</em> sub-directories. The corresponding output files from the OVITO DXA are also included in the sub-directory <em>Ni_disloc_const</em>. The files <em>disloc_data_*.ca</em> contain the raw output from the OVITO DXA and can be opened directly in OVITO. The <em>disloc_data_*.txt </em>files contain information extracted from the OVITO DXA in a format that can be read in and processed by the dislocation tracking code (<a href="https://github.com/geraldineanis/DislocCode/tree/v1.0.0">https://github.com/geraldineanis/DislocCode/tree/v1.0.0</a>). The wildcard character "*" is replaced with the simulation timestep. In each directory, the dislocation position vs. time data is included as a text file<em> </em>named<em> </em><em>perfect_pos_<seed>.txt</em>. The Mishin 2004 EAM interatomic potential file <em>NiAl_Mishin_2004.eam.alloy</em> used to generate the data is also provided.</p> <p>Additional simulations were carried out at a range of applied shear stresses (20 MPa - 50 MPa). Files needed to reproduce theses simulations are provided in <em>data_stress.zip </em>and follow the same format described above.</p> <p>The data provided here was generated with the <code>LAMMPS/29Sep2021-kokkos</code><em> </em>module on the SULIS Tier 2 HPC platform, which has been built with the OpenMP backend of the <code>kokkos</code> package and uses the <code>foss-2021b</code> toolchain (<a href="https://docs.easybuild.io/common-toolchains/">https://docs.easybuild.io/common-toolchains/</a>). For further information, please refer to <a href="https://sulis-hpc.github.io/appnotes/lammps.html">https://sulis-hpc.github.io/appnotes/lammps.html</a>.</p> <p>For more details on the calculations, please refer to the publication (in preparation). Please refer to the GitHub repository at <a href="https://github.com/geraldineanis/DislocCode/tree/v1.0.0">https://github.com/geraldineanis/DislocCode/tree/v1.0.0</a> for the analysis tools developed and for detailed instructuctions for their use.</p>
scRNA-seq data for article: Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis
<p>Single-cell RNA-seq dataset from sorted CD11bInt, F4/80Hi, CD64+ mouse liver cells in naive or Leishmania infantum-infected animals at 42 d.p.i.. Data analyses and results are described in manuscript: "Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis". Data files are Seurat objects in RDS format. Filtered-out potential doublets, low quality cells and dying cells (excluded cells with <1000 genes detected, cells with >6000 genes detected, cells with mitochondrial gene expression > 10% and cells with <5000 transcript molecules). Data normalization, scaling and integration performed using Seurat.</p> <p>Filtered dataset containing all KCs and macrophages is in the "pessenda_KC_Macro_seurat" file.</p> <p>Our data were then mapped onto a reference dataset published by Remmerie et al. (DOI: 10.1016/j.immuni.2020.08.004) for annotation consistent with the literature. The reference mapped object can be found in the "pessenda_refmap_KC_Macro_seurat" file.</p> <p>Dataset containing the additional analysis of CLEC4F-TIM4+ FACS-sorted KCs can be found in the "pessenda_refmap_KCTimPos_seurat" file.</p>
Dataset of article entitled: "Tuning magnonic devices with on-chip permanent micromagnets"
<p>These are the dataset relative to paper entitled "Tuning magnonic devices with on-chip permanent micromagnets" published in <em>Physical Review Applied</em> </p>
Data associated with the article 'Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis'
<p>The efficacy of oral language comprehension interventions varies, but the reasons for this variation have received little attention. A meta-analysis was conducted to examine intervention factors associated with the efficacy (as expressed with effect sizes) of oral language comprehension interventions in children under the age of 18 with or at risk for (Developmental) Language Disorder, (D)LD.</p> <p>The meta-analysis article together with this additional material comprise the content needed for a thorough understanding and replication of the results.</p> <p>This dataset is based on two systematic scoping reviews on oral language comprehension interventions (Tarvainen et al., 2020, 2021). Further information from the sourced articles was extracted for this study titled ‘Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis’. </p> <p>In the future, we hope that this data is used with a growing body of oral language comprehension interventions to conduct further and more detailed examinations of intervention factors associated with efficacy.</p> <p>References:</p> <p>Tarvainen, S., Launonen, K., & Stolt, S. (2021). Oral language comprehension interventions in school-age children and adolescents with developmental language disorder: A systematic scoping review. <em>Autism & Developmental Language Impairments</em>, <em>6</em>, 1–24. https://doi.org/10.1177/23969415211010423</p> <p>Tarvainen, S., Stolt, S., & Launonen, K. (2020). Oral language comprehension interventions in 1–8-year-old children with language disorders or difficulties: A systematic scoping review. <em>Autism & Developmental Language Impairments</em>, <em>5</em>, 1–24. https://doi.org/10.1177/2396941520946</p> <p> </p>
Supplemental Data from the article "The SmARTR pipeline: a modular workflow for the cinematic rendering of 3D scientific imaging data"
<h1><strong>Please, refer to <a href="https://github.com/MeVisLab/SmARTR-Networks">this GitHub repository</a> for additional info, updates, issue reports, and discussion<br></strong></h1> <p><strong>A collection of configuration files (SmARTR networks) published in "<a href="https://doi.org/10.1016/j.isci.2024.111475">The SmARTR Pipeline: a modular workflow for the cinematic rendering of 3D scientific imaging data</a>", enabling the creation of cinematic (photorealistic) renderings of 3D data in the FREE software <a href="https://www.mevislab.de/download">MeVisLab</a><br></strong></p> <ul> <li>Each folder in the archive contains one or more SmARTR network files, the scan and mask files required for the practical examples detailed in the <a href="https://www.cell.com/cms/10.1016/j.isci.2024.111475/attachment/8d79036b-acb6-4cda-a5ff-f56317691ebc/mmc1.pdf">Supplemental Data</a> of the article, and an additional folder with LUT presets.</li> </ul>
Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"
<p>This dataset results from an experiment on young oil palm plants (<em>Elaeis guineensis</em>) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), <em>i.e.</em>, a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%).</p> <p>Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.</p>
Data related to the article "A molecular perspective on induced charges on a metallic surface"
<p>Contains input files and data used to generate the figures of the article:</p> <p>A molecular perspective on induced charges on a metallic surface<br> (Giovanni Pireddu, Laura Scalfi, and Benjamin Rotenberg)</p> <p>arXiv: <a href="https://arxiv.org/abs/2110.11103">https://arxiv.org/abs/2110.11103</a></p> <p>The folder EXAMPLE_INPUT_FILES contains typical <a href="https://doi.org/10.21105/joss.02373">MetalWalls</a> (<a href="https://gitlab.com/ampere2/metalwalls">repository</a>) input files used to perform the simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper.</p> <p>The data files are named according to the ion distance considered:<br> - 'd1' corresponds to 1.50 Å;<br> - 'd2' corresponds to 3.14 Å;<br> - 'd3' corresponds to 5.40 Å;<br> - 'd4' corresponds to 7.03 Å;<br> - 'd5' corresponds to 15.00 Å.</p> <p>The data files are reported in three formats:<br> - induced charge density maps are in matrix format (arranged in several rows with each value corresponding to the values on the map).</p> <p> The coordinates of each point on the map are stored in the first row and first column.<br> - Solvent charge density maps are arranged in columns: (i) x coordinate, (ii) y coordinate, (iii) solvent charge density.<br> - Radial profiles are arranged in columns: (i) r, (ii) charge density, (iii) radial integral.</p>
Dataset for journal article: Nava et al. (2021) "Microalgae colonization of different microplastic polymers in experimental mesocosms across an environmental gradient"
<p>Dataset and R script for the article "Microalgae colonization of different microplastic polymers in experimental mesocosms across an environmental gradient" by Nava V., Matias M., Castillo-Escrivà A., Messyasz B. and Leoni B. accepted by Global Change Biology.</p>
Data set of article entitled: "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study"
<p>Data set of the immagies showed in figure 3 of the article entiteled "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study". All files contain the matrix of the dispersion relations of the two analysed Magnonic Crystals: the SAMPLE A and the SAMPLE B for different value of the interfacial Dzyaloshinskii-Moriya interaction (constant D). The first row is the set of values of k-vector, while the first column is the set of value of the frequencies. The other elements of the matrix are the values of the pixel related to the first row and first column. These elements are been obtanied by the Fast Fourier Transform in time and space of the micromagnetic simulations .</p>
Sentiment analysis of tech media articles using VADER package and co-occurrence analysis during the COVID-19 pandemic (01.2020-06.2020)
<p><strong>Sources: </strong></p> <ul> <li>Euractiv</li> <li>The Conversation</li> <li>Politico Europe </li> <li>IEEE Spectrum </li> <li>Techforge </li> <li>Fastcompany </li> <li>The Guardian (Tech) </li> <li>Arstechnica </li> <li>Reuters </li> <li>Gizmodo </li> <li>ZDNet </li> <li>The Register </li> <li>The Verge </li> <li>TechCrunch </li> </ul> <p> </p> <p><strong>Methodology</strong></p> <p>The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers.</p> <p>As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise.</p> <p>The process included the following main steps:</p> <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> <p>Therefore, the procedure has been repeated for 100 words for all identified social issues.</p> <p>The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down.</p> <p>The analysed paragraphs are selected the following way:</p> <ul> <li>The articles containing the given social issue are identified</li> <li>The paragraphs containing the social issue are selected for sentiment analysis</li> </ul> <p>*Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.</p>
Scholarly articles written by women extracted from Indexes of Archaeological Papers (1891-1907) and Gomme's Index of Archaeological Papers 1665-1890
<p>The dataset `List-of-Women-in-Archaeological-Indexes_cleaned.tsv` contains scholarly articles written by women extracted from annual <em>Indexes of Archaeological Papers</em> published between 1891 and 1907 inclusive and George Laurence Gomme's <a href="https://archive.org/details/indexofarchaeolo00gommrich/page/n5/mode/2up"><em>Index of Archaeological Papers 1665-1890</em></a>, referred to hereafter as the source datasets. These Indexes were published in London, initially by the Congress of Archaeological Societies directly, and from 1898 by Archibald Constable & Co. The Indexes were sent to Societies subscribing to the Congress, but could also be acquired separately. A list of indexes consulted in available on our <a href="https://www.zotero.org/groups/4509023/beyond_notability/collections/FDKSUL8J">Zotero library</a>.</p> <p>The dataset is published in .tsv and .xslx formats.</p> <p>This is v2 of the dataset, including some cleaned publication titles and the addition of the socities that published each journal (v2.1 fixes a faulty dataset export in v2).</p>
COSI-Article matrix: linking ISCB Communities of Special Interest to Wikipedia
<p>Wikipedia is regarded as one of the most important channels for the public communication of science; English Wikipedia has around 1,500 articles relating to computational biology, which are frequently accessed as an educational resource. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further quality improvement, primarily while comparing to related scientific fields such as genetics and medicine. Facilitating the involvement of members from ISCB COSIs (Communities of Special Interest) would improve a vital open educational resource in computational biology, additionally allowing COSIs to provide a quality educational resource particular to their subfield.</p> <p>This first version of the COSI-Article matrix is a binary matrix identifying relevant ISCB COSIs for all Wikipedia articles relating to computational biology, defining a domain-specific open educational resource for each COSI. In addition, quality and importance ratings for each article allow identification of areas where domain experts could improve computational biology representation.</p>
dataset related to article "Seizure activity and brain damage in a model of focal non-convulsive status epilepticus"
<p>Excell files with the values used on the construction of the graphical illustrations for the publication</p>
Supporting model output for article "Assessing the potential impact of river chemistry on Arctic coastal production"
<p>The following is a summary of processed model output data from a series of HiLAT model runs.<br> A description of the model runs and visualization of model output and analysis can be found in the<br> accompanying manuscript.</p> <p><br> Gibson G. A., Elliott, S., Piliouras, A. Clement Kinney, J., Jeffery, N. (2022) Assessing the potential<br> impact of river nitrate on coastal production in the Arctic. Frontiers in Marine Science: Coastal Ocean<br> Processes.</p> <p><br> This work was supported by the Regional and Global Model Analysis (RGMA) program of the US<br> Department of Energy’s Office of Science as a contribution to the HiLAT project. Additional support for<br> this project was provided by the National Science Foundation, under award #173886.<br> </p> <p><strong>River Nutrient Forcing</strong></p> <p>The experiments involved modifying the nutrient concentrations in the river nutrient forcing files.<br> The river nutrient files are specified during model setup. For use in the HiLAT model, GNEWS annual<br> river nutrient inputs were partitioned into twelve monthly forcing values. The nearest ocean grid point<br> to each of the GNEWS river mouth locations was identified and then, as with the runoff, the nutrient<br> inputs for each river basin were spatially mapped to surface ocean model grid cells, which are 10 meters<br> thick, such that the spatial pattern of river nutrient dispersion follows river water inputs to the oceans.</p> <p>The experiments were:<br> i) The baseline model simulation: The 12 monthly values for each grid cell were constant in time.<br> <strong>river_nutrients_GNEWS2000_gx1v6.nc</strong><br> ii) an experiment in which baseline Arctic River Nitrogen (NO 3 and NH 4 ) concentrations were doubled.<br> <strong>river_nutrients_GNEWS2000_gx1v6_x2Arctic.nc</strong><br> iii) an experiment in which baseline Arctic River Nitrogen (DON and DIN) concentrations were scaled to<br> the river volume discharge contained in <strong>runoff.daitren.iaf.20120419.nc</strong><br> <strong>river_nutrients_GNEWS2000_gx1v6_scaled_climatology.nc</strong><br> iv) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by two months.<br> <strong>river_nutrients_GNEWS2000_gx1v6_shifted2m_climatology.nc</strong><br> v) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by a month and<br> doubled in concentration.<br> <strong>river_nutrients_GNEWS2000_gx1v6_shifted_climatology_x2.nc</strong></p> <p>River nutrient fluxes are in units of nmol/cm2/s<br> Only concentrations within the domain TLONG>=60 &TLONG <=340 & TLAT >=60 were modified in<br> concentration/timing.</p> <p><br> Variables of interest:<br> din_riv_flux: dissolved inorganic nitrogen river flux<br> don_riv_flux: dissolved organic nitrogen river flux</p> <p>Each of the experiments is described in detail in Gibson et al (2022).</p> <p>---------------------------------------------<br> There are multiple versions of most output file types, corresponding to the river nutrient experiments that<br> were conducted.</p> <p><br> Many variables in the output files are <strong>regional averages</strong> where model regions are indicated by a number<br> *note - for aesthetics, the numbering used in the model output files differs slightly from the numbering<br> used in the accompanying manuscript. The numbers assigned in the analysis files aligns with the numbers<br> assigned to regions within the region mask provided in the grid file.</p> <p><strong>Grid File/region masks</strong><br> gx1v6_polar_mask_coast.5.22.20c.nc This file is an updated version of the standard grid file. It has been<br> updated to include the addition of a coastal Arctic region variable ‘Arctic_Coast_Mask’ which indicates<br> which grid cells are in the coastal regions used in the analysis and the Arctic_Region variable which<br> indicates which grid cells are in the broader regions.</p> <p><br> Variables contained in this file are:<br> Arctic_Coast_Mask: contains values 0-9 indicating which (if any) coastal region a grid cell is in<br> Arctic_Region Mask: contains values 0-11 indicating which (if any) region a grid cell is in</p> <p><br> TLAT: latitude of grid cell<br> TLONG: longitude of grid cell<br> TAREA: Area of grid cell<br> HT: Bathymetry of grid cell</p> <p> </p><table> <tbody> <tr> <td> </td> <td> <p><strong>Arctic_Region (seas)</strong></p> </td> <td> <p><strong>Arctic_Coast_Mask </strong><strong>(coast)</strong></p> </td> </tr> <tr> <td> <p><strong>Bering Sea</strong></p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p><strong>Chukchi Sea</strong></p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p><strong>East Siberian Sea</strong></p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p><strong>Laptev Sea</strong></p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p><strong>Beaufort Sea</strong></p> </td> <td> <p>5</p> </td> <td> <p>5</p> </td> </tr> <tr> <td> <p><strong>Barents Sea</strong></p> </td> <td> <p>6</p> </td> <td> <p>6</p> </td> </tr> <tr> <td> <p><strong>Canadian Basin</strong></p> </td> <td> <p>7</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Eurasian Basin</strong></p> </td> <td> <p>8</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Nordic Seas</strong></p> </td> <td> <p>9</p> </td> <td> <p>7</p> </td> </tr> <tr> <td> <p><strong>Labrador Sea</strong></p> </td> <td> <p>10</p> </td> <td> <p>8</p> </td> </tr> <tr> <td> <p><strong>Kara Sea</strong></p> </td> <td> <p>11</p> </td> <td> <p>9</p> </td> </tr> </tbody> </table> --------------<p></p> <p> </p><p>Model outputs that were analyzed in the manuscript are contained in three different kinds of output file.<br> For each file type a file exists for each river nutrient experiment.</p> <p></p> <p>The following series of files contains variables related to the particulate organic carbon flux to the<br> sediment, demineralization and remineralization rates.<br> bgc_T62_gx1GIF_nut-riv-BASELINE-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv_2XDC-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-seas_region-sed-137-157.nc</p> <p><br> Variables contained in these are:<br> POCTOSED_AVG* : Particulate organic carbon flux to sediment<br> PONTOSED_AVG* : Particulate organic nitrogen flux to sediment<br> SEDDENITRIF_AVG* : Sediment denitrification rate<br> POC_PROD_AVG* : Production of Particulate organic carbon<br> POC_FLUX_AVG* : Particulate organic carbon flux into layer/cell<br> DON_REMIN_AVG* : Dissolved Organic Nitrogen remineralization rate<br> DOC_REMIN_AVG* : Dissolved Organic Carbon remineralization rate<br> DIAT_N_LIM_AVG* : Diatom nitrogen limitation<br> DIAT_N_LIM_AVG* : Diatom nitrogen limitation<br> DIAT_P_LIM_AVG* : Diatom phosphorous limitation<br> DIAT_FE_LIM_AVG* : Diatom iron limitation<br> DIAT_LIGHT_LIM_AVG*: Diatom light limitation<br> SP_N_LIM_AVG* : Small phytoplankton nitrogen limitation<br> SP_P_LIM_AVG* : Small phytoplankton phosphorous limitation<br> SP_FE_LIM_AVG* : Small phytoplankton iron limitation<br> SP_LIGHT_LIM_AVG* : Small phytoplankton light limitation<br> Where * represents the coastal region number.<br> ----------------------------------------</p> <p><br> The following series of files contains primary production for the small and large phytoplankton groups<br> and the zooplankton biomass.</p> <p>Coastal regional averages – based on regions marked in the Arctic_Coast_Mask variable<br> bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_riv-BASELINE-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2XDC-region-prod-ACM-137-157.nc</p> <p>Regional seas averages – based on regions marked in the Arctic_Region variable<br> bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_riv-BASELINE-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-seas-137-157.nc</p> <p>Variables contained in these files are:<br> TAREA_SUM* – total area of the region<br> PPSP_REGSUM* – sum of primary production by small phytoplankton in a region<br> PPDIAT_REGSUM*– sum of primary production by diatoms in a region<br> ZOOC_AVG*– sum of zooplankton biomass in a region</p> <p>----------------------------------------<br> The following series of files contains ice associated variables and mixed layer nutrients</p> <p>bgc_T62_gx1GIF_nut_riv-2xArcticN-ice_coastal-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2XDC-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-ice_seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_seas-137-157.nc</p> <p>HI_REGAVG* : Regional averaged ice depth<br> HS_REGAVG* : Regional averaged snow depth<br> ICEAREA_REGSUM* : Regional sum ice area<br> ICEVOL_REGSUM*: Regional sum volume area<br> MLAM_REGAVG* : Regional average ammonium concentration in mixed layer<br> MLNIT_REGAVG* : Regional average nitrate concentration in mixed layer<br> PP_REGAVG* : Regional average primary production (ice algae)<br> PP_REGSUM* : Regional total primary production (ice algae)<br> TAREA_SUM* : Total area of region<br> TIME : time</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.