Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
357
datasets available to search
ShareScore release 0.9.0
Dataset results
357 results for “supplementary information”
Supplementary Information for "Performance evaluation of adaptive introgression classification methods"
<p>Supplementary information : supplementary figures and tables from "<em>Performance evaluation of adaptive introgression classification methods</em>", Romieu <em>et al., </em>2024 manuscript. ROC values, curves and score value by non-AI windows type for various demographic scenarios.</p>
Supplementary Information for "UV-Spectroscopic Detection of (Pyro-)Phosphate with the PUB module"
<p>This is the external Supplementary Information for our publication "UV-Spectroscopic Detection of (Pyro-)Phosphate with the PUB module".</p> <p>The .pdf file contains the Supplementary Information: author contributions, accessibility statement, experimental procedures, additional discussions and supplementary items, among others.</p> <p>The .zip file contains the raw data and metadata for all items (supplementary and main text) as well as the calculation results.</p> <p>To some extent, this work builds on and borrows from our previous publications on spectral unmixing (https://doi.org/10.3390/mps2030060, https://doi.org/10.1002/cbic.202000204) and thermodynamic reaction control (https://doi.org/10.1002/adsc.201901230, https://doi.org/10.1002/cphc.202000901, https://doi.org/10.1021/acscatal.1c02589).</p>
Supplementary Information: Investigating the detectability of hydrocarbons in exoplanet atmospheres with JWST
<p>Supplementary information containing additional figures of the journal article 'Investigating the detectability of hydrocarbons in exoplanet atmospheres with JWST' by D. Gasman, M. Min, and K. L. Chubb, published in Astronomy & Astrophysics (2022).</p>
Supplementary Information for Biogeography a key influence on distal forelimb variation in horses through the Cenozoic
<p>Locomotion in terrestrial tetrapods is reliant on interactions between distal limb bones (e.g. metapodials and phalanges). The metapodial-phalangeal joint in horse (Equidae) limbs is highly specialised, facilitating vital functions (shock absorption; elastic recoil). While joint shape has changed throughout horse evolution, potential drivers of these modifications have not been quantitatively assessed. Here, I examine the morphology of the forelimb metacarpophalangeal (MCP) joint of horses and their extinct kin (palaeotheres) using geometric morphometrics and disparity analyses, within a phylogenetic context. I also develop a novel alignment protocol that explores magnitude of shape change through time, correlated against body mass and diet. MCP shape was poorly correlated with mass or diet proxies, although significant temporal correlations were detected at 0–1 Ma intervals. A clear division was recovered between New and Old World hipparionin MCP morphologies. Significant changes in MCP disparity and high rates of shape divergence were observed during the Great American Biotic Interchange, with the MCP joint becoming broad and robust in two separate monodactyl lineages, possibly exhibiting novel locomotor behaviour. This large scale study of MCP joint shape demonstrates the apparent capacity for horses to rapidly change their distal limb morphology to overcome discrete locomotor challenges in new habitats. </p>
Supplementary Information for Coverage of in situ climatological observations in the world's mountains (Thornton et al.)
<p>Supplementary Information for "Coverage of in situ climatological observations in the world's mountains" (Thornton et al., Frontiers in Climate).</p>
Supplementary Information: Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b. Chubb and Min, A&A (2022).
<p>Supplementary information containing additional figures of the journal article 'Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b' by K. L. Chubb and M. Min, published in Astronomy & Astrophysics (2022).</p>
Supplementary information for 'Microstructure and pinning properties of CSD-grown SmBa2Cu3O7-δ films with and without BaHfO3 nanoparticles'
<p>Supplementary information containing TEM data and analysis for the article </p> <p>"<strong>Microstructure, pinning properties, and aging of CSD-grown SmBa<sub>2</sub>Cu<sub>3</sub>O<sub>7-δ</sub> films with and without BaHfO<sub>3</sub> nanoparticles</strong>"</p> <p>Link to paper: <a href="https://iopscience.iop.org/article/10.1088/1361-6668/ac7b4d">https://iopscience.iop.org/article/10.1088/1361-6668/ac7b4d</a></p> <p>Three folders are present after unzipping the archive:</p> <ul> <li>The folder <em>BHO-Nanoparticle-Size </em>contains a Jupyter notebook showing particle-size analysis. The projected particle sizes/areas were measured using Fiji using the LABKIT plugin by Arzt et al. (<a href="https://doi.org/10.3389/fcomp.2022.777728">https://doi.org/10.3389/fcomp.2022.777728</a>).</li> <li>The <em>Images</em> folder contains 16-bit STEM images in the tiff format. Some images were filtered with an average-background subtraction filter (ABSF).</li> <li>The <em>STEM-EDXS </em>folder shows Jupyter notebooks for STEM-EDXS data treatment using the HyperSpy Python package (<a href="https://hyperspy.org/">https://hyperspy.org/</a>).</li> </ul> <p>If there are any questions/bugs, feel free to contact me at lukas.gruenewald_at_kit.edu</p>
Supplementary Information for "Comment on UV-Spectroscopic Detection of (Pyro-)Phosphate with the PUB Module"
<p>This is the external Supplementary Information for our publication "Comment on UV-Spectroscopic Detection of (Pyro-)Phosphate with the PUB module".</p> <p>Unlike most of our publications, this short paper does not come with an official Supplementary Information. However, this zenodo entry contains the raw data and metadata for all items in the main text as well as all raw NMR spectra.</p> <p>For more details on this work, please see the origin publication detailing PUB (10.1021/acs.analchem.1c05356) as well as its external Supplementary Information on this platform (10.5281/zenodo.5760392).</p>
Supplementary information: The nuclear-spin-forbidden rovibrational transitions of water from first principles
<p><strong>Supplementary material to the manuscript <em>"The nuclear-spin-forbidden rovibrational transitions of water from first principles"</em> by Andrey Yachmenev, Guang Yang, Emil Zak, Sergei Yurchenko, and Jochen Küpper, <em>J. Chem. Phys., submitted. </em></strong><a href="https://arxiv.org/abs/2203.07945"> arXiv:2203.07945</a></p> <p>The data set contains hyperfine (spin-rovibrational) energies and dipole transition spectrum of water molecule (H<sub>2</sub><sup>16</sup>O), calculated using variational approach <a href="https://github.com/Trovemaster/TROVE">TROVE</a> and <a href="https://github.com/CFEL-CMI/richmol">RichMol</a>, and included spin-rotational and spin-spin hyperfine interactions.</p> <p>In addition, the data set includes HDF5-type richmol database file <strong><em>h2o_p48_j40_rovib.h5</em></strong> (see <a href="https://github.com/CFEL-CMI/richmol">https://github.com/CFEL-CMI/richmol</a>) containing rovibrational energies, matrix elements of nuclear spin-rotation, nuclear spin-spin, electric dipole, and electric quadrupole tensor operators of H<sub>2</sub><sup>16</sup>O, calculated using variational approach TROVE.</p> <ul> <li><strong>h2o_exomol_F.states </strong>and<strong> h2o_exomol_F.trans</strong> - hyperfine linelist of water stored in the ExoMol format (see, e.g., <a href="https://doi.org/10.1016/j.jms.2016.05.002">J. Molec. Spectrosc., 327, 73-94 (2016)</a>). The two files contain a set of hyperfine states with assignments and a set of dipole transitions (Einstein A-coefficients), respectively. The states in <strong>h2o_exomol_F.states</strong> file are arranged by quantum number of total angular momentum F = I + J (spin + rotation) in ascending order.</li> <li><strong>h2o_exomol_J.states </strong>and<strong> h2o_exomol_J.trans</strong> - contain same data as <strong>h2o_exomol_F.states </strong>and<strong> h2o_exomol_F.trans</strong> files, except that the states in <strong>h2o_exomol_J.states</strong> file are arranged by rotational quantum number (J) in ascending order.</li> <li><strong>h2o_p48_j40_rovib.h5<em> - </em></strong>Richmol HDF5 database file for H<sub>2</sub><sup>16</sup>O containing rovibrational energies (in cm<sup>-1</sup>), matrix elements of nuclear spin-rotation (in kHz), spin-spin (in kHz), molecular electric dipole moment (in Debye), and molecular electric quadrupole moment (in a.u.) operators. For details on how to read this file, see <a href="https://github.com/CFEL-CMI/richmol">Richmol GitHub repository</a> and <a href="https://richmol.readthedocs.io/en/latest/">Richmol documentation</a> (<em>or contact Andrey Yachmenev at andrey.yachmenev@cfel.de</em>).</li> <li><strong>ortho_para_transitions.txt</strong> - table with strongest predicted ortho-para transitions in H<sub>2</sub><sup>16</sup>O at T = 296 K with the 10<sup>−36</sup> cm/molecule intensity cut-off.<br> <br> <strong><em>An example of hyperfine energies and hyperfine dipole spectrum calculation for water using h2o_p48_j40_rovib.h5 file from this repository may be found in the <a href="https://github.com/CFEL-CMI/richmol">Richmol GitHub repository's</a> examples folder: <a href="https://github.com/CFEL-CMI/richmol/tree/develop/examples/hyperfine">https://github.com/CFEL-CMI/richmol/tree/develop/examples/hyperfine</a></em></strong></li> </ul> <p>Structure of<strong> h2o_exomol_F.states </strong>and<strong> h2o_exomol_J.states </strong>files:</p> <table align="left"> <thead> <tr> <th scope="col">Column No.</th> <th scope="col">Kind </th> <th scope="col">Meaning </th> </tr> </thead> <tbody> <tr> <td>1</td> <td>int</td> <td>state ID number</td> </tr> <tr> <td>2</td> <td>float</td> <td>hyperfine state energy relative to the ZPE, in cm<sup>-1</sup></td> </tr> <tr> <td>3</td> <td>int</td> <td>state degeneracy</td> </tr> <tr> <td>4</td> <td>int</td> <td>value of F quantum number (total spin-rotational angular momentum)</td> </tr> <tr> <td>5</td> <td>str</td> <td>state symmetry in C<sub>2v</sub></td> </tr> <tr> <td>6</td> <td>int</td> <td>value of J quantum number (total rotational angular momentum)</td> </tr> <tr> <td>7</td> <td>str</td> <td>symmetry of state's rotational component in C<sub>2v</sub></td> </tr> <tr> <td>8</td> <td>int</td> <td>value of k<sub>a</sub> quantum number (a-axis projection of rotational angular momentum)</td> </tr> <tr> <td>9</td> <td>int</td> <td>value of k<sub>c</sub> quantum number (c-axis projection of rotational angular momentum)</td> </tr> <tr> <td>10</td> <td>int</td> <td>value of v<sub>1</sub> vibrational quantum number</td> </tr> <tr> <td>11</td> <td>int</td> <td>value of v<sub>2</sub> vibrational quantum number</td> </tr> <tr> <td>12</td> <td>int</td> <td>value of v<sub>3</sub> vibrational quantum number</td> </tr> <tr> <td>13</td> <td>int</td> <td>value of I quantum number (total nuclear spin)</td> </tr> <tr> <td>14</td> <td>float</td> <td>reference rovibrational state energy (i.e., without hyperfine effects) relative to the ZPE, in cm<sup>-1</sup></td> </tr> </tbody> </table> <p>Structure of<strong> h2o_exomol_F.trans </strong>and<strong> h2o_exomol_J.trans </strong>files:</p> <table> <thead> <tr> <th scope="col">Column No.</th> <th scope="col">Kind</th> <th scope="col">Meaning</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>int</td> <td>ID number of final transition state (col. no. 1 in <strong>h2o_exomol_F.states </strong>or<strong> h2o_exomol_J.states </strong>file)</td> </tr> <tr> <td>2</td> <td>int</td> <td>ID number of initial transition state (col. no. 1 in <strong>h2o_exomol_F.states </strong>or<strong> h2o_exomol_J.states </strong>file)</td> </tr> <tr> <td>3</td> <td>float</td> <td>Einstein A-coefficient, in s<sup>-1</sup></td> </tr> <tr> <td>4</td> <td>float</td> <td>Transition wavenumber, in cm<sup>-1</sup></td> </tr> </tbody> </table> <p> </p>
Supplementary information for 'Distinct gene expression dynamics in developing and regenerating crustacean limbs', by Sinigaglia et al.
<p>Supplementary data and code for the manuscript <em>'Distinct gene expression dynamics in developing and regenerating crustacean limbs'</em>, by Sinigaglia et al.</p>
Supplementary Information for "G-type Halohydrin Dehalogenases Catalyze Ring Opening Reactions of Cyclic Epoxides with Diverse Anionic Nucleophiles"
<p>This is the external Supplementary Information for our publication "G-type Halohydrin Dehalogenases Catalyze Ring Opening Reactions of Cyclic Epoxides with Diverse Anionic Nucleophiles".</p> <p>The .zip files contain the raw NMR data for all compounds as well as the protein structural data described in the manuscript.</p>
Supplementary information for 'Crustacean leg regeneration restores complex microanatomy and cell diversity' by Almazán, Çevrim et al.
<p>Animals can regenerate complex organs, yet this frequently results in imprecise replicas of the original structure. In the crustacean <em>Parhyale</em>, embryonic and regenerating legs differ in gene expression dynamics but produce apparently similar mature structures. We examine the fidelity of <em>Parhyale </em>leg regeneration using complementary approaches to investigate microanatomy, sensory function, cellular composition and cell molecular profiles. We find that regeneration precisely replicates the complex microanatomy and spatial distribution of external sensory organs, and restores their sensory function. Single-nuclei sequencing shows that regenerated and uninjured legs are indistinguishable in terms of cell type composition and transcriptional profiles. This remarkable fidelity highlights the ability of organisms to achieve identical outcomes via distinct processes.</p>
Supplementary Information of "Introgression between highly divergent sea squirt genomes: an adaptive breakthrough?"
<p><strong>Supplementary Figures</strong></p> <p><strong>Figure S1</strong> Population genetic statistics calculated in non-overlapping 10 Kb windows along the 14 chromosomes in the sea squirt genome.<br> <strong>Figure S2</strong> <em>C. robusta</em> introgression into <em>C. intestinalis</em> shown across the 14 chromosomes.<br> <strong>Figure S3</strong> Population genetic statistics of the <em>C. robusta</em> introgressed coding sequences.<br> <strong>Figure S4 </strong>ABBA-BABA introgression patterns using<em> C. edwardsi </em>as an outgroup.<br> <strong>Figure S5</strong> Inference of the divergence history between <em>C. robusta</em> and <em>C. intestinalis</em> with moments.<br> <strong>Figure S6 </strong>Selection tests.<br> <strong>Figure S7 </strong><em>C. robusta</em> ancestry along chromosome 5 in <em>C. intestinalis</em> individuals.<br> <strong>Figure S8</strong> Neighbor-joining trees of 50 Kb windows framing the “missing data region” (grey band) at the center of the chromosome 5 hotspot.<br> <strong>Figure S9</strong> Copy number variation at candidate SNPs in the introgression hotspot on chromosome 5 (700 Kb - 1.5 Mb).<br> <strong>Figure S10</strong> Structural analysis of the “missing data region” on chromosome 5 (from 1,009,000 to 1,055,000 bp).</p> <p> </p> <p><strong>Supplementary Tables</strong></p> <p><strong>Table S1</strong> Sample information.<br> <strong>Table S2 </strong>Correlation between chromosomes of the individual <em>C. robusta </em>ancestry fraction.<br> <strong>Table S3</strong> Demographic results with moments – excluding chromosome 5.<br> <strong>Table S4</strong> Demographic results with moments – including chromosome 5.<br> <strong>Table S5 </strong>Description of the Supplementary Data.</p> <p> </p> <p><strong>Supplementary Scripts</strong></p> <p><em>Bioinformatic pipeline used for genotyping and haplotyping.</em></p> <p><strong>Script #1</strong>: prepare the reference genome for BWA and GATK.<br> reference_bwa_GATK_CF.sh<br> <strong>Script #2</strong>: mapping the reads to the reference with BWA.<br> mapping_bwa-mem_CF.sh<br> <strong>Script #3</strong>: indel realignment with GATK.<br> indel_realignment_CF.sh<br> <strong>Script #4</strong>: individual variant calling in gVCF format with GATK.<br> snpindel_callingGVCF_raw_CF.sh<br> <strong>Script #5</strong>: joint genotyping with GATK.<br> joint_genotyping_raw_CF.sh<br> <strong>Script #6</strong>: genotype refinement with GATK.<br> genotype_refinement_raw_CF.sh<br> <strong>Script #7</strong>: SNPs and indels recalibration with GATK.<br> snpindel_recalibration_CF.sh<br> <strong>Script #8</strong>: genotype refinement after recalibration with GATK.<br> genotype_refinement_recal_CF.sh<br> <strong>Script #9</strong>: genotype correction.<br> phase_by_transmission_correctCalling_CF@2020.sh<br> <strong>Script #10</strong>: phasing with GATK and BEAGLE.<br> phase_by_transmission_clean_CF@2020.sh</p> <p><em>Pipeline used for the demographic inferences with moments.</em></p> <p><strong>Script #11</strong>: define the demographic models.<br> moments_models_2pop_bb_parallel_folded_2periods.py<br> <strong>Script #12</strong>: run the demographic inferences.<br> moments_inference_dualanneal_bb_parallel_folded_2periods_bounds.py</p>
Supplementary information for yqiC and global transcriptome in Salmonella
<p>Supplementary information (Additional files 1-22, including 3 files, Table S1-S9, Fig. S1-S10) in the article entitled "Effects of colonization-associated gene <em>yqiC</em> on global transcriptome, cellular respiration, and oxidative stress in <em>Salmonella </em>Typhimurium"</p>
Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites: supplementary information and dataset
<p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>The addition of nano-sized filler particles enhances the mechanical performance of polymers. The resulting properties of the polymer nanocomposite depend on a complex interplay of influence factors such as material pairing, filler size, and content as well as filler-matrix adhesion. As a complement to experimental studies, numerical methods, such as molecular dynamics (MD), facilitate an isolated examination of the individual factors in order to understand their interaction better. However, particle-based simulations are, in general, computationally very expensive, rendering a thorough investigation of nanocomposites’ mechanical behavior both expensive and time-consuming. Therefore, this paper presents a fast coarse-grained MD model for a generic nanoparticle-reinforced thermoplastic. First, we examine the matrix and filler phase individually, which exhibit isotropic elasto-viscoplastic and anisotropic elastic behavior, respectively. Based on this, we demonstrate that the effect of filler size, filler content, and filler-matrix adhesion on the stiffness and strength of the nanocomposite corresponds very well with experimental findings in the literature. Consequently, the presented computationally efficient MD model enables the analysis of a generic polymer nanocomposite. In addition to the obtained insights into the mechanical behavior, the material characterization provides the basis for a future continuum mechanical description, which bridges the gap to the engineering scale. </p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All MD simulations were performed with LAMMPS [2], version: 29 Oct 2020 / 20201029</p> <p>Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11</p> <p>Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p>Installed packages<strong>:</strong><br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [3]</p> <p>Post-processing Matlab R2019b</p> <p>Evaluation of polymer entanglements with Z1-Algorithm [4]</p> <p> </p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, <strong>2022</strong>, 16.</p> <p>This dataset contains the results presented in [1] and the necessary data to obtain those as well as supplementary information.</p> <p><strong>Content:</strong></p> <p>supplementary material:</p> <p>supplementary_information.pdf</p> <p>data:<br> folder names vary depending on the context, explained in the following:</p> <p> </p> <p>01_matrix</p> <ul> <li> <p>01_equilibration<br> sample equilibration to different temperatures<br> nomenclature: equil_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>[-<batch_ID>]</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.1-1.0</p> </li> <li> <p>batch_ID: 2-5 </p> </li> </ul> </li> <li> <p>02_temperature_dependence<br> uniaxial tension simulations to identify temperature dependence<br> nomenclature: 01_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.1-1.0</p> </li> </ul> </li> <li> <p>03_directional_dependence<br> uniaxial tension simulations to prove isotropy in Y and Z direction; X direction in 04_rate_dependence<br> nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-<batchID></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-5</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>04_rate_dependence<br> uniaxial tension simulations to identify strain rate dependence<br> nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>[-<batchID>]</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>05_cyclic_loading<br> sinusoidal uniaxial deformation<br> nomenclature: 05_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4</p> </li> <li> <p>strain_amplitude: 0.01, 0.05, 0.15, 0.2</p> </li> </ul> </li> <li> <p>06_relaxation<br> relaxation subsequent to time-proportional deformation<br> nomenclature: 07_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude>_relax</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4</p> </li> <li> <p>strain_amplitude: 0.01, 0.05, 0.15, 0.2</p> </li> </ul> </li> <li> <p>07_simple_shear<br> time-proportional simple shear deformation with different strain rates<br> nomenclature: SS_P2VPSi-rate_<strain_rate>-<batchID></p> <ul> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>08_large_deformation<br> uniaxial deformation up to 100% strain<br> nomenclature: 02_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperatur>-strain_<max_strain></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>max_strain: 1</p> </li> </ul> </li> </ul> <p>02_filler</p> <ul> <li> <p>01_Silica_equilibration<br> sample equilibration</p> </li> <li> <p>02_time_proportional<br> time-proportional uniaxial and simple shear tests<br> nomenclature: Silica_BV-<loadcase>_<direction>-strain_<max_strain>-rate_<strain_rate></p> <ul> <li> <p>loadcase: uniaxial tension (UT), simple shear (SS)</p> </li> <li> <p>max_strain: 0.1</p> </li> <li> <p>direction: X, Y, Z (UT); XY, XZ, YZ (SS)</p> </li> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> </ul> </li> <li> <p>03_time_periodic<br> time-periodic uniaxial and simple shear tests<br> nomenclature: Silica_BV-<loadcase>_<direction>_sin-ampl_<strain_amplitude>-rate_<max_strain_rate></p> <ul> <li> <p>loadcase: uniaxial tension (UT), simple shear (SS)</p> </li> <li> <p>direction: X, Y, Z (UT); XY, XZ, YZ (SS)</p> </li> <li> <p>strain_amplitude: 0.025</p> </li> </ul> </li> </ul> <p>03_composite</p> <ul> <li> <p>01_equilibration<br> sample equilibration<br> nomenclature: equil_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID></p> <ul> <li> <p>filler_radius: 2.5-10.0</p> </li> <li> <p>filler_number: 1-160 (depending on filler_radius)</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>02_uniaxial-tension<br> uniaxial tension simulations<br> nomenclature: UT_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID></p> <ul> <li> <p>filler_radius: 2.5-10.0</p> </li> <li> <p>filler_number: 1-160 (depending on filler_radius)</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>03_filler-maxtrix-adhesion<br> equilibration and uniaxial deformation of samples with mid and weak filler-matrix adhesion (for strong adhesion see 01_equilibration and 02_uniaxial-tension<br> nomenclature: see above</p> </li> <li> <p>04_IP_equilibration<br> equilibration of samples to evaluate the microstructure for neat polymer and composites with filler radius 2.5-7.5<br> nomenclature: P2VPSi-<chains>x<chain_atoms>_rNP_<filler_radius>-nNP_<filler_number>_pos_<filler_pos>-<batchID></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>filler_radius: 0 (neat), 2.5, 5.0, 7.5</p> </li> <li> <p>filler_number: 0 (neat), 1</p> </li> <li> <p>batchID: 1-20</p> </li> </ul> </li> </ul> <p> </p> <p> </p> <p>Each simulation directory contains:</p> <ul> <li> <p>lammps input file (*.in) of the specific simulation</p> </li> <li> <p>data file (*.data) containing the initial sample configuration</p> </li> <li> <p>input.prm: input parameters of the specific simulation (read by the input file)</p> </li> <li> <p>meta.info: meta data of the specific simulation run</p> </li> <li> <p>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <ul> <li> <p>thermo_out.Dat: raw output </p> </li> <li> <p>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> </li> <li> <p>thermo_out_STD.Dat: standard deviation of raw output</p> </li> </ul> </li> </ul> <p> </p> <p>Output quantities (columns of *.Dat files):<br> Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <ul> <li> <p>Step: time step </p> </li> <li> <p>Time: time </p> </li> <li> <p>TotEng: total energy </p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy </p> </li> <li> <p>E_pair: pair energy </p> </li> <li> <p>E_bond: bond energy </p> </li> <li> <p>E_angle: angle energy </p> </li> <li> <p>E_dihed: dihedral energy </p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor </p> </li> <li> <p>Pyy: yy component of pressure tensor </p> </li> <li> <p>Pzz: zz component of pressure tensor </p> </li> <li> <p>Pxy: xy component of pressure tensor</p> </li> <li> <p>Pxz: xz component of pressure tensor</p> </li> <li> <p>Pyz: yz component of pressure tensor</p> </li> <li> <p>Volume: volume of simulation box </p> </li> <li> <p>Lx: box length in x direction </p> </li> <li> <p>Ly: box length in y direction </p> </li> <li> <p>Lz: box length in z direction </p> </li> <li> <p>Density: density </p> </li> <li> <p>c_RG: radius of gyration scalar </p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component) </p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component) </p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component) </p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component) </p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component) </p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component) </p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms </p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms </p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms </p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms </p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle </p> </li> <li> <p>c_angleave[4]: squared cosine of angle </p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction </p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction </p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction </p> </li> <li> <p>c_MSD[4]: total mean squared displacement </p> </li> <li> <p>c_COM[1]: x coordinate of center of mass </p> </li> <li> <p>c_COM[2]: y coordinate of center of mass </p> </li> <li> <p>c_COM[3]: z coordinate of center of mass </p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor </p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor </p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor </p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress </p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor </p> </li> <li> <p>v_Cauchy_yy: yy component of stress tensor</p> </li> <li> <p>v_Cauchy_zz: zz component of stress tensor</p> </li> <li> <p>v_Cauchy_xy: xy component of stress tensor </p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor </p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor </p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor </p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor </p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor </p> </li> </ul> <p><br> </p> <p><strong>References</strong>:</p> <p>[1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, <em>Express Polymer Letters</em>, <strong>2022</strong>, 16.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” <em>Journal of computational physics</em>, <strong>1995</strong>, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>
Supplementary information for the paper about foreign authors in russian journals (2000-2021, ca 600 journals)
<p>This is a supplementary dataset for the article about foreign authors in Russian Scopus-indexed journals (in Russian). It contains publication and citation data for countries, journals, country clusters, subject areas and topics</p> <p>Based on the set of ca.90000 papers with strictly foreign affiliations (without Russian affiliations) in ca 600 journals for 2000-2021</p>
Supplementary information for "Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability"
<p>Reproducibility data for the manuscript "<em>Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability</em>", it contains data and script for :</p> <ol> <li> <p>The R script <code>01_HDSfreq_Calibration.R</code> of the developed approach to estimate national breeding bird population size using Hierarchical Distance Sampling (HDS) and the secondary candidate set model selection method (Morin et al., 2020)</p> </li> <li> <p>The R script <code>02_pglmm_figures.R</code> for the calibration of the Phylogenetic Generalised Mixed Model (PGLMM) used in the manuscript to compare previous population size estimates to ones modelled using <code>01_HDSfreq_Calibration.R</code>, while accounting for species phylogenetic relatedness</p> </li> <li> <p>The R script <code>03_results_tables.R</code>, used to generate supplementary tables S2.1-3 and S6.1-2.</p> </li> </ol> <ul> <li> <p>Column names are highlighted in italics.</p> </li> </ul> <h2>Data description</h2> <h5>A. BirdPhylo_Burleigh_et_al.tre</h5> <p>A phylogenetic tree from Burleigh et al., 2015. Phylogenetic distances are used as random effect for the PGLMM in script <code>02_pglmm_figures.R</code></p> <h5>B. Conservation_status.txt</h5> <p>A <code>.txt</code> file of the conservation status for France (<em>Statut_FR</em>) and Europe (<em>Statut_EU</em>) for the studied species retrieved from (UICN France et al., 2016). Only <em>Statut_FR</em> is used for the table S6.1.</p> <h5>C. FBBS_trends_20122023.txt</h5> <p>A <code>.txt</code> file containing species trend of the French Breeding Bird Survey data from 2012 to 2023.</p> <ul> <li> <p>Species names (English, French) associated with FBBS trend estimated using data collected from 2012 - 2023</p> </li> <li> <p><em>Hab_specialization</em>, determined from Julliard et al. 2006 approach</p> </li> <li> <p><em>infPrec, supPerc, estimate, se, pval</em> : Species trends over 2012-2023 period in % | lower and upper confidence intervals, mean, standard error and significance</p> </li> </ul> <h5>D. PrepData_HDS.RData</h5> <p>A file containing <code>.RData</code> environment required to run <code>01_HDSfreq_Calibration.R</code> script, it contains :</p> <ul> <li> <p><strong>ATLAS12</strong> : A dataframe with breeding status information from 2012 breeding bird atlas (used to restrict model prediction grid, in regard of 2012 known breeding locations)</p> </li> <li> <p><strong>ConcordTBL</strong> : A concordance table for species names (English, French and scientific notation)</p> </li> <li> <p><strong>EPOC_ODF</strong> : observation dataset, each line corresponds to detected individuals</p> <ul> <li> <p><em>UUID, Ref, ID_liste, ID, ID_place, Grid_10x10</em> : Columns used to identify observations, lists, sites, locations, 10x10 grids</p> </li> <li> <p><em>ID_species_Biolovision, Nom_espece, english_name, scientific_name</em> : Species ID and names</p> </li> <li> <p><em>Date, Day, Month, Year, Julian_date, Obs_hour, Hour_list, Complete_checklist, Commentary, Project_name, Scheme, Observer, List_time, List_diversity, List_abundance</em> : Lists and Observation related effort covariates and metadata</p> </li> <li> <p><em>X_Lambert93_m, Y_Lambert93_m</em> : Observation locations in <code>(crs = 2154)</code></p> </li> <li> <p><em>GPS_loc_observer</em> : Logical, TRUE : location of observers corresponds to true GPS information ; FALSE : observer's location approximated as the barycenter of observations</p> </li> <li> <p><em>X_barycentre_L93, Y_barycentre_L93</em> : Observers location in <code>(crs = 2154)</code></p> </li> <li> <p><em>Use_distance_sampling, Observation_distance_m, Distance_bin_logical, Distance_class_0_25, Distance_class_25_100, Distance_class_100_200, Distance_class_200_more</em> : Distance sampling related informations</p> </li> <li> <p><em>Abudance_brut, Estimate, Number, Nb_male_identified, Nb_female_identified, Nb_juvenile_identified, Nb_grounded, Nb_flying, Nb_auditory, Nb_NA</em> : Observation metadata, used in case of <em>a priori</em> filter over male detection.</p> </li> </ul> </li> <li> <p><strong>grid_pred_envvar</strong> : Prediction grid with environmental covariates, see appendix S3 of the manuscript, covering metropolitan France</p> </li> <li> <p><strong>grid_pred.sf</strong> : corresponding sf object</p> </li> <li> <p><strong>L93_10x10</strong> : sf object corresponding to 10x10 grid used in 2012 atlas</p> </li> <li> <p><strong>ObsVar_EPOCODF</strong> : dataframe specifying lists effort covariates</p> </li> <li> <p><strong>OCCU_EPOC_ODF</strong> : Environmental covariate agregated over lists</p> </li> <li> <p><strong>OCCU_EPOC_ODF_sites_envvar</strong> : Environmental covariate agregated over sites</p> </li> <li> <p><strong>table.pheno</strong> : species table specifying related phenology filter</p> </li> </ul> <h5>E. ReadOutput_HDSfreq_comparison.csv</h5> <p>A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over areas determined as breeding in the 2012 atlas. <strong>Predictions were restrained over location known as breeding in 2012 for the sake of comparison.</strong></p> <ul> <li> <p><em>HDS_estimUnfenced_XXX</em> : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript)</p> </li> <li> <p><em>HDS_estim_ExtrapolFence_XXX</em> : average pop. size estimated with confidence interval after prediction post-treatment</p> </li> <li> <p><em>NB_data_calib</em> : Number of observations (distance data, not sites) used for calibration</p> </li> <li> <p><em>MALE_FILTERING</em> : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for the comparison with the previous atlas (in pairs). (cf . line 88-90 in <code>02_pglmm_figures.R</code>)</p> </li> <li> <p><em>EcartDTF_filtrage_maleOnly</em> : If MALE_FILTERING == T, proportion of the remaining data used for calibration after removal of list with individual tagged as female/juvenile (in %)</p> </li> <li> <p><em>Max_dist_breaks</em> : Maximal distance for detection function, after right-side truncation of 5%</p> </li> <li> <p><em>Chat</em> : Coefficient of overdisperion of the best model in the second candidate set</p> </li> <li> <p><em>MED_MEAN_Prob_Detect</em> (.._SE) : weighted averaged median of intercept from the availability state from HDS models, weigthed AICc-wise</p> </li> <li> <p><em>MED_MEAN_Density</em> : weighted averaged median of intercept from the abundance state from HDS models, weigthed AICc-wise</p> </li> <li> <p><em>Significant_phi/lambda</em> : Categorial (Significant/Near/Not), are availability/abundance intercepts significatively different from 0 (significant : alpha = 0.05, near : alpha = 0.1)</p> </li> <li> <p><em>KeyFun_used</em> : Key function used for distance sampling</p> </li> <li> <p><em>Mixtured_used</em> : Mixture used in the abundance state for HDS</p> </li> </ul> <h5>F. ReadOutput_HDSfreq_comparison_20212022.csv</h5> <p>A <code>.csv</code> table of species population size estimated using 2021-2022 EPOC-ODF data over areas determined as breeding in the 2012 atlas. Used for the robustness analysis of HDS estimated population size, see appendix S2 and table S2.2 of the manuscript.</p> <h5>G. ReadOutput_HDSfreq_EstimMetropole.csv</h5> <p>A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over <strong>metropolitan France</strong>.</p> <ul> <li> <p><em>HDS_estimUnfenced_XXX</em> : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript)</p> </li> <li> <p><em>HDS_estim_ExtrapolFence_XXX</em> : average pop. size estimated with confidence interval after prediction post-treatment</p> </li> <li> <p><em>MALE_FILTERING</em> : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for conversion to pop. size in breeding pairs</p> </li> <li> <p><em>Chat</em> : Coefficient of overdisperion of the best model in the second candidate set</p> </li> <li> <p><em>KeyFun_used</em> : Key function used for distance sampling</p> </li> <li> <p><em>Mixtured_used</em> : Mixture used in the abundance state for HDS</p> </li> </ul> <h5>H. TABLE_SpeciesFilters_and_2012Estimates.txt</h5> <p>A <code>.txt </code>table containing species names (English, French and scientific notation), filters and 2012 French atlas pop. size estimates</p> <ul> <li> <p><em>debut_jour</em> : starting day of the month for phenology filter</p> </li> <li> <p><em>debut_mois</em> : starting month for phenology filter</p> </li> <li> <p><em>fin_jour</em> : ending day of the month for phenology filter</p> </li> <li> <p><em>fin_mois</em> : ending month for phenology filter</p> </li> <li> <p><em>Estim_low/up_Atlas2012</em> : Lower and Upper interval of estimated pop. size in 2012 (number in breeding pairs)</p> </li> <li> <p><em>gregarious</em> : logical (0,1) specifying if the species is considered gregarious during its breeding season</p> </li> </ul> <h5>I. sessionInfo_script_XX</h5> <p>User R session information, obtained from <code>sessionInfo()</code> R function, used for running R script.</p> <h2>Code</h2> <h5>A. <code>01_HDSfreq_Calibration.R</code></h5> <p>R script showcasing data formatting and model calibration of the HDS based upon frequentist aproach from <code>unmarked</code> R package. For more details of the model calibration approach, see appendix S4 of the manuscript.</p> <h5>B. <code>02_pglmm_figures.R</code></h5> <p>Script for the calibration of the PGLMM and generation of figure 5 of the manuscript.</p> <h5>C. <code>03_results_tables.R</code></h5> <p>Script to generate tables depicted in Appendices S2 (S2.1-3) and S6 (S6.1-2)</p> <h5>D. <code>HDS_functions.R</code></h5> <p>R script called in <code>01_HDSfreq_Calibration.R</code>, contains 2 functions:</p> <ul> <li> <p><code>Try_HDS()</code> : Function implementing a try-catch permitting calibration of multiple species in a loop.</p> <ul> <li> <p>Species with non convergent models are skipped sending a notification to the user R interface.</p> </li> <li> <p>Used in all sub-candidate sets (i.e. "null", "p", "phi", "lambda")</p> </li> <li> <p>When phase="ALL" corresponding to the second candidate set (i.e. ensemble of best model candidates, with delta_AIC <= 10, from previous sub-candidate sets), it permits the use of previous sub-candidates set coefficients as starting values, with <code>StartValues </code>argument</p> </li> <li> <p>Later part of the function hack the call of the unmarkedFit class, in order to accommodate from calibrating a gdistsamp using characters formulas</p> </li> </ul> </li> <li> <p><code>fitstats()</code> : Function from unmarked::parboot(), available with <code>help(parboot)</code>. Allow estimation of multiple goodness-of-git statistic (Freeman-Tukey, Chi-squared and Sum of Squared Estimate of errors) through parametric bootstrap. In the manuscript, only chi-squared metric is used.</p> </li> </ul> <h5>E. <code>dsmextra_modif_function.R</code></h5> <p>R script called in <code>01_HDSfreq_Calibration.R</code>. Miscellaneous adjustment of core function from <code>dsmextra </code>package (main change being the integration of tolerance argument (<code>tol</code>) in the chain of function.</p> <h5>F. <code>misc_unmarked.R</code></h5> <p>R script called in <code>01_HDSfreq_Calibration.R</code>. modify Setmethods for unmarked function, in particular for <code>unmarked::parboot</code>, allowing parallelization of parametric bootstrap with prior unmarked version (<code>unmarked < 1.3.0</code>).</p> <h2>References</h2> <p>Data was derived from the following sources:</p> <ul> <li> <p>Burleigh, J.G., Kimball, R.T., Braun, E.L., 2015. Building the avian tree of life using a large-scale, sparse supermatrix. Molecular Phylogenetics and Evolution 84, 53–63. <a href="https://doi.org/10.1016/j.ympev.2014.12.003">https://doi.org/10.1016/j.ympev.2014.12.003</a></p> </li> </ul> <p>Other sources :</p> <ul> <li> <p>Julliard, R., Clavel, J., Devictor, V., Jiguet, F., Couvet, D., 2006. Spatial segregation of specialists and generalists in bird communities. Ecology Letters 9, 1237–1244. <a href="https://doi.org/10.1111/j.1461-0248.2006.00977.x">https://doi.org/10.1111/j.1461-0248.2006.00977.x</a></p> </li> <li> <p>Morin, D.J., Yackulic, C.B., Diffendorfer, J.E., Lesmeister, D.B., Nielsen, C.K., Reid, J., Schauber, E.M., 2020. Is your ad hoc model selection strategy affecting your multimodel inference? Ecosphere 11, e02997. <a href="https://doi.org/10.1002/ecs2.2997">https://doi.org/10.1002/ecs2.2997</a></p> </li> <li> <p>UICN France, MNHN, LPO, SEOF, ONCFS, 2016. La Liste rouge des espèces menacées en France - Chapitre Oiseaux de France métropolitaine. Paris, France.</p> </li> </ul>
Source Data for Supplementary Information of "Expanding the substrate scope of PylRS enzymes to include non-⍺-amino acids in vitro and in vivo"
<p>The attached excel file contains the source data for LC-MS traces shown in the Supplementary Information of the paper "Expanding the substrate scope of PylRS enzymes to include non-⍺-amino acids in vitro and in vivo." Each graph is contained in a tab and labeled with the Supplementary Figure number and panel with which it is associated.</p>
Supplementary information: Subsistence and Population development from the Middle Neolithic B (2800-2350 BCE) to the Late Neolithic (2350-1700 BCE) in Southern Scandinavia
<p>This is the supplementary information of the paper “Subsistence and Population development from the Middle Neolithic B (2800-2350 BCE) to the Late Neolithic (2350-1700 BCE) in Southern Scandinavia” (DOI: tba). Please consult the publication for in depth description of the data, its context and for the method applied on the data, as well as references to primary sources. Requirements to be installed to run the scripts: Python 3 (https://www.python.org/) with the packages numpy (https://numpy.org/), pandas (https://pandas.pydata.org/), matplotlib (https://matplotlib.org/), seaborn (https://seaborn.pydata.org/) and scipy (https://scipy.org/); all included in Ancaonda (Python-Distribution, https://www.anaconda.com/). R (https://cran.r-project.org/) with the packages here (https://cran.r-project.org/web/packages/here/index.html) and rcarbon (https://cran.r-project.org/web/packages/rcarbon/index.html), tidyverse, vegan, ggplot2, reshape2, RcppRoll. </p>
Supplementary Information for: Quantifying the potential for consumer-oriented policy to reduce domestic and foreign carbon emissions
<p>These files comprise the Supplementary Information for the paper "Quantifying the potential for consumer-oriented policy to reduce domestic and foreign carbon emissions" submitted to Climate Policy.</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.