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1,773 results for “Supplementary data,”
Data for: Alisch et al MAPLE Supplementary Materials
<p>Archive of experimental data for the manuscript: "MAPLE: a Modular Automated Platform for Large-scale Experiments, a low-cost robot for integrated animal-handling and phenotyping." Data are tracked animal centroids from various arenas, genotypes and experimental groups. Also included are MATLAB scripts for processing this data and producing the plots from the manuscript. A readme file describes the contents of the archive.</p>
Supplementary Data: Full Results: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the full output data from each of the scenarios considered in the above publication. They also include the post-processed input data, which might be useful if you want to rerun the scenarios with only small changes to the input data.</p> <p>The scripts to build the model, input data and result summaries can be found in a <a href="https://zenodo.org/record/1146665">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p>For each scenario, there is a <a href="https://github.com/PyPSA/PyPSA">PyPSA</a> network file in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> and a CSV of shadow prices.</p> <p>To read in a network file do:</p> <pre><code class="language-python">import pypsa network = pypsa.Network("network_file_name.h5")</code></pre> <p>All data is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
Supplementary data for: Comparison of transcriptomic profiles between HFPO-DA and prototypical PPARa, PPARg, and cytotoxic agents in mouse, rat, and pooled human hepatocytes
<p>Like many per- or polyfluorinated alkyl substances (PFAS), toxicity studies with HFPO-DA (ammonium,2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)-propanoate), a short-chain PFAS used in the manufacture of some types of fluorinated polymers, indicate that the liver is the primary target of toxicity in rodents following oral exposure. Although the current weight of evidence supports the PPARa mode of action (MOA) for liver effects in HFPO-DA-exposed mice, alternate MOAs have also been hypothesized including PPARg or cytotoxicity. To further evaluate the MOA for HFPO-DA in rodent liver, transcriptomic analyses were conducted on samples from primary mouse, rat and pooled human hepatocytes treated for 12, 24 or 72 hours with various concentrations of HFPO-DA, or agonists of PPARa (GW7647), PPARg (rosiglitazone), or cytotoxic agents (i.e., acetaminophen or d-galactosamine). Concordance analyses of enriched pathways across chemicals within each species demonstrated greatest concordance between HFPO-DA and PPARa agonist GW7647-treated hepatocytes compared to the other chemicals evaluated. These findings were supported by benchmark concentration modeling and predicted upstream regulator results. In addition, transcriptomic analyses across species demonstrated a greater transcriptomic response in rodent hepatocytes treated with HFPO-DA or agonists of PPARa or PPARg, indicating rodent hepatocytes are more sensitive to HFPO-DA or PPARa/g agonist treatment. These results are consistent with previously published transcriptomic analyses and further support that liver effects in HFPO-DA-exposed rodents are mediated through rodent-specific PPARa signaling mechanisms as part of the MOA for PPARa activator-induced rodent hepatocarcinogenesis. Thus, effects observed in mouse liver are not appropriate endpoints for toxicity value development for HFPO-DA in human health risk assessment.</p>
Supplementary data accompanying Hess et al. (2025) 'The I/Ca paleo-oxygenation proxy in planktonic foraminifera: A multispecies core-top calibration' published in Geochimica et Cosmochimica Acta
<p>This data accompanies Hess et al. (2025) 'The I/Ca paleo-oxygenation proxy in planktonic foraminifera: A multispecies core-top calibrations' published in Geochimica et Cosmochimica Acta.</p> <p>Data columns and explanation:</p> <table> <tbody> <tr> <td>reference</td> <td>reference for the I/Ca and Mg/Ca data</td> </tr> <tr> <td>site</td> <td>site name</td> </tr> <tr> <td>sample_depth_cm</td> <td>sample depth (cm below sediment surface)</td> </tr> <tr> <td>basin</td> <td>ocean basin</td> </tr> <tr> <td>site_depth_km</td> <td>site water depth (km)</td> </tr> <tr> <td>species</td> <td>foraminifera species</td> </tr> <tr> <td>calcification_depth</td> <td>foraminifera calcification depth</td> </tr> <tr> <td>size_fraction</td> <td>foraminifera size fraction</td> </tr> <tr> <td>cleaning_oxidative_reductive</td> <td>cleaning applied to sample before trace element analysis (O = oxidative, O+R = oxidative and reductive)</td> </tr> <tr> <td>local_O2_variability</td> <td>indication of whether the site experiences local O2 variability, rows with "yes" are excluded from Figure 4</td> </tr> <tr> <td>MgCa</td> <td>Mg/Ca (mmol/mol)</td> </tr> <tr> <td>MgCa_corr</td> <td>Mg/Ca corrected for effect of reductive cleaning, as necessary (mmol/mol)</td> </tr> <tr> <td>T_anand</td> <td>calcification temperature calculated from Mg/Ca using Anand et al. (2003) multispecies equation</td> </tr> <tr> <td>T_Hollstein_multispec</td> <td>calcification temperature calculated from Mg/Ca using Hollstein et al. (2017) multispecies equation</td> </tr> <tr> <td>T_Hollstein_spec</td> <td>calcification temperature calculated from Mg/Ca using species-specific Hollstein et al. (2017) equations</td> </tr> <tr> <td>T_Cleroux</td> <td>calcification temperature calculated from Mg/Ca using species-specific Cléroux et al. (2008) equations</td> </tr> <tr> <td>depth_anand</td> <td>calcification depth from water column temperature data (Moffett et al., 2020) and T_anand</td> </tr> <tr> <td>depth_Hollstein_multispec</td> <td>calcification depth from water column temperature data (Moffett et al., 2020) and T_Hollstein_multispec</td> </tr> <tr> <td>depth_Hollstein_spec</td> <td>calcification depth from water column temperature data (Moffett et al., 2020) and T_Hollstein_spec</td> </tr> <tr> <td>depth_Cleroux</td> <td>calcification depth from water column temperature data (Moffett et al., 2020) and T_Cleroux</td> </tr> <tr> <td>ICa</td> <td>I/Ca (µmol/mol)</td> </tr> <tr> <td>ICa_corr</td> <td>I/Ca corrected for effect of reductive cleaning, as necessary (µmol/mol)</td> </tr> <tr> <td>O2av_0-500m</td> <td>average oxygen concentration in the top 500 m of the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> <tr> <td>O2min_0-500m</td> <td>minimum oxygen concentration in the top 500 m of the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> <tr> <td>O2min_alldepths</td> <td>minimum oxygen concentration at any depth in the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> <tr> <td>O2av_0-100m</td> <td>average oxygen concentration in the top 100 m of the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> <tr> <td>O2min_0-100m</td> <td>minimum oxygen concentration in the top 100 m of the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> </tbody> </table>
Supplementary data: A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR
<p><strong>Content:</strong></p> <p>This dataset provides supplementary data to "A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR". It contains trained Neural Network Potentials (NNP), energy and force data used for accuracy evaluation of the NNPs. Energy and forces are stored as ASE trajectory files (traj), readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE). In addition, this repository contains the generated training database with DFT (SCAN+D3(BJ)) energies and forces as SchNetPack1.0 database (SiAlOHNa.db) file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>. Also, the structure files used to calculate NMR properties are involved.</p> <ul> <li>"nnps.zip" - (pytorch) NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)</li> <li>"SiAlOHNa.db" - DFT (SCAN+D3(BJ)) training database as SchNetPack1.0 database file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li> <li>"error_stats.zip" - traj files storing energies/forces at the DFT (SCAN+D3(BJ)) and NNP level for all test simulations to calcuate energy/force errors</li> <li>"Structures_CHA17.zip" - the structures files of CHA(17). </li> </ul>
Supplementary data
<p>Data and Jupyter Notebooks (for Python) that can be used to recreate the figures of "On the theory of the divergence method for quantifying source emissions from satellite observations".</p>
Supplementary data files for "Machine Learning Tools for Baraminology"
Open the record for dataset details and reuse information.
Open Access in Ukraine: characteristics and evolution from 2012 to 2021: supplementary data
<p>This dataset includes metadata for publications authored by researchers affiliated with Ukrainian research institutions and universities, gathered from Scopus, WoS, and Dimensions as of November 2022. Information on the papers' open access status, OA subtypes, publisher details and repository names was obtained from Unpaywall.</p>
Supplementary data for global distribution of mercury in foliage predicted by machine learning
<p>Global distribution of foliar mercury concentrations and pools with a spatial resolution of 0.25 latitude by 0.25 longitude, predicted by machine learning.</p>
Biotic interactions promote local adaptation to soil in plants - Supplementary data
<p>Although different ecological factors shape adaptative evolution in natural habitats, we know little about how their interactions impact local adaptation. Here we used eight generations of experimental evolution with outcrossing <em>Brassica rapa</em> plants as a model system, in eight treatment groups that varied in soil type, herbivory (with/without aphids), and pollination mode (hand- or bumblebee-pollination), to study how biotic interactions affect local adaptation to soil. First, we show that several plant traits evolved in response to biotic interactions in a soil-specific way. Second, using a reciprocal transplant experiment, we demonstrate that significant local adaptation to soil-type evolved in the "number of open flowers", a trait used as a fitness proxy, but only in plants that evolved with herbivory and bee pollination. Whole genome re-sequencing of experimental lines revealed that biotic interactions caused a 10-fold increase in the number of SNPs across the genome with significant allele frequency change, and that alleles with opposite allele frequency change in different soil types (antagonistic pleiotropy) were most common in plants with an evolutionary history of herbivory and bee pollination. Our results demonstrate that the interaction with mutualists and antagonists can facilitate local adaptation to soil type through antagonistic pleiotropy. </p>
Supplementary data to accompany Gernon, T.M., Hincks, T.K., Brune, S., Braun, J., Jones, S.M., Keir, D., Cunningham, A., & Glerum, A., Co-evolution of craton margins and interiors during continental breakup.
<p>Supplementary data to accompany Gernon, T.M., Hincks, T.K., Brune, S., Braun, J., Jones, S.M., Keir, D., Cunningham, A., & Glerum, A., <em>Co-evolution of craton margins and interiors during continental breakup</em>. Nature (Accepted in Principle at time of writing, 3 June 2024).</p> <p><strong>Constraining thermochron uncertainty</strong></p> <p>We utilise published thermochron model data for 46 sites across southern Africa from Brown et al. (2002); Green et al. (2017); Kounov et al. (2013) and (2009); Stanley et al. (2020), (2015) and (2013); Tinker et al. (2008), and Wildman et al. (2017), (2016) and (2015). </p> <p>The above studies present model uncertainty in slightly different ways. However, we have attempted to provide equivalent estimates of uncertainty across the board in our analysis. </p> <p>Stanley and Flowers (2020) provide individual simulation runs for 15 sites, and we use these directly to estimate maximum temperature drop and associated timing for each simulation. For the 12 sites provided by Wildman et al. (2017, 2016, 2015), we use the best fit and 95 percentile envelope, and assume the 'good fit' envelopes of Kounov et al. (2009) to be broadly equivalent. For 15 sites (see MinMax.csv) we utilise the best fit curve together with an estimate of the minimum and maximum plausible timing of the point of maximum temperature drop.</p> <p>Green et al. (2017) provides only a best fit curve, and in the absence of further data we cannot provide an uncertainty estimate here. </p> <p><br><strong>Files provided</strong></p> <p><strong>SourceData.csv</strong><br>Summary of each site, associated data source(s), coordinates and model uncertainty. Please see references listed within for complete thermochron model descriptions and original data.</p> <p><strong>MinMax.csv</strong><br>Name/Location and references for thermochron source data for 15 sites with best fit curves, and estimates of the min/max time of maximum temperature drop.<br>Tmin and Tmax (degrees C) are the minimum and maximum modelled temperatures for each location. t1_Ma and t2_Ma are the minimum and maximum times (Ma) where the model simulations (best, good or acceptable fit) reach the midpoint temperature Tmid= (Tmax -Tmin)/2<br>The most likely timing is taken from the best fit curve. </p> <p><strong>Files in Thermochron_bestfit</strong><br>Best fit thermochron curves (Age in Ma, and Temp in degrees C) for 31 sites digitized from the original publications. Names correspond to File Names in SourceData.csv, which also provides references.</p> <p><strong>Files in Thermochron_Envelopes</strong><br>Lower and Upper 95 percentile thermochron envelopes (denoted *_L95.csv or *_U95.csv) for 12 sites, digitized from Wildman et al. (2017, 2016, 2015).<br>Lower and Upper good fit thermochron envelopes (denoted *_L.csv or *_U.csv) for three sites, digitized from Kounov et al. (2009)<br>Age in Ma, and Temp in degrees C.<br> <br><strong>Files in Stanley2020_model_runs</strong><br>Individual model output directly from Stanley and Flowers 2020 for 15 sites (no modification of original published data). Note these files include modelled best fit curves.</p> <p>If any of the thermochron model data/summaries given here are re-used, please cite the original source(s) as provided below.</p> <p><br><strong>Complete references</strong></p> <p>R. W. Brown, M. A. Summerfield, and A. J. W. Gleadow. Denudational history along a transect across the Drakensberg Escarpment of southern Africa derived from apatite fission track thermochronology. Journal of Geophysical Research: Solid Earth, 107(B12), 2002.</p> <p>P. F. Green, I. R. Duddy, P. Japsen, J. M. Bonow, and J. A. Malan. Post-breakup burial and exhumation of the southern margin of Africa. Basin Research, 29(1):96–127, 2017.</p> <p>A. Kounov, G. Viola, I. Dunkl, and H. E. Frimmel. Southern African perspectives on the long-term morpho-tectonic evolution of cratonic interiors. Tectonophysics, 601:177–191, 2013.</p> <p>A. Kounov, G. Viola, M. deWit, and M. A. G. Andreoli. Denudation along the Atlantic passive margin: new insights from apatite fission-track analysis on the western coast of South Africa. Geological Society, London, Special Publications, 324(1):287–306, 2009.</p> <p>J. R. Stanley and R. M. Flowers. Mesozoic denudation history of the lower Orange River and eastward migration of erosion across the southern African Plateau. Lithosphere, 12(1):74–87, 2020.</p> <p>J. R. Stanley, R. M. Flowers, and D. R. Bell. Erosion patterns and mantle sources of topographic change across the southern African Plateau derived from the shallow and deep records of kimberlites. Geochemistry, Geophysics, Geosystems, 16(9):3235–3256, 2015.</p> <p>J. R. Stanley, R. M. Flowers, and D. R. Bell. Kimberlite (U-Th)/He dating links surface erosion with lithospheric heating, thinning, and<br>metasomatism in the southern African Plateau. Geology, 41(12):1243–1246, 2013.</p> <p>J. Tinker, M. de Wit, and R. Brown. Linking source and sink: Evaluating the balance between onshore erosion and offshore sediment accumulation since Gondwana break-up, South Africa. Tectonophysics, 455(1):94–103, 2008.</p> <p>M. Wildman, R. Brown, C. Persano, R. Beucher, F. M. Stuart, V. Mackintosh, K. Gallagher, J. Schwanethal, and A. Carter. Contrasting Mesozoic evolution across the boundary between on and off craton regions of the South African plateau inferred from apatite fission track and (U-Th-Sm)/He thermochronology. Journal of Geophysical Research: Solid Earth, 122(2):1517–1547, 2017.</p> <p>M. Wildman, R. Brown, R. Beucher, C. Persano, F. Stuart, K. Gallagher, J. Schwanethal, and A. Carter. The chronology and tectonic style of landscape evolution along the elevated Atlantic continental margin of South Africa resolved by joint apatite fission track and (U-Th-Sm)/He thermochronology. Tectonics, 35(3):511–545, 2016.</p> <p>M. Wildman, R. Brown, R. Watkins, A. Carter, A. Gleadow, and M. A. Summerfield. Post break-up tectonic inversion across the southwestern cape of South Africa: New insights from apatite and zircon fission track thermochronometry. Tectonophysics, 654:30–55, 2015.</p>
Supplementary Data of the project "Cross Cultural Tears - A Systematic Investigation of the Interpersonal Effects of Emotional Crying Across Different Cultural Backgrounds"
<p>Supplementary Data related to the publication: </p> <div> <p>Zickfeld, J. et al. (2021). Tears evoke the intention to offer social support: A systematic investigation of the interpersonal effects of emotional crying across 41 countries. Journal of Experimental Social Psychology, 95, 104137. <a href="https://doi.org/10.1016/j.jesp.2021.104137" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jesp.2021.104137 </a></p> </div>
The Gambian Pertussis Study (GaPs) Protocol: Supplementary data
<p>Attached are the following documents related to the protocol on 'The impact of acellular pertussis vaccination in pregnancy on the immunogenicity of acellular versus whole-cell pertussis vaccines in infancy: A protocol for a phase IV randomised, controlled mother-infant pertussis vaccine trial (The Gambian Pertussis Study, GaPs, ClinicalTrials.gov NCT03606096'</p> <ol> <li>Data dictionaries for the maternal and infant clinic visits </li> <li>GaPs informed consent document for pregnant participants</li> <li>SPIRIT checklist - signed</li> <li>Independent data and safety monitoring charter</li> </ol>
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 data for "Accelerated river mobility linked to water discharge variability"
<p>Supplementary data for the paper "Accelerated river mobility linked to water discharge variability", by Leenman et al.</p> <p>This repository contains 3 datasets underlying the figures in the manuscript and supporting material.</p> <p><strong>ds01_flow_gauge_metadata: </strong>Details and locations of flow records used in our analysis.</p> <p><strong>ds02_Qvar_and_TR: </strong>Floodplain reworking timescales, channel characteristics, sediment concentrations, and discharge variability metrics (i.e. all metrics used in our figures and modeling). Floodplain reworking timescale data are from <a href="https://doi.org/10.1029/2024GL108537">Greenberg et al., 2024</a>. Channel slopes, widths, planforms and catchment areas are from <a href="https://doi.org/10.1130/G49121.1">Galeazzi et al., 2021.</a> Bed-material sediment concentration estimates are generated from WBMsed (<a href="https://doi.org/10.1029/2021WR031583">Cohen et al., 2022)</a>. </p> <p><strong>ds03_channel_threads_GRWL: </strong>Data used to make one of our supplemental figures. Frequency count of the number of reaches that have a given number of channel threads in the Global River Widths from Landsat (GRWL) database (<a href="https://doi.org/10.1126/science.aat0636">Allen and Pavelsky, 2018</a>). Data downloaded via Google Earth Engine. Only reaches wider than 1000 m are included.</p> <p><strong>Code</strong> using these data to generate the plots in the paper / SM using these data can be found at <a href="https://github.com/a-leenman/Discharge-variability-river-mobility">https://github.com/a-leenman/Discharge-variability-river-mobility</a>. This is the 'live' code; the version at the time of paper submission is archived here: <a href="https://doi.org/10.5281/zenodo.12555069">https://doi.org/10.5281/zenodo.12555069</a>.</p>
Supplementary data for "Undergraduate Gender Diversity and the Direction of Scientific Research" (PART 3)
<p>Supplementary data for "Undergraduate Gender Diversity and the Direction of Scientific Research" (PART 3)</p> <p>Contains publicly available data from:</p> <ul> <li>Higher Education Research Institute. 1966–2006. “Cooperative Institutional Research Program(CIRP) Data Archives of the Freshman Survey Trends from 1966 to 2006.”</li> <li>National Center for Science and Engineering Statistics. 1972–1990. “Higher Education Research and Development Survey (HERD).</li> <li>U.S. National Center for Education Statistics. 1993. 120 Years of American Education: A Statistical Portrait. Washington, DC:U.S. Department of Education.</li> <li>U.S. National Center for Education Statistics. 2005. “Digest of Education Statistics, 2005.”</li> <li>U.S. National Center for Education Statistics. 2019. “Digest of Education Statistics, 2019.”</li> </ul> <p>To replicate the results in "Undergraduate Gender Diversity and the Direction of Scientific Research", unzip all folders in this repository and place all files and folders (except "PapersFieldsofStudy.txt.gz") in "data/raw". AEA Data and Code Repository openicpsr-204361 shows the expected file structure. </p> <p>Additionally, this repository contains "PapersFieldsofStudy.txt.gz" from Microsoft Academic Graph data (April 2018 Extract) used for "Undergraduate Gender Diversity and the Direction of Scientific Research." To replicate the results in "Undergraduate Gender Diversity and the Direction of Scientific Research", please decompress and place "PapersFieldsofStudy.txt.gz" into "data/raw/MAG/". The associated code repository can be found AEA Data and Code Repository openicpsr-204361.</p> <p>The attribution license of the Microsoft Academic Graph is <a href="https://opendatacommons.org/licenses/by/1-0/">ODC-BY</a></p> <p>Please cite the following paper in publications and reports using the Microsoft Academic Graph:</p> <p>Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 243-246. DOI=http://dx.doi.org/10.1145/2740908.2742839</p> <p> </p>
Supplementary Simulation Data for "A contact-based analysis of local energetic frustration dynamics identifies key residues enabling RfaH fold-switch"
<p>Additional simulation data for "A contact-based analysis of local energetic frustration dynamics identifies key residues enabling RfaH fold-switch"</p> <p><strong>Content:</strong></p> <p>'clusters_foldswitch': Contains representative structures in PDB format of the refolding landscape of RfaH using all-atom structure-based models. The QA and QB values indicated in each filename correspond to the fraction of native contacts contain in the representative structure in comparison to the total number of contacts in the structure of the autoinhibited ⍺-folded (A) and active β-folded (B) states of the C-terminal domain of RfaH.</p> <p>'input_one_fs': Contains a trajectory of RfaH refolding from the ⍺-folded to the β-folded state, with each frame contained into a separate PDB file, totalling 400 PDB files. These files can be used with the frustration-based windowing method scripts and Colab notebook made available at https://github.com/pb3lab/RfaH-frustration</p> <p>'input_many_fs': Contains several trajectory of RfaH reversible refolding between the ⍺-folded and β-folded states, with each frame contained into a separate PDB file, totalling 11,999 PDB files. These files can be used with the frustration-based windowing method scripts and Colab notebook made available at https://github.com/pb3lab/RfaH-frustration</p> <p>'output_one_fs': Contains output results from the analysis of local energetic frustration dynamics of the 400 frames contained in 'input_one_fs' using the windowing method available in the Colab notebook at https://github.com/pb3lab/RfaH-frustration.</p> <p>'output_many_fs': Contains output results from the analysis of local energetic frustration dynamics of the 11,999 frames contained in 'input_many_fs' using the windowing method available in the Python script at https://github.com/pb3lab/RfaH-frustration.</p>
Supplementary Data for 'Crossing host boundaries: the evolutionary drivers and correlates of viral host jumps'
<p>This version provides the raw maximum likelihood trees with ancestral host states annotated, as described in Tan et al. 2024 (https://doi.org/10.1038/s41559-024-02353-4). </p> <p> </p> <p>Tip labels are formatted as {genbank accession}|{host}|{collection_date}|{country}.</p> <p>Nodel labels are formatted as follows:</p> <ul> <li>rooted_trees_simplified: {node name}|{host}, where {host} is the most likely ancestral state (i.e., highest ancestral state likelihood)</li> </ul> <ul> <li>rooted_trees: {node name}|{host1}:{likelihood1}|{host2}:{likelihood2}...|{hostn}:{likelihoodn}, which provides the raw ancestral state likelihoods for each host state. </li> <li>All node names correspond to those provided in the Supplementary Tables in Tan et al. 2024.</li> </ul> <p> </p> <p> </p>
Supplementary Data for: Worldwide consequences of a mid-Holocene cold event in the Nordic Seas
<p>SupplementaryTable1.xlsx lists all the samples used to calculate the regional reservoir correction.</p> <p>SupplementaryTable2.xlsx lists all information on the individual radiocarbon dates of all the cores used in the study: LabID, Depth [cm], 14C age ± error, Calibrated age (95% confidence range and mean), and Dated species. Please note that the age-depth relationships have been modeled using a Bayesian approach so the Calibrated ages might differ from the ages that would be yielded by the calibration of individual radiocarbon ages.</p> <p>SupplementaryTable3.xlsx contains all the data used in the study:</p> <p>OCE2017-GR02:<br>- Depth [cm] <br>- Age [ka BP] (this study)<br>- <em>N. pachyderma </em>[%] (Telesiński et al. 2022)<br>- summer sSST 100m [°C] (this study)</p> <p>MSM5-5-723:<br>- Depth [cm]<br>- Age [ka BP] (this study)<br>- <em>N. pachyderma</em> [%] (Werner et al. 2015)<br>- summer sSST 100m [°C] (this study)<br>- P<sub>B</sub>IP<sub>25</sub> (Werner et al. 2015)<br>- Planktic foraminifera fragmentation [%] (Werner et al. 2003)</p> <p>JM10-330GC:<br>- Depth [cm]<br>- Age [ka BP] (this study)<br>- <em>N. pachyderma </em>[%] (Consolaro et al. 2018)<br>- summer sSST 100m [°C] (this study)<br>- Planktic foraminifera [ind./g] (Consolaro et al. 2018)</p> <p>MSM5-5-712:<br>- Depth [cm]<br>- Age [ka BP] (this study)<br>- <em>N. pachyderma </em>[%] (Werner et al. 2013)<br>- summer sSST 100m [°C] (this study)<br>- IRD flux [#/cm**2/kyr] (Werner et al. 2013, recalculated to the new age model)<br>- P<sub>B</sub>IP<sub>25 </sub>(Müller et al. 2012)</p> <p>M23258:<br>- Depth [cm]<br>- Age [ka BP] (this study)<br>- <em>N. pachyderma </em>[%] (Sarnthein et al. 2003)<br>- summer sSST 100m [°C] (this study)<br>- annual SST [°C] (Martrat et al. 2003)</p> <p>M17730-4<br>- Depth [cm]<br>- Age [ka BP] (this study)<br>- <em>N. pachyderma </em>[%] (Telesiński et al. 2015)<br>- summer sSST 100m [°C] (this study)<br>- Planktic foraminifera [ind./g] (Telesiński et al. 2015)</p> <p>MD95-2011<br>- Depth [cm]<br>- Age [ka BP] (this study)<br>- <em>N. pachyderma </em>[%] (Risebrobakken et al. 2003)<br>- summer sSST 100m [°C] (this study)<br>- Planktic foraminifera [ind./g] (Risebrobakken et al. 2003)</p> <p>MD99-2284<br>- Depth [cm]<br>- Age [ka BP] (this study)<br>- <em>N. pachyderma </em>[%] (Bakke et al. 2009, Eldevik et al. 2014)<br>- summer sSST 100m [°C] (this study)</p> <p>JM09-020<br>- Depth [cm]<br>- Age [ka BP] (this study)<br>- annual SST [°C] (Łącka et al. 2019)</p> <p>SupplementaryFigure1.pdf shows Age-depth relationships of cores used in the study as modeled using a Bayesian approach with the Bacon software (Blaauw and Christen, 2011). Dark shading indicates the more likely ages for a given depth, the grey dashed line indicates the 95% confidence range, and the red dashed line indicates the best-fit (mean) age model.</p> <p>SupplementaryFigure2.pdf shows Sea-ice concentration in (A) 6.83-6.85 ka BP and (B) 7.63-7.65 ka BP in the TraCE-21ka. Subsurface (92 m) temperature in (C) 6.81-6.83 ka BP and (D) 7.63-7.65 ka BP in the TraCE-21ka.</p>
Supplementary data to the paper: Toward a Novel Set of Pinna Anthropometric Features for Individualizing Head-Related Transfer Functions
<p>Supplementary research data to the <a href="https://doi.org/10.5281/zenodo.14338958" target="_blank" rel="noopener">paper</a>:</p> <blockquote> <p>Davide Fantini, Stavros Ntalampiras, Giorgio Presti, and Federico Avanzini. Toward a novel set of pinna anthropometric features for individualizing<br>head-related transfer functions. In <em>Proceedings of the 21th Sound and Music Computing Conference</em>, Porto, Portugal, July 2024.</p> </blockquote> <p>The repository includes the research data generated in the abovementioned paper. In particular, the repository includes:</p> <ul> <li><a href="../api/records/10805885/draft/files/README.md/content" target="_blank" rel="noopener noreferrer">README.md</a>: instructions for the data</li> <li><a href="../api/records/10805885/draft/files/pinna_images.mat/content" target="_blank" rel="noopener noreferrer">pinna_images.mat</a>: pinna depth images extracted from the 3D head meshes of the <a href="https://depositonce.tu-berlin.de/items/dc2a3076-a291-417e-97f0-7697e332c960">HUTUBS dataset</a></li> <li><a href="../api/records/10805885/draft/files/landmarks.mat/content" target="_blank" rel="noopener noreferrer">landmarks.mat</a>: coordinates of the landmarks manually annotated on pinna depth images</li> <li><a href="../api/records/10805885/draft/files/anthropometry.mat/content" target="_blank" rel="noopener noreferrer">anthropometry.mat</a>: anthropometric parameters automatically extracted from manually annotated landmarks</li> <li><a href="../api/records/10805885/draft/files/anthropometry_documentation.pdf/content" target="_blank" rel="noopener">anthropometry_documentation.pdf</a>: documentation of the pinna anthropometric parameters</li> <li><a href="../records/12698286/files/poster.pdf?download=1">poster.pdf</a>: poster presented at the SMC conference 2024</li> </ul> <p>The data are provided in the Matlab file format MAT. Nevertheless, the MAT files can be read with other programming languages, such as Python (<a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">scipy.io.loadmat</a>).</p> <p>A GitHub repository to automatically extract the pinna landmarks and features as described in the paper is available <a href="https://github.com/DavideFantini/pinna-anthropometry-extraction" target="_blank" rel="noopener">here</a>.</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.