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1,773 results for “Supplementary data,”
Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" (Data set and materials used for human-robot interaction experiment)
<p>Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" authored by Troels Aske Klausen, Ulrich Farhadi, Evgenios Vlachos, and Jonas Jørgensen.</p> <p>Contents of set 2:<br> - Data set and materials used for the human-robot interaction experiment and for data analysis</p> <p>Files:<br> - "Questionnaire.pdf": Questionnaire used for data collection.<br> - "Video links.txt": Weblinks to stimuli videos used.<br> - "Data set.xls": Collected raw data.<br> - "Matlab_DataAnalysis.mlx": Matlab script used to analyze raw data.<br> - "Linear_Arousal.png": Linear fit between the scoring of arousal and BPM.<br> - "Linear_Dominance.png": Linear fit between the scoring of dominance and BPM.<br> - "Linear_Pleasure.png": Linear fit between the scoring of pleasure and BPM.</p> <p>The experiment procedure is described in the paper.<br> The soft robot used for the experiment is open source and can be manufactured using design files available on Zenodo: 10.5281/zenodo.5565201</p>
Supplementary data - Modelling the role of dynamic topography and eustasy in the evolution of the Great Artesian Basin
<p>This data repository contains the supplementary data for the paper:</p> <p><strong>Modelling the role of dynamic topography and eustasy in the evolution of the Great Artesian Basin.</strong></p> <p>Carmen Braz<sup>1</sup>, Sabin Zahirovic<sup>1</sup>, Tristan Salles<sup>1</sup>, Nicolas Flament<sup>2</sup>, Lauren Harrington<sup>1</sup>, R. Dietmar Müller<sup>1</sup></p> <p><sup>1</sup> EarthByte Group, School of Geosciences, The University of Sydney, Sydney, Australia</p> <p><sup>2</sup> GeoQuEST Research Centre, School of Earth and Environmental Sciences, University of Wollongong, Wollongong, NSW, Australia</p> <p><em>Basin Research, https://doi.org/10.1111/bre.12606</em></p> <p>Included in this supplement are:</p> <ul> <li> All input files required for running Badlands models M1-M4</li> <li> Badlands digital output for preferred model M4</li> <li> Animations of topography and erosion-deposition through time for all four models presented in the paper </li> <li> Sediment layers for all time steps for preferred model M4 provided as netcdf grids.</li> </ul>
Supplementary Data for the manuscript 'The Rad9-Rad1-Hus1 DNA repair clamp is found in Microsporidia'
<p>This dataset contains the supplementary data files for the manuscript titled 'The Rad9-Rad1-Hus1 DNA repair clamp is found in Microsporidia'.</p>
Reconstruction of full-length LINE-1 progenitors from ancestral genomes (Supplementary Data)
<p><strong>Web Supplementary Files</strong></p> <ul> <li>Web Supplementary File 1 - FASTA files containing full-length reconstruction input sequences<strong>: full_length_reconstruction_input_sequence_fastas.zip</strong></li> <li>Web Supplementary File 2 - FASTA files containing Muscle alignments of the full-length reconstruction input sequences.<strong> full_length_reconstruction_input_sequence_alns.zip</strong></li> <li>Web Supplementary File 3 - FASTA file of full-length reconstructed sequences:<strong> full_length_reconstructions.fa</strong></li> <li>Web Supplementary File 4 - Table of full-length reconstruction statistics: <strong>full_length_reconstruction_stats.csv</strong></li> <li>Web Supplementary File 5 - FASTA files containing ORF reconstruction input sequences:<strong> orf_fastas.zip</strong></li> <li>Web Supplementary File 6 - FASTA files containing Macse alignments of the ORF reconstruction input sequences:<strong> ORF_reconstruction_input_sequence_alns.zip</strong></li> <li>Web Supplementary File 7 - Table of ORF reconstruction statistics: <strong>ORF_reconstructions.fa</strong></li> <li>Web Supplementary File 8 - Table of ORF reconstruction statistics: <strong>ORF_reconstruction_stats.csv</strong></li> <li>Web Supplementary File 9 - Table of Composite Sequences: <strong>bestfl_selection_fixed_CS_seqs.csv</strong></li> <li>Web Supplementary File 10 - Database of gold standards: <strong>L1_goldstandards.csv</strong></li> </ul> <p><strong>Data Underlying Figures</strong></p> <ul> <li>RepeatMasker scans of hg38 and ancestral genomes:<strong> </strong><strong>anc_gen_RM_out_files.zip</strong></li> <li><strong>Figure 4</strong> <ul> <li>4A <ul> <li>Source alignment of 54 composite sequences: <strong>220121_dropped12+L1ME3A_muscle.nt.afa</strong></li> <li>Tree produced using the alignment and FastTree: <strong>220121_dropped12+L1ME3A.tree</strong></li> </ul> </li> <li>4B <ul> <li>Source alignment of 67 Dfam L1 subfamily 3’ end models: <strong>200123_dfam_3ends.fa.muscle.aln</strong></li> <li>Tree produced using the alignment: <strong>200123_dfam_3ends.fa.muscle.aln.tree</strong></li> </ul> </li> </ul> </li> <li><strong>Figure 5</strong> <ul> <li>KZFP-TE enrichment p-values (from Barazandeh <em>et al</em> 2018):<strong> TE_KZFP_enrichment_pvals.xlsx</strong></li> <li>KZFP-TE top 500 peak overlap (from Barazandeh <em>et al</em> 2018): <strong>top500_peak_overlap.xlsx</strong></li> </ul> </li> <li><strong>Figure 6</strong> <ul> <li>RepeatMasker .out file for the Composite Sequence custom library queried against hg38: <strong>CS_RM_hg38.fa.out.gz</strong></li> </ul> </li> <li><strong>Figure S2</strong> <ul> <li>RepeatMasker scan .out file of hg38 (CG corrected Kimura Divergence values are in last column): <strong>hg38+KimDiv_RM.out</strong></li> <li>RepeatMasker scan .out file of the Progressive Cactus eutherian ancestral genome (CG corrected Kimura Divergence values are in last column): <strong>Progressive_Cactus_Euth+KimDiv_RM.out</strong></li> <li>RepeatMasker scan .out file of the Ancestors 1.1 eutherian ancestral genome (CG corrected Kimura Divergence values are in last column): <strong>Ancestors_Euth+KimDiv_RM.out</strong></li> </ul> </li> <li><strong>Figure S5</strong> <ul> <li>RepeatMasker scan .out files for Progressive Cactus simian and primate reconstructed ancestral genomes: <strong>progCactus_RM_outfiles.zip</strong></li> <li>S5A <ul> <li>FASTA files containing Cactus genome-derived reconstructed sequences equivalent to the L1MA2, L1MA4, and L1MD1-3 best full-length sequences: <strong>progCactus_reconstruction_bestFL_equivalents.zip</strong></li> </ul> </li> <li>S5B <ul> <li>FASTA files containing Muscle alignments of Cactus genome-derived full-length reconstruction input sequences: <strong>progCactus_reconstruction_input_sequence_alns.zip</strong></li> </ul> </li> </ul> </li> <li><strong>Figure S6</strong> <ul> <li>S6A <ul> <li>Results of Conserved Domain scans of Cactus genome-derived full-length reconstructed sequences: <strong>CD_search_results_short_nms.txt</strong></li> </ul> </li> <li>S6B-D <ul> <li>Character posterior probabilities of “best” full-length reconstructed sequences: <strong>best_fl_post_probs.zip</strong></li> </ul> </li> </ul> </li> <li><strong>Figure S7</strong> <ul> <li>S7B-C <ul> <li>Results of Conserved Domain scans of translated initial full-length reconstructed sequences: <strong>initial_recons_all_3frametrans_CD-search.txt</strong></li> <li>Results of Conserved Domain scans of translated reconstructed ORFs: <strong>recons_ORF1-2_all_3frametrans_CD-search.csv</strong></li> </ul> </li> </ul> </li> <li><strong>Figure S15</strong> <ul> <li>S15A <ul> <li>Source alignment of 67 composite sequences: <strong>bestfl_selection_fixed_CS_seqs_muscle.nt.afa</strong></li> <li>Tree produced using the alignment: <strong>bestfl_selection_fixed_CS_seqs_muscle.nt.afa.tree</strong></li> </ul> </li> <li>S15B-E <ul> <li>Source Muscle alignments for phylogenetic trees of reconstructed sequence components: <ul> <li>ORF2: <strong>ORF2_keep54_muscle.nt.afa</strong></li> <li>5’ UTR: <strong>5utr_keep54_muscle.nt.afa</strong></li> <li>ORF1: <strong>ORF1_keep54_muscle.nt.afa</strong></li> <li>3’ UTR: <strong>3utr_keep54_muscle.nt.afa</strong></li> </ul> </li> <li>Trees produced using above alignments: <ul> <li>ORF2: <strong>ORF2_keep54_muscle.nt.afa.tree</strong></li> <li>5’ UTR: <strong>5utr_keep54_muscle.nt.afa.tree</strong></li> <li>ORF1: <strong>ORF1_keep54_muscle.nt.afa.tree</strong></li> <li>3’ UTR: <strong>3utr_keep54_muscle.nt.afa.tree</strong></li> </ul> </li> </ul> </li> </ul> </li> <li><strong>Figure S17</strong> <ul> <li>Unfiltered BLAST results of Composite Sequences queried against hg38: <strong>CS_hg38_blastn.csv.zip</strong></li> <li>BED file of L1 instances annotated using BLAST pipeline: <strong>BLAST_L1_hits.bed</strong></li> </ul> </li> </ul>
Assessing ambitious nature conservation strategies in a below 2-degree and food-secure world – supplementary spatial data
<p><strong>Assessing ambitious nature conservation strategies in a below 2-degree and food-secure world – supplementary spatial data</strong></p><p><strong>Authors: </strong>Marcel Kok, Johan Meijer, Willem-Jan van Zeist, Jelle Hilbers, Marco Immovilli, Jan Janse, Elke Stehfest, Michel Bakkenes, Andrzej Tabeau, Aafke Schipper, Rob Alkemade</p><p><strong>Point of contact:</strong> <a href="mailto:Marcel.Kok@pbl.nl">Marcel.Kok@pbl.nl</a></p><p><strong>Research paper summary:</strong> Global biodiversity is projected to further decline under a wide range of future socio-economic development pathways, even in sustainability-oriented scenarios. This raises the question how biodiversity can be put on a path to recovery, the core challenge for the implementation of the CBD Kunming-Montreal Global Biodiversity Framework. We designed two ambitious global conservation strategies, 'Half Earth' (HE) and 'Sharing the Planet' (SP), and evaluated their ability to restore terrestrial and freshwater biodiversity and to provide nature's contributions to people (NCP), while also limiting global warming below 2 degrees and ensuring food security. We applied the integrated assessment framework IMAGE with the GLOBIO biodiversity model, using the 'Middle of the Road' Shared Socio-economic Pathway (SSP2) with its projected human population growth as baseline. We found that the HE strategy performs generally better for terrestrial biodiversity (biodiversity intactness (MSA), Area of Habitat, Living Planet Index, Red List Index) in currently still natural regions. The SP strategy yields more improvements for biodiversity in human-used areas, for freshwater biodiversity and for regulating NCP (pest control, pollination, erosion control, water quality). However, both strategies were insufficient to restore biodiversity and corresponded with considerable increases in food security risks and global temperature. Only when we combined the conservation strategies with a portfolio of 'integrated sustainability measures', including climate change mitigation and reductions of food waste and animal product consumption, our scenarios resulted in a restoration of biodiversity and NCP while keeping global warming below two degrees and food security risks below the baseline projection.</p><p><strong>Contents:</strong> This repository contains the supplementary spatial data describing the specific prioritization of conservation areas under the Half Earth (HE) and Sharing the Planet (SP) scenarios, and the resulting scenario land use and MSA data sets for the year 2050, including also a baseline (BL) scenario. All spatial data is in geotiff format at a 10 arcsecond resolution in WGS84 coordinate system. Detailed description of the methodology is provided in the paper listed under "related identifiers".</p><p><strong>Keywords:</strong> Nature conservation, Half Earth, Sharing the Planet, Climate Change, Food Security, Solution-oriented scenarios, Biodiversity, Nature's Contribution to People, NCP</p>
Supplementary data release for "Cosmology and modified gravitational wave propagation from binary black hole population models"
<p>We release the data products associated to the paper <a href="https://arxiv.org/abs/2112.05728">"Cosmology and modified gravitational wave propagation from binary black hole population models", </a><a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.064030"><em>Phys.Rev.D</em> 105 (2022) 6 </a>.</p> <p>The data can be used in conjunction with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> to reproduce the results of the paper. </p> <p>The data product contains the following folders:</p> <p>* injections_GWTC3: injections used to analyze the GWTC3 catalog, generated with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> . Injections are available separately for O1-O2, O3a, O3b for minimum SNR of 10, 11, 12 (folder names are self-explicative). Each folder contains a file named selected.h5 with the injections. For loading them, refer to the tutorial of the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> .</p> <p>* mock_BPL_5yr_GR : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to General Relativity (see the paper for details)</p> <p>* mock_BPL_5yr_MG : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to a modified gravity model with modified gravitational-wave propagation (see the paper for details)</p> <p>* injections_mock : injections for analyzing the mock datasets above</p>
Supplementary Data - "Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio"
<p>Supplementary data for the manuscript entitled "Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio".</p> <p>Includes data for topographic profiles of wrinkle ridges ("wrinkleridge_profiledata.xlsx"), COULOMB model inputs ("COULOMB_modelinputs.xlsx") and outputs ("COULOMB_modeloutputs.xlsx"), and GIS shapefile data for mapped wrinkle ridges (files labelled "allwrinkleridges" and "studiedwrinkleridges"), topographic profile lines (files labelled "topographicprofilelines"), and the regional profile (files labelled "regionalprofile"). </p>
"Agricultural trade and its impacts on cropland use and the global loss of species habitat." - Supplementary data
<p>This dataset and code is part of the following publication:<br> Schwarzmueller, F. & Kastner, T (2022), Agricultural trade and its impact on cropland use<br> and the global loss of species' habitats. Sustainability Science, doi: 10.1007/s11625-022-01138-7<br> </p> <p>There are three zip-folders accompanying this publication:</p> <p>Code.zip contains all the R-Scripts and input files neccessary for the calculation that were written by the authors.</p> <p>Data.zip contains the FAO-input data (as dowloaded in 2021). This exact data is not available anymore from the FAOSTAT website, which is why we included it in this repository.</p> <p>TradeMatrixFeed_import_dry_matter_1986-2013.zip contains the results from the calculation as shown in the paper.</p>
Supplementary Data: Cosmological constraints on decaying axion-like particles: a global analysis
<p><strong>Supplementary Data</strong></p> <p><em>Cosmological constraints on decaying axion-like particles: a global analysis</em></p> <p>This record contains the supplemetary data for the GAMBIT article, "Cosmological constraints on decaying axion-like particles: a global analysis". </p>
Supplementary data for manuscript titled: "Radiolitid Rudists: An Underestimated Archive for Cretaceous Climate Reconstruction"
<p>Supplementary data for manuscript titled: "Radiolitid Rudists: An Underestimated Archive for Cretaceous Climate Reconstruction"</p> <p>Containing raw stable isotope and trace element data used in the study</p>
supplementary data on the faunal remains from Nahal Rahaf 2
<p>S1 = archaeozoological database of the faunal remains from Nahal Rahaf 2</p> <p>S2 = osteometric data on the faunal remains from Nahal Rahaf 2, measurements follow von den Driesch (1976)</p> <p>S3 = skeletal element representation data</p> <p>S4 = comparable UP sites. </p>
Social Innovations for Circularity in the Built Environment – a Scoping Review and Classification. Supplementary Data to the Bibliometric Review.
<p>To transition the built environment (BE) towards circularity, i.e., maximizing the time resources spend in the BE, thus minimizing negative environmental impacts of resource usage, social innovations (SI) – understood as new ways of doing, organizing, framing, and knowing – are just as important as technological advancements. This article provides an overview of the state of the knowledge regarding SI that contribute to circularity in the BE, and proposes a framework for coherently classifying such SI in terms of their main categories and effects.</p> <p>To identify and understand the current knowledge regarding social innovations (SI) that contribute to circularity in the built environment (BE), a bibliometric review of scientific literature is conducted. It shows that the term social innovation is not frequently used in this contexts although the buzzwords circularity and circular economy are themselves often framed as SI. To assess the characteristics and contribution of SI to circularity in the BE, a scoping review that includes grey literature into the context was conducted and a framework developed to classify predominant SI using concepts from transition studies as well as the systems thinking approach. The framework is designed to help assess the potential of SI and to identify research and/or action gaps. Key findings are (1) There is a broad diversity of SIs that contribute to circularity in the BE already in the focus of research, although they are not always identified as such; (2) Most SI focus on either the design or the demolition phase (i.e., market related phases), whereas user-centered SI are less frequently discussed; and (3) It is crucial to also consider potential sustainability goal conflicts in order to guide policies that address SI as a solution.</p> <p>These datasets are the basis to the bibliometric literature review.</p>
Supplementary data and codes for "Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures"
<p>This file contains the data and codes used in the paper</p> <p>“Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures”, S. Williams et al, 2022.</p> <p>It includes the data used in all the figures and supplementary figures, as well as the codes used for simulations and escape rate estimation. The data files are in .mat format. The codes are Matlab codes with the exception of the analytical estimate of the escape rate which is a Mathematica worksheet.</p>
Supplementary data on microcantilever fatigue tests
<p>This data publication contains the results of microcantilever fatigue tests on a metallic glass and tungsten. It supplements the publication “A new method for microscale cyclic crack growth characterization from notched microcantilevers and application to single crystalline tungsten and a metallic glass” DOI: <a href="http://dx.doi.org/10.1557/s43578-022-00618-x">10.1557/s43578-022-00618-x</a>. During the experiments four Zr<sub>48</sub>Cu<sub>36</sub>Ag<sub>8</sub>Al<sub>8</sub> metallic glass microcantilevers and five tungsten single crystal microcantilevers were tested. The compositions, orientations and the production of the microcantilevers are described in the publication.</p> <p>The fatigue tests were performed by S. Gabel under the supervision of B. Merle at the Institute I - General Materials Properties of the Friedrich-Alexander Universität Erlangen-Nürnberg <a href="http://www.ww1.tf.fau.de/">(<em>Martensstr.5, 91058 Erlangen, Germany</em>)</a> using a NMT 03 in-situ nanoindenter <a href="https://www.femtotools.com/">(<em>FemtoTools, Switzerland</em>)</a> inside a 1540EsB Secondary Electron Microscope <a href="https://www.zeiss.com/corporate/int/home.html">(<em>Carl Zeiss, Germany</em>)</a>. The indenter was equipped with a wedge diamond tip, which had a length of ~10 µm. The experiments were performed, until the tip lost contact with the microcantilever due to bending.</p> <p>The data for the fatigue tests are provided in nine <strong>.csv</strong> files, each corresponding to a single test and being divided into two folders, sorted by the material. The numbers in the files names describe the initial cyclic stress intensity factor <em><strong>ΔK<sub>I</sub></strong></em>. This is also used in the publication to describe the single tests. The data in each <strong>.csv</strong> file are organized as follows:</p> <p>Each row contains thirteen semicolon separated characters, forming thirteen columns. The data description are given in row 1, e.g., "Stiffness". The physical unit is given in row 2, e.g., "N/m". From row 3 on the data for the individual experiment are given. The following information can be found in the respective columns:</p> <p>1: Cycle number. 2: Mean force <strong><em>P<sub>m</sub></em></strong> in µN. 3: Force amplitude <em><strong>ΔP/2</strong></em> in µN. 4: Displacement amplitude <em><strong>Δh/2</strong></em> in µm. 5: Stiffness <em><strong>S<sub>CSM </sub></strong></em>in N/m. 6: Crack growth length <em><strong>Δa</strong></em> in µm. 7: Stress intensity factor range <em><strong>ΔK<sub>I</sub></strong></em> in MPa√m. 8: Initial crack length <em><strong>a</strong></em> in µm. 9: Cantilever width <em><strong>W</strong></em> in µm. 10: Cantilever thickness <em><strong>T</strong></em> in µm. 11: Span length <em><strong>L</strong></em> in µm. 12: Phase shift in °. 13: Temperature in °C.</p> <p> </p> <p>One test with metallic glass was performed with a higher recording rate of 400 Hz, which can be found in the folder: <em>"Metallic_Glass_High_Recording_Rate"</em>. The data in the <strong>.csv</strong> file are sorted differently to the other tests:</p> <p>Each row contains fifteen semicolon separated characters, forming fifteen columns The data description is given in row 1, e.g., "Stiffness". The physical unit is given in row 2, e.g., "N/m". From row 3 on the data for the individual experiment are given. The following information can be found in the respective columns:</p> <p>1: Time in s. 2: Cycle number. 3: Mean displacement <em><strong>h<sub>m</sub></strong></em> in µm. 4: Mean force <strong><em>P<sub>m</sub></em></strong> in µN. 5: Force amplitude <em><strong>ΔP/2</strong></em> in µN. 6: Displacement amplitude <em><strong>Δh/2</strong></em> in µm. 7: The force <em><strong>P</strong></em> in µN. 8: Displacement <em><strong>h</strong></em> in µm. 9: Stiffness <em><strong>S<sub>CSM </sub></strong></em>in N/m. 10: Initial crack length <em><strong>a</strong></em> in µm. 11: Cantilever width <em><strong>W</strong></em> in µm. 12: Cantilever thickness <em><strong>T</strong></em> in µm. 13: Span length <em><strong>L</strong></em> in µm. 14: Phase shift in °. 15: Temperature in °C.</p>
Supplementary data for the time-bidirectional states tomography demonstration
<p>Supplementary data for the time-bidirectional tomography demonstration realized on ibm_oslo seven-qubit superconducting quantum processor.</p> <ul> <li>ibm_oslo_calibrations_data.csv contains calibration data at the time of the demonstration realized.</li> <li>input.json contains three input quantum circuits in qiskit format.</li> <li>output.json contains corresponding measurement counts in qiskit format.</li> </ul> <p> </p>
Olive orchard stress assessment with supplementary Sentinel-2 data
<p>This file contains ground truthing data from olive orchards in Halkidiki, N.Greece and Sentinel-2 data for the noted samples. The samples collected for ground truthing are polygons inside the borders of olive orchards in Halkidiki. Polygons or Sampling units contain information associated with biotic and abiotic stress-related assessments carried out by visual inspection and laboratory analysis of samples with ongoing symptoms. Assessments were recorded as percentages of symptoms observed in the total vegetation surface present in each sampling unit. Symptom percentages were attributed to three possible classes: Verticillium dahliae, Spilocaea oleaginea, Unidentified Stress Factors. These percentage values were used to characterize healthy trees and the incidence of V. dahliae, S. oleaginea and unidentified stress factors (USF) in the sample. USF was used for all other non-classified surveyed symptoms attributed to diseases, pests, or abiotic-related damage. Percentages were summed to compute the total stress present in each sampling unit. “Total stress” refers to the stress incidence value used together with different thresholds to create binary labels for each sample of “stressed” or “not stressed”.</p> <p>The polygon geographical information for each sample were recorded and stored in shapefile format using SW maps, a mobile mapping and GIS app.</p> <p>Sentinel-2 data was paired with each sample using the Feature Info Service (FIS) available from sentinel hub, now upgraded into the Statistical API tool ( & ). This API enables acquisition of statistics calculated based on satellite imagery without having to download images. In the Statistical API request the area of interest, time period, evalscript and statistical measures of interest can be calculated. The requested statistics are returned in the API response.</p> <p>Statistical API deployments:</p> <p><a href="https://creodias.sentinel-hub.com/api/v1/statistics">https://creodias.sentinel-hub.com/api/v1/statistics</a></p> <p><a href="https://services.sentinel-hub.com/api/v1/statistics">https://services.sentinel-hub.com/api/v1/statistics</a></p> <p><a href="https://services-uswest2.sentinel-hub.com/api/v1/statistics">https://services-uswest2.sentinel-hub.com/api/v1/statistics</a></p>
Early Neolithic polished stone tools analysis: Diploma thesis supplementary data
<p>Supplementary data of a master thesis: Early Neolithic polished stone tools analysis defended at Masaryk Univerzity, Brno, Czech Republic.</p> <p>The repository contains supplementary database and datasets concerning morphometric shape analysis of polished stone tools and related R scripts. The structure is described in attached read me file.</p>
Supplementary Data for NIPS Publication: Protein Interface Prediction using Graph Convolutional Networks.
<p>These data sets can be used to re-run the experiments from our paper, Protein Interface Prediction using Graph Convolutional Networks. The data are derived from protein complexes in the docking benchmark dataset v. 5.0. Each file is a python tuple that has been saved using cPickle and compressed using gzip.</p> <p>Links:</p> <p>Paper: https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks</p> <p>Poster: https://zenodo.org/record/1134154</p> <p>Code: https://github.com/fouticus/pipgcn</p> <p> </p> <p><strong>File Descriptions:</strong></p> <p>train.cpkl.gz and test.cpkl.gz have the data formatted for neighborhood based graph convolutions. The diffc_ files are the same data formatted for the diffusion convolutional neural networks that we compare against. </p> <p>train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of length 175 containing the PDB codes from the docking benchmark dataset</li> <li>element 1 is a list of length 175 containing features for each protein. Each element is a dictionary containing the following keys: <ul> <li>r_vertex: vertex (residue) features for the receptor. numpy array of shape (x, 70) where x is the number of residues in the receptor and 70 is the number of features.</li> <li>l_vertex: vertex (residue) features for the ligand. analogous to above, with shape (y, 70) where y is the number of residues in the ligand.</li> <li>complex_code: PDB code of the complex. matches the list of codes described above.</li> <li>l_edge: edge features for the neighborhood around each residue in the ligand. numpy array of shape (y, 20, 2) where y is defined as above. the second dimension is the edges to the 20 nearest neighboring residues, ordered by decreasing distance. The third dimension allows for two features per edge. </li> <li>r_edge: edge features for the neighborhood around each residue in the receptor. numpy array of shape (x, 20, 2) where x is as above. </li> <li>l_hood_indices: the index of the 20 closest residues to each residue, ordered by decreasing distance. numpy array of shape (y, 20, 1). "Index" means which row in l_vertex gives the vertex features for the closest neighbor, second closest neighbor, etc. </li> <li>r_hood_indices: analogous to above, shape (x, 20, 1).</li> <li>label: 1 or -1 label for each residue pair. numpy array of shape (x*y, 3). Each row looks like (i, j, k) where i is the index of the ligand residue, j is the index of the receptor residue, and k is either -1 (negative example) or 1 (positive example).</li> </ul> </li> </ul> <p>test.cpkl.gz matches the structure of train.cpkl.gz except it has the test set of 55 complexes. </p> <p>Descriptions of the vertex and edge features can be found in Appendix A of <a href="https://mountainscholar.org/handle/10217/185661">this.</a></p> <p>diffc_g2_p2_train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of the same 175 PDB codes as above. </li> <li>element 1 is a list of features for the 175 complexes. Each element is a dictionary of features with these keys: <ul> <li>r_vertex, l_vertex, complex_code, label: these are the same as described above. </li> <li>'r_power_series': Stacked diffusion matrices which are powers of the similarity matrix used in the DCNN method. numpy array of shape (x, 2, x) where x is the number of receptor residues. the middle dimension 2 indicates how many "hops" is used for that diffusion (1 vs. 2). In other words, element (i, 0, j) is the similarity after 1 hops between residues i and j. element (i, 1, j) is the similarity after 2 hops. See DCNN paper for details.</li> <li>'l_power_series': same as above but for the ligand. shape is (y, 2, y).</li> </ul> </li> </ul> <p>diffc_g2_p2_test.cpkl.gz is the same as diffc_g2_p2_train.cpkl.gz but for the 55 test complexes.</p> <p>diff_g2_p5_train.cpkl.gz and diff_g2_p5_test.cpkl.gz are the same as the p2 version above, except that the diffusion matrices have shape (x, 5, x) and (y, 5, y) because one of our comparisons against the DCNN model uses 5 hops instead of just 2. </p> <p> </p> <p>Note: these files were pickled with Python 2.7. If you're unpickling with Python 3.x you might have to specify encoding as 'latin1'. </p> <p> </p> <p>Please direct any questions to:</p> <ul> <li>Alex Fout (fout@colostate.edu)</li> <li>Jonathon Byrd (jonbyrd@colostate.edu)</li> <li>Basir Shariat (basir@cs.colostate.edu</li> <li>Asa Ben-Hur (asa@cs.colostate.edu)</li> </ul>
Supplementary data to `Do science maps from open access literature capture the overall topic structure of an academic field?`
<p>The dataset contains the 8,528 academic articles records related to Sustainable Food research sourced with the query `TS=("sustainab*" NEAR/2 "food*")` .</p> <p>They are the records present in the largest component of the citation network, as specified in the manuscript. </p> <p>The dataset was sourced from OpenAlex based on the original data used in the manuscript and it is composed of the following columns:</p> <table> <tbody> <tr> <td><em><strong>Column</strong></em></td> <td><em><strong>Description</strong></em></td> </tr> <tr> <td>Id</td> <td>OpenAlex ID</td> </tr> <tr> <td>DOI</td> <td>Document Object Identifier</td> </tr> <tr> <td>display_name</td> <td>The article title</td> </tr> <tr> <td>publication_year</td> <td>The publication year of the article</td> </tr> <tr> <td>open_access</td> <td>An object with details of the open access status of the article</td> </tr> </tbody> </table> <p>We choose the `.rdata` format for easy loading in R. Use the function `load()` to add the data frame to the enviroment. </p>
Supplementary data for the paper: "Resilient crystalline admixture in ultra-high performance self-healing concrete under cyclic freeze-thaw with de-icing salts"
<p>Supplementary data for the paper: "Resilient crystalline admixture in ultra-high performance self-healing concrete under cyclic freeze-thaw with de-icing salts"<br><br>Open data concerning experimental work. <span>This study investigates the influence of a crystalline admixture (CA) in Ultra-high performance (fibre-reinforced) concrete under freeze-thaw (FT) cycles with de-icing salts with focus on single cracks with a width of around 120 µm, specifically focusing on the ability of the healing products of CA to survive and the ability to re-heal after a healing regime following FT exposure. </span></p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.