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Dataset results
2,152 results for “outputs”
Climate model output from a study of tropical cyclones over the Shanghai region under climate change based on a convection-permitting modelling
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PacBio sequencing output increased through uniform and directional 5-fold concatenation
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DayCent simulations for California annual grasslands: Monthly data outputs
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[Model outputs] Identifying major hydrologic change drivers in a highly managed transboundary endorheic basin: integrating hydro‐ecological models and time‐series data mining techniques
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Photochemical model output data associated with: How to identify exoplanet surfaces using atmospheric trace species in hydrogen-dominated atmospheres
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Reproductive output: Traits: Competition and Resource Reduction for Five Grass Species Grown in Monoculture and Competition in Soils with Different Nitrogen Availabilities
This experiment was designed to determine the relationships between plant traits, successional status, and resource reduction for five grass species that were grown for three years in monoculture in replicated field plots on soils prepared to have different availabilities of nitrogen. It also determines the results of competition experiments among various combinations of these species as well as the differing feedback effects of each species on soil nitrogen mineralization rates. All of this work has motivated the desire to more fully understand the mechanisms of interactions among plants and their resources, in the belief that this might eventually allow predictions of the dynamics, diversity, and composition of plant communities.
Reproductive output--number of flowering heads: The Diversity and Abundance of Prairie Plant Communties
This study is divided into six experiments that test the influence of many factors on the diversity and abundance of prairie plant communities. Each "community" is a 2.25 meter square plot seeded with the same amount of seeds from 59 different species of plants. The first experiment is a nitrogen-fire-water factorial. The second experiment is a nitrogen-pH factorial. The third experiment is a nitrogen gradient and the fourth experiment is a nitrogen gradient with a diversity of seeds added each year. The fifth experiment is a disturbance gradient and the last experiment has three levels of soil heterogeneity (variance from the mean) for both nitrogen and pH. See trmte86, trmte103, trmte104, trmte105, trmte106, trmte107 for plot layout.
Reproductive output--number of flowering heads: Soil Heterogeneity With Disturbance
This study is one of six experiments (E086, E103, E104, E105, E106, E107, E108) that test the influence of many factors on the diversity and abundance of prairie plant communities. Each "community" is a 2.25 meter square plot seeded with the same amount of seeds from 59 different species of plants. The first experiment is a nitrogen-fire-water factorial. The second experiment is a nitrogen-pH factorial. The third experiment is a nitrogen gradient and the fourth experiment is a nitrogen gradient with a diversity of seeds added each year. This fifth experiment is a disturbance gradient and this experiment (E107) has three levels of soil heterogeneity (variance from the mean) for both nitrogen and pH.
Reproductive output--number of flowering heads: Soil Disturbance Gradient
This study is one of six experiments (E086, E103, E104, E105, E106, E107, E108) that test the influence of many factors on the diversity and abundance of prairie plant communities. Each community is a 2.25 meter square plot seeded with the same amount of seeds from 59 different species of plants. The first experiment is a nitrogen-fire-water factorial. The second experiment is a nitrogen-pH factorial. The third experiment is a nitrogen gradient and the fourth experiment is a nitrogen gradient with a diversity of seeds added each year. This experiment (E108) is a disturbance gradient and the last experiment has three levels of soil heterogeneity (variance from the mean) for both nitrogen and pH.
Supporting codes, data, and outputs to identify a type of lithofacies from rock-core digital photographs and to classify the lithofacies from well-log data
<p>This repository contains the codes and the data used in the study “Interpreting the Subsurface Lithofacies at High Lithological Resolution by Integrating Information from Well-log Data and Rock-core Digital Photographs”. The codes are used to identify the lithofacies type from rock-core digital photographs (clustering) and to predict the lithofacies from geophysical well-log data (classification). The lithofacies identification process, including feature extraction and clustering, was encoded by MATLAB, and the classification model was encoded by TensorFlow to construct a neural network-based classification model. </p>
Simulation output from "Multiscale MHD-Kinetic PIC Study of Energy Flux Caused by Reconnection"
<p>Results from an implicit particle-in-cell simulation (IPIC3D) of the energy fluxes from magnetic reconnection in Earth's magnetotail. The results consist of six files in HDF5 format that contain the electric field, magnetic field, the density, velocity and energy flux. Each file contains one time step. </p> <p>The IPIC3D simulation is described in</p> <p>Markidis, S., Lapenta, G. and Rizwan-uddin (2010) Multi-scale simulations of plasma with IPIC3D . Mathematics and Computers and Simulation, 80, 1509-1519.</p> <p>The energy conserving version of IPIC3D used in this study is described in</p> <p>Lapenta, G., (2017) Exactly energy conserving semi-implicit particle in cell formulation, J. Computational Physics, 334, 349-366.</p> <p>The application of IPIC3D to Earth's magnetosphere is described in </p> <p>Walker, R., Lapenta, G., Berchem, J., El-Alaoui, M., and Schriver, D., (2019) Embedding particle-in-cell simulations in global magnetohydrodynamic simulations of the magnetosphere, Journal of Plasma Physics, 85(1). </p>
Model code and output of CSIBv4 pan-Arctic sea ice-ocean DMS simulation
<p>here i deposit the model source code and output of the manuscript "Spatio-temporal variability in modelled bottom-ice and sea-surface dimethylsulfide concentrations and fluxes in the Arctic during 1979-2015" by Hayashida et al. (2020).</p> <p><strong>See "readme.txt" for information.</strong></p> <p>For question, contact Hakase Hayashida.</p>
Model output in the LS and SCS
<p>The model data or the samples used to produce the figures in the the manuscript "<em>An Examination of Circulation Characteristics in the Luzon Strait and the South China Sea using High-resolution Regional </em>Atmosphere-Ocean<em>Coupled Models</em>" submitted to <em>JGR-Oceans.</em></p>
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output.
Supplementary material 4 from: Augustinus BA, Lommen STE, Fogliatto S, Vidotto F, Smith T, Horvath D, Bonini M, Gentili RF, Citterio S, Müller-Schärer H, Schaffner U (2020) In-season leaf damage by a biocontrol agent explains reproductive output of an invasive plant species. NeoBiota 55: 117-146. https://doi.org/10.3897/neobiota.55.46874
Figure S1. Mean plant volume ± se of A. artemisiifolia plants measured during the experiment in the four experimental sites
Data and model output for figures in "Variable particle size distributions reduce the sensitivity of global export flux to climate change"
<p><strong>Associated publication</strong></p> <p>This dataset was used to generate analyses and figures in the following publication:</p> <p>Leung, S., Weber, T., Cram, J. A., & Deutsch, C. Variable particle size distributions reduce the sensitivity of global export flux to climate change. <em>Submitted to Biogeosciences.</em></p> <p><strong>Associated code</strong></p> <p>After downloading this dataset, run the associated MATLAB code at the following link to generate the figures and analyses in the above publication:</p> <p>https://doi.org/10.5281/zenodo.4117382</p>
Fig. 1 Viral sharing GAMM outputs and data distribution. a in Predicting the global mammalian viral sharing network using phylogeography
Fig. 1 Viral sharing GAMM outputs and data distribution. a Predicted viral sharing probability increases with increasing phylogenetic relatedness; the different coloured lines represent different geographic overlap values. b Predicted viral sharing probability increases with increasing geographic overlap; the different coloured lines represent different phylogenetic relatedness values. c The geographic overlap:phylogenetic similarity interaction surface, where the darker colours represent increased probability of viral sharing. White contour lines denote 10% increments of sharing probability. Labels have been removed from some contours to avoid overplotting. d Hexagonal bin chart displaying the data distribution, which was highly aggregated at low values of phylogenetic similarity and especially of geographic overlap.
Output Files of the 1-D Submodule in "The Photochemistry of Methane and Ethane in the Martian Atmosphere"
<p>netCDF output files and the python scripts (.ipynb) used to create the plots and work in the AGU submission "The Photochemistry of Methane and Ethane in the Martian Atmosphere".</p> <p>file_usage.txt matches the files to the relevant figures and plotting scripts.</p>
Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals (SDGs)"
<p><strong>This package contains data on five text analysis types (term extraction, contract analysis, topic modeling, network mapping), based on the survey data where researchers selected research output that are related to the 17 Sustainable Development Goals (SDGs). This is used as input to improve the current SDG classification model v4.0 to v5.0</strong></p> <p><a href="https://sustainabledevelopment.un.org/sdgs">Sustainable Development Goals</a> are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">Aurora SDG dashboard</a> is the initial outcome as <em>proof of practice</em>.)</p> <p>The initiative started from the Aurora Universities Network in 2017, in the working group "<a href="https://aurora-network.global/activity/societal-impact-and-relevance-of-research-sirr/">Societal Impact and Relevance of Research</a>", to investigate and to make visible 1. what research is done that are relevant to topics or challenges that live in society (for the proof of practice this has been scoped down to the SDGs), and 2. what the effect or impact is of implementing those research outcomes to those societal challenges (this also have been scoped down to research output being cited in policy documents from national and local governments an NGO's).</p> <p><strong>Context of this dataset | classification model improvement workflow</strong></p> <p>The classification model we have used are 17 different search queries on the Scopus database.</p> <ul> <li>SDG search queries version 4.0 (SQv4) have been created, Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3817443"><em>Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v4.0</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817443</a></li> </ul> </li> <li>A survey has been distributed to senior researchers to test the robustness of SQv4. Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3798385"><em>Survey data of "Mapping Research output to the Sustainable Development Goals SDGs"</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3798385</a></li> </ul> </li> <li>This text analysis has been made as one of the inputs to improve the classification model. Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3832090"><em>Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals SDGs"</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3832090</a></li> </ul> </li> <li>Improved SDG search queries version 5.0 (SQv5) have been created, Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3817445"><em>Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v5.0</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817445</a></li> </ul> </li> </ul> <p><strong>Methods used to do the text analysis</strong></p> <ol> <li><strong>Term Extraction</strong>: after text normalisation (stemming, etc) we extracted 2 terms in bigrams and trigrams that co-occurred the most per document, in the title, abstract and keyword</li> <li><strong>Contrast analysis</strong>: the co-occurring terms in publications (title, abstract, keywords), of the papers that respondents have indicated relate to this SDG (y-axis: True), and that have been rejected (x-axis: False). In the top left you'll see term co-occurrences that a clearly relate to this SDG. The bottom-right are terms that are appear in papers that have been rejected for this SDG. The top-right terms appear frequently in both and cannot be used to discriminate between the two groups.</li> <li><strong>Network map</strong>: This diagram shows the cluster-network of terms co-occurring in the publications related to this SDG, selected by the respondents (accepted publications only).</li> <li><strong>Topic model</strong>: This diagram shows the topics, and the related terms that make up that topic. The number of topics is related to the number of of targets of this SDG.</li> <li><strong>Contingency matrix</strong>: This diagram shows the top 10 of co-occurring terms that correlate the most.</li> </ol> <p><strong>Software used to do the text analyses</strong></p> <p>CorTexT: The <a href="https://www.cortext.net/">CorTexT Platform</a> is the digital platform of LISIS Unit and a project launched and sustained by IFRIS and INRAE. This platform aims at empowering open research and studies in humanities about the dynamic of science, technology, innovation and knowledge production.</p> <p><strong>Resource with interactive visualisations</strong></p> <p>Based on the text analysis data we have created a website that puts all the SDG interactive diagrams together. For you to scrall through. <a href="https://sites.google.com/vu.nl/sdg-survey-analysis-results/">https://sites.google.com/vu.nl/sdg-survey-analysis-results/</a></p> <p><strong>Data set content</strong></p> <p>In the dataset root you'll find the following folders and files:</p> <ul> <li><strong>/sdg01-17/</strong> <ul> <li>This contains the text analysis for all the individual SDG surveys.</li> </ul> </li> <li><strong>/methods/</strong> <ul> <li>This contains the step-by-step explanations of the text analysis methods using Cortext.</li> </ul> </li> <li><strong>/images/</strong> <ul> <li>images of the results used in this README.md.</li> </ul> </li> <li><strong>LICENSE.md</strong> <ul> <li>terms and conditions for reusing this data.</li> </ul> </li> <li><strong>README.md</strong> <ul> <li>description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.</li> </ul> </li> </ul> <p>Inside an <strong>/sdg01-17/</strong>-folder you'll find the following:</p> <ul> <li>This contains the step-by-step explanations of the text analysis methods using Cortext.</li> <li><strong>/sdg01-17/sdg04-sdg-survey-selected-publications-combined.db</strong> <ul> <li>his contains the title, abstract, keywords, fo the publications in the survey, including the and accept or rejection status and the number of respondents</li> </ul> </li> <li><strong>/sdg01-17/sdg04-sdg-survey-selected-publications-combined-accepted-accepted-custom-filtered.db</strong> <ul> <li>same as above, but only the accepted papers</li> </ul> </li> <li><strong>/sdg01-17/extracted-terms-list-top1000.csv</strong> <ul> <li>the aggregated list of co-occuring terms (bigrams and trigrams) extracted per paper.</li> </ul> </li> <li><strong>/sdg01-17/contrast-analysis/</strong> <ul> <li>This contains the data and visualisation of the terms appearing in papers that have been accepted (true) and rejected (false) to be relating to this SDG.</li> </ul> </li> <li><strong>/sdg01-17/topic-modelling/</strong> <ul> <li>This contains the data and visualisation of the terms clustered in the same number of topics as there are 'targets' within that SDG.</li> </ul> </li> <li><strong>/sdg01-17/network-mapping/</strong> <ul> <li>This contains the data and visualisation of the terms clustered in co-occuring proximation of appearance in papers</li> </ul> </li> <li><strong>/sdg01-17/contingency-matrix/</strong> <ul> <li>This contains the data and visualisation of the top 10 terms co-occuring</li> </ul> </li> </ul> <p>note: the .csv files are actually tab-separated.</p> <p><strong>Contribute and improve the SDG Search Queries</strong></p> <p>We welcome you to join the Github community and to fork, branch, improve and make a pull request to add your improvements to the new version of the SDG queries. <strong><a href="https://github.com/Aurora-Network-Global/sdg-queries">https://github.com/Aurora-Network-Global/sdg-queries</a></strong></p>
Model input and output, performance measures and modifications in the source code for PALM simulations on Mäkelänkatu in Helsinki, Finland
<p>This dataset is for air quality simulations conducted on Mäkelänkatu in Helsinki, Finland, using the PALM model system 6.0.</p> <p>By default, simulations use modelled data as boundary conditions. This includes modelled meteorological data from MEPS (MetCoOp Ensemble Prediction System) and air pollutant background concentrations from the ADCHEM model. Alternatively, measured meteorology from the Kivenlahti mast in Espoo, Finland, and aerosol size distribution from the SMEAR III station in Kumpula, Helsinki, is applied.</p> <p>Simulations:</p> <ul> <li>9 June morning: <ul> <li>0609_morning: use modelled boundary conditions for meteorology and air pollutants</li> <li>0609_morning_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution</li> <li>0609_morning_smear: use modelled boundary conditions for meteorology and measured for aerosol size distribution</li> <li>0609_morning_wd_smear: use modelled boundary conditions for meteorology, but modify the wind direction by using the measured wind direction at Kivenlahti. For aerosol size distribution, use the measured boundary conditions.</li> <li>0609_morning_wdk_smear: use modelled boundary conditions for meteorology, but modify the wind direction by using the measured wind direction at SMEAR III. For aerosol size distribution, use the measured boundary conditions.</li> <li>precursor_0609_morning: precursor with modelled boundary conditions for meteorology</li> <li>precursor_0609_morning_allmet: precursor with measured boundary conditions for meteorology</li> <li>precursor_0609_morning_wd: precursor with modelled boundary conditions, but the wind direction is modified by using the measured wind direction at Kivenlahti.</li> <li>precursor_0609_morning_wdk: precursor with modelled boundary conditions, but the wind direction is modified by using the measured wind direction at SMEAR III.</li> </ul> </li> <li>9 June evening: <ul> <li>0609_evening: use modelled boundary conditions for meteorology and air pollutants</li> <li>0609_evening_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution</li> <li>precursor_0609_evening: precursor with modelled boundary conditions for meteorology</li> <li>precursor_0609_evening_allmet: precursor with measured boundary conditions for meteorology</li> </ul> </li> <li>12 December morning: <ul> <li>1207_morning: use modelled boundary conditions for meteorology and air pollutants</li> <li>1207_morning_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution </li> <li>precursor_1207_morning: precursor with modelled boundary conditions for meteorology</li> <li>precursor_1207_morning_allmet: precursor with measured boundary conditions for meteorology</li> </ul> </li> </ul> <p> </p> <p>Datasets are given separately for the root (no suffix), parent (suffix _N02) and child (_N03) domain. The content is following:</p> <ul> <li>input_monitoring_output_usercode <ul> <li>Input data <ul> <li><run_identifier>_chemistry: emission data for gases</li> <li><run_identifier>_dynamic: initialisation and forcing data for meteorological variables and air pollutants</li> <li><run_identifier>_p3d: parameter file for model steering</li> <li><run_identifier>_salsa: emission data for aerosol particles</li> <li><run_identifier>_static: topography information</li> </ul> </li> <li>Simulation performance information <ul> <li><run_identifier>_cpu: information on the CPU time consumed</li> <li><run_identifier>_header: information about the selected model parameters</li> <li><run_identifier>_rc: time step control output</li> </ul> </li> <li>Output data <ul> <li><run_identifier>_av_masked_N03_M01.nc: temporally averaged wind speed data close to the ground</li> <li><run_identifier>_av_masked_N03_M04.nc: temporally averaged aerosol particle concentration data close to the ground</li> <li><run_identifier>_av_masked_N03_M06.nc: temporally averaged aerosol particle concentration data in a vertical column next to the air quality monitoring station on Mäkelänkatu</li> <li><run_identifier>_av_masked_N03_M07.nc: temporally averaged aerosol particle concentration data in a vertical column on the other side of the street from the air quality monitoring station on Mäkelänkatu</li> <li><run_identifier>_pr.nc: temporally vertical profile data on meteorological variables</li> <li><run_identifier>_ts.nc: flow statistics data</li> </ul> </li> <li>Modifications made to the source code (PALM revision, https://palm.muk.uni-hannover.de/trac/browser?rev=4416, last access: 10 Sept 2019) <ul> <li>chem_gasphase_mod.f90: chemical mechanism salsa+simple</li> <li>chem_emissions_mod.f90 (modifications indicated with "MONA")</li> <li>user_module.f90 (modification listed under "Current revisions")</li> <li>Makefile (modification listed under "Current revisions")</li> </ul> </li> </ul> </li> </ul> <p>See the PALM model webpage (https://palm.muk.uni-hannover.de) for details.</p> <p> </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.