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2,152 results for “outputs”
Marine heatwave projection output as in Hayashida et al. (2020)
<p><strong>There are two files</strong></p> <p><em>grid_spec_simple.nc</em> contains the grid information for OFAM3 (e.g. longitude, latitude, cell area).</p> <p><em>mhwout_1982-2018_OFAM_cmip5.nc</em> contains the MHW metrics over 1982-2050 simulated by OFAM3:</p> <ol> <li>It’s a 4D array [3, 69, 1200, 3600], where:</li> <li>The first dimension refers to the MHW metric (count, total_days, total_icum). ‘count’ is the frequency (number of MHW events in a year), ‘total_days’ is the annual MHW days, and ‘total_icum’ is the total cumulative intensity (degree days). To calculate mean MHW intensity (as in Hayashida et al. (2020)), you need to divide ‘total_icum’ by ‘total_days’.</li> <li>The second dimension is the years from 1982-2050.</li> <li>The third and fourth dimensions are the latitude and longitude grid points.</li> </ol> <p><strong>Reference </strong></p> <p>Hayashida, H., Matear, R.J., Strutton, P.G. <em>et al.</em> Insights into projected changes in marine heatwaves from a high-resolution ocean circulation model. <em>Nat Commun</em> <strong>11</strong>, 4352 (2020). https://doi.org/10.1038/s41467-020-18241-x</p>
Supplementary Information for "Modeling the circular economy in environmentally extended input–output: A web application"
<p>Supplementary Information for "Modeling the circular economy in environmentally extended input–output: A web application"</p> <p>Annex I: This supporting information provides the software review for native and web-based tools that could be used for Circular Economy and environmental analysis.</p> <p>Annex II: This supporting information provides the use-case result analysis.</p>
Input and Output dataset for "Robustness tests for an optical time scale"
<p>The uploaded dataset includes the data reported in the figures of the pubblication Robustness tests for an optical time scale (V Formichella et al 2022 Metrologia 59 015002, DOI 10.1088/1681-7575/ac3801, Euramet Pubblication REPRef-2989)</p>
Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling; Model input and output files
<p>These are the model input and output associated with the manuscript "Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling" in consideration for publication in the Journal of Volcanology and Geothermal Research.</p>
TreeAlign Input and Output datasets (initial submission)
<p>This repository provides the processed data necessary to reproduce the results for the initial TreeAlign paper submission. </p> <p>This includes the following:</p> <ul> <li>Single cell whole genome sequencing (scDNA) <ul> <li>Copy number profiles</li> <li>B allele frequency profiles</li> <li>phylogenetic trees computed with HDBSCAN</li> <li>benchmarking results vs other methods</li> </ul> </li> <li>10X single cell RNA sequencing (scRNA) <ul> <li>count matrices</li> <li>reference and total read counts at heterozygous SNPs</li> <li>meta data</li> </ul> </li> <li>TreeAlign output <ul> <li>Clone assignment for expression profiles</li> <li>p(k) scores</li> <li>p(a) scores</li> </ul> </li> </ul>
Chemical Networks and Model Output and for "Evidence of Photochemistry in an Exoplanet Atmosphere"
<p>The volume mixing ratio output of the key sulphur species computed by photochemical models for producing Fig. 1 </p> <p>Synthetic spectra in Fig. 2</p> <p>The photochemical networks used in each model.</p>
Output files for Carbon storage in the Earth's deep interior implied by carbonate-silicate-metal melt miscibility
<p>Density and speciation output files for Carbon storage in the Earth's deep interior implied by carbonate-silicate-metal melt miscibility.</p>
Model outputs and species-level data for "Functional traits and climate drive interspecific differences in disturbance-induced tree mortality"
<p>This repository is divided in three sub-directories: </p> <ul> <li><em><strong>sensitivity </strong></em>contains the posterior of each parameter estimated by the bayesian mortality model in a rdata file. This file was generated by the script https://github.com/jbarrere3/SalvageModel/tree/withFinland</li> <li><em><strong>climate </strong></em>contains for each tree species the climatic variables (mean annual temperature, minimum annual temperature and annual precipitation) extracted from CHELSA and the disturbance-related climatic indices (Fire Weather Index, Snow Water Equivalent and Gust Wind Speed)</li> <li><em><strong>traits </strong></em>contains the traits calculated directly with NFI data (bark thickness, height to dbh ratio, maximum growth), and a text file with the Species and Trait ID to request to TRY database. </li> </ul> <p>The content of this repository can be used to reproduce the analyses of the paper, with the script stored in in https://github.com/jbarrere3/DisturbancePaper</p> <p><strong>Edit (19/09/2023):</strong> A minor coding error was found in the pre-formatted data of the paper, which did not affect the main results but led to minor change in the value of the posterior estimates. An updated version of the posterior estimates of this dataset was made available at https://zenodo.org/record/8358921. </p>
Sequencing summaries & Nanopolish eventalign outputs for downstream analysis of yeast RNA and synthetic oligonucleotides
<p>This dataset contains:</p> <ol> <li>A set of text files from running the tool Nanopolish eventalign on several nanopore direct RNA sequencing data sets produced by Jay Hesselberth's lab at the University of Colorado (BioProject accession number <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA910992">PRJNA910992</a>), as well as external sequencing data sets from <a href="https://pubmed.ncbi.nlm.nih.gov/34893601/">PMID: 34893601</a> (synthetic oligonucleotides from Leger et al) and <a href="https://pubmed.ncbi.nlm.nih.gov/35252946/">PMID: 35252946</a> (yeast rRNA data from Stephenson et al).</li> <li>"Sequencing summary" files produced by MinKNOW, from nanopore sequencing of yeast mRNA and synthetic RNA oligos in the Hesselberth lab. We analyze these files in an associated manuscript to determine the "end status" of each read during the sequencing run.</li> </ol> <p>These files can be used as inputs to the R markdown documents at <a href="https://github.com/hesselberthlab/RNARePore">https://github.com/hesselberthlab/RNARePore</a> to reproduce the figures in the associated manuscript.</p>
Data, code, and output related to water lead levels
<p>The purpose of this research was to evaluate the need to repeatedly sample water from homes to identify infrequent, but high "spikes" in water lead levels due to the sporadic release of lead particulates. The Illinois and Pennsylvania data were used in establishing baseline (first round of sampling) water lead levels. The simulations were created using various assumptions of baseline prevalence and various probabilities of spike occurrence on the N+1 round of sampling, given the results on the N round.</p> <p>The findings of the research indicate that household tap water needs to be sampled at least 5–7 times to achieve a 50% probability of identifying hazardous spikes in water lead levels.</p>
Proof of Concept database with inputs and outputs of the Master thesis: Analyzing Software Delivery Performance behavior in popular Open Source Software Projects on a Release timeline basis through delivery metrics
<p>The software has become one of the main assets to deliver services today. Thus, software delivery has been dealing with a competitive and dynamic environment where the demand for faster and more assertive deliverables, called here Releases, only increases. Agile development methods emerged helping to accelerate software delivery, embracing industry and open source community. Since then, the software delivery frequency has expanded and improved bringing more adopters of rapid release cycles to reduce their time-to-market. However, using only rapid releases can not be enough as measuring software delivery can answer essential questions, like how software delivery is happening and how it should be. Some approaches for measuring software delivery appeared such as Software Delivery Performance (SDP) where software delivery is measured as a consequence of capabilities evolution. Popularity in Open Source Software Projects (OSSP) means that a project is mature enough in the community to fit the software demand and, therefore, is likely to be ready to be measured through a software delivery approach like SDP. In light of it, this work offers means to analyze SDP behavior in popular Open Source Software Projects on a Release timeline basis through delivery metrics. The results demonstrated that popularity is efficient filtering, as it improves the OSSP delivery, supporting the work's reliability and accuracy. The source code and methodology are published as a replication package to encourage reproducibility and future research.</p>
LULC input and CLM5 carbon output "Chemistry-albedo feedbacks offset up to a third of forestation's CO2 removal benefits."
<p>Land use land cover (LULC) files for the CESM and UKESM simulations performed for the study "Chemistry-albedo feedbacks from reforestation reduce climate benefits and crop yields. The input data was processed to make it compatible for UKESM as described in Weber et al (2022) https://doi.org/10.5194/egusphere-2022-748.</p>
Code and Output data associated with M.E. Orive, M, Barfield, R.D. Holt. Partial clonality expands the opportunity for spatial adaptation. Submitted to The American Naturalist.
<p>This contains the source code, all output files and sample input files used to create the graphs in the manuscript " M.E. Orive, M, Barfield, R.D. Holt. Partial clonality expands the opportunity for spatial adaptation," submitted to The American Naturalist. The readme.rtf file summarizes the manuscript, lists all code files and output files, and explains the output file format and which output data was used, organized by figure number.</p>
Model output data for 3D Climate modelling of LP 890-9 c with a modern Venus-like atmosphere
<p>We make available the output data from 3D climate modelling of LP 890-9 c with a modern Venus-like atmosphere. The data here has been produced for the publication submitted to Monthly Notices of the Royal Astronomical Society: Letters under the title: «3D Global Climate Model of an Exo-Venus: a modern Venus-like Atmosphere for the Nearby Super-Earth LP 890-9 c». The data includes the temperature profiles, emission (thermal) phase curves and transmission spectra files calculated for JWST/NIRSpec Prism. We also make available larger versions of the synthetic observable figures. Proper credit should be given to the authors. For further information, please get in touch with the corresponding author (Diogo Quirino) at: dfquirino@fc.ul.pt</p>
Simulation outputs required to generate figures for "Modeling Multi-Scale Deformation Cycles in Subduction Zones with a Continuum Visco-Elastic-Brittle Framework"
<p>This file contains all of the model simulation outputs necessary to produce the figures for the paper "Modeling Multi-Scale Deformation Cycles in Subduction Zones with a Continuum Visco-Elastic-Brittle Framework".</p> <p>Below are the details of which file is required to produce which figure:</p> <p> </p> <p><strong>Figure 5 (De_dam_fields.pdf) </strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/De_dam_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz">De_dam_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz</a></p> <p> </p> <p><strong>Figure 6 (convergence.pdf)</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_0_001_th_10_10_alpha_4_ddam10.tar.gz">comp_dt_We_0_001_th_10_10_alpha_4_ddam10.tar.gz </a></p> <p>Contains 4 files, each one for a different temporal resolution (delta t).</p> <p> </p> <p><strong>Figure C1 (convergence2.pdf, appendix)</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_0_1_th_10_9_alpha_4_ddam10.tar.gz">comp_dt_We_0_1_th_10_9_alpha_4_ddam10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_10_th_10_8_alpha_4_ddam10.tar.gz">comp_dt_We_10_th_10_8_alpha_4_ddam10.tar.gz </a></p> <p>Each contains 4 files, one for each temporal resolution (delta t).</p> <p> </p> <p><strong>Figure 7 (CPU_time.pdf)</strong></p> <p>No simulation output file: all of the necessary information (CPU times) are included in the associated MATLAB code, available in the Github repository.</p> <p> </p> <p><strong>Figure 8 (T_h.pdf)</strong></p> <p><strong>Left panels, a, c, e</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_0_001_dt_10_5_alpha_4_ddam_10.tar.gz">comp_th_We_0_001_dt_10_5_alpha_4_ddam_10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_0_1_dt_10_4_alpha_4_ddam_10.tar.gz">comp_th_We_0_1_dt_10_4_alpha_4_ddam_10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_10_dt_10_3_alpha_4_ddam_10.tar.gz">comp_th_We_10_dt_10_3_alpha_4_ddam_10.tar.gz </a></p> <p>Each contains 4 files, one for each healing time (T_h)</p> <p><strong>Right panels, b, d, f</strong></p> <p>comp_th_We_0_001_dt_10_5_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>Each contains 5 files, for 5 different realisations of the model simulations (same parameters, different initial noise on cohesion)</p> <p> </p> <p><strong>Figure 9 (comp_ddam_We_0_001.pdf)</strong></p> <p>comp_ddam_We_0_001_dt_10_5_th_10_10.tar.gz</p> <p>One file for each alpha value (2, 3, 4, 6, 8), one file for each delta d value (0.1, 0.3, 0.5, 0.7, 0.9)</p> <p> </p> <p><strong>Figure 10 (comp_ddam_We_0_1.pdf)</strong></p> <p>comp_ddam_We_0_1_dt_10_4_th_10_9.tar.gz</p> <p>One file for each alpha value (2, 3, 4, 6, 8), one file for each delta d value (0.1, 0.3, 0.5, 0.7, 0.9)</p> <p> </p> <p><strong>Figure 11 (discussion.pdf)</strong></p> <p>u_sfc_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>u_sfc_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_50.tar.gz</p> <p> </p> <p><strong>SI movie</strong></p> <p>SI_movie.tar.gz</p>
Output data: China's energy-water-land system co-evolution under carbon neutrality goal and climate impacts
<p>Outputs for Wang, J., Duan, Y., Wang, C., 2023. China’s energy-water-land system co-evolution under carbon neutrality goal and climate impacts. (In progress)</p> <p>Folder demeter contains the spatially downscaled land use/land cover datasets.<br> Folder tethys contains the spatially downscaled water withdrawal datasets.</p> <p>The sub-folder names correspond to the scenarios described in the paper.</p> <p>For landcover datasets, land type ratios in each grid are presented. Land types include water, forest, shrub, grass, urban, snow, sparse and crops.</p> <p>For water withdrawal datasets, “wd” = “water withdrawal spatially downscaled”, “twd” = “water withdrawal spatially and temporally downscaled”, “dom”=”domestic/municipal sector”, “elec”=”electricity sector”, ”irr”=”irrigation sector”, “liv”=”livestock sector”, “mfg”=”manufacturing/industry sector”, “min”=”mining/primary energy sector”, “nonag”=”non-agricultural sector”, “total”=”all sectors”.<br> </p>
MAOOAM model output
<p>Output of the typical MAOOAM atm-2x2 oc-2x4 (VDDG) model version. Produced with the <a href="https://github.com/Climdyn/MAOOAM">MAOOAM model</a>.</p> <p>The .nml model configuration files are also included, allowing the reproduction of the data.</p> <p>See also <a href="https://doi.org/10.5281/zenodo.594828">https://doi.org/10.5281/zenodo.594828</a>.</p> <p> </p>
Recognizing Preprint Peer Review Workshop Outputs
<p>These outputs were produced in connection with the Recognizing Preprint Peer Review workshop held at Janelia Research Campus & Online on December 1-2 by EMBO, HHMI, and ASAPbio.</p> <p>Original versions of some of these files, retaining original formatting and features, can be found on Google Docs:</p> <p><a href="https://docs.google.com/spreadsheets/d/1W24jrTYPqoLX9eJrjBd5ujtTwuW9X-MqN7Ti_rTqlZA/edit?usp=sharing">Survey results</a></p> <p><a href="https://docs.google.com/presentation/d/1h9xDkYXXyeETg1mgWy2-ZgEnYOdPrYxY8-F1ifkE5dc/edit?usp=sharing">Slides</a></p>
Model outputs associated with "Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"
<p>Model outputs associated with “Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"</p>
'Projected Landscape-scale Repercussions of Global Action for Climate and Biodiversity Protection' - model outputs
<p>Archive of model outputs produced for the MAgPIE v4.3.5 paper 'Projected Landscape-scale Repercussions of Global Action for Climate and Biodiversity Protection'.</p> <p>The model code of the MAgPIE and SEALS models can be accessed via:</p> <p><strong>MAgPIE model code</strong>: <a href="https://doi.org/10.5281/zenodo.5394196">https://doi.org/10.5281/zenodo.5394196</a> and <a href="https://github.com/magpiemodel/magpie">https://github.com/magpiemodel/magpie</a></p> <p><strong>MAgPIE model documentation</strong>: <a href="https://rse.pik-potsdam.de/doc/magpie/4.3.5/">https://rse.pik-potsdam.de/doc/magpie/4.3.5/</a></p> <p><strong>SEALS model code</strong>: <a href="https://doi.org/10.5281/zenodo.7795957">https://doi.org/10.5281/zenodo.7795957</a></p> <p>Data descriptions:</p> <p><strong>glosem_input.zip </strong>contains the spatially-explicit RLSK and C-factor data for each of the modelled scenarios at 10 arcseconds and the R code to estimate C-factor values based on the MAgPIE-SEALS outputs.</p> <p><strong>glosem_output.zip</strong> contains the spatially-explicit soil loss estimates for all scenarioso and the R code used to process the input data. The data was used to create Fig. 7.</p> <p><strong>magpie_ouput.zip</strong> contains the MAgPIE model outputs of all scenarios. The data is shown in Figs. 2, 3, 4, & 5.</p> <p><strong>pollination_sufficiency.zip</strong> contains the spatially-explicit pollination sufficiency estimates for all modelled scenarios and the R code used to derive the pollination sufficiency scores. The data is displayed in Fig. 6.</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.