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2,152 results for “outputs”
Sea ice model outputs developed in "Modelling Antarctic sea ice variability using a brittle rheology"
<p>Output files from the BBM, mEVP and mEVP+ runs developed in the paper entlited "Modelling Antarctic sea ice variability using a brittle rheology". </p> <p>Paper abstract:</p> <p>Sea ice is a composite solid material that sustains large fracture features at scales from metres to kilometres which can play an important role in coupled atmosphere-ocean processes. To model these features, brittle sea ice physics, via the Brittle-Bingham-Maxwell (BBM) rheology, has been implemented in the Lagrangian neXt generation Sea Ice Model (neXtSIM). In Arctic-only simulations the BBM rheology has shown a capacity to represent observationally consistent sea ice fracture patterns and breakup across a wide range of time and length scales. Still, it has not been tested whether this approach is suitable for the modelling of Antarctic sea ice. Here, we introduce a new 50-km grid-spacing Antarctic configuration of neXtSIM, neXtSIM-Ant, using the BBM rheology. We evaluate this simulation against observations of sea ice extent, drift, and thickness and compare it with identically-forced neXtSIM simulations that use the standard modified Elastic-Visco-Plastic (mEVP) rheology. In general, using BBM results in thicker sea ice and an improved representation of sea ice drift than mEVP. We suggest that this is related to short-duration breakup events caused by Antarctic storms that are not captured in the viscous-plastic model. </p>
Climate model (CM2.6) and regional model (ACM) outputs used to investigate the physical drivers and biogeochemical effects of the weakening of the northwest North Atlantic Shelfbreak Jet (Garcia-Suarez & Fennel., 2024; JAMES)
<p>Key variables from the climate model GFDL CM2.6 and the regional Atlantic Canada model (ACM) used to investigate the physical drivers and the biogeochemical effects of the weakening of the shelfbreak jet in the northwest North Atlantic Ocean. The dataset includes all model variables required to reproduce the key results in <em>Garcia-Suarez & Fennel (2024, JAMES)</em>. See <em>GarciaSuarezandFennel_JAMES_CM26_ACM_data_README.txt</em> for more details.</p>
Motor context dominates output from Purkinje cell functional regions during reflexive visuomotor behaviors
<p>Example electrophysiology datasets</p> <p>Variables are stored as .mat (MATLAB) and electrophysiological traces as .h5 files</p>
Volve 2016 reservoir simulation PRT output file
<p>Volve dataset 2016. Reservoir model simulation as-is. Released by Equinor. Used as source of datasets of cumulative production.</p>
CarloTopo synthetic GeoModeller model and relevant MCUE outputs
<p>Companion dataset to the paper:</p> <p><em>Topological Analysis in Monte Carlo Simulation for Uncertainty Estimation</em><em>, </em>Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, Mark Jessell</p> <p>This dataset contains twoarchives pertaining to Monte Carlo Uncertainty Estimation (MCUE) in implicit 3D geological models:</p> <ol> <li>CarloTopo.zip contains a synthetic GeoModeller three dimensional model.</li> <li>High_confidence_CarloTopo.zip contains MCUE outputs for Gaussian perturbation and topological clustering.</li> <li>Low_confidence_CarloTopo.zip contains MCUE outputs for Uniform perturbation and topological clustering.</li> </ol> <p>In the output archive files:</p> <ul> <li>xml files are the GeoModeller project saves, these can be opened with Intrepid's free viewer for GeoModeller 3.3 3.4 (available @: http://www.intrepid-geophysics.com/ig/index.php?page=geomodeller-update)</li> <li>.vo, .isi and .vop1 files are the lithological voxet exports in binary GOCAD format, these can be opened with the aforementionned viewer or Geocando (available @: https://sourceforge.net/projects/geocando)</li> <li>.vtk files are the MCUE uncertainty indexes outputs, these can be opened with Paraview (available @:https://www.paraview.org/download)</li> <li>the frequency.csv file is the MCUE probabilistic model output, it can be opened with any text editor /spreadsheet</li> <li>.csv files are the probabilistic geological models</li> <li>.task files are GeoModeller's API files</li> <li>.txt files are logs from CURE</li> </ul>
LAMMPS output from creep simulation of passivated silica sphere caps
<p>Results from creep simulation of silica sphere caps, passivated with hydroxy groups.</p> <p>50 nanoseconds of data, including log files and dump files.</p> <p>This description will be updated with a github repo and more information when possible.</p>
Meta-analysis of fat distribution traits in UK Biobank; meta-analysis output
<p>Some of my previously-uploaded files round a small number of p-values to zero. Uploading the METAL output so that the rounding errors can be avoided.</p> <p>Columns:</p> <ul> <li>SNP: rs identifier with reference and alternate alleles appended</li> <li>Allele1: first allele</li> <li>Allele2: second allele</li> <li>Freq1</li> <li>frequency of the variant collapsed across strata</li> <li>FreqSE: standard error of the frequency estimate</li> <li>MinFreq: minimum frequency</li> <li>MaxFreq: maximum frequency</li> <li>Effect: beta of the SNP</li> <li>StdErr: standard error</li> <li>P-value</li> <li>Direction: direction of effect in each stratum (UK Biobank, GIANT)</li> </ul> <p> </p> <p> </p>
Funding and Research Output Data
<p>The following dataset includes the anonymized and aggregated data underlying the following publication:</p> <p>Heyard, R., Hottenrott, H. The value of research funding for knowledge creation and dissemination: A study of SNSF Research Grants. <em>Humanit Soc Sci Commun</em> <strong>8</strong>, 217 (2021). <a href="https://doi.org/10.1057/s41599-021-00891-x">https://doi.org/10.1057/s41599-021-00891-x</a> </p> <p>The dataset includes researcher-year observations, so each row of the CSV represents one research, identified by an anonymised identifier, <strong>for a given year</strong>. Included researchers applied for funding by the Swiss National Science Foundation (SNSF). The variables are described in the data dictionnairy (see file data_dic.csv). </p> <p><span>Inclusion criteria:</span></p> <ul> <li>All researchers who applied to SNSF funding instrument project funding or Sinergia as<br>main applicant (e.g. PI) or co-applicant11 (e.g. co-PI) are included.</li> <li>Our observation period started in the year 2005 and ends in 2019.</li> <li>Dynamic, Researcher-specific Observation Period: it starts with the year in which the SNSF observes the researcher for the first time as (co-)PI to PF or as a career funding grantholder (after the postdoctoral level); the year the independent research career starts. However, this study period has its lower bound in 2005. The period ends in 2019 for everyone, and some researchers are observed for a longer period than others.</li> </ul> <p><span>Data anonymisation considerations:</span></p> <p>To ensure pseudo-anonymization of the data we only provide applicants’ age as categorical variable.</p> <p><span>Provenance of the data:</span></p> <p>The dataset was build by combining data owned by the Swiss National Science Foundation (SNSF) on researcher who submitted a proposal and were either funded or not, with data from Dimensions (<a href="https://www.dimensions.ai/">dimensions.ai</a>), a commercial database on scientific publications, which was access via an API (for which service the SNSF paid). All request regarding the process of data retrieval have to be addressed to the SNSF (datateam@snf.ch); same goes for the analysis code. </p>
APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r4)
<p>APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r4)</p>
Inputs and outputs of nep in GPUMD
<p>See the GPUMD manual for the descriptions of the inputs and outputs of the nep executable within GPUMD: https://gpumd.zheyongfan.org/index.php/Main_Page.</p>
Farfish FFDB Model outputs and indicators
<p> Data summary, estimates and projections stored in the FarFish FFDB</p>
OrthoFinder output from rice infecting Magnaporthe oryzae isolates
<p>Generated using OrthoFinder 2 and proteomes from 162 rice infecting Magnaporthe oryzae isolates</p>
Water potential data and model output
<ol> <li>Classifying the diverse ways that plants respond to hydrologic stress into generalizable 'water-use strategies' has long been an eco-physiological research goal. While many schemes for describing water-use strategies have proven to be quite useful, they are also associated with uncertainties regarding their theoretical basis and their connection to plant carbon and water relations. In this review, we discuss the factors that shape plant water stress responses and assess the approaches used to classify a plant's water-use strategy, paying particular attention to the popular but controversial concept of a continuum from isohydry to anisohydry.</li> <li>A generalizable and predictive framework for assessing plant water-use strategies has been historically elusive, yet recent advances in plant physiology and hydraulics provide the field with a way past these obstacles. Specifically, we promote the idea that many metrics that quantify water-use strategies are highly dynamic and emergent from the interaction between plant traits and environmental conditions, and that this complexity has historically hindered the development of a generalizable water-use strategy framework.</li> <li>This idea is explored using a plant hydraulics model to identify: 1) distinct temporal phases in plant hydraulic regulation during drought that underpin dynamic water-use responses, and 2) how variation in both traits and environmental forcings can significantly alter common metrics used to characterize plant water-use strategies. This modeling exercise can bridge the divide between various conceptualizations of water-use strategies and provides targeted hypotheses to advance understanding and quantification of plant water status regulation across spatial and temporal scales.</li> <li>Finally, we describe research frontiers that are necessary to improve the predictive capacity of the plant water-use strategies concept, including further investigation into the belowground determinants of plant water relations, targeted data collection efforts, and the potential to scale these concepts from individuals to whole regions.</li> </ol>
dataset + target vs output
<p>210 data points</p> <p><strong>meaning of each excel sheet:</strong></p> <p>IN - input variable values for each data point (each data point is one row) </p> <p>TARGET - target variable values for each data point (each data point is one row) </p> <p>VARS - presents the units used for each input (independent) and output/target (dependent) variables</p> <p>TARGET vs OUTPUT - presents the 210 expected (experimental) values and the ones obtained by the proposed ANN</p> <p> </p> <p><strong>Check reference below</strong> (to be added when the paper is published)</p> <p><a href="https://www.researchgate.net/publication/329932699_Neural_Networks_-_Shear_Strength_-_Corrugated_Web_Girders">https://www.researchgate.net/publication/329932699_Neural_Networks_-_Shear_Strength_-_Corrugated_Web_Girders</a></p>
Example mineral detection outputs
<p>Example output for Carbon Mapper mineral detection algorithm version 1 based on AVIRIS-NG flight line ang20200712t201415 available from the AVIRIS-NG data portal. </p>
Data simulation output for "Purging due to self-fertilization does not prevent accumulation of expansion load"
<p>Simulation output for manuscript "Purging due to self-fertilization does not prevent accumulation of expansion load".</p> <p>See https://github.com/LZeitler/selfing_expansion for details.</p> <p>Please cite the manuscript if you use this data. Preprint is available at https://doi.org/10.1101/2022.12.19.521096 .</p>
Whole database with all 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> <p> </p>
ChlOSPv1.0 output from 30 year pre-industrial simulation of CESMv2.2
<p>This dataset was compiled in support of the paper titled "The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2". These data were derived from a 30 year pre-industrial simulation of CESM2 with ChlOSP (the Chlorophyll Observation Simulator Package). The files stored here are the processed data that were used to generate the paper figures.</p>
MetROMS model output for manuscript "Dynamic response to ice shelf basal meltwater relevant to explain observed sea ice trends near the Antarctic continental shelf"
<p>This dataset contains output of the MetROMS ocean/sea-ice/ice-shelf model.</p><p>The output was used for analyses of the manuscript:</p><p>Huneke W. G. C., Hobbs W.R., Klocker A., Naughten K. A., 2023, Dynamic response to ice shelf basal meltwater relevant to explain observed sea ice trends near the Antarctic continental shelf, Geophysical Research Letters, 50, e2023GL105435, https://doi.org/10.1029/2023GL105435</p><p>The Github repository https://github.com/wghuneke/MetROMS_BasalMelt_Perturbation/tree/main contains analysis (python) scripts for processing and visualising the model output.</p><p>Contact wilma.huneke@anu.edu.au if you need further information.</p>
LarsAckermann GMD Iceberg 2023 - model output SPINUP
<p>This dataset contains model output from the experiment ID "spinup" with the Earth system model AWI-ESM2.1 and interactive icebergs published in https://doi.org/10.5194/egusphere-2023-2061</p><p>---------------------------------------------------------</p><p>It contains:</p><p>mean output over the last 100 model years of:<br> - 3D ocean temperature<br> - 3D salinity<br> - 2D sea-ice height<br> - zonal mean temperature for the Atlantic, Pacific and Indian Ocean basins<br> - zonal mean salinity for the Atlantic, Pacific and Indian Ocean basins</p><p>timeseries over 700 years of:<br> - global ocean temperature<br> - AABW for the IndoPacific sector<br> - global AABW</p><p>yearly data of:<br> - 3D ocean temperature<br> - 3D vertical velocity<br> - 3D vertical bolus velocity<br> - 3D Väisälä frequency<br> - 2D iceberg freshwater and heat fluxes<br> - buoys files with information about iceberg velocity, location, and dimension</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.