Find research datasets worth reusing

opencc-by-4.0May 2022View details →

The model output for the hypothermia case during the 2021 Ultramarathon

The datasets are WRF model outputs  for the hypothermia case during the 2021 Ultramarathon. The essential datasets are selected to support the study "Simulation of the effect of small-scale mountains on weather conditions during the May 2021 Ultramarathon in Gansu Province, China".

opencc-by-4.0May 2022View details →

Model output for PI, MH and LIG based on AWIESM1 and AWIESM2

This repository contains the AWIESM output of 3 PMIP4 simulations: pre-industrial (PI), mid-Holocene (MH) and last interglacial (LIG). List of variables: uwind =>  atmospheric zonal wind vwind =>  atmospheric meridional  wind slp => sea level pressure amoc => AMOC streamfuction salinity => ocean salinity temperature => ocean temperature_ temp2_pr_sic => surface temperature (tsurf), 2m air temperature (temp2), precipitation (aprc + aprl) and sea ice concentration (seaice)

opengpl-2.0May 2022View details →

Code: The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems

This is the code archive for the publication "The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems" in Earth's Future. Abstract: Studies have recently focused on using coupled natural–human systems (CNHS) to inform policymaking. However, model uncertainty can increase with model complexity and affect the variance of the model outcomes. Therefore, this study explores an uncertainty analysis of coupled hydrological and human decision models to better evaluate CNHS modeling properties. Five coupled models are proposed with different model complexities for human behavior settings (i.e., model structure and the number of calibrated parameters): one static, two adaptive, and two learning adaptive. Learning adaptive models (the most complex) have both a learning component (capturing long-term trends) and an adaptive component (capturing short-term variations), while adaptive models omit the learning component. The static model is the simplest, without learning or adaptive components. Applying the law of total variance, the model output uncertainty is decomposed into three sources: (1) climate change scenario uncertainty, (2) climate internal variability, and (3) different model configurations with parameter sets or model structures that are equally capable of producing similar outcomes. Our exploratory analysis demonstrated that model uncertainty would likely increase with model complexity given uncertain input data (e.g., climate forcing) and different model configurations; the inclusion of a learning mechanism in the human system can potentially offset the impact of the natural system on uncertainty through coupling natural and human systems. We also discuss other uncertainty sources, such as assumptions about model structure due to incomplete knowledge and metrics for calibration target selection for future studies.

Model output data and code for Zhang et al., Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality

Model output data and code for "Zhang et al., Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality" in Nature Communications.

Supplementary Data: OpenCOVID model output underlaying Figures 1 and 2 of "Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden"

Supplementary data files Figure_1.xlsx and Figure_2.xlsx contain the model simulation outcomes for Figures 1 and 2 of <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock et al</a> "Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden" (2022) <ul> <li>Figure 1: Peak daily hospital occupancy (number of beds per 100,000 population over the six-month simulation period) for three variant properties; infectivity (relative to Delta), immune evading capacity (%), and severity (relative to Delta)  </li> <li>Figure 2: Percentage of COVID-19 infections and deaths averted by third-dose vaccines for adults and vaccinating 5-11-year-olds with doses one and two.  </li> <li>Open access source-codes of the associated plotting functions are published <a href="http://zenodo.org/record/6532404#.Yqw7cezMKdb">here</a> on Zenodo.  </li> <li>Open access source-codes for the OpenCOVID model of all analyses as presented in <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock et al. (2022)</a> are publicly available at <a href="https://github.com/SwissTPH/OpenCOVID/tree/manuscript_december_2021/src">https://github.com/SwissTPH/OpenCOVID/tree/manuscript_december_2021/src</a>.  </li> <li>Detailed model descriptions and model equations of individual-based transmission model OpenCOVID are described in <a href="https://pubmed.ncbi.nlm.nih.gov/34923396/">Shattock et al. (2022)</a> and <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock et al. (2022).</a></li> </ul>

RDA Data Stewardship Organisational Models Survey 2021 Output Dataset

Data Stewardship comes in many forms and contexts, with the common goal of supporting data management. Yet that diversity can make it hard for the RDM community to further professionalise our work and services. The RDA Professionalising Data Stewardship Interest Group (PDS-IG) Models Task Group sought input from the research data community to help model different approaches to research data stewardship through an online survey in October to November 2021. An offline copy of the survey is available as a linked resource from this record. This dataset consists of a cleaned, anonymised responses from 136 respondents, though not all questions were answered by all respondents. The output are available in a CSV formatted file containing the full response set along with output from a thematic textual analysis undertaken on responses to a number of open-text questions in the survey.

Model output and PTt marker data for van Agtmaal et al., 2022 (in review), Frontiers in Earth Science

Model output for reproduction of key figures in the manuscript van Agtmaal et al. titled "Quantifying continental collision dynamics for Alpine-style orogens" currently under revision in Frontiers in Earth Science

opencc-by-4.0Jul 2022View details →

Model outputs of Wei et al. (2022): "Salt intrusion as a function of estuary length in periodically weakly stratified estuaries", published in Geophysical research Letters.

The .mat file includes all model data used in the study "Salt intrusion as a function of estuary length in periodically weakly stratified estuaries", published in Geophyscial Research Letters, 2022. The .txt file contains description of all physical variables contained in the .mat file.

dryad40/100

Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs

The data comes from common guillemots studied at Stora Karlsö, Sweden between 2019 and 2021. The common guillemots breed at an artificial cliff, and has been filmed continusly from above over three breeding seasons. Using the video material, a YOLOv5 model has been trained to detect adult birds, chicks and eggs. The dataset contains annotations (bounding boxes) used for training the model, the model itself, and outputs from the model (object detections). The data can be used and shared freely.

opencc-zeroSep 2022View details →

opencc-by-4.0Sep 2022View details →

Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice"

Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice", in review in Journal of Geophysical Research-Oceans, 2022. Details on CESM model settings and run setups can be found within the manuscript.

opencc-by-4.0Sep 2022View details →

MHD model output for Ganymede's magnetosphere during Juno's flyby

This dataset contains the complete simulation output from our MHD model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021 (Duling et al. 2022). The data was obtained by our application of the PLUTO simulation code v4.4 (Mignone et al. 2007) (http://plutocode.ph.unito.it/) described in Duling et al. 2022. The dataset includes the simulation variables on the simulation grid for a single timestep after steady state was reached. The grid has spherical geometry (r, theta, phi) with phi=0° longitude pointing towards Jupiter (positive y axis of the GPhiO system), phi=90° longitude pointing in the upstream direction (negative x axis of GPhiO) and theta=0° latitude at Ganymede's north pole (positive z axis of GPhiO). The following model variables are included: rho: plasma mass density prs: thermal plasma pressure vx1: plasma velocity radial component vx2: plasma velocity theta component vx3: plasma velocity phi component Bx1: magnetic field radial component Bx2: magnetic field theta component Bx3: magnetic field phi component Additionally the following derived variables are included: Jx1: electric current density radial component Jx2: electric current density theta component Jx3: electric current density phi component Bpx1: plasma magnetic field radial component Bpx2: plasma magnetic field theta component Bpx3: plasma magnetic field phi component Plasma magnetic field means that part of the total magnetic field that results from the plasma interaction. It equals the total magnetic field subtracted by the homogeneous upstream field and Ganymede's intrinsic and induced field. All values are in normalized units with these normalization factors: NORMR = 2.631e8  cm NORMV = 1.4e7  cm/s NORMRHO = 1.661e-22  g/cm^3 NORMPRS = 3.255e-08  dyne/cm^2 NORMB = 6.395e-04  Gauss NORMJ = 5.801e-03  statA/cm^2 In Duling et al. 2022 we present results of a model sensitivity study. This dataset includes model output from our best guess setup (default setup) only. Since the data is in PLUTO's binary format "flt" we provide a Python code snippet that reads the data to data arrays. grid.out This ASCII file contains the grid dimensions and coordinates of the cell boundaries. data.0020.flt This binary file contains the simulation variables on the cell centers of the grid. pluto.0.log This ASCII file contains the header of the PLUTO logfile. read_data.py This Python code snippet helps with reading the data.

openogl-uk-3.0Oct 2022View details →

Output from model simulations of a turbulent ice shelf-ocean boundary current

This dataset contains output from 2 LES and 19 MITgcm simulations of an idealised configuration of the ice shelf-ocean boundary current. Core fields are provided such as velocity and density and these are given as ice-plane averaged. The output was generated to make an inter-model comparison of the representation of dynamical processes at the ice shelf-ocean boundary. The two LES configurations differ in their sub-grid-scale parameterisation. The MITgcm simulations investigate the sensitivity to various parameter changes including: resolution, diffusivity coefficients, advection scheme and melt-parameterisation. All configurations are outlined in Patmore et al. (2022).

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 2. New neuronal model, based on matching a wave-like representation of the output.

For the new model, shown in Figure 2, the ‘differences’ between the outputs is measured and combined, to produce a kind of wave-like shape. If output point 1 has a value of 10, and output point 2 has a value of 5, for example, then this results in the shape moving down 5 points on the wave shape. This shape will, of course, change depending on the order that the dataset values are presented in. Also, if new data is presented, then that will produce a different shape. However, there is still an association between the input values and the output values and it is still this relation that is being learnt. The neural network needs to be able to learn a function that can generalise over the presented datasets, so that it can recognise the relation in previously unseen data as well. Generalising over trying to learn a wave-like shape, or trying to match the output values exactly looks quite similar, suggesting that the process is at least valid. In Figure 2, note that a transposition to move the learned shape up or down first is possible, before a weight value would try to scale it. If the initial match of the combined inputs can be as close as possible, then these adjustments will become less and will be more for fine tuning.

opencc-by-4.0Jun 2024View details →

ImpactX+MADX input and output files for a thin-kick model of the FNAL Booster

Input and output files for a thin-kick model of the Fermilab Booster ring in MAD-X (expressed in SXF format), together with a Python script to parse and run ImpactX using the SXF lattice file.

opencc-by-4.0Apr 2024View details →

Data for lodubay/galactic-dtd: Multi-Zone Galactic Chemical Evolution Model Outputs and APOGEE Sample

Data for <a href="github.com/lodubay/galactic-dtd">lodubay/galactic-dtd</a>, a project exploring different models for the Type Ia supernova delay-time distribution (DTD) in multi-zone galactic chemical evolution models with the <a href="github.com/giganano/VICE">VICE</a> package. This dataset contains two files: <code>multizone.tar.gz</code> is a compressed archive of all 33 multi-zone outputs (combinations of 8 DTDs x 4 star formation histories, plus one with an alternate stellar migration scheme), and <code>sample.csv</code> contains chemical abundance data from the <a href="https://www.sdss4.org/surveys/apogee/">APOGEE survey</a> (data release 17) and stellar ages from <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.tmp.1191L">Leung et al. (2023)</a>. This project is made reproducible with <code>showyourwork</code>, which will automatically download and extract all data from this deposit when it builds the article. This version contains a minor update to the APOGEE sample file.

Fig. 11 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 11: RMSE profiles for temperature (A) and salinity (C). All associated profiles differences (Argo-model) for temperature (B) and salinity (D) in 6 discrete depths (10 m dark blue, 20 m light blue, 30 m red, 40 m pink, 50 m green, 60 m yellow).

Fig. 10 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 10: Temperature (A) and salinity (C) average profiles with the associated STD for model (red) and Argo (blue), calculated from all the associated profiles of the study area (Fig. 1). Profile differences (Argo – model) of the average temperature (green line) and salinity (brown line) (B). T-S diagram of all Argo and model associated profiles for two depth layer zones (Argo: 200-800m light blue, 800-2000 m dark blue) (Model: 200-800 m pink, 800-2000 m red) (D).

Fig. 9 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 9: Temperature (A) and salinity (C) average profiles with the associated STD for model (red) and Argo (blue), calculated from the associated profiles during the "winter" periods (November – April). The associated profiles for the "summer" periods (May – October) are shown in (B) and (D) for the temperature and salinity respectively.

Fig. 7 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 7: A: Argo salinity average profiles in Southern Adriatic (SA) and Otranto Strait (OS) for the years 2010 (green) and 2012 (purple). B: Argo salinity average profiles in the Northern Ionian (NI) for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple). C: Model salinity average profiles in Southern Adriatic (SA) and Otranto Strait (OS) for the years 2010 (green) and 2012 (purple). D: Argo salinity average profiles in the Northern Ionian (NI) for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple).

Fig. 8 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 8: A: Argo salinity average profiles in the south-eastern Ionian for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple). B: Model salinity average profiles in the south-eastern Ionian for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple).