Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,028
datasets available to search
ShareScore release 0.7.1
Dataset results
1,028 results for “modelling & simulation”
UCI CTM model simulations used for deriving the spillover of tropospheric ozone into the stratosphere
<p>The world has made great strides in phasing out the halocarbons that drive ozone loss, such as the chlorofluorocarbons 11 and 12. While living with the well-documented depletion of the ozone layer, we are now watching the slow recovery (increase) of stratospheric ozone over this century after our phaseout of halocarbon production and use. Projecting this recovery date also depends on the impact of other changing greenhouse gases on stratospheric chemistry as well as changes in tropospheric ozone. Both observations and models identify tropospheric ozone as increasing due to air-quality pollution in the lower atmosphere. Here, using a global chemistry-transport model, we find that this ozone increase carries over into the stratosphere at rates affecting the recovery expected from the decay of atmospheric halocarbons. This process is inherently included in our chemistry-climate models but is not diagnosed as such. The ozone assessments need to consider that what happens in the troposphere does not stay in the troposphere, complicating our interpretation of ozone changes over this century.</p>
Supplementary Materials include results of simulation experiments to investigate the impact of phylogenetic regression with model violations.
<p>Modern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for disentangling evolutionary correlations from those arising from hierarchical phylogenetic relationships. Over the past few decades, the phylogenetic regression framework has become a paradigm of modern comparative biology that has been widely embraced as a remedy for shared ancestry. However, recent evidence has sown doubt over the efficacy of phylogenetic regression, and PCMs more generally, with the suggestion that many of these methods fail to provide an adequate defense against unreplicated evolution—the primary justification for using them in the first place. Importantly, some of the most compelling examples of biological innovation in nature result from abrupt lineage-specific evolutionary shifts, which current regression models are largely ill-equipped to deal with. Here we explore a solution to this problem by applying robust linear regression to comparative trait data. We formally introduce robust phylogenetic regression to the PCM toolkit with linear estimators that are less sensitive to model violations than the standard least-squares estimator, while still retaining high power to detect true trait associations. Our analyses also highlight an ingenuity of the original algorithm for phylogenetic regression based on independent contrasts, whereby robust estimators are particularly effective. Collectively, we find that robust estimators hold promise for improving tests of trait associations and offer a path forward in scenarios where classical approaches may fail. Our study joins recent arguments for increased vigilance against unreplicated evolution and a better understanding of evolutionary model performance in challenging–yet biologically important–settings.</p>
Simulations dataset and pre-trained models of "Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory" Ph.D. project
<p>Ph.D. project datasets and models release, <br><em>Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory.</em></p>
Dataset belonging to SNF project: Use of physiologically based pharmacokinetic modelling to simulate dosing requirements of long-acting intramuscular antiretroviral drugs in special populations and to manage drug-drug interactions
Open the record for dataset details and reuse information.
Simulation results with the EULAG research model for the publication: "Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost"
<p>Supplementary material for the publication</p> <ul> <li>Mark Schlutow, Tobias Stacke, Tom Doerffel, et al. Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost. ESS Open Archive . January 24, 2024. <a href="https://doi.org/10.22541/essoar.170612558.81370785/v1">https://doi.org/10.22541/essoar.170612558.81370785/v1</a></li> </ul> <p>The material contains all simulation results and raw outputs that are necessary to reproduce the figures and statistics of the publication. </p>
Synthetic Microstructure Evolution in SLM Processes: 2D Slices from Potts Model Simulations in SPPARKS
<p>The dataset comprises 2D slices of synthetic microstructures, which were simulated under various Selective Laser Melting (SLM) processing conditions.</p> <p>We employed the <em>Potts kinetic Monte Carlo model</em>, which is integrated within the open-source simulation tool, <a href="https://spparks.github.io/">SPPARKS</a>. </p> <p>For the base microstructural information, we utilized a 3D Electron Backscatter Diffraction (EBSD) dataset—specifically using Inconel 100 for simplicity—to generate a representative volume element (<a href="https://www.mdpi.com/2073-4352/10/10/944">RVE</a>). This RVE serves as the initial structure from which we evolve the microstructure across different processing conditions that are pertinent to SLM techniques. The description of the parameters is provided in the <a href="https://spparks.github.io/doc/app_am_ellipsoid.html">SPPARKS Docs</a>.</p> <p>The outcome is a dataset of 2D slices that reflect the potential microstructural variations resulting from specific manufacturing scenarios.</p> <p>Please follow the instructions in the <a href="https://github.com/sara-nl/spparks_hpc">SPPARKS_HPC Repo</a> to reproduce the results.</p>
Simulations of POPC bilayers and monolayers at three different sizes. CHARMM36 with OPC water model
<p>Simulations of POPC bilayers and monolayers with the CHARMM36 force field and OPC water. Three system sizes are used: small ("s", 64 lipids), medium ("m", 256 lipids), and large ("l", 1024 lipids). All simulations are 1 µs long. The simulations are performed using GROMACS, and for each system the following are provided for bilayers (without "mono" in the name) and monolayers (with "mono" in the name):</p> <ul> <li>run input file (tpr)</li> <li>energy file (edr)</li> <li>trajectory file (xtc)</li> <li>checkpoint file (cpt) </li> <li>final structure (gro)</li> </ul> <p>Additionally, the following are shared by the monolayer and bilayer, and their naming convention follows the bilayer one.</p> <ul> <li>index file (ndx)</li> <li>topology file (top)</li> </ul> <p>The simulation parameter files (mdp) are provided separately for bilayers and monolayers. The molecular topologies (top) are included in TOP.tar.</p> <p>The CHARMM36 force field is obtained from http://mackerell.umaryland.edu/charmm_ff.shtml</p>
Supplementary data to "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations"
<p>This dataset contains the data used to generate the results in the work "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations" [1].</p> <p>processed_data.zip contains the data used for the figures shown in [1], while raw_data.zip contains the original simulation results without further processing.</p> <p>To get an overview of the organization of the directories and a description of the data we recommend the README files in the top- and subdirectories.</p> <p>[1] C. Krämer et al., Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations, <a href="https://doi.org/10.48550/arXiv.2403.05223">10.48550/arXiv.2403.05223</a>, 2024</p>
Towards Immersive Process Simulation for Declarative Models - Accompanying Material
<div>This dataset contains the research protocol, transcripts and codings for the paper “Towards Immersive Process Simulation for Declarative Models.” Accepted for publication at the International Conference on Business Process Management - Forum. 2024.</div> <div> </div> <div>The documents are ordered by number. We describe their contents below:</div> <div> </div> <div>1. Appendix A - Consent Form used in research and validation interviews in section 4 and section 6</div> <div>2. Appendix B - DCR Training Material used to familiarize the research participants with DCR in section 4</div> <div>3. Appendix C - Elicitation Interview Script & Validation Session Interview Protocol used in sections 4 and 6</div> <div>4. Appendix D - First round of coding of Elicitation interviews</div> <div>5. Appendix E - Second round of coding of Elicitation interviews</div> <div>6. Appendix F - Third round of coding of Elicitation interviews</div> <div>7. Appendix G - First round of coding of Validation interviews</div> <div>8. Appendix H - Second round of coding of Validation interviews</div> <div>9. Appendix I1 - Third round of coding of Validation interviews, part 1</div> <div>10. Appendix I2 - Third round of coding of Validation interviews, part 2</div> <div>11. Appendix J - Validation participant DCR graph representation preferences: questionnaire and answers</div> <p>Moreover, we included all the transcripts of the interviews in raw form. </p> <div>The file transcript_ideation.pdf contains the transcripts of the requirement elicitation interviews conducted during the research phase.</div> <div>The file transcript_validation.pdf contains the the transcripts of the validation interviews.</div> <p> </p> <p>The github repository https://github.com/GloriousHypnotoad/DCR-Graph-artifact contains the source code of the software artifact used in the paper.</p>
Simulation scripts and data for the stochastic modelling of evolutionary rescue in resistance to pesticides
<p>Evolutionary rescue occurs when the genetic evolution of adaptation saves a population from extinction after environmental change. The evolution of resistance to pesticides is a special scenario of abrupt environmental change, where rescue occurs under strong selection for one or a few <em>de novo</em> resistance mutations of large effect. Here, we develop continuous-time approximations that accurately predict classic discrete-time dynamics in population genetics and population ecology in an integrated eco-evolutionary model of adaptive rescue through pesticide resistance. We derive analytical approximations for the key distributions and statistics that characterise the results, including the probability density function for the time to resistance and the probability of population extinction. The time to resistance shows a lag period, a narrow peak and a long tail, which implies that it can be difficult to predict when resistance will arise. The probability of population extinction shows a sharp transition, in that when extinction is possible, it is also highly likely, which can make eradication a theoretically achievable goal. Alongside these results contributing to the theory of evolutionary rescue, the methods have produced powerful approximations that lay the foundations of a flexible modelling framework for the applied study of eco-evolutionary dynamics to improve scientific resistance management.</p>
Studying the scale selection of mixed Rossby-gravity waves: Idealized simulations with the TIGAR model
<p>Mixed Rossby-gravity waves peak in two atmospheric regions in reanalysis: the upper troposphere and the upper stratosphere. The scales of MRG waves are different in these two regions, which can be seen e.g. on real-time MRG wave vertical profiles (https://modes.cen.uni-hamburg.de/products#MRG). In order to understand the MRG wave scale selection in these regions, we run idealized simulations with the TIGAR model (Vasylkevych and Zagar, 2021) with a symmetric initial height perturbation with respect to the equator and zonal wind profiles derived from ERA5 reanalysis (Hersbach et al, 2020). In addition, we also run TIGAR simulations with symmetric initial height perturbation and idealized zonal jets centered at various latitudes.</p>
Carbon dioxide (CO2) variations across India: Synthesis of observations and model simulations
<p>India is the 3rd largest emitter of fossil fuel carbon dioxide (CO<sub>2</sub>), making it essential to understand CO<sub>2</sub> dynamics to manage carbon emissions. This study examines CO<sub>2</sub> variability and its dynamics based on observations at eleven observational sites in India, satellite observations and model simulations. Results reveal distinct diurnal and seasonal patterns, along with an overall increasing trend in CO<sub>2</sub>. Deeper (shallower) seasonal cycle amplitudes (SCA) observed over northern (southern) part of India are due to the influence of the monsoon system, seasonal climate and terrestrial biosphere. The lowest SCA is observed over the high-altitude site, Hanle in north India (7.4 ppm), followed by the coastal sites, Pondicherry (8.0 ppm) and Thumba (8.4 ppm) in south India. Deepest SCA, 26.7 ppm, is observed at Mohali with one of the peaks observed in November attributed to crop residue burning activities in the Indo-Gangetic Plain. We have used an Atmospheric Chemistry Transport Model (ACTM) to simulate spatial and temporal variations in CO<sub>2</sub>. While the ACTM generally reproduces diurnal variability in January, it fails to capture the CO<sub>2 </sub>minima in July. The model simulates seasonal patterns at Thumba reasonably well, whereas underestimates the SCA at Gadanki. Satellite observations of column CO<sub>2</sub> (XCO<sub>2</sub>) show higher values (410–414 ppm) during the pre-monsoon season, while they remain lower (407–410 ppm) during winter and post-monsoon seasons during 2014–2024. Mean XCO<sub>2</sub> trend (2.41–2.46 ppm yr<sup>-1</sup>) and growth rate variations are similar to Mauna Loa observations.</p>
Simulated glacier runoff to 75 global major river basins (2000-2100, 3 glacier models)
<p>This dataset accompanies our study examining projected, century-scale, glacier runoff simulated by three global glacier evolution models (GloGEM, OGGM, and PyGEM). The dataset includes glacier model projections for all 75 major river basins of interest and includes four Shared Socioeconomic Pathways (SSPs) and twelve Global Climate Models (GCMs). Projections, originally provided as single glacier simulations for RGI (Randolph Glacier Inventory) regions of interest were aggregated at the basin scale using Jupyter notebooks. The dataset gives glacier runoff projections, for all three models, for each combination of GCM, SSP, and basin, with monthly resolution.</p>
Datasets used for the paper "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations"
<p>Atmospheric model and processed data for reproducing the results of "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations" currently submitted to Geophysical Research Letters.</p>
Global Physically-Constrained Deep Learning Water Cycle Model with Vegetation: Model Simulations
<p>Welcome to our repository, which features simulations from the Hybrid Hydrological Model with Vegetation (H2MV). This collection includes 11 NetCDF files, representing temporal model simulations on a monthly scale and the static output of maximum soil moisture capacity (also known as plant rooting water storage) derived from a 10-fold cross-validation (CV) setup:</p> <ul> <li><strong>Temporal Simulations</strong>: The files named "fold1.nc" through "fold10.nc" contain the temporal model simulations, aggregated to a monthly scale, from 10 fold cross-validation (CV) setup.</li> <li><strong>Static Output</strong>: The "sm_max.nc" file presents the H2MV's estimation of the maximum soil moisture capacity</li> </ul> <h3>Contents of the Temporal Simulation Files</h3> <p>Each of the "fold" files ("fold1.nc" to "fold10.nc") contains the following variables:</p> <ul> <li><strong>Snow Dynamics</strong> <ul> <li><span><code>snow_acc</code></span>: Snow accumulation (mm/day)</li> <li><span><code>snow_melt</code></span>: Snow melt (mm/day)</li> <li><span><code>swe</code></span>: Snow water equivalent (mm)</li> </ul> </li> <li><strong>Evapotranspiration and its components</strong> <ul> <li><span><code>Ei</code></span>: Interception evaporation (mm/day)</li> <li><span><code>Es</code></span>: Soil evaporation (mm/day)</li> <li><span><code>T</code></span>: Transpiration (mm/day)</li> <li><span><code>ET</code></span>: Evapotranspiration (mm/day)</li> </ul> </li> <li><strong>Recharge</strong> <ul> <li><span><code>r_soil</code></span>: Soil recharge (mm/day)</li> <li><span><code>r_gw</code></span>: Groundwater recharge (mm/day)</li> </ul> </li> <li><strong>Runoff</strong> <ul> <li><span><code>runoff_surface</code></span>: Surface runoff (mm/day)</li> <li><span><code>baseflow</code></span>: Baseflow (mm/day)</li> <li><span><code>runoff_total</code></span>: Total runoff (mm/day)</li> </ul> </li> <li><strong>Water Storages </strong> <ul> <li><span><code>GW</code></span>: Groundwater (mm)</li> <li><span><code>SM</code></span>: Soil moisture (mm)</li> <li><span><code>tws</code></span>: Terrestrial water storage (mm)</li> <li><span><code>tws_anomaly</code></span>: Anomalies of terrestrial water storage (mm)</li> </ul> </li> <li><strong>Vegetation</strong> <ul> <li><span><code>fapar</code></span>: Fraction of absorbed photosynthetically active radiation (-)</li> </ul> </li> </ul> <h3>Contents of the Static Output File</h3> <p>The "sm_max.nc" file contains 10 variables corresponding to the 10 folds of CV, with each variable (e.g., "fold1") referring to the respective fold.</p> <h3>Additional Information</h3> <p>It's important to note that the original model simulations were conducted with a daily temporal resolution, but the data shared here have been aggregated to a monthly scale. We are open to sharing the original daily simulations and additional variables not included in this repository upon request. Please feel free to reach out to us for more information or data requests.</p>
Data from: Connections between the Southern Ocean and the Eastern tropical Pacific in unforced and forced climate model simulations
<p>The sea surface temperature (SST) over the eastern tropical Pacific significantly influences global-mean climate feedback and may be driven in part by the SST over the Southern Ocean. Previous studies demonstrated a teleconnection from the Southern Ocean to the eastern tropical Pacific by perturbing the Southern Ocean climate. We investigate if this teleconnection holds in a fully coupled, freely running climate system using CMIP6 models. We assess the relationship between the Southern Ocean (SO) and the eastern tropical Pacific (SEP) by calculating correlations between SO and SEP SST timeseries within each model and regressions between mean SO and SEP SSTs across models. We show robust, positive SO-SEP relationships in an unforced climate using pre-industrial SSTs, in a forced climate using SST anomalies between pre-industrial and quadrupled CO<sub>2</sub> simulations, and in the SST pattern of the forced response relative to the global-mean SST anomaly. The strength of SO-SEP correlations is positively related to the stratocumulus cloud feedback off the west coast of South America, and negatively related to ocean heat uptake in the same region. As both shortwave cloud feedback and ocean heat uptake are underestimated in climate models, understanding their effects on SO-SEP teleconnections and their interactions is crucial for determining the strength of SO-SEP teleconnection in the real world and its trustworthiness in climate model projection.</p>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
Fig. 1 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 1. The system designed for simulating pest species dynamics in sugarcane.
Modeling the Depletion and Recovery of the Outer Radiation Belt During a Geomagnetic Storm: Combined MHD and Test Particle Simulations
<p>Data associated with JGR: Space Physics paper, "Modeling the Depletion and Recovery of the Outer Radiation Belt During a Geomagnetic Storm: Combined MHD and Test Particle Simulations".</p>
CICE6 simulations: Quality Control for Community Based Sea Ice Model Development
<p>CICE6 simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the CICE6 simulations by Elizabeth C. Hunke at Los Alamos National Laboratory described in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>It was produced using the CICE sea ice model version 6.0.0.alpha with the baseline namelist:</p> <p>&setup_nml<br> days_per_year = 365<br> use_leap_years = .false.<br> year_init = 1990<br> istep0 = 0<br> dt = 3600.0<br> npt = 43800<br> ndtd = 1<br> runtype = 'continue'<br> ice_ic = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/ic/gx1/iced_gx1_v5.nc'<br> restart = .true.<br> restart_ext = .false.<br> use_restart_time = .true.<br> restart_format = 'nc'<br> lcdf64 = .false.<br> restart_dir = './restart/'<br> restart_file = 'iced'<br> pointer_file = './ice.restart_file'<br> dumpfreq = 'm'<br> dumpfreq_n = 1<br> dump_last = .false.<br> bfbflag = .false.<br> diagfreq = 24<br> diag_type = 'stdout'<br> diag_file = 'ice_diag.d'<br> print_global = .true.<br> print_points = .true.<br> latpnt(1) = 90.<br> lonpnt(1) = 0.<br> latpnt(2) = -65.<br> lonpnt(2) = -45.<br> dbug = .false.<br> histfreq = 'm','d','x','x','x'<br> histfreq_n = 1 , 1 , 1 , 1 , 1<br> hist_avg = .true.<br> history_dir = './history/'<br> history_file = 'iceh'<br> write_ic = .true.<br> incond_dir = './history/'<br> incond_file = 'iceh_ic'<br> /</p> <p>&grid_nml<br> grid_format = 'bin'<br> grid_type = 'displaced_pole'<br> grid_file = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/grid/gx1/grid_gx1.bin'<br> kmt_file = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/grid/gx1/kmt_gx1.bin'<br> gridcpl_file = 'unknown_gridcpl_file'<br> kcatbound = 0<br> /</p> <p>&domain_nml<br> nprocs = 32<br> processor_shape = 'slenderX2'<br> distribution_type = 'cartesian'<br> distribution_wght = 'latitude'<br> ew_boundary_type = 'cyclic'<br> ns_boundary_type = 'open'<br> maskhalo_dyn = .false.<br> maskhalo_remap = .false.<br> maskhalo_bound = .false.<br> /</p> <p>&tracer_nml<br> tr_iage = .true.<br> restart_age = .false.<br> tr_FY = .true.<br> restart_FY = .false.<br> tr_lvl = .true.<br> restart_lvl = .false.<br> tr_pond_cesm = .false.<br> restart_pond_cesm = .false.<br> tr_pond_topo = .false.<br> restart_pond_topo = .false.<br> tr_pond_lvl = .true.<br> restart_pond_lvl = .false.<br> tr_aero = .false.<br> restart_aero = .false.<br> /</p> <p>&thermo_nml<br> kitd = 1<br> ktherm = 2<br> conduct = 'bubbly'<br> a_rapid_mode = 0.5e-3<br> Rac_rapid_mode = 10.0<br> aspect_rapid_mode = 1.0<br> dSdt_slow_mode = -5.0e-8<br> phi_c_slow_mode = 0.05<br> phi_i_mushy = 0.85<br> /</p> <p>&dynamics_nml<br> kdyn = 1<br> ndte = 120<br> revised_evp = .false.<br> advection = 'remap'<br> kstrength = 1<br> krdg_partic = 1<br> krdg_redist = 1<br> mu_rdg = 3<br> Cf = 17.<br> Ktens = 0.<br> e_ratio = 2.<br> basalstress = .false.<br> /</p> <p>&shortwave_nml<br> shortwave = 'dEdd'<br> albedo_type = 'default'<br> albicev = 0.78<br> albicei = 0.36<br> albsnowv = 0.98<br> albsnowi = 0.70 <br> ahmax = 0.3<br> R_ice = 0.<br> R_pnd = 0.<br> R_snw = 1.5<br> dT_mlt = 1.5<br> rsnw_mlt = 1500.<br> kalg = 0.6<br> /</p> <p>&ponds_nml<br> hp1 = 0.01<br> hs0 = 0.<br> hs1 = 0.03<br> dpscale = 1.e-3<br> frzpnd = 'hlid'<br> rfracmin = 0.15<br> rfracmax = 1.<br> pndaspect = 0.8<br> /</p> <p>&zbgc_nml<br> tr_brine = .false.<br> restart_hbrine = .false.<br> tr_zaero = .false.<br> modal_aero = .false.<br> skl_bgc = .false.<br> z_tracers = .false.<br> dEdd_algae = .false.<br> solve_zbgc = .false.<br> bgc_flux_type = 'Jin2006'<br> restore_bgc = .false.<br> restart_bgc = .false.<br> scale_bgc = .false.<br> solve_zsal = .false.<br> restart_zsal = .false.<br> bgc_data_dir = '/uknown_bgc_data_dir'<br> sil_data_type = 'default'<br> nit_data_type = 'default'<br> fe_data_type = 'default'<br> tr_bgc_Nit = .true.<br> tr_bgc_C = .true.<br> tr_bgc_chl = .false.<br> tr_bgc_Am = .true.<br> tr_bgc_Sil = .true.<br> tr_bgc_DMS = .false.<br> tr_bgc_PON = .true.<br> tr_bgc_hum = .true.<br> tr_bgc_DON = .false.<br> tr_bgc_Fe = .true. <br> grid_o = 0.006<br> grid_o_t = 0.006<br> l_sk = 0.024<br> grid_oS = 0.0<br> l_skS = 0.028<br> phi_snow = -0.3<br> initbio_frac = 0.8<br> frazil_scav = 0.8 <br> ratio_Si2N_diatoms = 1.8 <br> ratio_Si2N_sp = 0.0<br> ratio_Si2N_phaeo = 0.0<br> ratio_S2N_diatoms = 0.03 <br> ratio_S2N_sp = 0.03 <br> ratio_S2N_phaeo = 0.03<br> ratio_Fe2C_diatoms = 0.0033<br> ratio_Fe2C_sp = 0.0033<br> ratio_Fe2C_phaeo = 0.1<br> ratio_Fe2N_diatoms = 0.023 <br> ratio_Fe2N_sp = 0.023<br> ratio_Fe2N_phaeo = 0.7<br> ratio_Fe2DON = 0.023<br> ratio_Fe2DOC_s = 0.1<br> ratio_Fe2DOC_l = 0.033<br> fr_resp = 0.05<br> tau_min = 5200.0<br> tau_max = 173000.0<br> algal_vel = 0.0000000111<br> R_dFe2dust = 0.035<br> dustFe_sol = 0.005<br> chlabs_diatoms = 0.03<br> chlabs_sp = 0.01<br> chlabs_phaeo = 0.05<br> alpha2max_low_diatoms = 0.8<br> alpha2max_low_sp = 0.67<br> alpha2max_low_phaeo = 0.67<br> beta2max_diatoms = 0.018<br> beta2max_sp = 0.0025<br> beta2max_phaeo = 0.01<br> mu_max_diatoms = 1.44<br> mu_max_sp = 0.851<br> mu_max_phaeo = 0.851<br> grow_Tdep_diatoms = 0.06<br> grow_Tdep_sp = 0.06<br> grow_Tdep_phaeo = 0.06<br> fr_graze_diatoms = 0.0<br> fr_graze_sp = 0.1<br> fr_graze_phaeo = 0.1<br> mort_pre_diatoms = 0.007<br> mort_pre_sp = 0.007<br> mort_pre_phaeo = 0.007<br> mort_Tdep_diatoms = 0.03<br> mort_Tdep_sp = 0.03<br> mort_Tdep_phaeo = 0.03<br> k_exude_diatoms = 0.0<br> k_exude_sp = 0.0<br> k_exude_phaeo = 0.0<br> K_Nit_diatoms = 1.0<br> K_Nit_sp = 1.0<br> K_Nit_phaeo = 1.0<br> K_Am_diatoms = 0.3<br> K_Am_sp = 0.3<br> K_Am_phaeo = 0.3<br> K_Sil_diatoms = 4.0<br> K_Sil_sp = 0.0<br> K_Sil_phaeo = 0.0<br> K_Fe_diatoms = 1.0<br> K_Fe_sp = 0.2<br> K_Fe_phaeo = 0.1<br> f_don_protein = 0.6<br> kn_bac_protein = 0.03<br> f_don_Am_protein = 0.25<br> f_doc_s = 0.4<br> f_doc_l = 0.4<br> f_exude_s = 1.0<br> f_exude_l = 1.0<br> k_bac_s = 0.03<br> k_bac_l = 0.03<br> T_max = 0.0<br> fsal = 1.0<br> op_dep_min = 0.1<br> fr_graze_s = 0.5<br> fr_graze_e = 0.5<br> fr_mort2min = 0.5<br> fr_dFe = 0.3<br> k_nitrif = 0.0<br> t_iron_conv = 3065.0<br> max_loss = 0.9<br> max_dfe_doc1 = 0.2<br> fr_resp_s = 0.75<br> y_sk_DMS = 0.5<br> t_sk_conv = 3.0<br> t_sk_ox = 10.0<br> algaltype_diatoms = 0.0<br> algaltype_sp = 0.5<br> algaltype_phaeo = 0.5<br> nitratetype = -1.0<br> ammoniumtype = 1.0<br> silicatetype = -1.0<br> dmspptype = 0.5<br> dmspdtype = -1.0<br> humtype = 1.0<br> doctype_s = 0.5<br> doctype_l = 0.5<br> dontype_protein = 0.5<br> fedtype_1 = 0.5<br> feptype_1 = 0.5<br> zaerotype_bc1 = 1.0<br> zaerotype_bc2 = 1.0<br> zaerotype_dust1 = 1.0<br> zaerotype_dust2 = 1.0<br> zaerotype_dust3 = 1.0<br> zaerotype_dust4 = 1.0<br> ratio_C2N_diatoms = 7.0<br> ratio_C2N_sp = 7.0<br> ratio_C2N_phaeo = 7.0<br> ratio_chl2N_diatoms= 2.1<br> ratio_chl2N_sp = 1.1<br> ratio_chl2N_phaeo = 0.84<br> F_abs_chl_diatoms = 2.0<br> F_abs_chl_sp = 4.0<br> F_abs_chl_phaeo = 5.0<br> ratio_C2N_proteins = 7.0<br> /</p> <p>&forcing_nml<br> formdrag = .false.<br> atmbndy = 'default'<br> fyear_init = 1990<br> ycycle = 20<br> atm_data_format = 'bin'<br> atm_data_type = 'LYq'<br> atm_data_dir = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/forcing/gx1/COREII'<br> calc_strair = .true.<br> highfreq = .false.<br> natmiter = 5<br> calc_Tsfc = .true.<br> precip_units = 'mm_per_sec'<br> ustar_min = 0.0005<br> fbot_xfer_type = 'constant'<br> update_ocn_f = .false.<br> l_mpond_fresh = .false.<br> tfrz_option = 'mushy'<br> oceanmixed_ice = .true.<br> ocn_data_format = 'nc'<br> sss_data_type = 'ncar'<br> sst_data_type = 'ncar'<br> ocn_data_dir = '/usr/projects/climate/eclare/DATA/gx1v3/gx1v3/forcing/'<br> oceanmixed_file = 'oceanmixed_ice_depth.nc'<br> restore_sst = .false.<br> trestore = 90<br> restore_ice = .false.<br> /</p> <p>&icefields_nml<br> f_tmask = .true.<br> f_blkmask = .true.<br> f_tarea = .true.<br> f_uarea = .true.<br> f_dxt = .false.<br> f_dyt = .false.<br> f_dxu = .false.<br> f_dyu = .false.<br> f_HTN = .false.<br> f_HTE = .false.<br> f_ANGLE = .true.<br> f_ANGLET = .true.<br> f_NCAT = .true.<br> f_VGRDi = .false.<br> f_VGRDs = .false.<br> f_VGRDb = .false.<br> f_VGRDa = .true.<br> f_bounds = .false.<br> f_aice = 'm' <br> f_hi = 'm'<br> f_hs = 'm' <br> f_Tsfc = 'm' <br> f_sice = 'm' <br> f_uvel = 'm' <br> f_vvel = 'm' <br> f_uatm = 'm' <br> f_vatm = 'm' <br> f_fswdn = 'm' <br> f_flwdn = 'm'<br> f_snowfrac = 'x'<br> f_snow = 'x' <br> f_snow_ai = 'm' <br> f_rain = 'x' <br> f_rain_ai = 'm' <br> f_sst = 'm' <br> f_sss = 'm' <br> f_uocn = 'm' <br> f_vocn = 'm' <br> f_frzmlt = 'm'<br> f_fswfac = 'm'<br> f_fswint_ai = 'm'<br> f_fswabs = 'x' <br> f_fswabs_ai = 'm' <br> f_albsni = 'm' <br> f_alvdr = 'x'<br> f_alidr = 'x'<br> f_alvdf = 'x'<br> f_alidf = 'x'<br> f_alvdr_ai = 'x'<br> f_alidr_ai = 'x'<br> f_alvdf_ai = 'x'<br> f_alidf_ai = 'x'<br> f_albice = 'x'<br> f_albsno = 'x'<br> f_albpnd = 'x'<br> f_coszen = 'x'<br> f_flat = 'x' <br> f_flat_ai = 'm' <br> f_fsens = 'x' <br> f_fsens_ai = 'm' <br> f_fswup = 'x' <br> f_flwup = 'x' <br> f_flwup_ai = 'm' <br> f_evap = 'x' <br> f_evap_ai = 'm' <br> f_Tair = 'm' <br> f_Tref = 'x' <br> f_Qref = 'x'<br> f_congel = 'm' <br> f_frazil = 'm' <br> f_snoice = 'm' <br> f_dsnow = 'x' <br> f_melts = 'm'<br> f_meltt = 'm'<br> f_meltb = 'm'<br> f_meltl = 'm'<br> f_fresh = 'x'<br> f_fresh_ai = 'm'<br> f_fsalt = 'x'<br> f_fsalt_ai = 'm'<br> f_fbot = 'm'<br> f_fhocn = 'x' <br> f_fhocn_ai = 'm' <br> f_fswthru = 'x' <br> f_fswthru_ai = 'm' <br> f_fsurf_ai = 'x'<br> f_fcondtop_ai = 'x'<br> f_fmeltt_ai = 'x' <br> f_strairx = 'm' <br> f_strairy = 'm' <br> f_strtltx = 'x' <br> f_strtlty = 'x' <br> f_strcorx = 'x' <br> f_strcory = 'x' <br> f_strocnx = 'x' <br> f_strocny = 'x' <br> f_strintx = 'x' <br> f_strinty = 'x'<br> f_taubx = 'x'<br> f_tauby = 'x'<br> f_strength = 'm'<br> f_divu = 'm'<br> f_shear = 'm'<br> f_sig1 = 'm' <br> f_sig2 = 'm' <br> f_dvidtt = 'm' <br> f_dvidtd = 'm' <br> f_daidtt = 'm'<br> f_daidtd = 'm' <br> f_dagedtt = 'm'<br> f_dagedtd = 'm' <br> f_mlt_onset = 'm'<br> f_frz_onset = 'm'<br> f_hisnap = 'd'<br> f_aisnap = 'd'<br> f_trsig = 'm'<br> f_icepresent = 'm'<br> f_iage = 'm'<br> f_FY = 'x'<br> f_aicen = 'x'<br> f_vicen = 'x'<br> f_vsnon = 'x'<br> f_snowfracn = 'x'<br> f_keffn_top = 'x'<br> f_Tinz = 'x'<br> f_Sinz = 'x'<br> f_Tsnz = 'x'<br> f_fsurfn_ai = 'x'<br> f_fcondtopn_ai = 'x'<br> f_fmelttn_ai = 'x'<br> f_flatn_ai = 'x'<br> f_fsensn_ai = 'x'<br> /</p> <p>&icefields_mechred_nml<br> f_alvl = 'm'<br> f_vlvl = 'm'<br> f_ardg = 'm'<br> f_vrdg = 'm'<br> f_dardg1dt = 'x'<br> f_dardg2dt = 'x'<br> f_dvirdgdt = 'x'<br> f_opening = 'x'<br> f_ardgn = 'x'<br> f_vrdgn = 'x'<br> f_dardg1ndt = 'x'<br> f_dardg2ndt = 'x'<br> f_dvirdgndt = 'x'<br> f_krdgn = 'x'<br> f_aparticn = 'x'<br> f_aredistn = 'x'<br> f_vredistn = 'x'<br> f_araftn = 'x'<br> f_vraftn = 'x'<br> /</p> <p>&icefields_pond_nml<br> f_apondn = 'x'<br> f_apeffn = 'x'<br> f_hpondn = 'x'<br> f_apond = 'm'<br> f_hpond = 'm'<br> f_ipond = 'm'<br> f_apeff = 'm'<br> f_apond_ai = 'm'<br> f_hpond_ai = 'm'<br> f_ipond_ai = 'm'<br> f_apeff_ai = 'm'<br> /</p> <p>&icefields_bgc_nml<br> f_faero_atm = 'x'<br> f_faero_ocn = 'x'<br> f_aero = 'x' <br> f_fbio = 'm'<br> f_fbio_ai = 'm'<br> f_zaero = 'x'<br> f_bgc_S = 'm'<br> f_bgc_N = 'm'<br> f_bgc_C = 'x'<br> f_bgc_DOC = 'm'<br> f_bgc_DIC = 'x'<br> f_bgc_chl = 'x'<br> f_bgc_Nit = 'm'<br> f_bgc_Am = 'm'<br> f_bgc_Sil = 'm'<br> f_bgc_DMSPp = 'x'<br> f_bgc_DMSPd = 'x'<br> f_bgc_DMS = 'x'<br> f_bgc_DON = 'x' <br> f_bgc_Fe = 'm' <br> f_bgc_hum = 'm' <br> f_bgc_PON = 'm'<br> f_bgc_ml = 'm'<br> f_upNO = 'm'<br> f_upNH = 'm'<br> f_bTin = 'm'<br> f_bphi = 'm' <br> f_iDi = 'm'<br> f_iki = 'm'<br> f_fbri = 'm' <br> f_hbri = 'm'<br> f_zfswin = 'm'<br> f_bionet = 'm'<br> f_biosnow = 'm'<br> f_grownet = 'm'<br> f_PPnet = 'm'<br> f_algalpeak = 'm'<br> f_zbgc_frac = 'm'<br> /</p> <p>&icefields_drag_nml<br> f_drag = 'x'<br> f_Cdn_atm = 'x'<br> f_Cdn_ocn = 'x'<br> /</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.