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506 results for “assessment model”
OpenFOAM cases of the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers"
<p>This dataset contains the<em> underling data</em> for the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers”, submitted for the consideration and open review in Open Research Europe (ORE).</p> <p><strong>Validation1.tar.xz:</strong> OpenFOAM files and scripts for the simulation of flow and thermal structures in an enclosed environment (Wang and Chen, 2009).</p> <p><em>Wang, Miao; Chen, Qingyan (2009). Assessment of Various Turbulence Models for Transitional Flows in an Enclosed Environment (RP-1271). HVAC&R Research, 15(6), 1099–1119. doi:10.1080/10789669.2009.10390881</em></p> <p><strong>Validation2-kOmegaSSTModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using k-omega SST turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation2-RNGkEpsilonModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using RNG k-epsilon turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation3.tar.xz:</strong> OpenFOAM files and scripts for the simulation of strong natural convection in a model fire room (Murakami et al. 1995).</p> <p><em>Murakami, S., S. Kato, and R. Yoshie. 1995. Measurement of turbulence statistics in a model fire room by LDV. ASHRAE Transactions 101(2):287–301.</em></p> <p><strong>Validation4.tar.xz:</strong> OpenFOAM files and scripts for the simulation of thermal distribution in an open-aisle data center (Abdelmaksoud et al. 2013).</p> <p><em>W.A. Abdelmaksoud, T.Q. Dang, H. Ezzat Khalifa, R.R. Schmidt Improved computational fluid dynamics model for open-aisle air-cooled data center simulations J. Electron. Packag., 135 (2013), pp. 030901-30913</em></p> <p><strong>Results_Validation1.tar.xz:</strong> Simulation results of the Validation case 1.</p> <p><strong>Results_Validation2.tar.xz:</strong> Simulation results of the Validation case 2.</p> <p><strong>Results_Validation3.tar.xz:</strong> Simulation results of the Validation case 3.</p> <p><strong>Results_Validation4.tar.xz:</strong> Simulation results of the Validation case 4.</p> <p><strong>layout.csv:</strong> Input file for the Validation case 4.</p>
Datasets from study: "Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability"
<p>This repository contains the datasets needed to reproduce the figures from manuscript: Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability.</p> <p>In this study, we examine the potential of land surface temperature and vegetation data, which are not routinely assimilated in NWP models, for enhancing temperature forecast skill. We build surrogate models for NWP using Long Short-Term Memory.</p>
Data for "emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves"
<p>This dataset contains codes, data, tables, andd figures (high resolution) related to the following publication: Xiong, W., K. Tanaka, P. Ciais, D. J. A. Johansson, M. Lehtveer (2022) emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves. Submitted to arXiv on 23 December 2022.</p>
Atmospheric Halocarbon Observations at Beromünster, Switzerland, and Bayesian Inverse Modeling to assess Emissions
<p>Atmospheric halocarbon (CFCs, halons, HCFCs, HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub>, HFOs) and carbon monoxide (CO) observations (mole fractions) from the tall tower site at Beromünster, Switzerland (47.2 °N, 8.2 °E, 797 m a.s.l., 212 m a.g.l.), covering the period September 2019 to September 2020. The halocarbon measurements were conducted using a Medusa pre-concentration unit, coupled to gas chromatography (Agilent 6890N) and mass spectrometry (Agilent 5975, GC-MS).</p> <p>For further details see: Miller, B. R., Weiss, R. F., Salameh, P. K., Tanhua, T., Greally, B. R., Mühle, J., and Simmonds, P. G.: Medusa: A Sample Preconcentration and GC/MS Detector System for in Situ Measurements of Atmospheric Trace Halocarbons, Hydrocarbons, and Sulfur Compounds, Anal. Chem., 80, 1536–1545, https://doi.org/10.1021/ac702084k, 2008).</p> <p>The data format follows that used within the AGAGE network (see AGAGE data archive: <a href="http://agage.mit.edu/data/agage-data">http://agage.mit.edu/data/agage-data</a>).</p> <p>Data results for the Bayesian inversion conducted based on the measurement data from Beromünster to assess Swiss halocarbon emissions. Files are provided in netCDF format for the 28 individual substances discussed in (Rust, D. et al., 2022, <em>Swiss halocarbon emissions for 2019 to 2020 assessed from regional atmospheric observations</em>, Atmospheric Chemistry and Physics). Each file contains the a priori and a posteriori emissions as used or calculated in the Bayesian inversion. Data are provided on the grid used in the inversion (irregular longitude/latitude). Metadata are included as netCDF attributes. The netCDF files follow the CF conventions and are readable with any netcdf interface/tool.</p>
Thermophysical properties for the published article "Experiments and modelling on ASDEX Upgrade and WEST in support of tool development for tokamak reactor armour melting assessments"
<p>In order to model the macroscopic metallic melt motion realized in the poor-versus-efficient thermionic emitter leading edge exposures in the ASDEX-Upgrade outer divertor [1], the material library of the MEMENTO melt dynamics code, that previously only concerned tungsten [2] and beryllium [3], had to be extended to iridium and niobium. </p> <p>Reliable experimental data have been analyzed for the latent heats, specific isobaric heat capacity, electrical resistivity, thermal conductivity, mass density, vapor pressure, work function, total hemispherical emissivity and absolute thermoelectric power from the room temperature up to the normal boiling point of iridium and niobium as well as for the surface tension and the dynamic viscosity across the liquid state. Analytical expressions are recommended for the temperature dependence of these thermophysical properties, which involve high temperature extrapolations given the absence of extended liquid iridium and liquid niobium measurements. The analytical expressions, the details of their construction and the main references are included in the accompanying pdf.</p> <p>[1] S. Ratynskaia, K. Paschalidis, P. Tolias, K. Krieger, Y. Corre, M. Balden, M. Faitsch, A. Grosjean, Q. Tichit, R.A. Pitts, the ASDEX-Upgrade team, the WEST team and the Eurofusion MST1 team, "Experiments and modelling on ASDEX Upgrade and WEST in support of tool development for tokamak reactor armour melting assessments", Nucl. Mater. Energy 33 (2022) 101303.<br> [2] P. Tolias, "Analytical expressions for thermophysical properties of solid and liquid tungsten relevant for fusion applications", Nucl. Mater. Energy 13 (2017) 42.<br> [3] P. Tolias, "Analytical expressions for thermophysical properties of solid and liquid beryllium relevant for fusion applications", Nucl. Mater. Energy 31 (2022) 101195.</p>
Life cycle inventories for the article: Circular Battery Production in the EU: Insights from integrating Life Cycle Assessment into System Dynamics Modeling on Recycled Content and Environmental Impacts
<p>This repository provides the unregionalized life cycle inventories to the paper "<span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>".</p> <h2>Contents</h2> <p>The repository is split into 2 parts and comprises the following files:</p> <p><strong>01_production: </strong>contains the necessary life cycle inventories for battery production.</p> <ul> <li><strong>01_primary</strong>: contains the life cycle inventories for battery production from primary materials.</li> <li><strong>02_secondary</strong>: contains the life cycle inventories for battery production from secondary materials.</li> <li><strong>03_active_material</strong>: contains the life cycle inventories for the active battery materials from primary materials.</li> <li><strong>04_active_material</strong>: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul> <p> </p> <p><strong>02_recycling: </strong>contains the necessary inventories for battery recycling.</p> <ul> <li><strong>01_process</strong>: contains the life cycle inventories for battery recycling.</li> <li><strong>02_intermediate</strong>: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li><strong>03_output</strong>: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> <p>These files allow to reproduce the results of our study. Each file contains the life cycle inventory of one distinct battery capacity (20, 45, 68, 85, 95, 100 kWh) with a specific cell chemistry (LFP, NCA, NMC333, NMC532, NMC622, NMC811, NMC955) for battery production (based on Knehr et al. 2022) or for battery recycling (based on Blömeke et al. 2023).</p> <h2>Related publication</h2> <p>More details on the scientific context is provided in the publication itself:</p> <p><span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a></p> <h2>Funding</h2> <p>This publication (Raphael Ginster and Steffen Blömeke) was created within the Research Training Group CircularLIB, supported by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the Volkswagen Foundation (MWK | ZN3678).</p> <p>The publication on which this dataset is based were funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) under the grant numbers 03XP0302A (Christian Scheller) and 03XP0331A (Jan-Linus Popien). The authors are responsible for the contents of this publication.</p>
Logical model for Model checking to assess T-helper cell plasticity
<p>Logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g. stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity.</p> <p>In <a href="https://dx.doi.org/10.3389/fbioe.2014.00086">[1]</a>, it is presented an extended version of a published logical model of T-helper cell differentiation and plasticity, which accounts for novel cellular subtypes. The model encompasses 20 signaling pathways, a dozen of transcription factors, and about 30 cytokines, amounting to 101 components in total.</p> <p>Computational methods recently developed to efficiently analyze large models <a href="http://ginsim.org/node/185#ref1">[1]</a> are first used to study static properties of the model (i.e. stables states). Symbolic model checking is then applied to get further insights into reachability properties between Th canonical subtypes upon changes of specific prototypic environmental cues.</p> <p>The model reproduces novel reported Th subtypes (Tfh, Th9, Th22) and predicts additional Th hybrid subtypes in term of stables states. Using the model checker NuSMV-ARCTL, an abstract view of the dynamics, called reprograming graph, is produced providing a global and synthetic view of Th plasticity. The model is consistent with experimental data showing the polarization of naïve Th cells into the canonical Th subtypes. The model further predicts substancial plasticity of Th subtypes depending on the signalling environment.</p>
Datasets of the proposed models for carcinogenicity assessment based on slope factor.
<p>Datasets of the classification and regression models for carcinogenicity proposed in the paper "QSAR models for the human carcinogenicity: an assessment based on oral and inhalation slope factors" by Cosimo Toma<sup>1,2</sup>, Alberto Manganaro<sup>3</sup>, Giuseppa Raitano<sup>1</sup>, Marco Marzo<sup>1</sup>, Domenico Gadaleta<sup>1</sup>, Diego Baderna<sup>1</sup>*, Alessandra Roncaglioni<sup>1</sup>, Nynke Kramer<sup>2</sup>, Emilio Benfenati<sup>1</sup>*</p> <p><sup>1</sup> Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy</p> <p><sup>2</sup> Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands</p> <p><sup>3</sup> Kode Chemoinformatics s.r.l., Pisa, Italy</p> <p><strong>*</strong> Correspondence: diego.baderna@marionegri.it</p> <p>Paper is now published on Molecules by MDPI and it is available here DOI:10.3390/molecules26010127</p> <p>S5 dataset for models based on Oral Slope Factor and S5 dataset for models based on Inhalation Slope Factor.</p> <p><strong>Models were developed under the framework of LIFE-VERMEER project (LIFE 16ENV/IT7000167).</strong></p>
C2D2: An Open-Source, Pan-European, Harmonised Crop Development Database for Use in Regulatory Pesticide Exposure Modelling and Risk Assessment.
<p>There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonised, pan-European, CropLife Europe Crop Development Database, C2D2, that is fully aligned with this regulatory requirement utilising efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU) 1107/2009. Evaluation of C2D2 against an independent dataset showed good agreement for equivalent time periods, crop growth stages and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large dataset compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being under-represented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modelling framework (using the AppDate tool) are often indicated over some/many growth stages suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons licence to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection.</p>
A CO2 valorization plant to produce light hydrocarbons: kinetic model, process design and life cycle assessment
<p>Supplementary material: Reaction indexes, Conservation equations, boundary conditions and used coefficients. Additional experimental results, Experimental data fitting, Stream properties and composition of the CO2 plant, Life Cycle Assessment indicators, assumptions and data input </p>
An assessment of the (anti)androgenic properties of hexachloronaphthalene (HxCN) using a model of immature male rats (Hershberger Bioassay)
<p>The persistent organic pollutants (POPs) include polychlorinated naphthalenes (PCNs); of these, the most toxic, abundant and found in human tissues are the hexachloronaphthalenes (HxCNs). The aim of this study was to evaluate the (anti)androgenic action of HxCN using the Hershberger Bioassay (OECD 441). Castrated male Wistar rats were exposed per os to HxCN at daily doses ranging from 0.3-3.0 mg*kg b.w.-1 for 10 days. Testosterone propionate (TP) was used as the reference androgen, and flutamide (FLU) as the reference antiandrogen. Five assessor sex tissues (ASTs) were weighed: ventral prostate, seminal vesicles, levator ani-bulbocavernosus muscle (LABC), glans penis and Cowper gland. In addition to determining the absolute weight of the ASTs, a number of other tests were performed on serum hormone levels (testosterone [T], triiodothyronine 99 [T3], thyroxine [T4], LH and FSH) and the histopathology of the ASTs. </p>
Assessing evapotranspiration realism in rainfall-runoff models using evapotranspiration signatures
<p> </p> <p>This dataset contains simulated actual evapotranspiration (AET) data derived from five conceptual hydrological models and input data applied to 14 catchments in Australia. The data spans the period from 1980 to 2022. The five models included in this dataset are:</p> <ul> <li>SIMHYD</li> <li>IHACRES</li> <li>VIC</li> <li>SACRAMENTO</li> <li>GR4J</li> </ul> <p>These models were implemented in version 2.1 of the MaRRMoT framework.</p> <p>Here, the models were calibrated using two different approaches:</p> <ol> <li>Calibration based on discharge data only. (Folder: ModelCalQ_Data)</li> <li>Calibration using a composite objective function that incorporates both discharge and AET data. (Folder: ModelCalQnAET_Data)</li> </ol> <p>Example script is also included in each model folder, such as ‘<em>Run_Simhyd_MaRRMoT_Cal_Spartan.m’</em>, to facilitate the running of the models and understanding of the calibration process.</p> <p> </p>
Deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete (krkCMd)
<p>This is a deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete [1]. It relates to an experimental study on the autogenous self-healing of high-strength concrete [2]. Concrete specimens were prepared, matured, cracked, and exposed to self-healing. High-resolution scanning of the specimen surface and scale-invariant image processing were performed, multiple grid lines crossing cracks were established, and brightness degree profiles were extracted. Then, manual measurements of the crack widths were obtained by an operator.</p> <p>The dataset comprises 19,098 records of brightness profiles, reference crack width measurements, and benchmark measurements by deep learning and analytic models. The source images, which were stacked and marked with grid lines, are provided. The considerable number of brightness profiles coupled with manual reference measurements make the dataset well suited for developing an image-based deep learning models or analytic algorithms for assessing crack widths in concrete.</p> <p>The deposited data includes:</p> <ul> <li>krkCMd_table.csv: delimited, comma-separated text file containing a dataset of 19,098 crack brightness degree profiles, reference crack width measurements by operator, and benchmark measurements by a deep CNN metasensor and by an analytic edge detector.</li> <li>krkCMd_images.zip: archive containing source image files in folders by test series: <br>- stacked images of cracks in subsequent stages of self-healing (.tif files),<br>- zip archives assigned to image stacks and containing sets of ImageJ data files .roi,<br>- ImageJ .roi files specifying the locations of grid lines in the images.</li> <li>krkCMd_scripts.zip: archive containing custom scripts supporting image preprocessing and computing benchmark variables.</li> </ul> <p><span>For details please see the <a href="https://doi.org/10.1038/s41597-025-04485-z">data descriptor [1]</a>. When referring to the data in publications please cite [1].</span></p> <p>[1] Jakubowski, J., Tomczak, K. Dataset for developing deep learning models to assess crack width and self-healing progress in concrete. <em>Sci Data</em> <strong>12</strong>, 165 (2025). https://doi.org/10.1038/s41597-025-04485-z</p> <p>[2] Jakubowski, J. & Tomczak, K. Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete. <em>Constr. Build. Mater.</em> <strong>422</strong>, 135768 (2024). https://doi.org/10.1016/j.conbuildmat.2024.135768</p>
Data for Mellado et al. The impacts of marking on bats: mark-recapture models for assessing injury rates and tag loss. Journal of Mammalogy. 103:100-110. DOI:10.1093/jmammal/gyab153
<p>Data sets used in Mellado et al. The impacts of marking on bats: mark-recapture models for assessing injury rates and tag loss. Journal of Mammalogy. 103:100-110. (https://doi.org/10.1093/jmammal/gyab153)</p> <p>File Descriptions:</p> <p>CapHistTagLoss.txt - Capture histories for <em>Carollia perspicillata</em> identifying if individual was captured with both tags (B), arm bands (A), collar (C), not captured (0) or not monitored (dot). Covariates included are Sex, Forearm Length and Scaled Mass Index.<br> CaptHistTagInj.txt - Capture histories for <em>Carollia perspicillata</em> identifying if individual was captured with no lesions from arm band (A), minor injury (I), major injury (M), not captured (0) or not monitored (dot). Covariates included are Sex, Forearm Length and Scaled Mass Index.<br> LesionOccurrence.txt - Censored time-to-event data for survival analysis. Recorded events were the occurrence of lesions of any type due to arm bands.<br> RingCondition.txt - Censored time-to-event data for survival analysis. Recorded events were the occurrence of damage to arm bands.<br> SMI.txt - Longitudinal data for individual <em>Carollia perspicillata</em> Scaled Mass Index, identifying individual records, the occurrence of lesions, sex, month, year</p> <p> </p>
Supporting model output for article "Assessing the potential impact of river chemistry on Arctic coastal production"
<p>The following is a summary of processed model output data from a series of HiLAT model runs.<br> A description of the model runs and visualization of model output and analysis can be found in the<br> accompanying manuscript.</p> <p><br> Gibson G. A., Elliott, S., Piliouras, A. Clement Kinney, J., Jeffery, N. (2022) Assessing the potential<br> impact of river nitrate on coastal production in the Arctic. Frontiers in Marine Science: Coastal Ocean<br> Processes.</p> <p><br> This work was supported by the Regional and Global Model Analysis (RGMA) program of the US<br> Department of Energy’s Office of Science as a contribution to the HiLAT project. Additional support for<br> this project was provided by the National Science Foundation, under award #173886.<br> </p> <p><strong>River Nutrient Forcing</strong></p> <p>The experiments involved modifying the nutrient concentrations in the river nutrient forcing files.<br> The river nutrient files are specified during model setup. For use in the HiLAT model, GNEWS annual<br> river nutrient inputs were partitioned into twelve monthly forcing values. The nearest ocean grid point<br> to each of the GNEWS river mouth locations was identified and then, as with the runoff, the nutrient<br> inputs for each river basin were spatially mapped to surface ocean model grid cells, which are 10 meters<br> thick, such that the spatial pattern of river nutrient dispersion follows river water inputs to the oceans.</p> <p>The experiments were:<br> i) The baseline model simulation: The 12 monthly values for each grid cell were constant in time.<br> <strong>river_nutrients_GNEWS2000_gx1v6.nc</strong><br> ii) an experiment in which baseline Arctic River Nitrogen (NO 3 and NH 4 ) concentrations were doubled.<br> <strong>river_nutrients_GNEWS2000_gx1v6_x2Arctic.nc</strong><br> iii) an experiment in which baseline Arctic River Nitrogen (DON and DIN) concentrations were scaled to<br> the river volume discharge contained in <strong>runoff.daitren.iaf.20120419.nc</strong><br> <strong>river_nutrients_GNEWS2000_gx1v6_scaled_climatology.nc</strong><br> iv) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by two months.<br> <strong>river_nutrients_GNEWS2000_gx1v6_shifted2m_climatology.nc</strong><br> v) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by a month and<br> doubled in concentration.<br> <strong>river_nutrients_GNEWS2000_gx1v6_shifted_climatology_x2.nc</strong></p> <p>River nutrient fluxes are in units of nmol/cm2/s<br> Only concentrations within the domain TLONG>=60 &TLONG <=340 & TLAT >=60 were modified in<br> concentration/timing.</p> <p><br> Variables of interest:<br> din_riv_flux: dissolved inorganic nitrogen river flux<br> don_riv_flux: dissolved organic nitrogen river flux</p> <p>Each of the experiments is described in detail in Gibson et al (2022).</p> <p>---------------------------------------------<br> There are multiple versions of most output file types, corresponding to the river nutrient experiments that<br> were conducted.</p> <p><br> Many variables in the output files are <strong>regional averages</strong> where model regions are indicated by a number<br> *note - for aesthetics, the numbering used in the model output files differs slightly from the numbering<br> used in the accompanying manuscript. The numbers assigned in the analysis files aligns with the numbers<br> assigned to regions within the region mask provided in the grid file.</p> <p><strong>Grid File/region masks</strong><br> gx1v6_polar_mask_coast.5.22.20c.nc This file is an updated version of the standard grid file. It has been<br> updated to include the addition of a coastal Arctic region variable ‘Arctic_Coast_Mask’ which indicates<br> which grid cells are in the coastal regions used in the analysis and the Arctic_Region variable which<br> indicates which grid cells are in the broader regions.</p> <p><br> Variables contained in this file are:<br> Arctic_Coast_Mask: contains values 0-9 indicating which (if any) coastal region a grid cell is in<br> Arctic_Region Mask: contains values 0-11 indicating which (if any) region a grid cell is in</p> <p><br> TLAT: latitude of grid cell<br> TLONG: longitude of grid cell<br> TAREA: Area of grid cell<br> HT: Bathymetry of grid cell</p> <p> </p><table> <tbody> <tr> <td> </td> <td> <p><strong>Arctic_Region (seas)</strong></p> </td> <td> <p><strong>Arctic_Coast_Mask </strong><strong>(coast)</strong></p> </td> </tr> <tr> <td> <p><strong>Bering Sea</strong></p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p><strong>Chukchi Sea</strong></p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p><strong>East Siberian Sea</strong></p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p><strong>Laptev Sea</strong></p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p><strong>Beaufort Sea</strong></p> </td> <td> <p>5</p> </td> <td> <p>5</p> </td> </tr> <tr> <td> <p><strong>Barents Sea</strong></p> </td> <td> <p>6</p> </td> <td> <p>6</p> </td> </tr> <tr> <td> <p><strong>Canadian Basin</strong></p> </td> <td> <p>7</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Eurasian Basin</strong></p> </td> <td> <p>8</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Nordic Seas</strong></p> </td> <td> <p>9</p> </td> <td> <p>7</p> </td> </tr> <tr> <td> <p><strong>Labrador Sea</strong></p> </td> <td> <p>10</p> </td> <td> <p>8</p> </td> </tr> <tr> <td> <p><strong>Kara Sea</strong></p> </td> <td> <p>11</p> </td> <td> <p>9</p> </td> </tr> </tbody> </table> --------------<p></p> <p> </p><p>Model outputs that were analyzed in the manuscript are contained in three different kinds of output file.<br> For each file type a file exists for each river nutrient experiment.</p> <p></p> <p>The following series of files contains variables related to the particulate organic carbon flux to the<br> sediment, demineralization and remineralization rates.<br> bgc_T62_gx1GIF_nut-riv-BASELINE-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv_2XDC-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-seas_region-sed-137-157.nc</p> <p><br> Variables contained in these are:<br> POCTOSED_AVG* : Particulate organic carbon flux to sediment<br> PONTOSED_AVG* : Particulate organic nitrogen flux to sediment<br> SEDDENITRIF_AVG* : Sediment denitrification rate<br> POC_PROD_AVG* : Production of Particulate organic carbon<br> POC_FLUX_AVG* : Particulate organic carbon flux into layer/cell<br> DON_REMIN_AVG* : Dissolved Organic Nitrogen remineralization rate<br> DOC_REMIN_AVG* : Dissolved Organic Carbon remineralization rate<br> DIAT_N_LIM_AVG* : Diatom nitrogen limitation<br> DIAT_N_LIM_AVG* : Diatom nitrogen limitation<br> DIAT_P_LIM_AVG* : Diatom phosphorous limitation<br> DIAT_FE_LIM_AVG* : Diatom iron limitation<br> DIAT_LIGHT_LIM_AVG*: Diatom light limitation<br> SP_N_LIM_AVG* : Small phytoplankton nitrogen limitation<br> SP_P_LIM_AVG* : Small phytoplankton phosphorous limitation<br> SP_FE_LIM_AVG* : Small phytoplankton iron limitation<br> SP_LIGHT_LIM_AVG* : Small phytoplankton light limitation<br> Where * represents the coastal region number.<br> ----------------------------------------</p> <p><br> The following series of files contains primary production for the small and large phytoplankton groups<br> and the zooplankton biomass.</p> <p>Coastal regional averages – based on regions marked in the Arctic_Coast_Mask variable<br> bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_riv-BASELINE-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2XDC-region-prod-ACM-137-157.nc</p> <p>Regional seas averages – based on regions marked in the Arctic_Region variable<br> bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_riv-BASELINE-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-seas-137-157.nc</p> <p>Variables contained in these files are:<br> TAREA_SUM* – total area of the region<br> PPSP_REGSUM* – sum of primary production by small phytoplankton in a region<br> PPDIAT_REGSUM*– sum of primary production by diatoms in a region<br> ZOOC_AVG*– sum of zooplankton biomass in a region</p> <p>----------------------------------------<br> The following series of files contains ice associated variables and mixed layer nutrients</p> <p>bgc_T62_gx1GIF_nut_riv-2xArcticN-ice_coastal-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2XDC-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-ice_seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_seas-137-157.nc</p> <p>HI_REGAVG* : Regional averaged ice depth<br> HS_REGAVG* : Regional averaged snow depth<br> ICEAREA_REGSUM* : Regional sum ice area<br> ICEVOL_REGSUM*: Regional sum volume area<br> MLAM_REGAVG* : Regional average ammonium concentration in mixed layer<br> MLNIT_REGAVG* : Regional average nitrate concentration in mixed layer<br> PP_REGAVG* : Regional average primary production (ice algae)<br> PP_REGSUM* : Regional total primary production (ice algae)<br> TAREA_SUM* : Total area of region<br> TIME : time</p>
Data, plotting scripts, and figures for "Assessing diffusion model impacts on enstrophy and flame structure in lean premixed flames"
<p>This repository contains the data, plotting scripts, and figures associated with the paper "Assessing diffusion model impacts on enstrophy and flame structure in lean premixed flames" by Aaron J. Fillo, Peter E. Hamlington, and Kyle E. Niemeyer.</p> <p>See the README file for additional details.</p>
Data and code for: A conceptual model-based sediment connectivity assessment for patchy agricultural catchments
<p>Authors: Pedro V G Batista, Peter Fiener, Simon Scheper, Christine Alewell</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Abstract</p> <p>The accelerated sediment supply from agricultural soils to riverine and lacustrine environments leads to negative off-site consequences. In particular, the sediment connectivity from agricultural land to surface waters is strongly affected by landscape patchiness and the linear structures that separate field parcels (e.g. roads, tracks, hedges, and grass buffer strips). Understanding the interactions between these structures and sediment transfer is therefore crucial for minimising off-site erosion impacts. Although soil erosion models can be used to understand lateral sediment transport patterns, model-based connectivity assessments are hindered by the uncertainty in model structures and input data. In specific, the representation of linear landscape features in numerical soil redistribution models is often compromised by the spatial resolution of the input data and the quality of the process descriptions. Here we adapted the WaTEM/SEDEM model using high resolution spatial data (2 m x 2 m) to analyse the sediment connectivity in a very patchy mesoscale catchment (73 km<sup>2</sup>) of the Swiss Plateau. We used a global sensitivity analysis to explore model structural assumptions about how linear landscape features (dis)connect the sediment cascade, which allowed us to investigate the uncertainty in the model structure. Furthermore, we compared model simulations of hillslope sediment yields from five sub-catchments to tributary sediment loads, which were calculated with long-term water discharge and suspended sediment measurements. The sensitivity analysis revealed that the assumptions about how the road network (dis)connects the sediment transfer from field blocks to water courses had a much higher impact on modelled sediment yields than the uncertainty in model parameters. Moreover, model simulations showed a higher agreement with tributary sediment loads when the road network was assumed to directly connect sediments from hillslopes to water courses. Our results ultimately illustrate how a high-density road network combined with an effective drainage system increases sediment connectivity from hillslopes to surface waters in agricultural landscapes. This further highlights the importance of considering linear landscape features and model structural uncertainty in soil erosion and sediment connectivity research.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Metainformation</p> <p>This dataset includes:</p> <p>1 - The input data used for running the WaTEM/SEDEM model in the Baldegg catchment.</p> <p>2 - The discharge and sediment concentration data used for producing the sediment rating curves for the tributaries of the Lake Baldegg.</p> <p>3 - The model and sediment rating curve output data.</p> <p>4 - The R scripts for running the WaTEM/SEDEM model in the Baldegg catchment, the code for producing the sediment rating curves, and the code for summarising and analysing the model output data.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The sediment concentration and water discharge data were supplied by Robert Lovas, from the Department of Environment and Energy of the Canton of Lucerne.</p> <p>The model input data were adapted from freely available ©swisstopo geodata products:</p> <p>Swisstopo. SwissALTI3D. Das hoch aufgelöste Terrainmodell der Schweiz, 2014.</p> <p>Swisstopo. Swiss Map Vector 25 Beta, Das digitale Landschaftsmodell der Schweiz. 2018.</p> <p>Swisstopo. SwissTLM3D. Das grossmassstäbliche Topografische Landschaftsmodell der Schweiz, 2020.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further information we refer to our preprint: https://doi.org/10.5194/hess-2021-231</p> <p> </p> <p> </p>
Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling: Datasets.
<p>Datasets related to the publication [1].<br> Including:</p> <ul> <li>KRAS G12X mutations derived from COSMIC v.79 [http://cancer.sanger.ac.uk/cosmic/] (KRAS_G12X_mut_COSMICv79..xlsx)</li> <li>RMSFs (300-2000ns) of GDP-systems (300_2000rmsf_GDP_systems_RAW_AVG_SE.xlsx)</li> <li>RMSFs (300-2000ns) of GTP-systems (300_2000RMSF_GTP_systems_RAW_AVG_SE.xlsx)</li> <li>PyInteraph analysis data for salt-bridges and hydrophobic clusters (.dat files for each system in the PyInteraph_data.zip-file)</li> <li>Backbone trajectories for each system (residues 4-164; frames for every 1ns). Last number (e.g. _1) refers to the replica of the simulated system.</li> <li>backbone_4-164.gro/.pdb/.tpr -files (resid 4-164) </li> </ul> <p><br> [1] Pantsar T et al. Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling. <em>PLoS Comput Biol Submitted</em> (2018)</p>
Replication package for "An Empirical Assessment of Best-Answer Prediction Models in Technical Q&A Sites" (EMSE 2018)
<p>Replication package for the paper:</p> <blockquote> <p>F. Calefato, F. Lanubile, and N. Novielli (2018) “<a href="http://collab.di.uniba.it/fabio/wp-content/uploads/sites/5/2018/07/EMSE-D-17-00159_R3.compressed.pdf">An Empirical Assessment of Best-Answer Prediction Models in Technical Q&A Sites</a>.” Empirical Software Engineering Journal, DOI: <a href="http://dx.doi.org/10.1007/s10664-018-9642-5">10.1007/s10664-018-9642-5</a>.</p> </blockquote>
MAR-M-247 creep assessment through a modified theta projection model - Figures 2 and 5
<p>These two programs provide a way to rebuild the MAR-M-247 creep data presented in the paper:</p> <p>G. Maggiani, M.J. Roy, S. Colantoni, P.J. Withers, MAR-M-247 creep assessment through a modified theta projection model, Materialia, Volume 7, 2019, 100392, ISSN 2589-1529, https://doi.org/10.1016/j.mtla.2019.100392. http://www.sciencedirect.com/science/article/pii/S2589152919301887)<br> </p> <p>In Paper_Figure_2.m two coefficients of the paper itself are corrected and a comparison with what written in the paper and the corrected value is provided. One typo error for theta 1 at 982°C and 140 MPa where 6.9 must be 1.9. The other is for 1038°C 50 MPa theta4. In the paper it is written e^-11 while it actually should have been e^-10.</p> <p>Paper_Figure_5.m more decimal values are provided for the coefficients a, b, c and d that are used to rebuild the theta values.</p> <p> </p> <p> </p>
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Annotated Behaviour and Observability Dataset (ABODe)
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