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855 results for “model system”
Lagrangian drifter output in the Southeast Indian Ocean using the Connectivity Modelling System output forced with TROPAC01
<p>This dataset contains Lagrangian drifter trajectories from the Connectivity Modelling System (CMS) in the Southeast Indian Ocean. CMS was forced with ocean velocity fields from TROPAC01 and this experiment was focused on sources of the Leeuwin Current. TROPAC01 is a high-resolution ocean general circulation model, developed by the European Drakkar cooperation [Barnier et al., 2007] it is based on the NEMO [v3.2 Madec, 2008] code. Specifically, it is a 1/10 horizontal resolution model of the tropical Indo- Pacific region (spanning the area from 73°E - 63°W to 49°S - 31°N), nested within a half-degree global ocean/ sea-ice model. More information on the model configuration used for this experiment can be found in [van Sebille et al., 2014]. Using the velocity fields from TROPAC01 we then use the Connectivity Modelling System (CMS) v1.1 [Paris et al., 2013] to integrate the virtual particles in three-dimensional time-evolving flow.</p> <p>Version v1.0 of this dataset includes ascii raw model output of CMS trajectories and the forcing file (seed file) that enables a user to calculate absolute time of a particle's location. Variables are: particle_number, time, longitude, latitude, depth, exit_code.</p> <p>These experiments were executed by Christopher Bull of the ARC Centre of Excellence for Climate System Science (ARCCSS) research program "Mechanisms and attribution of past and future ocean circulation change", as part of Christopher's PhD candidature.</p> <p> </p> <p>References:</p> <p> Code and documentation for the CMS is available at:</p> <p> https://github.com/beatrixparis/connectivity-modeling-system</p> <p>Claire B. Paris, Judith Helgers, Erik van Sebille, Ashwanth Srinivasan, 2013.<br> Connectivity Modeling System: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean,<br> Environmental Modelling & Software, Volume 42, 2013, Pages 47-54, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2012.12.006.</p> <p>van Sebille, E., Sprintall, J., Schwarzkopf, F. U., Gupta, A. S., Santoso, A., England, M. H., Biastoch, A., and Böning, C. W. (2014), Pacific-to-Indian Ocean connectivity: Tasman leakage, Indonesian Throughflow, and the role of ENSO, <em>J. Geophys. Res. Oceans</em>, 119, 1365– 1382, doi:<a href="https://doi.org/10.1002/2013JC009525">10.1002/2013JC009525</a>.</p>
An Assessment of Nonhydrostatic and Hydrostatic Dynamical Cores at Seasonal Time Scales in the Energy Exascale Earth System Model (E3SMv1)
<p>This is the companion data for the manuscript of the same title, submitted to Journal of Advances in Modeling Earth Systems on 09/03/2021. </p> <p><strong>summer: </strong>this folder contains part of the model outputs I ran on NERSC Cori in 2020-2021 corresponding to the summer simulations in the manuscript.</p> <p><strong>winter: </strong>this folder contains part of the outputs I ran on NERSC Cori in 2020-2021 corresponding to the winter simulations in the manuscript.</p> <p><strong>ne256: </strong>this folder contains part of the outputs I ran on NERSC Cori in 2021 corresponding to the ne256 simulations in the manuscript.</p> <p><strong>bubble</strong>: this folder includes the namelists used in the rising bubble experiments.</p> <p><strong>script: </strong>this folder includes an example of a script to generate the realistic SCREAM simulation </p> <p> </p> <p>Unfortunately, model outputs are too large (~ 30 TB). Therefore, I only provide mean data, used directly to generate figures, in this repository. All model output are archived on tape at NERSC. </p> <p>For more details, refer to the manuscript, or contact me (wrliu@ucdavis.edu). </p>
Dataset part two to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"
<p>Additional dataset to dataset part one (doi: 10.5281/zenodo.4719596) to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"</p>
Data for the Eastern African power pool's energy systems model, developed in OSeMOSYS
<p>This repository consists of the following datasets</p> <p>1. EAPP_reference scenario_datafile.DD- This dataset is a model file that needs to be used with the code available in this <a href="https://github.com/KTH-dESA/OSeMOSYS/blob/master/OSeMOSYS_GNU_MathProg/osemosys_short.txt">GitHub</a> link. This data file (in concurrence with the OSeMOSYS code) can be used to create a linear programming file (LP file) to be solved using any mathematical optimisation solver like GLPSOL/C-PLEX/GUROBI/CBC.</p> <p>2. Main article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the main article.</p> <p>3. Supplementary article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the supplementary article.</p>
An Empirical Study of Textual and Structural Statistical Models for Maven-Based Build Systems
<p>Replication package for the manuscript entitled "An Empirical Study of Textual and Structural Statistical Models for Maven-Based Build Systems". See README.md file for details about how to use the package.</p>
Shortwave radiation budget under Arctic Sea ice in Earth System models
<p>Contains data and scripts of the study " Improving the representation of shortwave radiation budget under Arctic Sea ice in Earth System models using observations", submitted to Journal of Geophysical Research - Oceans. </p>
Thermo-hydro-chemical simulation of mid-ocean ridge hydrothermal systems: Static 2D models and effects of paleo-seawater chemistry
<p>DePaolo et al. Gcubed 2022 data files</p> <p><strong>Thermo-hydro-chemical simulation of mid-ocean ridge hydrothermal systems: </strong></p> <p><strong>Static 2D models and effects of paleo-seawater chemistry </strong></p> <p> </p> <p>In this folder are input and output files for v3.68 of TOUGHREACT that contain all of the files illustrated in the manuscript plus many more. Also included is v3 TOUGHREACT reference manual, which gives more information on all of the input and output files.</p> <p>In each folder there are a sequence of run folders, each containing input files (flow.inp, solute.inp, chemical.inp, MESH, GENER, plus a thermodynamic database with filename like “tkslth06acp3isi9.dat.” Also included are raw tecplot files (flowvector.tec, flowdata.tec, rct_sfarea.tec, rctn_rate.tec, min_SI.tec, minerals.tec, aqconc.tec) and other output files (all “.out” files). In some cases the .tec files, which are combined files with output for both fractures and matrix, have been separated into separate fracture and matrix files with names like “flowvector_frc.tec,” “flowvector_mtx.tec,” aqconc_frc.tec,” “aqconc_mtx.tec” to allow plotting of fracture and matrix properties separately.</p> <p>Some folders also contain .tiff or .png files that are 2D color contour plots as shown in the manuscript. All of these plots were made with Paraview (<a href="https://www.paraview.org/">https://www.paraview.org</a>) which is open-source.</p> <p>Each folder labeled like “Modern SW fastcpx Sr8…” contains several subfolders each labeled with the model year at which the run ends, like 2000, 2600, 2700, 2800, … which correspond to the warmup steps described in the manuscript:</p> <p>The typical procedure used to achieve the results reported here is (with some minor variations):</p> <ol> <li>Run the simulation for 2000 model years with 50% of the final heating from below and minimal chemical reactions. RSA for primary minerals in both matrix and fractures are set to 10<sup>-6</sup> cm<sup>2</sup>/g and 2 x 10<sup>-6</sup>cm<sup>2</sup>/g for secondary minerals, which yields chemical reaction rates about 500 times slower than for a more realistic system.</li> <li>Run for an additional 600 model years with the full heating from below and RSA’s at 10<sup>-6</sup> cm<sup>2</sup>/g and 2 x 10<sup>-6</sup> cm<sup>2</sup>/g. This step yields a steady state temperature and flow field with the full heating from below. Less time is needed than for the first phase because the fluid flow velocities are higher with higher heating rates.</li> <li>Run an additional 100 years; RSA’s increased to 10<sup>-5</sup> cm<sup>2</sup>/g and 2 x 10<sup>-5</sup> cm<sup>2</sup>/g</li> <li>Run 100 years; RSA’s at 10<sup>-4</sup> cm<sup>2</sup>/g and 2 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 100 years; RSA’s at 2 x 10<sup>-4</sup> cm<sup>2</sup>/g and 4 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 50 years; RSA’s at 3 x 10<sup>-4</sup> cm<sup>2</sup>/g and 5 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 50 years; RSA’s at 4 x 10<sup>-4</sup> cm<sup>2</sup>/g and 8 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 100 additional years*</li> </ol> <p>After step 8 the system has been running for 3100 model years, but only 150 years with full reactions, which is long enough to get close to quasi-steady state fluid chemistry (there is no true steady state for chemistry because the rock mineralogy is changing with time). For each of the steps marked with an asterisk, an alternative procedure is to use high RSA’s for fracture minerals, up to 50 times higher. </p> <p>In some folders there are additional subfolders extending in model time up to 3400 years.</p>
Redefining East African Rift System kinematics: Supplementary Model Files
<p>Supplementary kinematic model input and output files for the Geology publication Redefining East African Rift System kinematics. Model input files are for the open-source code TDEFNODE. </p>
A Scalable Data Management System Data Model facilitating the integration of any inspection system and the automated data digitalization process.
<p>In the context of the EU-funded project PILOTING (No. 871542), a scalable Data Management System (DMS) was deployed and facilitated the easy integration of nine different robotic systems and various payloads and the storing of all the data observations produced during the inspections. In particular, a three-phase methodology was adopted for the creation of the DMS Data Model (DMS-DM) in order to define the architectural design of the DMS. <br> The first phase was to semantically define the entities of the PILOTING ecosystem, identifying assets and activities that were important to the data providers, and including them in the data model design. The second phase was to identify relationships between the entities, focusing on information that must pre-exist for entities to be semantically accurate, hierarchies between entities, especially physical assets, and the data needed to form the inspection plan. For the third phase, the information collected by the previous two stages of the data model construction was used to create the overall data model, taking into consideration the possible integration with third parties and the needs of the I&M Visualization portal. <br> The constructed document is attempted to present the designed Entity-Relationship-Diagram (ERD) of the DMS-DM. Additional definitions of the existing entities and the relations between them are also depicted. </p>
Chemistry-weather interacted model system GRAPES_Meso5.1/CUACE CW V1.0
<p><span>The Chinese Meteorology Administration chemistry model CUACE is online integrated into the mesoscale operational weather prediction (NWP) model (GRAPES_Meso5.1) and aerosol-cloud-radiation interaction is achieved to establish the first version (V1) of chemistry-weather (CW) interacted model GRAPES-Meso5.1/CUACE CW V1. The most polluted winter 2016-2017 is selected to study the meteorology impacts on haze/fog prediction, the impact of aerosol-radiation, aerosol-cloud, and CW interaction (ARI, ACI, CWI) on haze/fog prediction, and NWP. Single way model without CWI displays reasonable PM<sub>2.5</sub> and visibility prediction in general. However, modeled PM<sub>2.5</sub> peaks are underestimated and visibility valleys are overestimated</span> <span>during haze/fog pollution, the underestimation of relative humidity (RH) contributes major to this misestimation; CWI model cut the negative errors of PM<sub>2.5</sub> peaks and the positive errors of visibility valleys. The improvement of 5km and 3km low visibility by CWI during severe haze/fog period is more obvious than that of 10 km, which just compensates for the largest deficiency in low visibility prediction related to severe haze/fog by single way model; The NWP including sea level pressures, relative humidity(RH), temperature, wind speed are also improved by CWI from surface to upper troposphere; ARI contributes larger to the predicted PM<sub>2.5</sub>, visibility and NWP improvement than that of ACI, their relative contributions varies with model vertical height and the overlapping condition of cloud and aerosols. Due to the joint contribution of RH and PM<sub>2.5</sub>, CWI's improvement in visibility is larger than PM<sub>2.5</sub>. This study illustrates the importance of including CWI in the air quality prediction model.</span></p>
An approach for modelling simultaneous fluid-phase and chemical reaction equilibria in multicomponent systems via Lagrangian duality: The reactive HELD algorithm.
<p>This is a data set associated with the paper <em>An approach for modeling simultaneous fluid-phase and chemical reaction equilibria in multicomponent systems via Lagrangian duality: The reactive HELD algorithm. </em>by Felipe A. Perdomo, George Jackson, Amparo Galindo, Claire S. Adjiman. The manuscript is presented as a proceeding of the 33<sup>rd</sup> European Symposium on Computer-Aided Process Engineering (ESCAPE33), June 18-21, 2023, in Athens, Greece.</p>
Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling; Model input and output files
<p>These are the model input and output associated with the manuscript "Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling" in consideration for publication in the Journal of Volcanology and Geothermal Research.</p>
CottonWeedDet12: a 12-class weed dataset of cotton production systems for benchmarking AI models for weed detection
<p>The dataset <strong>CottonWeedDet12</strong> consists of 5648 RGB images of 12-class weeds that are common in cotton fields in the southern U.S. states, with a total of 9370 bounding boxes. These images were acquired by either smartphones or hand-held digital cameras, under natural field light condition and throughout June to September of 2021. The images were manually labeled by qualified personnel for weed identification, and the labeling process was done using the VGG Image Annotator (version 2.10).</p> <p>The dataset, at the time of publication, is the largest publicly available multi-class dataset dedicated to weed detection. It expects to facilitate communicate efforts to exploit state-of-the-art deep learning method to push weed recognition to the next level. With the WeedDet12 dataset, a performance benchmark of a suite of YOLO object detectors has been built for weed detection. Detailed documentation of the dataset, model benchmarking and performance results is given in an accompanying journal paper: <a href="https://www.sciencedirect.com/science/article/pii/S0168169923000431">Dang, F., Chen, D., Lu, Y., Li, Z., 2023. YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems. Computers and Electronics in Agriculture 205, 107655. https://doi.org/10.1016/j.compag.2023.107655</a><a href="https://doi.org/10.1016/j.compag.2023.107655"> </a></p> <p>If you use the dataset on a published publication, please cite the dataset or the <a href="https://doi.org/10.1016/j.compag.2023.107655">journal article</a> above.</p>
Constraining Bedrock Groundwater Residence Times in a Mountain System with Environmental Tracer Observations and Bayesian Uncertainty Quantification: Modeling and Data Package
<p>Here we present field observations of dissolved noble gases (He, Ne, Ar, Kr, and Xe), Chloroflourcarbons (CFCs), Sulfurhexaflouride (SF6), and tritium (3H) sampled from the PLM1, PLM6, and PLM7 wells in the East River Colorado (USA) sampled in May, 2021. This observation dataset, along with the presented python modeling scripts to interpret the data, can aide in quantifying groundwater residence times and recharge conditions. The README files describes the directories and scripts.</p>
Embracing fine-root system complexity in terrestrial ecosystem modelling
<p>Model outputs for manuscript " <strong>Embracing fine-root system complexity in terrestrial ecosystem modelling</strong>".</p>
Modelling knowledge organization systems and structures
<p>In the last few decades, knowledge organization systems (KOS), especially thesauri,<br> classification schemes and lists of subject headings, have largely followed or conformed<br> with the established data models defined by standards, recommendations or best practices.<br> This long list contains some widely used models, such as ISO5964 Part 1, ISO2788, Z39.19,<br> BS 5723 and BS 6723, (Dextre Clarke, 2008) IFLA Principles Underlying Subject Heading<br> Languages (SHLs), and MARC 21 Format for Classification Data.<br> The FRSAD (Functional Requirements for Subject Authority Data) conceptual model is<br> the third member of the FRBR family, developed under the auspices of IFLA. The report<br> was approved in 2010 and will be published in 2011. FRSAD is a general conceptual model<br> that focuses on the subject relationship and therefore provides a theoretical framework for<br> all KOS and their data models. In addition, it also assists in the assessment of the potential<br> for international sharing and (re)use of subject authority data both within the library sector<br> and beyond.<br> In this paper FRSAD is compared to SKOS and SKOS XL as data models (with implementa-<br> tion examples).</p>
Model data and code supporting "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "
<p>Model data and analysis code supporting the Geoscientific Model Development manuscript "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "</p> <p> </p> <p> </p>
CCG: Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System)
<p>Seven clicSAND scenario files for <strong>Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System).</strong> </p> <p><strong>How to Visualise Results Online and Offline</strong> outline the steps required to re-run the scenarios on OSeMOSYS Cloud</p> <p><strong>Scenario Short Note</strong> outlines the steps to replicate the analysis and rebuild the scenarios</p> <p><strong>Annex - Input Data and Assumptions</strong> listing the data sources and assumptions in the scenarios</p>
Gravity change data used in the paper "Insights into the magmatic feeding system of the 2021 eruption at Cumbre Vieja (La Palma, Canary Islands) inferred from gravity data modeling"
<p>Gravity changes data observed in the network between July 2021 and January 2022 </p> <p>Reference:</p> <p>Insights into the magmatic feeding system of the 2021 eruption at Cumbre Vieja (La Palma, Canary Islands) inferred from gravity data modeling <br> F. G. Montesinos1,7, S. Sainz-Maza2,7, D. Gómez-Ortiz3, J. Arnoso4,7, I. Blanco-Montenegro5,7, M. Benavent1,7 E. Vélez4,7, N. Sánchez6 and T. Martín-Crespo3</p> <p>1 Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Plaza de Ciencias 3, 28040 Madrid, Spain.<br> 2 Observatorio Geofísico Central (IGN). C/ Alfonso XII, 3. 28014 Madrid, Spain.<br> 3 Dpt. Biología y Geología, Física y Química Inorgánica, ESCET, Universidad Rey Juan Carlos. C/Tulipán s/n, 28933 Móstoles, Madrid, Spain.<br> 4 Instituto de Geociencias (IGEO), CSIC-UCM. C/ Doctor Severo Ochoa, 7. 28040 Madrid, Spain.<br> 5 Departamento de Física, Escuela Politécnica Superior, Universidad de Burgos. Avda. de Cantabria s/n, 09006 Burgos, Spain.<br> 6 Instituto Geológico y Minero de España (IGME, CSIC), Unidad Territorial de Canarias, Alonso Alvarado, 43, 2A, 35003 Las Palmas de Gran Canaria, Spain.<br> 7 Research Group ‘Geodesia’, Universidad Complutense de Madrid, Spain.</p> <p><br> Corresponding author: Fuensanta G. Montesinos (fuensant@ucm.es)</p> <p>This research is supported by the project PID2019-104726GB-I00/AEI/10.13039/501100011033 funded by the Spanish Research Agency. Further, the University Complutense of Madrid (grants Financiación Grupos 2021, UCM 2022-GRFN14/22) and the Spanish Ministry of Science and Innovation (RD 1078/2021, funding for research activities of the CSIC-PIE project CSIC-LAPALMA-07) supported this research.</p>
Observing system simulation experiments to evaluate transport model error on CO2 flux estimates
<p>Atmospheric CO2 inversion using coarse-resolution transport model can cause large errors on surface carbon flux estimates. The transport model errors on flux estimates are isolated using observing system simulation experiments presented here.</p>
ScienceDex guides
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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.