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6,498 results for “Physicality”
RAW data for Correlation analysis of vibration modes in physical vapour deposited Bi2Se3 thin films probed by the Raman mapping technique
<p>Raw data for the "Correlation analysis of vibration modes in physical vapour deposited Bi2Se3 thin films probed by the Raman mapping technique" paper.</p>
Geochemical and physical characterization of lithic raw materials in the Olduvai Basin, Tanzania
<p>The invention and proliferation of stone tool technology in the Early Stone Age (ESA) marks a watershed in human evolution. Patterns of lithic procurement, manufacture, use, and discard have much to tell us about ESA hominin cognition and land use. However, these issues cannot be fully explored outside the context of the physical attributes and spatio-temporal availability of the lithic raw materials themselves. The Olduvai Basin of northern Tanzania, which is home to both a wide variety of potential toolstones and a rich collection of ESA archaeological sites, provides an excellent opportunity to investigate the relationship between lithic technology and raw material characteristics. Here, we examine two attributes of the basin's igneous and metamorphic rocks: spatial location and fracture predictability. A total of 244 geological specimens were analyzed with non-destructive portable XRF (pXRF) to determine the geochemical distinctiveness of five primary and secondary sources, while 110 geological specimens were subjected to Schmidt rebound hardness tests to measure fracture predictability. Element concentrations derived via pXRF show significant differences between sources, and multivariate predictive models classify geological specimens with 75–80% accuracy. The predictive models identify Naibor Soit as the most likely source for a small sample of three lithic artifacts from Bed II, which supports the idea that this inselberg served as a source of toolstone during the early Pleistocene. Clear patterns in fracture predictability exist within and between both sources and rock types. Fine-grained volcanics show high rebound values (associated with high fracture predictability), while finer-grained metamorphics and coarsegrained gneisses show intermediate and low rebound values, respectively. Artifact data from Bed I and II suggest that fracture predictability played a role in raw material selection at some sites, but other attributes like durability, expediency, and nodule size and shape were more significant.</p>
Meaningful and physically consistent efficiency definition for positive displacement pumps - continuation of the critical review of ISO 4391 and ISO 4409
<p>ISO 4391:1984 gives the common efficiency definition for positive displacement machines. ISO 4409:2019 uses this efficiency definition to specify the procedure for efficiency measurements. If the machine conditions do not correspond with an incompressible flow due to operation at high pressure levels, the compressibility of the fluid and the dead volume of a pump must be taken into account. On this point, ISO 4391:1984 is physically inconsistent.</p> <p>Achten et. al. address this issue in their paper at FPMC 2019 presenting a critical review of ISO 4409:2007. They introduce new definitions of the overall efficiency as well as the mechanical-hydraulic efficiency. At the same time, they question the validity of the volumetric efficiency definition. Li and Barkei continue on this issue in their paper at FPMC 2020 and give a new efficiency definition based on the introduction of a new quantity Φ which describes the volume specific enthalpy of the conveyed fluid.</p> <p>The motivation of this paper is to contribute to the ongoing and fruitful discussion. Our approach starts with the most general efficiency definition, namely the isentropic efficiency. Subsequently, we make assumptions concerning the fluid properties with respect to the compressibility of the conveyed fluid. On the basis of the ideal cycle of a positive displacement pump and the p-v diagram, we derive physically consistent and more meaningful representations of the overall, the mechanical-hydraulic and the volumetric efficiency that address the inconsistency of ISO 4391:1984. Furthermore, we compare our findings with the existing results of Achten et. al. and Li and Barkei.</p>
A Comprehensive Physical Model for the Contrasting Seismogenic Behaviour of Injection Wells in Western Canada
<p>Earthquake catalog for northern Montey play in northeastern British Columbia during 2017-2018.</p>
Differences in gut microbiome abundances and diversity by physical activity levels and BMI among patients with colorectal cancer
<p>We investigated associations of physical activity, BMI, and combinations of physical activity levels/BMI with gut microbiome diversity and differential abundances among colorectal cancer patients. Pre-surgery stool samples from 179 colorectal cancer patients were used to perform 16S rRNA gene sequencing.</p>
PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions
<p>Training, test datasets of the paper "PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions".</p>
Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-fine Scales
<p>Data, plots, and software to accompany (unpublished) paper: Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-fine Scales. This work uses an ultra-fine, holistic environmental and biometric sensing paradigm to generate empirical particulate matter models estimated by biometric variables.</p> <p>GitHub repository: <a href="https://github.com/mi3nts/DUEDARE">https://github.com/mi3nts/DUEDARE</a></p>
Supplementary scripts and data for Bastin et al.: Atlantic Equatorial Deep Jets in Argo Float Data, Journal of Physical Oceanography
<p>Analysis scripts used to obtain the results in the paper</p> <p>Bastin, S., M. Claus, P. Brandt and R. J. Greatbatch: Atlantic Equatorial Deep Jets in Argo Float Data. Submitted to Journal of Physical Oceanography.</p>
Dance Your Ph.D. 2018 Physics FINALIST: Dark energy and dark matter via probabilistic data analysis
<p>Dark matter (dancer in black mask; brings dancers together) and dark energy (dancer in black mask; pulls dancers apart) are the mysterious constituents of the universe that determine the evolution of the largest structures, clusters of galaxies (dancers in tie dye), and I (dancer in gold mask) want to learn about them them by analyzing a huge set of 3D galaxy positions. The telescope (dancer in silver mask) gives me 2D images of galaxies with no distances, so I have to estimate galaxy locations with "redshift," which is correlated with distance. The exciting telescopes of the next decades will build large samples of galaxies by taking lots of superficial color data that's not informative enough to determine the redshift. One thing that tells us about dark matter and dark energy is the redshift distribution (dancer in red stripes) -- I can still learn about dark energy and dark matter if I can figure out how many galaxies have each redshift. Since I don't actually know the redshifts of any galaxies, I consider the probability of redshift (dancers in white shirts) for each galaxy based on its limited color data. However, the redshift probabilities are biased by prior beliefs (dancer in gray scarf) about physics that are transmitted by the way we derive the probabilities from the galaxy colors. If unaccounted for, the prior beliefs could cause us to draw the wrong conclusions about dark matter and dark energy, meaning we won't learn anything from all this amazing telescope data we're going to have. For my thesis, I developed a method that puts the prior beliefs in their proper place and lets the data guide the inference of the redshift distribution, using an approach called Bayesian statistics. I audition many possible redshift distributions and check how likely each is to have caused the observed galaxy colors, with the redshift probabilities as the judges; this procedure is called Markov chain Monte Carlo (MCMC) sampling, and it leads to an ensemble of possible redshift distributions each with an associated probability of having produced the observed galaxy colors. By doing the math carefully and correctly, we will learn more about dark matter and dark energy even with the coarsest data!</p>
DATASET associated with the research of students' intrinsic motivation during practical work in physics
<p>This dataset is associated with the research of students' intrinsic motivation during practical work in physics.</p> <p><strong>Legend for specific columns</strong>:</p> <ul> <li><em>gender</em>: 1 = men; 2 = women</li> <li><em>intention to study STEM</em>: 1 = the highest intention; 7 = the lowest intention</li> <li><em>self-reported diligence</em>: 1 = the highest diligence; 7 = the lowest diligence</li> <li><em>the remaining columns (items 1-23)</em>: 1 = the highest feeling of the measured quality; 7 = the lowest feeling of the measured quality*</li> </ul> <p>* By measured quality we mean one of the following dimensions: interest/enjoyment (abbr. <strong>int</strong>), effort/impotance (<strong>eff</strong>), pressire/tension (<strong>ten</strong>), value/usefulness (<strong>val</strong>) and perceived competence (<strong>pc</strong>).</p> <p><strong>Note</strong>: Reverse-worded items have been already taken into account, the score in these tems has been calculated.</p>
[dataset] Runtime Equilibrium Verification for Resilient Cyber-physical Systems
<p>This package contains the <strong>raw data</strong> and <strong>scripts</strong> used to carry out the evaluation of the framework RUNE<sup>2</sup> (RUNtime Equilibrium verification and Enforcement).</p> <p>This package is paired with the following paper submitted for publication to the ACM Transactions on Autonomous and Adaptive Systems (TAAS). Invited contribution to the IEEE ACSOS 2021 Special Issue.</p> <p><strong>Title</strong>: Enforcing Resilience in Cyber-physical Systems via Equilibrium Verification at Runtime</p> <p><strong>Authors</strong>:<br> - Matteo Camilli, Free University of Bozen-Bolzano, Italy<br> - Raffaela Mirandola, Politecnico di Milano, Italy<br> - Patrizia Scandurra, University of Bergamo, Italy</p> <p>See README.md for further instructions.</p>
Supplementary material: Multi-wavelength view of the close-by GRB~190829A sheds light on gamma-ray burst physics
<p>This repository contains supplementary data regarding the article "Multi-wavelength view of the close-by GRB~190829A sheds light on gamma-ray burst physics" published by the Astrophysical Journal Letters.</p> <p>In particular, the repository contains:</p> <ul> <li>Markov Chain Monte Carlo samples for both the afterglow modelling and the circular gaussian fits to VLBI data</li> <li>clean radio images, residuals and UV coverage plots for all our VLBI epochs</li> </ul> <p>Data formats should be self-explanatory. Do not hesitate to contact us at omsharan.salafia@gmail.com for any question.</p>
Study On Hybrid Model Combining Super Learner And Physics-based Models For SHM In Bridges Using Low-cost BWIM
<p>The main objective of this project is to study and develop hybrid models of BWIM physics-based models incorporates with SHM inspection used by artificial intelligence (AI) techniques to predict structural damage. This study presents a comprehensive assessment of FE simulations leveraging contact method verified by vehicle-bridge interaction(VBI) theory and uses machine learning (ML) techniques to identify and predict structural damages from the structural response automatically. Bridges are a fundamental part of infrastructure management. The main challenge that we face is the aging of these transportation infrastructures without a tool to perform accurate structural assessments in a real-time manner. Unfortunately, this topic is still not completely developed due to the lack of study upon BWIM simulations designed with several different severity damages. The Contact method, a new approach to simulating moving-vehicle motion in BWIM simulation, is to carry out actual structural response verified by the VBI with comprehensive parameter studies. In order to simulation the reality complex condition of the bridge, the FE model is designed by four different classes of damages with three different damage locations applied with two different load conditions (e.g., static load and moving load). The responses collected from FE simulation are used for structural damage prediction leveraging the ML damage prediction model. Among ML methods, the XGBoost with assembly decision tree shows the most reliable results. The results in this project indicate that structural damage prediction can be achieved by using the ML technique and BWIM structural response, which provides high accuracy of damage prediction.</p>
1/8˚ resolution MOM6-COBALT daily physical and biogeochemical diagnostics for 2008
<p>The files in this dataset contain daily mean chlorophyll (µg/kg), pH, nitrate (mol/kg), dissolved oxygen (mol/kg), potential temperature (˚C) and salinity model outputs for 2008 for the region between 20-55˚N, 135-111˚W. Data was extracted from a global grid run with coupled ocean-ice model configured using the Modular Ocean Model 6 (MOM6, <a href="https://github.com/NOAA-GFDL/MOM6">https://github.com/NOAA-GFDL/MOM6</a> ) and Sea Ice Simulator (SIS2) developed at the NOAA Geophysical Fluid Dynamics Laboratory (Adcroft et al., 2019). The horizontal resolution of the grid is 1/8˚, which is considered eddying and no eddy parameterization was included. Vertically, the model uses 75 hybrid vertical-sigma2 layer coordinates that is remapped onto 35 World Ocean Atlas/Coupled Model Intercomparison Project standard depth levels. The atmospheric forcing was derived from the Japanese 55-year Reanalysis version 1.5 (JRA55 1.5, <a href="https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55">https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55</a>). The model is driven by river freshwater runoff from a monthly climatology derived from Dai and Trenberth (2002) and Dai et al. (2009), which can be assessed at <a href="https://rda.ucar.edu/datasets/ds551.0/">https://rda.ucar.edu/datasets/ds551.0/</a>. A remapping scheme was used to add freshwater into the appropriate coastal grid cells near the river mouths. The biogeochemical model used was the Carbon, Ocean Biogeochemistry and Lower Trophics (COBALTv2, Stock et al., 2020), which uses 33 tracers for representation of coupled elemental cycles of carbon, nitrogen, phosphorus, iron, silicon, alkalinity, oxygen and lithogenic matter and associated plankton food web dynamics. More details about the model setup are described in Liu et al. (2019) and Liu et al. (2021). This work was part of a PMEL-led project "A Pilot BGC Argo Float Array in the California Current Large Marine Ecosystem" funded by NOAA Research.</p> <p> </p> <p>References:</p> <p> </p> <p>Adcroft, A., Anderson, W., Blanton, C., Bushuk, M., Dufour, C.O., Dunne, J.P., Griffies, S.M. et al. (2019). The GFDL Global Ocean and Sea Ice Model OM4.0: Model description and simulation features. Journal of Advances in Modeling Earth System, doi: 10.1029/2019MS001726</p> <p> </p> <p>Dai, A., T. Qian, K. E. Trenberth, and J. D Milliman, 2009: Changes in continental freshwater discharge from 1948-2004. J. Climate, 22, 2773-2791</p> <p> </p> <p>Dai, A., and K. E. Trenberth, 2002: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeorol., 3, 660-687</p> <p> </p> <p>Liu, X., Dunne, J.P., Stock, C. A., Harrison, M.J., Adcroft, A., Resplandy, L. (2019). Simulating Water Residence Time in the Coastal Ocean: A Global Perspective. Geophysical Research Letters, 46, 22, 13910-13919. Doi:10.1029/2019GL085097</p> <p> </p> <p>Liu, X., Stock, C.A., Dunne, J.P., Lee, M., Shevliakova, E., Malyshev, S., Milly, P.C.D (2021). Simulated Global Coastal Ecosystem Responses to a Half-Century Increase in River Nitrogen Loads.</p> <p> </p> <p>Stock, C. A., Dunne, J. P., Fan, S., Ginoux, P., John, J., Krasting, J. P., et al. (2020). Ocean biogeochemistry in GFDL's Earth System Model 4.1 and its response to increasing atmospheric CO<sub>2</sub>. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>, e2019MS002043. https://doi.org/10.1029/2019MS002043</p>
1/8˚ resolution MOM6-COBALT daily physical and biogeochemical diagnostics for 2010
<p>The files in this dataset contain daily mean chlorophyll (µg/kg), pH, nitrate (mol/kg), dissolved oxygen (mol/kg), potential temperature (˚C) and salinity model outputs for 2010 for the region between 20-55˚N, 135-111˚W. Data was extracted from a global grid run with coupled ocean-ice model configured using the Modular Ocean Model 6 (MOM6, <a href="https://github.com/NOAA-GFDL/MOM6">https://github.com/NOAA-GFDL/MOM6</a> ) and Sea Ice Simulator (SIS2) developed at the NOAA Geophysical Fluid Dynamics Laboratory (Adcroft et al., 2019). The horizontal resolution of the grid is 1/8˚, which is considered eddying and no eddy parameterization was included. Vertically, the model uses 75 hybrid vertical-sigma2 layer coordinates that is remapped onto 35 World Ocean Atlas/Coupled Model Intercomparison Project standard depth levels. The atmospheric forcing was derived from the Japanese 55-year Reanalysis version 1.5 (JRA55 1.5, <a href="https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55">https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55</a>). The model is driven by river freshwater runoff from a monthly climatology derived from Dai and Trenberth (2002) and Dai et al. (2009), which can be assessed at <a href="https://rda.ucar.edu/datasets/ds551.0/">https://rda.ucar.edu/datasets/ds551.0/</a>. A remapping scheme was used to add freshwater into the appropriate coastal grid cells near the river mouths. The biogeochemical model used was the Carbon, Ocean Biogeochemistry and Lower Trophics (COBALTv2, Stock et al., 2020), which uses 33 tracers for representation of coupled elemental cycles of carbon, nitrogen, phosphorus, iron, silicon, alkalinity, oxygen and lithogenic matter and associated plankton food web dynamics. More details about the model setup are described in Liu et al. (2019) and Liu et al. (2021). This work was part of a PMEL-led project "A Pilot BGC Argo Float Array in the California Current Large Marine Ecosystem" funded by NOAA Research.</p> <p> </p> <p>References:</p> <p> </p> <p>Adcroft, A., Anderson, W., Blanton, C., Bushuk, M., Dufour, C.O., Dunne, J.P., Griffies, S.M. et al. (2019). The GFDL Global Ocean and Sea Ice Model OM4.0: Model description and simulation features. Journal of Advances in Modeling Earth System, doi: 10.1029/2019MS001726</p> <p> </p> <p>Dai, A., T. Qian, K. E. Trenberth, and J. D Milliman, 2009: Changes in continental freshwater discharge from 1948-2004. J. Climate, 22, 2773-2791</p> <p> </p> <p>Dai, A., and K. E. Trenberth, 2002: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeorol., 3, 660-687</p> <p> </p> <p>Liu, X., Dunne, J.P., Stock, C. A., Harrison, M.J., Adcroft, A., Resplandy, L. (2019). Simulating Water Residence Time in the Coastal Ocean: A Global Perspective. Geophysical Research Letters, 46, 22, 13910-13919. Doi:10.1029/2019GL085097</p> <p> </p> <p>Liu, X., Stock, C.A., Dunne, J.P., Lee, M., Shevliakova, E., Malyshev, S., Milly, P.C.D (2021). Simulated Global Coastal Ecosystem Responses to a Half-Century Increase in River Nitrogen Loads.</p> <p> </p> <p>Stock, C. A., Dunne, J. P., Fan, S., Ginoux, P., John, J., Krasting, J. P., et al. (2020). Ocean biogeochemistry in GFDL's Earth System Model 4.1 and its response to increasing atmospheric CO<sub>2</sub>. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>, e2019MS002043. https://doi.org/10.1029/2019MS002043</p>
1/8˚ resolution MOM6-COBALT daily physical and biogeochemical diagnostics for 2009
<p>The files in this dataset contain daily mean chlorophyll (µg/kg), pH, nitrate (mol/kg), dissolved oxygen (mol/kg), potential temperature (˚C) and salinity model outputs for 2009 for the region between 20-55˚N, 135-111˚W. Data was extracted from a global grid run with coupled ocean-ice model configured using the Modular Ocean Model 6 (MOM6, <a href="https://github.com/NOAA-GFDL/MOM6">https://github.com/NOAA-GFDL/MOM6</a> ) and Sea Ice Simulator (SIS2) developed at the NOAA Geophysical Fluid Dynamics Laboratory (Adcroft et al., 2019). The horizontal resolution of the grid is 1/8˚, which is considered eddying and no eddy parameterization was included. Vertically, the model uses 75 hybrid vertical-sigma2 layer coordinates that is remapped onto 35 World Ocean Atlas/Coupled Model Intercomparison Project standard depth levels. The atmospheric forcing was derived from the Japanese 55-year Reanalysis version 1.5 (JRA55 1.5, <a href="https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55">https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55</a>). The model is driven by river freshwater runoff from a monthly climatology derived from Dai and Trenberth (2002) and Dai et al. (2009), which can be assessed at <a href="https://rda.ucar.edu/datasets/ds551.0/">https://rda.ucar.edu/datasets/ds551.0/</a>. A remapping scheme was used to add freshwater into the appropriate coastal grid cells near the river mouths. The biogeochemical model used was the Carbon, Ocean Biogeochemistry and Lower Trophics (COBALTv2, Stock et al., 2020), which uses 33 tracers for representation of coupled elemental cycles of carbon, nitrogen, phosphorus, iron, silicon, alkalinity, oxygen and lithogenic matter and associated plankton food web dynamics. More details about the model setup are described in Liu et al. (2019) and Liu et al. (2021). This work was part of a PMEL-led project "A Pilot BGC Argo Float Array in the California Current Large Marine Ecosystem" funded by NOAA Research.</p> <p> </p> <p>References:</p> <p> </p> <p>Adcroft, A., Anderson, W., Blanton, C., Bushuk, M., Dufour, C.O., Dunne, J.P., Griffies, S.M. et al. (2019). The GFDL Global Ocean and Sea Ice Model OM4.0: Model description and simulation features. Journal of Advances in Modeling Earth System, doi: 10.1029/2019MS001726</p> <p> </p> <p>Dai, A., T. Qian, K. E. Trenberth, and J. D Milliman, 2009: Changes in continental freshwater discharge from 1948-2004. J. Climate, 22, 2773-2791</p> <p> </p> <p>Dai, A., and K. E. Trenberth, 2002: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeorol., 3, 660-687</p> <p> </p> <p>Liu, X., Dunne, J.P., Stock, C. A., Harrison, M.J., Adcroft, A., Resplandy, L. (2019). Simulating Water Residence Time in the Coastal Ocean: A Global Perspective. Geophysical Research Letters, 46, 22, 13910-13919. Doi:10.1029/2019GL085097</p> <p> </p> <p>Liu, X., Stock, C.A., Dunne, J.P., Lee, M., Shevliakova, E., Malyshev, S., Milly, P.C.D (2021). Simulated Global Coastal Ecosystem Responses to a Half-Century Increase in River Nitrogen Loads.</p> <p> </p> <p>Stock, C. A., Dunne, J. P., Fan, S., Ginoux, P., John, J., Krasting, J. P., et al. (2020). Ocean biogeochemistry in GFDL's Earth System Model 4.1 and its response to increasing atmospheric CO<sub>2</sub>. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>, e2019MS002043. https://doi.org/10.1029/2019MS002043</p>
Hadron Shower Simulation Data for Generative Models in Fundamental Physics
<p>Data set containing pion calorimeter showers used to train and evaluate our generative models for our Hadrons, Better, Faster, Stronger publication. Complete dataset consist of three hdf5 files:</p> <ul> <li>pion_train_uniform.hdf5 contains showers originating form pions with a uniformly distributed energy ranging form 10 GeV to 100 GeV. This set was used to train the models.</li> <li>pion_eval_uniform.hdf5 contains showers originating form pions with a uniformly distributed energy ranging form 10 GeV to 100 GeV. This set was used to evaluate the models.</li> <li>pion_eval_steps20to90.hdf5 contains showers originating form pions with discrete energies ranging form 20 GeV to 90 GeV in steps of 10 GeV. This set was used to evaluate the models.</li> </ul> <p>Each file contains a group called 'hcal_only'. This group has two dataset, 'energy' which contains the energy of the pions in GeV and 'layers' which contains the shower images, projected onto a 48x48x48 grid. For our model training this was reduced to 48x25x25 via slicing. The entries correspond to energy depositions in MeV.</p>
1/8˚ resolution MOM6-COBALT daily physical and biogeochemical diagnostics for 2011
<p>The files in this dataset contain daily mean chlorophyll (µg/kg), pH, nitrate (mol/kg), dissolved oxygen (mol/kg), potential temperature (˚C) and salinity model outputs for 2011 for the region between 20-55˚N, 135-111˚W. Data was extracted from a global grid run with coupled ocean-ice model configured using the Modular Ocean Model 6 (MOM6, <a href="https://github.com/NOAA-GFDL/MOM6">https://github.com/NOAA-GFDL/MOM6</a> ) and Sea Ice Simulator (SIS2) developed at the NOAA Geophysical Fluid Dynamics Laboratory (Adcroft et al., 2019). The horizontal resolution of the grid is 1/8˚, which is considered eddying and no eddy parameterization was included. Vertically, the model uses 75 hybrid vertical-sigma2 layer coordinates that is remapped onto 35 World Ocean Atlas/Coupled Model Intercomparison Project standard depth levels. The atmospheric forcing was derived from the Japanese 55-year Reanalysis version 1.5 (JRA55 1.5, <a href="https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55">https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55</a>). The model is driven by river freshwater runoff from a monthly climatology derived from Dai and Trenberth (2002) and Dai et al. (2009), which can be assessed at <a href="https://rda.ucar.edu/datasets/ds551.0/">https://rda.ucar.edu/datasets/ds551.0/</a>. A remapping scheme was used to add freshwater into the appropriate coastal grid cells near the river mouths. The biogeochemical model used was the Carbon, Ocean Biogeochemistry and Lower Trophics (COBALTv2, Stock et al., 2020), which uses 33 tracers for representation of coupled elemental cycles of carbon, nitrogen, phosphorus, iron, silicon, alkalinity, oxygen and lithogenic matter and associated plankton food web dynamics. More details about the model setup are described in Liu et al. (2019) and Liu et al. (2021). This work was part of a PMEL-led project "A Pilot BGC Argo Float Array in the California Current Large Marine Ecosystem" funded by NOAA Research.</p> <p> </p> <p>References:</p> <p> </p> <p>Adcroft, A., Anderson, W., Blanton, C., Bushuk, M., Dufour, C.O., Dunne, J.P., Griffies, S.M. et al. (2019). The GFDL Global Ocean and Sea Ice Model OM4.0: Model description and simulation features. Journal of Advances in Modeling Earth System, doi: 10.1029/2019MS001726</p> <p> </p> <p>Dai, A., T. Qian, K. E. Trenberth, and J. D Milliman, 2009: Changes in continental freshwater discharge from 1948-2004. J. Climate, 22, 2773-2791</p> <p> </p> <p>Dai, A., and K. E. Trenberth, 2002: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeorol., 3, 660-687</p> <p> </p> <p>Liu, X., Dunne, J.P., Stock, C. A., Harrison, M.J., Adcroft, A., Resplandy, L. (2019). Simulating Water Residence Time in the Coastal Ocean: A Global Perspective. Geophysical Research Letters, 46, 22, 13910-13919. Doi:10.1029/2019GL085097</p> <p> </p> <p>Liu, X., Stock, C.A., Dunne, J.P., Lee, M., Shevliakova, E., Malyshev, S., Milly, P.C.D (2021). Simulated Global Coastal Ecosystem Responses to a Half-Century Increase in River Nitrogen Loads.</p> <p> </p> <p>Stock, C. A., Dunne, J. P., Fan, S., Ginoux, P., John, J., Krasting, J. P., et al. (2020). Ocean biogeochemistry in GFDL's Earth System Model 4.1 and its response to increasing atmospheric CO<sub>2</sub>. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>, e2019MS002043. https://doi.org/10.1029/2019MS002043</p>
1/8˚ resolution MOM6-COBALT daily physical and biogeochemical diagnostics for 2012
<p>The files in this dataset contain daily mean chlorophyll (µg/kg), pH, nitrate (mol/kg), dissolved oxygen (mol/kg), potential temperature (˚C) and salinity model outputs for 2012 for the region between 20-55˚N, 135-111˚W. Data was extracted from a global grid run with coupled ocean-ice model configured using the Modular Ocean Model 6 (MOM6, <a href="https://github.com/NOAA-GFDL/MOM6">https://github.com/NOAA-GFDL/MOM6</a> ) and Sea Ice Simulator (SIS2) developed at the NOAA Geophysical Fluid Dynamics Laboratory (Adcroft et al., 2019). The horizontal resolution of the grid is 1/8˚, which is considered eddying and no eddy parameterization was included. Vertically, the model uses 75 hybrid vertical-sigma2 layer coordinates that is remapped onto 35 World Ocean Atlas/Coupled Model Intercomparison Project standard depth levels. The atmospheric forcing was derived from the Japanese 55-year Reanalysis version 1.5 (JRA55 1.5, <a href="https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55">https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55</a>). The model is driven by river freshwater runoff from a monthly climatology derived from Dai and Trenberth (2002) and Dai et al. (2009), which can be assessed at <a href="https://rda.ucar.edu/datasets/ds551.0/">https://rda.ucar.edu/datasets/ds551.0/</a>. A remapping scheme was used to add freshwater into the appropriate coastal grid cells near the river mouths. The biogeochemical model used was the Carbon, Ocean Biogeochemistry and Lower Trophics (COBALTv2, Stock et al., 2020), which uses 33 tracers for representation of coupled elemental cycles of carbon, nitrogen, phosphorus, iron, silicon, alkalinity, oxygen and lithogenic matter and associated plankton food web dynamics. More details about the model setup are described in Liu et al. (2019) and Liu et al. (2021). This work was part of a PMEL-led project "A Pilot BGC Argo Float Array in the California Current Large Marine Ecosystem" funded by NOAA Research.</p> <p> </p> <p>References:</p> <p> </p> <p>Adcroft, A., Anderson, W., Blanton, C., Bushuk, M., Dufour, C.O., Dunne, J.P., Griffies, S.M. et al. (2019). The GFDL Global Ocean and Sea Ice Model OM4.0: Model description and simulation features. Journal of Advances in Modeling Earth System, doi: 10.1029/2019MS001726</p> <p> </p> <p>Dai, A., T. Qian, K. E. Trenberth, and J. D Milliman, 2009: Changes in continental freshwater discharge from 1948-2004. J. Climate, 22, 2773-2791</p> <p> </p> <p>Dai, A., and K. E. Trenberth, 2002: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeorol., 3, 660-687</p> <p> </p> <p>Liu, X., Dunne, J.P., Stock, C. A., Harrison, M.J., Adcroft, A., Resplandy, L. (2019). Simulating Water Residence Time in the Coastal Ocean: A Global Perspective. Geophysical Research Letters, 46, 22, 13910-13919. Doi:10.1029/2019GL085097</p> <p> </p> <p>Liu, X., Stock, C.A., Dunne, J.P., Lee, M., Shevliakova, E., Malyshev, S., Milly, P.C.D (2021). Simulated Global Coastal Ecosystem Responses to a Half-Century Increase in River Nitrogen Loads.</p> <p> </p> <p>Stock, C. A., Dunne, J. P., Fan, S., Ginoux, P., John, J., Krasting, J. P., et al. (2020). Ocean biogeochemistry in GFDL's Earth System Model 4.1 and its response to increasing atmospheric CO<sub>2</sub>. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>, e2019MS002043. https://doi.org/10.1029/2019MS002043</p>
1/8˚ resolution MOM6-COBALT daily physical and biogeochemical diagnostics for 2013
<p>The files in this dataset contain daily mean chlorophyll (µg/kg), pH, nitrate (mol/kg), dissolved oxygen (mol/kg), potential temperature (˚C) and salinity model outputs for 2013 for the region between 20-55˚N, 135-111˚W. Data was extracted from a global grid run with coupled ocean-ice model configured using the Modular Ocean Model 6 (MOM6, <a href="https://github.com/NOAA-GFDL/MOM6">https://github.com/NOAA-GFDL/MOM6</a> ) and Sea Ice Simulator (SIS2) developed at the NOAA Geophysical Fluid Dynamics Laboratory (Adcroft et al., 2019). The horizontal resolution of the grid is 1/8˚, which is considered eddying and no eddy parameterization was included. Vertically, the model uses 75 hybrid vertical-sigma2 layer coordinates that is remapped onto 35 World Ocean Atlas/Coupled Model Intercomparison Project standard depth levels. The atmospheric forcing was derived from the Japanese 55-year Reanalysis version 1.5 (JRA55 1.5, <a href="https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55">https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55</a>). The model is driven by river freshwater runoff from a monthly climatology derived from Dai and Trenberth (2002) and Dai et al. (2009), which can be assessed at <a href="https://rda.ucar.edu/datasets/ds551.0/">https://rda.ucar.edu/datasets/ds551.0/</a>. A remapping scheme was used to add freshwater into the appropriate coastal grid cells near the river mouths. The biogeochemical model used was the Carbon, Ocean Biogeochemistry and Lower Trophics (COBALTv2, Stock et al., 2020), which uses 33 tracers for representation of coupled elemental cycles of carbon, nitrogen, phosphorus, iron, silicon, alkalinity, oxygen and lithogenic matter and associated plankton food web dynamics. More details about the model setup are described in Liu et al. (2019) and Liu et al. (2021). This work was part of a PMEL-led project "A Pilot BGC Argo Float Array in the California Current Large Marine Ecosystem" funded by NOAA Research.</p> <p> </p> <p>References:</p> <p> </p> <p>Adcroft, A., Anderson, W., Blanton, C., Bushuk, M., Dufour, C.O., Dunne, J.P., Griffies, S.M. et al. (2019). The GFDL Global Ocean and Sea Ice Model OM4.0: Model description and simulation features. Journal of Advances in Modeling Earth System, doi: 10.1029/2019MS001726</p> <p> </p> <p>Dai, A., T. Qian, K. E. Trenberth, and J. D Milliman, 2009: Changes in continental freshwater discharge from 1948-2004. J. Climate, 22, 2773-2791</p> <p> </p> <p>Dai, A., and K. E. Trenberth, 2002: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeorol., 3, 660-687</p> <p> </p> <p>Liu, X., Dunne, J.P., Stock, C. A., Harrison, M.J., Adcroft, A., Resplandy, L. (2019). Simulating Water Residence Time in the Coastal Ocean: A Global Perspective. Geophysical Research Letters, 46, 22, 13910-13919. Doi:10.1029/2019GL085097</p> <p> </p> <p>Liu, X., Stock, C.A., Dunne, J.P., Lee, M., Shevliakova, E., Malyshev, S., Milly, P.C.D (2021). Simulated Global Coastal Ecosystem Responses to a Half-Century Increase in River Nitrogen Loads.</p> <p> </p> <p>Stock, C. A., Dunne, J. P., Fan, S., Ginoux, P., John, J., Krasting, J. P., et al. (2020). Ocean biogeochemistry in GFDL's Earth System Model 4.1 and its response to increasing atmospheric CO<sub>2</sub>. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>, e2019MS002043. https://doi.org/10.1029/2019MS002043</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.