Find research datasets worth reusing

opencc-zeroFeb 2024View details →

Data from: Lineage diversity within a widespread endemic Australian skink to better inform conservation in response to regional-scale disturbance

This dataset was used to examine the phylogeographic genetic structure of Eastern three lined skink Bassiana duperreyi. It comprises SNP data used for population genetics and phylogenetic reconstruction. The data were used to provide foundational work for the detailed taxonomic re-evaluation of this species complex and to reinforce the need for biodiversity assessment to include an examination of cryptic species and/or cryptic diversity below the level of species. Such information on lineage diversity within species and its distribution in the context of disturbance at a regional scale can be factored into conservation planning regardless of whether a decision is made to formally diagnose new species taxonomically and nomenclaturally.

opencc-zeroFeb 2024View details →

A global meta-analysis of yield-scaled N2O emissions and its mitigation efforts for maize, wheat, and rice

Maintaining or even increasing crop yields while reducing nitrous oxide (N2O) emissions is necessary to reconcile food security and climate change, while the metric of yield-scaled N2O emission (i.e., N2O emissions per unit of crop yield) is at present poorly understood. Here we conducted a global meta-analysis with more than 6000 observations to explore the variation patterns and controlling factors of yield-scaled N2O emissions for maize, wheat, and rice and associated potential mitigation options. Our results showed that the average yield-scaled N2O emissions across all available data followed the order wheat (322 g N Mg-1, with the 95% confidence interval (CI): 301-346) > maize (211 g N Mg-1, CI: 198-225) > rice (153 g N Mg-1, CI: 144-163). Yield-scaled N2O emissions for individual crops were generally higher in tropical or subtropical zones than in temperate zones, and also showed a trend towards lower intensities from low to high latitudes. This global variation was better explained by climatic and edaphic factors than by N fertilizer management, while their combined effect predicted more than 70% of the variance. Furthermore, our analysis showed a significant decrease in yield-scaled N2O emissions with increasing N use efficiency or in N2O emissions for production systems with cereal yields > 10 Mg ha-1 (maize), 6.6 Mg ha-1 (wheat) or 6.8 Mg ha-1 (rice), respectively. This highlights that N use efficiency indicators can be used as valuable proxies for reconciling trade-offs between crop production and N2O mitigation. For all three major staple crops, reducing N fertilization by up to 30%, optimizing the timing and placement of fertilizer application or using enhanced-efficiency N fertilizers significantly reduced yield-scaled N2O emissions at similar or even higher cereal yields. Our data-driven assessment provides some key guidance for developing effective and targeted mitigation and adaptation strategies for the sustainable intensification of cereal production.

opencc-by-4.0Feb 2024View details →

Source code and data for manuscript "Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy"

Source code and data for manuscript "Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy", Nat Methods (2024), https://doi.org/10.1038/s41592-024-02275-w.

Data from: Higher avian biodiversity, increased shrub cover, and proximity to continuous forest may reduce pest insect crop loss in small-scale oil palm farming

One of the key ecosystem services offered by avian biodiversity within agricultural landscapes is natural predation. Nonetheless, the current use of biological control agents such as farmland birds in oil palm plantations is relatively limited. The present study aimed to assess the potential roles of avian biodiversity, particularly insectivores which provide natural predation against oil palm herbivorous insects. We also investigated the influence of local- and landscape-scale variables on foliage damage (crown or frond). Our data showed that crown damage decreased with increasing farmland bird richness (overall and insectivore), shrub cover, dried biomass, and elevation, but increased with epiphyte cover, oil palm height, and distance to continuous forest. Frond damage was negatively related to bird richness (overall, insectivore, and non-insectivore) and non-insectivore abundance, elevation, and shrub cover, while increased with insectivorous abundance, epiphyte cover, oil palm height, and distance to continuous forest. We also found that plantations (≥ 50 ha) were more susceptible to foliage damage from pest insects than smallholdings (<50 ha). There was no evidence that indicated the influence of forest patches on foliage damage. Our study highlights the economic value of conserving biodiversity, most notably, farmland birds and continuous forests with respect to biological control of defoliating pests and maintaining yield productivity in oil palm cultivation. Growers, particularly major plantation companies should make oil palm farming more biodiversity-friendly in order to increase the number of biological control agents such as birds.

Small-scale fires interact with herbivore feedbacks to create persistent grazing lawn environments

Fire-herbivory feedbacks strongly influence the formation of grazing lawns in savanna ecosystems. Preliminary findings suggest that small-scale (< 25 ha) fires can engineer grazing lawns by concentrating herbivores on the post-burn green flush; however, the persistence of such grazing lawns over the longer term and without repeated fire is unknown. We used high-resolution Light Detection and Ranging (LiDAR) to investigate the long-term effects of fire manipulation on short grass structure (height, cover, volume, and spatial continuity) and grazing lawn establishment in Kruger National Park, South Africa. We analysed the effects of fire exclusion and experimental burns applied over a 7-year period (2013-2019) followed by a one-year cessation of burning at varying spatial scales during the early and late dry seasons. Fires contributed a fourfold increase in short grass cover, regardless of fire season or size. The distribution of grass height differed significantly between fire-induced grazing lawns and recently unburnt parts of the landscape where controlled fires were excluded over the experimental period. The volume (corresponding to bulk density) of short grass on the landscape responded strongly to fires, with grass volume <20 cm in height increasing with both early and late dry season fires. Early dry season fires caused larger and more homogeneous short grass patches. Furthermore, early dry season fires were more influential in increasing the cover of the shortest grass height class (1-5 cm). Synthesis and applications. Our results demonstrate that fire-induced grazing lawns can persist over the longer-term, even when fires are no longer applied, leading to the creation of vertical and horizontal heterogeneity in the grass layer. Small-scale fires, therefore, represent a feasible management approach to expanding grazing lawn extent, potentially benefiting grazer coexistence and diversity.

Data from: Odor source distance is predictable from time-histories of odor statistics for large scale outdoor plumes

Odor plumes in turbulent environments are intermittent and sparse. Lab-scaled experiments suggest that information about the source distance may be encoded in odor signal statistics, yet it is unclear whether useful and continuous distance estimates can be made under real-world flow conditions. Here we analyze odor signals from outdoor experiments with a sensor moving across large spatial scales in desert and forest environments to show that odor signal statistics can yield useful estimates of distance. We show that achieving accurate estimates of distance requires integrating statistics from 5-10 seconds, with a high temporal encoding of the olfactory signal of at least 20 Hz. By combining distance estimates from a linear model with wind-relative motion dynamics, we achieved source distance estimates in a 60x60 m2 search area with median errors of 3-8 meters, a distance at which point odor sources are often within visual range for animals such as mosquitoes.

Data from: Stable isotope evidence for long-term stability of large-scale hydroclimate in the Neogene North American Great Plains

The Great Plains of North America host a stark climatic gradient, separating the humid and well-watered eastern US from the semi-arid and arid western US, and this gradient shapes the region's water availability, its ecosystems, and its economies. This climatic boundary is largely set by the influence of two competing atmospheric circulation systems that meet over the Great Plains—the wintertime westerlies bring dominantly dry air that gives way to moist, southerly air transported by the Great Plains Low-Level Jet in the warmer months. Climate model simulations suggest that, as CO2 rises, this low- level jet will strengthen, leading to greater precipitation in the spring, but less in the summer and, thus, no change in mean annual precipitation. Combined with rising temperatures that will increase potential evapotranspiration, semi-arid conditions will shift eastward, with potentially large consequences for the ecosystems and inhabitants of the Great Plains. We examine how hydroclimate in the Great Plains varied in the past in response to warmer global climate by studying the paleoclimate record within the Ogallala Formation, which underlies nearly the entire Great Plains and provides a spatially resolved record of hydroclimate during the globally warmer late Miocene. We use the stable isotopes of oxygen (δ18O) as preserved in authigenic carbonates hosted within the abundant paleosol and fluvial successions that comprise the Ogallala Formation as a record of past hydroclimate. Today, and coincident with the modern aridity gradient, there is a sharp meteoric water δ18O gradient with high (−6 to 0‰) δ18O in the southern Great Plains and low (−12 to −18‰) δ18O in the northern Plains. We find that the spatial pattern of reconstructed late Miocene precipitation δ18O is indistinguishable from the spatial pattern of modern meteoric water δ18O. We use a recently developed vapor transport model to demonstrate that this δ18O spatial pattern requires air mass mixing over the Great Plains between dry westerly and moist southerly air masses in the late Miocene—consistent with today. Our results suggest that the spatial extent of these two atmospheric circulation systems have been largely unchanged since the late Miocene and any strengthening of the Great Plains Low-Level Jet in response to warming has been isotopically masked by proportional increases in westerly moisture delivery. Our results hold implications for the sensitivity of Great Plains climate to changes in global temperature and CO2 and also for our understanding of the processes that drove Ogallala Formation deposition in the late Miocene.

Additional files for 'spatial scales of tropical isotope change' project

Additional files for tropical-d18O-scales repository on github. Associated code and other files are associated with DOI: 10.5281/zenodo.11053701.

openmit-licenseMar 2024View details →

Data from: Genome-scale annotation of protein binding sites via language model and geometric deep learning

The dataset contains the training and test sets of protein binding sites with DNA, RNA, peptide, protein, ATP, HEM, Zn2+, Ca2+, Mg2+ and Mn2+. Each protein is associated with 3 lines indicating the protein name (PDB accession code and chain), sequence and residue labels (0 for non-binding and 1 for binding), respectively. The ESMFold-predicted structures are also provided.

Data and codes for "Chapter 2: Particle-laden gravity currents: the lock-release slumping regime at the laboratory scale"

<div> <div> <h1>PALAGRAM Monograph</h1> </div> This repository contains the data used in the book chapter: <blockquote> Gadal, C., Schneider, J., Bonamy, C., Chauchat, J., Dossmann, Y., Kiesgen de Richter, S., Mercier, M.J., Naaim-Bouvet, F., Rastello, M. and Lacaze, L. (2025). Particle-laden Gravity Currents: The Lock-release Slumping Regime at the Laboratory Scale. In Particulate Gravity Currents. <a href="https://doi.org/10.1002/9781394216727.ch2">10.1002/9781394216727.ch2</a> </blockquote> <div> <h2>Repository organization</h2> </div> <div> <pre><code> palagram_monograph │ └───data: data are stored here │ └───input_data: input data as sent by everyone │ └─── ... : NETCDF files │ └───output_data: processed data output by analysis.py (also contains input_data) │ └─── ... : NETCDF files └───analysis: └───analysis.py: analysis code, that reads input_data and writes output_data └───paper: contains source files for article │ └───figures: contains source figures │ └─── ... : PDF files │ └─── figure_scripts: contains figure scripts that reads data in data/output_data and writes figures in paper/figures │ └─── *.py : python scripts for figures │ └─── ... : various files (.tex, .bib, ...) │ └─── main.pdf : article preprint </code></pre> <div> </div> </div> <div> <h2>Data organization</h2> </div> The CSV file <code>dataset_summary.csv</code> offers a summary of all runs and corresponding experimental parameters, allowing for easier access to the data. The folder <code>data/output_data</code> contains 287 netcdf4 files corresponding to each experimental run used in the paper. For each run, the structure of the NetCDF file is the following: <ul> <li> attributes: <ul> <li>particle_type: particle type used (silica sand, glass beads, etc..)</li> <li>label: filename</li> <li>lab: lab where this run has been performed</li> <li>run_oldID: Old filename, corresponding to the experimental notebook</li> <li>author: author(s) that acquired this run</li> <li>setup: setup used to acquire the data. See article.</li> <li>dataset: Dataset classification of this run, See paper.</li> </ul> </li> <li> dimensions(sizes): time(n) </li> <li> variables(dimensions): <ul> <li>At(): Atwood number</li> <li>Fr(): Froude number (adi. initial current velocity)</li> <li>H0(): initial heavy fluid height inside the lock</li> <li>H_a(): ambient fluid height outside the lock</li> <li>L0(): streamwise lock length</li> <li>L_1(): streamwise tank length after the lock</li> <li>Re(): Reynolds number</li> <li>S(): Settling number</li> <li>St(): Stokes number</li> <li>T_a(): ambient temperature</li> <li>T_f(): heavy fluid temperature inside the lock</li> <li>W0(): crossstream lock width</li> <li>a(): lock aspect ratio</li> <li>alpha(): bottom slope</li> <li>d(): particle diameter</li> <li>gprime(): specific gravity</li> <li>lamb(): adi. attenuation parameter</li> <li>nu_a(): ambient viscosity</li> <li>nu_f(): heavy fluid lock viscosity</li> <li>phi(): initial particle volume fraction inside the lock</li> <li>rho_a(): ambient fluid density</li> <li>rho_c(): heavy fluid mix density inside the lock</li> <li>rho_f():</li> <li>rho_p(): particle density</li> <li>t('time',): time vector</li> <li>t0(): characteristic timescale, t0 = L0/u0</li> <li>u0(): characteristic velocity scale, u0 = sqrt(gprime*H0)</li> <li>vs(): particle Stokes velocity</li> <li>x_front('time',): front position vector</li> </ul> </li> </ul> Variables can sometimes possess the following attributes: <ul> <li>unit: corresponding unit</li> <li>std: error(s) on the given quantity, calculated by error propagation from measurement uncertainties using the <code>uncertainties</code> module (<a href="https://pythonhosted.org/uncertainties/" rel="nofollow">https://pythonhosted.org/uncertainties/</a>) in Python.</li> <li>comments: comments on the given quantity (definition, formulas, etc ..)</li> </ul> <div> <h2>Related works</h2> </div> <ul> <li> Gadal, C., Schneider, J., Bonamy, C., Chauchat, J., Dossmann, Y., Kiesgen de Richter, S., Mercier, M.J., Naaim-Bouvet, F., Rastello, M. and Lacaze, L. (2025). Particle-laden Gravity Currents: The Lock-release Slumping Regime at the Laboratory Scale. In Particulate Gravity Currents. <a href="https://doi.org/10.1002/9781394216727.ch2">10.1002/9781394216727.ch2</a> </li> <li>Gadal, C., Mercier, M. J., Rastello, M., & Lacaze, L. (2023). Slumping regime in lock-release turbidity currents. Journal of Fluid Mechanics, 974, A4. <a href="https://doi.org/10.1017/jfm.2023.762" rel="nofollow">doi:10.1017/jfm.2023.762</a></li> <li> Gadal, C., Mercier, M., Rastello, M., & Lacaze, L. (2023). Data used in 'Slumping regime in lock-release turbidity currents' [Data set]. In Journal of Fluid Mechanics (Vol. 974, p. A4). Zenodo. <a href="https://doi.org/10.5281/zenodo.10058946" rel="nofollow">https://doi.org/10.5281/zenodo.10058946</a> </li> <li> Schneider, J., Dossmann, Y., Farges, O. et al. Investigation of particle laden gravity currents using the light attenuation technique. Exp Fluids, 64, 23 (2023). <a href="https://doi.org/10.1007/s00348-022-03562-y" rel="nofollow">doi:10.1007/s00348-022-03562-y</a> </li> <li> Chauchat, J., Cheng, Z., Nagel, T., Bonamy, C., and Hsu, T.-J. (2017) SedFoam-2.0: a 3-D two-phase flow numerical model for sediment transport, Geosci. Model Dev., 10, 4367-4392, <a href="https://doi.org/10.5194/gmd-10-4367-2017" rel="nofollow">doi:10.5194/gmd-10-4367-2017</a> and <a href="https://github.com/sedfoam/sedfoam">github</a> </li> </ul> </div>

Supplemental data and figures behind: Atom scale element and isotopic investigation of 25Mg-rich stardust from a H-burning supernova

We have discovered a presolar olivine from ALH 77307 with the highest 25Mg isotopic composition measured in a silicate to date (δ25Mg = 3025.1‰ ± 38.3‰). Its isotopic compositions challenge current stellar models, with modelling of Mg, Si and O showing a closest match to formation in a supernova where hydrogen ingestion occurred in the pre-supernova phase. Presolar grains within primitive astromaterials retain records of processes and environmental changes throughout stellar evolution. However, accessing these records has proved challenging due to the average grain size (~150 nm) of presolar silicates, their sensitivity to extraction agents and instrumental restrictions, limiting the range of isotopic and chemical signatures which can be studied per grain volume. Here, we present the first known detailed, geochemical study of a presolar silicate from a H-burning supernova, studied in 3D without contributions to the analysis volume and at unprecedented spatial resolutions (< 1 nm), essential for constraining physical and chemical processes occurring within this recently proposed stellar environment. From our results, we infer either; [1] condensation within an environment depleted of heavy elements compatible with the olivine lattice under the pressure and temperature conditions during condensation. [2] during periods of limited mixing either near the end of the pre-supernova phase or from a collapse so rapid localised pockets of different gas compositions formed.

Supplementary material for "A Constrained Spectral Approximation of Subgrid-Scale Orography on Unstructured Grids"

Supplementary material for: <ul> <li>Chew, R.; Dolaptchiev, S.; Wedel, M.-S.; Achatz, U.  A Constrained Spectral Approximation of Subgrid-Scale Orography on Unstructured Grids</li> </ul> The results_datasets.tar.gz archive contains the simulation datasets for the results presented in Fig. 4-9, 11-16, and B1. The Fig_10-wind_direction_study.tar.gz archive contains the simulation datasets for the results presented in Fig. 10. Input parameters necessary to reproduce these simulation runs are included as metadata (attributes) to the datasets. Otherwise, the input parameter scripts can be found in the <code>inputs</code> subpackage of the source code. Furthermore, the following results can be generated by the corresponding scripts: <table> <tbody> <tr> <td>Figures</td> <td>Scripts</td> </tr> <tr> <td>Fig. 1-3</td> <td><code>runs.idealised_isosceles</code></td> </tr> <tr> <td>Fig. D1</td> <td><code>runs.taper_test</code></td> </tr> <tr> <td>Fig. E1</td> <td><code>runs.idealised_delaunay</code></td> </tr> </tbody> </table>   Refer to the software repository URL for details on downloading the source code.

A matter of scale: Identifying the best spatial and temporal scale of environmental variables to model the distribution of a small cetacean

The importance of scale when investigating ecological patterns and processes is recognised across many species. In marine ecosystems, the processes that drive species distribution have a hierarchical structure over multiple nested spatial and temporal scales. Hence, multi-scale approaches should be considered when developing accurate distribution models to identify key habitats, particularly for populations of conservation concern. Here, we propose a modelling procedure to identify the best spatial and temporal scale for each modelled and remotely sensed oceanographic variable to model harbour porpoise (Phocoena phocoena) distribution. Harbour porpoise sightings were recorded during dedicated line-transect aerial surveys conducted in the summer of 2016, 2021 and 2022 in the Northeast Atlantic. Binary generalised additive models were used to assess the relationships between porpoise presence and oceanographic variables at different spatial (5, 20 and 40 km) and temporal (daily, monthly and across survey period) scales. Selected variables included sea surface temperature, thermal fronts, chlorophyll-a, sea surface height, mixed layer depth and salinity. A total of 30,514 km was covered on-effort with 216 harbour porpoise sightings recorded. Overall, the best spatial scale corresponded to the coarsest resolution considered in this study (40 km), while porpoise presence showed stronger association with oceanographic variables summarised at a longer temporal scale (monthly and averaged over survey period). Habitat models including covariates at coarse spatial and temporal scales may better reflect the processes driving availability and abundance of prey resources at the large scales covered during the surveys. These findings support the hypothesis that a multi-scale approach should be applied when investigating species distribution. Identifying suitable spatial and temporal scale would improve the functional interpretation of the underlying relationships, particularly when studying how a small marine predator interacts with its environment and responds to climate and ecosystem changes.

Functioning of a canopy-dominated intertidal community during emersion: highly productive but heterotrophic at the annual scale - R code

This repository contains the data and code for our paper: Claire Golléty, Jon Yearsley, Aline Migné, Dominique Davoult (2024). Functioning of a canopy-dominated intertidal community during emersion: highly productive but heterotrophic at the annual scale. Marine Biology https://doi.org/10.1007/s00227-024-04395-5

Small skeletons show size-specific scaling: an exploration of allometry in the mammalian lumbar spine

Studies of vertebrate bone biomechanics often focus on skeletal adaptations at upper extremes of body mass, ignoring the importance of skeletal adaptations at lower extremes. Yet mammals are ancestrally small and most modern species have masses under 5 kg, so the evolution of morphology and function at small size should be prioritized for understanding how mammals make a living. We examined allometric scaling of lumbar vertebrae in the small-bodied Philippine endemic rodents known as cloud rats, which vary in mass across two orders of magnitude (15.5g-2700g). External vertebral dimensions scale with isometry or positive allometry, likely relating to body size and nuances in quadrupedal posture. In contrast to most mammalian trabecular bone studies, bone volume fraction and trabecular thickness scale with positive allometry and isometry, respectively. It is physiologically impossible for these trends to continue to the upper extremes of mammalian body size, and we demonstrate a fundamental difference in trabecular bone allometry between large- and small-bodied mammals. These findings have important implications for the biomechanical capabilities of mammalian bone at small body size, for the selective pressures that govern skeletal evolution in small mammals, and for the way we define "small" and "large" in the context of vertebrate skeletons.

Data used in manuscript Direct CO2 emissions and uptake at neighbourhood scale over the urban area of Beijing

This dataset provides the data used in the manuscript "Direct CO2 emissions and uptake at neighbourhood scale over the urban area of Beijing". The folders are: 1. Modelled_CO2_Flux   This folder contains a portion of modelled CO2 fluxes generated by SUEWS. Fc is the net CO2 flux, FcPhoto the CO2 uptake by vegetation, FcRespi the CO2 release from soil and vegetation respiration, FcMetab the CO2 emissions from human metabolism, FcBuild the CO2 emissions from the local fuel combustion in buildings. Longitudes and latitudes denote the centroid of grid.     1.1 Fc_annual_2016_g_C_m-2_yr-1.nc is the annual CO2 fluxes in g C m-2 year-1.     1.2 Fc_monthly_2016_g_C_m-2_mon-1.nc is the monthly CO2 fluxes in g C m-2 month-1.     1.3 Fc_annual_2016_g_C_m-2_yr-1.tiff is the annual Fc (g C m-2 year-1) provided in GeoTiff format.     1.4 6_ring_EPSG4326 contains the ESRI Shapefile defining the study area (with the 6th Ring Road in Beijing as the boundary). 2. ModelRun   This folder includes SUEWS source code (Järvi et al., 2011; Ward et al., 2016; Järvi et al., 2019) and a model run sample.     2.1 SUEWS_SourceCode is a folder including SUEWS V2020b source Fortran codes. For detailed descriptions, readers are referred to SUEWS webpage (https://suews.readthedocs.io/en/latest/). Enter "make" through the command line and a SUEWS executive will be built under ".../ModelRun/Release".     2.2 EvaluationRun is a folder including the SUEWS run for model performance evaluation. To conduct a quick model run to reproduce the results demonstrated in the manuscript, use command line "./SUEWS_V2020b".  3. Observations   The unit for CO2 flux (Fc) is μmol m-2 s-1 under this folder.     3.1 co2_flux_140m_2016_rm_QC.csv is the Fc observations after quality control and resampled to hourly resolution.     3.2 Fc_gapfilled_with_MeanDC.csv is the Fc time series for the year 2016 gap-filled with the Mean Diurnal Cycle method on a seasonal basis.   Contact information: zhengyingqi@mail.iap.ac.cn [References] Järvi, L., Grimmond, C. S. B., & Christen, A. (2011). The surface urban energy and water balance scheme (SUEWS): Evaluation in Los Angeles and Vancouver. Journal of Hydrology, 411(3-4), 219-237. Ward, H. C., Kotthaus, S., Järvi, L., & Grimmond, C. S. B. (2016). Surface Urban Energy and Water Balance Scheme (SUEWS): development and evaluation at two UK sites. Urban Climate, 18, 1-32. Järvi, L., Havu, M., Ward, H. C., Bellucco, V., McFadden, J. P., Toivonen, T., ... & Grimmond, C. S. B. (2019). Spatial modeling of local‐scale biogenic and anthropogenic carbon dioxide emissions in Helsinki. Journal of Geophysical Research: Atmospheres, 124(15), 8363-8384.

opencc-by-4.0Apr 2024View details →

tRIBS benchmark: SNOTEL point-scale simulation, Happy Jack, AZ, USA.

This repository hosts the setup for executing a point-scale simulation (i.e. single computational element) of the <a href="https://wcc.sc.egov.usda.gov/nwcc/site?sitenum=969">Happy Jack SNOTEL Site</a> in northern Arizona, USA, using the TIN-Based Real-Time Integrated Basin Simulator (<a href="https://tribshms.readthedocs.io/en/latest/">tRIBS</a>).  This repository consists of :  (1) A data subdirectory, which contains all the necessary data to run tRIBS at Happy Jack and includes bias corrected dataset of SWE for model comparison.  2) A doc subdirectory, which contains a jupyter notebook for analyzing and visualizing this specific benchmark case, alongside additional documentation.  (3) A src subdirectory that is currently empty but intended to contain source code for the tRIBS executable, which can be obtained <a href="https://github.com/tribshms/tRIBS">here</a>.  (4) A bin subdirectory, that is currently empty but intended to store the tRIBS executable, with instructions to build the model <a href="https://tribshms.readthedocs.io/en/latest/man/Model_Execution.html#compilation-instructions">here</a>.  (5) A results subdirectory, which contains reference model output for results comparison. Instructions for running this benchmark are available in the README.html file within the repository, which can be opened with your web browser, and can also be found in the latest tRIBS <a href="https://tribshms.readthedocs.io/en/latest/man/Model_Execution.html#run-instructions">documentation</a>.  Data from this model was originally collated by Gretchen Hawkins and Josh Cederstrom for their Master’s Thesis work at Arizona State University and associated publications: Hawkins, G.A., Vivoni, E.R., Robles-Morua, A., Mascaro, G., Rivera, E., and Dominguez, F. 2015. A Climate Change Projection for Summer Hydrologic Conditions in a Semiarid Watershed of Central Arizona. Journal of Arid Environments. 118: 9–20. <a href="https://doi.org/10.1016/j.jaridenv.2015.02.022">https://doi.org/10.1016/j.jaridenv.2015.02.022</a>  Cederstrom, C.J., Vivoni, E.R., Mascaro, G., and Svoma, B. 2024. Forest Thinning Effects on Watershed Responses under Warming. Water Resources Research. (In Review).  Bias corrected SWE data is from: Sun N, Yan H, Wigmosta MS, Leung LR, Skaggs R, Hou Z. 2019. Regional Snow Parameters Estimation for Large-domain Hydrological Applications in the Western United States. Journal of Geophysical Research: Atmospheres. 124 : 5296–5313. Data for this model simulation were obtained from a number of public sources:  Meteorological forcing was obtained from the Happy Jack SNOTEL Site (https://www.nrcs.usda.gov/wps/portal/wcc/) and NLDAS-2 (https://ldas.gsfc.nasa.gov/nldas/v2/forcing). Soil properties were obtained from the Soil Survey Geographic Database (https://nrcs.app.box.com/v/soils) with derived parameters informed from the literature and calibration process. Vegetation parameters were obtained from the LANDFIRE REMAP data set (https://landfire.gov).   NOTE: If you encounter a situation where, after downloading and extracting the <code>happy_jack.gz</code> file, the extracted file has no extension, please follow these steps: <ol> <li> Rename the File: Manually rename the extracted file to <code>happy_jack.zip</code>. </li> <li> Extract Again: After renaming, unzip the <code>happy_jack.zip</code> file to access the full folder with all the necessary files. </li> </ol> This issue may occur if the file extraction process doesn't automatically add the <code>.zip</code> extension. By renaming the file and unzipping it again, you should be able to access the complete set of files without any issues.

opencc-by-4.0Apr 2024View details →

Mesh and velocity fields for "Resolving Pore-scale Concentration Gradients for Transverse Mixing and Reaction in Porous Media"

This dataset contains the data needed to reproduce the results in the manuscript "Resolving Pore-scale Concentration Gradients for Transverse Mixing and Reaction in Porous Media". The three folders refer to (1) uniform flow, (2) flow through a bead pack, and (3) flow through a Berea sample (from the 11 sandstones repository, Digital Rocks Portal). The meshes and the velocity fields (in the subfolder "velocity") are in a HDF5 format. The meshes and velocity fields can be read using FEniCS version 2019.2.0. The velocity fields need 2nd order Lagrange tetrahedral elements.

opencc-zeroApr 2024View details →

Data from: Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence

The everlasting pursuit for a more powerful artificial general intelligence (AGI) continuously demands for higher computing performance. Breaking the restrictions from the Moore's Law, integrated photonic neural networks have shown great potential in terms of superior processing speed and high energy-efficiency. However, the capacity and scalability are restricted by the unavoidable and time-varying on-chip errors that only simple tasks and shallow models (e.g., 10-category digits classification with thousand-parameter-level networks) can be on-chip realized optically. To support modern AGIs with optical computing, we innovate Taichi, large-scale photonic chiplets owning millions-of-neuron on-chip computing capability with 160 TOPS/W energy efficiency. For the first time, Taichi experimentally realized on-chip optical neural networks (ONNs) with over 13.96M neurons for thousand-category-level classification (tested 91.89% of accuracy for 1623-category Omniglot dataset) and high-fidelity artificial intelligence-generated content (AIGC) with state-of-the-art performances, while achieving up to 2 orders of magnitude improvement in computing efficiency than state-of-the-art AI chips. Based on an integrated diffractive-interference-hybrid design and a general distributed computing architecture, Taichi paves its own way for large-scale on-chip ONN models and advanced tasks, further exploiting the flexibility and potential of optical devices, which will lead to a giant leap in scalability, accuracy and efficiency to support modern AGI.