Find research datasets worth reusing

opencc-by-4.0Feb 2023View details →

Input and Output simulation data of the THOR GCM for the paper Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme

The input and ouput simulation data of the THOR GCM for Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme Global circulation models (GCMs) play an important role in contemporary investigations of exoplanet atmospheres. Different GCMs evolve various sets of dynamical equations which can result in obtaining different atmospheric properties between models. In this study, we investigate the effect of different dynamical equation sets on the atmospheres of hot Jupiter exoplanets. We compare GCM simulations using the quasi-primitive dynamical equations (QHD) and the deep Navier-Stokes equations (NHD) in the  GCM THOR. We utilise a two-stream non-grey "picket-fence" scheme to increase the realism of the radiative transfer scheme. We perform GCM simulations covering a wide parameter range grid of system parameters in the population of exoplanets. Our results show significant differences between simulations with the NHD and QHD equation sets at lower gravity, higher rotation rates or at higher irradiation temperatures. The parameter exploration shows the relevance of choosing dynamical equation sets dependent on system and planetary properties.Climate states of hot Jupiters seemed to be more diverse than previously thought. There are exceptions  to prograde superrotation. Overall, our study shows the evolution of different climate states which arise just due to different selection of Navier-Stokes equations and approximations. We show the shortcomings of approximations in GCMs made for Earth, but used for non Earth-like planets.

opencc-by-4.0Dec 2022View details →

Data for: "The effects of DEM resolution on the PyFLOWGO thermorheological lava flow model" paper

PyFLOWGO results for a topographic sensitivity analysis.  These are the 5m step size results presented in the publication, additional results upon request.

Accompanying data for the paper "Reduced order modeling of geometrically nonlinear rotating structures using the direct parametrisation of invariant manifolds"

Links<ul><li>isSupplementTo publication-article <a href="https://doi.org/10.46298/jtcam.10430">https://doi.org/10.46298/jtcam.10430</a></li><li>isSupplementedBy software <a href="https://archive.softwareheritage.org/swh:1:dir:97292192b4790c2af01e25f4694d024561c5638c;origin=https://github.com/MORFEproject/MORFEInvariantManifold.jl;visit=swh:1:snp:cbd3f3eaf0dc99efb1d6bed706c3b4c3b67a1077;anchor=swh:1:rev:f56492ccd78890ee2b82970ae8941d6e39c0c147">https://archive.softwareheritage.org/swh:1:dir:97292192b4790c2af01e25f4694d024561c5638c;origin=https://github.com/MORFEproject/MORFEInvariantManifold.jl;visit=swh:1:snp:cbd3f3eaf0dc99efb1d6bed706c3b4c3b67a1077;anchor=swh:1:rev:f56492ccd78890ee2b82970ae8941d6e39c0c147</a></li></ul>Language<ul><li>English</li></ul>License<ul><li>Creative Commons Attribution 4.0</li></ul>Contributions<ul><li>Adrien MARTIN carried out the main part of study, defined the examples, performed the numerical simulations and drafted the manuscript;</li><li>Andrea OPRENI and Alessandra VIZZACCARO developed the methodology and built the main parts of the Julia code implementing the reduction method;</li><li>Andrea OPRENI developed the first version of the HBFEM code which has been updated for rotation in collaboration with Adrien MARTIN;</li><li>Marielle DEBEURRE performed all the simulations shown in Appendix C related to the Timoshenko beam model with continuation;</li><li>Loïc SALLES supervised the work, discussed applications to blades, and helped in designing and understanding the twisted plate model;</li><li>Attilio FRANGI supervised the work and help in the development of the methodology;</li><li>Olivier THOMAS helped in all discussions related to the comparisons with the thin beam example and wrote Appendix C;</li><li>Cyril TOUZE supervised the work, carried out most of the writing and developed the methodology;</li></ul>All authors read and approved the final manuscript.Data collection: period and details<ul><li>Datasets produced between September and December 2022</li></ul>Funding sources<ul><li>Funding from AID (Agence de l'Innovation de Défense), project REMODEL, contract number 2020 65 0057 ENSTA</li></ul>Data structure and information<ul><li>README.md: Contains the general information concerning this dataset</li></ul>Figures<ul><li>fig_1: description of the rotating beam</li><li>fig_2(a,b,c,d): Linear characteristics of the rotating cantilever beam</li><li>fig_3(a,b): FRC of the rotating cantilever beam around 1F mode</li><li>fig_4: Convergence of the non-autonomous part of DPIM for the 1F mode</li><li>fig_5(a,b,c,d,e,f): Interpolation of the coefficients of the autonomous ROM</li><li>fig_6(a,c): Hardening/softening behaviour of the rotating beam; fig 6b is a zoom on fig 6a</li><li>fig_7(a,b,c): Comparisons of FRCs obtained from interpolated ROMs with FOM solution</li><li>fig_8a: FRC of the rotating cantilever beam around 2F mode; fig 8b is a zoom of fig 8a</li><li>fig_9(a,b,c,d): fig 9 a-b-c : geometry of the blade and some modes and static displacements; fig 9d : Campbell diagram of the blade</li><li>fig_10: FRC of the twisted plate</li><li>fig_11(a,b,c): Computing time and convergence analysis with respect to mesh refinement for the fan blade</li></ul>fig_12(a,b,c,d): FRC of interpolated ROMs with increasing degrees compared to reference solutionfig_A_1: Campbell diagram of the beam : impact of Coriolis effects<ul><li>fig_C_3(a,b,c,d,e,f,g,h,i): Comparison of the results on the beam studied between DPIM and article from Thomas for 1F and 2F modes</li><li>fig_C_2(a, b): Comparison of the results on the beam studied between : DPIM, article from Thomas and results from Debeurre</li></ul>

opencc-zeroJun 2023View details →

Data from: Evaluating the importance of individual heterogeneity in reproduction to Weddell seal population dynamics using integral projection models

<ol> <li>Identifying and accounting for unobserved individual heterogeneity in vital rates in demographic models is important for estimating population-level vital rates and identifying diverse life-history strategies, but much less is known about how this individual heterogeneity influences population dynamics.</li> <li>We aimed to understand how the distribution of individual heterogeneity in reproductive and survival rates influenced population dynamics using vital rates from a Weddell seal population by altering the distribution of individual heterogeneity in reproduction, which also altered the distribution of individual survival rates through the incorporation of our estimate of the correlation between the two rates and assessing resulting changes in population growth.</li> <li>We constructed an integral projection model (IPM) structured by age and reproductive state using estimates of vital rates for a long-lived mammal that has recently been shown to exhibit large individual heterogeneity in reproduction. Using output from the IPM, we evaluated how population dynamics changed with different underlying distributions of unobserved individual heterogeneity in reproduction.</li> <li>Results indicate that the changes to the underlying distribution of individual heterogeneity in reproduction cause very small changes in the population growth rate and other population metrics. The largest difference in the estimated population growth rate resulting from changes to the underlying distribution of individual heterogeneity was less than 1%.</li> <li>Our work highlights the differing importance of individual heterogeneity at the population level compared to the individual level. Although individual heterogeneity in reproduction may result in large differences in the lifetime fitness of individuals, changing the proportion of above- or below-average breeders in the population results in much smaller differences in annual population growth rate. For a long-lived mammal with stable and high adult-survival that gives birth to a single offspring, individual heterogeneity in reproduction has a limited effect on population dynamics. We posit that the limited effect of individual heterogeneity on population dynamics may be due to canalization of life-history traits.</li> </ol>

FESOM-REcoM model data: Lagrangian particle trajectories

This data set includes the results of Lagrangian particle tracking experiments with FESOM1.4-REcoM2. Particles were seeded at 596 positions near the Filchner Ice Shelf front at 78°S between Berkner Island and Coats Land and tracked forwards and backwards using daily mean model output. Particles were seeded every 10th day in 1990 and 1991 (historical, forward), 2009 and 2008 (historical, backward), 2080 and 2081 (future/SSP5-8.5 scenario, forward), and 2099 and 2098 (future/SSP5-8.5 scenario, backward). Particles were tracked outside of ice-shelf cavities for 20 (19) years or until the the particle left the domain of interest in the north (62°S), west (65°W), or east (2°E).  The following information for each particle is stored twice a day: longitude (blon in files), latitude (blat), time (bday and time), depth (bdepth), temperature (btemp), salinity (bsalt), density (bsigma0; potential density anomaly referenced to 0dbar) dissolved inorganic carbon (bdic), and total carbon (btotc). Each *tar.gz archive contains one experiment, i.e., all trajectories for e.g., forward/1990-2009 (seeding days 1,11,...,361 in 1990). For each seeding day, there are 12 *nc files, which together constitute the 596 particles seeded on a given day.   Naming convention / *tar.gz files:  Nissen2023_FESOM1.4_REcoM2_Trajectories_FT_EXPERIMENT_START_END_YEAR.tar.gz EXPERIMENT: bw (backward) or fw (forward) START_END_YEAR: 2009_1990, 2008_1990, 2098_2080, or 2099_2080 for backward experiments; 1990_2009, 1990_2008, 2080_2099, or 2081_2099 for forward experiments (EXAMPLE: Nissen2023_FESOM1.4_REcoM2_Trajectories_FT_fw_1990_2009.tar.gz) Naming convention / *nc files:  drifter_start_Filchner_ice_shelf_daySEEDING_DAY_START_END_YEAR_NUM_FILE_reduced.nc SEEDING_DAY: 1, 11, ..., 361 START_END_YEAR: same as above NUM_FILE: 1, ..., 12 (EXAMPLE: drifter_start_Filchner_ice_shelf_day1_1990_2009_1_reduced.nc)   NOTE:  The following files are duplicates and also contained in the larger *tar archives described above (disregard them if all tar archives starting with "Nissen2023_FESOM1.4_REcoM2_Trajectories_FT_" have been downloaded): Nissen2023_FESOM1.4_REcoM2_LagrangianExperiments_FT_fw_day91_1991_2009.tar.gz Nissen2023_FESOM1.4_REcoM2_LagrangianExperiments_FT_fw_day91_2080_2099.tar.gz   The Lagrangian particle trajectories have been analyzed here:  Nissen, C., Timmermann, R., van Caspel, M., and Wekerle, C.: Altered Weddell Sea warm- and dense-water pathways in response to 21st-century climate change, Ocean Sci., 20, 85–101, <a href="https://doi.org/10.5194/os-20-85-2024">https://doi.org/10.5194/os-20-85-2024</a>, 2024 The Eulerian fields underlying the Lagrangian experiments are described in more detail here:  Nissen, C., Timmermann, R., Hoppema, M. et al. Abruptly attenuated carbon sequestration with Weddell Sea dense waters by 2100. Nat Commun 13, 3402 (2022). <a href="https://doi.org/10.1038/s41467-022-30671-3">https://doi.org/10.1038/s41467-022-30671-3</a>  Nissen, C., R. Timmermann, M. Hoppema, and J. Hauck, 2023: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario. J. Climate, 36, 6613–6630, <a href="https://doi.org/10.1175/JCLI-D-22-0926.1">https://doi.org/10.1175/JCLI-D-22-0926.1</a>.  Original model output is available at the World Data Center for Climate (WDCC) under the following DOIs: <ul> <li>simA, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC</a></li> <li>simA, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC</a></li> </ul>

opencc-zeroJun 2023View details →

National forest inventory data for a size-structured forest population model

In forest communities, light competition is a key process for community assembly. Species' differences in seedling and sapling tolerance to shade cast by overstory trees is thought to determine species composition at late-successional stages. Most forests are distant from these late-successional equilibria, impeding a formal evaluation of their potential species composition. To extrapolate competitive equilibria from short-term data, we therefore introduce the JAB model, a parsimonious dynamic model with interacting size-structured populations, which focuses on sapling demography including the tolerance to overstory competition. We apply the JAB model to a two-"species" system from temperate European forests, i.e. the shade-tolerant species Fagus sylvatica L. and the group of all other competing species. Using Bayesian calibration with prior information from external Slovakian national forest inventory (NFI) data, we fit the JAB model to short timeseries from the German NFI. We use the posterior estimates of demographic rates to extrapolate that F. sylvatica will be the predominant species in 94% of the competitive equilibria, despite only predominating in 24% of the initial states. We further simulate counterfactual equilibria with parameters switched between species to assess the role of different demographic processes for competitive equilibria. These simulations confirm the hypothesis that the higher shade-tolerance of F. sylvatica saplings is key for its long-term predominance. Our results highlight the importance of demographic differences in early life stages for tree species assembly in forest communities.

opencc-zeroJun 2023View details →

Data for: Coupling dynamic energy budget and population dynamic models to inform stock enhancement in fisheries management

Extensive applications of fishery stock enhancement worldwide bring up broad concerns about its negative effects, creating a pivotal need for science-based assessment and planning of enhancement strategies. However, the lack of mechanistic understanding of enhanced population dynamics, particularly the density-dependent processes, leads to compromise in model development and limits the capacity in predicting enhancement effects. Here, we developed an individual-based model based on dynamic energy budget theory and full life history processes, to understand the mechanism of density dependence in population dynamics that emerge from individual-level processes. We demonstrated the utility of the model framework by applying it to an extensively enhanced species, Chinese prawn (Fenneropenaeus chinensis, Penaeidae). The model could yield projections reflecting the observed trajectory of population biomass and yields. The model also delineated the key effects of density dependence on the vital rates of growth, fecundity, and starvation mortality. Regarding the manifold effects of stock enhancement, we demonstrated a dampened shape in population biomass and yields with increasing magnitude of enhancement, and trade-offs between the ecological and economic objectives, i.e., pursuing high benefit might compromise the wild population without proper management. Furthermore, we illustrated the possibility of combining stock enhancement and harvest regulation in promoting population recovery while maintaining fisheries yields. We highlight the potential of the proposed model for understanding density dependence in enhancement program, and for designing integrated management strategies. The approach developed herein may serve as a general approach to assess the population dynamics in stock enhancement and inform enhancement management.

CROVER model output data for paper "Future Warming Decreases the Benefits of Irrigation in Global Food Production"

This data is output data simulated by the CROVER crop-river coupled model for the scientific paper entitled: “Future Warming Decreases the Benefits of Irrigation in Global Food Production”. Simulations are based on 120-year simulations (1981-2100) using 20 climate projections (four Representative Concentration Pathways (RCPs) (2.6, 4.5, 6.0, and 8.5) × five General Circulation Models (GCMs) (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M)). The dataset includes crop yield of the modeled five crops (maize, rice, soybean, spring wheat, and winter wheat) each for irrigated and rainfed with a spatial resolution of 1.125 degree. The file name consists of a series of identifiers, separated by underscore, according to the following pattern. crover_<gcm>_<rcp>_yield_<crop>_<irrigated|rainfed>.grd crover_<gcm>_<rcp>_yield_<crop>_<irrigated|rainfed>.ctl Format: grads binary (global, 1.125 degree) These files were compressed as a zip file for each GCM and RCP.

Data for 'Population density affects sexual selection in an insect model'

Data set (.csv file), analysis code (.R file) and readme (.txt file giving details for dataset and code) accompanying the publication 'Population density affects sexual selection in an insect model' (Winkler L, Eilhardt R, Janicke T, 2023).

Data from: Business and publication model of surgical journals: A bibliometric analysis

The dataset contains information about surgical journals included in the SCOPUS database used in our research. The following information is present in the data file: <ol> <li>Title</li> <li>Journal sub-specialty</li> <li>Country Origin</li> <li>Continent</li> <li>SJR</li> <li>Publisher</li> <li>Types of Publisher</li> <li>Publication Model</li> <li>Language</li> </ol>

Data for A multi-phase biogeochemical model for mitigating earthquake-induced liquefaction via microbially induced desaturation and calcium carbonate precipitation

This data accompanies the paper published in Biogeosciences, which can be found at https://doi.org/10.5194/egusphere-2022-1419.

Dataset for the paper submitted for peer-review with the title "Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model"

The proposed dataset is related to the following article submitted for peer review: Hasanyar, M., Flipo, N., Romary, T., Wang, S. (2023), Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model, UNDER PEER-REVIEW It consists of command files for the prose-pa0.74 software available here: https://gitlab.com/prose-pa/prose-pa  To run the model : 1. Compile prose-pa0.74 2. Copy the executable in the current directory 3. In a terminal launch > ./prose-pa0.74 simulation.COMM test.log The “simulation.COMM” holds the settings for the ProSe-PA simulation related to the paper mentioned in the front head of the current file.  The information on different parameters of “simulation.COMM” are included in “bathymetrie”, “Cmds”, “Inflows”, “layers”, “meteo”, “o2_obs”, “param_bio”, “Reaches” and “Singularities” folders. The “bathymetrie” folder holds the geometric information of several cross-sections along the river.  The Cmds folder holds the “simulation.COMM” file.  The “Inflows” folder the information about the boundary condition inflows to the river such as discharge, concentration of organic carbon, etc. The layer folder holds data of the initial conditions of the model (Table 2 in the article). The “meteo” folder holds the meteorological information. The “o2_obs” folder holds the observed oxygen data needed to do data assimilation.  The “param_bio” folder holds information on the physiology of bacteria, phytoplankton, and other model species. The “Reaches” folder holds information about river reaches and their manning coefficient.  The “param_range” file holds the variation range of model parameters considered in data assimilation together with their perturbation percentage. The output files are written in $HOME/Outputs folder. It is possible to change it directly in simulation.COMM, last entry “Output_folder”.

Data from: Partitioning variance in population growth for models with environmental and demographic stochasticity

<ol> <li>How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual 'realized' growth rates have gained in popularity.</li> <li>Realized LTREs have been used particularly to understand how variation in vital rates translates into variation in growth for populations under long-term study. For these, complete population models may be constructed by combining data in an integrated population model (IPM). IPMs are also used to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking. </li> <li>We extend realized LTREs in two ways. First, we further partition the contributions from vital rates into contributions from temporally varying factors that affect them. The decomposition allows us to compare the resultant effect on the growth rate of different environmental factors that may each act via multiple vital rates. Second, we show how realized growth rates can be decomposed into separate components from environmental and demographic stochasticity. The latter is typically omitted in LTRE analyses.</li> <li>We illustrate how to use the approach in an IPM for data from a 26-year study on northern wheatears (Oenanthe oenanthe), a migratory passerine bird breeding in an agricultural landscape. For this population, consisting of around 50–120 breeding pairs per year, we partition variation in realized growth rates into environmental contributions from temperature, rainfall, population density, and unexplained random variation via multiple vital rates, and from demographic stochasticity.</li> <li>The case study suggests that variation in first-year survival via the random component, and adult survival via temperature are two main factors behind environmental variation in growth rates. More than half of the variation in growth rates is suggested to come from demographic stochasticity, demonstrating the importance of this factor for populations of moderate size.</li> </ol>

opencc-zeroDec 2022View details →

Inferring the evolutionary model of community-structuring traits with convolutional kitchen sinks: Code and data

When communities are assembled through processes such as filtering or limiting similarity acting on phylogenetically conserved traits, the evolutionary signature of those traits may be reflected in patterns of community membership. We show how the model of trait evolution underlying community-structuring traits can be inferred from community membership data using both a variation of a traditional eco-phylogenetic metric--the mean pairwise distance (MPD) between taxa--and a recent machine learning tool, Convolutional Kitchen Sinks (CKS). Both methods perform well across a range of phylogenetically informative evolutionary models, but CKS outperforms MPD as tree size increases. We demonstrate CKS by inferring the evolutionary history of freeze tolerance in angiosperms. Our analysis is consistent with a late burst model of freeze tolerance, suggesting it evolved recently. We suggest that data ordered on phylogenies such as trait values, species interactions, or community presence/absence are good candidates for CKS modeling because the generative models produce structured differences between neighboring points that CKS is well-suited for. We introduce the R package kitchen to perform CKS for generic application of the technique.

Simulated Well Production Data using a Transient Well Model and a Developed Simulator

This is a simulated dataset of transient well production data. This dataset was used in my Masters thesis at King Abullah University of Science and Technology (KAUST), and it is shared for academic use and research work. The dataset has 100 wells simulated at time steps of 0.2 hours for an entire year. This gives 43,800 observations per well, and grand total of 4,380,000 observations in the entire dataset. The resulting production data is then perturbed with systemic and random gauge errors to better simulate real-world gauge readings. The simulator code used to generate this dataset can be found at: <a href="https://github.com/ykh-1992/TransientNodalAnalysis.jl">https://github.com/ykh-1992/TransientNodalAnalysis.jl</a> The data consists of three files: - "wells.csv": This file details the input parameters for each simulated well. - "data.zip": This file houses an 850 MB "data.csv" that includes the simulated well production data. - "auxiliary.csv": This file includes information related to the simulation run.

Data for: A model of ecological abundance: Terrestrial species inventories

Counts of species in ecological samples are important for two reasons: they tell us about community assembly processes and they form the basis of species diversity estimates. Previous models of count distributions are either complex, widely rejected, not grounded in population dynamics, or not able to predict high unevenness. I present a new one-parameter model assuming that individual counts track the geometric series. The series' governing parameter p is set to vary randomly among species. Communities differ only in the centering of the distribution of p. To find the probability distribution, a vector of evenly-spaced initial values called q is drawn from the range 0 to 1. Values are then scaled by (1) transforming each q into the odds o = q/(1 – q), (2) multiplying each o by a fitted parameter m, and (3) back-computing each p as m o/(m o + 1). This skews the values to match the centering of the actual counts. The distribution is consistent with a population dynamics model in which the number of offspring produced in each interval by each species is distributed geometrically, rising with the number of adults. Large-scale surveys of corals, fishes, butterflies, and trees are consistent with the distribution, as are local-scale inventories of trees and assorted vertebrate and insect groups. Each local survey is used to predict counts within biogeographically and taxonomically matched surveys. When only decisive differences are considered, the model's predictions outperform those of each rival in at least 86% of all pairwise comparisons. The new distribution's estimates haves no substantial sample size bias. Thus, it is preferable to other species diversity estimation methods in the frequent cases where it is a good fit to count data.

Model simulation data used in "The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)" (Righi et al., Earth Syst. Dynam., 2023)

This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (Earth Syst. Dynam., 2023). For details see the README.md file.

openmit-licenseJul 2023View details →

The exemplary basin modeling with Global Hydrologic Data Cloud (GHDC) and SHUD model.

GHDC = Global Hydrological Data Cloud SHUD = Simulator of Hydrologic Unstructured Domains   Files in the package:   <ul> <li> Conestoga - The data retrived from GHDC and the modeling result by SHUD model, for Gonestoga, Pennsylvania, USA. </li> <li> Gummara - The data retrived from GHDC and the modeling result by SHUD model, for Gummara, Ethiopia </li> <li> heihe - The data retrived from GHDC and the modeling result by SHUD model, for Heihe Headwater, Gansu, China </li> <li> threeBasin - The R analysis code for the three basin simulations, including loading data and visualization. </li> </ul>   The file structure in each basin folder: <table> <thead> <tr> <th>FOLDER</th> <th>FILE OR SUBFOLDER(BOLD)</th> <th>DESCRIPTION</th> </tr> </thead> <tbody> <tr> <td>ETV</td> <td>-</td> <td>General ETV data.</td> </tr> <tr> <td> </td> <td>dem.tif</td> <td>DEM subset of ASTER Global DEM.</td> </tr> <tr> <td> </td> <td>Soil.csv</td> <td>Soil texture of soil layer reclassified from HWSD subset.</td> </tr> <tr> <td> </td> <td>Geol.csv</td> <td>Soil texture of geology layer reclassified from HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.Geol.csv</td> <td>Soil texture, organic matter and bulk density of geology layer HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.Soil.csv</td> <td>Soil texture, organic matter and bulk density of soil layer HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.tif</td> <td>HWSD data subset.</td> </tr> <tr> <td> </td> <td>buff.shp, .dfb, .prj, .shx</td> <td>Shapefile of buffered polygon from user-provided watershed.</td> </tr> <tr> <td> </td> <td>outlets.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td>stm_dem.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td>wbd_dem.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td>TSD</td> <td>Time-series forcing files.</td> </tr> <tr> <td> </td> <td>GCS</td> <td>Subfolder, terriestial data in GCS</td> </tr> <tr> <td> </td> <td>PCS</td> <td>Subfolder, terriestial data in PCS.</td> </tr> <tr> <td> </td> <td>PCS/landuse.tif</td> <td>Landuse raster subset.</td> </tr> <tr> <td> </td> <td>PCS/soil.tif</td> <td>Soil classification raster subset.</td> </tr> <tr> <td> </td> <td>PCS/geology.tif</td> <td>Geology classification raster subset.</td> </tr> <tr> <td> </td> <td>Figure</td> <td>The figures during pre-processing.</td> </tr> <tr> <td> </td> <td>Figure/ETV_watershed_delineation</td> <td>Watershed delineation, include boundary, river, pourpoint.</td> </tr> <tr> <td> </td> <td>Figure/dem_buf</td> <td>The DEM, and buffer zone.</td> </tr> <tr> <td> </td> <td>Figure/ETV_LDAS</td> <td>The coverage of reanalysis grid.</td> </tr> <tr> <td> </td> <td>Figure/ETV_Landuse</td> <td>The landuse classification of the research area</td> </tr> <tr> <td> </td> <td>Figure/ETV_Soil</td> <td>The soil classification of the research area</td> </tr> <tr> <td> </td> <td>Figure/ETV_Geol</td> <td>The geology classification of the research area</td> </tr> <tr> <td>Modeling</td> <td>-</td> <td> </td> </tr> <tr> <td> </td> <td>Figure</td> <td>The figures during model deployment</td> </tr> <tr> <td> </td> <td>input</td> <td>Model input folder.</td> </tr> <tr> <td> </td> <td>GCS</td> <td>Spatial data for model deployment in GCS.</td> </tr> <tr> <td> </td> <td>PCS</td> <td>Spatial data for model deployment in PCS.</td> </tr> <tr> <td> </td> <td>deployConfig.txt</td> <td>The configuration file for model deployment script.</td> </tr> <tr> <td>StaticFiles</td> <td>-</td> <td>Static files, include the model, citation, description and executable model file.</td> </tr> <tr> <td> </td> <td>SHUD_model</td> <td>Source code of SHUD model</td> </tr> <tr> <td> </td> <td>Citation.bib</td> <td>Citation of Data and models.</td> </tr> <tr> <td> </td> <td>ReadMe_cn.html</td> <td>The readme file in Chinese.</td> </tr> <tr> <td> </td> <td>ReadMe_en.html</td> <td>The readme file in English.</td> </tr> <tr> <td> </td> <td>shud.exe</td> <td>The executable files of SHUD model, for Windows platform only.</td> </tr> <tr> <td>UserData</td> <td>-</td> <td>The files uploaded by user</td> </tr> </tbody> </table>

Data for: Considerations for fitting occupancy models to data from eBird and similar volunteer-collected data

An occupancy model makes use of data that are structured as sets of repeated visits to each of many sites, in order estimate the actual probability of occupancy (i.e., proportion of occupied sites) after correcting for imperfect detection using the information contained in the sets of repeated observations. We explore the conditions under which preexisting, volunteer-collected data from the citizen science project eBird can be used for fitting occupancy models. The data archived here are used to explore two ways in which the single-visit records could be used in occupancy models. First, we use empirical data contained within this archive to assess the potential for space-for-time substitution: aggregating single-visit records from different locations within a region into pseudo-repeat visits. The archived data are used to illustrate that the locations chosen for data collection by observers were not always representative of the habitat in the surrounding area, which would lead to biased estimates of occupancy probabilities when using space-for-time substitution. Second, create a large set of simulated data (output from the simulations contained in this archive) that we used to explore the utility of including data from single-visit records to supplement sets of repeated-visit data.

Mars Thermal + Non-Thermal Modeling Data from figures

This folder contains all the values from all the figures present in the paper titled "Evidence of Non-thermal Hydrogen in the Exosphere of Mars Resulting in Enhanced Water Loss", by Bhattacharyya et al.