Find research datasets worth reusing

opencc-zeroAug 2023View details →

Data from: The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models

Sowing flower strips along field edges is a widely adopted method for conserving pollinating insects in agricultural landscapes. To maximize the effect of flower strips given limited resources, we need spatially explicit tools that can prioritize their placement, and for identifying plant species to include in seed mixtures. We sampled bees and plant species as well as their interactions in a semi-controlled field experiment with roadside/field edge pairs with/without a sown flower strip at 31 sites in Norway and used a regional spatial model of solitary bee species richness to test if the effect of flower strips on bee species richness was predictable from the modelled solitary bee species richness. We found that sites with flower strips were more bee species rich compared to sites without flower strips and that this effect was greatest in areas that the regional solitary bee species richness model had identified to be particularly important for bees. Spatial models revealed that even within small landscapes there were pronounced differences between field edges in the predicted effect of sowing flower strips. Of the plant species that attracted the most bee species, the majority mainly attracted bumblebees and only few species also attracted solitary bees. Considering both the taxonomic diversity of bees and the species richness of bees attracted by plants we suggest that seed mixes containing Hieracium spp. such as Hieracium umbellatum and Pilosella officinarum; Taraxacum spp; Trifolium repens; Lotus corniculatus; Stellaria graminea; and Achillea millefolium would provide resources for diverse bee communities in our region. Spatial prediction models of bee diversity can be used to identify locations where flower strips are likely to have the largest effect and can thereby provide managers with an important tool for prioritizing how funding for agri-environmental schemes such as flower strips should be allocated. Such flower strips should contain plant species that are attractive to both solitary and bumblebees, and do not need to be particularly plant species rich as long as the selected plants complement each other.

Data observed at Kawajima land subsidence observatory (Japan) and ensemble model

This data set contains all data necessary to reproduce the results of the manuscript submitted by Akitaya & Aichi. The data set includes hydraulic head and land subsidence data observed at the Kawajima land subsidence observatory (Japan), and an ensemble model parameter set constructed using the evolutionary multimodal algorithm, ensemble simulation results, and predictive uncertainty analysis results obtained through ensemble model output statistics. Please refer to the "readme.txt" file provided in each individual folder for detailed instructions on how to read the data.

Data from: Sampling methodology influences habitat suitability modeling for Chiropteran species

Technological advances increase opportunities for novel wildlife survey methods. With increased detection methods, many organizations and agencies are creating habitat suitability models (HSMs) to identify critical habitats and prioritize conservation measures. However, multiple occurrence data types are utilized independently to create these HSMs with little understanding of how biases inherent to those data might impact HSM efficacy. We sought to understand how different data types can influence HSMs using three bat species (L. borealis, L. cinereus, and P. subflavus). We compared the overlap of models created from passive-only (acoustics), active-only (mist-netting and wind turbine mortalities), and combined occurrences to identify the effect of multiple data types and detection bias. For each species, the active-only models had the highest discriminatory ability to tell occurrence from background points and for two of the three species, active-only models performed best at maximizing the discrimination between presence and absence values. By comparing the niche overlaps of HSMs between data types, we found a high amount of variation with no species having over 45% overlap between the models. Passive models showed more suitable habitat in agricultural lands, while active models showed higher suitability in forested land, reflecting sampling bias. Overall, our results emphasize the need to carefully consider the influences of detection and survey biases on modeling, especially when combining multiple data types or using single data types to inform management interventions. Biases from sampling, behavior at the time of detection, false positive rates, and species life history intertwine to create striking differences among models. The final model output should consider biases of each detection type, particularly when the goal is to inform management decisions, as one data type may support very different management strategies than another.

Input data and modelling results of GXAJ, GIE and GXAJ-IE model

This dataset archived the underlying surface data and hydrometeorological data of the four typical watersheds in different hydrometeorological zones of China that used as input for GXAJ, GIE and GXAJ-IE model, and the simulation results produced by the three models.

opencc-by-4.0Oct 2022View details →

Data and Code Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models"

Code and Data Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models" =========================================================== This directory contains the data and scripts used to create the plots from our publication as well as the source code modifications necessary to run this test case within the CESM and MPAS models. Generating Plots --------------- The `netcdf` directory contains the nominal half-degree runs necessary to generate nearly all of the plots from the paper. The one plot which is not reproducible from these data is the volume-integrated Eddy Kinetic Energy in the Spectral Element model. Storing high-resolution 4D wind fields requires a prohibitive amount of space. These data can be provided by the corresponding author, O.K. Hughes (owhughes@umich.edu). However, because this is several hundred GB of data I would strongly recommend generating these high-resolution runs yourself on your local system if you need them. Using 288 Intel Skylake cores (that is, 8 nodes each with two 18C processors) ran on the order of an hour. In order to generate the plots from the paper, you need only install NCL and then run run.bash. Instructions for installing NCL can be found in the `run.bash` script. Source Code Modifications ---------------- CESM The `src` subdirectory contains the files `user_nl_cam` and `ic_baroclinic.F90`. Create a case using `--compset=FKESSLER` and `--run-unsupported` options when running `create_newcase`. If your case is located at `${CASE_DIR}`, then from within the directory containing this README, run `cp user_nl_cam ${CASE_DIR}/user_nl_cam`, and then run `cp ic_baroclinic.F90 ${CASE_DIR}/SourceMods/src.cam/`. Then build and run the model using the usual workflow. MPAS The MPAS code was run using a branch of the MPAS model provided by the model developers to the authors. While the source code modifications are provided in the `src` directory, I would strongly recommend contacting the corresponding author if you wish to run this test case in the MPAS codebase.

opencc-by-nc-nd-4.0Sep 2023View details →

Models and Data associated with: Single-cell gene expression prediction from DNA sequence at large contexts

This archive holds trained models and associated data for the <a href="https://www.biorxiv.org/content/10.1101/2023.07.26.550634v1">manuscript</a>: "Single-cell gene expression prediction from DNA sequence at large contexts" Structure: <ul> <li>configs - example configs for the workflows to produce publication data </li> <li>data_* - pre-processed single cell data used for publication</li> <li>models_* - model checkpoints, hyperparameters and training progress in tensorboard logs</li> <li>preprocessing - additional data required to reproduce the pre-processing workflow</li> </ul>   "Copyright 2023 GlaxoSmithKline Research & Development Limited. All rights reserved."

opencc-by-4.0Feb 2023View details →

Model input files and output data for Pluto clathrate formation modeling

Geochemical and thermal modeling input files and data for "Timing and abundance of clathrate formation controls ocean evolution in outer solar system bodies"

Supporting data for "Measuring the Loschmidt amplitude for finite-energy properties of the Fermi-Hubbard model on an ion-trap quantum computer"

This repository contains the supporting data for the publication:  "Measuring the Loschmidt amplitude for finite-energy properties of the Fermi-Hubbard model on an ion-trap quantum computer".

The data of "A comparison of citation-based clustering and topic modeling for science mapping"

These files consist of the data used in "A comparison of citation-based clustering and topic modeling for science mapping".

Data for: A catalytic model for SARS-CoV-2 reinfections: Performing simulation-based validation and extending the model to include nth infections

For code and more details see:  <ul> <li><code>inf_for_sbv.RDS</code> - simluated timeseries of primary infections used in the simulation-based validation of reinfections. </li> <li><code>inf_for_sbv_third.RDS</code> - simluated timeseries of primary infections used in the simulation-based validation of third infections. </li> <li><code>3_posterior_90_null_correctdata.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) when not considering a second lambda parameter (to third infections)</li> <li><code>3_posterior_90_null_l2_correctdata.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) when considering a second lambda parameter (to third infections)</li> <li><code>3_sim_90_null_correctdata.RDS</code> - simulation results when not considering a second lambda parameter for third infections (as used in the manuscript)</li> <li><code>3_sim_90_null_l2_correctdata.RDS</code> - simulation results when considering a second lambda parameter for third infections (as used in the manuscript)</li> </ul>

Associated model data for: Size-selective predation effects on juvenile Chinook salmon cohort survival off Central California evaluated with an individual-based model

This dataset corresponds to the paper "Size-selective predation effects on juvenile Chinook salmon cohort survival off Central California evaluated with an individual-based model" which is in press at Fisheries Oceanography. The abstract for this paper is as follows: Variation in the recruitment of salmon is often found to be correlated with marine climate indices, but mechanisms behind environment-recruitment relationships remain unclear and correlations often break down over time. We used an ecosystem modeling approach to explore bottom-up and top-down mechanisms linking a variable environment to salmon recruitment variations. Our ecosystem model incorporates a regional ocean circulation sub-model for hydrodynamics, a nutrient-phytoplankton-zooplankton sub-model for producing planktonic prey fields, and an individual-based model (IBM) representing juvenile Chinook salmon (Oncorhynchus tshawytscha), combined with observations of foraging distributions and diet of a seabird predator. The salmon IBM consists of modules, including a juvenile salmon growth module based on temperature and salmon-prey availability, a behavior-based movement module, and a juvenile salmon predation mortality module based on juvenile salmon size distribution and predator-prey interaction probability. Seabird-salmon interactions depend on spatial overlap and juvenile salmon size, whereby salmon that grow past the size range of the prey distribution of the predator will escape predation. We used a 21-year historical simulation to explore interannual variability in juvenile Chinook salmon growth and predation-mediated survival under a range of ocean conditions for sized-based mortality scenarios. We based a series of increasingly complex predation scenarios on seabird observational data to explore variability in predation mortality on juvenile Chinook salmon. We initially included information about the predator spatial distribution, then added population size, and finally, the predator's diet percentage made up of juvenile salmon. Model agreement improves with added predator complexity, especially during periods when predator abundance is high. Overall, our model found that when the fraction of juvenile salmon in seabird diet increased relative to alternate prey (e.g., Northern anchovy Engraulis mordax, and juvenile rockfish Sebastes spp.), there was a concomitant decrease in salmon cohort survival during their first year at sea.

Data from: A hierarchical model for external electrical control of an insect, accounting for inter-individual variation of muscle force properties

Cyborg control of insect movement is promising for developing miniature, high-mobility, and efficient biohybrid robots. However, considering the inter-individual variation of the insect neuromuscular apparatus and its neural control is challenging. We propose a hierarchical model including inter-individual variation of muscle properties of three leg muscles 14 involved in propulsion (retractor coxae), joint stiffness (pro- and retractor coxae), and stance-swing transition (protractor coxae and levator trochanteris) in the stick insect Carausius morosus. To estimate mechanical effects induced by external muscle stimulation, the model is based on the systematic evaluation of joint torques as functions of electrical stimulation parameters. A nearly linear relationship between the stimulus burst duration and generated torque was observed. This stimulus-torque characteristic holds for burst durations of up to 500ms, corresponding to the stance and swing phase durations of medium to fast walking stick insects. Hierarchical Bayesian modeling revealed that linearity of the stimulus-torque characteristic was invariant, with individually varying slopes. Individual prediction of joint torques provides significant benefits for precise cyborg control.

Data: Parameter regionalization with donor catchment clustering improves urban flood modeling in ungauged urban catchments

Data: Parameter regionalization with donor catchment clustering improves urban flood modeling in ungauged urban catchments for wrr Chen Hu, Jun Xia, Dunxian She !=== This dataset provides flood information for the paper submitted to wrr "Parameter regionalization with donor catchment clustering improves urban flood modeling in ungauged urban catchments",2023WR035071

opencc-zeroJul 2022View details →

Model simulation data used in "A global climatology of ice-nucleating particles under cirrus conditions derived from model simulations with EMAC-MADE3" (Beer et al., Atmos. Chem. Phys., 2022)

This dataset contains the output and the namelist setup of the EMAC-MADE3 global model simulations analysed and discussed in Beer et al. ("A global climatology of ice nucleating particles at cirrus conditions derived from model simulations with EMAC-MADE3", Atmos. Chem. Phys., 2022).

opencc-by-4.0Feb 2023View details →

Simulation outputs of DynSoM model and instrumental data for Samoylov Island's polygonal tundra

These datasets include the observational data, the input parameters, and the Dynamic Soil Model (DynSoM) outputs of a polygonal tundra arctic environment located in the quaternary sediments of Samoylov island, with and without considering heave due the segregation of ice. The data is gathered  (SamValidation.mat) and simulated at the centre and rim of one of the polygons, which correspond to wet and dry tundra conditions respectively. This dataset Is used as a primary data input for the simulation and output for the figures of the article "Mechanistic Modelling of Segregated Ice and Soil Heave Dynamics in Artic Soils" by Xavier Rodriguez-Lloveras, Melanie A. Thurner Philipp Porada & Christian Beer, Universität Hamburg. Submitted to "The Journal of Advances in Modeling Earth Systems (JAMES)" on February 2023. The datasets are published as MATLAB matrix format and require adequate MATLAB (or equivalent) software for visualization. The validation data (SamValidation.mat) is a subset of the data published by Boike et al. (2019, doi:10.1594/PANGAEA.905236) adapted to the DynSoM model requirements.

Data for modelling of bark beetle damage and outbreaks in Austria

The data shows salvage logging amounts caused by the European spruce bark beetle (Ips typographus) in Austrian political district resolution combined with climatic and forest structure parameters for the same year and district. The data set comprises of 6 files: <ul> <li>Data_Geography.csv  :  Collected data of the considered political districts of Austria, naming convention for the rest of the files, total area, population and population density of 2023, number of municipalities and the reference latitude, longitude and elevation for the "district center" chosen in this analysis.  </li> <li>Data_Climate_AUTDistrictCenters.csv  :  Data extracted or calculated from Copernicus E-OBS Dataset and mapped to reference district centers given in the Data_Geography.csv file. Climate parameters include temperatures (minimum, maximum, average), precipitation, humidity, radiation, windspeed plus bark beetle-relevant calculated parameters like degreedays, estimated start and end date of brooding season, and frostdays during winter that indicate overwintering success of bark beetles.  </li> <li>Data_BMLRT_AnnualLoggingReports.xlsx  :  Collected data from Austrian logging reports in political district-resolution, stating salvage logging from bark beetle and storm damage to deciduous trees yearly from 2003-2022. Units are Efm (harvest cubic meters), Vfm (stcok cubic meters) and ha (hectar). Around 95% of bark beetle damages to deciduous trees were identified to be caused by Ips typugraphus to Picea abies.  </li> <li>Data_BWF_DocumentationOfForestdamagefactors.xlsx  :  Collected data from Austrian documentation of forest damaging agents mostly in federal state-level resolution, stating bark beetle and storm damage to forests from 1989-2022.  </li> <li>Data_BWF_ForestInventory.csv  :  Collected data from Austrian forest inventories in political district-resolution yearly from 1996-2021, including total district area , total forest area, total coniferous forest area, total spruce area and associated shares and stocks, as well as some scarce data on dead wood.  </li> <li>Data_Merged_NaNcleared.csv  :  Table that contains all overlapping and imputed data from above, cleared of NaN values. Ready for further calculations or analysis.</li> </ul>   The python scripts for calculating the present (and more) bark beetle-relevant parameters can be found on GitHub: https://github.com/nadinefreistetter/barkbeetle_outbreak_scripts  in .ipynb format.   <blockquote> These data were prepared for the International Institute of Applied Systems Analysis Young Scientists Summer Program 2023. The report will be available online in 2024. The work of Freistetter has been carried out with funding from the Academy of Finland (decision number 341311). </blockquote>

Phase field modelling combined with data-driven approach to unravel the orientation influenced growth of interfacial Cu6Sn5 intermetallics under electric current stressing

Description: The datasets are constituted by two folders, namely, (A) data_features_and_metric.zip and (B) grain_area_prediction.zip.  (A) data_features_and_metric.zip: The following are the contents of this folder (i) grainTheta.csv file : The "grainTheta.csv" file consists the datasets generated from multiple phase field simulations. Name of the columns in the csv file are:  gnid = grain id number "n", ntheta = orientation angle of nth grain (o); nltheta = orientation angle of grain to the left of nth grain (o); nrtheta = orientation angle of grain to the right of nth grain (o); j = current density (A/m2); t = time (s); area = area of nth grain (m2); tl = horizontal length of the top edge of grain "n" (m) ; bl = horizontal length of the bottom edge of grain "n" (m)  The features  gnid, ntheta, nltheta and nrtheta for a given observation are determined during the design of initial conditions of  the corresponding phase field simulation. The value of "j" for the observation is determined via the boundary condition in the same numerical simulation. The result from the finite element method based phase field simulation has provided the numerical quantities for t, area, tl and bl attributes. The multiple observations in the data file have been obtained from multiple phase field simulations.  (ii) imc_theta.ipynb, imc_theta.py and  imc_theta.html files: These files contain the code to build the Pearson's Correlation Coefficient (PCC) heatmap analysis of the data contained in grainTheta.csv file.  (iii) comparison_mse.csv: This data file includes the information about  mean square error for training data (tmse) and mean square error for validation data (vmse)  at Epoch = 199  resulting from  10 different artificial neural network (ANN) models  distinguished by 10 different values of learning rates (lr) . Thus, the name of the columns in this csv file are modelno, lr, tmse and vmse.  (iv) mse_comparison.gnu: This file consists the codes required to output a png image from the data provided in comparison_mse.csv.  (v) train_loss.csv and val_loss.csv: These files consist  of the data of tmse and vmse at all points of Epochs  for the ANN model with lr = 2.5E-4 . Thus, the first column in train_loss.csv file corresponds to tmse whereas the second column is Epochs number. Similarly, vmse and Epochs represent the two columns in val_loss.csv file.    (vi) mse_lr2p5e-4.gnu : This file consists the codes required to output a png image from the data provided in train_loss.csv and val_loss.csv.  (B) grain_area_prediction.zip: Inside this folder, there is a folder named "prediction_of_grain_area" consisting of the following files: initial_area.csv file: This file consists the value of the initial grain area of grain 4. It is a constant at all orientation angle. predicted_result_00_5e4.csv: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for  grain 3 and grain 5 at orientation angles of 0o and 0o respectively, and for applied current density of 5.0E+4 J/m2  . predicted_result_00_5e5.csv:  This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for  grain 3 and grain 5 at orientation angles of 0o and 0o respectively, and for applied current density of 5.0E+5 J/m2  . predicted_result_9090_5e4.csv:  This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for  grain 3 and grain 5 at orientation angles of 90o and 90o respectively, and for applied current density of 5.0E+4 J/m2  . predicted_result_9090_5e5.csv: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for  grain 3 and grain 5 at orientation angles of 90o and 90o respectively, and for applied current density of 5.0E+5 J/m2  . area_00_adj.gnu : This gnu file contains the code to produce the png image from the data contained in predicted_result_00_5e4.csv and predicted_result_00_5e5.csv . The information about the initial area of grain 4 is obtained from initial_area.csv file by the code. area_9090_adj.gnu : This gnu file contains the code to produce the png image from the data contained in predicted_result_9090_5e4.csv and predicted_result_9090_5e5.csv . The information about the initial area of grain 4 is obtained from initial_area.csv file by the code.

Data from: genetic resources of macroalgae: development of an efficient method using microsatellite markers in non-model organisms

Red and brown seaweeds are species with high ecological and economic importance. Here we report the feasibility of cost-effective molecular marker development in 6 species from different clades. Microsatellites markers of two brown seaweed species Alaria esculenta, Pylaiella littoralis, and of four red seaweed species Calliblepharis jubata, Gracilaria gracilis, Gracilaria dura and Palmaria palmata were identified and characterized using genomic sequences of Double-Digest Restriction site Associated DNA (ddRAD). A total of 64,623,186 reads were generated from two runs of multiplexed Illumina Miseq sequencing for which 30,636 reads containing microsatellites and 15,443 microsatellite loci with primers pairs were found. Five hundred seventy-six primers pairs were selected for amplification trials and levels of polymorphism. From the 338 that gave a positive amplification, 142 primers pairs were polymorphic. For genetic analyses two or three populations per species from 13 different geographic locations were used. A total of 28 usable polymorphic markers for A. esculenta, 18 for P. littoralis, 11 for C. jubata, 14 for G. gracilis, 21 for G. dura and 13 for P. palmata were developed. The overall number of alleles per locus ranged from 2 to 22. These 105 new microsatellite markers will be useful for further studies of population genetics, breeding programs and conservation genetics of these species. Compared with traditional approaches, our study yielded thousands of microsatellite loci in a short time with affordable costs in six different species. This study based on ddRAD-sequencing for the development of microsatellite markers provides preliminary data using a few individuals from two distinct populations on the genetic structure and reproduction mode of a non-model species as shown with the detection of clonality for the two red algae, C. jubata and G. dura and the detection of highly genetically divergent populations corresponding probably to different cryptic species under the name of P. littoralis.

Simulated cycling data set and musculoskeletal models

This study used musculoskeletal modelling to explore the relationship between cycling conditions (power output and cadence) and muscle activation and metabolic power. We hypothesized that the cadence that minimized the simulated average active muscle volume would be higher than that which minimized the simulated metabolic power. We validated the simulation by comparing predicted muscle activation and fascicle velocities with experimental electromyography and ultrasound images. We found strong correlations for averaged muscle activations and moderate to good correlations for fascicle dynamics. These correlations tended to weaken when analyzed at the individual participant level. Our study revealed a curvilinear relationship between average active muscle volume and cadence, with the minimum active volume being aligned to the self-selected cadence. The simulated metabolic power was consistent with previous results and was minimized at lower cadences than that which minimized active muscle volume across power outputs. Whilst there are some limitations to the musculoskeletal modelling approach, the findings suggest that minimizing active muscle volume may be a more important factor than minimizing metabolic power for self-selected cycling cadence preferences. Further research is warranted to explore the potential of an active muscle volume-based objective function for control schemes across a wider range of cycling conditions.

Data set for model validation in "Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model"

This upload contains the data set for model validation in the manuscript "Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model".