Find research datasets worth reusing

Replication data for "Gekko gecko as a model organism for understanding aspects of laryngeal vocal evolution"

This dataset contains raw data and analysis code used in the preparation of the manuscript “Gekko gecko as a model organism for understanding aspects of laryngeal vocal evolution”.

Considerations for high-resolution regional meteorological wind modelling over complex terrain: a typhoon case study for assessing forestry damage (data)

This is the experiment data. The Weather Research and Forecasting (WRF) model is a popular and easily used as a numerical weather prediction (NWP) model, but configuring WRF to produce accurate results can be time-consuming. This is especially so when simulating extreme events, over complex terrain, or at high resolutions. In this study, a strong wind event from Tropical Cyclone (TC) Thad in year 1981 was simulated at 200 m resolution over an experiment forest in a mountainous region of Hokkaido island, Japan. The simulation configuration is challenging, in order to cover a larger area to produce a TC with appropriate track and intensity, and at the same time to resolve the smallest domain of sub-km grid spacing with computational stability. A mixed nesting method was applied with two-way nesting up for the first three domains, followed a separate simulation over the smallest domain. The mixed method could produce 10 min wind speed distributions similar to that of the full simulation with two-way nesting of all four domains, if a 30-minute boundary update interval was used for the separate simulation. Mixed nesting improves the efficiency of the simulation process, since the larger phenomenon scale and smaller human impact scale can be tuned separately.

Data for publication of "Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes"

Implementation of Gaussian process regression-based Bayesian optimisation (G-BO) using the emcee package (<a href="https://emcee.readthedocs.io/en/stable/" rel="nofollow">https://emcee.readthedocs.io/en/stable/</a>). For more information about the implementation of G-BO in optimising the Weather Research and Forecasting (WRF) model parameters, please refer to the paper - <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes</a>. <code>G-BO_script.ipynb</code> implements the GPR-based Bayesian optimisation using the Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. <ul> <li>QMC_sobol_samples: This file contains the 128 parameter samples across the parameter space of three sensitive parameters utilizing the Quasi Monte-Carlo (QMC) Sobol sequence design.</li> <li>nmae_all_128_ens_T_Rh: This file contains the normalised mean absolute error (NMAE) values of temperature (T) and relative humidity (Rh) of the 128 parameter sample WRF simulations. For more details, please refer to <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">this link</a>.</li> </ul>

Figure 2 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 2. Schematic view of Kernel Ridge Regression (KRR) model.

Figure 1 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 1. High-resolution flow chart of the Random Forest (RF) model.

opencc-zeroJun 2024View details →

dryad36/100

Data from: Integrating niche and occupancy models to infer the distribution of an endemic fossorial snake (Atractus lasallei)

Understanding species distribution and habitat preferences is crucial for effective conservation strategies. However, the lack of information about population responses to environmental change at different scales hinders effective conservation measures. In this study, we estimate the potential and realized distribution of Atractus lasallei, a semi-fossorial snake endemic to the northwestern region of Colombia. We modelled the potential distribution of A. lasallei based on ecological niche theory (using maxent), and habitat use was characterized while accounting for imperfect detection using a single-season occupancy model. Our results suggest that A. lasallei selects areas characterized by slopes below 10°, with high average annual precipitation (>2500mm/year) and herbaceous and shrubby vegetation. Its potential distribution encompasses the northern Central Cordillera and two smaller centers along the Western Cordillera, but its habitat is heavily fragmented within this potential distribution. When the two models are combined, the species' realized distribution sums up to 935 km2, highlighting its vulnerability. We recommend approaches that focus on variability at different spatio-temporal scales to better comprehend the variables that affect species' ranges and identify threats to vulnerable species. Prompt actions are needed to protect herbaceous and shrub vegetation in this region, highly demanded for agriculture and cattle grazing.

openmit-licenseJun 2024View details →

Data for Cell-type-specific alternative splicing in the cerebral cortex of a Schinzel-Giedion Syndrome patient variant mouse model

data.tar.gz contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files:   ./marvel: - This directory contains rds and Rdata objects that were created using the MARVEL R package cell_type_goresults.rds - This is the go results split by cell type marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting normalized_sj_expression.Rds - This object is the normalized splice junction expression Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition   ./seurat: - This directory contains all intermediate and final Seurat single-cell gene expression objects annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses. clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers. filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA. filtered_brain_samples.rds - This is the filtered Seurat object, before PCA. integrated_brain_samples.rds - This the integrated Seurat object, before other steps.   ./star: - All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space. J1/ - This directory contains outputs for brain sample J1 J13/ - This directory contains outputs for brain sample J13 J15/ - This directory contains outputs for brain sample J15 J2/ - This directory contains outputs for brain sample J2 J3/ - This directory contains outputs for brain sample J3 J4/ - This directory contains outputs for brain sample J4 K1/ - This directory contains outputs for kidney sample K1 K2/ - This directory contains outputs for kidney sample K2 K3/ - This directory contains outputs for kidney sample K3 K4/ - This directory contains outputs for kidney sample K4 K5/ - This directory contains outputs for kidney sample K5 K6/ - This directory contains outputs for kidney sample K6   ./star/genome: - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from<a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"> https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</a> chrLength.txt chrNameLength.txt chrName.txt chrStart.txt exonGeTrInfo.tab exonInfo.tab geneInfo.tab Genome genomeParameters.txt Log.out SA SAindex sjdbInfo.txt sjdbList.fromGTF.out.tab sjdbList.out.tab transcriptInfo.tab   ./star/J1: - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation<a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"> https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</a>  Log.final.out Log.out Log.progress.out SJ.out.tab Solo.out/ STARgenome/   ./star/J1/Solo.out:- This directory contains the outputs used for downstream analysis Barcodes.stats GeneFull_Ex50pAS/ SJ/   ./star/J1/Solo.out/GeneFull_Ex50pAS: - This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns) Features.stats filtered/ raw/ Summary.csv UMIperCellSorted.txt   ./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: - This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages) barcodes.tsv.gz - This file contains filtered cell barcodes features.tsv.gz - This file contains filtered features (genes) matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix   ./star/J1/Solo.out/GeneFull_Ex50pAS/raw: - This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered. barcodes.tsv features.tsv matrix.mtx   ./star/J1/Solo.out/SJ: - This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression Features.stats raw/ Summary.csv   ./star/J1/Solo.out/SJ/raw: - This directory contains the raw barcodes, features, and matrix files for splice junction expression barcodes.tsv - This file contains filtered cell barcodes features.tsv - This file contains filtered features (splice junctions) matrix.mtx - This file contains the filtered cell by gene expression count matrix   ./star/J1/_STARgenome: - This directory contains the STARgenome created and used by STAR for this sample. Detailed file descriptions available from<a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"> https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</a>  exonGeTrInfo.tab exonInfo.tab geneInfo.tab sjdbInfo.txt sjdbList.fromGTF.out.tab sjdbList.out.tab transcriptInfo.tab

Data for: Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model

This dataset, provided in NetCDF format, supports the research presented in the paper titled "Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model." Please contact Dr. Sun (ltsun@rams.colostate.edu) if you have any questions.

Model and Data Repository_CLEWs 4 Zambia

This repository for the research affiliated with the University of Edinburgh encompasses a comprehensive collection of data and model files, meticulously curated to facilitate detailed analysis and modelling using the CLEWs (Climate-Land-Energy-Water) framework. The repository includes the following essential files: <ol> <li>CLEWs Scenario Data Files</li> <li>CLEWs Reference Diagram for the Baseline:</li> <ul> <li>A comprehensive PDF diagram illustrating the baseline interconnections within the CLEWs framework for Zambia. This diagram serves as a visual aid to understand the foundational relationships and interactions between climate, land, energy, and water systems.</li> </ul> <li>CLEWs Number Crunching File:</li> <ul> <li>An Excel file containing detailed numerical analyses and computations essential for the CLEWs modelling. This file includes various datasets, calculations, and results that form the backbone of the scenario analyses.</li> </ul> <li>Signed Stakeholder Consent Forms:</li> <ul> <li>Documentation of consent from stakeholders who contributed to the research, ensuring ethical standards and transparency in data collection and usage.</li> </ul> <li>Detailed Stakeholder Responses:</li> <ul> <li>Comprehensive documentation of feedback and insights from stakeholders, providing valuable qualitative data that complement the quantitative analyses. These responses are crucial for understanding local perspectives and validating model assumptions.</li> </ul> <li>GEOCLEWs Inputs and Outputs for Zambia:</li> <ul> <li>A collection of files generated by the GeoCLEWs_ZM script, which automates data collection from sources such as GAEZ v4 and FAOSTAT. The outputs include agro-climatic potential yield, crop water deficit, precipitation, and land cover data, combined with electricity information for detailed CLEWs modelling. This dataset is vital for integrated analysis and visualization of the CLEWs components.</li> </ul> </ol> By centralising these critical resources, the Zenodo repository provides an invaluable tool for researchers, policymakers, and stakeholders engaged in sustainable development and climate resilience efforts in Zambia. Each file has been meticulously prepared and uploaded to ensure ease of access, facilitating robust analysis and informed decision-making within the context of the CLEWs framework.

Data-driven surrogate model for wind turbine damage equivalent load

There are four zip files in this data set: <ul> <li>PythonCode_OpenFAST: The code used to generate 32768 OpenFAST fst files to build the database.</li> <li>ML_TrainingCode: The code that used to train the TCN-FCNN and FCNN models for both free stream and wake</li> <li>Trained_Models: All the trained models are saved in Keras format. The models with max in their filenames were trained on maximum values. The models with XY in their naming were trained on wind in the X and Y directions.</li> <li>data: It includes all the CSV files for training and testing.</li> </ul>

Accompanying data for the paper "Making Sense of Wildlife Habitat Use on Active Oil Sands Mines: Quasi-experiments, Occupancy Models, Trends Assessments, and Upland Habitat Reclamation"

This data set contains both the raw species detection records and the derived occupancy model data used to assess usage patterns for the nine species of wildlife.  Data have been anonymized by using non-identifying company and lease names. These attributes are not required to reproduce the results in this paper and was done per contractual requirements between LGL Limited and its clients. Data is currently being reviewed by the client and will be shared publicly once final approval has been received.

Data for impacts of topography-based subgrid scheme and downscaling of atmospheric forcing on modeling land surface processes in the conterminous US

The effects of small-scale topography-induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). A topography-based subgrid structure and methods of downscaling of atmospheric forcing from the atmospheric grid to the subgrids of the land model grid (TGUs) have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to improve representation of the effects of small-scale topography-induced land surface heterogeneity on land surface processes. This study evaluates the impacts of the topography-based subgrid structure and downscaling of atmospheric forcing on modeling land surface processes in E3SM over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5-degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS-level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional-level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small-scale surface heterogeneity in ESMs and motivate future research to understand their effects on land-atmosphere interactions, streamflow, and water resources management over mountainous regions. The data utilized to evaluate effects of the topography-based subgrid structure and downscaling of atmospheric forcing in land surface modeling include a TGU level land surface data file, atmospheric forcing to drive the land model, ELM user name list configuration parameters, regionalization variables (topographic regions, snow fraction regions, water versus energy limited regions, and regions of season of maximum precipitation), model restart files for both ELM configurations, and model outputs (grid and subgrid levels), model outputs aggregated to TGUs and grid levels. The data files include: <ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/daily_prism_precip.zip?versionId=be97ca8d-182a-4f1e-9ae3-9da3f2b87e24">DELM.zip</a>: Directory containing the following files relevant to the D ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_all_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file:  grd_level_output_disag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_disag_daily_20220512.nc</li> <li>TGU-level monthly output file: tgu_level_output_all_disag_20221109.nc</li> </ol> <li> <a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/dem_4km4.nc">NoDELM.zip</a>: Directory containing the following files relevant to the NoD ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_no_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_nodisag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_nodisag_daily_20220512.nc  </li> <li>TGU-level monthly output file: tgu_level_output_no_disag_20221109.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">shared.zip</a>: Directory containing the following files relevant to both the D ELM and NoD ELM configurations.</li> <ol> <li>Subgrid-based surface data file: MASKED.half_degree_merge.surfdata_0.5x0.5_simyr1850_c200924.pft17.10262022v2.nc</li> <li>Regionalization file used to generate regions based on snow fraction, water versus energy limited state, and seasons of maximum precipitation: half_deg_budyko_curve_analysis_20230104_disag.nc</li> <li>Topographic ratio file used to generate topography-based regions: grd_level_output_nodisag_run_11_new_20221109.nc</li> <li>TGU-level surface elevation data file where surface elevation data are derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013]:  half_deg_subgrids_with_PFTs_and_stat_20210403.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">SNOTEL_files.zip</a>: Directory containing the following SNOTEL data related files used to evaluate model performance.</li> <ol> <li> SNOTEL list of stations file: SNOTEL_halfdegree_intersect4.csv</li> <li>SNOTEL data files/folders: csv</li> </ol> </ol>   References Lehner, B., et al. (2008). "New Global Hydrography Derived From Spaceborne Elevation Data." Eos, Transactions American Geophysical Union 89(10): 93-94. Lehner, B. and G. Grill (2013). "Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems." Hydrological Processes 27(15): 2171-2186.

Supporting data for Emerging AI-based weather prediction models as downscaling tools

Supporting data for "Emerging AI-based weather prediction models as downscaling tools" by Nikolay Koldunov, T. Rackow, Christian Lessig, S. Danilov, S. Cheedela, D. Sidorenko, Irina Sandu, Thomas Jung <a href="https://t.co/PSUCUvh9lf" target="_blank" rel="noopener noreferrer nofollow">https://doi.org/10.48550/arXiv.2406.17977</a>

openmit-licenseApr 2024View details →

eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies (data)

Dataset and results used for the simulations in following publication: Carsten Wegkamp, Henrik Wagner, Eike Niehs, Julien Essers, Marcel Lüdecke, Mattias Hadlak, Bernd Engel: "eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies", Open Source Modelling and Simulation of Energy Systems (OSMSES) 2024, Vienna, Austria, 2024   This contains the input (scenario) files for the building & grid scenario and the results of the two simulations. It uses the elenia Energy Library (eELib) with release version 1.0.0: https://gitlab.com/elenia1/elenia-energy-library

Data used in: Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city

This dataset contains the processed data and analysis code used in the article: <div>Hall, T.W., Blunn, L., Grimmond, S., McCarroll, N., Merchant, C.J., Morrison, W., et al. (2024) Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city. Quarterly Journal of the Royal Meteorological Society, 150(760), 1771–1790. Available from: <div><a href="https://doi.org/10.1002/qj.4669">https://doi.org/10.1002/qj.4669</a></div> <div> </div> <div>The data consists of LST data, UM100 model output and ancillary files (all netCDF format).</div> <div> </div> <div>LST_data contains:</div> </div> <ol> <li>Landsat LST data retrieved in this study (CALC) on four study days, LST data from FORTH and NASA JPL on two days</li> <li>MODIS LST data for 2018-07-15</li> </ol> UM100_output contains model output from initial and final runs for the four study days The python script plot.py can be used to generate the figures shown in this article.

opencc-zeroJul 2024View details →

dryad36/100

Modeling data and R code for Chrysodeixis chalcites ecological niche

The golden twin-spot moth, Chrysodeixis chalcites Esper (Lepidoptera: Noctuidae), is a polyphagous, polyvoltine crop pest occurring natively from northern Europe to Mediterranean Africa and the Canary Islands. Larvae feed on a wide variety of naturally occurring plants as well as soybean and other legume crops, short staple cotton, tomato, potato, peppers, tobacco, and banana. Chrysodeixis chalcites has been recorded in agricultural lands in the Ontario peninsula in eastern Canada and in northern counties of Indiana, USA. Given the strong potential for C. chalcites to invade USA crop lands, it is important to identify environments most likely to sustain growing populations of this pest. Though C. chalcites is native to Europe and North Africa, it has invaded sub-Saharan Africa. Using occurrence data form the native and invaded ranges, and environmental predictors including bioclimatic conditions and human disturbance, we trained three ecological niche models to estimate an ensemble prediction of environmental suitability in the contiguous US. Because human impact is potentially a confounding predictor, models were trained both with and without it. High environmental suitability was projected for the Atlantic coast from New England to Florida, the Gulf coast, the lower Midwest, and the Pacific coast and Central Valley of California.

The output data of the 1D Venusian chemistry-diffusion model of Dai et al. (2024) and the adopted chemical network

To use these data, please cite the paper: Dai et al. (2024, doi: 10.1051/0004-6361/202450552)  Supplemental_Tables_for_Dai_et_al_2024.pdf: the chemical network adopted in the model Nominal.txt: the chemical network adopted in the model modify_chem.py: the additional adjustments of the reactions Nominal_Bkzz_SO2.vul and A_Dkzz_SO2.vul: the output data of the nominal model and model A, respectively reading_data.py: the methods to read the output files   *Errata:  1) R296 in Supplemental Table: "1×10^7+0.05n_atm" should have been "1×10^17+0.05n_atm" 2) R329 in Supplemental Table: should have been removed The errata do not affect the results of this study.

Improving Volcanic SO2 Cloud Modeling Through Data Fusion and Trajectory Analysis: A Case Study of 2022 Hunga Tonga Eruption

Dataset Overview: This dataset comprises approximately 500 clusters of aggregated observational data collected from January 16 to 20 during the ascending (ASC) and descending (DES) periods. We grouped a large number of observation points into these clusters and calculated trajectories from the center of each cluster. The choice of 500 clusters was driven by pragmatic considerations, aiming for a balance between computational feasibility and the level of detail needed for our analysis. Data Unit Description: The "mass" values in this dataset for each cluster are calculated by multiplying the mass per unit area (g/m2) of individual data points by the area covered by each point, thus providing the total mass in grams (g). The "heights" are presented in units of kilometers (km), representing the observed top heights of each cluster.

Data of resulting velocity and anisotropic models, and Moho depth of the North China Craton

<pre>1. NCC_Pn_Velocity_Anisotropy_Model.txt        Data of our resulting velocity and anisotropic models. Format: longitude, latitude, vel_value (km/s), ani_value (km/s), azimuth_anisotropy (degree), 2. NCC_Moho_Depth_Model.txt        Data of our resulting Moho depth. Format: longitude, latitude, moho_depth (km)</pre>