Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Code and data for RCEMIP-II: Mock-Walker Simulations as Phase II of the Radiative-Convective Equilibrium Model Intercomparison Project

Model configuration code and post-processed data for simulations with SAM6.11.2 (Khairoutdinov and Randall, 2003) and CAM6 (https://github.com/ESCOMP/CESM/releases/tag/release-cesm2.1.3) needed to reproduce figures in the protocol paper for RCEMIP-II (Wing et al., 2023): Wing, A. A., Silvers, L. G., and Reed, K. A.: RCEMIP-II: Mock-Walker Simulations as Phase II of the Radiative-Convective Equilibrium Model Intercomparison Project, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-235, in review, 2023. SAM6.11.2 data (SAM6.11.2-lambda6000.zip and SAM6.11.2-lambda6144.zip): <ul> <li>lambda6000: simulations with wavelength 6000 km</li> <li>lambda6144: simulations with wavelength 6144 km</li> <li>Each simulation, for a given mean SST ($SST) and delta SST ($dT) has the following data files <ul> <li>crh_avg_$SST_$dT.mat: column relative humidity averaged over the short (y) dimension, as a function of x and time.</li> <li>mockwalker2048x128x74_3km_12s_$SST_$dT.nc: domain-averaged 0D (function of t) and 1D (function of z and t) data <ul> <li>The "long" simulations, which have a domain that is twice as long as normal, instead have files with names mockwalker4096x128x74_3km_12s_$SST_$dT.nc</li> <li>The "wide" simulations, which have a domain that is twice as wide as normal, instead have files with names mockwalker2048x256x74_3km_12s_$SST_$dT.nc</li> <li>The "longwide" simulations, which have a domain that is twice as long and twice as wide as normal, instead have files with names mockwalker4096x256x74_3km_12s_$SST_$dT.nc</li> </ul> </li> <li>SAM_CRM_MW_$SST_$dT_1D_cldfrac_avg.nc: domain cloud fraction profile (function of z and t) following cfv2 definition of Stauffer and Wing (2022)</li> </ul> </li> </ul> SAM6.11.2 configuration files (SAM6.11.2-lambda6000-config.zip and SAM6.11.2-lambda6144.zip): <ul> <li>lambda6000: simulations with wavelength 6000 km</li> <li>lambda6144: simulations with wavelength 6144 km</li> <li>Each simulation, for a given mean SST and delta SST has the following configuration files <ul> <li>snd: Initial sounding</li> <li>prm: Namelist parameters</li> <li>grd: Vertical grid</li> <li>domain.f90: Domain size and number of subdomains</li> <li>simpleocean.f90: SST specification</li> </ul> </li> </ul> CAM6 data (CAM6.zip): <ul> <li>Each simulation, for a given mean SST ($SST) and delta SST ($dT) has the following data files <ul> <li>MockWalk54_humidity_HCF_$dT_$SST.nc: column relative humidity averaged over 4 longitude points, as a function of latitude and time.</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_0D_rlut_avg.nc: domain-averaged longwave flux at the top of the atmosphere</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_0D_rsut_avg.nc: domain-averaged upwelling shortwave flux at the top of the atmosphere</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_0D_rsdt_avg.nc: domain-averaged downwelling shortwave flux at the top of the atmosphere</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_1D_cldfrac_avg.nc: domain-averaged cloud fraction profile (function of z and t)</li> </ul> </li> </ul> CAM6 configuration files (CAM6-MW295dT1p25-config.tar, CAM6-MW300dT1p25-config.tar, CAM6-MW305dT1p25-config.tar): Contains model initialization and configuration files for simulations with delta SST = 1.25 K. Simulations with other delta SST values need only change the delta SST parameter.

Data for: The meta-analysis of the effects of spatial sampling bias correction on presence only species distribution models

This dataset contains information extracted from 70 studies identified through a systematic review of the peer-reviewed literature (Web of Science and SCOPUS databases both searched on the 13/02/2023) to evaluate the effect of spatial sampling bias correction methods in presence-only species distribution models.

opencc-by-4.0Jan 2021View details →

Data and scripts for the submission "A locally smoothed terrain-following vertical coordinate to improve the simulation of fog and low stratus in numerical weather prediction models"

Dataset and scripts used to generate Figures for "A locally smoothed terrain-following vertical coordinate to improve the simulation of fog and low stratus in numerical weather prediction models", submitted to the Journal of Advances in Modeling Earth Systems (JAMES). Scripts: Python and NCL Data: Netcdf, PNG, Python pickled objects

Colville River Delta Sea Ice Model and Output Data Files

In this study, we used a 1D Delft3D-FLOW model to simulate the temporal development of the Colville River Delta, AK during the most active Arctic seasons. Simulations focused on the deltaic clinoform (i.e., the cross-sectional view of a delta) and used a floating barge structure to mimic the effects of sea ice on surface waters. Delft3D simulations were coupled with modules written in MATLAB and outputs were process in MATLAB.The dataset includes an example model run file (Delft3D-FLOW and MATLAB) and output of results from simulations used to assess Arctic delta development under sea ice.  Files include:<ol><li>Example Delft3D-FLOW model setup file, MATLAB run script, and ice files for a 1500-year simulation.</li><li>Processed MATLAB structures and metadata for model results<ol><li>Long-term Delta Developmental Outputs (1500-year simulations)<ol><li>Ice-free</li><li>Ice-affected</li><li>Ice-free with waves</li><li>Ice-affected with waves</li></ol></li><li>Varying Sea Ice Characteristics Outputs (500-year simulations)<ol><li>Ice matrix (six simulations)</li></ol></li><li>Future Arctic Delta Scenarios Outputs (450-year simulations)  <ol><li>Scenario A</li><li>Scenario B</li></ol></li></ol></li></ol>

opencc-by-4.0Nov 2023View details →

Simulation data and surrogate model for the DTU 10MW reference wind turbine including down-regulation, power boosting and individual blade control

This contribution provides the simulated data and surrogate models for the DTU 10 MW reference wind turbine in an onshore configuration simulated with FAST v8.16.00. The dimensions include mean wind speed, turbulence intensity, and power level, as well as the application of an individual blade control (IBC) loop. Down-regulation up to 50% is considered using two controller trajectories. The constTSR trajectory considers only pitching for down-regulation, maintaining a constant tip speed ratio, and the lin70 trajectory considers both pitch and rotational speed reduction to achieve down-regulation. Power boosting is performed up to 130% power level by following the optimal Cp trajectory until the requested power level is reached. The regression is done with two methods: a spline-based interpolation and a  Gaussian Process Regression (GPR). The raw data, smoothened data, and the trained GPR models are provided along with scripts for generating the surrogate model's predictions with both methods. A short description of the simulation parameters and variables considered is given in the supplementary pdf file. The dataset is part of the doctoral thesis 'Wind Turbine Operational Optimization Considering Revenue and Fatigue Objectives' by Vasilis Pettas at the University of Stuttgart (<a href="http://dx.doi.org/10.18419/opus-13959">http://dx.doi.org/10.18419/opus-13959</a>) and the journal publication 'Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities' <a href="https://doi.org/10.3390/en17061284">(https://doi.org/10.3390/en17061284</a>). Detailed analysis of the controller design and validation of the surrogate models can be found in these publications.

Digital research data from: Evaluation of a pH- and time-dependent model for the sorption of heavy metal cations by poultry litter-derived biochar

This is digital research data corresponding to a published manuscript, Evaluation of a pH- and time-dependent model for the sorption of heavy metal cations by poultry litter-derived biochar. Chemosphere (2024), 347, 140688. https://doi.org/10.1016/j.chemosphere.2023.140688. Common isotherm and kinetic models cannot describe the pH-dependent sorption of heavy metal cations by biochar. In this paper, we evaluated a pH-dependent, equilibrium/kinetic model for describing the sorption of cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) by poultry litter-derived biochar (PLB). We performed sorption experiments across a range of solution pH, initial metal concentration, and reaction time.

Foot shape-function model data

The human foot is a complex structure that plays an important role in our capacity for upright locomotion. Comparisons of our feet to those of our closest extinct and extant relatives have linked shape features (e.g., the longitudinal and transverse arches, heel size and toe length) to specific mechanical functions. However, foot shape varies widely across the human population, so it remains unclear if and how specific shape variants are related to locomotor mechanics. Here we construct a statistical shape-function model (SFM) from 100 healthy participants to directly explore the relationship between the shape and function of our feet. We also examined if we could predict the joint motion and moments occurring within a person's foot during locomotion based purely on shape features. The SFM revealed that the longitudinal and transverse arches, relative foot proportions and toe shape along with their associated joint mechanics were the most variable. However, each of these only accounted for small proportions of the overall variation in shape, deformation, and joint mechanics, most likely due to the high structural complexity of the foot. Nevertheless, a leave-one-out analysis showed that the SFM can accurately predict joint mechanics of a novel foot, based on its shape and deformation.

Data from: Effect of stretching on inflammation in a subcutaneous carrageenan mouse model analyzed at single-cell resolution

Understanding the factors that influence the biological response to inflammation is crucial, due to its involvement in physiological and pathological processes, including tissue repair/healing, cancer, infections, and autoimmune diseases. We have previously demonstrated that in vivo stretching can reduce inflammation and increase local pro-resolving lipid mediators in rats, suggesting a direct mechanical effect on inflammation resolution. Here, we aimed to explore further the effects of stretching at the cellular/molecular level in a mouse subcutaneous carrageenan-inflammation model. Stretching for 10 minutes twice a day reduced inflammation, increased the production of pro-resolving mediator pathway intermediate 17-HDHA at 48h post carrageenan injection, and decreased both pro-resolving and pro-inflammatory mediators (e.g., PGE2 and PGD2) at 96h. ScRNAseq analysis of inflammatory lesions at 96h showed that stretching increased the expression of both pro-inflammatory (Nos2) and pro-resolution (Arg1) genes in M1 and M2 macrophages at 96 hours. An intercellular communication analysis predicted specific ligand-receptor interactions orchestrated by neutrophils and M2a macrophages, suggesting a continuous neutrophil presence recruiting immune cells such as activated macrophages to contain the antigen while promoting resolution and preserving tissue homeostasis.

opencc-by-4.0Jul 2023View details →

Model outputs and data used in the study

The aqua_chl.rar is Chla satellite remote sensing data. The Cempaka.txt is the Typhoon Cempaka data obtained fron CMA.The Lupit.txt is the Typhoon Lupit data obtained fron CMA.The PRE_mod.mat is the model outputs.The diagnose_budget.rar is diagnostic output from the model.The salinity.mat file contains both the measured and simulated salinity data.

ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries – Data and Models

This repository contains data and models used in the following paper.   Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J. C., Shivnaraine, R. V., & Zou, J. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries. In review.   The data and models are meant to be used with the <a href="https://github.com/swansonk14/admet_ai">ADMET-AI</a> code, which runs the ADMET-AI web server at <a href="https://admet.ai.greenstonebio.com/">admet.ai.greenstonebio.com</a>.   The data.zip file has the following structure. data   drugbank: Contains files with drugs from the <a href="https://go.drugbank.com/">DrugBank</a> that have received regulatory approval. drugbank_approved.csv contains the full set of approved drugs along with ADMET-AI predictions, while the other files contain subsets of these molecules used for testing the speed of ADMET prediction tools.   tdc_admet_all: Contains the data (.csv files) and RDKit features (.npz files) for all 41 single-task ADMET datasets from the <a href="https://tdcommons.ai/">Therapeutics Data Commons</a> (TDC).   tdc_admet_multitask: Contains the data (.csv files) and RDKit features (.npz files) for the two multi-task datasets (one regression and one classification) constructed by combining the tdc_admet_all datasets.   tdc_admet_all.csv: A CSV file containing all 41 ADMET datasets from tdc_admet_all. This can be used to easily look up all ADMET properties for a given molecule in the TDC.   tdc_admet_group: Contains the data (.csv files) and RDKit features (.npz files) for the 22 TDC ADMET Benchmark Group datasets with five splits per dataset.   tdc_admet_group_raw: Contains the raw data (.csv files) used to construct the five splits per dataset in tdc_admet_group.   The models.zip file has the following structure. Note that the ADMET-AI website and Python package use the multi-task Chemprop-RDKit models below. models   tdc_admet_all: Contains Chemprop and Chemprop-RDKit models trained on all 41 single-task TDC ADMET datasets.   tdc_admet_all_multitask: Contains Chemprop and Chemprop-RDKit models trained on the two multi-task TDC ADMET datasets (one regression and one classification).   tdc_admet_group: Contains Chemprop and Chemprop-RDKit models trained on the 22 TDC ADMET Benchmark Group datasets.

The Sediment Thickness Model for Andalusia (STMA) data, intermediate data and scripts

This dataset contains the Sediment Thickness Model for Andalusia (South of Spain), the GroundHog files and the consulting scripts linked to the paper "Thickness model of Andalusian's nearshore and coastal inland topography " under review in Journal of Marine Science and Engineering.The following ZIP files can be found here:=============== STMA.zipA dataset of 108 ESRI ASCII grid files group by province (Almeria, Cadiz, Granada, Huelva and Malaga) and by physiographic zone (18). Each zone has six different files named as:PZZ_V_T_S.ascwhere,P: It is the first letter of the province (e.g. A for Almeria, C for Cadiz…)ZZ: number of physiographic zone from a minimum number of 2 to 6 in each province. Each province was divided by different overlapping rectangles following on the orientation of the coastline, the shape of the continental shelf, river intersections, capes, the main sediment type, and the level of influence from atmospheric and maritime weathering agents, primarily.V: the model versionT: Type of sediment, Consolidated (C) and Unconsolidated (U)S: Grain size, Fine (F), Sand (S) and Gravel (G)e.g. the file A01_1_C_F.asc is the province of Almeria, zone 01, version 1, Consolidated sediment and Fine fraction.All files have a projection file (EPSG 25830) with the same name but a *.prj extension and a auxiliary file (*.asc.aux.xml) with additional information about the projection used. =============== Script.zipSpecifically, three Pyqgis scripts and three Model Qgis: <ul><li>1_Add_STMA_by_province_and_zone.py: to add the STMA to QGIS grouping the layers by province and zones.</li><li>2_Point_value_STMA.py: to add the STMA value to a point sample layer.</li><li>3_Zonal_statistics_STMA.py: to calculate the zonal statistics of STMA in a polygon area.</li><li>1_Volume_STMA.model3: Calculate the volume of each material (six raster layer) in one zone.</li><li>2_Volume_STMA_Clip.model3: Calculate the volume of each material in a clip area using a polygon layer.</li><li>3_Zonal_statistics_STMA.model3: Zonal statistics of the STMA in a polygon layer. It is a model version of the last pyqgis script 3_Zonal_statistics_STMA.py.</li></ul>The first two models could be used in a batch processing if the user needs information of more than one zone.=============== GroundHog.zipFive folders, one for each province, with information to open the models in Groundhog software.

Modelling data for: Short-course combination treatment for experimental chronic Chagas disease

Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, affects millions of people in the Americas and across the world leading to considerable morbidity and mortality. Current treatment options, benznidazole (BNZ) and nifurtimox, offer limited efficacy and often lead to adverse side effects due to long treatment durations. Better treatment options are therefore urgently required. Here we describe a pyrrolopyrimidine series, identified through phenotypic screening, that offers a clear opportunity to improve on current treatments. In vitro cell-based washout assays demonstrate that compounds in the series are incapable of killing all parasites, however, combining these pyrrolopyrimidines with a sub-efficacious dose of BNZ can clear all parasites in vitro after five days. Importantly, these findings were replicated in a clinically predictive in vivo model of chronic Chagas disease, where five days of treatment with the combination was sufficient to prevent parasite relapse. Comprehensive mechanism of action studies, supported by ligand-structure modelling, show that compounds from this pyrrolopyrimidine series inhibit the Qi active site of T. cruzi cytochrome b, part of the cytochrome bc1 complex of the electron transport chain. Knowledge of the molecular target enabled a cascade of assays to be assembled to evaluate selectivity over the human cytochrome b homologue. As a result, a highly selective and efficacious lead compound was identified. The combination of our lead compound with BNZ rapidly clears T. cruzi parasites, both in vitro and in vivo, and shows great potential to overcome key issues associated with currently available treatments.

opencc-by-4.0Aug 2023View details →

Additional data and models for the article "Role of metasomatism in the development of the East African Rift at the Northern Tanzanian Divergence: Insights from 3D magnetotelluric modelling."

Additional data and models for the article "Role of metasomatism in the development of the East African Rift at the Northern Tanzanian Divergence: Insights from 3D magnetotelluric modelling." This data package includes: 1-ModEM rho and dat format files of the final preferred model. 2-vtk version of this model 3-Scripts to plot the MT models 4-Water content models calculated with MATE 5-Scripts to plot the water content models 6-Parameter files used in water calculation with MATE 7- EDI files used in the model.

Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be

Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be

Data from: Spectral wear modelling of rubber friction on a hard substrate with large surface roughness

Soft-hard matter friction is a long-standing tribology problem that remains unclarified, requiring engineers to empirically predict the wear life. To clarify this issue, this study examines the transient running-in regime of rubber friction on a hard rough substrate and models the temporal wear progression using the spectrum curves of surface roughness for both materials. Performing a series of friction tests and three-dimensional surface-height measurements, the time-dependent behaviours of the power spectral densities (PSDs) are divided into two phases, namely the initial non-steady and long-term steady phases. The detailed spectral analyses of worn rubber surfaces in the initial phase lead to a blended PSD function between self-affine and K-correlation surface models, consisting of one variable (the Hurst exponent) that is saturated by the substrate self-affinity. Supported by the Greenwood–Williamson theory concerning rough contact mechanics, the volumetric estimate with the blended PSD function is used to assess the volume rate of wear debris in the steady phase, which is validated experimentally. These findings not only improve the wear predictions of soft materials from previous measurements of worn surfaces but also help clarify the constrained multiscale mechanism of wear.

Data from: Gene modelling and annotation for the Hawaiian bobtail squid, Euprymna scolopes

Coleoid cephalopods possess numerous complex, species-specific morphological and behavioural adaptations, e.g., a uniquely structured nervous system that is the largest among the invertebrates. The Hawaiian bobtail squid Euprymna scolopes is one of the most established cephalopod species. With its recent publication of the chromosomal-scale genome assembly and regulatory genomic data, it also emerges as a key model for cephalopod gene regulation and evolution. However, the latest genome assembly has been lacking a native gene model set. Our manuscript describes the generation of new long-read transcriptomic data and, combined with a plethora of available transcriptomic datasets, a new reference annotation for E. scolopes.

Data underpinning "Quantitative functional renormalization for three-dimensional quantum Heisenberg models"

Data and script for creation of the plots. To install the software environment start julia in the directory of the files, type `] activate .` then `instantiate`

Data for "The Green's Function Model Intercomparison Project (GFMIP) Protocol"

Data used in the analysis for the GFMIP Protocol paper, in the form of .jld2 files, to be used in conjunction with <a href="../doi/10.5281/zenodo.7697344">https://zenodo.org/doi/10.5281/zenodo.7697344</a>

Data for evaluation of modelled versus observed NMVOC compounds at EMEP sites in Europe

Data (model outputs, emission files, python scripts) for the publication of "Evaluation of modelled versus observed NMVOC compounds at EMEP sites in Europe".

Supporting Data for "Local exposure misclassification in national models: relationships with urban infrastructure and demographics"

Overview: This dataset accompanies the recent publication "Local exposure misclassification in national models: relationships with urban infrastructure and demographics" (DOI: 10.1038/s41370-023-00624-z). It provides essential data for replicating and extending the analysis conducted in the study. The script for the analysis is available at https://github.com/SEChambliss-AQ/LD-analysis. The dataset consists of four key files. Files Included: <ol> <li> Gridded OSM and GSV Data (gridded_OSM_GSV.RDS): <ul> <li>This R object offers a 100mx100m grid covering select neighborhoods in the San Francisco Bay Area.</li> <li>Each grid cell includes average air pollution levels (Ultrafine Particle Count in thousand count per cubic meter; Nitrogen Dioxide in ppb) from mobile monitoring.</li> <li>The file also provides normalized z-scores of local density of urban infrastructure related to air pollutants based on OpenStreetMap (OSM) data.</li> <li>Further details can be found in the associated publication.</li> </ul> </li> <li> BartMachine Outputs (bartMachine R objects.zip): <ul> <li>A collection of R object outputs from the bartMachine package, an R-Java Bayesian Additive Regression Trees implementation.</li> <li>These objects, which can be regenerated using the provided script, are included to reduce computational requirements for future analyses.</li> <li>The script for generating these outputs is available at the above <a href="https://github.com/SEChambliss-AQ/LD-analysis">GitHub Repository</a>.</li> </ul> </li> <li> NO2 Predictions (CACES_criteria.csv): <ul> <li>Land Use Regression (LUR) data for Nitrogen Dioxide, provided by the Center for Air, Climate, and Energy Solutions.</li> <li>Methodologies for integrating these data with the gridded dataset are described in the publication and script.</li> </ul> </li> <li> UFP Predictions (CACES_UFP.csv): <ul> <li>Similar to the NO2 dataset, this file contains LUR data for Ultrafine Particle predictions.</li> <li>Methods for data integration are detailed in the publication and available script.</li> </ul> </li> </ol> Usage: These files are intended for researchers and analysts aiming to replicate or build upon the study's findings. They provide a source of data for exploring the complex interplay between air pollution, urban infrastructure, and demographic factors in urban environments. For detailed methodology and analysis, refer to the original publication and the accompanying GitHub repository.