Find research datasets worth reusing

Development of a Predictive Model for In-Hospital Mortality in COVID-19 Patients Using CAR, IL-6, IL-6/LY, and NLR: A Single-Center Study in Indonesia

Table 1. Demographic data of the subject *mean (+SD); #median (Q1-Q3)   Table 2. CAR, IL-6, IL-6/LY, NLR cut-off values and performance in determining mortality

opencc-zeroMay 2023View details →

Predicting species richness and diversity using satellite remote sensing and random forest machine learning algorithm

Aims: Remote sensing approaches could be beneficial for monitoring and compiling essential biodiversity data because it is cost-effective and allows for coverage of large areas over a short period. This study investigated the relationship between multispectral remote sensing data from Landsat 8 and Sentinel 2 and species richness and diversity in mountainous and protected grasslands. Locations: Golden Gate Highlands National Park, Free State, South Africa. Methods: In-situ data of plant species composition and cover from 142 plots with 16 releves each were distributed across the study site and used to calculate species richness and Shannon-wiener species diversity index (species diversity. We used a machine-learning random forest algorithm to optimise the prediction of species richness and diversity. The algorithm was used to identify the optimal spectral bands and vegetation indices for estimating species richness and diversity. Subsequently, the selected bands and vegetation indices were used to estimate species richness through random forest regression. Results: This research found weak relationships between remote sensing vegetation indices and the diversity metrics, but significant relationships were found between some spectral bands and diversity metrics. Moreover, using machine learning random forest, the multispectral datasets exhibited strong predictive powers. In this investigation, for both sensors, near-infrared (NIR) seemed to be the most selected band to explain species diversity in mountainous grasslands. Main conclusions: This finding further ascertains the efficiency of using NIR in vegetation mapping. This research shows that NIR, SAVI and EVI are the most adequate for predicting species richness and diversity in mountainous grasslands with relatively good accuracies.

opencc-zeroMay 2023View details →

Genetic factors predict hybrid formation in the British flora

Natural hybridization can have a profound evolutionary impact, with consequences ranging from the extinction of rare taxa to the origin of new species. Natural hybridization is particularly common in plants; however, our understanding of the general factors that promote or prevent hybridization is hampered by the highly variable outcomes in different lineages. Here, we quantify the influence of different predictors on hybrid formation across species from an entire flora. We combine estimates of hybridization with ecological attributes and a new species-level phylogeny for over 1,100 UK flowering plant species. Our results show that genetic factors, particularly parental genetic distance, as well as phylogenetic position and ploidy, are key determinants of hybrid formation, whereas many other factors such as range overlap and genus size explain much less variation in hybrid formation. Overall, intrinsic genetic factors shape the evolutionary and ecological consequences of natural hybridization across species in a flora.

Dataset of Flow Velocity Prediction in Vegetated Alluvial Channels Comparing Empirical and State-of-the-art Hybrid Machine Learning Models

We compiled 447 datasets from different sources and lab- and field-based measurements. These datasets included Einstein and Banks (1950), Fenzl (1962), Kouwen et al. (1969), Ree and Crow (1977), Murota (1984), Tsujimoto and Kitamura (1990), Tsujimoto (1991), Tsujimoto (1993), Shimizu (1994), Dunn et al. (1996), Ikeda and Kanazawa (1996), Meijer (1998), Jarvela (2002), Rowinski and Kubrak (2002), Stone and Shen (2002), Poggi et al. (2004), Carollo et al. (2005), and Murphy et al. (2007).

Img2brain: Predicting the neural responses to visual stimuli of naturalistic scenes using machine learning

The data for this project is part of the <a href="https://doi.org/10.1038/s41593-021-00962-x">Natural Scenes Dataset</a> (NSD), a massive dataset of 7T fMRI responses to images of natural scenes coming from the <a href="https://cocodataset.org/#home">COCO dataset</a>. The training dataset consists of brain responses measured at 10.000 brain locations (voxels) to 8857 images (in jpg format) for one subject. The 10.000 voxels are distributed around the visual pathway and may encode perceptual and semantic features in different proportions. The test dataset comprises 984 images (in jpg format), and the goal is to predict the brain responses to these images. The zip file contains the following folders: 1. trainingIMG: contains the training images (8857) in jpg format. The numbering corresponds to the order of the rows in the brain response matrix. 2. testIMG: contains test images (984) in jpg format. 3. trainingfMRI: contains a npy file with the fMRI responses measured at 10000 brain locations (voxels) to the training images. The matrix has 8857 rows (one for each image) and 10000 columns (one for each voxel).

opencc-by-4.0Dec 2022View details →

A Deep Learning Approach to Afternoon Convection Prediction Using Heterogeneous Weather Data: Taking the Important Areas in Taiwan as an Example

The following partial dataset is provided by the authors for the Afternoon Convection Prediction  method presented in the paper. afternoon_rainfall_label is labeled as afternoon convection data weather_image is weather images

Data for 'Evaluating WRF-GC v2.0 predictions of boundary layer and vertical ozone profiles during the 2021 TRACER-AQ campaign in Houston, Texas'

This repository provides observation datasets, model configuration files, model boundary conditions, model input files, and scripts used in the manuscript titled 'Evaluating WRF-GC v2.0 predictions of boundary layer and vertical ozone profiles during the 2021 TRACER-AQ campaign in Houston, Texas'.

Data associated with Cell Reports publication: Dura-Bernal et al. 2023, "Multiscale model of primary motor cortex circuits predicts in vivo cell type-specific, behavioral state-dependent dynamics"

This dataset includes experimental data used to constrain and validate the model, and model simulation output data. The source code for the associated M1 model and data analysis can be found here: https://github.com/suny-downstate-medical-center/M1_NetPyNE_CellReports_2023 Please download the data_v2.zip file, which contains the most updated and complete version of the data. For more information please contact: salvador.dura-bernal@downstate.edu

DOX_BDW: Incorporating Solvation and Desolvation Effects of Cavity Water into Nonfitting Protein–Ligand Binding Affinity Prediction

structures.zip:  including the coordinates of all optimized proteinligand complex structure obtained by DOX_BDW calculation. (compressed PDB file). These pdb files could also be  used as input for the binding energy calculation,as illustrated in SI section 8.  mdinput.zip: Including the input files,parameter files, topology files needed to run MD simulation for water mapping, as illustrated in SI section 8. Note that all of the parameter files and topology files would be automatically generated using the RUNMD program we uploaded with the example file.  example.zip: The programs and input files needed to run an example, as illustrated in SI section 9. And all the output files except MD trajectories are in there,too.

opencc-zeroJun 2023View details →

Prediction of current and future suitable habitats for three invasive freshwater fish species (Lepomis gibbosus, Perccottus glenii and Pseudorasbora parva) in Europe

Climate change can have a significant impact on the earth's ecosystems. Invasive species will respond to climate change, and their responses will have ecological and economic implications. Habitat suitability models (HSMs) are one of the most important tools currently available to assess the potential impacts of climate change on species. Projections of models of suitable conditions for species, built using Maxent based on the occurrence throughout the range (native and invasive), on the current climate of Europe and on the forecast climate data for the 2050s and 2070s under the SSP2 and SSP5 scenarios are present here.

A New GFSv15 based Climate Model Large Ensemble and Its Application to Understanding Climate Variability, and Predictability

Data and analysis scripts for figures of Journal article (A New GFSv15 based Climate Model Large Ensemble and Its Application to Understanding Climate Variability, and Predictability)

opencc-zeroJun 2023View details →

Data for: Functional response metrics explain and predict high but differing ecological impacts of juvenile and adult lionfish

Recent accumulation of evidence across taxa indicates that the ecological impacts of invasive alien species are predictable from their Functional Response (FR; e.g. the maximum feeding rate) and Functional Response Ratio (FRR; the FR attack rate/handling time ratio). Here, we experimentally derive these metrics to predict the ecological impacts of both juvenile and adult lionfish (Pterois volitans), one of the world's most damaging invaders, across representative and likely future prey types. Potentially prey-population destabilising Type II FRs were exhibited by both life stages of lionfish towards four prey species: Artemia salina, Gammarus oceanicus, Palaemonetes varians and Nephrops norvegicus. FR magnitudes revealed ontogenetic shifts in lionfish impacts, while lionfish FRR values were substantially higher than mean FRR values across known damaging invasive taxa. Thus, both life stages of lionfish are predicted to contribute to differing but high ecological impacts across prey communities, including commercially important species. With lionfish invasion ranges currently expanding across multiple regions globally, efforts to reduce lionfish numbers and population size structure, and provision of prey refugia through habitat complexity, might reduce their impacts. However, early detection and complete eradication of individuals located in new regions is advised.

Unlocking the Predictive Power of Quantum-Inspired Representations for Intermolecular Properties in Machine Learning

Dataset associated with the manuscript entitled "Unlocking the Predictive Power of Quantum-Inspired Representations for Intermolecular Properties in Machine Learning".  See Readme file (markdown format) for details on how the data is structured in the "database" file.

TBAQ, TCAQ and TBQ1-5 structures predicted at the BMK/6-31G(d,p) (+PCM) level of theory

This dataset contains the BMK/6-31G(d,p) structures of possible conformers for TBAQ and TCAQ and the structures of the TBQ1-5 dimers in CH2Cl2, using the PCM solvation model

Dataset for Dynamic expectations: Behavioral and electrophysiological evidence of sub-second updates in reward predictions

This is the dataset for the paper entitled "Dynamic expectations: Behavioral and electrophysiological evidence of sub-second updates in reward predictions" by Marciano et al.

ResNet 34 Ensemble Predictions on TinyImageNet

Predictions generated by an ensemble of 4 ResNet 34 Deep Neural Networks Trained on TinyImagenet, as used in repository https://anonymous.4open.science/r/ensemble_attention-7616/README.md. Ensembles are trained to encourage/discourage predictive diversity. Each timestamped folder contains individual training runs, with the labels and probabilistic predictions of the ensemble on 1) the training set (train_labels.npy, train_preds.npy) and 2) the test set (ind_labels.npy, ind_preds.npy) for tinyimagenet. The file (tinyimagenet/resnet34/version_0/hparams.yaml) contains specific hyperparameters used on a particular training run. Figures visualizing training results can be generated by: 1.unzipping the four folders in to the directory `ensemble_attention/scripts/outputs/`  2. running the script `ensemble_attention/scripts/vis_scripts/all_weights_resnet34_tinyimagenet.py`.

Dataset: Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture

Dataset for Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture manuscript submitted to Plant Phenomics. The dataset contains raw and processed root architecture images, RhizoVision trait outputs, and the associated R scripts for statistical analysis and model parameterization.

A federated learning framework based on transfer learning and knowledge distillation for targeted advertising-Click-Through Rate Prediction Dataset

https://www.kaggle.com/c/avazu-ctr-prediction

openMay 2023View details →

Fast Indoor Radio Propagation Prediction Using Deep-Learning Dataset

We show a dataset composed by Radio Maps Estimation (RME) and Cells Maps Estimation (CME) for the 5GHz band WIFI in indoor scenarios: it has 60 indoor constructions plans and 1000 distributions initially for a training process and 20 indoor constructions plans and 50 distributions aditionals for a test process of access points to even construction. These distributions are random and several WLAN's structures: 1 to 5 access points. The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels. Every coverage map have like maximum power delivered is Pr = Pt = 26 dBm (according to data from <a href="https://www.cisco.com/c/en/us/products/wireless/catalyst-9100ax-access-points/index.html">current commercial equipment</a>) and like minimum power a value noise established in Pr = KTB, where K is the Boltzmann's constant, T the enviroment temperatura equal to 290°K and B the band width equal to 80MHz. Dataset DeepFIRP is the result of a lot of simulations by a <a href="https://doi.org/10.5281/zenodo.7983595">own software developed in MATLAB</a> that work with the<a href="https://mentor.ieee.org/802.11/dcn/14/11-14-0882-04-00ax-tgax-channel-model-document.docx"> IEEE 802.11ax channel model</a>. The pictures have a depth of 8 bits and size of 256pixels X 256pixels equivalents to indoor constructions of 20 X 20 m2. These ones make reference to offices's spaces at general or classroom.  A application to this dataset and the codes used for generate it is found <a href="https://github.com/johanflorez98/Fast-Indoor-Radio-Propagation-Prediction-Using-Deep-Learning">here</a>, where we implement a U-Net model for theRME and CME in indoor enviroments. This investigation contribute in novels methods for estimate by fast way coverage and cells maps using deep-learning in comparation with the conventional phisics methods like dominath-path model or ray-tracing. Whats allows save a lot of amount time in the WLANs's designs.