Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
575
datasets available to search
ShareScore release 0.9.0
Dataset results
575 results for “Modeling Codes”
Data and R-Code from: How to account for behavioral states in step-selection analysis: a model comparison
<p>This repository provides the R-code and data used for the simulation and case study of the research paper: "How to account for behavioral states in step-selection analysis: a model comparison".</p><p>The folder "<strong>Pohle_et_al_2023_BehavioralStates_iSSA_Data</strong>" contains the landscape rasters used for data generation in the simulation study, and the bank vole (<i>Myodes glareolus</i>) movement data used in the case study on bank vole interactions:</p><ul><li>landscape10.RData and landscape50.RData: Landscape rasters for the simulation study.</li><li>Vole_case_control.rds: Case-control bank vole data for the case study.</li><li>Info_replicates.rds: Information about bank vole indiviuals and corresponding replicates for the case study.</li><li>Codebook_case_study.xlsx: Codebook for the case study data sets.</li><li>Read_me.txt</li></ul><p>The folder "<strong>Pohle_et_al_2023_BehavioralStates_iSSA_RCode</strong>" contains the R-scripts for the simulation and case study:</p><ul><li>Functions.R: Functions to apply HMMs, TS-iSSAs, and HMM-iSSAs to movement data; used for the simulation and case study.</li><li>Simulation_study.R: R-Code to run the simulation study. Parallel computation is used.</li><li>Results_simulation_study.R: R-Code to create the result figures and tables for the simulation study.</li><li>Case_study.R: R-Code to run the bank vole interaction case study. Parallel computation is used.</li><li>Results_case_study.R: R-Code to create the result figures and tables for the case study.</li><li>Read_me.txt</li></ul><p>Besides the simulation and case study from the paper, the included functions (<i>Functions.R</i>) can generally be used to perform an HMM-iSSA analysis.</p><p>For the bank vole movement data without control locations, see: Schlägel, U.E. et al. (2019). Data from: Estimating interactions between individuals from concurrent animal movements [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.rt535m8">https://doi.org/10.5061/dryad.rt535m8</a>.</p><p><strong>Acknowledgements</strong></p><p>We thank Sophie Eden, Angela Puschmann and Pauline Lange for help with the bank vole data collection and maintenance of the outdoor enclosures.</p><p> </p><p> </p>
Supplements for "The Proteomic Code: Novel Amino Acid Residue Pairing Models "Encode" Protein Folding and Protein-Protein Interactions"
<p><strong>Supplements for "The Proteomic Code: Novel Amino Acid Residue Pairing Models “Encode” Protein Folding and Protein-Protein Interactions"</strong></p> <p>This supplement includes the following files:</p> <ul> <li>Main_v02.R --- Script in R language to process protein structures and produce the datasets.</li> <li>datasets_v02.zip --- final filtered version of datasets produced in R language (by "Main_v02.R"). </li> <li>Dataset key v02.txt --- key and descriptions to column names in ("datasets.zip"). </li> </ul> <p>The article featuring these datasets is submitted to:</p> <p>Journal: Computers in Biology and Medicine (Reference: CBM_110033)<br>Title: "The Proteomic Code: Novel Amino Acid Residue Pairing Models "Encode" Protein Folding and Protein-Protein Interactions"<br>Authors: Hameduh, Tareq; Miller, Andrew D. ; Heger, Zbynek; Haddad, Yazan</p> <p> </p> <p> </p>
Brown dwarf atmosphere intensity grid produced by PICASO radiative transfer code and based on Sonora model atmospheres
<p>Specific intensity in erg s^−1 Hz^−1 sr^−1 cm^−2 on a grid of the cosine of the viewing angle 𝜇 = cos 𝜙, wavelength 𝜆, surface effective temperature, and surface gravitational acceleration that was computed from the Sonora pressure-temperature and abundance profiles, using version 2.3 of the open source code PICASO (Batalha et al. 2019; Batalha et al. 2022), which has previously been used to compute the thermal emission spectra of brown dwarfs (e.g. Mang et al. 2022) and exoplanets (e.g. Robbins-Blanch et al. 2022).</p> <p>This grid served as input to software PARS (Paint the Atmospheres of Rotating Stars; Lipatov and Brandt 2020) in an article that predicts the observational effects of rotation in brown dwarfs (Lipatov, Brandt, and Batalha 2022).</p>
Replication Package of ''Improving the Sustainability of Neural Code Summarization Models: An Empirical Study"
<p>This repository contains the replication package of "Improving the Sustainability of Neural Code Summarization Models: An Empirical Study".</p>
Data and code for training and testing a ResMLP model with experience replay for machine-learning physics parameterization
<p>This directory contains the training data and code for training and testing a ResMLP with experience replay for creating a machine-learning physics parameterization for the Community Atmospheric Model. </p> <p>The directory is structured as follows:</p> <p>1. Download training and testing data: https://portal.nersc.gov/archive/home/z/zhangtao/www/hybird_GCM_ML</p> <p>2. Unzip nncam_training.zip</p> <p>nncam_training</p> <p> - models</p> <p> model definition of ResMLP and other models for comparison purposes</p> <p> - dataloader </p> <p> utility scripts to load data into pytorch dataset</p> <p> - training_scripts</p> <p> scripts to train ResMLP model with/without experience replay</p> <p> - offline_test</p> <p> scripts to perform offline test (Table 2, Figure 2)</p> <p>3. Unzip nncam_coupling.zip</p> <p>nncam_srcmods</p> <p> - SourceMods</p> <p> SourceMods to be used with CAM modules for coupling with neural network</p> <p> - otherfiles</p> <p> additional configuration files to setup and run SPCAM with neural network</p> <p> - pythonfiles</p> <p> python scripts to run neural network and couple with CAM</p> <p> - ClimAnalysis</p> <p> - paper_plots.ipynb</p> <p> scripts to produce online evaluation figures (Figure 1, Figure 3-10)</p> <p> </p>
Toward emulating an explicit organic chemistry mechanism with a random forest model: dataset and training code
<p>This repository contains the dataset created with the GECKO-A model and the code (training_gecko_rf_final.py) used to train and test random forests for predicting secondary organic aerosol formation.</p> <p>For each simulation, results are distributed in two separate files identified as such:</p> <ul> <li><precursor>_library_<id>_predictors.csv and <precursor>_library_<id>_outcomes.csv.</li> <li><precursor> is either ARO1 (toluene) or dodecane_4gen (dodecane).</li> <li><id> is a unique simulation identifier.</li> <li>the *predictors.csv files contain the state of the predictors for each timestep at the beginning of the chemical solver integration step.</li> <li>the *outcomes.csv files contain the state of the outcomes at the end of the chemical solver integration step.</li> </ul> <p>The TRAINING_* directories contain training simulations. TRAINING_ALL contains all the training data, used for the default random forest configuration. TRAINING_*NOX contain sorted training data matching LOW, MID and HIGH NOx initial regimes (see associated article) to train the specialized random forests.</p> <p>Similarly, the VALIDATION_* directories contain validation simulations, used to test the random forests after training.</p> <p>The TESTING* directories contain the results of testing the random forest for comparison with the VALIDATION simulations.</p>
Models and data in support of "EMFEM: a parallel 3D modeling code for frequency-domain electromagnetic method using goal-oriented adaptive finite element method"
<p>These directories contain the model and data files for "EMFEM: a parallel 3D modeling code for frequency-domain electromagnetic method using goal-oriented adaptive finite element method".<br> </p>
The code and data for "Deep-Learning Correction Methods for WRF Model Precipitation Forecasting from 2014 Through 2022 in Zhengzhou city, China"
<p>The code and data for “Deep-Learning Correction Methods for WRF Model Precipitation Forecasting from 2014 Through 2022 in Zhengzhou city, China”</p>
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1) - MONICA model source code and data
<p>Zip file consisting of the data used in the manuscript "Evaluation and optimisation of the soil carbon turnover routine in the MONICA model".<br> The data consists of 11 German long term experimenting field sites, formatted in MONICA readable input files. Each consisting of one dataset for weather information (.met), and three .json files describing the management, site and soil properties of each treatment. The validation datasets consist of soil temperature and soil moisture measurement and are distinguishable by the .vals file type/format.</p> <p>For further information please refer to the manuscript.</p>
Open source code for Brain-inspired bodily self-perception model for robot rubber hand illusion
<p><a href="https://github.com/Brain-Cog-Lab/RHI#rhi">RHI</a></p> <p><a href="https://github.com/Brain-Cog-Lab/RHI#rhi_matlab">RHI_Matlab</a></p> <p>This file is the open-source code for 'Brain-inspired bodily self-perception model for robot rubber hand illusion', mainly used in simulation environments, and can reproduce various rubber hand illusion experiments.</p> <p><a href="https://github.com/Brain-Cog-Lab/RHI#rhi_braincog">RHI_BrainCog</a></p> <p>We are building an open source spiking neural network based brain-inspired cognitive intelligence engine for Brain-inspired Artificial Intelligence and brain simulation. Therefore, we also implemented the core Proprioceptive drift experiment of the rubber hand illusion experiment using Braincog.</p> <p><a href="https://github.com/BrainCog-X/Brain-Cog/tree/main/examples/Embodied_Cognition/RHI">The open source code built by BrainCog</a></p> <p>BrainCog provides essential and fundamental components to model biological and artificial intelligence. The current version of BrainCog contains at least 18 functional spiking neural network algorithms (including but not limited to perception and learning, decision making, knowledge representation and reasoning, motor control, social cognition, etc.) built based on BrainCog infrastructures, and BrainCog also provide brain simulations to drosophila, rodent, monkey, and human brains at multiple scales based on spiking neural networks at multiple scales.</p>
Data and Code for "An updated end-to-end ecosystem model of the Northern California Current"
<p>This upload contains all data and code for "An updated end-to-end ecosystem model of the Northern California Current reflecting ecosystem changes due to recent marine heat waves", submitted to PLOS ONE. Manuscript abstract is pasted below:</p> <p>The Northern California Current is a highly productive marine upwelling ecosystem that is economically and ecologically important. It is home to both commercially harvested species and those that are federally listed under the U.S. Endangered Species Act. Recently, there has been a global shift from single-species fisheries management to ecosystem-based fisheries management, which acknowledges that more complex dynamics can reverberate through a food web. Here, we have integrated new research into an end-to-end ecosystem model (i.e., physics to fisheries) using data from long-term ocean surveys, phytoplankton satellite imagery paired with a vertically generalized production model, a recently assembled diet database, fishery catch information, species distribution models, and existing literature. This spatially-explicit model includes 90 living and detrital functional groups ranging from phytoplankton, krill, and forage fish to salmon, seabirds, and marine mammals, and nine fisheries that occur off the coast of Washington, Oregon, and Northern California. This model was updated from previous regional models to account for more recent changes in the Northern California Current (e.g., increases in market squid and some gelatinous zooplankton such as pyrosomes and salps), to expand the previous domain to increase the spatial resolution, to include data from previously unincorporated surveys, and to add improved characterization of endangered species, such as Chinook salmon (<em>Oncorhynchus tshawytscha</em>) and southern resident killer whales (<em>Orcinus orca</em>). Our model is mass-balanced, ecologically plausible, without extinctions, and stable over 100-year simulations. Ammonium and nitrate availability, total primary production rates, and model-derived phytoplankton time series are within realistic ranges. As we move towards holistic ecosystem-based fisheries management, we must continue to openly and collaboratively integrate our disparate datasets and collective knowledge to solve the intricate problems we face. As a tool for future research, we provide the data and code to use our ecosystem model.</p>
Input files accompanying the paper "Instrumenting Stellar Oscillation Codes with Parfait Models"
<p>A zip archive containing input files accompanying the paper "Instrumenting Stellar Oscillation Codes with Parfait Models" by R. H. D. Townsend and R. V. Kuenzi.</p>
Multidisciplinary Network OSA Code (Obstructive Apnea Syndrome): Digital Operative Model in Public Health for an Early Diagnosis and Therapy Monitoring
ClinicalTrials.gov study NCT06677580. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Distinct Hippocampal Expression Profiles of Long Non-coding RNAs in an Alzheimer’s Disease Model
GEO Series GSE129015. Rattus norvegicus. 6 samples. Type: Non-coding RNA profiling by array; Expression profiling by array.
Microarray expression profile analysis of long non-coding RNAs and mRNAs in A Rat Carotid Artery Balloon Injury Model
GEO Series GSE121839. Rattus norvegicus. 6 samples. Type: Non-coding RNA profiling by array; Expression profiling by array.
Data from: Impaired spatial memory codes in a mouse model of Rett syndrome
Open the record for dataset details and reuse information.
A novel in vivo model of Glioblastoma radiation resistance identifies long non-coding RNAs and targetable kinases
GEO Series GSE206225. Homo sapiens. 45 samples. Type: Expression profiling by high throughput sequencing.
A primary hierarchically organized patient-derived model enables in depth interrogation of stemness driven by the coding and non-coding genome [RNA-Seq]
GEO Series GSE211595. Homo sapiens. 29 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic Analysis of Long Non-coding RNAs and mRNAs in a Mouse Model of Venous Thrombosis
GEO Series GSE269352. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Enhancer associated long non-coding RNA transcription and immune gene regulation in experimental models of rickettsial infection
GEO Series GSE121808. Mus musculus. 6 samples. Type: Non-coding RNA profiling by high throughput sequencing.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.