Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

SITE OCCUPANCY AND ABUNDANCE MODELS FOR ANALYZING MULTIPLE-VISIT DETECTION/NONDETECTION DATA

Data and R codes for the paper: Site occupancy and abundance models for analyzing multiple-visit detection/nondetection data.

opencc-zeroJan 2024View details →

spatiAlign: An Unsupervised Contrastive Learning Model for Data Integration of Spatially Resolved Transcriptomics

Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Here, we propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.

Data and code of Covid-19 SIRDS model with fuzzy transitions between epidemic periods

Repository for code and data of project that implement SIRDS model with fuzzy transitions between epidemic periods.

Data needed to reproduce analysis from "Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests"

Data to reproduce analysis from "Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests". This includes: <ol> <li>Tree ring data (meyer, bdn, principe, dittmar)</li> <li>LPJ-GUESS model output (frost_validation, frost_sensitivity, runs_22012024_revision)</li> <li>Data used for plotting</li> </ol>

opencc-by-4.0Jul 2023View details →

2023_Turkey_doublet_Data_Model

Data and model files for our geodetic slip model, kinematic slip models, and dynamic rupture models, for the 2023 Kahramanmaraş Mw 7.8-7.7 earthquake doublet.

Evaluating the Robustness of Deep-learning Algorithm-selection Models by Evolving Adversarial Instances - Code and Data

This repository contains the code and data for reproducibility of the paper 'Evaluating the Robustness of Deep-learning Algorithm-selection Models by Evolving Adversarial Instances'.  The following files are included: <ul> <li>Data.zip : contains the original instances in the datasets;</li> <li>Models.zip : trained Deep Neural Networks models used in the paper;</li> <li>New_instances.zip : generated instances using the approach;</li> <li>Parsed_data.zip : results and statistics of the experiments;</li> <li>script_adversarial_v3.py : Python script used to generate the results</li> </ul>

The evaluation data and source codes of a new conceptual coupled Earth system model and the MOC box model.

The dataset contains the results of a conceptual Atmosphere-Ocean-Ice-Land coupled Earth system model and a MOC box model and the evaluation data of their.

opencc-by-4.0Jun 2023View details →

Model Output and Validation Data for Online Determination of GNSS Differential Code Biases using Rao-Blackwellized Particle Filtering

This dataset contains the outputs of 10 test runs of the A-CHAIM data assimilation model used to evaluate the performance of the bias estimation procedure used by the model. The 5-minute output files for each test run are included in a separate folder. The IGS DCBs in SINEX format and the various global ionospheric maps used in the study are included. This dataset also includes all of the processed GNSS data used in the study, which were used to generate the DCBs used in each test run. The in-situ electron density measurements from DMSP as well as the ionosonde data from GIRO which were used to measure the performance are also included.

opencc-by-4.0Aug 2020View details →

Data from: Future projections of biodiversity and ecosystem services in Europe with two integrated assessment models

Projections of future changes in biodiversity and ecosystem services (BES) are of increasing importance to inform policy and decision-making on options for conservation and sustainable use of BES. Scenario-based modelling is a powerful tool to assess these future changes. This study assesses the consequences for BES in Europe under four socio-environmental scenarios designed from a BES perspective. We evaluated these scenarios using two integrated assessment models (IMAGE-GLOBIO and CLIMSAVE IAP, respectively).  Veerkamp, C. J., Dunford. R. W., Harrison, P. A., Mandryk, M., Priess, J. A., Schipper, A. M., Stehfest, E., & Alkemade, R. (2020). Future projections of biodiversity and ecosystem services in Europe with two integrated assessment models. Regional Environmental Change, 20, 103. <a href="https://doi.org/10.1007/s10113-020-01685-8">https://doi.org/10.1007/s10113-020-01685-8</a>

Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment

Hydrological data measured in Lompolonjängänoja (LJO) catchment and used in Nousu et al.   ET_fluxes.csv - Eddy-covariance based, daily evapotranspiration (ET) fluxes [mm/d] at Kenttärova (NFOR) and Lompolojänkkä (NWET) stations GW_levels.csv - Observed groundwater levels [m] relative to the ground surface measured around the LJO catchment Q_runoff.csv - Observed specific discharge [mm/d] at the LJO catchment outlet THETA_kenttarova.csv - Automatically measured soil moisture (i.e. volumetric water content [m3/m3]) around Kenttärova stations THETA_spatial.csv - Manually measured soil moisture (i.e. volumetric water content [m3/m3]) around the LJO catchment

Code and Data for Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0

Includes the code used for all simulations and grid configurations for the paper entitled "Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0" submitted to Geoscientific Model Development.  Also included are the model output files for all cases and grid configurations used to generate the analysis and figures in the paper.

opencc-by-4.0Feb 2024View details →

N/OFQ-NOPa models data

This enty contains: <ul> <li>MD input files and scripts to run the simulations of six different predicted N/OFQ-NOP complexes obtained with different modelling approaches, as follows:  <table> <tbody> <tr> <td>Model name</td> <td>Method</td> </tr> <tr> <td>5C1M_HM_P4</td> <td> Homology modelling + peptide docking</td> </tr> <tr> <td>6DDE_HM_P3   </td> <td>Homology modelling + peptide docking</td> </tr> <tr> <td>AF_MS1_P2</td> <td>AI-based modelling + peptide docking</td> </tr> <tr> <td>AF_MS2_P1</td> <td>AI-based modelling + peptide docking</td> </tr> <tr> <td>AF_R2M5</td> <td>AI-based modelling</td> </tr> <tr> <td>AFc_R1M5  </td> <td>AI-based modelling</td> </tr> </tbody> </table> </li> <li>MD topology (.psf) and trajectory (.dcd) files of all six systems plus vmd script (.vmd ) to visualise the  trajectory with lowest peptide and receptor RMSD and RMSF profiles </li> </ul>

TRANS2Am Phase I EAIM Model Data

<div> This code and model input and output data was used as part of the work submitted to JGR:Atmospheres titled "Inorganic Nitrogen Gas-Aerosol Partitioning in and around Animal Feeding Operations in Northeastern Colorado in Late Summer 2021." </div>

Supplementary data: Predicting grid frequency short-term dynamics with Gaussian processes and sequence modeling

This repository contains data and result files for the paper "Predicting grid frequency short-term dynamics with Gaussian processes and sequence modelling".  The code to generate the models and reproduce the results of the comparative study in the above paper is available on this  <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository</a> Supplementary data: - The trained_models folder contains the results of the trained models. - The folder data contains data needed for for the comparative study for the year 2019 in the paper above.  This data set (except knn_point_predictions.npy) is generated with the code in this <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">github repository</a>. knn_point_predictions.npy is generated with the code in this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository </a>.

Model Weights and Data

Open the record for dataset details and reuse information.

Parcels-WAOM model data: Lagrangian particle trajectories

This data set includes the results of Lagrangian particle tracking experiments with Parsels in Weddell and Ross seas.  The following information for each particle is stored once a day: longitude (lon in files), latitude (lat), time (time), depth (z), temperature (temp), salinity (sal), ice draft (ice). Each *.zip archive contains one experiment in netcdf file for one of the seas (Weddell  or Ross seas) and for one of four seasons during 20 years of simulations.

$swint vision model training data

Training language models to see using strings that represent pixelized images.

Model data for: Analysis of the global atmospheric background sulfur budget in a multi-model framework

The present dataset contains all model data used in the model intercomparison in ACP. All data is provided as monthly means. For more data, please contact the first author. V2 addresses inconsistencies in the time axes, vertical coordinates, and variable names between models.

Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques

Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center. Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority.  The R script(s) for machine learning models can also be found at <a href="../api/records/10901736/draft/files/Code%20for%20machine%20learning%20classification%20model.R/content" target="_blank" rel="noopener noreferrer">Code for machine learning classification model.R</a>.