Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

Second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'

This is the second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'. The paper was published in Geophysical Research Letters. We provide the data that has been smoothed by moving filter and not. The data can be loaded by the raster package in R. Note that the unit is ppmv. Please note that both of these files must be in the same directory to open in R properly. Please get in touch with the authors if you have any issues, email: xliu21@smail.nju.edu.cn or wanghk@nju.edu.cn

opencc-by-sa-4.0Jul 2018View details →

R-code for publication: Ensembles of Ensembles: Combining the Predictions from Multiple Machine Learning Methods

This is the R-code as well as the underlying data needed to reproduce the results of the springer book chapter: "Ensembles of Ensembles: Combining the Predictions from Multiple Machine Learning Methods" For more information contact: dlieske@mta.ca

opencc-by-4.0Aug 2024View details →

CREMP-CycPeptMPDB: Conformer-rotamer ensembles of macrocyclic peptides for machine learning with permeability annotations

CREMP-CycPeptMPDB: A resource generated for the rapid development and evaluation of machine learning models for permeable macrocyclic peptides. CREMP-CycPeptMPDB contains 3,258 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). Altogether, this dataset contains nearly 8.7 million unique macrocycle geometries, each annotated with energies derived from semi-empirical tight-binding DFT calculations and with experimental membrane permeability measurements obtained from the <a href="http://cycpeptmpdb.com/" target="_blank" rel="noopener">CycPeptMPDB</a> database. We anticipate that this dataset will enable the development of machine learning models that can improve peptide design and optimization for novel therapeutics. This dataset complements the <a title="CREMP" href="../doi/10.5281/zenodo.7931444" target="_blank" rel="noopener">CREMP dataset</a>, which contains a larger selection of conformer ensembles for homodetic macrocyclic peptides. We provide the data in two available formats, either as Python pickle files, which provide quick read access with RDKit version 2022.09.5 or later, and as text-based SDF files with associated metadata in JSON format. Each file is named based on its amino acid sequence, with residues separated by periods, using standard one-letter codes with lowercase letters representing D-amino acids and "Me" prefixes representing N-methylated amino acids. The sequences are in no particular order, e.g., "C.R.E.M.P" and "R.E.M.P.C" correspond to the same peptide macrocycle. The filename extensions are ".pickle", ".sdf", and ".json". Each file in the “pickle” folder contains a Python dictionary with amino acid sequence, SMILES, CREST metadata, and a single RDKit molecule object containing all conformers. All files in the folder were compressed into a single “pickle.tar.gz” archive. In the “sdf_and_json” folder, each individual SDF file contains all conformers, each associated with its own JSON file that contains CREST metadata. Similarly, all are compressed into another single archive, “sdf_and_json.tar.bz2”. A single summary CSV file is also provided containing ”sequence”, “smiles”, “num_monomers”, “num_atoms”, “num_heavy_atoms”, along with the CREST metadata “totalconfs”, “uniqueconfs”, “lowestenergy”, “poplowestpct”, “temperature”, “ensembleenergy”, “ensembleentropy”, and “ensemblefreeenergy”. The number of unique conformers with different 3D structures is given by “uniqueconfs”, while “totalconfs” includes the number of rotamers in addition. The unzipped sizes of the archives are approximately 13 GB for "pickle.tar.gz" and 84 GB for "sdf_and_json.tar.bz2". If you encounter errors when trying to load the pickle files, please make sure your RDKit version is at least 2022.09.5. If that doesn't work, try other Python versions.

opencc-by-4.0Sep 2024View details →

Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data

<h2>Sub-dataset: WRB soil types probabilities (part 1)</h2> <h2>Disclaimer</h2> This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk. <h2>Description</h2> This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics. This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset. This data set includes: <ul> <li>Soil types classification and relative entropy: This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li>Soil types probabilities (part 1): This data includes 92 averaged probabilities (0-1) maps for classes from abruptic.acrisols to gleyic.arenosols. The probabilites were scaled 100x (0-100). </li> <li>Soil types probabilities (part 2): This data includes 93 averaged probabilities maps for classes from gleyic.cambisols to vitric.andosols. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li>Time period: long term.</li> <li>Type of data: Soil types classification and model probabilities.</li> <li>How the data was collected or derived: The data was derived using ensemble ML models.</li> <li>Statistical methods used: Relative entropy (Kullback-Leibler divergence)</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif, the fields are: <ol> <li>generic variable name: soil.types = soil types</li> <li>variable procedure combination: ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li>Position in the probability distribution/variable type: m = mean | c = class | p = probability</li> <li>Spatial support: 30m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20220101 = 2022-01-01</li> <li>Time reference end time: 20221231 = 2022-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: v20240917 = version from 2024-09-17</li> </ol>

opencc-by-4.0Sep 2024View details →

Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data

<h2>Sub-dataset: WRB soil types probabilities (part 2)</h2> <h2>Disclaimer</h2> This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk. <h2>Description</h2> This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics. This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset. This data set includes: <ul> <li>Soil types classification and relative entropy: This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li>Soil types probabilities (part 1): This data includes 92 averaged probabilities (0-1) maps for classes from abruptic.acrisols to gleyic.arenosols. The probabilites were scaled 100x (0-100). </li> <li>Soil types probabilities (part 2): This data includes 93 averaged probabilities maps for classes from gleyic.cambisols to vitric.andosols. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li>Time period: long term.</li> <li>Type of data: Soil types classification and model probabilities.</li> <li>How the data was collected or derived: The data was derived using ensemble ML models.</li> <li>Statistical methods used: Relative entropy (Kullback-Leibler divergence)</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif, the fields are: <ol> <li>generic variable name: soil.types = soil types</li> <li>variable procedure combination: ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li>Position in the probability distribution/variable type: m = mean | c = class | p = probability</li> <li>Spatial support: 30m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20220101 = 2022-01-01</li> <li>Time reference end time: 20221231 = 2022-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: v20240917 = version from 2024-09-17</li> </ol>

opencc-by-4.0Sep 2024View details →

Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data

<h2>Sub-dataset: WRB soil types classification and relative entropy</h2> <h2>Disclaimer</h2> This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk. <h2>Description</h2> This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics. This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset. This data set includes: <ul> <li>Soil types classification and relative entropy: This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li>Soil types probabilities (part 1): This data includes 92 averaged probabilities (0-1) maps for classes from abruptic.acrisols to gleyic.arenosols. The probabilites were scaled 100x (0-100). </li> <li>Soil types probabilities (part 2): This data includes 93 averaged probabilities maps for classes from gleyic.cambisols to vitric.andosols. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li>Time period: long term.</li> <li>Type of data: Soil types classification and model probabilities.</li> <li>How the data was collected or derived: The data was derived using ensemble ML models.</li> <li>Statistical methods used: Relative entropy (Kullback-Leibler divergence)</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif, the fields are: <ol> <li>generic variable name: soil.types = soil types</li> <li>variable procedure combination: ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li>Position in the probability distribution/variable type: m = mean | c = class | p = probability</li> <li>Spatial support: 30m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20220101 = 2022-01-01</li> <li>Time reference end time: 20221231 = 2022-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: v20240917 = version from 2024-09-17</li> </ol>

opencc-by-4.0May 2023View details →

CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning

CREMP: A resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). Altogether, this dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical tight-binding DFT calculations. We anticipate that this dataset will enable the development of machine learning models that can improve peptide design and optimization for novel therapeutics. We provide the data in two available formats, either as Python pickle files, which provide quick read access with RDKit version 2022.09.5 or later, and as text-based SDF files with associated metadata in JSON format. Each file is named based on its amino acid sequence, with residues separated by periods, using standard one-letter codes with lowercase letters representing D-amino acids and "Me" prefixes representing N-methylated amino acids. The sequences are in no particular order, e.g., "C.R.E.M.P" and "R.E.M.P.C" correspond to the same peptide macrocycle. The filename extensions are ".pickle", ".sdf", and ".json". Each file in the “pickle” folder contains a Python dictionary with amino acid sequence, SMILES, CREST metadata, and a single RDKit molecule object containing all conformers. All files in the folder were compressed into a single “pickle.tar.gz” archive. In the “sdf_and_json” folder, each individual SDF file contains all conformers, each associated with its own JSON file that contains CREST metadata. Similarly, all are compressed into another single archive, “sdf_and_json.tar.bz2”. A single summary CSV file is also provided containing ”sequence”, “smiles”, “num_monomers”, “num_atoms”, “num_heavy_atoms”, along with the CREST metadata “totalconfs”, “uniqueconfs”, “lowestenergy”, “poplowestpct”, “temperature”, “ensembleenergy”, “ensembleentropy”, and “ensemblefreeenergy”. The number of unique conformers with different 3D structures is given by “uniqueconfs”, while “totalconfs” includes the number of rotamers in addition. The unzipped sizes of the archives are approximately 32 GB for "pickle.tar.gz" and 210 GB for "sdf_and_json.tar.bz2". If you encounter errors when trying to load the pickle files, please make sure your RDKit version is at least 2022.09.5. If that doesn't work, try other Python versions.

opencc-by-4.0Oct 2023View details →

Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR

General Description The dataset contains composites at 250 m spatial resolution of (1)  monthly potential FAPAR for the year 2021 from ensemble ML model predictions, (2) the model deviance for each prediction, (3) the yearly average of potential FAPAR, (4) the yearly average of actual FAPAR and (5) the yearly average of the difference between actual and potential (actual minus potential) FAPAR. The dataset is based on the <a href="https://zenodo.org/record/8392976">95th percentile of the monthly aggregated FAPAR</a> derived from <a href="http://glass.umd.edu/Overview.html">250 m 8 d GLASS V6 FAPAR</a>. Potential FAPAR was predicted by fitting an ensemble ML model using globally distributed training points (cca 3 Mio) and a set of 52 biophysical covariates including several layers related to human pressure. The code for modeling potential FAPAR is openly available at <a href="http://github.com/Open-Earth-Monitor/Global_FAPAR_250m">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m</a>. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping.  Data Details <ul> <li>Time period: January 2021 - December 2021</li> <li>Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li>How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR</li> <li>Statistical methods used: Ensemble machine learning</li> <li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li> <li>Coordinate reference system: EPSG:4326</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li>Spatial resolution: 1/480 d.d. = 0.00208333 (250m)</li> <li>Image size: 172,800 x 71,698</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a> Reference Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a>   Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: <ol> <li>generic variable name: pot.fapar = Potential Fraction of Absorbed Photosynthetically Active Radiation</li> <li>variable procedure combination: eml = ensemble machine learning</li> <li>Position in the probability distribution / variable type: m = mean</li> <li>Spatial support: 250m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20210101 = 2021-01-01</li> <li>Time reference end time: 20211231 = 2021-12-31</li> <li>Bounding box: go = global (without Antarctica)</li> <li>EPSG code: epsg.4326 = EPSG:4326</li> <li>Version code: v20230924 = 2023-09-24 (creation date)</li> </ol>

opencc-by-4.0Apr 2018View details →

New Ideas for Brain Modelling 4-Figure 4. LHS relates to neuron binding ensemble mass, with central column activated. RHS relates to hierarchy, with a direct mapping. The two red lines show where the ensemble is missing and so it needs to be learned. The blue lines show extra neurons from the hierarchy back to the ensemble, but can be removed as error. The other paired black squares represent where the patterns match and can oscillate together.

This paper continues the research that considers a new cognitive model based strongly on the human brain, last updated in Greer (2016). In particular, it considers figure 4 of that paper (Figure  below) and how it might be useful in practice. The paper also describes some new methods in the areas of image processing and behaviour simulation. The image processing introduces a most classical form of pattern cross-referencing, while the behaviour equations used feedback for a memory-type of cross-referencing. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with ‘full linking’, if you like. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared with. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its current state and course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. Sensory input may be static and binary, but cross- references result in variable comparisons that make the input more dynamic. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.

opencc-by-4.0Aug 2024View details →

Ensemble Learning of Catchment-Wise Optimized LSTMs Enhances Regional Rainfall-Runoff modelling - Case Study: Basque Country, Spain - Data

<div> <div>This data and results are for paper: "Ensemble Learning of Catchment-Wise Optimized LSTMs Enhances Regional Rainfall-Runoff modelling - Case Study: Basque Country, Spain" by Hosseini et al. 2024 (Preprint - Under review J.Hydro 2024) Available at SSRN: <a href="https://ssrn.com/abstract=4918782" target="_blank" rel="noopener">https://ssrn.com/abstract=4918782</a></div> <div> </div> </div>

opencc-by-4.0Jan 2023View details →

Predictors and predictands for "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"

Predictors and predictands for "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain". Training predictors from the ERA5 reanalysis, projecting predictors from CORDEX EUR11, and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rds" format, to be read from R, except for CORDEX files in NetCDF.

opencc-by-4.0Jun 2023View details →

Arabic news credibility on Twitter using sentiment analysis and ensemble learning

Arabic news credibility on Twitter using sentiment analysis and ensemble learning.   WHAT IS IT? ----------- an Arabic news credibility model on Twitter using sentiment analysis and ensemble learning. Here we include the Collected dataset and the source code of the proposed model written in Python language and using Keras library with Tensorflow backend.   Required Packages ------------------ <ol> <li>Keras (<a href="https://keras.io/">https://keras.io/</a>).</li> <li>Scikit-learn (<a href="http://scikit-learn.org/)">http://scikit-learn.org/)</a></li> <li>Imnlearn (<a href="https://imbalanced-learn.org/stable/">imbalanced-learn documentation — Version 0.10.1</a>)</li> </ol>     To Run the model --------------- One data file is required to run the model which are:   <ol> <li>The data that were used are the collected dataset in the file, set the path of the required data file in the code.</li> </ol>   The dataset --------------- <ol> <li>There are the dataset file with all features, you can choose the features that you need and apply it on the model.</li> <li>There are a description file that describe each feature in the news credibility dataset</li> <li>The file Tweet_ID contains the list of tweets id in the dataset.</li> <li>The annotated replies based on credibility is provided.</li> </ol>         CONTACTS -------- <ul> <li>If you want to report bugs or have general queries email to <duha_atif@yahoo.com></li> </ul>

dryad36/100

Neural ensemble reactivation in REM and SWS coordinate with muscle activity to promote rapid motor skill learning

Neural activity patterns of recent experiences are reactivated during sleep in structures critical for memory storage, including hippocampus and neocortex. This reactivation process is thought to aid memory consolidation. Although synaptic rearrangement dynamics following learning involve an interplay between slow-wave sleep (SWS) and rapid eye movement sleep (REM), most physiological evidence implicates SWS directly following experience as a preferred window for reactivation. Here we show that reactivation occurs in both REM and SWS, and that coordination of REM and SWS activation on the same day is associated with rapid learning of a motor skill. We performed 6-hour recordings from cells in rats' motor cortex as they were trained daily on a skilled reaching task. In addition to SWS following training, reactivation occurred in REM, primarily during the pre-task rest period, and REM and SWS reactivation occurred on the same day in rats that acquired the skill rapidly. Both pre-task REM and posttask SWS activation were coordinated with muscle activity during sleep, suggesting a functional role for reactivation in skill learning. Our results provide the first demonstration that reactivation in REM sleep occurs during motor skill learning, and that coordinated reactivation in both sleep states on the same day, although at different times, is beneficial for skill learning.

opencc-zeroMar 2020View details →

opencc-by-4.0Dec 2021View details →

EnGRaiN : A Supervised Ensemble Learning Method for Recovery of Large-scale Gene Regulatory Networks

EnGRaiN is a supervised machine learning method to construct ensemble networks. To benefit from the typical accuracy advantages of supervised learning methods while taking into account the impossibility of knowing true networks for training, we devised a method that uses small training datasets of true positives and true negatives among gene pairs. The datasets used to evaluate the performance of EnGaiN include (i) simulated datasets generated from Yeast networks and (ii) A. thaliana gene expression datasets.

opencc-by-4.0Dec 2021View details →

Artifact for "MAAT: A Novel Ensemble Approach to Addressing Fairness and Performance Bugs for Machine Learning Software"

This artifact is for the paper entitled “MAAT: A Novel Ensemble Approach to Addressing Fairness and Performance Bugs for Machine Learning Software”, which is accepted by ESEC/FSE 2022. MAAT is a novel ensemble approach to improving the fairness-performance trade-off for ML software. It outperforms state-of-the-art bias mitigation methods. The artifact has also been placed on GitHub (https://github.com/chenzhenpeng18/FSE22-MAAT) under the Apache License, publicly accessible to other researchers. In this artifact, we provide the source code of MAAT and other existing bias mitigation methods that we use in our study, as well as the intermediate results, the installation instructions, and a replication guideline (included in the README). The replication guideline provides detailed steps to replicate all the results for all the research questions.

opencc-by-4.0Jan 2023View details →

Multivariate projected ensemble of "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"

Multivariate statistically downscaled projected ensemble for different combinations of GCM-RCMs of both stochastic and deterministic runs for eight historical runs, eight RCP85 runs and one RCP26 run. Selection of good perfoming GCM-RCM combinations in NetCDF format. Variables: precipitation, water vapour pressure, radiation, wind speed, and, maximum, mean and minimum temperature.

dryad36/100

Data from: Predicting regional carbon price in China based on multi-factor HKELM by combining secondary decomposition and ensemble learning

Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In light of the complex characteristics of the regional carbon price in China, this paper proposes a model to forecast carbon price based on the multi-factor hybrid kernel-based extreme learning machine (HKELM) by combining secondary decomposition and ensemble learning. Variational mode decomposition (VMD) is first used to decompose the carbon price into several modes, and range entropy is then used to reconstruct these modes. The multi-factor HKELM optimized by the sparrow search algorithm is used to forecast the reconstructed subsequences, where the main external factors innovatively selected by maximum information coefficient and historical time-series data on carbon prices are both considered as input variables to the forecasting model. Following this, the improved complete ensemble-based empirical mode decomposition with adaptive noise and range entropy are respectively used to decompose and reconstruct the residual term generated by VMD. Finally, the nonlinear ensemble learning method is introduced to determine the predictions of residual term and final carbon price. In the empirical analysis of Guangzhou market, the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the model are 0.1716, 0.1218 and 0.0026, respectively. The proposed model outperforms other comparative models in predicting accuracy. The work here extends the research on forecasting theory and methods of predicting the carbon price.

opencc-zeroMay 2023View details →

dryad36/100

Data for: Learning in ensembles of proteinoid microspheres

Proteinoids are thermal proteins which form microspheres in water in presence of salt. Ensembles of proteinoid microspheres exhibit passive non-linear electrical properties and active neuron-like spiking of electrical potential. We propose that various neuromorphic computing architectures can be prototyped from the proteinoid microspheres. A key feature of a neuromorphic system is a learning. Through the use of optical and resistance measurements, we study mechanisms of learning in ensembles of proteinoid microspheres. We anlyse 16 types of proteinoids, study their intrinsic morphology and electrical properties. We demonstrate that proteinoids can learn, memorize, and habituate, making them a promising candidate for novel computing.

opencc-zeroJul 2023View details →

openother-openJun 2023View details →

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at global scale (v1.0)

This study investigates the estimation of daily SSM using eight optimised ML algorithms and ten ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using the international soil moisture network (ISMN) data collected from 1722 stations distributed across the World.