Research Dataset Catalog and ShareScore Ratings | ScienceDex

zenodo32/100

Replication Data for: ``Toward machine learning-augmented, bathymetry-aware parameterizations of mesoscale eddy buoyancy fluxes across upwelling slope fronts''

This dataset contains the Python scripts for training the Artificial Neural Networks (ANNs), the trained ANNs, configuration files for the reference 2D MITgcm simulations, and model outputs used in the paper.

opencc-by-4.0Jan 2023View details →

zenodo32/100

Analyzing the Knowledge, Attitude, and Practices of Healthcare workers towards the awareness of nosocomial infections- A Global Survey

Raw data for our article titled "Analyzing the Knowledge, Attitude, and Practices of Healthcare workers towards the awareness of nosocomial infections- A Global Survey"

opencc-by-4.0Apr 2023View details →

zenodo32/100

Supplementary material 2 from: Colombari F, Battisti A (2023) Citizen science at school increases awareness of biological invasions and contributes to the detection of exotic ambrosia beetles. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 211-229. https://doi.org/10.3897/neobiota.84.95177

Slides of the lecture on 'Monitoring of insect species harmful to trees and forests' and link to educational videos

opencc-zeroMay 2023View details →

zenodo32/100

Wyniki zawarte w Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey

This resource contains the results included in Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey

opencc-by-4.0Aug 2022View details →

zenodo32/100

Transformer Models for Disconnection-Aware Triple Transformer Loop

Models of the Triple Transformer Loop for retrosynthesis trained using OpenNMT. Full details in <a href="https://doi.org/10.1039/d3sc01604h">Chemical Science</a>.             <div> <div> <div class="highlighter--icon highlighter--icon-copy"> </div> <div class="highlighter--icon highlighter--icon-change-color"> </div> <div class="highlighter--icon highlighter--icon-delete"> </div> </div> </div>

opencc-by-4.0Jul 2023View details →

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CASENet: Deep Category-Aware Semantic Edge Detection

Introduction This dataset includes initial and trained model weights (caffemodel files) for SBD/Cityscapes datasets which is used in our CASENet software available (<a href="https://github.com/merlresearch/CASENet">https://github.com/merlresearch/CASENet</a>). Citation If you use the data, please cite the following <a href="https://www.merl.com/publications/TR2017-100">TR2017-100</a>: <pre><code>@inproceedings{Yu2017jul, author = {Yu, Zhiding and Feng, Chen and Liu, Ming-Yu and Ramalingam, Srikumar}, title = {CASENet: Deep Category-Aware Semantic Edge Detection}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = 2017, month = jul, doi = {10.1109/CVPR.2017.191}, url = {https://www.merl.com/publications/TR2017-100} }</code></pre> Copyright and License The CASENet dataset is released under<a href="https://creativecommons.org/licenses/by-sa/4.0/"> CC-BY-SA-4.0 license</a>. All data: <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2017, 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>

opencc-by-sa-4.0Apr 2023View details →

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WIA-SZZ: Work Item Aware SZZ - Cloned Repository Files (6/7)

For replication purposes, these 7 Zenodo records contain a copy of the cloned repositories from Rosa et. al, ICSE 2021, <a href="https://github.com/grosa1/icse2021-szz-replication-package">Evaluating SZZ implementations through a developer-informed oracle</a>, downloaded as of to Dec. 2022. These repositories were used for the evaluation and analysis of our paper WIA-SZZ: Work Item Aware SZZ. A script to download and unzip all files in these Zenodo records is provided as well. 1. https://zenodo.org/record/8285106    (doi: 10.5281/zenodo.8285106) 2. https://zenodo.org/record/8285119    (doi: 10.5281/zenodo.8285119) 3. https://zenodo.org/record/8285121    (doi: 10.5281/zenodo.8285121) 4. https://zenodo.org/record/8285125    (doi: 10.5281/zenodo.8285125) 5. https://zenodo.org/record/8285127    (doi: 10.5281/zenodo.8285127) 6. https://zenodo.org/record/8285138    (doi: 10.5281/zenodo.8285138) 7. https://zenodo.org/record/8285140    (doi: 10.5281/zenodo.8285140)