Find research datasets worth reusing

opencc-by-4.0Dec 2008View details →

Text-fig. 1. D&E tree of Endress and Doyle (2009), from the combined morphological and molecular analysis of Doyle and Endress (2000), with modifications based on more recent data, showing the inferred evolution of the reticulum grading character (39). Boxes under names of taxa indicate their character state; shading of branches indicates their reconstructed state based on parsimony optimization with MacClade (Maddison and Maddison 2003). Nymph = Nymphaeales, Aust = Austrobaileyales, Chlor = Chloranthaceae, Piper = Piperales, Ca = Canellales, Magnol = Magnoliales. in Early Cretaceous Monocots: A Phylogenetic Evaluation

Text-fig. 1. D&E tree of Endress and Doyle (2009), from the combined morphological and molecular analysis of Doyle and Endress (2000), with modifications based on more recent data, showing the inferred evolution of the reticulum grading character (39). Boxes under names of taxa indicate their character state; shading of branches indicates their reconstructed state based on parsimony optimization with MacClade (Maddison and Maddison 2003). Nymph = Nymphaeales, Aust = Austrobaileyales, Chlor = Chloranthaceae, Piper = Piperales, Ca = Canellales, Magnol = Magnoliales.

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>

Accompanying data for publication: "Learning the Optimal Power Flow: Environment Design Matters"

All the data created for the publication "Learning the Optimal Power Flow: Environment Design Matters" by Wolgast and Nieße. The dataset contains all training runs performed, including the final neural network weights, meta-data about the training run, and various metrics during the course of training, which were used to generate the results and plots. The source code to re-produce the plots for the publication (and everything else) can be found on GitHub: https://github.com/Digitalized-Energy-Systems/rl-opf-env-design

opencc-by-4.0Mar 2010View details →

Fig. 1 in Procedures for Obtaining Optimal SEM Images of Coccolithophore Debris in Coccolith Limestones

Fig. 1. CCI images of coccolith material in different stages of fragmentation within faecal pellets in coccolith limestone. Cross−sections of complete and dismembered, variously oriented, coccospheres allow to observe their structures. A. Perpendicular cross−section of a coccosphere with well visible distal (d) and proximal (p) shields of placoliths joined by parallel calcite crystallites in wide central area (c). Interlocking of adjacent edges of placolith shields is particularly well visible. The structure of the central area is well visible as a net in horizontal cross−section of the plate in the right (c1), ZNG PAN B−III−75/3. B. Perpendicular cross−sections of coccospheres clearly show distal (d) and proximal (p) shields of placoliths, and central areas (c). The central areas are seen as narrow massive or open structures, depending on the orientation of cross−sections (compare c and c1). Note the very tight interlocking of adjacent edges of placolith shields and additional placoliths (x) within the coccosphere. The structures closing the central area are seen as a narrow open structure in horizontal view of placoliths (c2), ZNG PAN B−III−75/5. Both images were obtained in ESEM using SE under low vacuum, from non−coated polished thin section at WD, 10.1 mm, SS, 37 s/frame.

Maximized response by structural optimization of soft elastic composite systems

This dataset contains the underlying data and numerical optimization codes for the paper L. Fischer and Andreas M. Menzel Maximized response by structural optimization of soft elastic composite systems PNAS Nexus 3, pgae353 (2024) (DOI: <a href="https://doi.org/10.1093/pnasnexus/pgae353" target="_blank" rel="noopener">10.1093/pnasnexus/pgae353</a>). For more information, please see the included "Readme.txt" in the dataset "Zenodo.zip".

Fig. 2 in Optimizing torula bait for Anastrepha suspensa (Diptera: Tephritidae) trapping in the Dominican Republic

Fig. 2. Effect of weekly and biweekly renewal and no renewal of baits on the cumulative captures of females (A1) and males (A2) from Oct to Dec 2019, and females (B1) and males (B2) from Jan to Feb 2020 of Caribbean fruit fly (Anastrepha suspensa) per wk in guava production in San Cristobal, Dominican Republic.

Fig. 1 in Optimizing torula bait for Anastrepha suspensa (Diptera: Tephritidae) trapping in the Dominican Republic

Fig. 1. Effect of torula yeast and benzalkonium chloride on the cumulative captures of females (A1) and males (A2) from Oct to Dec 2019, and females (B1) and males (B2) from Jan to Feb 2020 of Caribbean fruit fly (Anastrepha suspensa) per wk in guava production in San Cristobal, Dominican Republic.

Fig. 4 in Optimizing torula bait for Anastrepha suspensa (Diptera: Tephritidae) trapping in the Dominican Republic

Fig. 4. Minimum, average, and maximum air temperature, and relative humidity at the experimental site between 23 Oct 2019 and 23 Feb 2020 in San Cristobal, Dominican Republic; (1.8 × °C) + 32 = °F.

Fig. 3 in Optimizing torula bait for Anastrepha suspensa (Diptera: Tephritidae) trapping in the Dominican Republic

Fig. 3. Daily pH changes of torula yeast pellets (34.2% torula yeast + 57.2% borax) (two 4 g pellets in 200 mL of water) and torula yeast pellets + benzalkonium chloride (80% purity) (two 4 g pellets in 200 mL of water + 16 mL of benzalkonium chloride) under laboratory conditions in San Cristobal, Dominican Republic.

opencc-by-4.0Sep 2024View details →

RNA oxidation in soybean seedlings in optimal conditions and under Cd treatment

The project aims at identification of oxidized (8-hydroxyguanosine, 8-OHG, 8-oxoG enriched) transcripts in soybean seedlings grown in control (optimal) conditions and exposed to Cd at the concentration 10 mg/l for 2 h.  The transcripts were identified through Illumina sequencing.  The raw data are submitted in the NCBI database under the proejct: <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1158244">PRJNA1158244</a>. The genes were annotated to soybean genome derived from Ensembl Plants (http://plants.ensembl.org/index.html) using featureCounts. The list of identified genes is included in the attached Excel file.

opencc-by-4.0Sep 2024View details →

Supplementary Data: Data Imbalance in Drug Response Prediction: Multi-Objective Optimization Approach in Deep Learning Setting

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2024View details →

RGB-Based Behavior Cloning Dataset for Surgical Robotics: 99,522 Episodes of Optimal Demonstrations

<h3>Dataset Description:</h3> This dataset contains 99,522 episodes of RGB-based state-action-reward expert demonstrations collected from a reaching task within a surgical robotics simulation environment, LapGym (Scheikl et al.). The data was generated using the LapGym ReachEnv, where a robotic grasper is tasked with reaching a specific point in 3D space. Each episode consists of a series of RGB images (64x64 pixels), corresponding actions, rewards, and terminal flags, designed for training behavior cloning and offline RL algorithms. This dataset was created for the paper "Assessing Behavior Cloning with RGB Inputs in Surgical Robotics Through Dataset Ablation". The expert demonstrations were collected using an optimal agent, where actions were computed based on the known locations of the grasper and the point of interest. The specific settings for the ReachEnv environment used to collect the dataset are as follows: <ul> <li>Environment: <code>ReachEnv</code></li> <li>Observation Type: <code>RGB</code></li> <li>Render Mode: <code>HUMAN</code></li> <li>Action Type: <code>CONTINUOUS</code></li> <li>Distance to Target Threshold: <code>0.01</code></li> <li>Image Shape: <code>(64, 64)</code></li> <li>Frame Skip: <code>1</code></li> <li>Time Step: <code>0.1</code></li> <li>Reward Amounts: <ul> <li>Distance to Target: <code>0.0</code></li> <li>Delta Distance to Target: <code>0.0</code></li> <li>Successful Task: <code>100.0</code></li> <li>Time Step Cost: <code>0.0</code></li> <li>Workspace Violation: <code>0.0</code></li> </ul> </li> <li>Sphere Radius: <code>0.020</code></li> </ul> Key features of the dataset include: <ul> <li>RGB Inputs: Each episode includes 64x64 RGB frames representing the environment's visual state.</li> <li>Optimal Demonstrations: All actions represent optimal behavior for completing the reach task.</li> <li>Sparse Rewards: Rewards are only provided upon successful task completion, offering a challenging learning scenario.</li> <li>Varied Episode Lengths: Episodes vary in length, depending on how quickly the task is completed.</li> </ul> <h3>Applications:</h3> This dataset is designed for research in: <ul> <li>Behavior cloning with RGB image inputs.</li> <li>Data efficiency and sample efficiency in imitation learning.</li> <li>Offline reinforcement learning with visual inputs.</li> </ul> <h3>Structure:</h3> <ul> <li>Observations: Images stored as 64x64 RGB pixel arrays.</li> <li>Actions: Continuous actions corresponding to the robotic grasper’s movements.</li> <li>Rewards: Sparse rewards indicating task success.</li> <li>Terminals: Terminal flags for task completion.</li> </ul> <h3>How to Use:</h3> This dataset can be used to train and evaluate offline models for robotic control tasks in conjunction with LapGym, particularly in the domain of surgical robotics. It is especially suited for behavior cloning experiments, offline reinforcement learning, and studies on data efficiency. <h3>Citation:</h3> Please cite this dataset in any publications as: Acs and Zhong (2024). RGB-Based Behavior Cloning Dataset for Surgical Robotics: 99,522 Episodes of Optimal Demonstrations.

Visualization of the numerical pose optimization with the JAMDA scoring function using the BFGS and the LSL-BFGS algorithm

These videos demonstrate the behavior of two different optimization algorithms (BFGS and LSL-BFGS) during pose optimization using the JAMDA protein-ligand scoring function. Flachsenberg et al. (2020) (<a href="http://doi.org/10.1021/acs.jcim.0c01095" target="_blank" rel="noopener">10.1021/acs.jcim.0c01095</a>) describes the JAMDA protein-ligand scoring function and the LSL-BFGS algorithm. The data for these videos stems from Experiment 5 in Flachsenberg et al. (2020). In this experiment, the crystal structure of a ligand was numerically optimized in the binding site with respect to the JAMDA scoring function to create the JAMDA-minimized crystal structure. The JAMDA-minimized crystal structure was randomly deflected to generate various starting poses for the numerical optimization. These videos demonstrate the behavior of two optimization algorithms (BFGS and LSL-BFGS) when optimizing one of the generated starting poses. The chosen example for the videos is a structure of ribonuclease A with a 5'-deoxy-5'-N-piperidinouridine inhibitor (PDB code 3d6q, <a href="https://doi.org/10.2210/pdb3D6Q/pdb" target="_blank" rel="noopener">10.2210/pdb3D6Q/pdb</a>, <a href="https://doi.org/10.1021/jm800724t" target="_blank" rel="noopener">10.1021/jm800724t</a>). Each of the videos shows all the intermediate steps the optimization algorithm takes until convergence. The main observation (that is discussed in detail in Flachsenberg et al. (2020)) is that the BFGS algorithm tends to take inappropriately large steps when clashes are present in the structure, resulting in unwanted binding mode changes. This is not the case for the LSL-BFGS algorithm. <h3>Legend</h3> For each iteration, the JAMDA score value, the RMSD to the JAMDA-minimized crystal structure (yellow), and the RMSD to the optimization's starting point (blue) are given. Furthermore, the gradient's norm (representing the main convergence criterion) is shown. In addition to the optimized ligand, also the JAMDA-minimized crystal structure (yellow) and the optimization's starting structure (blue) are shown. Each optimization algorithm is shown in two videos: In one video, the optimized ligand is colored by elements. Here, atoms with clashes (positive JAMDA scores) are marked with orange balls. In the other video, the atoms and bonds of the optimized ligand are colored by their individual JAMDA score. <h3>Used Software</h3> The snapshots of the optimization algorithms were rendered using PyMOL 3.0 (<a href="https://pymol.org/" target="_blank" rel="noopener">https://pymol.org/</a>) and further processed using the Pillow 10.4 Python library (<a href="https://doi.org/10.5281/zenodo.12606429" target="_blank" rel="noopener">10.5281/zenodo.12606429</a>). Videos were created from the individual snapshots using FFmpeg 7.0 (<a href="https://www.ffmpeg.org/" target="_blank" rel="noopener">https://www.ffmpeg.org/</a>).

opencc-by-4.0Jun 2024View details →

Two-Streams Revisited: General Equations, Exact Coefficients, and Optimized Closures (data and code)

Code and data necessary to reproduce figures presented in our paper  "Two-Streams Revisited: General Equations, Exact Coefficients, and Optimized Closures" (Ho, and Pincus 2024). The figures and code to reproduce them are in the Jupyter Notebook `Two-stream_revisited_code.ipynb`  which includes 3D interactive versions of Figures 6, 7, 10. We also provided code to further explore the dataset of coupling coefficients `twostreams_revisited_data.npz`. Identical GitHub repository: <a href="https://github.com/LDEO-CREW/Two-streams_revisited_code">https://github.com/LDEO-CREW/Two-streams_revisited_code</a>. This record is for the post-review (final) version of the paper, with a slight code change to accomodate `PythonicDISORT > 0.9.1`. You may contact me, Dion, through dh3065@columbia.edu if you have any questions. Requirements <ul> <li>Python 3.8+</li> <li>`PythonicDISORT >= 0.8.0` (A minor code change will be required for `PythonicDISORT <= 0.9.1`)</li> <li>`numpy >= 1.8.0`</li> <li>`scipy >= 1.8.0`</li> <li>`matplotlib >= 3.6.0`</li> <li>`jupyter > 1.0.0`</li> <li>`notebook > 6.5.2`</li> <li>`autograd >= 1.5`</li> <li>`plotly >= 5.22.0`</li> </ul>

opencc-by-4.0Oct 2024View details →

UNSUPERVISED MACHINE LEARNING AND VECTOR MODELS IN DESIGNING AND OPTIMIZATION OF TELECOM RETAIL CHANNELS

This paper examines the use of unsupervised machine learning and vector models in the design and optimization of retail channels for telecommunications services. Unsupervised machine learning allows you to analyze and identify hidden patterns in large volumes of untagged data, which is especially important in a dynamically changing consumer market. Vector models, in turn, provide high accuracy of demand forecasting and inventory management, contributing to an increase in the efficiency of trading channels. The synergy of these technologies allows companies to improve customer experience, optimize operational processes and increase competitiveness in the market. The main focus of the work is on data processing methods, including correlation analysis, the use of the support vector machine (SVM) method and its adaptation to solve problems related to predicting customer behavior and optimizing logistics processes.

opencc-by-4.0Nov 2023View details →

Real-world Optimization Benchmark from Vehicle Dynamics - Data and Code

Data and Code of five 2D single-objective optimization problems from vehicle dynamics design for benchmarking Conference Paper at ECTA Real-world Optimization Benchmark from Vehicle Dynamics: Specification of Problems in 2D and Methodology for Transferring (Meta-)Optimized Algorithm Parameters

opencc-by-4.0Oct 2024View details →

Dataset of the manuscript: Predictive design to determine optimal absorber placement in colloidal photonic crystals

This data publication is based on the metadata and datasets underlying the manuscript "Predictive design to determine optimal absorber placement in colloidal photonic crystals". The Data is roughly organized by the figure of appearance. Figure 1 contained no result data Figure "Figure 2" contains: <ul> <li>Simulated and experimental reflectance spectra of bare PS colloidal crystal and CIELab color coordinates of simulated bare PS colloidal crystal.</li> </ul> Figure "Figure 3+4" contains: <ul> <li>Data for particle based and layer based designs for chroma optimization according to Eq. 2 </li> <ul> <li>convergence history J(steps)</li> <li>Optimized design absorber distributions (average of layers)</li> <li>CIELab color coordinates</li> <li>Spectra</li> </ul> <li>CIELab color coordinates and Chroma of all predictive designs sorted by threshold L value according to Eq. 3 + comparative designs: bottom absorber, top absorber, homogeneous.</li> </ul> Figure "Figure 5" contains: <ul> <li>Chemdraw File containing chemical structures</li> <li>Pendant drop surface tension measurements </li> <li>Surface pressure increase on Langmuir-Blodgett trough</li> </ul> Figure "Figure 6" contains: <ul> <li>SEM images of mono- and multilayers labeled in accordance to design and composition</li> </ul> Figure "Figure 7+8" contains: <ul> <li>Photographs of fabricated multilayers</li> <ul> <li>Homogeneous designs labeled in accordance to composition</li> <li>Layered designs labeled in accordance to design type (XBA: bottom absorber with X absorbing numbers; XTA: bottom absorber with X absorbing numbers; Ld_XX: predictive design with L threshold of XX)</li> </ul> <li>Spectra of all samples including their error determined from 2 measurements</li> <li>Average spectra of all designs (averaged from all samples of that design) including their error estimated using gaussian error propagation</li> <li>Color data of all samples calculated from spectra </li> <ul> <li>CIELab coordinates and Chroma</li> <li>xyz values</li> <li>RGB values</li> </ul> </ul> Figure "Figure 9" contains: <ul> <li>Optimized design absorber distributions, spectra and CIELab color coordinates and chroma for colloidal crystals of varying primary particle size</li> </ul>

opencc-zeroOct 2024View details →

Comprehensive Dataset on Optimization of Production Systems

This dataset consists of an Excel file containing eight sheets, each corresponding to different aspects of production line optimization, engineering problems, and home healthcare scheduling. The data have been used in various published works and provide valuable resources for research in these fields.  The data have been used and validated in the following publications: - A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment; - Buffer allocation design for unreliable production lines using genetic algorithm and finite perturbation analysis; - Adaptive Dynamic Jumping Particle Swarm Optimization for Buffer Allocation in Unreliable Production Lines; - Simultaneous allocation of buffer capacities and service times in unreliable production lines; - Joint Allocation of Buffer Sizes and Service Times in Unreliable Assembly-Disassembly Systems; - Exponential Particle Swarm Optimization for Global Optimization.

opencc-by-4.0Oct 2024View details →

Optimization of nuclei isolation for high-molecular weight DNA extraction of wild plants

Optimization of nuclei isolation for high-molecular weight DNA extraction from wild plants Authors Irene Martínez-García1; Martina Degli Alberti2; Aureliano, Bombarely2; Mario Xavier Ruiz-González1; Santiago Vilanova1; Silvia Manrique1   Affiliations 1 Instituto Universitario para la Conservación y Mejora de la Agrodiversidad Valenciana (COMAV), Universidad Politécnica de Valencia (UPV), Camino de Vera s/n 46022, Valencia, Spain) 2 Instituto de Biología Molecular y Celular de Plantas (IBMCP) Primo-Yufera, Consejo Superior de Investigaciones Científicas (CSIC)- Universidad Politécnica de Valencia (UPV), Camino de Vera s/n 46022, Valencia, Spain)   Abstract Obtaining high-quality, high-molecular-weight (HMW) DNA is mandatory for constructing reference genomes, yet it remains a significant challenge, particularly for non-model plant species. Many plants contain polysaccharides and secondary metabolites such as polyphenols and tannins, which hinder DNA extraction. Exposure to multiple environmental stresses along their lives exacerbate this issue in wild plants collected from nature, as they may synthesize additional compounds that impair DNA extraction. This study investigates the efficacy of various DNA extraction protocols on four recalcitrant plant species—Pistacia lentiscus, Phyllirea angustifolia, Sarcocornia fruticosa, and Limbarda crithmoides—each possessing unique traits complicating DNA extraction (e.g., succulence, lignification, coloration). Samples were collected from El Saler (Valencia), and multiple protocols for nuclei isolation and DNA extraction were tested. The results were systematically evaluated to compile a comprehensive best practices guide, aiding researchers in selecting optimal methods tailored to their species of interest. This guide serves as a valuable resource for future endeavours in genome research, facilitating advancements in understanding and conservation efforts for diverse plant species. Funding:  This work was supported by Erasmus+ Program (to MD), Programa María Zambrano 2021 (to SM and MXRG, UPV Ministerio de Universidades, Plan de Recuperación, Transformación y Resiliencia - Financiado por la Unión Europea – NextGenerationEU) and Catalan Initiative For the Earth Biogenome Project (Call1A-2023 to SM and call 2-2023 to AB and SM) and Primeros Proyectos UPV (PAID-06-22 to MXRG).