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5,573 results for “optimization”
Emotion Category and Face Perception Task Optimized for Multivariate Pattern Analysis
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Dataset for "An Alternative Chlorine-Assisted Optimization of CdS/Sb2Se3 Solar Cells: Towards Understanding of Chlorine Incorporation Mechanism"
<p>The current strategies in the development of Sb2Se3 thin film solar cells involve fabrication and optimization of<br>superstrate and substrate device architectures, with the preferable choice for TiO2 and CdS heterojunction layers.<br>For CdS-based superstrate cells, several studies reported the necessity to apply CdCl2 or other metal halide-based<br>post-deposition treatment (PDT), highlighting improvement of CdS/Sb2Se3 device efficiency. However, the need,<br>effect, and mechanism of such PDT are very often not described. Additionally, the fact that many groups have not<br>succeeded in demonstrating its benefits suggests that this strategy is not straightforward, requiring a deeper<br>understanding towards a more unified concept. The present study proposes an alternative approach to the<br>challenging CdCl2 PDT of CdS in CdS/Sb2Se3 device, involving controllable Cl incorporation in CdS films by<br>systematically varying the concentration of NH4Cl in the CBD precursor solution from 1 to 8 mM. Structural and<br>electrical characterizations are correlated with advanced measurements of Scanning Kelvin Probe, surface<br>photovoltage, and atomic force microscopy to understand the impact of Cl incorporation on the properties of CdS<br>films and CdS/Sb2Se3 devices. The validity of Cl incorporation in the CdS lattice and interdiffusion processes at<br>the CdS-Sb2Se3 interface is confirmed by secondary ion mass spectrometry analysis. It is demonstrated that<br>incorporation of 1 mM of NH4Cl, as a Cl source in CBD CdS, can boost the PCE of CdS/Sb2Se3 by ~20 %. With this<br>approach, we offer new perspectives on the optimization methodology for Cl-based CdS/Sb2Se3 device processing<br>and complementary understanding of the physiochemistry behind these processes.</p>
Optimizing laboratory cultures of <i>Gammarus fossarum</i> (Crustacea: Amphipoda) as a study organism in environmental sciences and ecotoxicology
<p>Supplemental code and data for Alther, Krähenbühl, Bucher & Altermatt (2022) 'Optimizing laboratory cultures of <em>Gammarus fossarum</em> (Crustacea: Amphipoda) as a study organism in environmental sciences and ecotoxicology' (DOI: 10.1016/j.scitotenv.2022.158730). The repository folder contains three text files and a corresponding R script.</p> <p>Rerunning the analysis and producing figures requires two raw data files: LabdataAK_v6_210616_Daylength_input.txt and Nutrition_Exp_KaplanMeier_v1_input.txt. In order to reproduce the analysis and figures, run 'AmphipodHusbandry_20220919.R'. Make sure that your working directory is the folder containing all data files, easily achieved by (re)starting R (or R Studio) by double-clicking the R script file in the folder. The analysis script will produce all the figures from the paper, organized in a folder 'Results' and a subfolder 'Supplement'. Figures are prepared as pixel graphics (PNG).</p> <p>The R script was tested in R ver. 4.1.1 (Windows 10, version 21H1), 4.1.3 (macOS 11.6), and 4.2.0 (Ubuntu 22.04. Required packages are survival (version 3.2-13 worked), survminer (version 0.4.9 worked), and vioplot (version 0.3.7 worked).</p>
Optimization of Solid-State Anaerobic Digestion of Prairie Biomass and Beef Manure
This dataset supports the evaluation and optimization of solid-state anaerobic digestion (SSAD) using prairie biomass and beef manure mixtures under varying total solids (TS) contents, particle sizes, and percolation frequency. It includes raw and processed data on biogas and methane production, volatile solids composition, carbon-to-nitrogen ratios, theoretical biochemical methane potential (BMP), energy balances, and water activity.
Production line dataset for task scheduling and energy optimization - Demand Response Participation
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> it was simulated an announcement of a demand response program at period 757, describing a demand response event from period 937 (Friday at 21:00h) to 960 (Friday at 23:00h) , where each period represents five minutes. The demand response program imposed a limit consumption, during its event, of 2.5 kWh. The announcement of the demand response allowed the use of the proposed solution to limit the energy consumption. For that, the algorithm described in section 3.3 was executed at period 769 (Friday at 7:00h).</p> <p>The API can be found at <<a href="http://www.gecad.isep.ipp.pt/api/spear/%3E">http://www.gecad.isep.ipp.pt/api/spear/</a>></p> <p>File Description:</p> <ul> <li>Input_JSON_Demand_Response_Optimization - JSON input data for the demand response participation</li> <li>Output_JSON_Demand_Response_Optimization - JSON output data for the demand response participation</li> <li>Output_Statistics_Demand_Response_Optimization - Excel output demand response participation statistics</li> <li>Comparison_Output_Statistics_Demand_Response - Excel output statistics comparing the before and after the demand response participation</li> </ul>
Production line dataset for task scheduling and energy optimization - Schedule Optimization
<p>The case study of this dataset uses real production data, provided by a textile company that manufactures hang tags. Their working schedule is from 7h00 of Monday to 23h00 of Saturday. This dataset uses a period of 5 minutes for all task durations and energy data. The case study considers a six-day period from 7h00 of Monday to 23h00 of Saturday. The scheduling algorithm was used for three machines that share the same cell.<br> <br> The API can be found at <http://www.gecad.isep.ipp.pt/api/spear/><br> <br> File Description:</p> <ul> <li>Input_JSON_Schedule_Optimization - JSON input data for the schedule optimization</li> <li>Output_JSON_Schedule_Optimization - JSON output data for the schedule optimization</li> <li>Output_Statistics_Schedule_Optimization - Excel output schedule optimization statistics</li> </ul>
Nearly optimal coverings of orientation space
<p>We give various sets of orientations which cover orientation space nearly optimally. These are suitable for searching orientation space and for integrating over orientation (together with the provided weights). The data is identical to that provided in March, 2006. The documentation has been updated for this release; this is available at https://github.com/cffk/orientation/tree/v1.1</p>
Optimal Displacement Increment for Numerical Frequencies (Dataset)
<p>1<em>H</em>-pyrrolo[3,2-<em>h</em>]quinoline [Gorski, 2012] was optimized in ORCA v3.0.3 [Neese, 2012; http://orcaforum.cec.mpg.de] using RPBE [Perdew, 1992 and 1996] with the def2-TZVP basis sets [Weigend, 1998], and the def2-TZVP/J auxiliary bases [Weigend, 2006] for the RI approximation [Vahtras, 1992]. The nuclear Hessian, normal modes, and harmonic vibrational frequencies were then computed using analytical (ANFREQ) and numerical (NUMFREQ) methodologies. The numerical Hessians were computed with nuclear (Cartesian) displacement increments ranging from 0.0001 Bohr to 0.1 Bohr. The geometry optimization was conducted using the parameters of the TIGHTOPT simple input keyword; KS-SCF and CP-SCF calculations used VERYTIGHTSCF thresholds.</p> <p>An analysis of the deviation of normal modes and harmonic frequencies for each numerical Hessian computation from the analytical Hessian results was presented as a single-figure presentation (SFP) at the 2016 Virtual Winterschool on Computational Chemistry (http://winterschool.cc). This SFP can be found at doi:10.5281/zenodo.44807.</p> <p>For the initial OPT and ANFREQ, the following files are provided:<br /> PQ_OPT_AFQ.engrad -- Gradient data<br /> PQ_OPT_AFQ.gbw -- Wavefunction <br /> PQ_OPT_AFQ.hess -- Hessian data<br /> PQ_OPT_AFQ.out -- Computation output<br /> PQ_OPT_AFQ.trj -- Optimization trajectory (multi-frame OpenBabel XYZ)<br /> PQ_OPT_AFQ.txt -- ORCA input file<br /> PQ_OPT_AFQ.xyz -- Optimized geometry</p> <p>For each following NUMFREQ, the following files are provided, where the number at the end of the filename indicates the nuclear displacement increment in Bohrs:<br /> PQ_NFQ_0.####.hess -- Hessian data<br /> PQ_NFQ_0.####.out -- Computation output<br /> PQ_NFQ_0.####.txt -- ORCA input file</p> <p>Since ORCA does not report non-mass-weighted normal modes, these are provided separately for each calculation as modes_0.####.csv (modes_A.csv for the analytical Hessian.)</p> <p>The dot products of each normal mode from the numerical Hessian computations with the corresponding mode in the analytical Hessian calculation (modes ordered as presented in the ORCA output) are provided in modes_dot_products.csv. The MAD of these data are plotted in the LH figure of the above-referenced SFP.</p> <p>For those numerical Hessian computations with normal modes out of sequence relative to the analytical calculation, permutation matrices to bring them back in accord with the analytical Hessian modes are included as swaps_0.####.csv.</p> <p>A table of the calculated vibrational frequencies for each computation, re-ordered as necessary to bring the normal modes in accord with the analytical Hessian run, is included as freqs_swapped.csv. The MAD and maximum absolute deviation of these data are plotted in the RH figure of the above-referenced SFP.</p>
Heliostat field positions after optimization with DLR's software HFLCAL
<p>The dataset provides the design of the heliostat field layout, in terms of number and positions. The filed layout is optimized by minimizing the estimated LCOE of the system, using the DLR tool Visual HFLCAL software</p> <p>The datasets could help other people design a heliostat field.</p> <p>For detailed analysis, please refer to Deliverable 1.2 (Process Parameters of Solar Particle Cycle) to be downloaded at: <a href="https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf">https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf</a></p>
Microbial Proteomes with/without experimental optimal growth temperature
<p>This repository contains proteomes of microorganisms with/without experimentally determined optimal growth temperature (OGT), used in the paper '<strong>Li G, Rabe KS, Nielsen J & Engqvist MKM (2019) Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. </strong><em>ACS Synth. Biol.</em><strong> 8: 1411–1420</strong>'. There are two .tar.gz files:</p> <p>(1) classified.tar.gz. It contains 5761 proteomes with experimental OGT. The name format of each proteome is '{ogt}_{organism_name}_{organism domain}.fasta'. For example, '36_escherichia_coli_bacteria.fasta' for <em>Escherichia coli.</em></p> <p>(2) not_classified.tar.gz. It contains 1803 proteomes without experimental OGT. The name format is similar as in classified.tar.gz. The only different is to use 'tt' to represent the unknown OGT value. For example, 'tt_candidatus_azobacteroides_bacteria.fasta'.</p> <p>All proteomes are in fasta format. </p> <p>If you used the dataset, please kindly cite the paper mentioned above.</p>
SCG Dataset from Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
<p><strong>Abstract:</strong> Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical ML and other deep learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.</p>
Docked structures from "Optimizing active learning for free energy calculations"
<p>This archive contains the docked TYK2 structures used in the paper "Optimizing active learning for free energy calculations" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ailsci.2022.100050" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ailsci.2022.100050</span></span></a>). AM1-BCC charges are stored in the field "AM1Cache" in the SD file. The charges can be extracted using the code sample below. </p> <p> </p> <pre><code>from rdkit import Chem import base64 import pickle suppl = Chem.SDMolSupplier("10k_most_similar_tyk2_charged.sdf", removeHs=False) for mol in suppl: am1 = mol.GetProp("AM1Cache") am1_charges = pickle.loads(base64.b64decode(mol.GetProp("AM1Cache"))) assert len(am1_charges) == mol.GetNumAtoms(), "Charge cache has different number of charges than mol atoms"</code></pre>
Novel estimates of the leaf relative uptake rate of carbonyl sulfide from optimality theory
<p>Data and Matlab scripts for repeating the analysis presented in the paper. In addition, global monthly climatological LRUs are provided at 0.05° resolution for the period 2001-2010 as nc-files. </p>
Accepted Artifact for Profiling and Optimizing Java Streams
<p>The accepted artifact for the journal article "Profiling and Optimizing Java Streams" published in Volume 7, Issue 3 of <a href="https://programming-journal.org/"><em>The Art, Science, and Engineering of Programming</em></a>.</p> <p>Please refer to the README.md to work with the artifact and reproduce results from the article.</p>
The optimal period for oocyte retrieval after the administration of recombinant human chorionic gonadotropin in in vitro fertilization
<p>This is the dataset of the study called "The optimal period for oocyte retrieval after the administration of recombinant human chorionic gonadotropin in in vitro fertilization".</p> <p><strong>Abstract</strong></p> <p>Background</p> <p>Our objective was to investigate the existence of an optimal period for oocyte retrieval in regards to the clinical pregnancy occurrence after the administration of recombinant human chorionic gonadotropin (rhCG) (Ovitrelle®).</p> <p>Methods</p> <p>We studied the digital records of 3362 middle eastern couples who underwent in vitro fertilization (IVF) treatment between 2019 and 2021.</p> <p>Results</p> <p>Through statistical testing, we found that there is a significant positive correlation between the oocyte retrieval period and the clinical pregnancy occurrence up to the 37th hour, where retrieval at the 37th hour was found to provide the most optimal outcome, especially in the case of gonadotropin-releasing hormone agonist (GnRHa) long protocol.</p> <p>Conclusions</p> <p>This cohort study recommends retrieval at hour 37 after ovulation triggering under the described conditions.</p>
Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"
<p>This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude <B> and the sun's radio flux at 10.7 cm <F10.7>. <B> measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.</p>
Optimized structures of the stationary points on the potential energy surface of the OH(2Π) + C2H4 reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the OH(<sup>2</sup>Π) + C<sub>2</sub>H<sub>4</sub> potential energy surface published in our article “OH(<sup>2</sup>Π) + C<sub>2</sub>H<sub>4</sub> Reaction: A Combined Crossed Molecular Beam and Theoretical Study” (P<em>hys. Chem. A</em> 2023, 127, 21, 4609–4623), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c08662">https://doi.org/10.1021/acs.jpca.2c08662</a>.</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized structures of the stationary points on the potential energy surface of the O(3P, 1D) + HCCCN(X1Σ+) reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the O(<sup>3</sup>P, <sup>1</sup>D) + HCCCN(X<sup>1</sup>Σ<sup>+</sup>) potential energy surface published in our article “Reactions O(<sup>3</sup>P, <sup>1</sup>D) + HCCCN(X<sup>1</sup>Σ<sup>+</sup>) (Cyanoacetylene): Crossed-Beam and Theoretical Studies and Implications for the Chemistry of Extraterrestrial Environments” (<em>J. Phys. Chem. A</em> 2023, 127, 3, 685–703), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c07708">https://doi.org/10.1021/acs.jpca.2c07708</a>.</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized structures of the stationary points on the potential energy surface of the dissociation of the CH3OH˙+ cation
<p>This Zip file contains the optimized stationary points structures of the potential energy surface (PES) for the dissociation of the CH3OH˙+ cation.</p> <p>The PES has been published in our paper “Fragmentation of interstellar methanol by collisions with He˙<sup>+</sup>: an experimental and computational study” (<em><strong>Phys. Chem. Chem. Phys.</strong></em>, 2022, <strong>24</strong>, 22437-22452), that can be found in https://doi.org/10.1039/D2CP02458F .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01 and the structures were optimized at ωB97X-D/aug-cc-pVTZ level of theory.</p>
Dataset: Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
<p>This dataset contains the data used in the paper</p> <blockquote> <p>de Dumast, P., Sanchez, T., Lajous, H., Bach Cuadra, M. (2023). Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution Reconstruction. MICCAI 2023. LNCS, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_32</p> </blockquote> <p>A preprint can also be found on <a href="https://arxiv.org/abs/2211.14274">arXiv</a>. If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This paper studied the impact of the regularization parameter <span class="math-tex">\(\alpha \)</span> on the super-resolution reconstruction of fetal brain magnetic resonance (MR) images. It used simulated T2-weighted data MR images generated using FaBiAN v2.0, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation. The dataset contains the raw simulated data, the corresponding ground truths as well as corresponding super-resolution (SR) reconstructions using MIALSRTK and NiftyMIC with varying regularization parameters <span class="math-tex">\(\alpha \)</span>.</p> <p>Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.</p>
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.