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5,573 results for “optimality”
Data in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes to replicate the results and the figures presented in "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"</p>
Geometry optimizations and electronic excitation spectra of an OLED emitter
<p>This dataset contains the main input and output files for the simulations reported in the study Wagner, J.; Kumar, D.; Kochman, M. A.; Gryber, T.; Grzelak, M.; Kubas, A.; Data, P.; Lindner, M. Facile Functionalization of Ambipolar, Nitrogen-Doped PAHs toward Highly Efficient TADF OLED Emitters. <em>ACS Appl. Mater. Interfaces</em> <strong>2023</strong>, <em>15</em>, 37728−37740. DOI: 10.1021/acsami.3c07552.</p> <p>The electronic structure calculations were performed within the program Turbomole, version 6.3.1. The ground electronic state of the model compound was treated at the SOS-MP2/6-31G(d) level of theory, while its excited electronic states were described at the SOS-ADC(2)/6-31G(d) level. </p> <p>The geometry optimizations were carried out by interfacing Turbomole to the program Gaussian 16. In this setup, Gaussian acts as a "wrapper" around Turbomole, and calls Turbomole for the calculation of the energy and the gradient of the chosen electronic states. The interface consists of the C++ programs "get_forces_opt.cpp", "format_gradient.cpp", and the script "run_tm.bash". The number of atoms in the molecule is defined in the file "settings". </p> <p>A detailed summary of the computational parameters will be provided in a correction to our article.</p>
Memory-optimized round-3 Dilithium in pure C
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The dataset of predicting the potential habitat suitability of Saussurea species in China under future climate change using the optimized Maximum Entropy (MaxEnt) model
<p><strong>Description:</strong></p> <p>This dataset accompanies the study on the Saussurea species, renowned for its biodiversity and medicinal significance in high-elevation regions, which faces endangerment due to climate change and human activities. Despite its importance, conservation research on Saussurea has been limited. To address this gap, the study employed the optimized MaxEnt model to simulate Saussurea's habitat suitability and analyze key environmental factors influencing its distribution.</p> <p>The dataset includes:</p> <ol> <li><strong>Model and Parameter Optimization Code</strong>: The code used for optimizing the MaxEnt model parameters, ensuring reproducibility of the habitat suitability models.</li> <li><strong>Saussurea Distribution Points</strong>: Georeferenced points indicating the observed locations of Saussurea species.</li> <li><strong>Current Environmental Variables</strong>: Data on key environmental factors influencing Saussurea distribution, such as Elevation, Isothermality (Bio3), and Temperature Annual Range (Bio7).</li> <li><strong>Future Environmental Variables: </strong>Data on key environmental factors influencing Saussurea distribution under SSP126, SSP245, SSP370 and SSP585 in 2020-2100s.</li> </ol>
Data availability Optimized inventory control for zero waste in new product development
<p>This data elucidates the research focus using the ABC method, the distribution of lead times based on material types, and the comparative analysis between initial and proposed conditions.</p>
CO2 reforming with methane for syngas production using a dielectric barrier discharge plasma coupled with Ni/γ-Al2O3 catalysts: Process optimization through response surface methodology
<p>XRD data, TG data, experimental data</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Optimized geometries of acids and their deprotonated ions on various metal surfaces.
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Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
The supporting data for the paper "Synergistic Enhancement of LSTM Time Series Prediction via Companion Strategy and Decay Operator-Improved Aquila Optimization"
<p>数据生成程序</p> <p>该数据集是使用 The Investor's Exchange API 生成的,脚本会定期获取标准普尔 500 指数中所有公司的历史股价。详细说明和脚本可以在 GitHub 存储库中找到。该数据每5年更新一次,最近一次更新于2018年2月。</p> <p> </p> <p>数据处理方法和步骤</p> <p>数据处理的主要步骤包括:</p> <p> </p> <p>数据采集:使用 API 获取每只股票的历史数据,存储在.csv文件中。</p> <p>数据清理:删除重复条目,纠正格式错误,确保数据完整性。</p> <p>数据合并:将单个股票数据合并到一个大.csv文件中,以便于使用。</p> <p>数据验证:通过检查时间序列的连续性和完整性来验证数据的准确性。</p> <p>使用的设备和工具</p> <p>数据采集工具:Python 脚本</p> <p>数据处理工具:用于数据清洗和处理的 Pandas 库</p> <p>数据存储:CSV文件格式</p> <p>时间和地理范围</p> <p>时间范围:数据涵盖过去 5 年的历史股票价格,最新更新于 2018 年 2 月。</p> <p>地理范围:数据涵盖标准普尔500指数中的所有公司,主要是美国市场数据。</p> <p>时间和空间分辨率</p> <p>时间分辨率:每日数据,每个交易日一条记录。</p> <p>空间分辨率:无地理空间分辨率;数据按公司分组。</p> <p>表格数据</p> <p>条目总数:条目总数取决于标准普尔500指数中的公司数量和总交易日数。</p> <p>行标题和列标题:</p> <p>日期:交易日期格式为yy-mm-dd</p> <p>开盘价:开盘价(美元)</p> <p>最高价:当日最高价(美元)</p> <p>最低价:当日最低价格(美元)</p> <p>收盘价:收盘价(美元)</p> <p>交易量:成交股数</p> <p>名称:以股票代码的名义</p> <p>缺失数据</p> <p>数据集在某些交易日可能缺少数据,主要是由于非交易日(例如节假日)或API数据采集过程中的临时网络问题。这些缺失的数据通常不会影响整体分析结果。</p> <p> </p> <p>数据错误</p> <p>由于数据源是第三方 API,因此数据错误的可能性很低。如果发现错误,通常是由于 API 数据采集过程中的临时网络问题造成的。数据清理过程旨在最大限度地减少和纠正这些错误。</p> <p> </p> <p>数据文件说明</p> <p>数据文件类型:</p> <p>all_stocks_5yr.csv:包含所有股票的合并数据文件。</p> <p>individual_stocks_5yr文件夹:包含每个股票的单个.csv文件。</p> <p>文件内容和格式:文件采用 CSV 格式,每个文件包含日期、开盘价、最高价、最低价、收盘价、成交量和股票名称列。</p> <p>文件大小:文件大小取决于特定股票的交易数据量,通常从几MB到几十MB不等。</p> <p>文件格式说明</p> <p>数据以通用的 CSV 格式存储,可以使用 Excel、Notepad++ 或任何支持 CSV 文件的工具打开和查看。对于进一步的数据处理和分析,可以使用 Python Pandas 库。</p> <p> </p> <p>总结</p> <p>该数据集提供过去5年标准普尔500指数中所有公司的历史股价数据,包括开盘价、最高价、最低价、收盘价、交易量等详细信息。它适用于各种财务数据分析和建模应用。数据通过 API 获取并处理,以确保准确性和完整性。</p>
Dataset for paper "Direct multiple shooting for computationally efficient train trajectory optimization" (Kouzoupis et al.)
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Solvothermal Synthesis Combined with Design of Experiments—Optimization Approach for Magnetite Nanocrystal Clusters
<p>The upload archive consists of all the obtained results based on DLS ( commonly known as <strong>dynamic light scattering</strong>) measurements. More detain you could find in the cited article above.</p>
Data repository of the paper "Expectation Values from the Single-Layer Quantum Approximate Optimization Algorithm on Ising Problems"
<p>This data repository contains the I-set instances of the paper "Expectation Values from the Single-Layer Quantum Approximate Optimization Algorithm on Ising Problems". </p>
Sector-coupled input data for optimizing the German transmission grid
<p>This repository contains input data for the open-source python tool <a href="https://github.com/openego/eTraGo">eTraGo</a> (<strong>e</strong>lectricity <strong>Tra</strong>nsmission <strong>G</strong>rid <strong>o</strong>ptimization) version 0.9.0.<br> This data will be uploaded to the <a href="https://openenergy-platform.org/">OpenEnergy Platform</a> which can be accessed by eTraGo. This dataset is an intermediate solution until the data is uploaded.</p> <p>The published data includes the sector-coupled transmission grid data for two scenarios (eGon2035 and eGon2035_lowlfex). It was created with the open-source tool <a href="https://github.com/openego/eGon-data/">eGon-data</a> within the research project <a href="https://ego-n.org">eGon</a>. All input data sets as well as the code are available under open source licenses.</p> <p>The data is stored as a PostgreSQL database in the attached backup file. First, the required schemas and extensions have to be created within the database by running the following SQL statements:</p> <p><code>CREATE SCHEMA grid;<br> CREATE SCHEMA boundaries;<br> CREATE EXTENSION postgis;</code></p> <p>Afterwards the data can be restored by using e.g. pgAdmin or via PostgreSQL's <a href="https://www.postgresql.org/docs/current/app-pgrestore.html">pg_restore</a> command (replace <code>HOST</code>, DATABASE_<code>NAME, PORT</code> and <code>USER</code> by your settings):</p> <p><code>pg_restore --host HOST --port PORT --username USER --no-password --dbname </code>DATABASE_<code>NAME --no-owner --no-privileges --verbose "etrago_data_egon2035.backup"</code></p>
Replication package for: House price dynamics, optimal LTV limits and the liquidity trap
<p>This package contains all the code necessary to replicate the figures in Ferrero, A., Harrison, R. and Nelson, B. (forthcoming) "House price dynamics, optimal LTV limits and the liquidity trap", Review of Economic Studies.</p>
data set used for Application of Blockchain Technology Based on Artificial Intelligence Optimization in Communication Data Security Service
<p>This is the data set used to implement the project Application of Blockchain Technology Based on Artificial Intelligence Optimization in Communication Data Security Service</p>
Sentiment analysis of media's political bias in micro-blogging - A computational framework for optimized recommendation systems
<p>This dataset contains tweets from four Pakistani news channels, namely DAWN, GEO, ARY, and 24News, collected using the twarc command line tool with a Twitter academic researcher account. The tweets were collected between Nov, 2015, and April 4, 2022, and relate to three major political parties in Pakistan, PTI, PMLN, and PPP through their official names in the relevant news channel page tweets. </p>
Metadane dla Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey
<p>Metadata for Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey</p>
Rysunki dla Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints
<p>This resource contains the figures for Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints</p>
Dataset of "Supersonic: Learning to Generate Source Code Optimizations in C/C++"
<p>This is the dataset of "<a href="https://arxiv.org/abs/2309.14846">Supersonic: Learning to Generate Source Code Optimizations in C/C++</a>".</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.