Find research datasets worth reusing

Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Maintenance Optimization

Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> a maintenance optimization scenario was formulated to validate the scheduler's ability to schedule tasks as well as maintenance activities while also minimizing the total costs. Accordingly, it was considered an optimization weight of 1 for the total cost and 0 for machine occupancy deviation, as well as a 2-hour execution time for the genetic algorithm. Each maintenance activity, for every machine, has a duration of 6 hours and 10 minutes, with a labor cost of 3,22 EUR/hour during the stipulated maintenance hours and a monetary penalty, that doubles the cost (i.e., 6.44 EUR/hour) if done out of maintenance hours.   File Description: <ul> <li>Input_JSON_Maintenance_Optimization - JSON input data for the maintenance optimization</li> <li>Output_JSON_Maintenance_Optimization - JSON output data for the maintenance optimization</li> <li>Output_Statistics_Maintenance_Optimization - Excel output maintenance optimization statistics</li> </ul>

Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Total Cost and Machine Occupancy Deviation Optimization

Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> a total cost and machine occupancy deviation optimization scenario was formulated that aims to demonstrate how the proposed scheduler is able to balance tasks between machines while also reducing overall costs. For this scenario, it was considered an optimization weight of 0.5 for both the total costs and machine occupancy deviation objectives and the genetic algorithm was executed for 2 hours.   File Description: <ul> <li>Input_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON input data for the total cost and machine occupancy deviation optimization</li> <li>Output_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON output data for the total cost and machine occupancy deviation optimization</li> <li>Output_Statistics_Total_Cost_Machine_Occupancy_Deviation_Optimization - Excel output total cost and machine occupancy deviation optimization statistics</li> </ul>

Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Energy Cost Optimization with Energy Selling

Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> an energy cost optimization considering the presence of an energy buyer is proposed to validate the scheduler’s ability to maximize profits while also minimizing energy costs. The scenario considers an added sales value corresponding to 50% of the buying. For this scenario, the genetic algorithm was executed for 2 hours, with 1 and 0 for the optimization weights total cost and machine occupancy deviation, respectively.   File Description: <ul> <li>Input_JSON_Energy_Cost_Energy_Selling_Optimization - JSON input data for the energy cost optimization with energy selling</li> <li>Output_JSON_Energy_Cost_Energy_Selling_Optimization - JSON output data for the energy cost optimization with energy selling</li> <li>Output_Statistics_Energy_Cost_Energy_Selling_Optimization - Excel output energy cost optimization with energy selling statistics</li> </ul>

Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Joint Optimization of Production and Maintenance

Using the previous datasets at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>>,  <<a href="https://zenodo.org/record/7055698">https://zenodo.org/record/7055698</a>>, <<a href="https://zenodo.org/record/7055580">https://zenodo.org/record/7055580</a>>, and <<a href="https://zenodo.org/record/7055573">https://zenodo.org/record/7055573</a>>  a joint optimization of production and maintenance scenario was formulated which aims at combining all the features from the cited scenarios. For energy selling, it was considered an added sales value corresponding to 50% of the buying. Regarding maintenance activities, it was simulated an announcement of a maintenance activity for MAQ118 from Monday at 07:00 (i.e., period 1) to Monday at 17:00 (i.e., period 120), and another for MAQ120 which can be done at any time. These maintenance activities take 6 hours and 10 minutes to complete and have an associated labor cost of 3,22 EUR/hour in maintenance hours, and a double cost penalty (i.e., 6.44 EUR/hour) if done out of maintenance hours. The scenario was executed in 2 hours, with the corresponding optimization weights of 0.8 and 0.2 for the total cost and machine occupancy deviation, respectively.   File Description: <ul> <li>Input_JSON_Joint_Optimization_Production_Maintenance - JSON input data for the joint optimization of production and maintenance</li> <li>Output_JSON_Joint_Optimization_Production_Maintenance - JSON output data for the joint optimization of production and maintenance</li> <li>Output_Statistics_Joint_Optimization_Production_Maintenance - Excel output joint optimization of production and maintenance statistics</li> </ul>

opencc-by-4.0Apr 2019View details →

Moving window optimization IHTP

bulk.txt - bulk temperature at times given in time.txt hflux.txt - calculated values of heat flux on rocket skin hflux0.txt - initial (guess) values of heat flux on rocket skin movingwindow.py - Python 2.7 script performing moving window optimization solving Inverse Heat Transfer Problem rx.mac - Ansys APDL script solving Direct Heat Transfer Problem temp_bulkhead.csv - raw temperature data temperatura.txt - calculated values of temperature from Ansys time.txt - time stamps for heat flux

opencc-by-4.0Nov 2023View details →

Apples to Apples: Shift from Mass Ratio to Additive Molecules per Electrode Area to Optimize Li-Ion Batteries

Electrolyte additives in liquid electrolyte batteries can trigger the formation of a protective interphase (SEI) atthe electrodes that aims to suppress side reactions at the electrodes. Studies of varying amounts of additives have been done over the last years, providing a comprehensive understanding of the impact of the electrolyte formulation on the lifetime of the cells. However, these studies mostly focus on the variation of the mass fraction of additive in the electrolyte while disregarding the ratio (radd) of the additive's amount of substance (nadd) to the electrode area (Aelectrode). Herein we utilize our extremely accurate automatic battery assembly system (AUTOBASS) to vary electrode area and amount of substance of the additive. The data provides strong evidence that reporting the mass ratios of electrolyte components is insufficient and the mol of additive relative to the electrodes' area should be reported. Herein, the two most utilized additives, namely fluoroethylene carbonate (FEC) and vinylene carbonate (VC) were studied. Each additive was varied from 0.1 wt.-% - 3.0 wt.-% for VC, and 5 wt.-% - 15 wt.-% for FEC for two mass loadings of 1 mAh/cm2 and 3 mAh/cm2. To engage the community to find better descriptors, such as the proposed radd, we publish the dataset alongside this manuscript. Codes and mechanical parts of the project: AutoBASS 2.0: <a href="https://github.com/Helge-Stein-Group/AutoBASS/tree/AutoBASS_2.0">GitHub - Helge-Stein-Group/AutoBASS at AutoBASS_2.0</a>

opencc-by-4.0May 2024View details →

Data for "Optimization of decoder priors for accurate quantum error correction"

Datasets of surface code and repetition code memory experiments executed on Google's Sycamore quantum processor. See README at the root of each zip archive for detailed description of each dataset.

opencc-by-4.0Mar 2018View details →

Survey on the usage of Mathematical Modelling, Simulation and Optimization software

This dataset contains the result of a survey we carried out in the context of the MSO4SC project in order to know which kinds of tools for simulation were using our stakeholders. The purpose was to prioritize functionalities depending on stakeholders' preferences. It was a survey with 41 questions grouped in 10 areas (impact of simulation software on their entities, usage of pre/post-processing, usage of visualization, etc...). The pdf file includes the list of questions for clarification. Such survey was answered by academia and industry from several European countries.

opencc-by-4.0Jul 2018View details →

Circulation type classifications for surface temperature and precipitation optimized for Italy

The four files are two couple of files for two circulation type classifications (pct9 and san9) optimized for Italy, in order to stratify precipitation and surface temperature respectively. "pct9.cla" and "san.cla" are the circulation type daily series between 1979 and 2015 computed on mean sea level pressure (MSLP) and geopotential height at 500 hPa (500HGT) respectively. Meteorological fields are extracted by the NCEP-NCAR Reanalysis 2 dataset. "pct-nc.txt" and "san9-nc.txt" are the centroid values of MSLP and 500HGT respectively, computed on 9 classes over a spatial domain of 7 X 7 grid points across Italy. These files are created through the COST733 software package (DOI: 10.1002/joc.3920).  The pct9 and san9 classifications were selected as the best performing for the stratifacation of precipitation and surface temperature respectively across Italian peninsula, through a sensitivity analysis detailed in a specific study (DOI: 10.1002/joc.5219). In summary several circulation type classifications were computed with different classification methods, number of types and classification variables (i.e. predictands). Then such classifications were compared through the use of proper statistical indexes in order to assess the stratification of the ground-level precipitation and the surface air temperature across Italian peninsula. These two classifications could be evaluated also for other meteorological or environmental variables.

opencc-zeroJul 2019View details →

Genome alignments for the project "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - Protocol optimization

Genome alignments for data generated in the project "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol – Optimization of the protocol." Files names indicate unique identifiers of MOIRAI workflow runs, with the following structure: library name, dot, workflow ID (OP-WORKFLOW-CAGEscan-short-reads-v2.0.), dot, timestamp. The raw (FASTQ) data of each library is also deposited in Zenodo (<a href="https://doi.org/10.5281/zenodo.250156">10.5281/zenodo.250156</a>). Library names correspond to the following runs: <ul> <li> NC33: 151007_M00528_0161_000000000-AEBDC</li> <li> NC37: 151204_M00528_0173_000000000-AEBEF</li> <li> NC38: 151211_M00528_0175_000000000-AE9PJ</li> <li> NC39: 160122_M00528_0185_000000000-AEB18</li> <li> NC42: 160302_M00528_0192_000000000-AELYK</li> </ul> This data can be analysed using the "CAGEr" software package available from Bioconductor.  The "multiplex_files.zip" file contains tables indicating which samples are biological replicates of each other or negative controls.

opencc-by-4.0Nov 2019View details →

Climate change impact and mitigation cost data - The economically optimal warming limit of the planet

This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper: Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. <a href="https://doi.org/10.5194/esd-10-741-2019">https://doi.org/10.5194/esd-10-741-2019</a> Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).   Climate change impact data File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries.   File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).   File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action). In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, <a href="https://tntcat.iiasa.ac.at/SspDb/">https://tntcat.iiasa.ac.at/SspDb/</a>, last accessed 15Nov 2019).   Climate change mitigation cost data The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2]. File 4: REMIND_scenario_results_economic_data.csv File 5: REMIND_scenarios_climate_data.csv Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature. In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios. The first dimension specifies the climate policy regime (delayed action, baseline scenarios): 1xx: climate action from 2010 5xx: climate action from 2015 2xx climate action from 2020 (used in this study) 3xx climate action from 2030 4x1 weak policy baseline (before Paris agreement) The second dimension specifies the technology portfolio and assumptions: x1x Full technology portfolio (used in this study) x2x noCCS: unavailability   of   CCS x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed x4x NucPO: phase out of investments into nuclear energy x5x Limited SW: penetration of   solar   and   wind   power   limited x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases) x6x noBECCS: unavailability   of   CCS   in   combination   with   bioenergy The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.). xx1   0$/tCO2   (baseline) xx2   10$/tCO2 xx3   30$/tCO2 xx4   50$/tCO2  xx5   100$/tCO2 xx6   200$/tCO2 xx7   500$/tCO2 xx8   40$/tCO2 xx9   20$/tCO2 xx0   5$/tCO2 For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price).   [1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a. [2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.

opencc-by-4.0Sep 2024View details →

Optimization of abdominal CT based on a model of total risk minimization by putting radiation risk in perspective with imaging benefit

Population of one million cases simulating a liver cancer scenario. The demographic information was taken from the USA 2019 Census Population Estimates by Age, Sex, Race, and Hispanic Origin. The patient population was simulated in eight different cohorts with genders of male and female, and races/ethnicities of white, Black, Hispanic, and Asian. The total population sample size allowed the inclusion of a significant number of cases in each group. For each simulated patient, the age was randomly sampled from a uniform distribution, which spanned the age range of the individual demographic groups. Radiation risk, clinical risk, and total imaging procedure risk were also calculated.

opencc-by-4.0Sep 2024View details →

Optimizing the Shelling Process of InP/ZnS Quantum Dots Using a Single-Source Shell Precursor: Implications for Lighting and Display Applications

This is the data supporting the manuscript "Optimizing the Shelling Process of InP/ZnS Quantum Dots Using a Single-Source Shell Precursor: Implications for Lighting and Display Applications". Abstract InP/ZnS core/shell quantum dots (QDs), recognized as highly promising heavy-metal-free emitters, are increasingly utilized in lighting and display applications. Their synthesis in a tubular flow reactor enables production in a highly efficient, scalable, and reproducible manner, particularly when combined with a single-source shell precursor, such as zinc diethyldithiocarbamate (Zn(S2CNEt2)2). However, the photoluminescence quantum yield (PLQY) of QDs synthesized with this route remains significantly lower compared to those synthesized in batch reactors involving multiple steps for the shell growth. Our study identifies the formation of absorbing, yet non-emissive ZnS nanoparticles during the ZnS shell formation process as a main contributing factor to this discrepancy. By varying the shelling conditions, especially the shelling reaction temperature and InP core concentration, we investigated the formation of pure ZnS nanoparticles and their impact on the optical properties, particularly PLQY, of the resultant InP/ZnS QDs through UV-vis absorption, steady-state and time-resolved photoluminescence (PL) spectroscopy, scanning transmission electron microscopy (STEM) and analytical ultracentrifugation (AUC) measurements. Our results suggest that process conditions, such as lower shelling temperatures or reduced InP core concentrations (resulting in a lower external surface area), encourage the homogeneous nucleation of ZnS. This reduces the availability of shell precursors necessary for an effective passivation of the InP core surfaces, ultimately resulting in lower PLQYs. These findings explain the origin of persistently underperformed PLQY of InP/ZnS QDs synthesized from this synthesis route and suggest further optimization strategies to improve their emission for lighting and display applications. The data are sorted per techniques used for characterization. Information about the measurement details can be found in README files attached to each technique folder.

Quantum chemical optimizations of Ala-Gly di- and tripeptides

Quantum chemical simulations of reactants and products for aa dimerization Software: Gaussian v16 RevA.03 The "thermochemistry" archive refers to thermochemical calculations of reactants and products for condensation of Alanine, Glycine and boric acid. Geometry optimizations were carried out at the B3LYP/6-311G** level of theory, both in vacuo and in water in a polarized continuum fashion.  See readme.txt file for a detailed list of files and directories.

opencc-by-4.0Sep 2023View details →

Polyubiquitin ligand-induced phase transitions are optimized by spacing between ubiquitin units

These are the original data used to make figures for the manuscript titled "Polyubiquitin ligand-induced phase transitions are optimized by spacing between ubiquitin units" by Sarasi Galagedera et al.

Efficiency circularity assessment and optimization tool_NL

Inputs and results for the WIDER UPTAKE H2020 project's Dutch case study of manufacturing bio-composites uising the resources recovered from the urban water chain.

Efficiency circularity assessment and optimization tool_IT

Inputs and results for the WIDER UPTAKE H2020 project's Italian case study of reusing treated wastewater for agricultural irrigation.

Data and code for "Optimizing cover crop practices as a sustainable solution for global agroecosystem services"

Data and code for "Optimizing cover crop practices as a sustainable solution for global agroecosystem services" (Qiu et al. 2025), including source data, R scripts, and output results.

opencc-by-4.0Nov 2024View details →

Data for "Quantum combinatorial optimization beyond the variational paradigm: simple schedules for hard problems"

Contains instances of combinatorial optimizations problems (Sherrington-Kirkpatrick and MAX 2-SAT) as well as further results and plotting notebooks for the paper "Quantum combinatorial optimization beyond the variational paradigm: simple schedules for hard problems".