PCE Price Index Annual Change in the United States increased to 2.30 percent in May from 2.20 percent in April of 2025. This dataset includes a chart with historical data for the United States PCE Price Index Annual Change.

View data of PCE, an index that measures monthly changes in the price of consumer goods and services as a means of analyzing inflation.

Core PCE Price Index Annual Change in the United States increased to 2.70 percent in May from 2.60 percent in April of 2025. This dataset includes a chart with historical data for the United States Core Pce Price Index Annual Change.

Graph and download economic data for Personal Consumption Expenditures: Chain-type Price Index (PCEPI) from Jan 1959 to May 2025 about chained, headline figure, PCE, consumption expenditures, consumption, personal, inflation, price index, indexes, price, and USA.

Personal consumption expenditures (PCE) is the value of the goods and services purchased by, or on the behalf of, Iowa residents. Per capita PCE is calculated by dividing the PCE by the Census Bureau’s annual midyear (July 1) population estimates.

PCE Prices QoQ in the United States increased to 3.70 percent in the first quarter of 2025 from 2.40 percent in the fourth quarter of 2024. This dataset includes a chart with historical data for the United States PCE Prices QoQ.

Graph and download economic data for Personal Consumption Expenditures Excluding Food and Energy (Chain-Type Price Index) (PCEPILFE) from Jan 1959 to May 2025 about chained, core, energy, headline figure, PCE, consumption expenditures, consumption, personal, inflation, price index, indexes, price, and USA.

Core PCE Price Index MoM in the United States increased to 0.20 percent in May from 0.10 percent in April of 2025. This dataset includes a chart with historical data for the United States Core Pce Price Index MoM.

PCE Price Index Monthly Change in the United States increased to 0.10 percent in April from 0 percent in March of 2025. This dataset includes a chart with historical data for the United States PCE Price Index Monthly Change.

Alaska Energy Authority Power Cost Equalization (PCE) program by community. The power cost equalization program supports rural Alaskans who live in areas where energy costs are significantly higher than urban areas in meeting the cost of electricity."AEA determines eligibility of community facilities and residential customers and authorizes payment to the electric utility. Commercial customers are not eligible to receive PCE credit. Participating utilities are required to reduce each eligible customer’s bill by the amount that the State pays for PCE. RCA determines if a utility is eligible to participate in the program and calculates the amount of PCE per kWh payable to the utility. More information about the RCA may be found at www.state.ak.us/rca."(AEA, 2017)Source: Alaska Energy AuthorityThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data - it has been primarily compiled from AEA PCE Fiscal Year Utility Report PDFs. For more information and for questions about this data, see: AEA Power Cost Equalization

Power sources of participating Power Cost Equalization entities by community they serve.The power cost equalization program supports rural Alaskans who live in areas where energy costs are significantly higher than urban areas in meeting the cost of electricity. Eligibility is determined by the Regulatory Commission of Alaska under Alaska Statutes 42.45.100-170.Source: Alaska Energy AuthorityThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: AEA Power Cost Equalization

Content:

This dataset contains time-resolved in situ data acquired during the formation of blade-coated perovskite thin films, which were subsequently processed into functional perovskite solar cells. The time series data capture the vacuum quenching process - a critical step in perovskite layer formation - using photoluminescence (PL) and diffuse reflection imaging. The dataset is intended to support deep learning applications for predicting both material-level properties (e.g., precursor composition) and device-level performance metrics (e.g., power conversion efficiency, PCE).

Unlike the previous dataset, this dataset includes perovskite solar cells fabricated under varied process conditions. Specifically, the quenching duration, precursor solution molarity, and molar ratio were systematically changed to enhance the diversity of the data.

To monitor the vacuum quenching process, a PL imaging setup captured four channels of time series image data (2D+t), including one diffuse reflection channel and three PL spectrum channels filtered for different wavelengths. All images were cropped into 65x56 pixel patches, isolating the active area of individual solar cells. However, currently, the dataset provides only the time transients of these four channels, where the spatial mean intensity was calculated for each time step. This dimensionality reduction transforms the high-dimensional video data into compact temporal transients, highlighting the critical dynamics of thin-film formation.

The dataset consists of two parts:

1. Samples finalized into functional solar cells:

   • Includes photovoltaic (PV) performance metrics as target variables:
     (1) power conversion efficiency (PCE),
     (2) open-circuit voltage (VOC),
     (3) short-circuit current density (JSC),
     (4) fill factor (FF), measured in forward and backward sweeps.

2. Samples not finalized into functional solar cells:

   • Does not include PV metrics. These samples are suitable for classification tasks, such as predicting precursor solution molarity and molar ratio.

Further information on the experimental procedure and data processing is detailed in the corresponding paper: Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication. Please cite this paper when using the dataset.

This dataset is related to the 100D function benchmark case. A detailed description of the benchmark case can be found on the public online community website UQWorld: https://uqworld.org/t/benchmark-case-100d-function/.

The experimental designs include datasets with 400, 800, 1200, 1600, and 2000 samples, each generated using optimized maximin distance Latin Hypercube Sampling (LHS) with 1000 iterations. Each dataset is replicated 20 times. The validation set contains 100,000 samples generated by Monte Carlo simulation. Each dataset contains input samples and the corresponding computational model responses.

Description of the dataset file

The dataset file includes two variables:

• ExpDesigns, and
• ValidationSet.

Both variables are Matlab structures with fields X, Y, and nSamples. Variable ExpDesigns is a non-scalar structure sized according to the number of experimental design groups. Each field of X for the i-th element of the struct array contains replicated datasets, forming a matrix of size [number of samples] x [dimensionality] x [number of replications]. Similarly, each field of Y for the i-th element contains replicated computational model responses that correspond to the experimental design of the same replication, sized [number of samples] x [number of model outputs] x [number of replications]. The same structure logic applies to the ValidationSet variable, except it contains only one dataset per benchmark case.

The structure can be summarized as follows:

• ExpDesigns(i).X(j,k,l)
  • i: dataset group,
  • j: sample index,
  • k: variable index, and
  • l: replication index.

• ExpDesigns(i).Y(j,m,l)
  • i, j, l: same as above,
  • m: computational model output index.

• ValidationSet.X(j,k)
  • j, k: same as above.

• ValidationSet.Y(j,m)
  • j, m: same as above.

Description of benchmarked metamodel competitors

The selection of competitors was based on our experience with meta-modeling and includes various metamodel types: Polynomial Chaos Expansions (PCE), Polynomial Chaos Kriging (PCK), and Kriging. Given that each metamodel has many hyperparameters, we chose the most general settings to address different benchmark case difficulties, including dimensionality, nonlinearity, and non-monotonicity.

For Polynomial Chaos Expansions (PCE), we used a polynomial degree and q-norm adaptivity approach. This approach adaptively increases the maximum polynomial degree and truncation q-norm until the estimated leave-one-out error starts increasing. Maximum polynomial interaction terms were limited to 2 due to the memory requirements for large model dimensionality and large experimental designs. We tested three different solvers to calculate the PCE coefficients: Least Angle Regression (LARS), Orthogonal Matching Pursuit (OMP), and Subspace Pursuit (SP).

Polynomial Chaos Kriging (PCK) employs a sequential combination strategy of PCE and Kriging. PCE uses degree adaptivity with a fixed q-norm. The maximum number of interactions is again set to 2 with the LARS solver. Ordinary Kriging is applied using the Matérn-5/2 correlation family, ellipsoidal, and anisotropic correlation function. We used a hybrid genetic algorithm to optimize the hyperparameters.

We benchmarked both linear and ordinary Kriging, including Matérn-5/2 and Gaussian correlation families and separable and ellipsoidal correlation, resulting in eight different Kriging competitors. The hyperparameters were calculated using a hybrid covariance matrix adaptation-evolution strategy optimization.

For further details on the settings, please refer to the competitors.m file and UQLab user manuals:

• S. Marelli, N. Luethen, B. Sudret, UQLab User Manual – Polynomial Chaos Expansions, Report UQLab-V2.1-104, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.
• C. Lataniotis, D. Wicaksono, S. Marelli, B. Sudret, UQLab User Manual – Kriging (Gaussian Process Modeling), Report UQLab-V2.1-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.
• R. Schoebi, S. Marelli, B. Sudret, UQLab User Manual – Polynomial Chaos Kriging, Report UQLab-V2.0-109, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.

Description of the results file

The results file contains one variable: Metrics. It is a Matlab structure with fields corresponding to each competitor (currently 12). Each competitor field contains data of type non-scalar struct array. The performance metrics included are RelMSE, RelRMSE, RelMAE, MAPE, Q2, and RelCVErr. Each field of Metrics.(CompetitorName) for the i-th element of the struct array contains metrics corresponding to the replicated dataset and the competitor, structured as follows:

• Metrics.(CompetitorName)(i).(MetricName)(l)
  
  • i: dataset group,
  • l: replication index.

The description of the performance measures (metrics) can be found here: https://uqworld.org/t/metamodel-performance-measures/.

Additional files

We provide files in three languages (MATLAB, Python, and Julia) to showcase how to work with datasets, results, and their visualization. The files are called working_with_datafiles.* (the extension depends on the selected language).

Acknowledgment

This project was supported by the Open Research Data Program of the ETH Board under Grant number EPFL SCR0902285. The calculations were run on the Euler cluster of ETH Zürich using the MATLAB-based UQLab software developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich.

Simplified molecular-input line-entry system (SMILES) notation and inbuilt Monte Carlo algorithm of CORAL software were employed to construct generative and prediction QSPR models for the analysis of the power conversion efficiency (PCE) of 215 phenothiazine derivatives. The dataset was divided into four splits and each split was further divided into four sets. A hybrid descriptor, a combination of SMILES and hydrogen suppressed graph (HSG), was employed to build reliable and robust QSPR models. The role of the index of ideality of correlation (IIC) was also studied in depth. We performed a comparative study to predict PCE using two target functions (TF1 without IIC and TF2 with IIC). Eight QSPR models were developed and the models developed with TF2 was shown robust and reliable. The QSPR model generated from split 4 was considered a leading model. The different statistical benchmarks were computed for the lead model and these were rtraining set2=0.7784; rinvisible training set2=0.7955; rcalibration set2=0.7738; rvalidation set2=0.7506; Qtraining set2=0.7691; Qinvisible training set2=0.7850; Qcalibration set2=0.7501; Qvalidation set2=0.7085; IICtraining set = 0.8590; IICinvisible training set = 0.8297; IICcalibration set = 0.8796; IICvalidation set = 0.8293, etc. The promoters of increase and decrease of endpoint PCE were also extracted.

This dataset is related to the Two-dimensional heat diffusion model benchmark case. A detailed description of the benchmark case can be found on the public online community website UQWorld: https://uqworld.org/t/benchmark-case-two-dimensional-heat-diffusion-model/.

The experimental designs include datasets with 400, 800, 1200, 1600, and 2000 samples, each generated using optimized maximin distance Latin Hypercube Sampling (LHS) with 1000 iterations. Each dataset is replicated 20 times. The validation set contains 100,000 samples generated by Monte Carlo simulation. Each dataset contains input samples and the corresponding computational model responses.

Description of the dataset file

The dataset file includes two variables:

• ExpDesigns, and
• ValidationSet.

Both variables are Matlab structures with fields X, Y, and nSamples. Variable ExpDesigns is a non-scalar structure sized according to the number of experimental design groups. Each field of X for the i-th element of the struct array contains replicated datasets, forming a matrix of size [number of samples] x [dimensionality] x [number of replications]. Similarly, each field of Y for the i-th element contains replicated computational model responses that correspond to the experimental design of the same replication, sized [number of samples] x [number of model outputs] x [number of replications]. The same structure logic applies to the ValidationSet variable, except it contains only one dataset per benchmark case.

The structure can be summarized as follows:

• ExpDesigns(i).X(j,k,l)
  • i: dataset group,
  • j: sample index,
  • k: variable index, and
  • l: replication index.

• ExpDesigns(i).Y(j,m,l)
  • i, j, l: same as above,
  • m: computational model output index.

• ValidationSet.X(j,k)
  • j, k: same as above.

• ValidationSet.Y(j,m)
  • j, m: same as above.

Description of benchmarked metamodel competitors

The selection of competitors was based on our experience with meta-modeling and includes various metamodel types: Polynomial Chaos Expansions (PCE), Polynomial Chaos Kriging (PCK), and Kriging. Given that each metamodel has many hyperparameters, we chose the most general settings to address different benchmark case difficulties, including dimensionality, nonlinearity, and non-monotonicity.

For Polynomial Chaos Expansions (PCE), we used a polynomial degree and q-norm adaptivity approach. This approach adaptively increases the maximum polynomial degree and truncation q-norm until the estimated leave-one-out error starts increasing. Maximum polynomial interaction terms were limited to 2 due to the memory requirements for large model dimensionality and large experimental designs. We tested three different solvers to calculate the PCE coefficients: Least Angle Regression (LARS), Orthogonal Matching Pursuit (OMP), and Subspace Pursuit (SP).

Polynomial Chaos Kriging (PCK) employs a sequential combination strategy of PCE and Kriging. PCE uses degree adaptivity with a fixed q-norm. The maximum number of interactions is again set to 2 with the LARS solver. Ordinary Kriging is applied using the Matérn-5/2 correlation family, ellipsoidal, and anisotropic correlation function. We used a hybrid genetic algorithm to optimize the hyperparameters.

We benchmarked both linear and ordinary Kriging, including Matérn-5/2 and Gaussian correlation families and separable and ellipsoidal correlation, resulting in eight different Kriging competitors. The hyperparameters were calculated using a hybrid covariance matrix adaptation-evolution strategy optimization.

For further details on the settings, please refer to the competitors.m file and UQLab user manuals:

• S. Marelli, N. Luethen, B. Sudret, UQLab User Manual – Polynomial Chaos Expansions, Report UQLab-V2.1-104, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.
• C. Lataniotis, D. Wicaksono, S. Marelli, B. Sudret, UQLab User Manual – Kriging (Gaussian Process Modeling), Report UQLab-V2.1-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.
• R. Schoebi, S. Marelli, B. Sudret, UQLab User Manual – Polynomial Chaos Kriging, Report UQLab-V2.0-109, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.

Description of the results file

The results file contains one variable: Metrics. It is a Matlab structure with fields corresponding to each competitor (currently 12). Each competitor field contains data of type non-scalar struct array. The performance metrics included are RelMSE, RelRMSE, RelMAE, MAPE, Q2, and RelCVErr. Each field of Metrics.(CompetitorName) for the i-th element of the struct array contains metrics corresponding to the replicated dataset and the competitor, structured as follows:

• Metrics.(CompetitorName)(i).(MetricName)(l)
  
  • i: dataset group,
  • l: replication index.

The description of the performance measures (metrics) can be found here: https://uqworld.org/t/metamodel-performance-measures/.

Additional files

We provide files in three languages (MATLAB, Python, and Julia) to showcase how to work with datasets, results, and their visualization. The files are called working_with_datafiles.* (the extension depends on the selected language).

Acknowledgment

This project was supported by the Open Research Data Program of the ETH Board under Grant number EPFL SCR0902285. The calculations were run on the Euler cluster of ETH Zürich using the MATLAB-based UQLab software developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to May 2025 about savings, personal, rate, and USA.

Communities served by entities that are eligible for the Alaska Energy Authority's (AEA) Power Cost Equalization (PCE) program. The power cost equalization program supports rural Alaskans who live in areas where energy costs are significantly higher than urban areas in meeting the cost of electricity. Eligibility is determined by the Regulatory Commission of Alaska under Alaska Statutes 42.45.100-170."AEA determines eligibility of community facilities and residential customers and authorizes payment to the electric utility. Commercial customers are not eligible to receive PCE credit. Participating utilities are required to reduce each eligible customer’s bill by the amount that the State pays for PCE. RCA determines if a utility is eligible to participate in the program and calculates the amount of PCE per kWh payable to the utility. More information about the RCA may be found at www.state.ak.us/rca."(AEA, 2017)Source: Alaska Energy AuthorityThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: AEA Power Cost Equalization

Core consumer prices in Canada increased 2.50 percent in May of 2025 over the same month in the previous year. This dataset provides - Canada Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.