Graph and download economic data for Real Potential Gross Domestic Product (GDPPOT) from Q1 1949 to Q4 2035 about projection, real, GDP, and USA.

Comprehensive real potential gdp data with real-time values, historical trends, charts, and economic analysis. Track real potential gdp indicators for informed investment decisions.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Realizing the Real Potential of REAL Stock?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

China Potential(GDP) Gross Domestic ProductGrowth: Volume data was reported at 4.539 % in 2025. This records a decrease from the previous number of 4.578 % for 2024. China Potential(GDP) Gross Domestic ProductGrowth: Volume data is updated yearly, averaging 9.556 % from Dec 1991 (Median) to 2025, with 35 observations. The data reached an all-time high of 12.953 % in 1991 and a record low of 4.539 % in 2025. China Potential(GDP) Gross Domestic ProductGrowth: Volume data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.EO: GDP: Potential Output and Output Gap: Forecast: Non OECD Member: Annual. GDPVTR_ANNPCT - Potential output, volume, growth. Percentage change compared to the previous period. Quarterly growth expressed at annual rate.

United States - Nominal Potential Gross Domestic Product was 45188.50000 Bil. of $ in October of 2035, according to the United States Federal Reserve. Historically, United States - Nominal Potential Gross Domestic Product reached a record high of 45188.50000 in October of 2035 and a record low of 274.03395 in January of 1949. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Nominal Potential Gross Domestic Product - last updated from the United States Federal Reserve on July of 2025.

Australia Potential over Actual GDP: Ratio data was reported at 1.008 Ratio in 2026. This records a decrease from the previous number of 1.011 Ratio for 2025. Australia Potential over Actual GDP: Ratio data is updated yearly, averaging 0.999 Ratio from Dec 1985 (Median) to 2026, with 42 observations. The data reached an all-time high of 1.042 Ratio in 2020 and a record low of 0.978 Ratio in 1989. Australia Potential over Actual GDP: Ratio data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.EO: GDP: Potential Output and Output Gap: Forecast: OECD Member: Annual. IFU3 - Ratio of potential to actual real GDP of the total economy OECD calculation, see OECD Economic Outlook database documentation

United States (DC)Potential Real GDP data was reported at 15,335.900 USD bn in Dec 2019. This records an increase from the previous number of 15,252.000 USD bn for Sep 2019. United States (DC)Potential Real GDP data is updated quarterly, averaging 5,905.600 USD bn from Mar 1949 (Median) to Dec 2019, with 284 observations. The data reached an all-time high of 15,335.900 USD bn in Dec 2019 and a record low of 1,654.200 USD bn in Mar 1949. United States (DC)Potential Real GDP data remains active status in CEIC and is reported by Congressional Budget Office. The data is categorized under Global Database’s USA – Table US.A106: NIPA 2013: Potential Gross Domestic Product.

The ability to control the location of nanoscale objects in liquids is essential for fundamental and applied research from nanofluidics to molecular biology. To overcome their random Brownian motion, the electrostatic fluid trap creates local minima in potential energy by shaping electrostatic interactions with a tailored wall topography. However, this strategy is inherently static; once fabricated, the potential wells cannot be modulated. Here, we propose and experimentally demonstrate that such a trap can be controlled through a buried gate electrode. We measure changes in the average escape times of nanoparticles from the traps to quantify the induced modulations of 0.7 kBT in potential energy and 50 mV in surface potential. Finally, we summarize the mechanism in a parameter-free predictive model, including surface chemistry and electrostatic fringing, that reproduces the experimental results. Our findings open a route toward real-time controllable nanoparticle traps.

Real-world datasets.

Virtual reality environments offer great opportunities to study the performance of brain-computer interfaces (BCIs) in real-world contexts. As real-world stimuli are typically multimodal, their neuronal integration elicits complex response patterns. To investigate the effect of additional auditory cues on the processing of visual information, we used virtual reality to mimic safety-related events in an industrial environment while we concomitantly recorded electroencephalography (EEG) signals. We simulated a box traveling on a conveyor belt system where two types of stimuli – an exploding and a burning box – interrupt regular operation. The recordings from 16 subjects were divided into two subsets, a visual-only and an audio-visual experiment. In the visual-only experiment, the response patterns for both stimuli elicited a similar pattern – a visual evoked potential (VEP) followed by an event-related potential (ERP) over the occipital-parietal lobe. Moreover, we found the perceived severity of the event to be reflected in the signal amplitude. Interestingly, the additional auditory cues had a twofold effect on the previous findings: The P1 component was significantly suppressed in the case of the exploding box stimulus, whereas the N2c showed an enhancement for the burning box stimulus. This result highlights the impact of multisensory integration on the performance of realistic BCI applications. Indeed, we observed alterations in the offline classification accuracy for a detection task based on a mixed feature extraction (variance, power spectral density, and discrete wavelet transform) and a support vector machine classifier. In the case of the explosion, the accuracy slightly decreased by –1.64% p. in an audio-visual experiment compared to the visual-only. Contrarily, the classification accuracy for the burning box increased by 5.58% p. when additional auditory cues were present. Hence, we conclude, that especially in challenging detection tasks, it is favorable to consider the potential of multisensory integration when BCIs are supposed to operate under (multimodal) real-world conditions.

For each real network, we consider five different ensembles of synthetic networks with approximately the same average degree, sampling 100 realizations of each ensemble. We construct a distribution of RMSE between the potentials associated to the real network and to each syntetic network. Mean and standard deviation of each distribution are reported in the table, with network models closest to the real ones highlighted in boldface. The number in brackets represents the RMSE between the real network potential and the ensemble median potential.

United States Potential Real GDP: 2009p data was reported at 17,329.900 USD bn in Dec 2017. This records an increase from the previous number of 17,254.200 USD bn for Sep 2017. United States Potential Real GDP: 2009p data is updated quarterly, averaging 7,162.350 USD bn from Mar 1949 (Median) to Dec 2017, with 276 observations. The data reached an all-time high of 17,329.900 USD bn in Dec 2017 and a record low of 2,005.800 USD bn in Mar 1949. United States Potential Real GDP: 2009p data remains active status in CEIC and is reported by Congressional Budget Office. The data is categorized under Global Database’s USA – Table US.A106: NIPA 2013: Potential Gross Domestic Product.

supplementary information file to the manuscript

This study reconciles discrepancies between practical electrochemical conditions and theoretical density functional theory (DFT) frameworks, evaluating three graphyne-confined single-atom catalysts (Mo-TEB, Mo@GY, and Mo@GDY). Using both constant charge models in vacuum and constant potential models with continuum implicit solvation, we closely mimic real-world electrochemical environments. Our findings highlight the crucial role of explicitly incorporating electrode potential and pH in the constant potential model, providing enhanced insights into the nitrogen reduction reaction (NRR) mechanisms. Notably, the superior NRR performance of Mo-TEB is attributed to the d-band center’s proximity to the Fermi level and enhanced magnetic moments at the atomic center. This research advances our understanding of graphyne-confined single-atom catalysts as effective NRR platforms and underscores the significance of the constant potential model for accurate DFT studies of electrochemical reactions.

Dravet Syndrome Market report features an extensive study of the current landscape and the likely future potential of single-use system...

Australia Potential(GDP) Gross Domestic ProductGrowth: Volume data was reported at 2.208 % in 2026. This records a decrease from the previous number of 2.230 % for 2025. Australia Potential(GDP) Gross Domestic ProductGrowth: Volume data is updated yearly, averaging 2.960 % from Dec 1986 (Median) to 2026, with 41 observations. The data reached an all-time high of 3.862 % in 1998 and a record low of 2.141 % in 2020. Australia Potential(GDP) Gross Domestic ProductGrowth: Volume data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.EO: GDP: Potential Output and Output Gap: Forecast: OECD Member: Annual. GDPVTR_ANNPCT - Potential output, volume, growth. Percentage change compared to the previous period. Quarterly growth expressed at annual rate.

This feature class is shown on the Potential and actual acid sulfate soils overlay map (map reference: OM-016.1).This feature class includes the following sub-categories:(a) Potential and actual acid sulfate soils sub-category;(b) Land at or below 5m AHD sub-category;(c) Land above 5m AHD and below 20m AHD sub-category.For information about the overlay and how it is applied, please refer to the Brisbane City Plan 2014 document.

Results of functional annotation clustering in the top 100 most abundant proteins, performed with the Database for Annotation, Visualization and Integrated Discovery (DAVID).

Since its first experimental signatures, the so called “critical brain hypothesis” has been extensively studied. Yet, its actual foundations remain elusive. According to a widely accepted teleological reasoning, the brain would be poised to a critical state to optimize the mapping of the noisy and ever changing real-world inputs, thus suggesting that primary sensory cortical areas should be critical. We investigated whether a single barrel column of the somatosensory cortex of the anesthetized rat displays a critical behavior. Neuronal avalanches were recorded across all cortical layers in terms of both multi-unit activities and population local field potentials, and their behavior during spontaneous activity compared to the one evoked by a controlled single whisker deflection. By applying a maximum likelihood statistical method based on timeseries undersampling to fit the avalanches distributions, we show that neuronal avalanches are power law distributed for both multi-unit activities and local field potentials during spontaneous activity, with exponents that are spread along a scaling line. Instead, after the tactile stimulus, activity switches to a transient across-layers synchronization mode that appears to dominate the cortical representation of the single sensory input.

The prognosis for people with the high-grade brain tumor glioblastoma is very poor, due largely to low cell death in response to genotoxic therapy. The transcription factor BCL6, a protein that normally suppresses the DNA damage response during immune cell maturation, and a known driver of B-cell lymphoma, was shown to mediate the survival of glioblastoma cells. Expression was observed in glioblastoma tumor specimens and cell lines. When BCL6 expression or activity was reduced in these lines, increased apoptosis and a profound loss of proliferation was observed, consistent with gene expression signatures suggestive of anti-apoptotic and pro-survival signaling role for BCL6 in glioblastoma. Further, treatment with the standard therapies for glioblastoma—ionizing radiation and temozolomide—both induced BCL6 expression in vitro, and an in vivo orthotopic animal model of glioblastoma. Importantly, inhibition of BCL6 in combination with genotoxic therapies enhanced the therapeutic effect. Together these data demonstrate that BCL6 is an active transcription factor in glioblastoma, that it drives survival of cells, and that it increased with DNA damage, which increased the survival rate of therapy-treated cells. This makes BCL6 an excellent therapeutic target in glioblastoma—by increasing sensitivity to standard DNA damaging therapy, BCL6 inhibitors have real potential to improve the outcome for people with this disease.