Graph and download economic data for Economic Policy Uncertainty Index for United States (USEPUINDXD) from 1985-01-01 to 2025-10-20 about academic data, uncertainty, indexes, and USA.

Global climate policy uncertainty was assessed using approximately 11.27 million news articles from 2000 to 2023 in twelve countries of the G20 spanning six continents: Asia (China, Japan, Korea, and India), Africa (South Africa), North America (the US and Canada), South America (Brazil), Europe (the UK, France, and Germany), and Australasia (Australia). The data is categorized into global and national indices with different frequencies including daily, weekly, and monthly intervals.Citation:Ma, D., Zhang, D., Guo, K., & Ji, Q. (2024). Coupling between global climate policy uncertainty and economic policy uncertainty. Finance Research Letters, 69, 106180.Ji, Q., Ma, D., Zhai, P., Fan, Y., & Zhang, D. (2024). Global climate policy uncertainty and financial markets. Journal of International Financial Markets, Institutions and Money, 102047.

Key information about United States Categorical Economic Policy Uncertainty Index: United States

• United States Categorical Economic Policy Uncertainty Index was reported at 313.268 Index in Feb 2025.
• This records an increase from the previous number of 236.696 Index for Jan 2025.
• US Categorical Economic Policy Uncertainty Index data is updated monthly, averaging 93.847 Index from Jan 1985 to Feb 2025, with 482 observations.
• The data reached an all-time high of 503.012 Index in Apr 2020 and a record low of 37.266 Index in Jul 2007.
• US Categorical Economic Policy Uncertainty Index data remains active status in CEIC and is reported by Economic Policy Uncertainty.
• The data is categorized under World Trend Plus’s Economic Policy Uncertainty Index – Table: Categorical Economic Policy Uncertainty Index: By Country.

Table 3 gives details of the estimated statistical uncertainties associated with each colony. This is based on the robust regression analysis and the image quality of each VHR image. The uncertainty from the robust regression is estimated using Monte Carlo analysis (see Statistical Procedure section of the main text). The uncertainty based upon the image quality has been estimated using multiple analyses of images of differing quality. From this the survey has been broken into four classes as discussed in the Accuracy and uncertainty in the Discussion section.

Bathymetric, topographic, and grain-size data were collected in April 2011 along a 27-mi (43.5 – km) reach of the Colorado River in Grand Canyon National Park, Arizona. The study reach begins at river mile 61.1, about 0.6 -mi (1 –km) above the confluence of the Colorado and Little Colorado Rivers and ends at river mile 88.1 at the upstream boundary of the Bright Angel Rapid (Phantom Ranch boat beach). Channel bathymetry was mapped using multibeam and singlebeam echosounders, subaerial topography was mapped using ground-based total-stations, and bed-sediment grain-size data were collected using an underwater digital microscope system. These data were combined to produce digital elevation models, spatially variable estimates of digital elevation model uncertainty, georeferenced grain-size data, and bed-sediment distribution maps. These data were created by the Southwest Biological Science Center, Grand Canyon Monitoring and Science Center as a component of a larger effort to monitor the status and trends of sand storage along the Colorado River in Grand Canyon National Park. This dataset is a 1-meter resolution elevation uncertainty model generated by fuzzy inference system modeling associated from the 1-meter resolution digital elevation model (DEM_EGC_Apr2011.tif) associated with this data release.

Key information about China Trade Policy Uncertainty Index

• China Trade Policy Uncertainty Index was reported at 343.008 Index in Feb 2025.
• This records an increase from the previous number of 281.900 Index for Jan 2025.
• China Trade Policy Uncertainty Index data is updated monthly, averaging 90.815 Index from Jan 2000 to Feb 2025, with 302 observations.
• The data reached an all-time high of 1,425.160 Index in Jun 2019 and a record low of 0.000 Index in Jul 2011.
• China Trade Policy Uncertainty Index data remains active status in CEIC and is reported by Economic Policy Uncertainty.
• The data is categorized under World Trend Plus’s Economic Policy Uncertainty Index – Table: Trade Policy Uncertainty Index: By Country.

Key information about Australia Economic Policy Uncertainty Index

• Australia Economic Policy Uncertainty Index was reported at 134.660 Index in Jan 2025.
• This records a decrease from the previous number of 221.221 Index for Dec 2024.
• Australia Economic Policy Uncertainty Index data is updated monthly, averaging 96.435 Index from Jan 1997 to Jan 2025, with 337 observations.
• The data reached an all-time high of 337.044 Index in Aug 2011 and a record low of 25.662 Index in Jan 2007.
• Australia Economic Policy Uncertainty Index data remains active status in CEIC and is reported by Economic Policy Uncertainty.
• The data is categorized under World Trend Plus’s Economic Policy Uncertainty Index – Table: Economic Policy Uncertainty Index: By Country.

Graph and download economic data for World Uncertainty Index: Global: Simple Average (WUIGLOBALSMPAVG) from Q1 1990 to Q2 2025 about uncertainty, average, World, and indexes.

The GitHub site has the dataset used to create the manuscript figures, which examined which simulation parameters influenced the spread of contamination within a water distribution system. The dataset includes the values for the metrics of interest, which were extent of contamination, population impacted, extent of contamination in the unknown zone, and the population impacted in the unknown zone. These values changed according to the simulation parameter combination set that was used in modeling the affects of a contamination incident within a water distribution system. These parameters included demand, valve closure, contaminant reaction coefficient, injection start time, injection duration, and injection location (as listed in Table 1 of the paper). Two different water distribution system models were used in the paper, the KL network and the N6 network. In addition, some of the simulation model input files that were used to create the data are also provided on the GitHub site, but not all of the input files are provided since there were approximately 25 million simulations. A README file is provided on the GitHub site for more explanation of the files provided. This dataset is associated with the following publication: Hart, D., J.S. Rodriguez, J. Burkhardt, B. Borchers, C. Laird, R. Murray, K. Klise, and T. Haxton. Quantifying hydraulic and water quality uncertainty to inform sampling of drinking water distribution systems. JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT. American Society of Civil Engineers (ASCE), Reston, VA, USA, 145(1): ., (2019).

Economic Policy Uncertainty for United States was 413.76000 Index in October of 2025, according to the United States Federal Reserve. Historically, Economic Policy Uncertainty for United States reached a record high of 1026.38000 in January of 2024 and a record low of 3.32000 in August of 2015. Trading Economics provides the current actual value, an historical data chart and related indicators for Economic Policy Uncertainty for United States - last updated from the United States Federal Reserve on October of 2025.

COVID-19 surveillance across the United States is essential to tracking and mitigating the pandemic, but data representing cases and deaths may be impacted by attribute, spatial, and temporal uncertainties. COVID-19 case and death data are essential to understanding the pandemic and serve as key inputs for prediction models that inform policy-decisions; consistent information across datasets is critical to ensuring coherent findings. We implement an exploratory data analytic approach to characterize, synthesize, and visualize spatial-temporal dimensions of uncertainty across commonly used datasets for case and death metrics (Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres). We scrutinize data consistency to assess where and when disagreements occur, potentially indicating underlying uncertainty. We observe differences in cumulative case and death rates to highlight discrepancies and identify spatial patterns. Data are assessed using pairwise agreement (Cohen’s kappa) and agreement across all datasets (Fleiss’ kappa) to summarize changes over time. Findings suggest highest agreements between CDC, JHU, and NYT datasets. We find nine discrete type-components of information uncertainty for COVID-19 datasets reflecting various complex processes. Understanding processes and indicators of uncertainty in COVID-19 data reporting is especially relevant to public health professionals and policymakers to accurately understand and communicate information about the pandemic.

uncertainty-in-planning/hf_dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Key information about Greece Monetary Policy Uncertainty Index

• Greece Monetary Policy Uncertainty Index was reported at 224.245 Index in Dec 2024.
• This records a decrease from the previous number of 468.713 Index for Nov 2024.
• Greece Monetary Policy Uncertainty Index data is updated monthly, averaging 94.361 Index from Jan 1998 to Dec 2024, with 324 observations.
• The data reached an all-time high of 468.713 Index in Nov 2024 and a record low of 11.776 Index in Aug 2020.
• Greece Monetary Policy Uncertainty Index data remains active status in CEIC and is reported by Economic Policy Uncertainty.
• The data is categorized under World Trend Plus’s Economic Policy Uncertainty Index – Table: Categorical Economic Policy Uncertainty Index: By Country.

Key information about Japan Monetary Policy Uncertainty Index

• Japan Monetary Policy Uncertainty Index was reported at 96.884 Index in Feb 2025.
• This records a decrease from the previous number of 110.113 Index for Jan 2025.
• Japan Monetary Policy Uncertainty Index data is updated monthly, averaging 95.049 Index from Jan 1987 to Feb 2025, with 458 observations.
• The data reached an all-time high of 365.093 Index in Aug 2011 and a record low of 14.718 Index in Oct 1988.
• Japan Monetary Policy Uncertainty Index data remains active status in CEIC and is reported by Economic Policy Uncertainty.
• The data is categorized under World Trend Plus’s Economic Policy Uncertainty Index – Table: Categorical Economic Policy Uncertainty Index: By Country.

Description of the dataset

In order to study the expression of uncertainty in scientific articles, we have put together an interdisciplinary corpus of journals in the fields of Science, Technology and Medicine (STM) and the Humanities and Social Sciences (SHS). The selection of journals in our corpus is based on the Scimago Journal and Country Rank (SJR) classification, which is based on Scopus, the largest academic database available online. We have selected journals covering various disciplines, such as medicine, biochemistry, genetics and molecular biology, computer science, social sciences, environmental sciences, psychology, arts and humanities. For each discipline, we selected the five highest-ranked journals. In addition, we have included the journals PLoS ONE and Nature, both of which are interdisciplinary and highly ranked.

Based on the corpus of articles from different disciplines described above, we created a set of annotated sentences as follows:

593 were pre-selected automatically, by studying the occurrences of the lists of uncertainty indices proposed by Bongelli et al. (2019), Chen et al. (2018) and Hyland (1996).

The remaining sentences were extracted from a subset of articles, consisting of two randomly selected articles per journal. These articles were examined by two human annotators to identify sentences containing uncertainty and to annotate them.

600 sentences not expressing scientific uncertainty were manually identified and reviewed by two annotators

The sentences were annotated by two independent annotators following the annotation guide proposed by Ningrum and Atanassova (2024). The annotators were trained on the basis of an annotation guide and previously annotated sentences in order to guarantee the consistency of the annotations. Each sentence was annotated as expressing or not expressing uncertainty (Uncertainty and No Uncertainty).Sentences expressing uncertainty were then annotated along five dimensions: Reference , Nature, Context , Timeline and Expression. The annotators reached an average agreement score of 0.414 according to Cohen's Kappa test, which shows the difficulty of the task of annotating scientific uncertainty.Finally, conflicting annotations were resolved by a third independent annotator.

Our final corpus thus consists of a total of 1 840 sentences from 496 articles in 21 English-language journals from 8 different disciplines.The columns of the table are as follows:

journal: name of the journal from where the article originates

article_title: title of the article from where the sentence is extracted

publication_year: year of publication of the article

sentence_text: text of the sentence expressing or not expressing uncertainty

uncertainty: 1 if the sentence expresses uncertainty and 0 otherwise;

ref, nature, context, timeline, expression: annotations of the type of uncertainty according to the annotation framework proposed by Ningrum and Atanassova (2023). The annotation of each dimension in this dataset are in numeric format rather than textual. The mapping betwen textual and numeric labels is presented in the Table below.

Dimension 1 2 3 4 5

Reference Author Former Both

Nature Epistemic Aleatory Both

Context Background Methods Res&Disc Conclusion Others

Timeline Past Present Future

Expression Quantified Unquantified

This gold standard has been produced as part of the ANR InSciM (Modelling Uncertainty in Science) project.

References

Bongelli, R., Riccioni, I., Burro, R., & Zuczkowski, A. (2019). Writers’ uncertainty in scientific and popular biomedical articles. A comparative analysis of the British Medical Journal and Discover Magazine [Publisher: Public Library of Science]. PLoS ONE, 14 (9). https://doi.org/10.1371/journal.pone.0221933

Chen, C., Song, M., & Heo, G. E. (2018). A scalable and adaptive method for finding semantically equivalent cue words of uncertainty. Journal of Informetrics, 12 (1), 158–180. https://doi.org/10.1016/j.joi.2017.12.004

Hyland, K. E. (1996). Talking to the academy forms of hedging in science research articles [Publisher: SAGE Publications Inc.]. Written Communication, 13 (2), 251–281. https://doi.org/10.1177/0741088396013002004

Ningrum, P. K., & Atanassova, I. (2023). Scientific Uncertainty: An Annotation Framework and Corpus Study in Different Disciplines. 19th International Conference of the International Society for Scientometrics and Informetrics (ISSI 2023). https://doi.org/10.5281/zenodo.8306035

Ningrum, P. K., & Atanassova, I. (2024). Annotation of scientific uncertainty using linguistic patterns. Scientometrics. https://doi.org/10.1007/s11192-024-05009-z

This dataset provides 5 x 5 km gridded estimates of soil organic carbon (SOC) across Latin America that were derived from existing point soil characterization data and compiled environmental prediction factors for SOC. This dataset is representative for the period between 1980 to 2000s corresponding with the highest density of observations available in the WoSIS system and the covariates used as prediction factors for soil organic carbon across Latin America. SOC stocks (kg/m2) were estimated for the SOC and bulk density point measurements and a spatially explicit measure of the SOC estimation error was also calculated. A modeling ensemble, using a linear combination of five statistical methods (regression Kriging, random forest, kernel weighted nearest neighbors, partial least squared regression and support vector machines) was applied to the SOC stock data at (1) country-specific and (2) regional scales to develop gridded SOC estimates (kg/m2) for all of Latin America. Uncertainty estimates are provided for the two model predictions based on independent model residuals and their full conditional response to the SOC prediction factors.

N/A. This dataset is associated with the following publication: Groff, L., J. Grossman, A. Kruve, J. Minucci, C. Lowe, J. McCord, D. Kapraun, K. Phillips, S. Purucker, A. Chao, C. Ring, A. Williams, and J. Sobus. Uncertainty estimation strategies for quantitative non-targeted analysis. Analytical and Bioanalytical Chemistry. Springer, New York, NY, USA, 414(17): 4919-4933, (2022).

Business Uncertainty: Employment Growth: Smoothed data was reported at 0.055 % in Sep 2020. This records an increase from the previous number of 0.054 % for Aug 2020. Business Uncertainty: Employment Growth: Smoothed data is updated monthly, averaging 0.041 % from Jan 2015 (Median) to Sep 2020, with 69 observations. The data reached an all-time high of 0.060 % in Jun 2020 and a record low of 0.036 % in Mar 2019. Business Uncertainty: Employment Growth: Smoothed data remains active status in CEIC and is reported by Federal Reserve Bank of Atlanta. The data is categorized under Global Database’s United States – Table US.S018: Business Uncertainty Index.

This dataset represents expected location uncertainty of seismic events, when using sensors from the Netherlands seismic network and sensors just across the boarder in Belgium and Germany. The computation has been done over a grid covering the Netherlands, with a fixed source depth of 3 km. For each scenario event, the location probability density function has been determined, both as function of depth and as function of the horizontal coordinates. The assumption has been made that these probability density functions can be approximated as being (multivariate) normal distributions, such that they can be described with standard deviations. The uncertainty in the horizontal plane is parameterized with σ1 (S1), σ2 (S2) and θ (T). S1 is the standard deviation in the direction with maximum uncertainty, which occurs at a certain angle (T) with north. S2 is the standard deviation in the perpendicular direction with minimum uncertainty. In the vertical direction, the probability density function is computed at the most likely epicenter. The corresponding uncertainty is parameterized with SZ. Additionally, the azimuthal gap (AG) has been computed and is included in the dataset. This parameter describes how well the azimuthal coverage of stations is for event location. Details are described in the following technical report: https://cdn.knmi.nl/knmi/pdf/bibliotheek/knmipubTR/TR405.pdf.

The Checkpoints dataset as trained and used in A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors published at ICLR 2024. All models all trained and uploaded in a float16 format to reduce the memory footprint.

  Usage





  Untar the models

Just untar the desired models available in models, for instance with: tar -xvf models/cifar10-resnet18/cifar10-resnet18-0-1023.tgz

Most of them are regrouped in tar files containing 1024 models each. This will create a new… See the full description on the dataset page: https://huggingface.co/datasets/torch-uncertainty/Checkpoints.