The dataset contains information about protective policy response (PPI) measures public health government responses to COVID-19 at all levels of government throughout the world. The PPI measure considers the extent of COVID-19 policy responses in the following categories: state of emergencies, border closures, school closures, social gathering and social distancing limitations, home-bound policies, medical isolation policies, closure/restriction of businesses and services, and mandatory personal protection equipment. The coding for public health policies is based on government websites and reputable news sources reporting adoption of these policies. Total, National, and Subnational Indices are calculated based on the standing public health policies adopted at various levels of government for each unit (state, province, etc.) for each day, by adding together the highest values across levels of government in each category on that day. Data is accessible to people who have an OPEN ICPSR account.

Producer Price Indices (PPIs) are a series of economic indicators that measure the price movement of goods bought and sold by UK manufacturers.

The producer price index (PPI) is an statistical indicator that measures changes in price of industrial production over a specific period. It typically includes production prices from sectors such as mining, manufacturing, and (electricity, gas, and water production).

This dataset shows the Producer price index (2010=100) for local production by commodity sections (SITC 4), 2010-2023 (Jan-Jul), Malaysia (Monthly) Footnote Sections Weight Total 100.00 Food 7.454 Beverages & Tobacco 1.307 Crude, Materials, Inedible 5.769 Mineral Fuels, Lubricants,etc. 21.075 Animal & Vegetables Oils & Fats 9.287 Chemicals 8.557 Manufactured Goods 13.672 Machinery & Transport Equipment 25.611 Miscellaneous Manufactured 7.268 Commencing reference month January 2018, the Producer Price Index uses updated basket of goods based on the Economics Census of 2016 and from relevant government agencies. The new basket contains 1063 products. Updating of basket of goods will ensure that the products selected for PPI computation can measures the average price change as imposed by producers of goods in an industry. Source: Department of Statistics, Malaysia

Producer Price Inflation MoM in the United States increased to 0.10 percent in May from -0.20 percent in April of 2025. This dataset includes a chart with historical data for the United States Producer Price Inflation MoM.

This table contains figures on the average price development of the selling prices, the import prices and the domestic consumption of industrial products with a base year of 2015=100. This data is available for both domestic and foreign sales. The products are classified based on the goods classification PRODCOM (PRODuction COMmunautaire).

Data available from January 2012 up to and including December 2023.

Status of the figures: The data for August 2023 up to and including December 2023 and the 2023 annual rate are provisional. Since this table has been stopped, the data is no longer made definitive.

Changes as of March 6th 2024 None, this table is stopped.

When will new figures be published? The results in this series are based on 2015=100. Due to the base shift this table is stopped. Figures based on 2021=100 are published in table Producer Price Index (PPI), output and importprices by product, 2021=100. Further information, see Base Year Revision Industrial Producer Price Index, 2021=100 in paragraph 3.

United States PPI: ME: Misc Instruments: Measuring & Controlling (MC) data was reported at 159.900 Jun1985=100 in Jun 2018. This records an increase from the previous number of 159.800 Jun1985=100 for May 2018. United States PPI: ME: Misc Instruments: Measuring & Controlling (MC) data is updated monthly, averaging 137.000 Jun1985=100 from Jun 1985 (Median) to Jun 2018, with 397 observations. The data reached an all-time high of 159.900 Jun1985=100 in Jun 2018 and a record low of 99.900 Jun1985=100 in Jul 1985. United States PPI: ME: Misc Instruments: Measuring & Controlling (MC) data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I017: Producer Price Index: By Commodities.

Producer Prices in China decreased 3.60 percent in June of 2025 over the same month in the previous year. This dataset provides the latest reported value for - China Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The production price index (PPI) for construction materials and components in the United States decreased slightly in 2024. Up until 2020, construction prices had been rising fairly steadily. However, in the years after that construction producer prices have been very unstable. Production price index A PPI of *** in 2022, indicates that the real-world price has risen by *** percent in comparison to the base year - 1982 in this case. Similarly, under the same baseline, the PPI for construction machinery and equipment has also risen steadily until 2018. Like all prices, there are regional differences within the United States. The PPI acts as a measurement for the average changes in prices that domestic producers receive for their output. In the United States, the PPI is one of the oldest continuous statistical datasets published by the government. Common construction materials Some building materials are essential to construction work, and the decision on which to use is important for the life and the endurance of the building. Materials such as cement, steel, and sand are essential to many construction projects. The production of cement is tightly linked to the demand that comes from the construction industry. The durability and potency of steel gives it an advantage over wood and concrete, providing buildings with a higher resistance but a cheaper price tag. Sand is commonly used in buildings, but it is especially common in roads that require stones of various grades and granulation.

Producer Prices in Australia increased 3.70 percent in March of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Australia Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Spearman rank correlation coefficients are provided in parentheses.aNumber of proteins in PPI data set having abundance measurements.bThe symbol “

Contains a range of producer price indexes. Firstly, economy-wide indexes are presented within a Stage of Production (SOP) framework, followed by a set of partial, stand-alone measures relating to specific industry sectors of the economy (selected manufacturing, construction, mining and service industries).

About this Dataset

The Consumer Price Index (CPI) for food is a component of the all-items CPI. The CPI measures the average change over time in the prices paid by urban consumers for a representative market basket of consumer goods and services. While the all-items CPI measures the price changes for all consumer goods and services, including food, the CPI for food measures the changes in the retail prices of food items only. ERS's monthly update is usually released on the 25th of the month; however, if the 25th falls on a weekend or a holiday, the monthly update will be published on either the 23rd or 24th. This report provides a detailed outline of ERS's forecasting methodology, along with measures to test the precision of the estimates (May 2015). At ERS, work on the CPI for food consists of several activities. ERS reports the current index level for food, examines changes in the CPI for food, and constructs forecasts of the CPI for food for the next 12-18 months. Forecasting the CPI for food has become increasingly important due to the changing structure of food and agricultural economies and the important signals the forecasts provide to farmers, processors, wholesalers, consumers, and policymakers. As a natural extension of ERS's work with the CPI for food, ERS also analyzes and models forecasts for the Producer Price Index (PPI). The PPI is similar to the CPI in that it measures price changes over time; however, instead of measuring changes in retail prices, the PPI measures the average change in prices paid to domestic producers for their output. The PPI collects data for nearly every industry in the goods-producing sector of the economy. Changes in farm-level and wholesale-level PPIs are of particular interest in forecasting food CPIs. cpi

Category

Economic

Keywords

cpi,restaurant,wholesale-food-prices

Row Count

68

Price

Free

Spearman rank correlation coefficients are provided in parentheses.aNumber of proteins in PPI data set having abundance measurements.bThe symbol “

Producer Prices in China decreased to 104.90 points in March from 105.30 points in February of 2025. This dataset provides - China Producer Prices - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Identifying key proteins from protein-protein interaction (PPI) networks is one of the most fundamental and important tasks for computational biologists. However, the protein interactions obtained by high-throughput technology are characterized by a high false positive rate, which severely hinders the prediction accuracy of the current computational methods. In this paper, we propose a novel strategy to identify key proteins by constructing reliable PPI networks. Five Gene Ontology (GO)-based semantic similarity measurements (Jiang, Lin, Rel, Resnik, and Wang) are used to calculate the confidence scores for protein pairs under three annotation terms (Molecular function (MF), Biological process (BP), and Cellular component (CC)). The protein pairs with low similarity values are assumed to be low-confidence links, and the refined PPI networks are constructed by filtering the low-confidence links. Six topology-based centrality methods (the BC, DC, EC, NC, SC, and aveNC) are applied to test the performance of the measurements under the original network and refined network. We systematically compare the performance of the five semantic similarity metrics with the three GO annotation terms on four benchmark datasets, and the simulation results show that the performance of these centrality methods under refined PPI networks is relatively better than that under the original networks. Resnik with a BP annotation term performs best among all five metrics with the three annotation terms. These findings suggest the importance of semantic similarity metrics in measuring the reliability of the links between proteins and highlight the Resnik metric with the BP annotation term as a favourable choice.

Five-fold Cross validation was performed on the S432 dataset with ANN_BAGGING and SVM_BAGGING. Independent test was performed on the S142 dataset with the best ANN_BAGGING predictors from the five-fold cross validation. The benchmark measurements are defined in Equations (6)∼(11).

The Predictive Phenomics Science & Technology Initiative (PPI) at Pacific Northwest National Laboratory are tackling the grand challenge of understanding and predicting phenotype by identifying the molecular basis of function and enable function-driven design and control of biological systems. Research projects within this initiative are divided into three Thrust Areas (TAs): TA1) Enhancing Multi-Scale Phenomics Measurements, TA2) Identifying Molecular Patterns of Biological Function, and TA3) Computational Methods - Phenotypic Signatures. In efforts to enable discovery, reproducibility, and reuse of PPI-funded digital research data generated or used through the course of the proposed research-funded lifecycles, all corresponding digital data assets conducted under the Laboratory Directed Research and Development Program at PNNL are linked to this PPI dataset catalog collection.

Overview Long-range scanning Doppler lidar located on Gordon Ridge. The WindTracer provides high-resolution, long-range lidar data for use in the WFIP2 program. Data Details The system is configured to take data in three different modes. All three modes take 15 minutes to complete and are started at 00, 15, 30, and 45 minutes after the hour. The first nine minutes of the period are spent performing two high-resolution, long-range Plan Position Indicator (PPI) scans at 0.0 and -1.0 degree elevation angles (tilts). These data have file names annotated with HiResPPI noted in the "optional fields" of the file name; for example: lidar.z09.00.20150801.150000.HiResPPI.prd. The next six minutes are spent performing higher altitude PPI scans and Range Height Indicator (RHI) scans. The PPI scans are completed at 6.0- and 30.0-degree elevations, and the RHI scans are completed from below the horizon (down into valleys, as able), up to 40 degrees elevation at 010-, 100-, 190-, and 280-degree azimuths. These files are annotated with PPI-RHI noted in the optional fields of the file name; for example: lidar.z09.00.20150801.150900.PPI-RHI.prd *The last minute is spent measuring a high-altitude vertical wind profile. Generally, this dataset will include data from near ground level up to the top of the planetary boundary layer (PBL), and higher altitude data when high-level cirrus or other clouds are present. The Velocity Azimuth Display (VAD) is measured using six lines of sight at an elevation angle of 75 degrees at azimuth angles of 000, 060, 120, 180, 240, and 300 degrees from True North. The files are annotated with VAD in the optional fields of the file name; for example: lidar.z09.00.20150801.151400.VAD.prd. LMCT does have a data format document that can be provided to users who need programming access to the data. This document is proprietary information but can be supplied to anyone after signing a non-disclosure agreement (NDA). To initiate the NDA process, please contact Keith Barr at keith.barr@lmco.com. The data are not proprietary, only the manual describing the data format. Data Quality Lockheed Martin Coherent Technologies (LMCT) has implemented and refined data quality analysis over the last 14 years, and this installation uses standard data-quality processing procedures. Generally, filtered data products can be accepted as fully data qualified. Secondary processing, such as wind vector analysis, should be used with some caution as the data-quality filters still are "young" and incorrect values can be encountered. Uncertainty Uncertainty in the radial wind measurements (the system's base measurement) varies slightly with range. For most measurements, accuracy of the filtered radial wind measurements have been shown to be within 0.5 m/s with accuracy better than 0.25 m/s not uncommon for ranges less than 10 km. Constraints Doppler lidar is dependent on aerosol loading in the atmosphere, and the signal can be significantly attenuated in precipitation and fog. These weather situations can reduce range performance significantly, and, in heavy rain or thick fog, range performance can be reduced to zero. Long-range performance depends on adequate aerosol loading to provide enough backscattered laser radiation so that a measurement can be made.

Producer Prices in the United Kingdom increased 0.30 percent in January of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United Kingdom Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.