Price index of consumer goods and services is calculated on the basis of the results of:
- surveys on prices of consumer goods and services on the retail market,
- surveys on household budgets, providing data on average expenditures on consumer goods and services; these data are then used for compilation of a weight system.

Calculating price index of consumer goods and services is done on the basis of the Classification of Individual Consumption by Purpose (COICOP) adapted for the use of Harmonized Indices of Consumer Prices (HICP).

The price index of a representative in the region included in the price survey results from relating its average monthly price to an average annual price from the previous yea The all-Polish price index of a representative included in the survey is calculated as geometric mean of price indices from all regions. Calculating price indices of groups of consumer goods and services at the lowest level of weight system aggregation is done on the basis of price indices of the representatives included in price survey in a given group by using geometric mean. They are then used by applying weight system to calculate indices of higher level of aggregation up to the price index of total consumer goods and services. price index is calculated in line with the Laspeyress’s formula by applying weights from the year preceding the reference year.

This dataset includes soil wet aggregate stability measurements from the Upper Mississippi River Basin LTAR site in Ames, Iowa. Samples were collected in 2021 from this long-term tillage and cover crop trial in a corn-based agroecosystem. We measured wet aggregate stability using digital photography to quantify disintegration (slaking) of submerged aggregates over time, similar to the technique described by Fajardo et al. (2016) and Rieke et al. (2021). However, we adapted the technique to larger sample numbers by using a multi-well tray to submerge 20-36 aggregates simultaneously. We used this approach to measure slaking index of 160 soil samples (2120 aggregates). This dataset includes slaking index calculated for each aggregates, and also summarized by samples. There were usually 10-12 aggregates measured per sample. We focused primarily on methodological issues, assessing the statistical power of slaking index, needed replication, sensitivity to cultural practices, and sensitivity to sample collection date. We found that small numbers of highly unstable aggregates lead to skewed distributions for slaking index. We concluded at least 20 aggregates per sample were preferred to provide confidence in measurement precision. However, the experiment had high statistical power with only 10-12 replicates per sample. Slaking index was not sensitive to the initial size of dry aggregates (3 to 10 mm diameter); therefore, pre-sieving soils was not necessary. The field trial showed greater aggregate stability under no-till than chisel plow practice, and changing stability over a growing season. These results will be useful to researchers and agricultural practitioners who want a simple, fast, low-cost method for measuring wet aggregate stability on many samples.

One of the various sorts of damage to asphalt concrete is cracking. Repeated loads, the deterioration or aging of material combinations, or structural factors can contribute to the development of cracks. Asphalt concrete’s crack resistance is represented by the CT index. 107 CT Index data samples from the University of Transport Technology’s lab are measured. These data samples are used to establish a database from which a Machine Learning (ML) model for predicting the CT Index of asphalt concrete can be built. For creating the highest performing machine learning model, three well-known machine learning methods are introduced: Random Forest (RF), K-Nearest Neighbors (KNN), and Multivariable Adaptive Regression Spines (MARS). Monte Carlo simulation is used to verify the accuracy of the ML model, which includes the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The RF model is the most effective ML model, according to analysis and evaluation of performance indicators. By SHAPley Additive exPlanations based on RF model, the input Aggregate content passing 4.75 mm sieve (AP4.75) has a significant effect on the variation of CT Index value. In following, the descending order is Reclaimed Asphalt Pavement content (RAP) > Bitumen content (BC) > Flash point (FP) > Softening point > Rejuvenator content (RC) > Aggregate content passing 0.075mm sieve (AP0.075) > Penetration at 25°C (P). The results study contributes to selecting a suitable AI approach to quickly and accurately determine the CT Index of asphalt concrete mixtures.

Price index of consumer goods and services is calculated on the basis of the results of: - surveys on prices of consumer goods and services on the retail market, - surveys on household budgets, providing data on average expenditures on consumer goods and services; these data are then used for compilation of a weight system. Calculating price index of consumer goods and services is done on the basis of the Classification of Individual Consumption by Purpose (COICOP) adapted for the use of Harmonized Indices of Consumer Prices (HICP). The price index of a representative in the region included in the price survey results from relating its average monthly price to an average annual price from the previous yea The all-Polish price index of a representative included in the survey is calculated as geometric mean of price indices from all regions. Calculating price indices of groups of consumer goods and services at the lowest level of weight system aggregation is done on the basis of price indices of the representatives included in price survey in a given group by using geometric mean. They are then used by applying weight system to calculate indices of higher level of aggregation up to the price index of total consumer goods and services. price index is calculated in line with the Laspeyress’s formula by applying weights from the year preceding the reference year.

Price index of consumer goods and services is calculated on the basis of the results of: - surveys on prices of consumer goods and services on the retail market, - surveys on household budgets, providing data on average expenditures on consumer goods and services; these data are then used for compilation of a weight system. Calculating price index of consumer goods and services is done on the basis of the Classification of Individual Consumption by Purpose (COICOP) adapted for the use of Harmonized Indices of Consumer Prices (HICP). The price index of a representative in the region included in the price survey results from relating its average monthly price to an average annual price from the previous yea The all-Polish price index of a representative included in the survey is calculated as geometric mean of price indices from all regions. Calculating price indices of groups of consumer goods and services at the lowest level of weight system aggregation is done on the basis of price indices of the representatives included in price survey in a given group by using geometric mean. They are then used by applying weight system to calculate indices of higher level of aggregation up to the price index of total consumer goods and services. price index is calculated in line with the Laspeyress’s formula by applying weights from the year preceding the reference year.

Monthly indexes and percentage changes for major components and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the corresponding month of the previous year, the previous month and the current month. The base year for the index is 2002=100.

This table contains figures about the price developments of a package of goods and services purchased by the average Dutch household, also known as the consumer price index (CPI). The table also includes the derived consumer price index: this price index excludes the effect of changes in the rates of product-related taxes (e.g. VAT and excise duty on alcohol and tobacco) and subsidies and consumption-related taxes (e.g. motor vehicle tax).

The table also includes the month-on-month and year-on-year changes of the CPI. The year-on-year change of total consumer expenditures is known as inflation. The figures are shown for 338 product groups in 2025. Furthermore, 34 combinations of product groups (special aggregates) are displayed in the table. The weighting coefficient shows how much Dutch consumers spend on each product group in relation to their total expenditure. The total weighting is 100,000.

CPI figures are published every month. In addition, an annual figure is published at the end of the year. The CPI of a calendar year is calculated as the average of the indices of the twelve months of that year.

Data available from: January 1996.

Status of the figures: Figures of the flash estimate are published at the end of a reporting month, or shortly thereafter. At the flash estimate, annual rates of change and monthly rates of change are made available for the all items category and for a selection of special aggregates. These figures are calculated on the basis of still incomplete source data. These figures are not suitable for indexation purposes. Therefore, the publication of the flash estimate does not include indices. The rates of change of the flash estimate are characterized as provisional.

The flash estimate is followed by the first publication of all indices and rates of change for the reporting month. These figures are also provisional. The figures for that same reporting month become final one month later. Differences between the provisional and final indices are caused by source material that has become available after the provisional publication.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

Changes as of 13 February 2025: Starting in the reporting month of January 2025, price changes will be published for expenditure categories 053290 Other small electric household appliances and 103000 Post-secondary non-tertiary education. The base period for this new index series is December 2024. This means that the index level of 100 is the price level measured in December 2024.

Changes as of 8 February 2024: Starting in the reporting month of January 2024, a price change will be published for expenditure category 063000 Hospital Services. The base period for this new index series is December 2023. This means that the index level of 100 is the price level measured in December 2023. Previously, between 2006 and 2009, an index was published for the same expenditure category. The base year for that index series was 2006=100. It was discontinued after December 2009. The current series starts again from 100 in December 2023.

Changes as of 1 June 2016: For all series new data is added for the period 1996 to January 2015. To complete the data, the existing series that were terminated before 2015 are added to the table.

This concerns the series: 2006=100: 011320 Frozen fish 031100 Clothing materials 031420 Repair and hire of clothing 032200 Repair and hire of footwear 043210 Services of plumbers 043230 Maintenance of heating systems 043250 Services of carpenters 043290 Oth. maint. services for dwell. 051300 Repair of furniture etc. 053190 Other major household appliances 063000 Services of hospitals 091420 Unrecorded recording media 094240 Hire of equipment for culture 096010 Package domestic holidays 2000=100: 134000 Real estate tax

These series do not have base year 2015=100. 2006=100 and 2000=100 are added for these series. When another base year applies, it is mentioned explicitly in the description.

When will new figures be published? The figures of the flash estimate are published on the last working day of the month to which the figures relate, or shortly thereafter.

The new indices are usually published between the first and second Thursday of the month following on the reporting month. The figures of the previous reporting month then become final.

All CPI publications are announced on the publication calendar.

This table includes figures on the price developments of a package of goods and services purchased by consumers in the Netherlands. The figures are consistent with European directives also known as the harmonised consumer price index (HICP). In all member states of the European Union (EU), these indices are compiled in a similar manner to facilitate comparison between the various EU countries.

This table also contains the HICP at constant taxes: this price index excludes the effect of changes in the rates of product-related taxes (e.g. VAT and excise duty on alcohol and tobacco).

The table also includes the month-on-month and year-on-year changes of the HICP. The year-on-year change of total consumer expenditure is known as inflation. The figures are shown for 327 product groups in 2025. Furthermore, 34 combinations of product groups (special aggregates) are displayed. The weighting coefficient shows how much consumers in the Netherlands spend on each product group in relation to their total expenditure. The total weighting is 100,000.

HICP figures are published every month. In addition, an annual figure is published at the end of the year. The HICP of a calendar year is calculated as the average of the indices of the twelve months of that year.

Data available from: January 1996.

Status of the figures: Figures of the flash estimate are published at the end of a reporting month, or shortly thereafter. At the flash estimate, figures are made available for the all items category and for a selection of special aggregates. These figures are calculated on the basis of still incomplete source data. The results of the flash estimate are characterized as provisional.

In most cases, the figures are final in the second publication of the same reporting month. Differences between the provisional and final indices are caused by source material that has become available after the flash estimate. The results of the HICP are only marked as provisional in the second publication if it is already known at the time of publication that data are still incomplete, a revision is expected in a later month, or in special circumstances such as the corona crisis. In that case, the figures become final one month later.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

Changes as of 13 February 2025: Starting in the reporting month of January 2025, price changes will be published for expenditure categories 053290 Other small electric household appliances and 103000 Post-secondary non-tertiary education. The base period for this new index series is December 2024. This means that the index level of 100 is the price level measured in December 2024.

Changes as of 8 February 2024: Starting in the reporting month of January 2024, a price change will be published for expenditure category 063000 Hospital Services. The base period for this new index series is December 2023. This means that the index level of 100 is the price level measured in December 2023. Previously, between 2000 and 2009, an index was published for the same expenditure category. The base year for that index series was 2005=100. It was discontinued after December 2009. The current series starts again from 100 in December 2023.

When will new figures be published? The figures of the flash estimate are published on the last working day of the month to which the figures relate, or shortly thereafter.

Final figures will usually be published between the first and second Thursday of the month following on the reporting month.

All CPI and HICP publications are announced on the publication calendar.

Field nomenclature * Attributes containing _APP refer to measures taken with Appmosfera. * Attributes containing _M refer to measurements taken with SMAQ_mobile. * Attributes containing _F refer to measurements made with SMAQ_fixed. * Gases that do not contain any of the above attributes (e.g., o3, o3_level,....) correspond to the aggregate calculation of fixed SMAQ mobile and SMAQ. * Measurements are taken in ug/m³ (micrograms/cubic meter). * Attributes with global text (e.g. “iuca.level_APP_global”) corresponds to the calculated globals of each device or the global of all devices. In the IUCA (Urban Air Quality Index) PM1 is not taken into account. NOTA: Preview of the file compatible with Firefox and Chrome browsers.

Reporting of new Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. This dataset will receive a final update on June 1, 2023, to reconcile historical data through May 10, 2023, and will remain publicly available.

Aggregate Data Collection Process Since the start of the COVID-19 pandemic, data have been gathered through a robust process with the following steps:

• A CDC data team reviews and validates the information obtained from jurisdictions’ state and local websites via an overnight data review process.
• If more than one official county data source exists, CDC uses a comprehensive data selection process comparing each official county data source, and takes the highest case and death counts respectively, unless otherwise specified by the state.
• CDC compiles these data and posts the finalized information on COVID Data Tracker.
• County level data is aggregated to obtain state and territory specific totals.

This process is collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provide the most up-to-date numbers on cases and deaths by report date. CDC may retrospectively update counts to correct data quality issues.

Methodology Changes Several differences exist between the current, weekly-updated dataset and the archived version:

• Source: The current Weekly-Updated Version is based on county-level aggregate count data, while the Archived Version is based on State-level aggregate count data.
• Confirmed/Probable Cases/Death breakdown:  While the probable cases and deaths are included in the total case and total death counts in both versions (if applicable), they were reported separately from the confirmed cases and deaths by jurisdiction in the Archived Version.  In the current Weekly-Updated Version, the counts by jurisdiction are not reported by confirmed or probable status (See Confirmed and Probable Counts section for more detail).
• Time Series Frequency: The current Weekly-Updated Version contains weekly time series data (i.e., one record per week per jurisdiction), while the Archived Version contains daily time series data (i.e., one record per day per jurisdiction).
• Update Frequency: The current Weekly-Updated Version is updated weekly, while the Archived Version was updated twice daily up to October 20, 2022.

Important note: The counts reflected during a given time period in this dataset may not match the counts reflected for the same time period in the archived dataset noted above. Discrepancies may exist due to differences between county and state COVID-19 case surveillance and reconciliation efforts.

Confirmed and Probable Counts In this dataset, counts by jurisdiction are not displayed by confirmed or probable status. Instead, confirmed and probable cases and deaths are included in the Total Cases and Total Deaths columns, when available. Not all jurisdictions report probable cases and deaths to CDC.* Confirmed and probable case definition criteria are described here:

Council of State and Territorial Epidemiologists (ymaws.com).

Deaths CDC reports death data on other sections of the website: CDC COVID Data Tracker: Home, CDC COVID Data Tracker: Cases, Deaths, and Testing, and NCHS Provisional Death Counts. Information presented on the COVID Data Tracker pages is based on the same source (total case counts) as the present dataset; however, NCHS Death Counts are based on death certificates that use information reported by physicians, medical examiners, or coroners in the cause-of-death section of each certificate. Data from each of these pages are considered provisional (not complete and pending verification) and are therefore subject to change. Counts from previous weeks are continually revised as more records are received and processed.

Number of Jurisdictions Reporting There are currently 60 public health jurisdictions reporting cases of COVID-19. This includes the 50 states, the District of Columbia, New York City, the U.S. territories of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, Puerto Rico, and the U.S Virgin Islands as well as three independent countries in compacts of free association with the United States, Federated States of Micronesia, Republic of the Marshall Islands, and Republic of Palau. New York State’s reported case and death counts do not include New York City’s counts as they separately report nationally notifiable conditions to CDC.

CDC COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths, available by state and by county. These and other data on COVID-19 are available from multiple public locations, such as:

https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html

https://www.cdc.gov/covid-data-tracker/index.html

https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

https://www.cdc.gov/coronavirus/2019-ncov/php/open-america/surveillance-data-analytics.html

Additional COVID-19 public use datasets, include line-level (patient-level) data, are available at: https://data.cdc.gov/browse?tags=covid-19.

Archived Data Notes:

November 3, 2022: Due to a reporting cadence issue, case rates for Missouri counties are calculated based on 11 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 3, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Due to a reporting cadence change, case rates for Alabama counties are calculated based on 13 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 10, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Per the request of the jurisdiction, cases and deaths among non-residents have been removed from all Hawaii county totals throughout the entire time series. Cumulative case and death counts reported by CDC will no longer match Hawaii’s COVID-19 Dashboard, which still includes non-resident cases and deaths. 

November 17, 2022: Two new columns, weekly historic cases and weekly historic deaths, were added to this dataset on November 17, 2022. These columns reflect case and death counts that were reported that week but were historical in nature and not reflective of the current burden within the jurisdiction. These historical cases and deaths are not included in the new weekly case and new weekly death columns; however, they are reflected in the cumulative totals provided for each jurisdiction. These data are used to account for artificial increases in case and death totals due to batched reporting of historical data.

December 1, 2022: Due to cadence changes over the Thanksgiving holiday, case rates for all Ohio counties are reported as 0 in the data released on December 1, 2022.

January 5, 2023: Due to North Carolina’s holiday reporting cadence, aggregate case and death data will contain 14 days’ worth of data instead of the customary 7 days. As a result, case and death metrics will appear higher than expected in the January 5, 2023, weekly release.

January 12, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0. As a result, case and death metrics will appear lower than expected in the January 12, 2023, weekly release.

January 19, 2023: Due to a reporting cadence issue, Mississippi’s aggregate case and death data will be calculated based on 14 days’ worth of data instead of the customary 7 days in the January 19, 2023, weekly release.

January 26, 2023: Due to a reporting backlog of historic COVID-19 cases, case rates for two Michigan counties (Livingston and Washtenaw) were higher than expected in the January 19, 2023 weekly release.

January 26, 2023: Due to a backlog of historic COVID-19 cases being reported this week, aggregate case and death counts in Charlotte County and Sarasota County, Florida, will appear higher than expected in the January 26, 2023 weekly release.

January 26, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0 in the weekly release posted on January 26, 2023.

February 2, 2023: As of the data collection deadline, CDC observed an abnormally large increase in aggregate COVID-19 cases and deaths reported for Washington State. In response, totals for new cases and new deaths released on February 2, 2023, have been displayed as zero at the state level until the issue is addressed with state officials. CDC is working with state officials to address the issue.

February 2, 2023: Due to a decrease reported in cumulative case counts by Wyoming, case rates will be reported as 0 in the February 2, 2023, weekly release. CDC is working with state officials to verify the data submitted.

February 16, 2023: Due to data processing delays, Utah’s aggregate case and death data will be reported as 0 in the weekly release posted on February 16, 2023. As a result, case and death metrics will appear lower than expected and should be interpreted with caution.

February 16, 2023: Due to a reporting cadence change, Maine’s

The spatial context of invasions is increasingly recognized as important for the success and efficiency of management actions. This information can be key for managing invasive grasses that threaten native ecosystems. We calculated landscape metrics and circuit-based centrality for invasive grasses using a source input raster of weighted-average annual herbaceous cover from 2016-2018 (Maestas et al. 2020, 30 meter resolution) in the Great Basin, USA. This shapefile data product includes the summarized landscape metrics and connectivity metrics for 15 kilometer grid cells (n = 2408) across the Great Basin, USA. Metrics for each grid cell include: mean patch area (area_mn), class area (ca), number of patches (np), largest patch index (lpi), mean Euclidean nearest neighbor distance (enn_mn), mean patch contiguity (cntg_mn), aggregation index (ai), and centrality (cntrlty). We also calculated dominant abundance class (dm_bnd_) for comparison with these metric values.