Congo, The Democratic Republic of the CD: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data was reported at 41.686 % in 2017. This records an increase from the previous number of 4.349 % for 2016. Congo, The Democratic Republic of the CD: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data is updated yearly, averaging 30.795 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 26,765.858 % in 1994 and a record low of -1.156 % in 2015. Congo, The Democratic Republic of the CD: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Democratic Republic of Congo – Table CD.World Bank: Inflation. Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years.; ; World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.; ;

Cytomegalovirus (CMV) has been shown to induce large populations of CD8 T-effector memory cells that unlike central memory persist in large quantities following infection, a phenomenon commonly termed “memory inflation”. Although murine models to date have shown very large and persistent CMV-specific T-cell expansions following infection, there is considerable variability in CMV-specific T-memory responses in humans. Historically such memory inflation in humans has been assumed a consequence of reactivation events during the life of the host. Because basic information about CMV infection/re-infection and reactivation in immune competent humans is not available, we used a murine model to test how primary infection, reinfection, and reactivation stimuli influence memory inflation. We show that low titer infections induce “partial” memory inflation of both mCMV specific CD8 T-cells and antibody. We show further that reinfection with different strains can boost partial memory inflation. Finally, we show preliminary results suggesting that a single strong reactivation stimulus does not stimulate memory inflation. Altogether, our results suggest that while high titer primary infections can induce memory inflation, reinfections during the life of a host may be more important than previously appreciated.

This dataset provides economic statistics in real prices in regions - using regional output producer index (ROPI) when available - is recommended for users who wish to query a large amount of data. It is not designed for visualising results using the Table and Chart buttons. To access the ‘Developer API query builder’, click on the ‘Developer API’ button above.

To get started check the API documentation

Dataflows covered

• Real gross domestic product (ROPI-adjusted for inflation) - Regions
• Real gross value added by main economic activity (ROPI-adjusted for inflation) - Regions
• Real labour productivity by main economic activity (ROPI-adjusted for inflation) - Regions

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Regions and territorial levels

Regions are subnational units below national boundaries and correspond to administrative divisions defined autonomously by countries according to different criteria. The OECD classifies regions into two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). This classification facilitates greater comparability of geographic units at the same territorial level.

The list and maps of OECD regions are presented in the OECD Territorial grid (pdf).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link).

Cite this dataset

OECD Regions, cities and local areas database (Economic statistics ROPI-adjusted for inflation - Regions (for 'Developer API'), http://oe.cd/geostats.

For any question or comment, please write to RegionStat@oecd.org

Memory T cell inflation is a process in which a subset of cytomegalovirus (CMV) specific CD8 T cells continuously expands mainly during latent infection and establishes a large and stable population of effector memory cells in peripheral tissues. Here we set out to identify in vivo parameters that promote and limit CD8 T cell inflation in the context of MCMV infection. We found that the inflationary T cell pool comprised mainly high avidity CD8 T cells, outcompeting lower avidity CD8 T cells. Furthermore, the size of the inflationary T cell pool was not restricted by the availability of specific tissue niches, but it was directly related to the number of virus-specific CD8 T cells that were activated during priming. In particular, the amount of early-primed KLRG1- cells and the number of inflationary cells with a central memory phenotype were a critical determinant for the overall magnitude of the inflationary T cell pool. Inflationary memory CD8 T cells provided protection from a Vaccinia virus challenge and this protection directly correlated with the size of the inflationary memory T cell pool in peripheral tissues. These results highlight the remarkable protective potential of inflationary CD8 T cells that can be harnessed for CMV-based T cell vaccine approaches.

Graph and download economic data for Producer Price Index by Commodity for Miscellaneous Products: Audio Discs, Full-Length (Including CDs and Vinyl Records) (WPU159C01011) from Dec 2010 to Jan 2019 about recording, miscellaneous, commodities, PPI, inflation, price index, indexes, price, and USA.

US Recorded Music Revenues by Format

US Recorded Music Revenues by Format in 2019 (Inflation Adjusted)

By Throwback Thursday [source]

About this dataset

This dataset contains comprehensive information about the US recorded music industry in 2019 Week 10. It includes details on the various formats of recorded music, such as CDs, vinyl records, digital downloads, and more. The dataset also provides data on the respective years in which these records were made, allowing for accurate historical comparison and analysis.

Key metrics provided include the number of units sold for each format, as well as corresponding revenue generated from their sales. In addition to the raw revenue figures, this dataset offers an extra column that presents inflation-adjusted revenue values. These adjusted figures take into account changes in purchasing power over time and enable a fair comparison of different years' revenues.

Overall, this dataset offers valuable insights into the US recorded music industry's performance in terms of format popularity and economic gains throughout a specific week in 2019. Researchers, analysts, and music professionals can utilize this comprehensive dataset to explore trends within specific formats while considering both absolute revenue and inflation-adjusted figures

How to use the dataset

Introduction:

• Understanding the Columns: a) Format: This column categorizes the format of the recorded music, such as CD, vinyl, digital download, etc. b) Year: This column represents the year in which the data was recorded. c) Units: The number of units sold for a particular format of recorded music. d) Revenue: The revenue generated from sales for a specific format. e) Revenue (Inflation Adjusted): The column that shows revenue adjusted for inflation.

• Analyzing Formats: By exploring and analyzing the Format column in this dataset, you can gain insights into changing consumer preferences over time. You can identify which formats have gained popularity or declined over different years or periods.

• Understanding Revenue Generation: To understand revenue patterns in relation to various formats and years, analyze both Revenue and Revenue (Inflation Adjusted) columns separately. Comparing these two columns will help you assess changes due to inflation accurately.

• Exploring Units Sold: The column Units provides insight into how many units were sold for each format within a specific year or period. Analyzing this data helps understand consumer demand across various formats.

• Calculating Inflation-Adjusted Revenue: Utilize the Revenue (Inflation Adjusted) column when analyzing long-term trends or comparisons across different periods without worrying about how inflation affects purchasing power over time.

• Comparing Multiple Years or Periods: This dataset includes information specifically for 2019 Week 10. However, you can use this dataset in conjunction with other datasets covering different years to compare revenue, units sold, and format performance across multiple years.

• Creating Visualizations: Visualizations such as line charts or bar graphs can help represent patterns and trends more comprehensively. Consider creating visualizations based on formats over multiple years or comparing revenue generated by different formats.

• Deriving Insights: Make use of the information provided to identify trends, understand customer preferences, and make informed decisions related to marketing strategies or product offerings in the music industry.

Conclusion:

Research Ideas

• Analyzing the impact of different music formats on revenue: This dataset provides information on the revenue and units sold for different recorded music formats such as CDs, vinyl, and digital downloads. By analyzing this data, one can identify which format generates the highest revenue and understand how consumer preferences have shifted over time.
• Tracking changes in purchasing power over time: The dataset includes both revenue and inflation-adjusted revenue figures, allowing for a comparison of how purchasing power has changed over the years. This can be useful in understanding trends in consumer spending habits or evaluating the success of marketing campaigns.
• Assessing market performance by year: With data on both units sold and revenue by year, this dataset can be used to assess the overall performance of the US recorded music industry over time. By comparing different years, one can identify periods of growth or decline and gain insights into factors driving these changes, such as technological advancements or shifts in consumer behavior

Acknowledgements

&...

We demonstrate that the disruption index (CD) recently applied to publication and patent citation networks by Park et al. (Nature, 2023) systematically decreases over time due to secular growth in research and patent production, following two distinct mechanisms unrelated to innovation – the first structural and the second behavioral. The structural explanation follows from ‘citation inflation’ (CI) (Petersen et al., Research Policy, 2018), an inextricable feature of real citation networks. One driver of CI is the ever-increasing length of reference lists, which causes the CD index to systematically decrease. The behavioral explanation reflects shifts in scholarly citation practice (e.g. self-citation) that increase the rate of triadic closure in citation networks and confounds efforts to measure disruptive innovation using CD. Combined, these two mechanisms render CD unsuitable for cross-temporal analysis, and call into question the interpretations provided by Park et al.

This dataset provides statistics on real gross domestic product (GDP) and real GDP per capita for subnational regions. Real values are deflation-adjusted using the Regional Producer Price Index (ROPI), where available.

Data source and definition

Regional gross domestic product data is collected at current prices, in millions of national currency from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites.

To allow comparability over time and between countries, data at current prices are transformed into constant prices and purchasing power parity measures. Regional GDP per capita is calculated by dividing regional GDP by the average annual population of the region.

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

This dataset provides statistics on labour productivity for large and small regions. Real values are deflation-adjusted using the Regional Producer Price Index (ROPI), where available.

Data source and definition

Labour productivity is measured as gross value added per employment at place of work by main economic activity. Regional gross value added and employment data are collected from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites. In order to allow comparability over time and across countries, data in current prices are transformed into constant prices and PPP measures.

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

CD：通货膨胀：国内生产总值平减指数在12-01-2017达41.686%，相较于12-01-2016的4.349%有所增长。CD：通货膨胀：国内生产总值平减指数数据按年更新，12-01-1961至12-01-2017期间平均值为31.719%，共57份观测结果。该数据的历史最高值出现于12-01-1994，达26,765.858%，而历史最低值则出现于12-01-1970，为-2.837%。CEIC提供的CD：通货膨胀：国内生产总值平减指数数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的刚果民主共和国 – 表 CD.世界银行：通货膨胀。

India Consumer Price Index (CPI): Miscellaneous: CD, DVD, Audio or Video Cassette, Etc data was reported at 127.200 2012=100 in Oct 2018. This records a decrease from the previous number of 131.300 2012=100 for Sep 2018. India Consumer Price Index (CPI): Miscellaneous: CD, DVD, Audio or Video Cassette, Etc data is updated monthly, averaging 120.500 2012=100 from Jan 2014 (Median) to Oct 2018, with 58 observations. The data reached an all-time high of 132.900 2012=100 in Jul 2018 and a record low of 107.100 2012=100 in Feb 2014. India Consumer Price Index (CPI): Miscellaneous: CD, DVD, Audio or Video Cassette, Etc data remains active status in CEIC and is reported by Central Statistics Office. The data is categorized under India Premium Database’s Inflation – Table IN.IA017: Consumer Price Index: 2012=100: Miscellaneous.

Congo, The Democratic Republic of the CD: Real Interest Rate data was reported at -14.868 % pa in 2017. This records a decrease from the previous number of 14.084 % pa for 2016. Congo, The Democratic Republic of the CD: Real Interest Rate data is updated yearly, averaging 21.006 % pa from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 29.583 % pa in 2010 and a record low of -14.868 % pa in 2017. Congo, The Democratic Republic of the CD: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Democratic Republic of Congo – Table CD.World Bank.WDI: Interest Rates. Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator.; ;

This dataset is the Vulnerability Indices for Mortgage, Petroleum and Inflation Risks and Expenditure (VAMPIRE) for Australian Capital Cities for the year of 2001. The data has been calculated for each Census Collection District (CCD) within the Greater Capital City regions following the 2001 Australian Standard Geographical Classification (ASGC). The VAMPIRE index developed at Griffith University's Urban Research Program provides a measure of socio-economic oil price vulnerability in Australian cities based on an analysis of socio-economic indicators from the Australian Bureau of Statistics (ABS) Census Data.

Graph and download economic data for Producer Price Index by Industry: Software and Other Prerecorded Compact Disc, Tape, and Record Reproducing: Audio Discs, Full-Length (Including Compact Discs and Vinyl Records) (PCU33461433461421) from Dec 2003 to Jan 2019 about recording, software, PPI, industry, inflation, price index, indexes, price, and USA.

This dataset provides statistics on real gross value added by broad 10 activities for regions. Real values are deflation-adjusted using the Regional Producer Price Index (ROPI), where available.

Data source and definition

Regional gross value added data is collected at current prices, in millions of national currency from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites. In order to allow comparability over time and across countries, data in current prices are transformed into constant prices and PPP measures.

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

India Consumer Price Index (CPI): Miscellaneous: CD, DVD, Audio or Video Cassette, Etc data was reported at 0.005 % in Oct 2018. This stayed constant from the previous number of 0.005 % for Sep 2018. India Consumer Price Index (CPI): Miscellaneous: CD, DVD, Audio or Video Cassette, Etc data is updated monthly, averaging 0.005 % from Jan 2014 (Median) to Oct 2018, with 58 observations. The data reached an all-time high of 0.005 % in Oct 2018 and a record low of 0.005 % in Oct 2018. India Consumer Price Index (CPI): Miscellaneous: CD, DVD, Audio or Video Cassette, Etc data remains active status in CEIC and is reported by Central Statistics Office. The data is categorized under India Premium Database’s Inflation – Table IN.IA018: Consumer Price Index: 2012=100: Miscellaneous: Weights.

The efficacies of many new T cell vaccines rely on generating large populations of long-lived pathogen-specific effector memory CD8 T cells. However, it is now increasingly recognized that prior infection history impacts on the host immune response. Additionally, the order in which these infections are acquired could have a major effect. Exploiting the ability to generate large sustained effector memory (i.e. inflationary) T cell populations from murine cytomegalovirus (MCMV) and human Adenovirus-subtype (AdHu5) 5-beta-galactosidase (Ad-lacZ) vector, the impact of new infections on pre-existing memory and the capacity of the host’s memory compartment to accommodate multiple inflationary populations from unrelated pathogens was investigated in a murine model. Simultaneous and sequential infections, first with MCMV followed by Ad-lacZ, generated inflationary populations towards both viruses with similar kinetics and magnitude to mono-infected groups. However, in Ad-lacZ immune mice, subsequent acute MCMV infection led to a rapid decline of the pre-existing Ad-LacZ-specific inflating population, associated with bystander activation of Fas-dependent apoptotic pathways. However, responses were maintained long-term and boosting with Ad-lacZ led to rapid re-expansion of the inflating population. These data indicate firstly that multiple specificities of inflating memory cells can be acquired at different times and stably co-exist. Some acute infections may also deplete pre-existing memory populations, thus revealing the importance of the order of infection acquisition. Importantly, immunization with an AdHu5 vector did not alter the size of the pre-existing memory. These phenomena are relevant to the development of adenoviral vectors as novel vaccination strategies for diverse infections and cancers. (241 words)

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.

CD1 CD PRIVATE EQUITY FUND I

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

Brazil IPCA-15: YoY: Personal Expenses: Recreation: CD and DVD data was reported at 0.870 % in Jun 2019. This records an increase from the previous number of 0.440 % for May 2019. Brazil IPCA-15: YoY: Personal Expenses: Recreation: CD and DVD data is updated monthly, averaging -0.300 % from Feb 2012 (Median) to Jun 2019, with 89 observations. The data reached an all-time high of 6.510 % in Jun 2013 and a record low of -6.410 % in Jun 2014. Brazil IPCA-15: YoY: Personal Expenses: Recreation: CD and DVD data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Inflation – Table BR.IA015: Consumer Price Index: Broad Category-15 (IPCA-15): POF 2008-2009: Dec1993=100: Year on Year.

Brazil IPCA: YoY: Sao Luis: Personal Expenses: Recreation: CD and DVD data was reported at 2.880 % in Jun 2018. This records an increase from the previous number of 0.800 % for May 2018. Brazil IPCA: YoY: Sao Luis: Personal Expenses: Recreation: CD and DVD data is updated monthly, averaging 1.840 % from May 2018 (Median) to Jun 2018, with 2 observations. The data reached an all-time high of 2.880 % in Jun 2018 and a record low of 0.800 % in May 2018. Brazil IPCA: YoY: Sao Luis: Personal Expenses: Recreation: CD and DVD data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Inflation – Table BR.IC050: Consumer Price Index: POF 2008-2009: Broad Category (IPCA): Dec1993=100: YoY: By Municipality: Sao Luis.