19 datasets found
  1. d

    Year-wise Cost Inflation Index

    • dataful.in
    Updated Nov 20, 2025
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    Dataful (Factly) (2025). Year-wise Cost Inflation Index [Dataset]. https://dataful.in/datasets/1319
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    application/x-parquet, xlsx, csvAvailable download formats
    Dataset updated
    Nov 20, 2025
    Dataset authored and provided by
    Dataful (Factly)
    License

    https://dataful.in/terms-and-conditionshttps://dataful.in/terms-and-conditions

    Area covered
    India
    Variables measured
    Cost Inflation Index
    Description

    This dataset contains year-wise data of Cost Inflation Index (CII). The CII number is used to arrive at the inflation-adjusted cost price of assets transferred for computing long-term capital gains.

  2. Cost inflation index in India FY 2002-2026

    • statista.com
    Updated Sep 1, 2025
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    Statista (2025). Cost inflation index in India FY 2002-2026 [Dataset]. https://www.statista.com/statistics/1360962/india-cost-inflation-index/
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    Dataset updated
    Sep 1, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    India
    Description

    During the financial year 2026, the cost inflation index (CII) in India stood at ***. This was an increase from the previous year's figure of ***. The CII is used to compute an asset's inflation-adjusted cost price. It is used to assess the inflation value of assets like land, houses, jewelry etc.

  3. Crown Illumination Index (CII) Values for Light on Tree Crowns at Harvard...

    • search.dataone.org
    • portal.edirepository.org
    Updated Apr 5, 2019
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    John Grady (2019). Crown Illumination Index (CII) Values for Light on Tree Crowns at Harvard Forest LTER [Dataset]. https://search.dataone.org/view/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fmsb-tempbiodev%2F1111168%2F1
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    Dataset updated
    Apr 5, 2019
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    John Grady
    Area covered
    Variables measured
    CII, Line, Site, Year, Order, Taxon, ENQ_Tag_#, Main_Stem, LTER_Tag_#, Comments_JG, and 1 more
    Description

    Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. This dataset was based on trees contained in five Gentry plots set up at Harvard Forest by the Enquist Lab (PI, Brian Enquist) from the University of Arizona as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836. The CII stands for Crown Illumination Index, which is a ranked measure of how closed or open a canopy is. It was developed by Clark and Clark (1992), and focuses on the extent to which the crown of any sized tree receives overhead vertical light (directly above) and lateral light coming from the sides. For instance, a '5' corresponds to an exposed tree, a '1' to a completely shaded tree. Trees were classified by CII at Harvard Forest LTER by John Grady of the University of New Mexico.

  4. d

    1939 Quay County CII Aerial Photo Index

    • catalog.data.gov
    • gstore.unm.edu
    Updated Dec 2, 2020
    + more versions
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    Earth Data Analysis Center (Point of Contact) (2020). 1939 Quay County CII Aerial Photo Index [Dataset]. https://catalog.data.gov/dataset/1939-quay-county-cii-aerial-photo-index
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    Dataset updated
    Dec 2, 2020
    Dataset provided by
    Earth Data Analysis Center (Point of Contact)
    Area covered
    Quay County
    Description

    Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth Data Analysis Center.

  5. f

    Changes of mean Cognitive Impairment Index (CII) values and of the mean FSS...

    • figshare.com
    xls
    Updated Dec 2, 2015
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    Pietro Iaffaldano; Rosa Gemma Viterbo; Damiano Paolicelli; Guglielmo Lucchese; Emilio Portaccio; Benedetta Goretti; Vita Direnzo; Mariangela D'Onghia; Stefano Zoccolella; Maria Pia Amato; Maria Trojano (2015). Changes of mean Cognitive Impairment Index (CII) values and of the mean FSS Score over 1 and 2 years of NTZ treatment. [Dataset]. http://doi.org/10.1371/journal.pone.0035843.t003
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    xlsAvailable download formats
    Dataset updated
    Dec 2, 2015
    Dataset provided by
    PLOS ONE
    Authors
    Pietro Iaffaldano; Rosa Gemma Viterbo; Damiano Paolicelli; Guglielmo Lucchese; Emilio Portaccio; Benedetta Goretti; Vita Direnzo; Mariangela D'Onghia; Stefano Zoccolella; Maria Pia Amato; Maria Trojano
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    *Wilcoxon signed-rank test;†Wilcoxon signed-rank test pair-wise comparison: Year 1 vs Baseline;§Wilcoxon signed-rank test pair-wise comparison: Year 2 vs Baseline;θWilcoxon signed-rank test pair-wise comparison: Year 1 vs Year 2.

  6. Construction output price indices

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Nov 13, 2025
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    Office for National Statistics (2025). Construction output price indices [Dataset]. https://www.ons.gov.uk/businessindustryandtrade/constructionindustry/datasets/interimconstructionoutputpriceindices
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    xlsxAvailable download formats
    Dataset updated
    Nov 13, 2025
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Construction Output Price Indices (OPIs) from January 2014 to September 2025, UK. Summary

  7. Construction cost index in the UK 2014-2024, by type

    • statista.com
    Updated Jun 30, 2025
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    Statista (2025). Construction cost index in the UK 2014-2024, by type [Dataset]. https://www.statista.com/statistics/1292727/construction-output-prices-index-in-the-uk-by-type/
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    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United Kingdom
    Description

    From 2015 to 2024, the construction output prices of public and private housing increased by ***** percent in the United Kingdom (UK). Meanwhile, the prices of industrial buildings increased by ***** percent during that period, and infrastructure prices by ***** percent. Housing and industrial are the segments that increased the most during that period. Balfour Beatty ranked in the past years as the construction firm with the largest revenue in the UK.

  8. Three-tier CSI Industry indices and respective symbols.

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Haishu Qiao; Yue Xia; Ying Li (2023). Three-tier CSI Industry indices and respective symbols. [Dataset]. http://doi.org/10.1371/journal.pone.0156784.t001
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    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Haishu Qiao; Yue Xia; Ying Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Three-tier CSI Industry indices and respective symbols.

  9. CII CI RESOURCES LIMITED (Forecast)

    • kappasignal.com
    Updated Apr 7, 2023
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    KappaSignal (2023). CII CI RESOURCES LIMITED (Forecast) [Dataset]. https://www.kappasignal.com/2023/04/cii-ci-resources-limited.html
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    Dataset updated
    Apr 7, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    CII CI RESOURCES LIMITED

    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

  10. f

    Data from: Identifications of good and bad structural fragments of...

    • tandf.figshare.com
    xlsx
    Updated Jun 5, 2023
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    P. Kumar; R. Singh; A. Kumar; A.P. Toropova; A.A. Toropov; M. Devi; S. Lal; J. Sindhu; D. Singh (2023). Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis [Dataset]. http://doi.org/10.6084/m9.figshare.21080772.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    P. Kumar; R. Singh; A. Kumar; A.P. Toropova; A.A. Toropov; M. Devi; S. Lal; J. Sindhu; D. Singh
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r2VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.

  11. f

    Logistic regression (CII worsening as response variable).

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Dec 31, 2013
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    Motta, Caterina; Bozzali, Marco; Centonze, Diego; Nucci, Carlo; Studer, Valeria; Bari, Monica; Sottile, Fabrizio; Mastrangelo, Nicolina; Cercignani, Mara; Mancino, Raffaele; Buttari, Fabio; Maccarrone, Mauro; Rossi, Silvia; Bernardini, Sergio; Mori, Francesco; Gravina, Paolo; Castelli, Maura (2013). Logistic regression (CII worsening as response variable). [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001664962
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    Dataset updated
    Dec 31, 2013
    Authors
    Motta, Caterina; Bozzali, Marco; Centonze, Diego; Nucci, Carlo; Studer, Valeria; Bari, Monica; Sottile, Fabrizio; Mastrangelo, Nicolina; Cercignani, Mara; Mancino, Raffaele; Buttari, Fabio; Maccarrone, Mauro; Rossi, Silvia; Bernardini, Sergio; Mori, Francesco; Gravina, Paolo; Castelli, Maura
    Description

    CII, Cognitive Impairment Index; EDSS, Expanded Disability Status Scale; SE, Standard Error; OR, Odds Ratio.

  12. p

    Data from: Global structure and dynamics of human apolipoprotein CII in...

    • bmrb.pdbj.org
    • bmrb.io
    Updated Mar 14, 2003
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    Janusz Zdunek; Gary Martinez; Jurgen Schleucher; Per-Olof Lycksell; Yinliang Yin; Solveig Nilsson; Yan Shen; Gunilla Olivecrona; Sybren Wijmenga (2003). Global structure and dynamics of human apolipoprotein CII in complex with micelles: evidence for increased mobility of the helix involved in the activation of lipoprotein lipase [Dataset]. http://doi.org/10.13018/BMR5534
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    Dataset updated
    Mar 14, 2003
    Dataset provided by
    Biological Magnetic Resonance Data Bank
    Authors
    Janusz Zdunek; Gary Martinez; Jurgen Schleucher; Per-Olof Lycksell; Yinliang Yin; Solveig Nilsson; Yan Shen; Gunilla Olivecrona; Sybren Wijmenga
    Description

    Biological Magnetic Resonance Bank Entry 5534: Global structure and dynamics of human apolipoprotein CII in complex with micelles: evidence for increased mobility of the helix involved in the activation of lipoprotein lipase

  13. Data from: Monetary policy in Brazil in pandemic times

    • scielo.figshare.com
    tiff
    Updated Jun 1, 2023
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    Carmem Feijó; Eliane Cristina Araújo; Luiz Carlos Bresser-Pereira (2023). Monetary policy in Brazil in pandemic times [Dataset]. http://doi.org/10.6084/m9.figshare.19965335.v1
    Explore at:
    tiffAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    SciELOhttp://www.scielo.org/
    Authors
    Carmem Feijó; Eliane Cristina Araújo; Luiz Carlos Bresser-Pereira
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Brazil
    Description

    ABSTRACT The paper discusses the determination of inflation in Brazil, especially after the great recession of 2015-2016, to assess the adequacy of manipulating interest rates to control the rise in prices due to permanent cost pressure. The burden of using the interest rate to fight cost inflation is to create a highly conventional level of the real interest rate, which benefits the rentier class in a financialized economy. In the light of the post-Keynesian macroeconomics, a high-interest rate convention keeps the economy with a low growth rate and a low investment rate, which in the case of the Brazilian economy has resulted in a regression in the productive matrix and productivity stagnation, and both contribute to perpetuating cost pressures on prices. The empirical analysis corroborates the discussion about recent inflation having its origin in cost pressures over which the interest rate impact for its control is limited. We complement the empirical analysis by testing the response to the SELIC interest rate of the variables used to explain the fluctuation of market prices and administered prices: commodity price index, exchange rate and activity level. As expected, the impact of an increase in the interest rate appreciates the exchange rate, favouring inflation control and reducing the level of activity but has no impact on the commodity price index.

  14. Dynamic GMM-regression results.

    • plos.figshare.com
    xls
    Updated May 30, 2023
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    Haishu Qiao; Yue Xia; Ying Li (2023). Dynamic GMM-regression results. [Dataset]. http://doi.org/10.1371/journal.pone.0156784.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Haishu Qiao; Yue Xia; Ying Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Dynamic GMM-regression results.

  15. Clinicopathological characteristics according to colon inflammatory index.

    • plos.figshare.com
    xls
    Updated Jun 5, 2023
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    Mitsutoshi Ishii; Tetsuro Tominaga; Takashi Nonaka; Shosaburo Oyama; Masaaki Moriyama; Keizaburo Maruyama; Terumitsu Sawai; Takeshi Nagayasu (2023). Clinicopathological characteristics according to colon inflammatory index. [Dataset]. http://doi.org/10.1371/journal.pone.0273167.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Mitsutoshi Ishii; Tetsuro Tominaga; Takashi Nonaka; Shosaburo Oyama; Masaaki Moriyama; Keizaburo Maruyama; Terumitsu Sawai; Takeshi Nagayasu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Clinicopathological characteristics according to colon inflammatory index.

  16. Univariate and multivariate analysis for predicting relapse-free survival.

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Mitsutoshi Ishii; Tetsuro Tominaga; Takashi Nonaka; Shosaburo Oyama; Masaaki Moriyama; Keizaburo Maruyama; Terumitsu Sawai; Takeshi Nagayasu (2023). Univariate and multivariate analysis for predicting relapse-free survival. [Dataset]. http://doi.org/10.1371/journal.pone.0273167.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Mitsutoshi Ishii; Tetsuro Tominaga; Takashi Nonaka; Shosaburo Oyama; Masaaki Moriyama; Keizaburo Maruyama; Terumitsu Sawai; Takeshi Nagayasu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Univariate and multivariate analysis for predicting relapse-free survival.

  17. Univariate and multivariate analysis predicting overall survival.

    • plos.figshare.com
    xls
    Updated May 31, 2023
    + more versions
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    Mitsutoshi Ishii; Tetsuro Tominaga; Takashi Nonaka; Shosaburo Oyama; Masaaki Moriyama; Keizaburo Maruyama; Terumitsu Sawai; Takeshi Nagayasu (2023). Univariate and multivariate analysis predicting overall survival. [Dataset]. http://doi.org/10.1371/journal.pone.0273167.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Mitsutoshi Ishii; Tetsuro Tominaga; Takashi Nonaka; Shosaburo Oyama; Masaaki Moriyama; Keizaburo Maruyama; Terumitsu Sawai; Takeshi Nagayasu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Univariate and multivariate analysis predicting overall survival.

  18. f

    The criterion of coupling degree of CII and RED.

    • figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated Aug 5, 2024
    + more versions
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    Yong Xiang; Yonghua Chen; Ailing Wan; Yangyang Su; Renkai Xiong (2024). The criterion of coupling degree of CII and RED. [Dataset]. http://doi.org/10.1371/journal.pone.0308127.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Aug 5, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Yong Xiang; Yonghua Chen; Ailing Wan; Yangyang Su; Renkai Xiong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In numerous developing nations, challenges such as insufficient investment in innovation and limited capabilities for conversion impede the growth of the construction sector, thus affecting the overall economic well-being of these regions. This paper focuses on construction industry innovation (CII) and its correlation with region economic development (RED), providing valuable insights to overcome these challenges and promote sustainable economic advancement. This study references existing literature to devise an evaluation indicator system dedicated for CII and RED. It then proceeds with an empirical analysis of the integration and synergy between CII and the economic development across 31 Chinese provinces from 2012 to 2021. Furthermore, this paper employs ArcGIS and Geoda software to meticulously dissect the spatial distribution characteristics underlying this coordination. The main conclusions are succinctly summarized as follows: CII in China is intricately connected to RED, exhibiting a strong connection that diminishes from south to north. Nonetheless, the coordination level between these factors remains relatively low, with notable regional disparities, particularly from southeast to northwest. The primary obstacles to effective coordination are related to innovation input, output, and economic scale. Additionally, spatial correlation analysis demonstrates pronounced regional clustering, showing stability despite slight fluctuations over the study period. This research underscores the concept of coupling coordination between CII and RED, underpinned by scientific analytical methods. The outcomes provide a definitive guide for advancing the transformation and enhancement of the construction industry while promoting RED.

  19. f

    The criterion of coupling coordination degree of CII and RED.

    • figshare.com
    xls
    Updated Aug 5, 2024
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    Yong Xiang; Yonghua Chen; Ailing Wan; Yangyang Su; Renkai Xiong (2024). The criterion of coupling coordination degree of CII and RED. [Dataset]. http://doi.org/10.1371/journal.pone.0308127.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Aug 5, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Yong Xiang; Yonghua Chen; Ailing Wan; Yangyang Su; Renkai Xiong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The criterion of coupling coordination degree of CII and RED.

  20. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Dataful (Factly) (2025). Year-wise Cost Inflation Index [Dataset]. https://dataful.in/datasets/1319

Year-wise Cost Inflation Index

Explore at:
application/x-parquet, xlsx, csvAvailable download formats
Dataset updated
Nov 20, 2025
Dataset authored and provided by
Dataful (Factly)
License

https://dataful.in/terms-and-conditionshttps://dataful.in/terms-and-conditions

Area covered
India
Variables measured
Cost Inflation Index
Description

This dataset contains year-wise data of Cost Inflation Index (CII). The CII number is used to arrive at the inflation-adjusted cost price of assets transferred for computing long-term capital gains.

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