61 datasets found
  1. Brazil Broad Producer Price Index: IPA-EP-M: Intermediate Goods: Materials &...

    • ceicdata.com
    Updated Mar 5, 2023
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    CEICdata.com (2023). Brazil Broad Producer Price Index: IPA-EP-M: Intermediate Goods: Materials & Components for Construction [Dataset]. https://www.ceicdata.com/en/brazil/broad-producer-price-index-by-processing-stages/broad-producer-price-index-ipaepm-intermediate-goods-materials--components-for-construction
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    Dataset updated
    Mar 5, 2023
    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Feb 1, 2020 - Jan 1, 2021
    Area covered
    Brazil
    Variables measured
    Producer Prices
    Description

    Brazil Broad Producer Price Index: IPA-EP-M: Intermediate Goods: Materials & Components for Construction data was reported at 924.749 Aug1994=100 in Jan 2021. This records an increase from the previous number of 901.073 Aug1994=100 for Dec 2020. Brazil Broad Producer Price Index: IPA-EP-M: Intermediate Goods: Materials & Components for Construction data is updated monthly, averaging 388.973 Aug1994=100 from Sep 1994 (Median) to Jan 2021, with 317 observations. The data reached an all-time high of 924.749 Aug1994=100 in Jan 2021 and a record low of 98.731 Aug1994=100 in Oct 1994. Brazil Broad Producer Price Index: IPA-EP-M: Intermediate Goods: Materials & Components for Construction data remains active status in CEIC and is reported by Getulio Vargas Foundation. The data is categorized under Brazil Premium Database’s Inflation – Table BR.FGV: Broad Producer Price Index: by Processing Stages.

  2. Brazil National Construction Cost Index: INCC-10: by Stages

    • ceicdata.com
    Updated Feb 2, 2021
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    CEICdata.com (2021). Brazil National Construction Cost Index: INCC-10: by Stages [Dataset]. https://www.ceicdata.com/en/brazil/national-construction-cost-index/national-construction-cost-index-incc10-by-stages
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    Dataset updated
    Feb 2, 2021
    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Jan 1, 2022 - Dec 1, 2022
    Area covered
    Brazil
    Variables measured
    Construction Cost
    Description

    Brazil National Construction Cost Index: INCC-10: by Stages data was reported at 243.124 Feb2009=100 in Dec 2022. This records an increase from the previous number of 241.531 Feb2009=100 for Nov 2022. Brazil National Construction Cost Index: INCC-10: by Stages data is updated monthly, averaging 0.000 Feb2009=100 from Mar 2009 (Median) to Dec 2022, with 166 observations. Brazil National Construction Cost Index: INCC-10: by Stages data remains active status in CEIC and is reported by Getulio Vargas Foundation. The data is categorized under Brazil Premium Database’s Construction and Properties Sector – Table BR.FGV: National Construction Cost Index.

  3. f

    S1 Data -

    • plos.figshare.com
    txt
    Updated Jul 21, 2023
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    Zhixiang LIU; Kang ZOU; Zhan SUN (2023). S1 Data - [Dataset]. http://doi.org/10.1371/journal.pone.0288753.s004
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    txtAvailable download formats
    Dataset updated
    Jul 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Zhixiang LIU; Kang ZOU; Zhan SUN
    License

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

    Description

    Underground roadway excavation is a complex process, especially roadway curved excavation. In addition, the rationality of the design of coal mine roadway excavation scheme directly affects the speed of roadway excavation. The more reasonable the scheme design, the more conducive to rapid excavation. In order to avoid the influence of invalid construction on the efficiency of roadway excavation, this paper studies the forming of roadway bend. Based on the analysis of the tunneling process of the roadway curve, the mathematical model of the roadway curve is established. Taking the turning radius of the roadway curve as the evaluation index, the influence of various factors on the roadway curve excavation is analyzed. The research shows that the radius of the roadway curve increases with the increase of the feed rate, the working space position of the roadheader and the required width of the roadway, and decreases with the increase of the working space angle. Then, combined with the advantages of KNN algorithm, an interpolation model for calculating the radius of the curve is established based on RBF algorithm, and the radius of the tunnel curve is reconstructed and predicted. It provides a basis for the rational design of the construction process of the roadway bend and a reliable numerical algorithm for the design of the radius of the roadway bend. It also provides a theoretical basis for improving the efficiency of high roadway excavation in coal mines.

  4. g

    Development Economics Data Group - Logistics performance index: Efficiency...

    • gimi9.com
    Updated May 16, 2023
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    (2023). Development Economics Data Group - Logistics performance index: Efficiency of customs clearance process (1=low to 5=high) | gimi9.com [Dataset]. https://gimi9.com/dataset/worldbank_wb_wdi_lp_lpi_cust_xq/
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    Dataset updated
    May 16, 2023
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated efficiency of customs clearance processes (i.e. speed, simplicity and predictability of formalities), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents.

  5. f

    Data from: Study approach and step by step development of a fish-based...

    • scielo.figshare.com
    jpeg
    Updated Jun 1, 2023
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    Maria Letizia Petesse (2023). Study approach and step by step development of a fish-based multimetric index for reservoirs: a case study presentation from a neotropical cascade system [Dataset]. http://doi.org/10.6084/m9.figshare.7367660.v1
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    jpegAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    SciELO journals
    Authors
    Maria Letizia Petesse
    License

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

    Description

    Abstract Aim The present paper approached some issues related to the construction and adaptation of the Reservoir Fish Assemblage Index (RFAI). Method the case study presents the step by step construction of the multimetric index adopted for the Tietê cascade reservoir system, and it is discussed the comparison between discrete and continuous scoring criteria. Results The main questions related to the adaptation of multimetric indexes to reservoir were synthesized as: i – terminology question; ii - representative fish sampling; iii – reference condition. The construction of the RFAI for the Tietê cascade reservoir system resulted in seven steps, and the continuous scoring criterion showed to increase the accuracy of the final index when compared to the discrete scoring especially for the most degraded environment. Conclusion Biological criteria for the assessment of aquatic ecosystems are widely recognized and accepted by scientific community. The increasing value of water resource makes reservoirs important object of scientific and social interest, justifying the definition of proper tools for their assessment and monitoring. Among the available tools, the multimetric approach is one of the most popular. The observed results showed the validity of the approach also for artificial environments, expecting for its official incorporation in biomonitoring programs in Brazil.

  6. f

    Phase 1 Results: Relative Importance Index.

    • plos.figshare.com
    xls
    Updated May 7, 2025
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    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor (2025). Phase 1 Results: Relative Importance Index. [Dataset]. http://doi.org/10.1371/journal.pone.0322295.t004
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    xlsAvailable download formats
    Dataset updated
    May 7, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor
    License

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

    Description

    The construction industry has long been struggling with excessive costs, delays, and compromised quality due to wasteful practices, particularly in developing nations. While international efforts have focused on identifying waste-related factors, there’s a research gap in pinpointing micro-level causes and analyzing their specific impacts on cost, quality, and time. This study delves into the prevalent and crucial causes of construction waste in Pakistan, evaluating their collective influence on project cost, schedule, quality, and material management. A mix of literature study findings, on-site inspections, and a survey from industry experts was used to determine the causes of non-value-adding/waste generation in construction. Using two rounds of questionnaire survey, 65 valid responses (48% of response rate) were obtained. Following the initial round, the most significant causes and their consequences on waste-related features were identified using the Relative Importance Index (RII) and mean value indexing. To confirm the influence of these waste-generating causes on Pakistani construction projects, assessments from 21 industrial experts were conducted in the second round. The data alignment between the two phases confirmed the impact of the indicated causes on waste. The study determined that inadequate worker training and awareness, planning long duration leading to material escalation, and poor workmanship are the main causes of construction waste in Pakistan. Insights from 21 construction industry experts were also acquired, offering helpful strategies to cut waste in these projects. The outcome of this study clarifies the main reasons for the negative effects of construction waste on the sector and offers recommendations for future steps to reduce their frequency.

  7. f

    Phase 1 Results: Mean Value Indexing, Inferences, and RII Scores.

    • figshare.com
    xls
    Updated May 7, 2025
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    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor (2025). Phase 1 Results: Mean Value Indexing, Inferences, and RII Scores. [Dataset]. http://doi.org/10.1371/journal.pone.0322295.t005
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    xlsAvailable download formats
    Dataset updated
    May 7, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor
    License

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

    Description

    Phase 1 Results: Mean Value Indexing, Inferences, and RII Scores.

  8. South Korea SPPI: Intermediate Materials: Construction

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). South Korea SPPI: Intermediate Materials: Construction [Dataset]. https://www.ceicdata.com/en/korea/stages-of-processing-index-2005100/sppi-intermediate-materials-construction
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    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Dec 1, 2011 - Nov 1, 2012
    Area covered
    South Korea
    Variables measured
    Producer Prices
    Description

    Korea SPPI: Intermediate Materials: Construction data was reported at 146.600 2005=100 in Nov 2012. This records a decrease from the previous number of 147.500 2005=100 for Oct 2012. Korea SPPI: Intermediate Materials: Construction data is updated monthly, averaging 67.000 2005=100 from Jan 1980 (Median) to Nov 2012, with 395 observations. The data reached an all-time high of 151.600 2005=100 in Apr 2012 and a record low of 33.400 2005=100 in Jan 1980. Korea SPPI: Intermediate Materials: Construction data remains active status in CEIC and is reported by The Bank of Korea. The data is categorized under Global Database’s Korea – Table KR.I048: Stages of Processing Index: 2005=100.

  9. T

    United States Building Permits

    • tradingeconomics.com
    • ko.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 24, 2025
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    TRADING ECONOMICS (2025). United States Building Permits [Dataset]. https://tradingeconomics.com/united-states/building-permits
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    csv, xml, json, excelAvailable download formats
    Dataset updated
    Jul 24, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Jan 31, 1960 - Jun 30, 2025
    Area covered
    United States
    Description

    Building Permits in the United States decreased to 1393 Thousand in June from 1394 Thousand in May of 2025. This dataset provides the latest reported value for - United States Building Permits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  10. The reliability statistics.

    • plos.figshare.com
    xls
    Updated Jun 10, 2023
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    Junlong Peng; Jing Zhou; Fanyi Meng; Yan Yu (2023). The reliability statistics. [Dataset]. http://doi.org/10.1371/journal.pone.0252138.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 10, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Junlong Peng; Jing Zhou; Fanyi Meng; Yan Yu
    License

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

    Description

    The reliability statistics.

  11. Annual Employment Survey 2000 - Sri Lanka

    • dev.ihsn.org
    • nada.statistics.gov.lk
    • +1more
    Updated Apr 25, 2019
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    Statistics Division, Department of Labour (2019). Annual Employment Survey 2000 - Sri Lanka [Dataset]. https://dev.ihsn.org/nada//catalog/74191
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    Dataset updated
    Apr 25, 2019
    Dataset provided by
    Department of Employment and Labourhttp://www.labour.gov.za/
    Authors
    Statistics Division, Department of Labour
    Time period covered
    2000
    Area covered
    Sri Lanka
    Description

    Abstract

    The Annual Employment Survey of Sri Lanka conducted by the Department of Labour has been designed to measure the levels and trends of the private sector and semi-government sector employment of the country over a period of time. The main objective of the survey is to collect employment related information in the organised sector establishments of the country

    Geographic coverage

    National coverage

    Although this is a national level survey, coverage of Nothern and Estern provinces was not at a sufficient level of response due to the prevailing situation in this area.

    The coverage was also limited because of the fact that there was no complete list of establishments for the whole country at the time of the survey.

    Analysis unit

    The survey covers establishments in both Public and Private sectors

    Universe

    The survey covered establishments with five or more paid employees in private sector and also semi-government institutions in public sector.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A complete list of establishments ( survey frame ) was available at the time of the first survey started in 1971. This list was compiled along with the list of buildings in the country prepared for the Census of Population and Housing 1971. There were about 22,500 establishments with 5 or more paid employees and thereafter this survey frame was updated annually, taking new registrations from the Employees Provident Fund lists.

    Construction of indices: Index numbers of employment are calculated with 1971 as the base year, and using the link relative method. Indices are computed for each major industry division,

    Weighting of sample results: The employment estimates are not weighted

    Adjustments: Non response : there are no adjustments for non response Other bias : no adjustments have been made for any other bias. Use of bench mark data : none Seasonal variations : not relevant

    Indicators of reliability of the estimates : Coverage of the sampling frame : coverage is limited, as there is no complete list of establishments for the whole country. Sampling error/Sampling variance : not relevant Non response rate :

    Non sampling errors : the main known source of bias is the limited coverage of the survey due to the lack of a complete list of establishments for the whole country

    Sampling deviation

    The coverage of the survey was badly affected due to the limiting factors such as the non availability of a complete list of establishments for the whole country and the nature of the method of data collection (postal survey method).

    Mode of data collection

    Mail Questionnaire [mail]

    Research instrument

    The questionnaire used in the Annual Employemnet Survey consists of three parts.

    They are : 1. Establishment particulars 2. Classification of employees 3. Occupational category

    The questionnaire was printed in Sinhala, Tamil and English.

    Cleaning operations

    An edit program was run to edit the raw data files.

    Depending on the absence or presence of errors, the records in the raw data file were seperated into an error free file and a rejects file respectively. From the rejects file, a error report was generated indicating the reason for rejection of each record. The errors were checked and manually corrected on the report by referring to the questionnaire. Based on the corrected report, error records were updated by data entry operators.

    The edit program was run again to edit the rejects file and the error free records were added to the error free file. If the rejects file was not empty the edit process was repeated.

  12. f

    Causes Contributing to the Generation of Waste on Construction Projects.

    • figshare.com
    xls
    Updated May 7, 2025
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    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor (2025). Causes Contributing to the Generation of Waste on Construction Projects. [Dataset]. http://doi.org/10.1371/journal.pone.0322295.t002
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    xlsAvailable download formats
    Dataset updated
    May 7, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor
    License

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

    Description

    Causes Contributing to the Generation of Waste on Construction Projects.

  13. f

    Data from: THE CONSTRUCTION OF SPORTS PUBLIC SERVICE SYSTEM FOR THE ELDERLY...

    • scielo.figshare.com
    jpeg
    Updated Feb 12, 2024
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    Jie Sun; Ke Hu (2024). THE CONSTRUCTION OF SPORTS PUBLIC SERVICE SYSTEM FOR THE ELDERLY FROM THE PERSPECTIVE OF HEALTHY AGING [Dataset]. http://doi.org/10.6084/m9.figshare.14285404.v1
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    jpegAvailable download formats
    Dataset updated
    Feb 12, 2024
    Dataset provided by
    SciELO journals
    Authors
    Jie Sun; Ke Hu
    License

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

    Description

    ABSTRACT With the gradual improvement of people's quality of life, the average life expectancy of our country has been extended. It is estimated that the total number of the elderly population in China will exceed 250 million by the end of 2020. Therefore, promoting healthy aging is a necessary measure to cope with the coming aging society in China, and physical exercise is an important method to keep the physical and mental health of the elderly. In order to promote the development of healthy aging in China, this study focused on the current level of public sports services for the elderly. This study uses the expert consultation method to test the rationality of the evaluation index, and uses the method of questionnaire survey to score each evaluation index, and uses the analytic hierarchy process (AHP) to calculate the weight of the index. Finally, the paper takes the public sports service of the elderly in a city as an example to verify. The results show that the level of sports public service for the elderly in this city is only 77,928. Especially the two aspects of allocation of sports venues and the use of funds are the most unsatisfactory ones. It can be targeted to improve these two aspects, so as to improve the level of public sports services for the elderly. It is hoped that, through this study, we can provide some reference for improving the level of sports public service for the elderly from the perspective of healthy aging.

  14. f

    Frequency of Causes Contributing to the Generation of Waste on Construction...

    • figshare.com
    xls
    Updated May 7, 2025
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    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor (2025). Frequency of Causes Contributing to the Generation of Waste on Construction Projects in Literature. [Dataset]. http://doi.org/10.1371/journal.pone.0322295.t003
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    xlsAvailable download formats
    Dataset updated
    May 7, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor
    License

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

    Description

    Frequency of Causes Contributing to the Generation of Waste on Construction Projects in Literature.

  15. f

    Proposed Mitigation Strategies by Subject Matter Experts.

    • plos.figshare.com
    xls
    Updated May 7, 2025
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    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor (2025). Proposed Mitigation Strategies by Subject Matter Experts. [Dataset]. http://doi.org/10.1371/journal.pone.0322295.t008
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    xlsAvailable download formats
    Dataset updated
    May 7, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor
    License

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

    Description

    Proposed Mitigation Strategies by Subject Matter Experts.

  16. f

    Waste Generation by %, Mix, and Country of Origin.

    • plos.figshare.com
    xls
    Updated May 7, 2025
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    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor (2025). Waste Generation by %, Mix, and Country of Origin. [Dataset]. http://doi.org/10.1371/journal.pone.0322295.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 7, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor
    License

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

    Description

    Waste Generation by %, Mix, and Country of Origin.

  17. South Korea PPI: Raw and Intermediate Materials: Construction: Import

    • ceicdata.com
    Updated Mar 15, 2023
    + more versions
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    CEICdata.com (2023). South Korea PPI: Raw and Intermediate Materials: Construction: Import [Dataset]. https://www.ceicdata.com/en/korea/stages-of-processing-index-2000100/ppi-raw-and-intermediate-materials-construction-import
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    Dataset updated
    Mar 15, 2023
    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Jun 1, 2007 - May 1, 2008
    Area covered
    South Korea
    Variables measured
    Producer Prices
    Description

    Korea PPI: Raw and Intermediate Materials: Construction: Import data was reported at 158.900 2000=100 in May 2008. This records an increase from the previous number of 147.900 2000=100 for Apr 2008. Korea PPI: Raw and Intermediate Materials: Construction: Import data is updated monthly, averaging 75.100 2000=100 from Jan 1980 (Median) to May 2008, with 341 observations. The data reached an all-time high of 158.900 2000=100 in May 2008 and a record low of 31.270 2000=100 in Jan 1980. Korea PPI: Raw and Intermediate Materials: Construction: Import data remains active status in CEIC and is reported by The Bank of Korea. The data is categorized under Global Database’s Korea – Table KR.I049: Stages of Processing Index: 2000=100.

  18. Israel Residential Building Input Price Index: MP: QM: MA: Stairs

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). Israel Residential Building Input Price Index: MP: QM: MA: Stairs [Dataset]. https://www.ceicdata.com/en/israel/residential-building-input-price-index-jan2004100/residential-building-input-price-index-mp-qm-ma-stairs
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    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Aug 1, 2010 - Jul 1, 2011
    Area covered
    Israel
    Variables measured
    Producer Prices
    Description

    Israel Residential Building Input Price Index: MP: QM: MA: Stairs data was reported at 131.100 Jan2004=100 in Jul 2011. This records a decrease from the previous number of 131.200 Jan2004=100 for Jun 2011. Israel Residential Building Input Price Index: MP: QM: MA: Stairs data is updated monthly, averaging 119.100 Jan2004=100 from Jan 2004 (Median) to Jul 2011, with 91 observations. The data reached an all-time high of 131.200 Jan2004=100 in Jun 2011 and a record low of 100.000 Jan2004=100 in Jan 2004. Israel Residential Building Input Price Index: MP: QM: MA: Stairs data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.I021: Residential Building Input Price Index: Jan2004=100.

  19. f

    Phase 1 and Phase 2 Results Comparison for Validation.

    • plos.figshare.com
    xls
    Updated May 7, 2025
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    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor (2025). Phase 1 and Phase 2 Results Comparison for Validation. [Dataset]. http://doi.org/10.1371/journal.pone.0322295.t007
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    xlsAvailable download formats
    Dataset updated
    May 7, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Usman Aftab; Mughees Aslam; Aman Ulhaq; Farrokh Jaleel; Sohail Malik; Hafiz Zahoor
    License

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

    Description

    Phase 1 and Phase 2 Results Comparison for Validation.

  20. South Korea ExPI: Won: MI: ME: SPM: AC: Construction & Mineral Process...

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    CEICdata.com, South Korea ExPI: Won: MI: ME: SPM: AC: Construction & Mineral Process Machinery [Dataset]. https://www.ceicdata.com/en/korea/export-price-index-won-basis-2015100/expi-won-mi-me-spm-ac-construction--mineral-process-machinery
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    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    May 1, 2018 - Apr 1, 2019
    Area covered
    South Korea
    Description

    South Korea ExPI: Won: MI: ME: SPM: AC: Construction & Mineral Process Machinery data was reported at 103.020 2015=100 in Apr 2019. This records an increase from the previous number of 102.770 2015=100 for Mar 2019. South Korea ExPI: Won: MI: ME: SPM: AC: Construction & Mineral Process Machinery data is updated monthly, averaging 102.235 2015=100 from Jan 2018 (Median) to Apr 2019, with 16 observations. The data reached an all-time high of 103.400 2015=100 in Jul 2018 and a record low of 100.790 2015=100 in Jan 2018. South Korea ExPI: Won: MI: ME: SPM: AC: Construction & Mineral Process Machinery data remains active status in CEIC and is reported by The Bank of Korea. The data is categorized under Global Database’s South Korea – Table KR.I056: Export Price Index (Won Basis): 2015=100.

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CEICdata.com (2023). Brazil Broad Producer Price Index: IPA-EP-M: Intermediate Goods: Materials & Components for Construction [Dataset]. https://www.ceicdata.com/en/brazil/broad-producer-price-index-by-processing-stages/broad-producer-price-index-ipaepm-intermediate-goods-materials--components-for-construction
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Brazil Broad Producer Price Index: IPA-EP-M: Intermediate Goods: Materials & Components for Construction

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Dataset updated
Mar 5, 2023
Dataset provided by
CEIC Data
License

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

Time period covered
Feb 1, 2020 - Jan 1, 2021
Area covered
Brazil
Variables measured
Producer Prices
Description

Brazil Broad Producer Price Index: IPA-EP-M: Intermediate Goods: Materials & Components for Construction data was reported at 924.749 Aug1994=100 in Jan 2021. This records an increase from the previous number of 901.073 Aug1994=100 for Dec 2020. Brazil Broad Producer Price Index: IPA-EP-M: Intermediate Goods: Materials & Components for Construction data is updated monthly, averaging 388.973 Aug1994=100 from Sep 1994 (Median) to Jan 2021, with 317 observations. The data reached an all-time high of 924.749 Aug1994=100 in Jan 2021 and a record low of 98.731 Aug1994=100 in Oct 1994. Brazil Broad Producer Price Index: IPA-EP-M: Intermediate Goods: Materials & Components for Construction data remains active status in CEIC and is reported by Getulio Vargas Foundation. The data is categorized under Brazil Premium Database’s Inflation – Table BR.FGV: Broad Producer Price Index: by Processing Stages.

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