100+ datasets found
  1. Cognitive Style Index dataset

    • zenodo.org
    csv
    Updated Jan 24, 2020
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    Félix Cuneo; Félix Cuneo (2020). Cognitive Style Index dataset [Dataset]. http://doi.org/10.5281/zenodo.1438755
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    csvAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Félix Cuneo; Félix Cuneo
    License

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

    Description

    Items are in order, and not yet reversed.

  2. Zalando fashion transparency index score 2017-2023

    • statista.com
    Updated May 20, 2025
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    Statista (2025). Zalando fashion transparency index score 2017-2023 [Dataset]. https://www.statista.com/statistics/1175011/zalando-fashion-transparency-index-score/
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    Dataset updated
    May 20, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Germany
    Description

    Berlin-based online fashion retailer Zalando's sustainability and transparency about its sourcing and supply chain processes have declined in recent years. According to the most recent fashion transparency index scores, Zalando scored ** out of 100 in 2023, down from ** in 2021. According to the source, this score means that Zalando is "disclosing their first tier manufacturers as well as detailed information about their policies, procedures, social and environmental goals, governance, supplier assessment and remediation processes."

  3. Ranking of luxury fashion cities consumer industry index forecast 2030

    • statista.com
    Updated Jun 27, 2025
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    Statista (2025). Ranking of luxury fashion cities consumer industry index forecast 2030 [Dataset]. https://www.statista.com/statistics/1248346/global-fashion-cities-consumer-ranking/
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    Dataset updated
    Jun 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2021
    Area covered
    Worldwide
    Description

    According to the latest results of IFDAQ's Global Cities Consumer IPX (Index), by 2030 Paris is expected to become the leading city for fashion, with an index value of ***** points. IFDAQ's forecast for 2030 put New York and London in the second and third place, respectively.The index measures global cities taking into account factors such as GDP, brand presence, wealth, consumption and creative power.

  4. Crop Index Model

    • data.ca.gov
    • data.cnra.ca.gov
    • +5more
    Updated Mar 22, 2024
    + more versions
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    California Energy Commission (2024). Crop Index Model [Dataset]. https://data.ca.gov/dataset/crop-index-model
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    arcgis geoservices rest api, htmlAvailable download formats
    Dataset updated
    Mar 22, 2024
    Dataset authored and provided by
    California Energy Commissionhttp://www.energy.ca.gov/
    License

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

    Description

    Cropland Index


    The Cropland Index evaluates lands used to produce crops based on the following input datasets: Revised Storie Index, California Important Farmland data, Electrical Conductivity (EC), and Sodium Adsorption Ratio (SAR). Together, these input layers were used in a suitability model to generate this raster. High values are associated with better Croplands


    California Important Farmland data – statistical data used for analyzing impacts on California’s agricultural resources from the Farmland Mapping and Monitoring Program. Agricultural land is rated according to soil quality and irrigation status. The maps are updated every two years (on even numbered years) with the use of a computer mapping system, aerial imagery, public review, and field reconnaissance.

    Cropland Index Mask - This is a constructed data set used to define the model domain. Its footprint is defined by combining the extent of the California Important Farmland data (2018) classifications listed above and the area defined by California Statewide Crop Mapping for the state of California.

    Prime Farmland – farmland with the best combination of physical and chemical features able to sustain long term agricultural production. This land has the soil quality, growing season, and moisture supply needed to produce sustained high yields. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date.

    Farmland of Statewide Importance – farmland similar to Prime Farmland but with minor shortcomings, such as greater slopes or less ability to store soil moisture. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date.

    Unique Farmland – farmland of lesser quality soils used for the production of the state’s leading agricultural crops. This land is usually irrigated but may include Non irrigated orchards or vineyards as found in some climatic zones in California. Land must have been cropped at some time during the four years prior to the mapping date.

    Gridded Soil Survey Geographic Database (gSSURGO) a database containing information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS. The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories.

    California Revised Storie Index - is a soil rating based on soil properties that govern a soils potential for cultivated agriculture in California. The Revised Storie Index assesses the productivity of a soil from the following four characteristics: Factor A, degree of soil profile development; factor B, texture of the surface layer; factor C, slope; and factor X, manageable features, including drainage, microrelief, fertility, acidity, erosion, and salt content. A score ranging from 0 to 100 percent is determined for each factor, and the scores are then multiplied together to derive an index rating.

    Electrical Conductivity - is the electrolytic conductivity of an extract from saturated soil paste, expressed as Deci siemens per meter at 25 degrees C. Electrical conductivity is a measure of the concentration of water-soluble salts in soils. It is used to indicate saline soils. High concentrations of neutral salts, such as sodium chloride and sodium sulfate, may interfere with the adsorption of water by plants because the osmotic pressure in the soil solution is nearly as <span

  5. South Africa Index: FTSE/JSE: All Share Style Value

    • ceicdata.com
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    CEICdata.com, South Africa Index: FTSE/JSE: All Share Style Value [Dataset]. https://www.ceicdata.com/en/south-africa/johannesburg-stock-exchange-index/index-ftsejse-all-share-style-value
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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
    Jul 1, 2017 - Jun 1, 2018
    Area covered
    South Africa
    Variables measured
    Securities Exchange Index
    Description

    South Africa Index: FTSE/JSE: All Share Style Value data was reported at 358.994 02Jan2002=100 in Oct 2018. This records a decrease from the previous number of 372.309 02Jan2002=100 for Sep 2018. South Africa Index: FTSE/JSE: All Share Style Value data is updated monthly, averaging 291.870 02Jan2002=100 from Oct 2004 (Median) to Oct 2018, with 169 observations. The data reached an all-time high of 425.157 02Jan2002=100 in Apr 2015 and a record low of 103.070 02Jan2002=100 in Oct 2004. South Africa Index: FTSE/JSE: All Share Style Value data remains active status in CEIC and is reported by Johannesburg Stock Exchange. The data is categorized under Global Database’s South Africa – Table ZA.Z001: Johannesburg Stock Exchange: Index.

  6. Thailand SET: Index: Fashion

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). Thailand SET: Index: Fashion [Dataset]. https://www.ceicdata.com/en/thailand/the-stock-exchange-of-thailand-index/set-index-fashion
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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, 2017 - Jul 1, 2018
    Area covered
    Thailand
    Variables measured
    Securities Exchange Index
    Description

    Thailand SET: Index: Fashion data was reported at 746.370 31Dec2003=100 in Nov 2018. This records an increase from the previous number of 736.550 31Dec2003=100 for Oct 2018. Thailand SET: Index: Fashion data is updated monthly, averaging 690.790 31Dec2003=100 from Jan 2005 (Median) to Nov 2018, with 167 observations. The data reached an all-time high of 869.430 31Dec2003=100 in Oct 2014 and a record low of 426.520 31Dec2003=100 in Nov 2008. Thailand SET: Index: Fashion data remains active status in CEIC and is reported by The Stock Exchange of Thailand. The data is categorized under Global Database’s Thailand – Table TH.Z001: The Stock Exchange of Thailand: Index.

  7. Fashion Token Index Market Research Report 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jun 28, 2025
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    Dataintelo (2025). Fashion Token Index Market Research Report 2033 [Dataset]. https://dataintelo.com/report/fashion-token-index-market
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    csv, pptx, pdfAvailable download formats
    Dataset updated
    Jun 28, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Fashion Token Index Market Outlook



    According to our latest research, the Fashion Token Index market size reached USD 2.18 billion globally in 2024, reflecting the rapid integration of blockchain and tokenization within the fashion industry. The market is projected to grow at a robust CAGR of 20.7% from 2025 to 2033, reaching an estimated USD 13.89 billion by the end of the forecast period. This impressive growth is primarily driven by the rising adoption of digital assets, NFTs, and decentralized platforms in the fashion sector, enabling new revenue streams, enhanced transparency, and improved consumer engagement.




    One of the most significant growth factors for the Fashion Token Index market is the increasing demand for digital fashion and virtual goods. As consumers, particularly Gen Z and Millennials, spend more time in virtual environments and the metaverse, fashion brands are leveraging blockchain-based tokens to create, sell, and authenticate digital apparel and accessories. This trend not only creates new monetization opportunities for brands and designers but also fosters a thriving secondary market for digital collectibles. The integration of NFTs and other token types into fashion collections is redefining the concept of exclusivity, allowing brands to offer limited edition items, unique experiences, and digital ownership, thereby driving market expansion.




    Another key driver is the growing emphasis on authenticity and provenance in the fashion industry. Counterfeiting remains a persistent challenge, costing brands billions annually and eroding consumer trust. Blockchain-powered Fashion Token Index solutions enable brands to embed authentication and provenance data directly into digital tokens, providing immutable proof of origin and ownership. This level of transparency not only protects brand integrity but also empowers consumers to make informed purchasing decisions. As regulatory bodies and industry associations increasingly mandate traceability and sustainability disclosures, the adoption of tokenized solutions is expected to accelerate further, fueling market growth.




    The emergence of brand loyalty programs powered by fashion tokens is also contributing to the market's upward trajectory. By leveraging tokenization, fashion brands can create innovative loyalty ecosystems where consumers earn, trade, and redeem tokens for exclusive rewards, early access to collections, or personalized experiences. Such programs foster deeper customer engagement, drive repeat purchases, and enhance brand differentiation in a highly competitive landscape. As more brands experiment with tokenized loyalty initiatives and integrate them with e-commerce and social platforms, the Fashion Token Index market is poised for sustained growth throughout the forecast period.




    Regionally, North America currently leads the global Fashion Token Index market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. The dominance of North America is attributed to the strong presence of leading fashion houses, advanced blockchain infrastructure, and a high concentration of tech-savvy consumers. Europe is witnessing rapid adoption, particularly in fashion capitals such as Paris, Milan, and London, where brands are pioneering digital fashion initiatives. The Asia Pacific region, led by China, Japan, and South Korea, is emerging as a significant growth engine, driven by a young, digitally native population and the proliferation of online platforms. The Middle East & Africa and Latin America are also experiencing increased interest, albeit at a more nascent stage, as brands and consumers in these regions begin to explore the benefits of fashion tokenization.



    Component Analysis



    The Component segment of the Fashion Token Index market is bifurcated into Platform and Services, each playing a pivotal role in shaping the industry landscape. The Platform sub-segment encompasses the underlying blockchain infrastructure, token issuance tools, smart contract development environments, and marketplaces that facilitate the creation, management, and trading of fashion tokens. These platforms are crucial for enabling seamless interoperability, scalability, and security, which are essential for mainstream adoption. Leading platforms are investing heavily in user-friendly interfaces, robust compliance features, and integrations with payment gateways, thereby lowering the entry barriers

  8. Fashion Token Index Market Research Report 2033

    • growthmarketreports.com
    csv, pdf, pptx
    Updated Jun 29, 2025
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    Growth Market Reports (2025). Fashion Token Index Market Research Report 2033 [Dataset]. https://growthmarketreports.com/report/fashion-token-index-market
    Explore at:
    csv, pdf, pptxAvailable download formats
    Dataset updated
    Jun 29, 2025
    Dataset authored and provided by
    Growth Market Reports
    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Fashion Token Index Market Outlook




    According to our latest research, the global Fashion Token Index market size reached USD 1.28 billion in 2024, reflecting a robust expansion driven by the digital transformation in the fashion and retail sectors. The market is projected to grow at a compelling CAGR of 22.7% from 2025 to 2033, reaching an estimated value of USD 9.02 billion by the end of the forecast period. This remarkable growth trajectory is primarily fueled by increased adoption of blockchain technology, rising consumer interest in digital assets, and the proliferation of virtual fashion experiences. As per the latest research, the Fashion Token Index market is witnessing rapid evolution, with both established fashion houses and emerging digital-native brands leveraging tokenization to enhance customer engagement, drive loyalty, and unlock new revenue streams.




    One of the key growth factors propelling the Fashion Token Index market is the increasing convergence of fashion and technology. The integration of blockchain-based tokens within the fashion industry enables brands to offer unique digital experiences, authenticate products, and facilitate transparent supply chains. Utility tokens and NFTs are being utilized to provide exclusive access to digital fashion shows, limited-edition collections, and immersive virtual environments. This trend is particularly pronounced among Gen Z and millennial consumers, who are highly receptive to digital ownership and the gamification of brand interactions. The ability to tokenize fashion assets not only enhances consumer engagement but also opens up innovative monetization pathways for designers and brands, further accelerating market growth.




    Another significant driver of the Fashion Token Index market is the rise of virtual goods and digital fashion. The burgeoning popularity of the metaverse and online gaming platforms has created a thriving market for digital apparel and accessories, which can be bought, sold, and traded using fashion tokens. Non-fungible tokens (NFTs) are at the forefront of this movement, allowing consumers to own verifiable, scarce digital fashion items. As virtual environments become increasingly sophisticated, brands are investing in NFT collaborations, digital runway events, and avatar customization, thereby expanding the utility and appeal of fashion tokens. The seamless integration of payment and loyalty tokens into these ecosystems further incentivizes consumer participation and fosters brand loyalty.




    Furthermore, the Fashion Token Index market is benefiting from the growing emphasis on sustainability and transparency within the fashion industry. Blockchain-powered tokens facilitate traceability, enabling consumers to verify the provenance and ethical credentials of their purchases. Security tokens are being leveraged to fractionalize ownership of high-value fashion assets, democratizing investment opportunities and fostering greater inclusivity. Additionally, the adoption of tokenized loyalty programs is streamlining customer rewards and enhancing the overall shopping experience. As regulatory frameworks around digital assets mature, institutional adoption is expected to rise, paving the way for sustained market expansion.




    Regionally, North America and Europe are leading the Fashion Token Index market, driven by advanced digital infrastructure, high consumer awareness, and a vibrant ecosystem of fashion-tech startups. The Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, a burgeoning middle class, and widespread adoption of mobile payment solutions. Latin America and the Middle East & Africa are also witnessing increasing interest, with local brands experimenting with tokenization to differentiate their offerings and tap into global audiences. While regional dynamics vary, the overarching trend is clear: the fusion of blockchain technology and fashion is transforming industry paradigms, creating new value propositions for stakeholders across the value chain.





    Token Type Analysis


    &

  9. CEC Cropland Index Model (Classified)

    • data.cnra.ca.gov
    • data.ca.gov
    • +5more
    Updated Mar 14, 2023
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    California Energy Commission (2023). CEC Cropland Index Model (Classified) [Dataset]. https://data.cnra.ca.gov/dataset/cec-cropland-index-model-classified
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    xlsx, kml, geojson, html, zip, gdb, csv, arcgis geoservices rest api, txt, gpkgAvailable download formats
    Dataset updated
    Mar 14, 2023
    Dataset authored and provided by
    California Energy Commissionhttp://www.energy.ca.gov/
    License

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

    Description

    For lands used to produce crops, CEC developed a suitability model to simultaneously evaluate several factors that impact an area’s relative implication for croplands. In the CEC land use screens, implication is defined as a possible significance or a likely consequence of an action. For example, planning for energy infrastructure development in areas with more factors that support high-value croplands has implications for opportunities to preserve agricultural land. The variables used in the CEC Cropland Index Model contain information on soil quality (CA Revised Storie Index, Electrical Conductivity, and Sodium Adsorption Ratio), farmland designations (Prime Farmland, Unique Farmland and Farmland of Statewide Importance), and current existence of crops (as indicated by the California Statewide Crop Mapping). The CEC Cropland Index Model does not include statewide information for grazing lands or rangelands, and it is only applied to solar technology.

    Each input data layer is transformed onto a common scale and weighted according to each dataset’s relative importance. The result is a summation of the input data layers into a single-gridded map. This final model output provides a numerically weighted index of importance for croplands at a given location. The classified version of the model output, given in this dataset, partitions the CEC Cropland Index Model at the mean into areas of high and low implication. The high implication area is used as an exclusion in the CEC Land Use Screens for solar technology. These regions have a relatively higher implication for cropland than the lower implication region.

    The table below provides data sources that the CEC Cropland Index Model relies on. For a complete description of the model and its use in the 2023 CEC Land-Use Screens, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

    Dataset Name

    Source

    Usage

    Gridded Soil Survey Geographic (gSSURGO) Database

    Soil Survey Staff. 2020. "The Gridded Soil Survey Geographic (gSSURGO) Database for California." United States Department of Agriculture, Natural Resources Conservation Service. https://gdg.sc.egov.usda.gov/

    Provides CA Revised Storie Index, Electrical Conductivity, and Sodium Adsorption Ratio for the CEC Cropland Index Model for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential

    California Important Farmland

    2022. "2018 California Important Farmland.” Farmland Mapping and Monitoring Program." California Department of Conservation. https://www.conservation.ca.gov/dlrp/fmmp

    Prime Farmland, Unique Farmland, and Farmland of Statewide Importance is used in the CEC Cropland Index Model for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential

    California Statewide Crop Mapping (2019)

    2022. "'https://data.cnra.ca.gov/dataset/statewide-crop-mapping' rel='nofollow ugc'>2019 California Statewide Crop Mapping." California Department of Water Resources. https://data.cnra.ca.gov/dataset/statewide-crop-mapping

    The footprint is used as part of the mask for the CEC Cropland Index Model’s domain of analysis for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential

  10. Leading fashion brands worldwide 2021, by Social Index score

    • statista.com
    Updated Jul 9, 2025
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    Statista (2025). Leading fashion brands worldwide 2021, by Social Index score [Dataset]. https://www.statista.com/statistics/1322440/fashion-brands-social-index-score/
    Explore at:
    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2021
    Area covered
    Worldwide
    Description

    In 2021, sports brand Nike gained the highest score among fashion brands in the Social Index. Nike's high score indicates a high volume of brand-related conversations and a high ratio of positive mentions in these conversations. It was followed by Gucci and Adidas which both scored over ** thousand points.

  11. f

    Index list to analyze size, style, and sector effects.

    • figshare.com
    xls
    Updated Apr 17, 2024
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    Chulyoung Cho; Jinseok Yang; Beakcheol Jang (2024). Index list to analyze size, style, and sector effects. [Dataset]. http://doi.org/10.1371/journal.pone.0300393.t001
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    xlsAvailable download formats
    Dataset updated
    Apr 17, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Chulyoung Cho; Jinseok Yang; Beakcheol Jang
    License

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

    Description

    Index list to analyze size, style, and sector effects.

  12. South Africa Index: FTSE/JSE: All Share Style Growth

    • ceicdata.com
    Updated Jan 15, 2025
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    CEICdata.com (2025). South Africa Index: FTSE/JSE: All Share Style Growth [Dataset]. https://www.ceicdata.com/en/south-africa/johannesburg-stock-exchange-index/index-ftsejse-all-share-style-growth
    Explore at:
    Dataset updated
    Jan 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
    Jul 1, 2017 - Jun 1, 2018
    Area covered
    South Africa
    Variables measured
    Securities Exchange Index
    Description

    South Africa Index: FTSE/JSE: All Share Style Growth data was reported at 591.118 02Jan2002=100 in Nov 2018. This records an increase from the previous number of 590.045 02Jan2002=100 for Oct 2018. South Africa Index: FTSE/JSE: All Share Style Growth data is updated monthly, averaging 325.790 02Jan2002=100 from Oct 2004 (Median) to Nov 2018, with 170 observations. The data reached an all-time high of 711.997 02Jan2002=100 in Nov 2017 and a record low of 119.420 02Jan2002=100 in Oct 2004. South Africa Index: FTSE/JSE: All Share Style Growth data remains active status in CEIC and is reported by Johannesburg Stock Exchange. The data is categorized under Global Database’s South Africa – Table ZA.Z001: Johannesburg Stock Exchange: Index.

  13. D

    Topo Map Index

    • data.nsw.gov.au
    • researchdata.edu.au
    arcgis rest service
    Updated May 29, 2025
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    Spatial Services (DCS) (2025). Topo Map Index [Dataset]. https://data.nsw.gov.au/data/dataset/1-585654eb02d449cfbc46ed801303b9cf
    Explore at:
    arcgis rest serviceAvailable download formats
    Dataset updated
    May 29, 2025
    Dataset provided by
    Spatial Services (DCS)
    Description

    Export DataAccess API

    Includes the URLs for all collar on and collar off products within the Topo Index map sheets.

    This is the most updated version for GeoPDF Topo Index. If there is any further query, please contact Spatial Services Customer Hub (https://customerhub.spatial.nsw.gov.au/servicedesk/customer/portals).

    Metadata

    Content TitleTopo Map Index
    Content TypeHosted Feature Layer
    DescriptionIncludes the URLs for all collar on and collar off products within the Topo Index Map sheets
    Initial Publication Date13/12/2023
    Data Currency13/12/2023
    Data Update FrequencyYearly
    Content SourceAPI
    File TypeESRI Shapefile (*.shp)
    Attribution
    Data Theme, Classification or Relationship to other Datasets
    Accuracy
    Spatial Reference System (dataset)WGS84
    Spatial Reference System (web service)EPSG:4326
    WGS84 Equivalent ToGDA94
    Spatial Extent
    Content Lineage
    Data ClassificationUnclassified
    Data Access PolicyOpen
    Data Quality
    Terms and ConditionsCreative Commons
    Standard and Specification
    Data CustodianDCS Spatial Services
    346 Panorama Ave
    Bathurst NSW 2795
    Point of ContactPlease contact us via the Spatial Services Customer Hub
    Data Aggregator
    Data Distributor
    Additional Supporting Information
    TRIM Number

  14. Hong Kong SAR, China Composite CPI: Weights: AT: Foreign-Style Winess

    • ceicdata.com
    Updated Jan 15, 2025
    + more versions
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    CEICdata.com (2025). Hong Kong SAR, China Composite CPI: Weights: AT: Foreign-Style Winess [Dataset]. https://www.ceicdata.com/en/hong-kong/composite-consumer-price-index-1009910100-weights/composite-cpi-weights-at-foreignstyle-winess
    Explore at:
    Dataset updated
    Jan 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
    Jan 1, 2016 - Dec 1, 2016
    Area covered
    Hong Kong
    Variables measured
    Consumer Prices
    Description

    Hong Kong Composite Consumer Price Index (CPI): Weights: AT: Foreign-Style Winess data was reported at 0.100 % in Dec 2016. This stayed constant from the previous number of 0.100 % for Nov 2016. Hong Kong Composite Consumer Price Index (CPI): Weights: AT: Foreign-Style Winess data is updated monthly, averaging 0.100 % from Oct 2004 (Median) to Dec 2016, with 147 observations. The data reached an all-time high of 0.100 % in Dec 2016 and a record low of 0.100 % in Dec 2016. Hong Kong Composite Consumer Price Index (CPI): Weights: AT: Foreign-Style Winess data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR – Table HK.I006: Composite Consumer Price Index: 10/09-9/10=100: Weights.

  15. ifo index in fashion & shoe trade Germany 2023-2024

    • ai-chatbox.pro
    • statista.com
    Updated Sep 19, 2024
    + more versions
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    Statista Research Department (2024). ifo index in fashion & shoe trade Germany 2023-2024 [Dataset]. https://www.ai-chatbox.pro/?_=%2Ftopics%2F5868%2Ftextile-and-clothing-retail-in-germany%2F%23XgboD02vawLbpWJjSPEePEUG%2FVFd%2Bik%3D
    Explore at:
    Dataset updated
    Sep 19, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Germany
    Description

    In June 2024, the ifo business climate index for the German fashion trade showed -38.4 points. The shoe trade indicator was -44.9 points. What is the ifo Business Climate? The ifo Business Climate is an indicator for economic activity in Germany. It is based on approximately 9,000 monthly responses from businesses in manufacturing, the service sector, trade, and construction. Companies are asked to give their assessments of the current business situation and their expectations for the next six months. They can describe their situation as “good,” “satisfactory,” or “poor” and their business expectations for the next six months as “more favorable,” “unchanged,” or “less favorable.” The balance value of the current business situation is the difference in the percentage shares of the responses “good” and “poor”; the balance value of expectations is the difference in the percentage shares of the responses “more favorable” and “less favorable.” The business climate is a transformed mean of the balances of the business situation and the expectations. To calculate the index values, the transformed balances are all normalized to the average for the year 2015.

  16. w

    Global Commodity Index Funds Market Research Report: By Investment Objective...

    • wiseguyreports.com
    Updated Jul 19, 2024
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    wWiseguy Research Consultants Pvt Ltd (2024). Global Commodity Index Funds Market Research Report: By Investment Objective (Diversification, Inflation Hedging, Performance Enhancement), By Asset Class (Broad Commodity Index Funds, Sector-Specific Commodity Index Funds, Single Commodity Index Funds), By Index Provider (S&P GSCI, Bloomberg Commodity Index (BCI), Thomson Reuters/CoreCommodity CRB Index), By Investment Style (Active Commodity Index Funds, Passive Commodity Index Funds), By Investor Profile (Institutional Investors, Accredited Investors, Retail Investors) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2032. [Dataset]. https://www.wiseguyreports.com/reports/commodity-index-funds-market
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    Dataset updated
    Jul 19, 2024
    Dataset authored and provided by
    wWiseguy Research Consultants Pvt Ltd
    License

    https://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy

    Time period covered
    Jan 7, 2024
    Area covered
    Global
    Description
    BASE YEAR2024
    HISTORICAL DATA2019 - 2024
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    MARKET SIZE 2023377.63(USD Billion)
    MARKET SIZE 2024401.23(USD Billion)
    MARKET SIZE 2032651.97(USD Billion)
    SEGMENTS COVEREDInvestment Objective ,Asset Class ,Index Provider ,Investment Style ,Investor Profile ,Regional
    COUNTRIES COVEREDNorth America, Europe, APAC, South America, MEA
    KEY MARKET DYNAMICSIncreased demand for alternative investments Growing popularity of passive investing Rise in commodity prices Geopolitical uncertainty Technological advancements
    MARKET FORECAST UNITSUSD Billion
    KEY COMPANIES PROFILEDiShares MSCI Commodity Swap Index Fund ,Rogers International Commodity Index ,S&P GSCI ,MSCI Commodity Index ,UBS Bloomberg Constant Maturity Commodity Index ,PowerShares DB Commodity Tracking Fund ,Bloomberg Commodity Index ,DB Commodity Index ,Solactive Commodity Index ,Thomson Reuters/CoreCommodity CRB Index ,Invesco DB Commodity Index Tracking Fund ,CRB Commodity Index ,Dow Jones Commodity Index ,ETFS Physical Swiss Gold Shares ,WisdomTree Enhanced Commodity Tracking Fund
    MARKET FORECAST PERIOD2024 - 2032
    KEY MARKET OPPORTUNITIESGrowing demand for diversification Increased investor interest in commodities Technological advancements
    COMPOUND ANNUAL GROWTH RATE (CAGR) 6.25% (2024 - 2032)
  17. China CN: YiWu Small Commodity Price Index: Apparel & Accessory: Fashion

    • ceicdata.com
    + more versions
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    CEICdata.com, China CN: YiWu Small Commodity Price Index: Apparel & Accessory: Fashion [Dataset]. https://www.ceicdata.com/en/china/yiwu-small-commodity-price-index-weekly/cn-yiwu-small-commodity-price-index-apparel--accessory-fashion
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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 6, 2024 - Jul 22, 2024
    Area covered
    China
    Variables measured
    Economic Outlook Survey
    Description

    China YiWu Small Commodity Price Index: Apparel & Accessory: Fashion data was reported at 99.870 Jul2006=100 in 22 Jul 2024. This records a decrease from the previous number of 99.880 Jul2006=100 for 15 Jul 2024. China YiWu Small Commodity Price Index: Apparel & Accessory: Fashion data is updated daily, averaging 100.420 Jul2006=100 from Sep 2006 (Median) to 22 Jul 2024, with 869 observations. The data reached an all-time high of 112.800 Jul2006=100 in 19 Nov 2007 and a record low of 88.580 Jul2006=100 in 03 Nov 2008. China YiWu Small Commodity Price Index: Apparel & Accessory: Fashion data remains active status in CEIC and is reported by Yiwu City Government. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OQ: YiWu Small Commodity Price Index: Weekly.

  18. A

    CDC Social Vulnerability Index 2018 - USA

    • data.amerigeoss.org
    • anrgeodata.vermont.gov
    • +2more
    esri rest, html
    Updated Mar 18, 2020
    + more versions
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    ESRI (2020). CDC Social Vulnerability Index 2018 - USA [Dataset]. https://data.amerigeoss.org/pl/dataset/cdc-social-vulnerability-index-2018-usa
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    html, esri restAvailable download formats
    Dataset updated
    Mar 18, 2020
    Dataset provided by
    ESRI
    Area covered
    United States
    Description
    • This feature layer visualizes the 2018 overall SVI for U.S. counties and tracts
    • Social Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. county and tract

    • 15 social factors grouped into four major themes

    • Index value calculated for each county for the 15 social factors, four major themes, and the overall rank
    What is CDC Social Vulnerability Index?
    ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.

    The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:
    • Socioeconomic
    • Housing Composition and Disability
    • Minority Status and Language
    • Housing and Transportation
    Variables
    For a detailed description of variable uses, please refer to the full SVI 2018 documentation.

    Rankings
    We ranked counties and tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of counties in multiple states, or across the U.S. as a whole. Rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each county and tract, we generated its percentile rank among all counties and tracts for 1) the fifteen individual variables, 2) the four themes, and 3) its overall position.

    Overall Rankings:
    We totaled the sums for each theme, ordered the counties, and then calculated overall percentile rankings. Please note: taking the sum of the sums for each theme is the same as summing individual variable rankings.

    The overall tract summary ranking variable is RPL_THEMES.

    Theme rankings:
    For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are:
    • Socioeconomic theme - RPL_THEME1
    • Housing Composition and Disability - RPL_THEME2
    • Minority Status & Language - RPL_THEME3
    • Housing & Transportation - RPL_THEME4

    Flags
    Counties in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Counties below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each county as the total number of all variable flags.

  19. C

    Air quality - ATMO index from 2021

    • ckan.mobidatalab.eu
    Updated Aug 22, 2023
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    Direction de la Transition Écologique et du Climat - Ville de Paris (2023). Air quality - ATMO index from 2021 [Dataset]. https://ckan.mobidatalab.eu/dataset/quality-of-lair-index-atmo-from-2021
    Explore at:
    https://www.iana.org/assignments/media-types/text/csv, https://www.iana.org/assignments/media-types/application/jsonAvailable download formats
    Dataset updated
    Aug 22, 2023
    Dataset provided by
    Direction de la Transition Écologique et du Climat - Ville de Paris
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Time period covered
    Dec 30, 2021
    Description

    Dataset allows to determine the number of days when the air quality is considered to be "good" to "extremely bad", based on the new ATMO index as of January 1, 2021.

    The ATMO index was revised and adopted by the Ministry of Ecological Transition on January 1, 2021.

    The ATMO index incorporates the main regulated air pollutants, tracers of transport, urban and industrial activities PM10, PM2.5, nitrogen dioxide, ozone, sulfur dioxide.< o:p>

    It comes in six qualifiers defined according to different classes for these five pollutants: "good", "medium", "degraded", "bad", "very bad", "extremely bad".


    For each pollutant, a sub-index is calculated. Each sub-index is determined each day from the maximum levels of the pollutant considered. This is the maximum sub-index that constitutes the final ATMO index characterizing the overall air quality for the day in question.


    https://www.airparif.asso.fr/sites/default/files/pages/Grille-des-sous -indices.jpg" style="width: 532.889px;"/>

    < /p>

    The data provider is Airparif >> Index History | Airparif

    Link Paris.fr >> Air and sound environment - City of Paris; Air quality status in Paris - City of Paris

  20. Peru Equity Market Index

    • dr.ceicdata.com
    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). Peru Equity Market Index [Dataset]. https://www.dr.ceicdata.com/en/indicator/peru/equity-market-index
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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
    Mar 1, 2024 - Feb 1, 2025
    Area covered
    Peru
    Variables measured
    Securities Exchange Index
    Description

    Key information about Peru General

    • Peru General closed at 28,546.8 points in Feb 2025, compared with 28,984.3 points at the previous month end
    • Peru Equity Market Index: Month End: Lima Stock Exchange: S&P/BVL: General data is updated monthly, available from Jan 1992 to Feb 2025, with an average number of 13,086.4 points
    • The data reached an all-time high of 30,470.5 points in Oct 2024 and a record low of 108.5 points in Jan 1992

    Lima Stock Exchange provides daily data on several major stock market indices, but the IGBVL index is the one most closely monitored by analysts. Effective May 4th, 2015, the IGBVL became a modified market capitalization weighted index - based on float-adjusted market capitalization and liquidity criteria

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Félix Cuneo; Félix Cuneo (2020). Cognitive Style Index dataset [Dataset]. http://doi.org/10.5281/zenodo.1438755
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Cognitive Style Index dataset

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csvAvailable download formats
Dataset updated
Jan 24, 2020
Dataset provided by
Zenodohttp://zenodo.org/
Authors
Félix Cuneo; Félix Cuneo
License

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

Description

Items are in order, and not yet reversed.

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