1) Data Introduction • The AI Global Index Dataset is a comprehensive index that benchmarks 62 countries based on the level of AI investment, innovation, and implementation, including seven key indicators (human resources, infrastructure, operational environment, research, development, government strategy, commercialization) and general information by country (region, cluster, income group, political system).

2) Data Utilization (1) AI Global Index Dataset has characteristics that: • This dataset consists of a total of 13 columns with 5 categorical variables (regions, clusters, etc.) and 8 numerical variables (scores for each indicator), covering 62 countries. • The seven key indicators are classified into three pillars: △ implementation (human resources/infrastructure/operational environment) △ innovation (R&D) △ investment (government strategy/commercialization), and assess each country's overall AI ecosystem capabilities in multiple dimensions. (2) AI Global Index Dataset can be used to: • Global AI leadership pattern analysis: Correlation analysis between seven indicators can identify AI strengths and weaknesses by country and perform group comparisons by region and income level. • Machine learning-based predictive model: It can be used for data science education and application, such as country-specific index prediction through regression analysis or classification of AI development types through clustering.

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Index: Standard & Poors: 500 data was reported at 5,569.060 1941-1943=10 in Apr 2025. This records a decrease from the previous number of 5,611.850 1941-1943=10 for Mar 2025. Index: Standard & Poors: 500 data is updated monthly, averaging 1,038.565 1941-1943=10 from Jan 1976 (Median) to Apr 2025, with 592 observations. The data reached an all-time high of 6,040.530 1941-1943=10 in Jan 2025 and a record low of 87.040 1941-1943=10 in Feb 1978. Index: Standard & Poors: 500 data remains active status in CEIC and is reported by S&P Global. The data is categorized under Global Database’s United States – Table US.Z002: S&P Global: US Index.

The Global Cybersecurity Index (GCI) is a trusted reference that measures the commitment of countries to cybersecurity at a global level – to raise awareness of the importance and different dimensions of the issue. As cybersecurity has a broad field of application, cutting across many industries and various sectors, each country's level of development or engagement is assessed along five pillars – (i) Legal Measures, (ii) Technical Measures, (iii) Organizational Measures, (iv) Capacity Development, and (v) Cooperation – and then aggregated into an overall score.​

The Cline Center Global News Index is a searchable database of textual features extracted from millions of news stories, specifically designed to provide comprehensive coverage of events around the world. In addition to searching documents for keywords, users can query metadata and features such as named entities extracted using Natural Language Processing (NLP) methods and variables that measure sentiment and emotional valence. Archer is a web application purpose-built by the Cline Center to enable researchers to access data from the Global News Index. Archer provides a user-friendly interface for querying the Global News Index (with the back-end indexing still handled by Solr). By default, queries are built using icons and drop-down menus. More technically-savvy users can use Lucene/Solr query syntax via a ‘raw query’ option. Archer allows users to save and iterate on their queries, and to visualize faceted query results, which can be helpful for users as they refine their queries. Additional Resources: - Access to Archer and the Global News Index is limited to account-holders. If you are interested in signing up for an account, please fill out the Archer Access Request Form so we can determine if you are eligible for access or not. - Current users who would like to provide feedback, such as reporting a bug or requesting a feature, can fill out the Archer User Feedback Form. - The Cline Center sends out periodic email newsletters to the Archer Users Group. Please fill out this form to subscribe to it. Citation Guidelines: 1) To cite the GNI codebook (or any other documentation associated with the Global News Index and Archer) please use the following citation: Cline Center for Advanced Social Research. 2023. Global News Index and Extracted Features Repository [codebook], v1.2.0. Champaign, IL: University of Illinois. June. XX. doi:10.13012/B2IDB-5649852_V5 2) To cite data from the Global News Index (accessed via Archer or otherwise) please use the following citation (filling in the correct date of access): Cline Center for Advanced Social Research. 2023. Global News Index and Extracted Features Repository [database], v1.2.0. Champaign, IL: University of Illinois. Jun. XX. Accessed Month, DD, YYYY. doi:10.13012/B2IDB-5649852_V5 *NOTE: V4 is suppressed and V5 is replacing V4 with updated ‘Archer’ documents.

The Global Gridded Relative Deprivation Index (GRDI), Version 1 (GRDIv1) data set characterizes the relative levels of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest. GRDIv1 is built from sociodemographic and satellite data inputs that were spatially harmonized, indexed, and weighted into six main components to produce the final index raster. Inputs were selected from the best-available data that either continuously vary across space or have at least administrative level 1 (provincial/state) resolution, and which have global spatial coverage.

Indeks Daya Saing Global Indonesia dilaporkan sebesar 64.629 Score pada 2019. Rekor ini turun dibanding sebelumnya yaitu 64.935 Score untuk 2018. Data Indeks Daya Saing Global Indonesia diperbarui tahunan, dengan rata-rata 64.629 Score dari 2017 sampai 2019, dengan 3 observasi. Data ini mencapai angka tertinggi sebesar 64.935 Score pada 2018 dan rekor terendah sebesar 63.488 Score pada 2017. Data Indeks Daya Saing Global Indonesia tetap berstatus aktif di CEIC dan dilaporkan oleh World Economic Forum. Data dikategorikan dalam Global Competitiveness Index (GCI) World Trend Plus – Table GCI 4.0: Overall Index: Individual Countries.

Cbonds collects and normalizes indices data, offering daily updated and historical data on over 40,000 indices, including macroeconomic indicators, yield curves and spreads, currency markets, stock and funds markets, and commodities. Using the Indices API, you can access an index's holdings, such as its assets, sectors, and weight, as well as basic data on the asset. You can obtain end-of-day, and historical API indicator prices in CSV, XLS, and JSON formats. Cbonds provides a free Indices API for a limited test period of two weeks or for a longer period with a limited number of instruments.

United States Import Value Index data was reported at 126.779 2015=100 in 2021. This records an increase from the previous number of 103.958 2015=100 for 2020. United States Import Value Index data is updated yearly, averaging 51.384 2015=100 from Dec 1980 (Median) to 2021, with 42 observations. The data reached an all-time high of 126.779 2015=100 in 2021 and a record low of 11.319 2015=100 in 1982. United States Import Value Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Trade Index. Import value indexes are the current value of imports (c.i.f.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's import value indexes are reported for most economies.;United Nations Conference on Trade and Development;;

For the first time, the full results from the Global Green Economy Index (GGEI) are available in the public domain. Historically, only the aggregate results have been publicly accessible. The full dataset has been paywalled and accessible to our subscribers only. But the way in which we release GGEI data to the public is changing. Read on for a quick explanation for how and why.

First, the how. The GGEI file publicly accessible today represents that dataset officially compiled in 2022. It contains the full results for each of the 18 indicators in the GGEI for 160 countries, across the four main dimensions of climate change & social equity, sector decarbonization, markets & ESG investment and the environment. Some (not all) of these data points have since been updated, as new datasets have been published. The GGEI is a dynamic model, updating in real-time as new data becomes available. Our subscribing clients will still receive this most timely version of the model, along with any customizations they may request.

Now, the why. First and foremost, there is huge demand among academic researchers globally for the full GGEI dataset. Academic inquiry around the green transition, sustainable development, ESG investing, and green energy systems has exploded over the past several years. We receive hundreds of inquiries annually from these students and researchers to access the full GGEI dataset. Making it publicly accessible as we are today makes it easier for these individuals and institutions to use these GGEI to promote learning and green progress within their institutions.

More broadly, the landscape for data has changed significantly. A decade ago when the GGEI was first published, datasets existed more in silos and users might subscribe to one specific dataset like the GGEI to answer a specific question. But today, data usage in the sustainability space has become much more of a system, whereby myriad data sources are synthesized into increasingly sophisticated models, often fueled by artificial intelligence. Making the GGEI more accessible will accelerate how this perspective on the global green economy can be integrated to these systems.

India's Global Innovation Index data - score and ranking across all sub-indexes, in addition to comparison with global peers.

The total annual freezing and thawing indices are defined as thecumulative number of degree-days when air temperatures are below andabove zero degrees Celsius. The total annual freezing index has beenwidely used to predict permafrost distribution; estimate themaximum thickness of sea, lake, and river ice, and the maximum depth ofground-frost penetration; and classify snow types. The annual totalthawing index has been used to predict permafrost distribution and toestimate the maximum depth of thaw in frozen ground. Both totalfreezing and thawing indices are important parameters for engineeringdesign in cold regions.Data coverage is global. The data set contains the total annual freezing and thawing indices with a spatial resolution of 0.5 degreeslatitude by 0.5 degrees longitude. Two data files are available, forthe freeze and thaw indices respectively, in flat binary format. Eachfile is approximately 1 MB in size. The total annual freezing andthawing indexes were calculated based upon the monthly mean airtemperature by Legates and Willmott (1990).

This dataset provides values for GLOBAL COMPETITIVENESS INDEX GCI WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Explore LSEG S&P Global Purchasing Managers Index (PMI) for monthly surveys that provide up-to-date, accurate, and unique indicators of economic trends.

Global Aridity Index and Potential Evapotranspiration Database: CMIP_6 Future Projections(Future_Global_AI_PET)Robert J. Zomer 1, 2, 3, Antonio Trabucco1,41. Euro-Mediterranean Center on Climate Change, IAFES Division, Sassari, Italy. 2. Centre for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Science, Kunming, Yunnan, China3. CIFOR-ICRAF China Program, World Agroforestry (ICRAF), Kunming, Yunnan. China4. National Biodiversity Future Center (NBFC), Palermo, ItalyThe Global Aridity Index and Potential Evapotranspiration (Global AI-PET) Database: CMIP_6 Future Projections – Version 1 (Future_Global_AI_PET) provides a high-resolution (30 arc-seconds) global raster dataset of average monthly and annual potential evapotransipation (PET) and aridity index (AI) for two historical (1960-1990; 1970-2000) and two future (2021-2040; 2041-2060) time periods for each of 22 CMIP6 Earth System Models across four emission scenarios (SSP: 126, 245, 370, 585). The database also includes three averaged multi-model ensembles produced for each of the four emission scenarios:· All Models: includes all of the 22 ESM, as available within a particular SSP.· High Risk: includes 5 ESM identified as projecting the highest increases in temperature and precipitation and lying outside and significantly higher than the majority of estimates.· Majority Consensus: includes 15 ESM, that is, all available ESM excluding the ESM in the “High Risk” category, and those missing data across all of the 4 SSP. Further herein referred to as the “Consensus” category.These geo-spatial datasets have been produced with the support of Euro-Mediterranean Center on Climate Change, IAFES Division; Centre for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Science; CIFOR-ICRAF China Program, World Agroforestry (CIFOR-ICRAF) and the National Biodiversity Future Center (NBFC).These datasets are provided under a CC_BY 4.0 License (please attribute), in standard GeoTiff format, WGS84 Geographic Coordinate System, 30 arc seconds or ~ 1km at the equator, to support studies contributing to sustainable development, biodiversity and environmental conservation, poverty alleviation, and adaption to climate change, among other global, regional, national, and local concerns.The Future_Global_AI_PET is available online from the Science Data Bank (ScienceDB) at: https://doi.org/10.57760/sciencedb.nbsdc.00086Previous versions of the Global Aridity Index and PET Database are available online here:https://figshare.com/articles/dataset/Global_Aridity_Index_and_Potential_Evapotranspiration_ET0_Climate_Database_v2/7504448/6Technical questions regarding the datasets can be directed to Robert Zomer: r.zomer@mac.com or Antonio Trabucco: antonio.trabucco@cmcc.it Methods:Based on the results of comparative validations, the Hargreaves model has been evaluated as one of the best fit to model PET and Aridity index globally with the available high resolution downscaled and bias corrected climate projections and chosen for the implementation of the Global-AI_PET- CMIP6 Future Projections. This method performs almost as well as the Penman-Monteith method, but requires less parameterization, and has significantly lower sensitivity to error in climatic inputs (Hargreaves and Allen, 2003). The currently available downscaled CMIP6 projections (available from WorldClim) do provide fewer climate variables idoneous for implementation of temperature-based evapotranspiration methods, such as the Hargreaves method. Hargreaves (1985, 1994) uses mean monthly temperature (Tmean), mean monthly temperature range (TD) and extraterrestrial radiation (RA, radiation on top of the atmosphere) to calculate ET0, as shown below: PET = 0.023 * RA * (Tmean + 17.8) * TD0.5where RA is extraterrestrial radiation at the top of the atmosphere, TD is the difference between mean maximum temperatures and mean minimum temperatures (Tmax - Tmin), and Tmean is equal to Tmax + Tmin divided by 2. The Hargreaves equation has been implemented globally on a per grid cell basis at 30 arc seconds resolution (~ 1km2 at the equator), in ArcGIS (v11.1) using Python v3.2 (see code availability section) to estimate PET/AI globally using future projections provided by the CMIP6 collaboration. The data to parametrize the equation were obtained from the Worldclim (worldclim.org) online data repository, which provides bias-corrected downscaled monthly values of minimum temperature, maximum temperature, and precipitation for 25 CMIP6 Earth System Models (ESMs), across four Shared Socio-economic Pathways (SSPs): 126, 245, 370 and 585. PET/AI was estimated for two historical periods, WorldClim 1.4 (1960-1990) and WorldClim 2.1 (1970-2000), representing on average a decades change, by applying the Hargreaves methodology described above. Similarly, PET/AI was estimated for two future time periods, namely 2021-2040 and 2041-2060, for each of the 25 models across their respective four SSP scenarios (126, 245, 370,585). Aridity Index Aridity is often expressed as an Aridity Index, comprised of the ratio of precipitation over PET, and signifying the amount of precipitation available in relation to atmospheric water demand and quantifying the water (from rainfall) availability for plant growth after ET demand has been met, comparing incoming moisture totals with potential outgoing moisture. The AI for the averaged time periods has been calculated on a per grid cell basis, as: AI = MA_Prec/MA_PETwhere: AI = Aridity Index MA_Prec = Mean Annual Precipitation MA_PET = Mean Annual Reference EvapotranspirationUsing the mean annual precipitation (MA_Prec) values obtained from the CMIP6 climate projections, while ET0 datasets estimated on a monthly average basis by the method described above were aggregated to mean annual values (MA_PET). Using this formulation, AI values are unitless, increasing with more humid condition and decreasing with more arid conditions.Multi-Model Averaged EnsemblesBased upon the distribution of the various scenarios along a gradient of their projected temperature and precipitation estimates for the each of the four SSP and two future time period, three multi-model ensembles, each articulated by their four respective SSPs, were identified. The three parameters of monthly minimum temperature, monthly maximum temperature and monthly precipitation for ESM’s included within each of these ensemble categories were averaged for each of their respective SSPs. These averaged parameters were then used to calculate the PET/AI as per the above methodology.Code Availablity:The algorithm and code in Python used to calculate PET and AI is available on Figshare at this link below:https://figshare.com/articles/software/Global_Future_PET_AI_Algorithm_Code_Python_-_Calculate_PET_AI/24978666DATA FORMATPET datasets are available as monthly averages (12 datasets, i.e. one dataset for each month, averaged over the specified time period) or as an annual average (1 dataset) for the specified time period. Aridity Index grid layers are available as one grid layer representing the annual average over the specified period. The following nomenclature is used to describe the dataset: Zipped Files - Directory Names refer to: Model_SSP_Time-PeriodFor example: ACCESS-CM2_126_2021-2040.zip Model: ACCESS-CM2 / SSP:126 / Time-Period: 2021-2040Prefix of Files (TIFFs) is either:pet_ for PET layers aridity_index for Aridity Index (no suffix)Suffix For PET Files is either:1, 2, ... 12 Month of the yearyr Yearly averagesd Standard DeviationExamples:pet_02.tif is the PET average for the month of February.pet_yr.tif is the PET annual average.’pet_sd.tif is the standard deviation of the annual PETaridity_index.tif is the annual aridity index. The PET values are defined as total mm of PET per month or per year. The Aridity Index values are unit-less.The geospatial dataset is in geographic coordinates; datum and spheroid are WGS84; spatial units are decimal degrees. The spatial resolution is 30 arc-seconds or 0.008333 degrees. Arc degrees and seconds are angular distances, and conversion to linear units (like km) varies with latitude, as below:The Future-PET and Future-Aridity Index data layers have been processed and finalized for distribution online as GEO-TIFFs. These datasets have been zipped (.zip) into monthly series or individual annual layers, by each combination of climate model/scenarios, and are available for online access. Data Storage HierarchyThe database is organized for storage into a hierarchy of directories (see ReadMe.doc):( Individual zipped files are generally about 1 GB or less) Associated Peer Reviewed Journal Article:Zomer RJ, Xu J, Spano D and Trabucco A. 2024. CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060. Open Research Europe 4:157 https://doi.org/10.12688/openreseurope.18110.1For further info, please refer to these earlier paper describing the database and methodology:Zomer, R.J.; Xu, J.; Trabucco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Scientific Data 9, 409.Zomer, R. J; Bossio, D. A.; Trabucco, A.; van Straaten, O.; Verchot, L.V. 2008. Climate Change Mitigation: A Spatial Analysis of Global Land Suitability for Clean Development Mechanism Afforestation and Reforestation. Agric. Ecosystems and Environment. 126:67-80.Trabucco, A.; Zomer, R. J.; Bossio, D. A.; van Straaten, O.; Verchot, L.V. 2008. Climate Change Mitigation through Afforestation / Reforestation: A global analysis of hydrologic

This dataset provides a global 0.25 degree x 0.25 degree gridded monthly mean leaf area index (LAI) climatology as averaged over the period from August 1981 to August 2015. The data were derived from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g version 2, a bi-weekly data product from 1981 to 2015 (GIMMS-LAI3g version 2). The LAI3g version 2 (raw) data were first regridded from 1/12 x 1/12 degree to 0.25 x 0.25 degree resolution, then processed to remove missing and unreasonable values, scaled to obtain LAI values, and the bi-weekly LAI values were averaged for every month. Finally, the monthly long-term mean LAI (1981-2015) was calculated.

Global Peace Index for India - overall score, ranking and values for performance indicators, and comparison with global peers.

This dataset provides values for GLOBAL COMPETITIVENESS INDEX GCI WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

NASDAQ: Index: Total Return: NASDAQ US Benchmark Consumer Staples Index data was reported at 1,470.920 NA in Apr 2025. This records a decrease from the previous number of 1,482.620 NA for Mar 2025. NASDAQ: Index: Total Return: NASDAQ US Benchmark Consumer Staples Index data is updated monthly, averaging 1,276.255 NA from Sep 2020 (Median) to Apr 2025, with 56 observations. The data reached an all-time high of 1,482.620 NA in Mar 2025 and a record low of 974.100 NA in Oct 2020. NASDAQ: Index: Total Return: NASDAQ US Benchmark Consumer Staples Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: NASDAQ: Total Return: Monthly.