This research presents a transfer learning approach for deep learning models to predict monthly average index of Standard and Poor's 500(S&P 500) and Taiwan Stock Exchange Capitalization Weighted Stock Index(TAIEX) and use it to simulate trading E-mini S&P 500 and Mini-TAIEX futures contracts for evaluation. It conducts three experiments to show that the approach can gain stable profits. The first experiment is to analyze the results of different types of data preprocessing and trading strategies and find a general one for the following experiments. Second, we compared the results between the original and transfer learning methods to prove that our techniques are able to get consistent earnings. Finally, we proposed some ensemble models and found that the ensemble methods were more effective and stable to make profits.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Alteryx: The Data Analytics Company That's Undervalued

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Costa Rica: PISA science scores: The latest value from 2022 is 410.987 index points, a decline from 415.622 index points in 2018. In comparison, the world average is 449.005 index points, based on data from 78 countries. Historically, the average for Costa Rica from 2012 to 2022 is 418.892 index points. The minimum value, 410.987 index points, was reached in 2022 while the maximum of 429.351 index points was recorded in 2012.

Jordan: PISA science scores: The latest value from 2022 is 374.527 index points, a decline from 429.252 index points in 2018. In comparison, the world average is 449.005 index points, based on data from 78 countries. Historically, the average for Jordan from 2006 to 2022 is 409.863 index points. The minimum value, 374.527 index points, was reached in 2022 while the maximum of 429.252 index points was recorded in 2018.

Meta stock price for past 10 years. Following technical indicators added.

1. Date: This column represents the date for which the data is recorded.
2. Open: The opening price of a stock on a particular trading day.
3. High: The highest price at which a stock traded during the trading day.
4. Low: The lowest price at which a stock traded during the trading day.
5. Close: The closing price of a stock on a particular trading day. This is the final price at which the stock is valued for the day.
6. Volume: The number of shares or contracts traded in a security or an entire market during a given period, usually one trading day.
7. RSI_7: 7-day Relative Strength Index. It's a momentum indicator measuring the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock.
8. RSI_14: 14-day Relative Strength Index. Similar to RSI_7 but calculated over 14 days.
9. CCI_7: 7-day Commodity Channel Index. It’s a technical indicator that measures the difference between the current price and the historical average price. When calculated over 7 days, it gives short-term trends.
10. CCI_14: 14-day Commodity Channel Index. Like CCI_7, but over 14 days for more medium-term trends.
11. SMA_50: 50-day Simple Moving Average. It averages the closing prices of a stock over the past 50 days.
12. EMA_50: 50-day Exponential Moving Average. Similar to SMA_50, but gives more weight to recent prices, making it more responsive to new information.
13. SMA_100: 100-day Simple Moving Average. It averages the closing prices over the past 100 days.
14. EMA_100: 100-day Exponential Moving Average. Like SMA_100, but more responsive to recent price changes.
15. MACD: Moving Average Convergence Divergence. This indicator shows the relationship between two moving averages of a stock’s price.
16. Bollinger: Bollinger Bands. A type of price envelope developed by John Bollinger.
17. TrueRange: Typically used in calculating the Average True Range (ATR), it is a measure of volatility that considers the range between the high, low, and previous close of a stock.
18. ATR_7: 7-day Average True Range. It measures market volatility by decomposing the entire range of a stock for that period.
19. ATR_14: 14-day Average True Range. Similar to ATR_7, but calculated over 14 days.

Target

Next_Day_Close: Represents the closing price of the stock for the next day. It is useful for predictive models trying to forecast future prices.

Browse LSEG's I/B/E/S Estimates, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivalled data and delivery mechanisms.

Value of this dataset:The High Resolution Model Intercomparison Project (HighResMIP) is one of the major projects for the Coupled Model Intercomparison Project 6 (CMIP6) (Haarsma et al., 2016). It, for the first time, adopts a multi-model approach to systematicly investigate the impacts of model horizonal resolution on historical and future climate variabilities and changing trends. Functioning as one of the required drivers for the HighResMIP, this mean seasonal cycle of leaf area index (LAI) product will be adopted by different modeling groups to quantify the sensitivity of land-atmosphere feedbacks to the high resolution LAI datasets. Moreover, through the comprehensive evaluation of LAI-related simulaitons against multi-source observations, the CMIP6 community will better understand the sources of their model uncertainties. Data Description: This mean seasonal cycle of LAI version 1 (LAI_V1_for_HighResMIP.nc) was reprocessed from the monthly AVHRR GIMMS LAI3g version 2 (1981/08 to 2015/09, Mao et al., 2013; Zhu et al., 2013). It is a gridded mean product (0.25 degree by 0.25 degree) specially designed for the abovementioned HighResMIP of CMIP6. Data Collection Methods: We downloaded the raw monthly LAI3g time series from http://pan.baidu.com/s/1kUXTQp5. We then made necessary processes including the remapping to produce multi-year LAI means. More detailed information can be found in the “Data Processing Steps” below. Data Processing Steps: All missing values (e.g., 25000) and unreasonable values higher than 7000 in the raw LAI3g data were first replaced by 0, and a scaling factor of 0.001 was adopted to obtain the actual LAI. The raw 1/12 degree by 1/12 degree LAI3g data were then regridded to 0.25 degree by 0.25 degree using the bilinear interpolation. Since the raw LAI3g data provide bi-weekly LAI, i.e., two LAI values for a certain grid every month, monthly mean LAI for the new product was calculated as the average of the two values for the remapped data. The 34-year or 35-year mean monthly LAI was then calculated for each 12-month based on the data availability. For example, the LAI climatology for January was calculated as the average of 1982 to 2015, but the LAI climatology for August was computed as the average of 1981 to 2015.Data Set Coverage:Temporal Coverage: Start Date: JanuaryTemporal Coverage: End Date: DecemberTemporal Resolution: MonthlySpatial Coverage: GlobeSpatial Resolution: 0.25 degree by 0.25 degreeParameters: time, lat, lon and LAIPI Contact Information: Title: R&D Staff ScientistFirst Name: JiafuLast Name: Mao Organization: Oak Ridge National LaboratoryE-mail: maoj@ornl.govCountry: USAPhone: 865-576-7815 Technical Contact Information:First Name: BinyanLast Name: YanOrganization: the University of Texas at AustinE-mail: byan@utexas.eduCountry: USAPhone: 512-994-9968

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Advanced Health Intelligence (AHI): Revolutionizing Healthcare through AI and Data Analytics?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

AYX Stock Forecast: The Next Big Thing in Data Science and Analytics

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset contains all data and code necessary to reproduce the analysis presented in the manuscript: Winzeler, H.E., Owens, P.R., Read Q.D.., Libohova, Z., Ashworth, A., Sauer, T. 2022. 2022. Topographic wetness index as a proxy for soil moisture in a hillslope catena: flow algorithms and map generalization. Land 11:2018. DOI: 10.3390/land11112018. There are several steps to this analysis. The relevant scripts for each are listed below. The first step is to use the raw digital elevation data (DEM) to produce different versions of the topographic wetness index (TWI) for the study region (Calculating TWI). Then, these TWI output files are processed, along with soil moisture (volumetric water content or VWC) time series data from a number of sensors located within the study region, to create analysis-ready data objects (Processing TWI and VWC). Next, models are fit relating TWI to soil moisture (Model fitting) and results are plotted (Visualizing main results). A number of additional analyses were also done (Additional analyses). Input data The DEM of the study region is archived in this dataset as SourceDem.zip. This contains the DEM of the study region (DEM1.sgrd) and associated auxiliary files all called DEM1.* with different extensions. In addition, the DEM is provided as a .tif file called USGS_one_meter_x39y400_AR_R6_WashingtonCO_2015.tif. The remaining data and code files are archived in the repository created with a GitHub release on 2022-10-11, twi-moisture-0.1.zip. The data are found in a subfolder called data.

2017_LoggerData_HEW.csv through 2021_HEW.csv: Soil moisture (VWC) logger data for each year 2017-2021 (5 files total). 2882174.csv: weather data from a nearby station. DryPeriods2017-2021.csv: starting and ending days for dry periods 2017-2021. LoggerLocations.csv: Geographic locations and metadata for each VWC logger. Logger_Locations_TWI_2017-2021.xlsx: 546 topographic wetness indexes calculated at each VWC logger location. note: This is intermediate input created in the first step of the pipeline.

Code pipeline To reproduce the analysis in the manuscript run these scripts in the following order. The scripts are all found in the root directory of the repository. See the manuscript for more details on the methods. Calculating TWI

TerrainAnalysis.R: Taking the DEM file as input, calculates 546 different topgraphic wetness indexes using a variety of different algorithms. Each algorithm is run multiple times with different input parameters, as described in more detail in the manuscript. After performing this step, it is necessary to use the SAGA-GIS GUI to extract the TWI values for each of the sensor locations. The output generated in this way is included in this repository as Logger_Locations_TWI_2017-2021.xlsx. Therefore it is not necessary to rerun this step of the analysis but the code is provided for completeness.

Processing TWI and VWC

read_process_data.R: Takes raw TWI and moisture data files and processes them into analysis-ready format, saving the results as CSV. qc_avg_moisture.R: Does additional quality control on the moisture data and averages it across different time periods.

Model fitting Models were fit regressing soil moisture (average VWC for a certain time period) against a TWI index, with and without soil depth as a covariate. In each case, for both the model without depth and the model with depth, prediction performance was calculated with and without spatially-blocked cross-validation. Where cross validation wasn't used, we simply used the predictions from the model fit to all the data.

fit_combos.R: Models were fit to each combination of soil moisture averaged over 57 months (all months from April 2017-December 2021) and 546 TWI indexes. In addition models were fit to soil moisture averaged over years, and to the grand mean across the full study period. fit_dryperiods.R: Models were fit to soil moisture averaged over previously identified dry periods within the study period (each 1 or 2 weeks in length), again for each of the 546 indexes. fit_summer.R: Models were fit to the soil moisture average for the months of June-September for each of the five years, again for each of the 546 indexes.

Visualizing main results Preliminary visualization of results was done in a series of RMarkdown notebooks. All the notebooks follow the same general format, plotting model performance (observed-predicted correlation) across different combinations of time period and characteristics of the TWI indexes being compared. The indexes are grouped by SWI versus TWI, DEM filter used, flow algorithm, and any other parameters that varied. The notebooks show the model performance metrics with and without the soil depth covariate, and with and without spatially-blocked cross-validation. Crossing those two factors, there are four values for model performance for each combination of time period and TWI index presented.

performance_plots_bymonth.Rmd: Using the results from the models fit to each month of data separately, prediction performance was averaged by month across the five years of data to show within-year trends. performance_plots_byyear.Rmd: Using the results from the models fit to each month of data separately, prediction performance was averaged by year to show trends across multiple years. performance_plots_dry_periods.Rmd: Prediction performance was presented for the models fit to the previously identified dry periods. performance_plots_summer.Rmd: Prediction performance was presented for the models fit to the June-September moisture averages.

Additional analyses Some additional analyses were done that may not be published in the final manuscript but which are included here for completeness.

2019dryperiod.Rmd: analysis, done separately for each day, of a specific dry period in 2019. alldryperiodsbyday.Rmd: analysis, done separately for each day, of the same dry periods discussed above. best_indices.R: after fitting models, this script was used to quickly identify some of the best-performing indexes for closer scrutiny. wateryearfigs.R: exploratory figures showing median and quantile interval of VWC for sensors in low and high TWI locations for each water year. Resources in this dataset:Resource Title: Digital elevation model of study region. File Name: SourceDEM.zipResource Description: .zip archive containing digital elevation model files for the study region. See dataset description for more details.Resource Title: twi-moisture-0.1: Archived git repository containing all other necessary data and code . File Name: twi-moisture-0.1.zipResource Description: .zip archive containing all data and code, other than the digital elevation model archived as a separate file. This file was generated by a GitHub release made on 2022-10-11 of the git repository hosted at https://github.com/qdread/twi-moisture (private repository). See dataset description and README file contained within this archive for more details.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

CRCT Stock: The Data Analytics Company That Could Soar in the Next 3 Months

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

El Salvador: PISA science scores: The latest value from 2022 is 373.141 index points, unavailable from index points in . In comparison, the world average is 449.005 index points, based on data from 78 countries. Historically, the average for El Salvador from 2022 to 2022 is 373.141 index points. The minimum value, 373.141 index points, was reached in 2022 while the maximum of 373.141 index points was recorded in 2022.

Guatemala: PISA science scores: The latest value from 2022 is 372.963 index points, unavailable from index points in . In comparison, the world average is 449.005 index points, based on data from 78 countries. Historically, the average for Guatemala from 2022 to 2022 is 372.963 index points. The minimum value, 372.963 index points, was reached in 2022 while the maximum of 372.963 index points was recorded in 2022.

Consumer price indexes. Also includes average prices paid for commodities, utilities and fuels, CPI for older Americans, chained CPI, department store inventory price indexes and CPI research series Data

This data set includes daily, population-weighted mean values of various heat metrics for every county in the contiguous United States from 2000-2020. The dataset methodology, usage notes, and additional citations are published in Scientific Data (see reference below for Spangler et al. [2022]). Minimum, maximum, and mean ambient temperature, dew-point temperature, humidex, heat index, net effective temperature, wet-bulb globe temperature, and Universal Thermal Climate Index are included. Note that Monroe County, Florida (FIPS: 12087) and Nantucket County, Massachusetts (FIPS 25019) are missing due to unavailability of ERA5-Land data for Key West, Florida and Nantucket, MA. To use these data, assign the data from the .Rds file to a new data frame in R using the readRDS() function. Please cite the use of this data set with the following reference. Note that additional citations for specific variables can be found in Table 2.

K.R. Spangler, S. Liang, and G.A. Wellenius. "Wet-Bulb Globe Temperature, Universal Thermal Climate Index, and Other Heat Metrics for US Counties, 2000-2020." Scientific Data (2022). doi: 10.1038/s41597-022-01405-3

This data set contains modified Copernicus Climate Change Service information (2022), as described and cited in the manuscript referenced above. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. This data set is provided “as is” with no warranty of any kind.

The data provided at this page includes the planetary wave index (PWI) of Irving and Simmonds (2015), the zonal wave three (ZW3) index of Raphael (2004) and a simple meridional index (MI) that is the average magnitude of the meridional wind over the interval 40-70S.All three were calculated using monthly timescale 500 hPa meridional wind (for PWI and MI) or 500 hPa geopotential (for ZW3) data from the ERA-Interim project:http://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=pl/The code referred to in the global history attribute of each file can be found at:https://github.com/DamienIrving/climate-analysisand the details of the software environment in which that code was run at:https://anaconda.org/DamienIrving/wisconsin/filesI've also been playing around with some basic visualisation and analysis of the data, which can be found at:https://github.com/DamienIrving/climate-analysis/blob/master/development/wisconsin.ipynbIrving D, Simmonds I (2015). A novel approach to diagnosing Southern Hemisphere planetary wave activity and its influence on regional climate variability. Journal of Climate. 28, 9041-9057. doi:10.1175/JCLI-D-15-0287.1Raphael M (2004). A zonal wave 3 index for the Southern Hemisphere. Geophysical Research Letters, 31, L23212. doi:10.1029/2004GL020365

Note: Version 3.1 supersedes all previous releases. Version 3.0 has been deprecated due to the discovery of a data inconsistency in the calculation of net longwave radiation in the source code used to generate the dataset. As a result, there is a general positive bias in potential evapotranspiration (ET0) and a consequent lower (drier) bias in the Aridity Index (AI) in affected outputs. This issue has been fully corrected in v3.1, and all ET0 and AI products have been recomputed using the corrected method. We are grateful for the numerous feedback from users and in particular to Dr. Pushpendra Raghav, Research Scientist, Department of Civil Engineering, University of Alabama, for identifying and bringing this issue to our attention. We recommend all users migrate to Version 3.1 and discontinue use of the previous v3.0.***********************************************************************************************************************************NOTE: The recently released Future Global Aridity Index and PET Database (CMIP_6) is now available at:https://doi.org/10.57760/sciencedb.nbsdc.00086High-resolution (30 arc-seconds) global raster datasets of average monthly and annual potential evapotranspiration (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 CIMP6 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:**************************************************************************************************************************The Global Aridity Index (Global-AI) and Global Reference Evapo-Transpiration (Global-ET0) datasets provided in Version 3.1 of the Global Aridity Index and Potential Evapo-Transpiration (ET0) Database (Global-AI_PET_v3.x1) provide high-resolution (30 arc-seconds) global raster data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon implementation of the FAO-56 Penman-Monteith Reference Evapotranspiration (ET0) equation.Aridity Index represent the ratio between precipitation and ET0, thus rainfall over vegetation water demand (aggregated on annual basis). Under this formulation, Aridity Index values increase for more humid conditions, and decrease with more arid conditions. The Aridity Index values reported within the Global-AI geodataset have been multiplied by a factor of 10,000 to derive and distribute the data as integers (with 4 decimal accuracy). This multiplier has been used to increase the precision of the variable values without using decimals. The Readme File is provided with a detailed description of the dataset files. A peer-reviewed article is now available with a description of the methodology and a technical evaluation.The Global-AI_PET_v3 datasets are provided for non-commercial use in standard GeoTiff format, at 30 arc seconds or ~ 1km at the equator.The Python programming source code used to run the calculation of ET0 and AI is provided and available online on Figshare at:https://figshare.com/articles/software/Global_Aridity_Index_and_Potential_Evapotranspiration_Climate_Database_v3_-_Algorithm_Code_Python_/20005589Peer-Review Reference and Proper Citation:Zomer, R.J.; Xu, J.; Trabuco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Scientific Data 9, 409. https://www.nature.com/articles/s41597-022-01493-1

The MOD13C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MOD13C1 Version 6.1 data product.

The MOD13C1 Version 6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.

The Climate Modeling Grid (CMG) consists 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MOD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer MOD13A2 data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution.

Known Issues * The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MOD09 surface reflectance products may have impacted MOD13 Vegetation Index data products particularly over arid bright surfaces. * Corrections were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions * The 16-day composite VI is generated using the two 8-day composite surface reflectance granules (MOD09A1) in the 16-day period. * This surface reflectance input is based on the minimum blue compositing approach used to generate the 8-day surface reflectance product. * The product format is consistent with the Version 5 product generated using the Level 2 gridded daily surface reflectance product. * A frequently updated long-term global CMG Average Vegetation Index product database is used to fill the gaps in the CMG product suite.

Panama: PISA science scores: The latest value from 2022 is 387.768 index points, an increase from 364.624 index points in 2018. In comparison, the world average is 449.005 index points, based on data from 78 countries. Historically, the average for Panama from 2018 to 2022 is 376.196 index points. The minimum value, 364.624 index points, was reached in 2018 while the maximum of 387.768 index points was recorded in 2022.

Sweden: PISA science scores: The latest value from 2022 is 493.549 index points, a decline from 499.445 index points in 2018. In comparison, the world average is 449.005 index points, based on data from 78 countries. Historically, the average for Sweden from 2006 to 2022 is 494.943 index points. The minimum value, 484.799 index points, was reached in 2012 while the maximum of 503.334 index points was recorded in 2006.