Dataset

This dataset was created by Akshatha Aravind

Released under Apache 2.0

Contents

The Photochemical Reflectance Index (PRI) corresponds to the de-epoxidation state of the xanthophyll cycle and is one of the few pigment-based vegetation indices sensitive to rapid plant physiological responses. As such, new remotely-sensed PRI products present opportunities to study diurnal and seasonal processes in evergreen conifer forests, where complex vegetation dynamics are not well reflected by the small annual changes in chlorophyll content or leaf structure. Because PRI is tied explicitly to short and long term changes in xanthophyll pigments which are responsible for regulatig stress, this study characterized PRI in a semi-arid, sub-alpine mixed conifer forest, in order to assess its potential as a proxy for water stress by extension of its association with photoprotection. To determine the sensitivity of PRI to seasonal changes in ecohydrological variability and gross primary productivity, canopy spectral measurements were combined with eddy covariance flux and sap flow methods. Seasonally, there was a significant relationship between PRI and sap flow velocity (R2=0.56), and multiple linear regression analysis demonstrated the PRI response to dynamic water and energy limitations in this system. Although PRI was an effective indicator of stomatal response to ecohydrological constraints on a seasonal time scale, top-of-canopy leaf-level gas exchange, chlorophyll fluorescence, and hyperspectral reflectance measurements suggest that diurnal PRI saturates under conditions of severe light stress. This research indicates that remotely-sensed PRI has potential to fill spatial and temporal gaps in the ability to distinguish how water availability influences carbon dynamics of forested ecosystems. Methods Study Site: The study location was a sub-alpine mixed conifer forest in the Coronado National Forest on Mt. Bigelow, northeast of Tucson, Arizona. The site is at 2573 m elevation in an area of significant topographical complexity. The climate is semi-arid with a mean annual temperature of 9.4 °C andmean annual precipitation of 750 mm. Of this, ~50% falls during the North American Monsoon in late summer (Adams et al., 1997). The site is dominated by mature second-growth Douglas fir (Pseudotsuga menziesii), ponderosa pine (Pinus ponderosa), and southwestern white pine (Pinus strobiformis), withlittle understory vegetation. The forest exhibits a complex and bimodal pattern of primary production, with an initial spring peak following snow melt, a dry pre-monsoon mid-season depression (May-June), and a secondary productivity peak during the wet monsoon (July-Sept), remaining active through fall.

Sap flow: We measured sap flow on the north and south sides of three P. strobiformisand two P. ponderosaindividuals using the thermal dissipation probe method (Granier 1985; Granier 1987). Data were logged at 30 min resolution using an upper heated probe and lower reference probe (TDP-30, Dynamax Inc., Houston, TX, USA) implanted in the sapwood of the tree approximately 40 mm apart. Sap flow velocity (cm hr-1) was calculated according to: , where and dT is the difference in temperature (°C) between the two needles, and dTM is the maximum temperature difference between midnight and 7:00 am.

Canopy Spectral Reflectance: On July 3, 2018, we installed an autonomous Spectral Reflectance Sensor (METER Group, Inc., Pullman, WA, USA), and began collecting PRI reflectance at 10-min intervals. For a complete description of the sensor see: http://manuals.decagon.com/Manuals/14597_SRS_Web.pdf and Magney et al., 2016. The PRI sensors use photodiodes with narrow bandpass filters centered at the 532 nm and 570 nm wavelengths with 10 nm full width half maximum bandwidths. It uses a hemispherical upward-looking sensor, and a field stop downward-looking sensor to measure incoming and upwelling radiation (W m-2sr-1nm-1), respectively. PRI was calculated as:

Where is the spectral reflectance value at center wavelength of 532 nm and is the spectral reflectance value at center wavelength of 570 nm. Downward looking sensor interference filters restrict the field of view (FOV) to 36°. The sensor was at 24 m height, roughly 12 m above the top of the canopy titled off-nadir at an angle of 20°, resulting in a field of view (FOV) of ~50m2. The PRI sensor faced west and therefore measured eastern facing needles. Within the sensor FOV were full or partial canopies of five trees (three P. ponderosaand two P. strobiformis, no understory vegetation), four of which were equipped with sap flow sensors.

Leaf Level gas exchange, chlorophyll fluorescence, and hyperspectral reflectance: We collected leaf-level measurements on September 13-14 for one P. ponderosaand one P. strobiformismature tree on attached top of canopy needles (13m height) using a canopy access crane. We measured four branches on each tree every hour from 9:00 -16:00 MST. Two sunlit fascicles were measured for simultaneous gas exchange and fluorescence (see below). Immediately after, the same needles, plus two more fascicles, were measured with a spectroradiometer for PRI (see below). When measuring under intermittent cloudiness, measurements were aborted if the needles were not exposed to sunlight immediately prior to both gas exchange and spectral measurements.

Spot gas exchange and simultaneous chlorophyll fluorescence were measured using the Li-6800 Portable Photosynthesis System infrared gas analyzer (LICOR Inc., Lincoln, NE, USA) with a standard 6 cm2 leaf chamber. For each round of branch measurements air temperature (Tair) and photosyntheticall active radiation (PAR) were characterized, and the internal chamber conditions were set to match the ambient environment. We performed leaf area analysis on ten samples of each species to derive an average leaf area within the chamber (2.24 cm2 ±0.16 cm2and 2.22 cm2 ±0.22 cm2 for P. ponderosaand P. strobiformis, respectively) assuming each sample clamped the same approximate amount of leaf area. We used the multiphase flash method to obtain greater accuracy in Fm’ acquisition compared to typical rectangular flash methods (Loriaux et al. 2013). To obtain dark adapted parameters, rectangular flash measurements were taken at pre-dawn on September 14 for a minimum of eight samples per branch to obtain branch averaged Fo and Fm.

We measured leaf-level hyperspectral reflectance with an ASD FieldSpec3 (ASD Inc., Boulder, CO, USA) spectroradiometer with plant probe. The plant probe has a low intensity light source for non-destructive data collection. Prior to each measurement, a white reflectance reference was taken using a calibrated Spectralon reference standard. Needles were arranged in a single plane to minimize gaps without overlapping (Rajewicz et al., 2019). After clamping onto the needles and turning on the light source, 2-3 spectra were taken within a few seconds to prevent jumps in PRI due to an altered light condition (Mottus et al. 2017).

Comprehensive financial statements and analysis for Primerica, Inc. (PRI) including income statement, balance sheet, cash flow, and financial ratios.

Analysis of ‘Home Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/PythonforSASUsers/hpindex on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Federal Housing Finance Agency House Price Index (HPI) is a broad measure of the movement of single-family house prices. The HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. The technical methodology for devising the index, collection, and publishing the data is at: http://www.fhfa.gov/PolicyProgramsResearch/Research/PaperDocuments/1996-03_HPI_TechDescription_N508.pdf

Content

Contains monthly and quarterly time series from January 1991 to August 2016 for the U.S., state, and MSA categories. Analysis variables are the aggregate non-seasonally adjusted value and seasonally adjusted index values. The index value is 100 beginning January 1991.

Acknowledgements

This data is found on Data.gov

Inspiration

Can this data be combined with the corresponding census growth projections either at the state or MSA level to forecast 24 months out the highest and lowest home index values?

--- Original source retains full ownership of the source dataset ---

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.

Primerica (PRI) Stock: A Safe Haven in Turbulent Times?

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

Dataset

This dataset was created by Ragul V L

Released under MIT

Contents

Dataset

This dataset was created by PedroLucchetti

Contents

In Asia Pacific, the diamino sulfanilide prices for Q2 2024 reached 4620 USD/MT in June. The market experienced a downturn because of steady production alongside subdued demand, particularly in sectors like textiles. India, despite growth in textiles, saw stagnating demand because of seasonal influences that dampened industrial activity. Stable supplies combined with reduced industrial usage created a generally bearish trend in the region.

We analyse the pattern of daily price of a collection of artistic non-fungible tokens, namely, the “Bored Ape Yacht Club” (BAYC) collectibles, over the first year of their life, from May 2021 to May 2022. Taking a time-series analysis approach, we consider the daily average price, and other variants of daily price index, derived from hedonic regression model. Aesthetic features of the collectibles do matter. At the same time, the price series emerge to be non-stationary, integrated of order 1, with their first difference exhibiting heteroscedasticity and autoregressive variance. Models of ARCH/GARCH class are appropriate to describe the dynamics. Though the price series of BAYC collectibles and their daily movements share many characteristics with the series of financial assets, they do not appear to be related to financial variables from both the crypto- and the real (i.e., not crypto) world.

Q1-2022 U.S. Solar Photovoltaic System and Energy Storage Cost Benchmarks With Minimum Sustainable Price Analysis Data File

PURPOSE (possible uses)

Non-professional investors often try to find an interesting stock among those in an index (such as the Standard and Poor's 500, Nasdaq, etc.). They need only one company, the best, and they don't want to fail (perform poorly). So, the metric to optimize is accuracy, described as:

Accuracy = True Positives / (True Positives + False Positives)

And the predictive model can be a binary classifier.

The data covers the price and volume of shares of 31 NASDAQ companies in the year 2022.

Context

Every data set I found to predict a stock price (investing) aims to find the price for the next day, and only for that stock. But in practical terms, people like to find the best stocks to buy from an index and wait a few days hoping to get an increase in the price of this investment.

Content

Rows are grouped by companies and their age (newest to oldest) on a common date. The first column is the company. The following are the age, market, date (separated by year, month, day, hour, minute), share volume, various traditional prices of that share (close, open, high...), some price and volume statistics and target. The target is mainly defined as 1 when the closing price increases by at least 5% in 5 days (open market days). The target is 0 in any other case.

Complex features and target were made by executing: https://www.kaggle.com/code/luisandresgarcia/202307

Thanks

Many thanks to everyone who participates in scientific papers and Kaggle notebooks related to financial investment.

Analysis of ‘Mobile Price Classification’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/iabhishekofficial/mobile-price-classification on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Bob has started his own mobile company. He wants to give tough fight to big companies like Apple,Samsung etc.

He does not know how to estimate price of mobiles his company creates. In this competitive mobile phone market you cannot simply assume things. To solve this problem he collects sales data of mobile phones of various companies.

Bob wants to find out some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.

In this problem you do not have to predict actual price but a price range indicating how high the price is

--- Original source retains full ownership of the source dataset ---

About this dataset

Context

The dataset tabulates the median household income in Price town. It can be utilized to understand the trend in median household income and to analyze the income distribution in Price town by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Price, Wisconsin Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Price, Wisconsin: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Price, Wisconsin
• Price, Wisconsin households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Price town median household income. You can refer the same here

During the second quarter of 2024, the maleic anhydride prices in the United States reached 1393 USD/MT in June. The market saw significant price increases driven by robust seasonal demand and rising shipping costs. In Q2 of 2024, maleic anhydride prices in Japan saw significant price instability, with a slight increase driven by limited supply chains and robust seasonal growth. During the second quarter of 2024, maleic anhydride pricing in Germany experienced a slight price increase, fueled by intricate supply chains, including container shortages and freight delays. The quarter ended with maleic anhydride priced at 1287 USD/MT.

About this dataset

Context

The dataset tabulates the data for the Price, UT population pyramid, which represents the Price population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Price, UT, is 36.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Price, UT, is 25.3.
• Total dependency ratio for Price, UT is 62.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Price, UT is 4.0.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Price population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Price for the selected age group is shown in the following column.
• Population (Female): The female population in the Price for the selected age group is shown in the following column.
• Total Population: The total population of the Price for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Price Population by Age. You can refer the same here

We analyse the pattern of daily price of a collection of artistic non-fungible tokens, namely, the “Bored Ape Yacht Club” (BAYC) collectibles, over the first year of their life, from May 2021 to May 2022. Taking a time-series analysis approach, we consider the daily average price, and other variants of daily price index, derived from hedonic regression model. Aesthetic features of the collectibles do matter. At the same time, the price series emerge to be non-stationary, integrated of order 1, with their first difference exhibiting heteroscedasticity and autoregressive variance. Models of ARCH/GARCH class are appropriate to describe the dynamics. Though the price series of BAYC collectibles and their daily movements share many characteristics with the series of financial assets, they do not appear to be related to financial variables from both the crypto- and the real (i.e., not crypto) world.

The price per unit is forecast to experience significant growth in all segments in 2030. The trend observed from 2020 to 2030 remains consistent throughout the entire forecast period. There is a continuous increase in the price per unit across all segments. Notably, the Fresh Meat segment achieves the highest value of ***** U.S. dollars at 2030. Find further statistics on other topics such as a comparison of the revenue in China and a comparison of the revenue in India. The Statista Market Insights cover a broad range of additional markets.

We analyse the pattern of daily price of a collection of artistic non-fungible tokens, namely, the “Bored Ape Yacht Club” (BAYC) collectibles, over the first year of their life, from May 2021 to May 2022. Taking a time-series analysis approach, we consider the daily average price, and other variants of daily price index, derived from hedonic regression model. Aesthetic features of the collectibles do matter. At the same time, the price series emerge to be non-stationary, integrated of order 1, with their first difference exhibiting heteroscedasticity and autoregressive variance. Models of ARCH/GARCH class are appropriate to describe the dynamics. Though the price series of BAYC collectibles and their daily movements share many characteristics with the series of financial assets, they do not appear to be related to financial variables from both the crypto- and the real (i.e., not crypto) world.

Dataset

This dataset was created by darth manav

Contents

Detailed price prediction analysis for HOME on Jul 7, 2025, including bearish case ($0.019), base case ($0.021), and bullish case ($0.021) scenarios with Buy trading signal based on technical analysis and market sentiment indicators.