This dataset was used to produce the results of following paper:Title: Risk-constrained Optimal Dynamic Trading Strategies Under Short- and Long-term Uncertainties

Overview This dataset provides a comprehensive collection of daily stock price data for Nvidia Corporation (NVDA), spanning a 20-year period from January 2, 2004, to January 1, 2024. Nvidia, a global leader in graphics processing units (GPUs) and AI technologies, has experienced exponential growth, particularly in recent years as it became a major player in artificial intelligence, gaming, and autonomous vehicles. This dataset captures key market movements and trends during Nvidia’s significant rise to prominence.

About the Dataset The dataset contains crucial financial data for Nvidia's stock, including opening, high, low, and closing prices, as well as trading volume for each day in the 20-year period. This data is ideal for conducting a variety of financial analyses, ranging from simple trend observation to complex predictive modeling using machine learning algorithms such as LSTM (Long Short-Term Memory). Traders, financial analysts, and data scientists can use this dataset to backtest trading strategies, develop stock market prediction models, and perform time series analysis on stock price movements.

Attribute Information Date: The date of the stock price record. Open: The stock price at the beginning of the trading day. High: The highest price Nvidia stock reached during the day. Low: The lowest price Nvidia stock reached during the day. Close: The stock price at the end of the trading day. Volume: The total number of Nvidia shares traded during the day.

Key Insights from the Dataset Over the past two decades, Nvidia has gone through various phases of growth, most notably its dramatic rise after the 2010s, which was fueled by the growing demand for GPUs in the gaming industry, as well as Nvidia’s breakthroughs in artificial intelligence (AI) and deep learning. The dataset captures periods of market volatility, growth spurts, and consolidations, providing ample opportunities for in-depth analysis of Nvidia's market behavior.

Usage This dataset can be used for various types of financial analysis and machine learning tasks, including:

Trend Analysis: Track Nvidia’s stock price trends over time and identify key inflection points. Technical Analysis: Use the provided historical data to calculate various technical indicators such as Moving Averages, RSI (Relative Strength Index), Bollinger Bands, MACD (Moving Average Convergence Divergence), and more, allowing for a deeper understanding of price patterns.

Stock Price Prediction: Leverage machine learning algorithms like LSTM and ARIMA to predict future stock price movements based on the historical data. Backtesting Trading Strategies: Test and optimize trading strategies using Nvidia’s historical price data to simulate real-world trading conditions.

Data Collection Methodology The data was collected from Yahoo Finance using the yfinance Python library, which provides easy access to historical stock price data. This dataset covers the daily stock prices of Nvidia Corporation (NVDA) from January 2, 2004, to January 1, 2024, with each entry representing a single trading day. The data includes fields for open, high, low, and close prices, along with the trading volume for each day.

In order to avoid missing representative features, we should select a lot of features as far as possible when using machine learning algorithms in stock trading. Meanwhile, these high dimensional features can lead to redundancy of information and reduce the efficiency, and accuracy of learning algorithms. It is worth noting that dimensionality reduction operation (DRO) is one of the main means to deal with stock high-dimensional data. However, there are few studies on whether DRO can significantly improve the trading performance of deep neural network (DNN) algorithms. Therefore, this paper selects large-scale stock datasets in the American market and in the Chinese market as the research objects. For each stock, we firstly apply four most widely used DRO, namely principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), classification and regression trees (CART), and autoencoder (AE) to deal with original features respectively, and then use the new features as inputs of the most six popular DNN algorithms such as Multilayer Perceptron (MLP), Deep Belief Network (DBN), Stacked Auto-Encoders(SAE), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), Gated Recurrent Unit(GRU) to generate trading signals. Finally, we apply the trading signals to conduct a lot of daily trading back-testing and non-parameter statistical testing. The experiments show that LASSO can significantly improve the performance of RNN, LSTM, and GRU. In addition, any DRO mentioned in this paper do not significantly improve trading performance and the speed of generating trading signals of the other DNN algorithms.

This dataset provides a comprehensive historical record of the daily market performance of NVIDIA Corporation (NASDAQ: NVDA), a leading technology company renowned for its graphics processing units (GPUs) and semiconductor products. The dataset includes a wealth of information spanning several years, encompassing daily open, high, low, and closing prices, as well as volume traded. Analysts, investors, and researchers can leverage this dataset to explore patterns, trends, and correlations in NVIDIA's stock behavior, aiding in market analysis, algorithmic trading strategies, and financial research. Whether you're examining long-term investment opportunities or short-term trading dynamics, this dataset offers valuable insights into one of the most prominent players in the semiconductor industry.

The American Samoa Local Action Strategies (LAS) are the result of a nearly two-year process that saw input from territorial agencies, non-profit groups, interested individuals, and other stake-holders such as local fishers, and federal agency partners. This process was initiated through the American Samoa Coral Reef Advisory Group (CRAG), a voluntary committee comprised of numerous agencies and academic institutions in the territory concerned with coral reef issues. Since its inception in 1998, CRAG has overseen many successful management and science activities, has increased member-agency collaboration and has improved alignment and cooperation with non-CRAG agencies that have common interests. To address LAS focus areas, CRAG developed both short- and long-term action plans that prioritize activities for funding. Where possible, current and ongoing activities were incorporated into each LAS to provide continuity and networking, and to underscore that individual agency mandates and projects are supported by the CRAG as a whole. Each LAS consists of goals, success indicators, projects and timelines, and will continue to evolve and develop as new resources are brought to bear, and as projects are completed.Available onlineCall Number: [EL]Physical Description: 2 Pages

Abstract

The application of machine learning has become commonplace for problems in modern data science. The democratization of the decision process when choosing a machine learning algorithm has also received considerable attention through the use of meta features and automated machine learning for both classification and regression type problems. However, this is not the case for multistep-ahead time series problems. Time series models generally rely upon the series itself to make future predictions, as opposed to independent features used in regression and classification problems. The structure of a time series is generally described by features such as trend, seasonality, cyclicality, and irregularity. In this research, we demonstrate how time series metrics for these features, in conjunction with an ensemble based regression learner, were used to predict the standardized mean square error of candidate time series prediction models. These experiments used datasets that cover a wide feature space and enable researchers to select the single best performing model or the top N performing models. A robust evaluation was carried out to test the learner's performance on both synthetic and real time series.

Proposed Dataset

The dataset proposed here gives the results for 20 step ahead predictions for eight Machine Learning/Multi-step ahead prediction strategies for 5,842 time series datasets outlined here. It was used as the training data for the Meta Learners in this research. The meta features used are columns C to AE. Columns AH outlines the method/strategy used and columns AI to BB (the error) is the outcome variable for each prediction step. The description of the method/strategies is as follows:

Machine Learning methods:

NN: Neural Network

ARIMA: Autoregressive Integrated Moving Average

SVR: Support Vector Regression

LSTM: Long Short Term Memory

RNN: Recurrent Neural Network

Multistep ahead prediction strategy:

OSAP: One Step ahead strategy

MRFA: Multi Resolution Forecast Aggregation

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines


    Forex Pair
    Headline
    Sentiment
    Explanation




    GBPUSD 
    Diminishing bets for a move to 12400 
    Neutral
    Lack of strong sentiment in either direction


    GBPUSD 
    No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
    Positive
    Positive sentiment towards GBPUSD (Cable) in the near term


    GBPUSD 
    When are the UK jobs and how could they affect GBPUSD 
    Neutral
    Poses a question and does not express a clear sentiment


    JPYUSD
    Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
    Positive
    Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply


    USDJPY
    Dollar rebounds despite US data. Yen gains amid lower yields 
    Neutral
    Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other


    USDJPY
    USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
    Negative
    USDJPY is expected to reach a lower value, with the USD losing value against the JPY


    AUDUSD

    <p>RBA Governor Lowe’s Testimony High inflation is damaging and corrosive </p>

    Positive
    Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.

Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

Asset Price Dataset Description

This dataset is a comprehensive collection of historical financial data on a specific asset, covering a wide range of information related to daily prices, trading volume and technical indicators. It is designed to provide a detailed, multi-faceted view of asset performance over time, enabling in-depth analysis and the application of various financial strategies.

Information on the columns of the dataset

1. Date: The specific date of the entry.
2. Opening: The opening price of the asset at the beginning of the day.
3. High: The highest price reached by the asset during the day.
4. Low: The lowest price reached by the asset during the day.
5. Closing: The price of the asset at the end of the day.
6. Adjusted Closing: The closing price adjusted for dividends and stock splits.
7. Volume: The number of shares traded during the day.
8. Amplitude: The difference between the highest and lowest price of the day (High - Low).
9. MA7: Moving average of the closing price of the last 7 days.
10. MA14: Moving average of the closing price of the last 14 days.
11. MA30: Moving average of the closing price over the last 30 days.
12. Daily Return: The percentage change in the closing price in relation to the previous day.
13. ATR (Average True Range): Moving average of the True Range (TR) for a given period, used to measure volatility.
14. RSI (Relative Strength Index): Relative Strength Index, a momentum indicator that measures the speed and change of price movements.
15. Annual growth percentage: Percentage of annual growth.
16. Percentage of daily growth: Percentage of daily growth.
17. Absolute Daily Growth: Daily absolute growth, the absolute difference in the closing price compared to the previous day.
18. Day: The day of the week.
19. Month: The month of the year.
20. TR (True Range): The biggest difference between:
21. The maximum price of the day minus the minimum price of the day.
22. The maximum price of the day minus the closing price of the previous day.
23. The minimum price of the day minus the closing price of the previous day.

Applicability

1. Trend Analysis:
   • Through historical data, it is possible to identify short and long-term price trends, helping analysts and investors make informed decisions about buying and selling assets.
2. Development of Negotiation Strategies:
   • The data can be used to develop and test automated trading strategies, including the use of moving averages, relative strength indexes (RSI), and other technical indicators.
3. Volatility Study:
   • With metrics such as Average True Range (ATR), the dataset allows measuring asset volatility over time, essential for risk management strategies and understanding asset stability.
4. Performance Assessment:
   • The detailed history of opening, closing, high and low prices, as well as trading volume, allows an accurate assessment of the asset's performance in different periods.
5. Modeling and Forecasting:
   • The data can be used to build predictive models using machine learning and statistical analysis techniques, providing predictions about future price movements.
6. Education and Research:
   • For students and researchers, the dataset offers a rich source of real data to study financial markets, test hypotheses and perform simulations.

Importance

1. Informed Decision Making:
   • Access to detailed historical data allows investors and analysts to make evidence-based decisions, reducing uncertainty and risk associated with financial markets.
2. Backtesting:
   • It is possible to apply trading strategies to historical data to verify their effectiveness before implementing them in the real market, a crucial process for developing robust trading systems.
3. Comparative Performance Analysis:
   • With consistent data, you can compare the asset's performance over different periods or with other assets, providing a clear perspective on its relative performance.
4. Pattern Identification:
   • The dataset allows the identification of patterns and anomalies in price movements, which can be explored to develop trading strategies or to better understand the factors that influence the market.
5. Risk Management:
   • Analyzing volatility and price behavior over time helps in building risk management strategies, essential for preserving capital and optimizing returns.

This dataset is a valuable tool for anyone involved in financial markets, from individual investors to market analysts and academic researchers, providing the necessary foundation for detailed analysis and informed financial decisions.

We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits. We utilize elementary head-motion units named kinemes, atomic facial movements termed action units and speech features to estimate these human-centered traits. Empirical results confirm that kinemes and action units enable discovery of multiple trait-specific behaviors while also enabling explainability in support of the predictions. For fusing cues, we explore decision and feature-level fusion, and an additive attention-based fusion strategy which quantifies the relative importance of the three modalities for trait prediction. Examining various long-short term memory (LSTM) architectures for classification and regression on the MIT Interview and First Impressions Candidate Screening (FICS) datasets, we note that: (1) Multimodal approaches outperform unimodal counterparts, achieving the highest PCC of 0.98 for Excited-Friendly traits in MIT and 0.57 for Extraversion in FICS; (2) Efficient trait predictions and plausible explanations are achieved with both unimodal and multimodal approaches, and (3) Following the thin-slice approach, effective trait prediction is achieved even from two-second behavioral snippets. Our implementation code is available at: https://github.com/deepsurbhi8/Explainable_Human_Traits_Prediction.

The dataset contains the file required for training and testing and split accordingly.

There are two groups of features that you can use for prediction:

1. Fundamentals and ratios: Values collected form statements and balance sheets for each ticker
2. Technical indicators and strategy flags: Technical indicators calculated on close value of each day and buy and sell signals generated using some commonly used trading strategies.

Files found in Fundamentals folder is a processed format of the files found in raw folder. Ratios and other values are stretched to match the length of the closing price column such that the value in the pe_ratio column for example is the PE ratio from the most recent quarter and this applies for every column.

Technical indicators are calculated with the default parameters used in Pandas_TA package.

Data is collected form finance.yahoo.com and macrotrends.net Timeframe for the given data is different from one ticker to another because of unavailability of some stocks for a given time frame on either of the websites.

All code required to collect the data and perform preprocessing and feature engineering to get the data in the given format can be found in the following notebooks:

1. https://www.kaggle.com/code/mohammedobeidat/us-stocks-data-collection
2. https://www.kaggle.com/code/mohammedobeidat/us-stocks-technicals-feature-engineering-and-eda
3. https://www.kaggle.com/code/mohammedobeidat/us-stocks-fundamentals-preprocessing-and-eda

Files

• {<>_ticker_train}.csv - the training set
• {<>_ticker_train}.csv - the test set

Columns

Columns names are supposed to be self-explanatory assuming you are familiar with the stock market. Some acronyms you may encounter:

1. tmm is short for Trailing Twelve Months
2. pe is short for Price to Earnings
3. pb is short for Price to Book Value
4. ps is short for Price to Sales
5. fcf is short for Free Cash Flow
6. eps is short for Earnings per Share

The present dataset contains information about the categorisation of Business Models for Short Food Supply Chains, in the context of the agroBRIDGES project (Horizon 2020, GA No. 101000788). Regional information about existing and new business models for Short Food Supply Chains were collected from 12 regions and countries of focus for the project (Beacon Regions) through desk research, interviews and co-creation workshops.

The datasets collected information provided by Beacon Region Leaders in the co-creation workshops about models applied and newly or potentially developed ones and available in the literature, to categorise them into stylized models with specific and distinguishable features and attributes, enabling a systematic approach in the development of the sustainability assessment framework. An initial ranking of local business models was provided at a regional / country level.

Through analysis of the aggregate ranking results of business models at local level, a final list of business models for SFSCs was derived. Five different Business Model types have been addressed and developed ( i. Community Supported Agriculture - CSA, ii. Face to face trade, iii. Online Food Trade, iv. Local Food Trade, and v. Improved Logistics). The final categorization list provided valuable insights in order to represent all regional contexts and eventually offer value-added information about the main factors for developing a successful business model that can enhance market success in the long term; as well as to strengthen farmer´s strategy that eventually enhances their position within the whole value chain and improve their customer experience.

The Value Proposition Canvas and Business Model Canvas was defined for each of the most common SFSC represented in each of the targeted regions.

The dataset contains:
• agroBRIDGES_SFSCs-BMModels-Categorisation_2021.12.01_v1 [zip file]: The initial list of business models collected from the co-creation workshops
• agroBRIDGES_SFSCs-BMModels-Categorisation-Tool_2021.12.01_v1 [.xlsx file]: Internal Assessment tool for Business models regional data collection: A data collection tool was built in-house and shared to all Beacon Region Leaders in order to enable them to fill it regarding their assumptions and conclusions after the co-creation participants discussed and presented their insights from regional activities.
• agroBRIDGES_SFSCs-BModels-CategorisationResults_2021.12.01_v1 [.xlsx file]: SFSCs BM type results in each of the regions.
• agroBRIDGES_BusinessModels_Canvas_2021.12.01_v1 [zip file]: The Business Model and Value Proposition Canvas of the 5 BM categories

The aim of the project is to provide a clearer picture of what men and women actually did to support themselves in early modern Sweden (approximately 1550 to 1800) and to analyze short- and long term consequences of survival strategies in general and of the gender division of labour in particular.

Complex incubation strategies have evolved to solve the trade-off between parent survival and care for their eggs with often brief departures (recesses) that maximise egg survival or infrequent extended recesses maximising adult condition. Here we examined incubation behaviour of Sanderlings (Calidris alba), a species that exhibits both bi- and uniparental incubation behaviour. During 11 breeding seasons in Greenland, we have quantified incubation variability with thermologgers placed in nests. We estimated the impact of environmental conditions and individual characteristics on the occurrence and the duration of recesses. We found that extended recesses are a unique feature of uniparentals, and their frequency and duration increased in colder temperatures. The relationship was mediated by body condition, with individuals in poor condition performing longer extended recesses in colder temperatures. This suggests that extended recesses may represent a shift towards self-maintenance at th..., This dataset (2011-2021) is a long-term monitoring based on fieldwork in Greenland. The field team collects behavioural data at Sanderling nests by putting thermologgers in the nest cups during the incubation period. Loggers are then retrieved at the end of the breeding season and data is processed with TinyTag Explorer Software. One dataset is at the recess (departure from the nest to forage) scale, and the other is at the daily scale (see Methods). Datasets are used in conjunction with the provided R code., , # Extended incubation recesses in Sanderlings are impacted by temperature and body condition

Description of the data and file structure

We collected behavioural information on Sanderlings' incubation ecology. We collected information on their incubation recesses at two different time scales. Firstly, we collected information at the recess scale: the recess is the statistical unit. Secondly, we pooled these recesses at a daily scale. For that, we focus on the Total Duration of Recesses (TDR), summing all the recesses during a 24h period. Therefore, we obtained two datasets; "Recesses", at the recess scale, and "TDR" at the daily scale.

Description of the column names:

‘Recesses.txt’ dataset.

·      date: date-hour of each recess performed YY-MM-DD HH:MM:SS.

·      recess_size_max: duration of each recess in minutes.

·      recesslong: 0/1 for each recess, to describe if this recess is extended (≥ 120min) or short (<120min).

·      strategy: description of the nest’s s...

This Second County Integrated Development Plan is a five-year blue print that highlights the socio-economic challenges faced by the County, strategies for resources mobilization, projects and programmes to be implemented in order to address the socio-economic challenges. The CIDP II articulates medium term policies and objectives which are further translated into short term strategies, programmes and projects to be implemented under the Medium Term Expenditure Framework (MTEF).

The research project on the breeding strategies of desert plants in hexi region of gansu province belongs to the national natural science foundation "environment and ecological science in western China" major research plan, led by professor an lizhe of lanzhou university. The project runs from January 2004 to December 2007. Remittance data of the project: 1. Effect of super - dry preservation on seeds The data is in Word format and contains a lot of analysis charts. A comparative study was conducted on the changes of vitality of overlord seeds and rhizoma coptidis seeds stored at 45℃, room temperature and 15℃ respectively, and the effects of dampening treatment, artificial aging and ultra-dry treatment on electrical conductivity and physiological activity indexes of seeds were conducted.The details are as follows: Energy change of seeds was preserved at 45℃ FIG. 1 germination rate (%) of overlord seeds stored at 45℃、FIG. 2 germination index of overlord seeds stored at 45℃、FIG. 3 vigor index of the seeds stored at 45℃. Change of seed vigor at room temperature FIG. 4 germination rate (%) of overlord seeds stored at room temperature、FIG. 5 germination index of overlord seeds stored at room temperature、FIG. 6 vigor index of overlord seeds preserved at room temperature. 15℃ preservation of seed vitality changes FIG. 7 germination rate of overlord seeds stored at 15℃ (%)、FIG. 8 germination index of alba seeds stored at 15℃、FIG. 9 vigor index of the seeds stored at 15℃. Changes of seed vigor of rhizoma coryzae at 45℃ FIG. 10 germination rate (%) of rhizoma coptidis seeds stored at 45℃、FIG. 11 germination index of the seeds of rhizoma coryzae at 45℃、FIG. 12 vigor index of seeds of corydalis corydalis preserved at 45℃. Changes of seed vigor of rhizoma coryzae at room temperature FIG. 13 germination rate (%) of rhizoma corydalis seeds preserved at room temperature、FIG. 14 germination index of seeds preserved at room temperature、FIG. 15 vigor index of seeds of corydalis corydalis preserved at room temperature Changes of seed vigor of rhizoma corydalis in 15℃ storage FIG. 16 germination rate (%) of rhizoma coptidis seeds stored at 15℃、FIG. 17 germination index of the seeds of rhizoma coptidis preserved at 15℃、FIG. 18 vigor index of seeds of corydalis sativus preserved at 15℃ Effect of slow wetting treatment on relative conductivity of seeds FIG. 28 changes in the relative conductivity of arrobatus seeds without dampening treatment、FIG. 29 changes of relative conductivity of overlord seeds after slow wetting treatment、FIG. 31 changes of relative electrical conductivity of seeds of rhizoma coryzae after dampening treatment Effects of artificial aging treatment on seed of archaea chinensis l FIG. 34 effects of artificial aging treatment on germination rate of overlord seeds、FIG. 35 effect of artificial aging treatment on seed vigor index、FIG. 36 effects of artificial aging treatment on the relative conductivity of overlord seeds Effects of artificial aging treatment on seeds of coryza sativa l FIG. 37 effect of artificial aging treatment on germination rate of seeds of coryza sativa l、FIG. 38 effect of artificial aging treatment on seed vigor index of rhizoma coryzae、FIG. 39 effects of artificial aging treatment on the relative electrical conductivity of the seeds of coryza sativa l Effects of artificial aging on the content of aldehydes in seeds after 15 days FIG. 52 effects of artificial aging treatment on the content of aldehydes in the seeds after 15 day、FIG. 53 effects of artificial aging treatment on the content of aldehydes in seeds of prunus chinense after 15 days, Effect of super - dry treatment on physiological activity index of seed Table 31 effect of super - dry treatment on physiological activity index of monkshood seed Table 32 influence of hyperdrying treatment on physiological activity index of seeds of coryza sativa l 2. Micromorphological and structural characteristics of the skin of desert plants (including experimental conditions, microscopic images of the skin microstructure and analysis of distribution of 47 plants, genus, species code, list of length and weight of long and short axes of seeds, and list of seed elements)

At Crawlbee, we take pride in presenting our comprehensive Consumer Database, a treasure trove of essential data touchpoints that will empower your marketing endeavors.

Why Choose Crawlbee's Consumer Data:

Our database is meticulously crafted from a multitude of trusted sources, including real estate transactional data. This vast compilation ensures the utmost accuracy and relevance, making it a resource for businesses seeking to reach their target audiences effectively. Whether you're in need of Audience Data, B2C Data, or specialized insights such as US Household Data, Housing Data, or Mortgage Data, our database has it all.

Personalized Targeting:

Our data allows for highly versatile targeting across a wide spectrum of use cases. Be it refining your Audience Data for precise marketing campaigns, acquiring in-depth Consumer Data for analytical insights, or accessing specific US Household Data for residential market analysis, Crawlbee's database empowers you to achieve your goals with confidence.

Flexible Pricing:

This versatility ensures that our data fits seamlessly into your budget and operational plans, whether you're seeking B2C Data for short-term projects or a continuous stream of Mortgage Data for long-term strategies.

Exceptional Value:

When you choose Crawlbee, you're selecting a partner dedicated to your success. Our Consumer Data, Audience Data, B2C Data, US Household Data, Housing Data, and Mortgage Data are designed to help you stand out in your industry, outperform competitors, and reach your business goals with precision.

Experience the difference of data that's built on reliability, precision, and performance. Unlock the potential of your marketing campaigns and analytical endeavors with Crawlbee's comprehensive data offerings. Get started today and take a step towards unparalleled success, backed by Consumer Data, Audience Data, B2C Data, US Household Data, Housing Data, and Mortgage Data that meet your unique needs.

This dataset is part of the database compiled as an outcome of Work Area 1 in project OrganicYieldsUP. Variable definitions can be found here: https://doi.org/10.5281/zenodo.15276082

Work Package 3 (WP3) of the OrganicYieldsUP project focused on compiling off-site data from peer-reviewed scientific literature to complement the on-site experimental data gathered in WP2. The goal was to identify, extract, and structure data on yield-enhancing strategies under organic management across Europe and comparable climate zones. This process was essential for broadening the project’s evidence base and informing subsequent analysis and modelling activities in WP4 and WP5. WP3 followed a systematic approach aligned with PRISMA methodology to ensure transparent and consistent literature screening. A total of 751 publications were initially identified based on defined search criteria. After applying inclusion and exclusion filters, 170 studies passed the first screening phase. From these, data were successfully extracted from 60 scientific publications and entered into the standard WP2/WP3 data template developed in WP1.

The screening of published scientific papers focused on papers published between 2009 and 2024. This time frame was chosen to ensure the use of the most current and relevant studies reflecting recent developments in organic farming methods, data quality standards, and policy frameworks. The screening prioritized English-language publications to maintain consistency in terminology and ensure broad understanding across project partners. Only original peer-reviewed research articles were considered, including case study reports where applicable. The search excluded reviews, editorials, and opinion papers due to the risk of duplicating data already included in WP2. Studies needed to focus explicitly on the impact of organic crop management strategies on yields. Only field trials, long-term experiments, and case studies were included, while pot experiments and single-year studies were excluded to avoid misleading conclusions caused by seasonal anomalies or short-term effects. All included studies had to come from Europe or similar climate zones such as North America or North Africa. During initial screening, titles, abstracts, and keywords had to contain terms related to "yield" and "organic" to be considered relevant. Full-text screening followed, using specific keywords aligned with the WP1 database. Only studies containing the obligatory data fields identified in WP1 were accepted. Publications that had already been included in the WP2 analysis were not considered again in WP3.

Access to an increasing amount of data opens for the application of machine learning models to predict the best combination of models and strategies for bidding of hydro power in a de-regulated market for any given day. This data-set describe the historical performance-gap of two given bidding strategies over several years (2016-2018). Data from two different bidding strategies are presented in the data-set. The first is bidding the expected volume. The expected volumes are found by deterministic optimization against forecasted price and inflow using the SHOP software, and are submitted as fixed hourly bids to the Nord Pool power exchange. The second strategy is stochastic bidding. The stochastic model is based on the deterministic method, but allows for a stochastic representation of inflow to the reservoir and day-ahead market prices. SHOP is a software tool for optimal short-term hydropower scheduling developed by SINTEF Energy Research, used by many hydropower producers in the Nordic market. The total performance-gap for the two strategies in the data-set are calculated as the difference between the optimum value for the relevant bidding date and the value of the investigated strategy. A high number for indicate poor performance. In addition, a set of of relevant variables accessible prior to bidding have been collected and are published in the data-set. Realized- and prognosed prices in the data-set are prices for the NO2 area in Nordpool. The reservoir and watervalue in the data-set are associated with a river system located in south-western Norway

Data Description: Through multi-source data fusion, considering multiple countries, spatial scales, energy types (including biomass), and unifying economic sectors, a carbon dioxide emission inventory for 11 Southeast Asian and South Asian countries (including 7 Southeast Asian countries including Myanmar, Cambodia, Laos, Philippines, Indonesia, Thailand, Malaysia, and 4 South Asian countries including Pakistan, India, Sri Lanka, and Iran) from 2010 to 2020 was constructed to identify the multidimensional heterogeneity of carbon dioxide emissions in each country. This database provides scientific insights to support developing economies in crafting both short- and long-term energy transition strategies, as well as designing context-specific CO₂ emission reduction policies at the national and regional levels.

Consumer Insurance Experience & Demographic Profile

This dataset provides a detailed view of how individuals engage with insurance products, paired with demographic and lifestyle attributes to enable powerful segmentation, behavioral analysis, and customer journey mapping. By combining real-world insurance experiences with contextual information about each respondent’s background and preferences, this dataset supports a wide range of data-driven decision-making for insurance providers, policy designers, marketing teams, and product strategists.

Value of the Dataset Understanding how consumers perceive and interact with insurance offerings is critical to building products that resonate and services that retain. This dataset offers that visibility across multiple dimensions—capturing not only what type of insurance consumers hold and how they purchased it, but also what drives their satisfaction, loyalty, and likelihood to switch. Paired with demographic details like income, education, family status, and lifestyle, this information becomes a foundation for more personalized outreach, better-designed offerings, and improved customer experiences.

Because the data reflects lived experiences across diverse markets, it is particularly valuable for benchmarking consumer sentiment in emerging economies, identifying service delivery gaps, or evaluating potential uptake of new policy formats such as digital or personalized insurance.

Example Use Cases 1. Targeted Product Design A health insurer looking to launch short-term, digital-first plans could filter this dataset for consumers with low policy tenure, high digital communication preference, and dissatisfaction with current providers. This segment would inform feature design and positioning.

1. Competitive Analysis A provider evaluating churn risk can identify patterns among users who have filed claims but report dissatisfaction—indicating operational areas that may be driving customer loss and where improvements could increase retention.

2. Communication Channel Optimization By analyzing preferred communication methods across different demographic segments, insurers can tailor outreach strategies (e.g., SMS vs. in-app chat) to improve engagement and reduce support costs.

3. Market Expansion & Localization International insurers can explore regional variations in satisfaction drivers, awareness levels, and price sensitivity to refine go-to-market strategies in countries like Senegal, Tanzania, or the UAE.

4. Personalized Policy Offer Design Using data on interest in personalized policies and lifestyle indicators, providers can build customizable offerings for consumers more likely to value flexibility, such as frequent travelers or those with irregular incomes.

Insurance-Specific Fields & Descriptions Current Insurance Type Captures the kind of insurance the individual currently holds, with a focus on health insurance in this dataset.

Purchase Method Indicates how the insurance was obtained—through an agent, online, employer, etc.—to understand acquisition channels.

Policy Length Duration of the current policy, categorized (e.g., less than 1 year, 1–3 years, more than 5 years) to analyze tenure-based behaviors.

Satisfaction Self-reported satisfaction with the current insurance provider, useful for benchmarking sentiment.

Top Factor in Choosing Provider Highlights what influenced the purchase decision most—such as coverage options, customer service, pricing, or brand reputation.

Policy Review Frequency Shows how often individuals revisit their policy details or compare with alternatives, revealing levels of engagement or passive behavior.

Filed Claim A yes/no indicator showing whether the consumer has ever filed a claim, useful for analyzing downstream service experiences.

Claim Satisfaction Measures satisfaction with how past claims were handled, providing insight into operational effectiveness.

Primary Value Sought Captures what consumers value most from their insurance—e.g., peace of mind, financial protection, access to quality care.

Likelihood to Recommend Acts as a proxy for Net Promoter Score (NPS), indicating brand advocacy and potential referral behavior.

Biggest Areas for Improvement Open-ended or multi-select responses identifying where insurers can do better—lower premiums, faster claims, more digital tools, etc.

Preferred Method of Communication Indicates how consumers want to be contacted—via online chat, phone, email, SMS—supporting channel strategy optimization.

Preferred Services Details the types of updates or services consumers want—such as claims status, policy changes, or coverage recommendations.

Insurance Awareness Score Self-reported awareness of how insurance works, including policy options, rights, and terms.

Interest in Personalized Policies Captures whether the individual is open to customized insurance plans, an important indicator for usage-ba...