United States - Domestic Financial Sectors; Open Market Paper; Liability, Transactions was 49717.00000 Mil. of $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Domestic Financial Sectors; Open Market Paper; Liability, Transactions reached a record high of 275915.00000 in January of 2006 and a record low of -453542.00000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Domestic Financial Sectors; Open Market Paper; Liability, Transactions - last updated from the United States Federal Reserve on November of 2025.

A survey of consumers at three farmers' markets (FMs) was done near Vancouver, British Columbia. The markets span urban and suburb locales, and the survey's 234 respondents were asked questions about shopping behavior, attitudes toward FMs, and demographic information. The focus of the analysis is on the differences between regulars and non-regulars to the market, where a regular is considered a shopper who shops weekly or bi-weekly. The results show that regulars spend more ($46.36 vs. 33.19 for non-regulars), are much more likely to expect higher prices compared to grocery stores than non-regulars, and buy more products (4.15 vs. 3.1). Regulars also value attributes of FMs differently: they value variety, organic products, and being locally-grown more highly. Organic purchasing behavior is also significantly different with regulars much more likely to say they “always” or “usually” buy organic products. As this is the first study to explicitly analyze regulars at FMs, suggested research directions and methods are offered to help guide future research.

United States - Domestic Financial Sectors; Open Market Paper; Liability, Transactions was 1889.00000 Mil. of $ in April of 2025, according to the United States Federal Reserve. Historically, United States - Domestic Financial Sectors; Open Market Paper; Liability, Transactions reached a record high of 688049.00000 in July of 2006 and a record low of -682602.00000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Domestic Financial Sectors; Open Market Paper; Liability, Transactions - last updated from the United States Federal Reserve on November of 2025.

Text data from the documents surrounding Federal Open Market Committee meetings. Data is scraped from the Federal Reserve website using a web scraper I made using the Scrapy framework which can be found on GitHub at https://github.com/rw19842/Fed-Scraper.

United States - Domestic Financial Sectors; Open Market Paper; Liability, Level was 737780.00000 Mil. of $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Domestic Financial Sectors; Open Market Paper; Liability, Level reached a record high of 1739988.00000 in January of 2006 and a record low of 220.00000 in January of 1945. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Domestic Financial Sectors; Open Market Paper; Liability, Level - last updated from the United States Federal Reserve on November of 2025.

ABSTRACT Food prices play a major role in setting inflation rates, and in recent years’ global climatic conditions has worsened a lot while global demand is increasing due to the growth of the middle class in countries such as China and India. Rising food prices remains a key concern for the government of Saudi Arabia. Saudi Arabia remains vulnerable to increases in food prices due to its high dependence on imports. The Saudi economy is an open-market based economy which is reflected by data of foreign trade with trading partners of the Kingdom. High degree of economic openness of a country causes the domestic inflation rate to be affected by change in the prices of goods in the country of origin. Saudi government is facing the challenge of limiting inflation amid a spike in global food prices. Another major challenge to the effectiveness of the Saudi monetary policy is the lack of autonomy due to the pegged exchange rate system with the US dollar. This paper attempts to study the market dynamics of the kingdom of Saudi Arabia, drivers responsible for inflation and measures that has been taken by the government to deal with the situation.

Graph and download economic data for FOMC Summary of Economic Projections for the Growth Rate of Real Gross Domestic Product, Median (GDPC1MD) from 2025 to 2028 about projection, median, real, GDP, rate, and USA.

The New York State Department of Labor has labor market analysts in 10 regions across the state. This data set outlines where the labor market analysts are located by labor market region.

United States - Domestic Financial Sectors; Open Market Paper; Asset, Transactions was 282489.00000 Mil. of $ in April of 2025, according to the United States Federal Reserve. Historically, United States - Domestic Financial Sectors; Open Market Paper; Asset, Transactions reached a record high of 592687.00000 in July of 2006 and a record low of -635855.00000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Domestic Financial Sectors; Open Market Paper; Asset, Transactions - last updated from the United States Federal Reserve on November of 2025.

Note: Updates to this data product are discontinued. ERS collected data from USDA-accredited State and private certification groups to calculate the extent of certified organic farmland acreage and livestock in the United States. These are presented in tables showing the change in U.S. organic acreage and livestock numbers from 1992 to 2011 (see the National tables section). Data for 1997 and 2000-11 are presented by State and commodity (see the State tables section).This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

This is the supplementary document of interview schedule questions for the data published titled "Survey Data on Farmers Awareness, Perception towards Futures Market" The Interview Schedule includes demographic, training, farming, cost and marketing details of farmers. In addition, it also includes the questions measuring the awareness, and perception of farmers towards futures markets and ends with the details on information sources used by the farmers for taking pricing decisions.

Price risk is one of the major source of risk faced by all farmers. This data set captures the information sources used by the farmers for taking pricing decisions. A survey was carried out with 314 Chillies farmers at Ramanathapuram District of TamilNadu, 383 Turmeric Cultivators in the Erode District of TamilNadu and 221 Cardamom growers in Theni District of TamilNadu. The awareness level and perception towards commodity futures market and farmers preference towards usage of marketing alternatives in selling their produce were recorded in the dataset. The dataset is in MS-Excel format.

Acta Universitatis Sapientiae Economics and Business Abstract & Indexing - ResearchHelpDesk - Acta Universitatis Sapientiae Economics and Business - As a broad-based professional and international journal, the Acta Universitatis Sapientiae, Economics and Business welcomes submissions of applied and theoretical research papers in all fields of Economics and Business. The journal especially encourages submissions on research from management, marketing, quantitative methods and their applications, regional economics, regional development, agricultural economics, and finance. However, The journal is open to research coming from other areas of Economics and Business as well. The primary interest is to publish academic research, but the journal also welcomes good quality research coming from practitioners. Abstracting & Indexing Baidu Scholar Cabell's Whitelist CNKI Scholar (China National Knowledge Infrastructure) CNPIEC - cnpLINKer Dimensions DOAJ (Directory of Open Access Journals) EBSCO (relevant databases) EBSCO Discovery Service Genamics JournalSeek Google Scholar J-Gate JournalTOCs KESLI-NDSL (Korean National Discovery for Science Leaders) Microsoft Academic MyScienceWork Naviga (Softweco) Primo Central (ExLibris) Publons QOAM (Quality Open Access Market) ReadCube Research Papers in Economics (RePEc) Semantic Scholar Sherpa/RoMEO Summon (ProQuest) TDNet Ulrich's Periodicals Directory/Ulrich web WanFang Data WorldCat (OCLC)

The datasets for the Role of Financial Investors on Commodity Futures Risk Premium are weekly datasets for the period from 1995 to 2015 for three commodities in the energy market: crude oil (WTI), heating oil, and natural gas. These datasets contain futures prices for different maturities, open interest positions for each commodity (long and short open interest positions), and S&P 500 composite index. The selected commodities are traded on the New York Mercantile Exchange (NYMEX). The data comes from the Thomson Reuters Datastream and from the Commodity Futures Trading Commission (CFTC).

Acta Universitatis Sapientiae Economics and Business CiteScore 2024-2025 - ResearchHelpDesk - Acta Universitatis Sapientiae Economics and Business - As a broad-based professional and international journal, the Acta Universitatis Sapientiae, Economics and Business welcomes submissions of applied and theoretical research papers in all fields of Economics and Business. The journal especially encourages submissions on research from management, marketing, quantitative methods and their applications, regional economics, regional development, agricultural economics, and finance. However, The journal is open to research coming from other areas of Economics and Business as well. The primary interest is to publish academic research, but the journal also welcomes good quality research coming from practitioners. Abstracting & Indexing Baidu Scholar Cabell's Whitelist CNKI Scholar (China National Knowledge Infrastructure) CNPIEC - cnpLINKer Dimensions DOAJ (Directory of Open Access Journals) EBSCO (relevant databases) EBSCO Discovery Service Genamics JournalSeek Google Scholar J-Gate JournalTOCs KESLI-NDSL (Korean National Discovery for Science Leaders) Microsoft Academic MyScienceWork Naviga (Softweco) Primo Central (ExLibris) Publons QOAM (Quality Open Access Market) ReadCube Research Papers in Economics (RePEc) Semantic Scholar Sherpa/RoMEO Summon (ProQuest) TDNet Ulrich's Periodicals Directory/Ulrich web WanFang Data WorldCat (OCLC)

This dataset has been supplied by the project ‘Understanding and Enhancing the Community Value of Traditional Retail Markets in UK cities’ (Project Reference: ES/P010547/1) conducted by the University of Leeds, the Open University, the National Market Traders Federation, and the New Economics Foundation. The dataset contains interview transcripts, workshop notes, and focus group transcripts that are applicable for qualitative analysis, and survey data that is applicable for statistical analysis.

The qualitative dataset includes transcripts of interviews, workshops, and focus groups about the community value of Traditional Retail Markets (TRM); the context and particularities of Bury Market, Grainger Market, and Queen’s Market; and the market users’ everyday life experiences of these three markets. The quantitative dataset includes the responses of Bury Market, Grainger Market, and Queen’s Market users regarding the economic, social, and cultural value of these markets from a user perspective.

Supporting documentation for the qualitative data includes a Data listing, Information sheets, Consent forms, and Topic guides (Full list in ReadMe file). Supporting documentation for the quantitative data includes Recruitment leaflets, Information sheets, Questionnaires, and Variables description (Full list in ReadMe file). More information about the project can be found at https://trmcommunityvalue.leeds.ac.uk/.

Traditional Retail Markets (TRM) have played a significant role in UK’s towns and cities for centuries but their central community role is threatened by radical changes in retail trends, public sector cuts and, more recently, the increased pressures created by the COVID-19 crisis. Our study provides a new way to understand the community value which traditional markets offer, which we have defined as constituted by three interconnected dimensions: 1. Economic: TRMs as places that provide affordable food, products and services as well as create opportunities for low-cost business start-ups. 2. Social: TRMs as platforms for social mobility and the development of community ties and trust leading to better social inclusion. 3. Cultural: TRMs as spaces for experiencing a diversity of cultures and ethnicities and provide a sense of place for migrants, ethnic minorities and generally vulnerable citizens.

Between February 2018 and September 2021, this project has collected and analysed qualitative and quantitative data to propose a new understanding of the community value that markets can bring about. In addition to reviewing industry, academic, community, media and policy publications to understand the current national and international public discourse around traditional markets, we developed a mixed methods, collaborative and action-oriented research approach. We interviewed over 50 experts and ran workshops with policymakers, market traders, market operators and managers, and representatives from charity organisations and community groups. In order to gain an in-depth understanding of market users’ experiences, we surveyed 1500 market users and run 6 focus groups in three case-study markets: Bury Market, Grainger Market, and Queen's Market.

To extend the potential of this study and have a real societal impact, we have co-produced our research with non-academic partners from the TRM sector (National Market Traders Federation, NMTF) and experts in community economics (New Economics Foundation, NEF). In this way, we have developed usable outputs and tools to support the work of all the groups we have worked with.

I'm fascinated how banking and large hedge funds are utilizing the huge amount of data they possess to drive the stock market in either direction. Many companies do gather these data and sell them again, while they are already public information. I'm a beginner in this world and I want to make majority of the scattered data available to everyone in one place to ease process of analysis with minimal cost possible. his dataset contains the prices for stocks since establishment until November 1st, 2021. The stocks divided based on the market index (Dow Johns, Nasdaq 100, S&P 500, and Russell 3000).

Hope you find dataset is useful.

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Open-Source LLM Market Size 2025-2029

The open-source LLM market size is valued to increase by USD 54 billion, at a CAGR of 33.7% from 2024 to 2029. Increasing democratization and compelling economics will drive the open-source LLM market.

Market Insights

North America dominated the market and accounted for a 37% growth during the 2025-2029.
By Application - Technology and software segment was valued at USD 4.02 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 575.60 million 
Market Future Opportunities 2024: USD 53995.50 million
CAGR from 2024 to 2029 : 33.7%

Market Summary

The Open-Source Large Language Model (LLM) market has experienced significant growth due to the increasing democratization of artificial intelligence (AI) technology and its compelling economics. This global trend is driven by the proliferation of smaller organizations seeking to leverage advanced language models for various applications, including supply chain optimization, compliance, and operational efficiency. Open-source LLMs offer several advantages over proprietary models. They provide greater flexibility, as users can modify and adapt the models to their specific needs. Additionally, open-source models often have larger training datasets, leading to improved performance and accuracy. However, there are challenges to implementing open-source LLMs, such as the prohibitive computational costs and critical hardware dependency. These obstacles necessitate the development of more efficient algorithms and the exploration of cloud computing solutions.
A real-world business scenario illustrates the potential benefits of open-source LLMs. A manufacturing company aims to optimize its supply chain by implementing an AI-powered system to analyze customer demand patterns and predict inventory needs. The company chooses an open-source LLM due to its flexibility and cost-effectiveness. By integrating the LLM into its supply chain management system, the company can improve forecasting accuracy and reduce inventory costs, ultimately increasing operational efficiency and customer satisfaction. Despite the challenges, the market continues to grow as organizations recognize the potential benefits of advanced language models. The democratization of AI technology and the compelling economics of open-source solutions make them an attractive option for businesses of all sizes.

What will be the size of the Open-Source LLM Market during the forecast period?

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The Open-Source Large Language Model (LLM) Market continues to evolve, offering businesses innovative solutions for various applications. One notable trend is the increasing adoption of explainable AI (XAI) methods in LLMs. XAI models provide transparency into the reasoning behind their outputs, addressing concerns around bias mitigation and interpretability. This transparency is crucial for industries with stringent compliance requirements, such as finance and healthcare. For instance, a recent study reveals that companies implementing XAI models have achieved a 25% increase in model acceptance rates among stakeholders, leading to more informed decisions. This improvement can significantly impact product strategy and budgeting, as businesses can confidently invest in AI solutions that align with their ethical and regulatory standards.
Moreover, advancements in LLM architecture include encoder-decoder architectures, multi-head attention, and self-attention layers, which enhance feature extraction and model scalability. These improvements contribute to better performance and more accurate results, making LLMs an essential tool for businesses seeking to optimize their operations and gain a competitive edge. In summary, the market is characterized by continuous innovation and a strong focus on delivering human-centric solutions. The adoption of explainable AI methods and advancements in neural network architecture are just a few examples of how businesses can benefit from these technologies. By investing in Open-Source LLMs, organizations can improve efficiency, enhance decision-making, and maintain a responsible approach to AI implementation.

Unpacking the Open-Source LLM Market Landscape

In the dynamic landscape of large language models (LLMs), open-source solutions have gained significant traction, offering businesses competitive advantages through data augmentation and few-shot learning capabilities. Compared to traditional models, open-source LLMs enable a 30% reduction in optimizer selection time and a 25% improvement in model accuracy for summarization tasks. Furthermore, distributed training and model compression techniques allow businesses to process larger training dataset sizes with minimal tokenization process disruptions, result

This paper examines the effects of oil demand and supply shocks on emerging market economies in Latin America using a Bayesian vector autoregressive (VAR) model that combines zero and sign restrictions. Our results highlight the importance of separately identify the oil market shocks. The higher price of oil driven by increased global demand produces higher output growth in Brazil, Colombia, and Chile. The results are more persistent for Brazil and Colombia likely due to increased income from oil exports, as both economies are net oil exporters. The better times in the domestic economies result in lower uncertainty and appreciation of the exchange rate in all countries in the sample. Oil supply shocks and oil-specific demand shocks are not statistically significant for most variables. Our results provide important insights into the appropriate exchange rate policy in emerging market economies.

United States - All Sectors; Open Market Paper; Liability, Level was 1389706.00000 Mil. of $ in April of 2025, according to the United States Federal Reserve. Historically, United States - All Sectors; Open Market Paper; Liability, Level reached a record high of 2111666.00000 in April of 2007 and a record low of 315.00000 in October of 1945. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - All Sectors; Open Market Paper; Liability, Level - last updated from the United States Federal Reserve on November of 2025.