We offer three easy-to-understand equity data packages to fit your business needs. Visit intrinio.com/pricing to compare packages.

Bronze

The Bronze package is ideal for developing your idea and prototyping your platform with high-quality EOD equity pricing data, standardized financial statement data, and supplementary fundamental datasets.

When you’re ready for launch, it’s a seamless transition to our Silver package for additional data sets, 15-minute delayed equity pricing data, expanded history, and more.

• Historical EOD equity prices & technicals (10 years history)
• Security reference data
• Standardized & as-reported financial statements (5 years history)
• 7 supplementary fundamental data sets

Bronze Benefits:

• Web API access
• 300 API calls/minute limit
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Silver

The Silver package is ideal for startups that are in development, testing, or in the beta launch phase. Hit the ground running with 15-minute delayed and historical intraday and EOD equity prices, plus our standardized and as-reported financial statement data with nine supplementary data sets, including insider transactions and institutional ownership.

When you’re ready to scale, easily move up to the Gold package for our full range of data sets and full history, real-time equity pricing data, premium support options, and much more.

• 15-minute delayed & historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (10 years history)
• 9 supplementary fundamental data sets

Silver Benefits:

• Web API access
• 2,000 API calls/minute limit
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Gold

The Gold package is ideal for funded companies that are in the growth or scaling stage, as well as institutions that are innovating within the fintech space. This full-service solution offers our complete collection of equity pricing data feeds, from real-time to historical EOD, plus standardized financial statement data and nine supplementary feeds.

You’ll also have access to our wide range of modern access methods, third-party data via Intrinio’s API with licensing assistance, support from our team of expert engineers, custom delivery architectures, and much more.

• Real-time equity prices
• Historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (full history)
• 9 supplementary fundamental data sets

Gold Benefits:

• No exchange fees
• No user reporting or variable per-user exchange fees
• High liquidity (6%+)
• Web API & WebSocket access
• 2,000 API calls/minute limit
• Customizable access methods (Snowflake, FTP, etc.)
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support
• Access to engineering team
• Concierge customer success team
• Comarketing & promotional initiatives

Platinum

Don’t see a package that fits your needs? Our team can design premium custom packages for institutions.

Get access to a curated dataset of over 160,000 products from Target.com, all featuring a 30% or greater discount. This collection is ideal for anyone studying pricing trends, consumer deal behavior, or building retail pricing intelligence platforms.

The data spans categories including home goods, electronics, fashion, beauty, and personal care, offering insights into Target’s promotional strategies and markdown inventory.

What’s Included:

• Product Title & URL

• Original & Discounted Prices

• % Discount

• Brand, Category

• Image links, Description

• Availability (in stock / out of stock)

• Scraped Date

Use Cases:

• Build daily deal apps or deal newsletters

• Monitor Target’s price drops and markdown strategy

• Analyze clearance vs. everyday discount trends

• Create dashboards for pricing analytics

• Feed retail bots or price comparison engines

Updates:

This dataset can be refreshed weekly or monthly upon request.

The All CMS Data Feeds dataset is an expansive resource offering access to 118 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis tools and platforms. This ensures that users can easily integrate the data into their existing wo...

USDA Economic Research Service (ERS) compares prices paid by consumers for food with prices received by farmers for corresponding commodities. This data set reports these comparisons for a variety of foods sold through retail food stores such as supermarkets and super centers. Comparisons are made for individual foods and groupings of individual foods-market baskets-that represent what a typical U.S. household buys at retail in a year. The retail costs of these baskets are compared with the money received by farmers for a corresponding basket of agricultural commodities.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.

Wheat fell to 519 USd/Bu on September 26, 2025, down 1.52% from the previous day. Over the past month, Wheat's price has risen 3.34%, but it is still 10.52% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Wheat - values, historical data, forecasts and news - updated on September of 2025.

This product provides information on Non-Board Feed Grain Prices, over a ten-year period. Comparison of the $/tonne prices for Wheat, Oats, Barely in Lethbridge, Calgary, Red Deer, Edmonton, Peace River/Grande Prairie and Vermilion are included.

This product provides information on Non-Board Feed Grain Prices, over a ten-year period. Comparison of the $/tonne prices for Wheat, Oats, Barely in Lethbridge, Calgary, Red Deer, Edmonton, Peace River/Grande Prairie and Vermilion are included.

Corn fell to 421.50 USd/BU on September 26, 2025, down 1.00% from the previous day. Over the past month, Corn's price has risen 10.20%, and is up 0.84% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on September of 2025.

Fruit Juice Retail Mapping – In-Store Product Availability, Pricing, and Shelf Visibility

This dataset offers granular, on-the-ground intelligence on the presence, pricing, shelf positioning, and availability of packaged fruit juice brands across various retail outlets. Captured by field agents directly from stores, the data includes structured inputs such as outlet attributes, product barcodes, pricing, shelf photos, and product availability checks. It is designed to help FMCG teams track in-store performance, benchmark competitors, and optimize retail execution strategies in real time.

Core Value Proposition Retail environments are dynamic, and winning at the shelf requires timely, accurate data on how products are being positioned and priced across thousands of locations. This dataset bridges that gap by providing a real-world, store-level view into the execution of fruit juice products—across both modern and traditional retail formats.

It enables stakeholders to move beyond assumptions and market averages, offering visibility into specific brands, SKUs, and store types. Teams can assess the effectiveness of distribution strategies, monitor compliance with planograms or promotional campaigns, and uncover competitive gaps across different regions.

Use Cases by Role Trade Marketing Teams

Verify on-shelf product presence and identify visibility gaps across retail partners

Monitor planogram compliance with real photo documentation

Compare pricing vs. competitors in-store to ensure promotional pricing is effective

Track availability of new SKUs or promotional bundles

Sales & Field Operations

Prioritize store visits based on stockout frequency or missing SKUs

Identify retailers not carrying key products or brands and target them for onboarding

Validate retail execution of in-market activations or price drops

Map payment method availability for potential POS integrations or retail enablement

Brand & Category Managers

Measure retail footprint and market penetration at the brand level

Benchmark share of shelf and price positioning versus competitors across retail types

Identify regional pricing inconsistencies or availability issues

Understand consumer-facing shelf experience using storefront and shelf photos

Insights & Strategy Teams

Segment retail environments by outlet type, city, or region for performance benchmarking

Identify trends in availability, pricing, and product assortment

Support business cases for expanding into underserved channels or cities

Feed data into forecasting or market sizing models using actual in-store coverage

Revenue Growth & Pricing Teams

Monitor how price strategies are being executed in the field

Identify unauthorized discounting or pricing inconsistencies by outlet

Evaluate price sensitivity by comparing prices across similar store types

Use competitor pricing benchmarks to refine promotional calendars

Key Data Components Outlet Details

Outlet Name, Type, Address, City, Country, Latitude, Longitude These fields provide context around where the product data was captured, supporting regional and channel segmentation.

Storefront & Section Photos

Storefront Photo, Juice Section Photo Visual confirmation of retail execution and visibility, allowing internal teams to audit displays and merchandising quality.

Product Availability & Pricing

Is [Brand] Available? fields for each juice brand (e.g., Chivita, Capri-sun, Ribena, etc.)

Price, Barcode, and Shelf Photo for each product These fields allow for detailed, SKU-level tracking of which products are available, at what price, and how they appear on the shelf.

Additional Retail Attributes

Payment Methods, Products Offered, Additional Attributes These help teams understand store-level characteristics that may influence sales strategy, such as whether the outlet supports mobile payments or carries complementary categories.

Competitive Tracking Brands included in the dataset (e.g., Chivita Orange, Happy Hour, Active, Capri-sun, Ribena, 5Alive, Frudi, LaCasera, Sosa, Wilson’s Lemonade, etc.) are all tracked for:

On-shelf presence (yes/no)

Price

Barcode

Shelf-level photo capture

This makes the dataset a strong foundation for competitive audits, pricing analysis, and retail presence benchmarking across brands and territories.

Summary The Fruit Juice Retail Mapping dataset provides the ground truth for how fruit juice products are presented, priced, and positioned at the point of sale. It’s built to enable smarter decision-making across marketing, sales, trade, and insights functions—helping teams move faster, identify gaps, and act on opportunities with precision. Whether the goal is to improve coverage, enforce pricing policy, design promotions, or win more shelf space, this data offers the visibility needed to execute with confidence.

The Yoox Products Database is a comprehensive, ready-to-use dataset featuring over 250,000 product listings from the Yoox online fashion platform. This database is ideal for eCommerce analytics, price comparison tools, trend forecasting, competitor research, and building product recommendation engines.

Inside, you’ll find structured CSV files neatly compressed in a ZIP archive, making it simple to import into any BI tool, database, or application.

Key Data Fields:

• Product IDs & SKUs

• Product Titles & Descriptions

• Categories & Subcategories

• Brand Information

• Pricing & Discounts

• Availability & Stock Status

• Image Links

Perfect for data analysts, developers, marketers, and online retailers looking to harness fashion retail insights.

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

Dynamic Ticket Pricing AI Market Outlook

According to our latest research, the global Dynamic Ticket Pricing AI market size reached USD 1.42 billion in 2024, reflecting the rapid adoption of artificial intelligence in ticketing strategies across diverse industries. The market is expanding robustly, with a CAGR of 21.7% projected from 2025 to 2033. By the end of 2033, the market is expected to achieve a value of USD 10.34 billion. This significant growth is primarily driven by the increasing demand for real-time pricing optimization and revenue maximization, as organizations in sports, entertainment, transportation, and hospitality sectors leverage AI-powered solutions to respond dynamically to fluctuating market demand and consumer behavior.

A primary growth factor in the Dynamic Ticket Pricing AI market is the growing sophistication of AI algorithms that enable highly granular and real-time pricing adjustments. As consumer purchasing patterns become increasingly unpredictable, traditional static pricing models are proving inadequate for maximizing occupancy and revenue. AI-powered dynamic ticket pricing systems utilize machine learning, historical data, and predictive analytics to continuously assess demand, competitor pricing, and numerous external variables, allowing organizations to offer the right price at the right time. This capability is especially critical in sectors such as sports and live entertainment, where ticket demand can spike or plummet rapidly based on team performance, artist popularity, or even weather conditions. The ability to automate price changes and personalize offers is leading to higher conversion rates, improved customer satisfaction, and increased profitability for event organizers and ticketing platforms alike.

Another significant driver is the digital transformation sweeping through the transportation and hospitality sectors. Airlines, rail operators, and hotel chains are increasingly relying on dynamic pricing AI to manage their perishable inventory and optimize yield. The proliferation of online booking platforms and mobile ticketing applications has made it easier to collect and analyze vast amounts of consumer data, which in turn feeds more accurate and responsive pricing models. Furthermore, the integration of AI-driven dynamic pricing with CRM and marketing automation tools is enabling organizations to deliver targeted promotions and upsell opportunities, thereby enhancing overall customer lifetime value. The growing emphasis on operational efficiency and data-driven decision-making is compelling both large enterprises and SMEs to invest in advanced pricing technologies.

Additionally, the expansion of the Dynamic Ticket Pricing AI market is fueled by the increasing pressure on event organizers and service providers to remain competitive in an era of hyper-connectivity and instant access to information. Consumers today are more price-sensitive and have greater visibility into pricing trends, thanks to comparison websites and social media. Dynamic pricing AI offers a strategic advantage by enabling organizations to react swiftly to competitor moves, market trends, and real-time feedback. This agility not only helps in capturing incremental revenue during periods of high demand but also in filling seats or rooms that might otherwise go unsold. As regulatory frameworks around pricing transparency and consumer protection continue to evolve, AI-powered solutions are also being designed with compliance and fairness in mind, further accelerating their adoption across regions and industries.

In the realm of sports, the adoption of dynamic pricing strategies has become increasingly prevalent, particularly with the advent of Sports Ticket Dynamic Pricing Consulting services. These services are designed to assist sports organizations in navigating the complexities of real-time pricing adjustments, ensuring that ticket prices are aligned with current market demand and consumer expectations. By leveraging advanced analytics and AI-driven insights, sports teams and event organizers can optimize ticket sales, enhance fan engagement, and maximize revenue streams. The integration of dynamic pricing with fan loyalty programs and personalized offers further amplifies the value proposition, creating a more tailored and rewarding experience for sports enthusiasts. As the sports industry continues to evolve, consulting services play a pivot

Soybeans rose to 1,014 USd/Bu on September 26, 2025, up 0.17% from the previous day. Over the past month, Soybeans's price has fallen 1.29%, and is down 4.86% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Soybeans - values, historical data, forecasts and news - updated on September of 2025.

Graph and download economic data for Global price of Wheat (PWHEAMTUSDM) from Jan 1990 to Jun 2025 about wheat, World, and price.

Poultry fell to 8.14 BRL/Kgs on September 26, 2025, down 0.12% from the previous day. Over the past month, Poultry's price has risen 13.21%, and is up 8.10% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Poultry - values, historical data, forecasts and news - updated on September of 2025.

View Refinitiv's New York Stock Exchange (NYSE) Market Data and benefit from full-depth market-by-price data, available as real-time and historical records.

Feeder Cattle rose to 356.96 USd/Lbs on September 26, 2025, up 0.82% from the previous day. Over the past month, Feeder Cattle's price has fallen 2.31%, but it is still 44.37% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Feeder Cattle - values, historical data, forecasts and news - updated on September of 2025.

The Oil Swap Model service by TraditionData provides a real-time source for oil swaps pricing data, drawn from a combination of electronic data feeds and broker input.

• Combines electronic data and broker insights for real-time pricing.
• Coverage of crude and refined products with extensive forward curve coverage.
• Enables users to spot arbitrage opportunities and assess market risk.

Discover more about this service at Oil Swap Model.

This repository includes python scripts and input/output data associated with the following publication:

[1] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States Electricity Markets". Renewable & Sustainable Energy Reviews 2019. https://doi.org/10.1016/j.rser.2019.109594

Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.

Many of the scripts, data, and descriptive text in this repository are shared with the following publication:

[2] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles". Applied Energy 2019, 256, 113734. https://doi.org/10.1016/j.apenergy.2019.113734

All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.

Contents

pvvm/*.py : Python module with functions for modeling PV generation and calculating PV energy revenue, capacity value, and emissions offset.

notebooks/*.ipynb : Jupyter notebooks, including:

pvvm-vos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, assign nodes to capacity zones, download NSRDB input data, and reproduce some figures in [1]

pvvm-example-generation.ipynb: Example scripts demonstrating the use of the PV generation model and a sensitivity analysis of PV generator assumptions

pvvm-example-plots.ipynb: Example scripts demonstrating different plotting functions

validate-pv-monthly-eia.ipynb: Scripts and plots for comparing modeled PV generation with monthly generation reported in EIA forms 860 and 923, as discussed in SI Note 3 of [1]

validate-pv-hourly-pvdaq.ipynb: Scripts and plots for comparing modeled PV generation with hourly generation reported in NREL PVDAQ database, as discussed in SI Note 3 of [1]

pvvm-energyvalue.ipynb: Scripts for calculating the wholesale energy market revenues of PV and reproducing some figures in [1]

pvvm-capacityvalue.ipynb: Scripts for calculating the capacity credit and capacity revenues of PV and reproducing some figures in [1]

pvvm-emissionsvalue.ipynb: Scripts for calculating the emissions offset of PV and reproducing some figures in [1]

pvvm-breakeven.ipynb: Scripts for calculating the breakeven upfront cost and carbon price for PV and reproducing some figures in [1]

html/*.html : Static images of the above Jupyter notebooks for viewing without a python kernel

data/lmp/*.gz : Day-ahead nodal locational marginal prices (LMPs) and marginal costs of energy (MCE), congestion (MCC), and losses (MCL) for CAISO, ERCOT, MISO, NYISO, and ISONE.

At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will be linked here with the complete dataset.

results/*.csv.gz : Simulation results associated with [1], including modeled energy revenue, capacity credit and revenue, emissions offsets, and breakeven costs for PV systems at all LMP nodes

Data notes

ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (https://opensource.org/licenses/MIT), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.

ISO-specific notes on LMP data:

CAISO data from http://oasis.caiso.com/mrioasis/logon.do are used pursuant to the terms at http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse.

ERCOT data are from http://www.ercot.com/mktinfo/prices.

MISO data are from https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/ and https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/.

PJM data were originally downloaded from https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx and https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx. At the time of this writing these data are currently hosted at https://dataminer2.pjm.com/feed/da_hrl_lmps and https://dataminer2.pjm.com/feed/rt_hrl_lmps.

NYISO data from http://mis.nyiso.com/public/ are used subject to the disclaimer at https://www.nyiso.com/legal-notice.

ISONE data are from https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly and https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.

Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-vos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.

Usage notes

Code is provided under the MIT License, as specified in the pvvm/LICENSE file and at the top of each *.py file.

Updates to the code, if any, will be posted in the non-static repository at https://github.com/patrickbrown4/pvvm_vos. The code in the present repository has the following version-specific dependencies:

matplotlib: 3.0.3

numpy: 1.16.2

pandas: 0.24.2

pvlib: 0.6.1

scipy: 1.2.1

tqdm: 4.31.1

To use the NSRDB download functions, you will need to modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from https://developer.nrel.gov/signup/. Locations can be specified by passing (latitude, longitude) floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, you will need to request a googlemaps API key (https://developers.google.com/maps/documentation/javascript/get-api-key) and insert it in the "settings.py" file.

Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: https://github.com/catalyst-cooperative/pudl.