Context:

“The Universal Product Code (UPC) is a barcode symbology that is widely used in the United States, Canada, United Kingdom, Australia, New Zealand, in Europe and other countries for tracking trade items in stores.

“UPC (technically refers to UPC-A) consists of 12 numeric digits, that are uniquely assigned to each trade item. Along with the related EAN barcode, the UPC is the barcode mainly used for scanning of trade items at the point of sale, per GS1 specifications.[1] UPC data structures are a component of GTINs and follow the global GS1 specification, which is based on international standards. But some retailers (clothing, furniture) do not use the GS1 system (rather other barcode symbologies or article number systems). On the other hand, some retailers use the EAN/UPC barcode symbology, but without using a GTIN (for products, brands, sold at such retailers only).”

-- Tate. (n.d.). In Wikipedia. Retrieved August 18, 2017, from https://en.wikipedia.org/wiki/Plagiarism. Text reproduced here under a CC-BY-SA 3.0 license.

Content:

This dataset contains just over 1 million UPC codes and the names of the products associated with them.

Acknowledgements:

While UPC’s themselves are not copyrightable, the brand names and trademarks in this dataset remain the property of their respective owners.

Inspiration:

• Can you use this dataset to generate new product names?
• Can you use this in conjunction with other datasets to disambiguate products?

Global Food Safety Big Data market size 2025 was XX Million. Food Safety Big Data Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Open Food Facts Nutrition table detection dataset

This dataset was used to train the nutrition table object detection model running in production at Open Food Facts. Images were collected from the Open Food Facts database and labeled manually. Just like the original images, the images in this dataset are licensed under the Creative Commons Attribution Share Alike license (CC-BY-SA 3.0).

  Fields

image_id: Unique identifier for the image, generated from the barcode and… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/nutrition-table-detection.

This dataset represents the list of CA WIC authorized food items identified by food category and subcategory. Each item is uniquely identified by a Universal Product Code (UPC) or Price Look-Up code (PLU) for WIC electronic benefit transfer (EBT). The WIC Authorized vendors use the CA Authorized Product List (APL) to transact WIC food items at cash registers. The APL plays a crucial role in supporting WIC EBT purchases. WIC EBT requires vendor systems to maintain the APL to match the scanned food items' UPCs to ensure they are on the APL. The food items identified by UPC and PLU can be found in the data files below. When you download the files, Excel may prompt you to automatically format the data. If prompted, you may want to hit ‘Don’t Convert’ so that Excel leaves the data as is without any formatting or data conversions.

The Women, Infants and Children (WIC) Program is a federally-funded health and nutrition program that provides assistance to pregnant women, new mothers, infants, and children under age five. WIC helps California families by providing food benefits to individual participants based on their nutritional need and risk assessment. The food benefits can be used to purchase healthy supplemental foods from approximately 3,800 WIC authorized vendor stores throughout the State. WIC also provides nutritional education, breastfeeding support, healthcare referrals, and other community services. Participants must meet income guidelines and other criteria. Currently, 84 WIC agencies provide services monthly to approximately one million participants at several hundred sites in local communities throughout the State.

Open Prices

What is Open Prices?

Open Prices is a project to collect and share prices of products around the world. It's a publicly available dataset that can be used for research, analysis, and more. Open Prices is developed and maintained by Open Food Facts.

There are currently few companies that own large databases of product prices at the barcode level. These prices are not freely available, but sold at a high price to private actors, researchers and other organizations that can afford them.

Open Prices aims to democratize access to price data by collecting and sharing product prices under an open licence. The data is available under the Open Database License (ODbL), which means that it can be used for any purpose, as long as you credit Open Prices and share any modifications you make to the dataset. Images submitted as proof are licensed under the Creative Commons Attribution-ShareAlike 4.0 International.

Dataset description

This dataset contains in Parquet format all price information contained in the Open Prices database. The dataset is updated daily.

Here is a description of the most important columns:

• id: The ID of the price in DB
• product_code: The barcode of the product, null if the product is a "raw" product (fruit, vegetable, etc.)
• category_tag: The category of the product, only present for "raw" products. We follow Open Food Facts category taxonomy for category IDs.
• labels_tags: The labels of the product, only present for "raw" products. We follow Open Food Facts label taxonomy for label IDs.
• origins_tags: The origins of the product, only present for "raw" products. We follow Open Food Facts origin taxonomy for origin IDs.
• price: The price of the product, with the discount if any.
• price_is_discounted: Whether the price is discounted or not.
• price_without_discount: The price of the product without discount, null if the price is not discounted.
• price_per: The unit for which the price is given (e.g. "KILOGRAM", "UNIT")
• currency: The currency of the price
• location_osm_id: The OpenStreetMap ID of the location where the price was recorded. We use OpenStreetMap to identify uniquely the store where the price was recorded.
• location_osm_type: The type of the OpenStreetMap location (e.g. "NODE", "WAY")
• location_id: The ID of the location in the Open Prices database
• date: The date when the price was recorded
• proof_id: The ID of the proof of the price in the Open Prices DB
• owner: a hash of the owner of the price, for privacy.
• created: The date when the price was created in the Open Prices DB
• updated: The date when the price was last updated in the Open Prices DB
• proof_file_path: The path to the proof file in the Open Prices DB
• proof_type: The type of the proof. Possible values are RECEIPT, PRICE_TAG, GDPR_REQUEST, SHOP_IMPORT
• proof_date: The date of the proof
• proof_currency: The currency of the proof, should be the same as the price currency
• proof_created: The datetime when the proof was created in the Open Prices DB
• proof_updated: The datetime when the proof was last updated in the Open Prices DB
• location_osm_display_name: The display name of the OpenStreetMap location
• location_osm_address_city: The city of the OpenStreetMap location
• location_osm_address_postcode: The postcode of the OpenStreetMap location

How can I download images?

All images can be accessed under the https://prices.openfoodfacts.org/img/ base URL. You just have to concatenate the proof_file_path column to this base URL to get the full URL of the image (ex: https://prices.openfoodfacts.org/img/0010/lqGHf3ZcVR.webp).

Can I contribute to Open Prices?

Of course! You can contribute by adding prices, trough the Open Prices website or through Open Food Facts mobile app.

To participate in the technical development, you can check the Open Prices GitHub repository.

The global Barcode Data Capture System market is experiencing robust growth, driven by the increasing adoption of automation and digitization across various industries. While the exact market size for 2025 isn't provided, considering the common sizes of similar technology markets and a plausible CAGR of 8% (a reasonable estimate given the consistent demand for efficiency improvements in logistics, manufacturing, and healthcare), we can project a market valuation of approximately $2.5 billion for 2025. This growth is fueled by several key factors, including the rising demand for real-time inventory management, improved supply chain visibility, and the need for efficient data collection in diverse sectors like food and beverage, healthcare, and general manufacturing. The market is segmented by device type (linear number, linear alphanumeric, 2D design) and application, reflecting the diverse needs of different industries. Trends such as the integration of barcode scanners with cloud-based platforms, the rise of mobile barcode scanning, and increasing adoption of advanced imaging technologies are further boosting market expansion. However, factors such as high initial investment costs and the need for specialized technical expertise can restrain market growth to some extent. The forecast period of 2025-2033 is expected to witness continued growth, potentially reaching over $4 billion by 2033, driven by ongoing technological advancements and increasing adoption across emerging markets. The competitive landscape is characterized by a mix of established players and emerging companies offering a range of barcode data capture solutions. Key players are focusing on innovation and strategic partnerships to gain a competitive edge. Companies like Fishbowl, Automation-Plus, Inc., and Diamond Technologies, Inc. are actively contributing to market growth through their product offerings and technological advancements. Regional market distribution will likely see continued dominance from North America and Europe initially, however, the Asia-Pacific region is poised for significant growth due to increasing industrialization and expanding e-commerce sectors. This dynamic market presents opportunities for both established companies and new entrants to capitalize on the growing demand for efficient and reliable barcode data capture systems.

We present the database of nine commercially important clam species in Chile. They were collected in the coasts of Chile.

These data are intended to support the barcode gene sequence databases to identify these species.

The database includes sampling records (and include photography of the shell, Photograph, shell, DNA and DNA sequences).

QR Code Nutrition Label Market Outlook

According to our latest research, the QR Code Nutrition Label market size reached USD 1.3 billion globally in 2024, and it is poised to grow at a robust CAGR of 13.2% during the forecast period, reaching an estimated USD 3.7 billion by 2033. The growth in this market is primarily driven by increasing consumer demand for transparent and easily accessible nutritional information, stringent regulatory requirements, and the rapid adoption of digital technologies across the food, beverage, and pharmaceutical sectors. As per our latest research, the industry is witnessing a paradigm shift towards digital labeling solutions, with QR code-based nutrition labels emerging as a preferred choice for both manufacturers and consumers seeking real-time, dynamic, and interactive product information.

One of the primary growth factors propelling the QR Code Nutrition Label market is the rising consumer awareness regarding health and wellness. Modern consumers are increasingly conscious about the nutritional content of the products they consume, demanding more detailed and accurate information than what traditional labels offer. QR code nutrition labels bridge this gap by providing instant access to comprehensive data, including ingredients, allergens, sourcing, and even sustainability metrics. This shift is further amplified by the growing prevalence of smartphones and mobile internet, which makes scanning QR codes a seamless experience. The adoption of QR code labels is also supported by global health initiatives and campaigns that encourage transparent labeling, thereby fostering consumer trust and brand loyalty.

Another significant growth driver is the evolving regulatory landscape governing food safety and labeling standards. Governments and regulatory bodies across the globe are mandating stricter disclosure norms and traceability in food and pharmaceutical products. QR code nutrition labels offer a cost-effective and scalable solution to comply with these requirements, enabling real-time updates and ensuring that consumers receive the most current information. The ability to dynamically update nutritional data in response to product reformulations or regulatory changes without the need to reprint physical labels is a substantial advantage for manufacturers. This regulatory impetus is especially pronounced in regions such as North America and Europe, where compliance with nutritional transparency is increasingly enforced.

Technological advancements in digital labeling solutions constitute another critical factor driving market expansion. Innovations in QR code generation, data analytics, and integration with enterprise resource planning (ERP) systems are enhancing the functionality and reliability of QR code nutrition labels. These technologies enable manufacturers and retailers to gather valuable insights into consumer behavior, track product journeys, and optimize supply chain management. Furthermore, QR code nutrition labels support multilingual information delivery and accessibility features, making them suitable for diverse consumer bases across geographies. As digital transformation accelerates across industries, the QR Code Nutrition Label market is anticipated to witness sustained growth, with increased investments in software, hardware, and services.

From a regional perspective, North America currently dominates the QR Code Nutrition Label market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The robust presence of leading food, beverage, and pharmaceutical companies, coupled with high consumer awareness and stringent regulatory frameworks, underpins the market’s strength in these regions. Meanwhile, the Asia Pacific region is expected to exhibit the fastest growth rate over the forecast period, driven by rapid urbanization, rising disposable incomes, and increasing digital penetration. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by emerging regulatory initiatives and growing consumer demand for transparency in product labeling.

Market Overview:

The Global Industrial Barcode Scanners Market Size is expected to grow at a CAGR of 5.5% from 2022 to 2030. The market growth is attributed to the increasing demand for barcode scanners in various industries such as medical, transportation, and manufacturing. Additionally, the growing demand for 2D imagers and laser scanners is also contributing to the growth of the global Industrial Barcode Scanner market. North America dominates the global industrial barcode scanner market followed by Europe and Asia Pacific.

Product Definition:

An industrial barcode scanner is a device that scans barcodes and outputs their data to a computer or other electronic device. Industrial barcode scanners are used in factories, warehouses, and other industrial settings to speed up the process of scanning items and tracking their movement.

Laser Scanners:

Laser scanners are automated machines that read and capture data from objects by scanning them with a laser beam. They work on the principle of optical recognition wherein the machine analyzes an object and if it is a match, it gives out a positive reading while if not, it gives out a negative reading. These devices have gained popularity in recent years owing to their ability to scan and read barcodes at high speeds without any errors or misreadings.

2D Imagers:

2D images are used in industrial barcode scanners to convert the 2D image of the object into a digital image which is further processed by the computer. The main function of 2D imagers is to provide an interface between human and machine so that user can easily understand what he/she is scanning and where he/she wants to go with it. It helps in providing feedback for users while they are using barcode scanners, thus making them easy and intuitive to use.

Linear Imagers:

Linear imagers are devices that generate image data through a line scan process. They can be found in industrial barcode scanners because they produce images quickly, which helps speed up the scanning process. Linear imagers are also useful for scanning small or difficult-to-read areas of a barcode.

Application Insights:

The other application segment includes food, retail, and logistics applications. The demand for barcode scanners in other applications is expected to grow at a significant rate over the forecast period owing to their increasing use in warehouses, stores, and shops for inventory management as well as tracking purposes. Industrial barcode scanners are used in the medical field for various purposes such as drug tracking, inventory control, and patient data capture. Industrial Barcode Scanners are used in transportation for the purposes of automating inventory, tracking shipments, and fraud prevention. The main uses of industrial barcode scanners are in manufacturing. They are used to track and inventory products, as well as to automate processes.

Regional Analysis:

Asia Pacific dominated the global market in terms of revenue share at over 38.0% and is expected to continue its dominance throughout the forecast period. The regional growth is mainly driven by China, which accounted for a revenue share of over 80.0% owing to high industrialization coupled with government initiatives such as Made In and Make In India encouraging manufacturing activities within the country has led to an increase in Barcode Label printing, thereby driving demand across numerous industries including food & beverages, pharmaceuticals, chemicals, and others.

Growth Factors:

• Increasing demand for industrial barcode scanners in the automotive and manufacturing sectors due to the growing trend of automation and globalization.
• The proliferation of handheld industrial barcode scanners that are easy to use and offer superior performance, which is resulting in their increased adoption across a wide range of industries.
• Growing demand for ruggedized industrial barcode scanners that can withstand harsh environmental conditions, thus expanding their deployment in various end-user industries such as oil & gas, mining, and construction.
• The rising popularity of 2D barcodes among businesses owing to its ability to store more data than traditional 1D linear codes, thereby driving the demand for high-speed 2D industrial barcode scanners.

Report Scope

The global food traceability technology market is experiencing robust growth, driven by increasing consumer demand for transparency and safety in the food supply chain. Stringent government regulations regarding food safety and traceability, coupled with the rising incidence of foodborne illnesses, are further fueling market expansion. The market is segmented by application (fresh food, dairy products, meat, and others) and by technology (RFID/RTLS, GPS, barcodes, infrared, and biometrics). RFID/RTLS currently holds the largest market share due to its ability to provide real-time tracking and monitoring capabilities, enhancing efficiency and minimizing risks throughout the supply chain. However, the adoption of GPS and barcode technologies remains significant, particularly in logistics and distribution. The high initial investment costs associated with some technologies, like RFID, and the lack of standardization across different systems present challenges to market growth. Nonetheless, the integration of advanced analytics and AI-powered solutions is transforming the landscape, offering improved data insights and predictive capabilities for better inventory management and risk mitigation. This trend is expected to drive further adoption, particularly within larger food processing and distribution companies. Technological advancements, coupled with ongoing efforts to improve data security and interoperability, will shape the market's future trajectory. The North American and European regions currently dominate the food traceability technology market, driven by high consumer awareness and established regulatory frameworks. However, Asia-Pacific is poised for significant growth, fueled by increasing middle-class incomes and rising demand for safe and high-quality food products in countries like China and India. Companies such as Honeywell, SAP, and Trimble are leading players in the market, offering comprehensive solutions that integrate various technologies. The competitive landscape is dynamic, with smaller companies specializing in niche applications and technologies contributing to innovation. The market's future growth trajectory will depend on continued technological advancements, the implementation of effective regulatory frameworks, and the increasing collaboration between industry stakeholders to ensure seamless data exchange and transparency across the food supply chain. Focus on sustainability and reducing food waste through improved traceability is further contributing to market expansion.

Taxonomic identification of biological materials can be achieved through DNA barcoding, where an unknown “barcode” sequence is compared to a reference database. In many disciplines, obtaining accurate taxonomic identifications can be imperative (e.g., evolutionary biology, food regulatory compliance, forensics). The Barcode of Life DataSystems (BOLD) and GenBank are the main public repositories of DNA barcode sequences. In this study, an assessment of the accuracy and reliability of sequences in these databases was performed. To achieve this, 1) curated reference materials for plants, macro-fungi and insects were obtained from national collections, 2) relevant barcode sequences (rbcL, matK, trnH-psbA, ITS and COI) from these reference samples were generated and used for searching against both databases, and 3) optimal search parameters were determined that ensure the best match to the known species in either database. While GenBank outperformed BOLD for species-level identification of insect taxa (53% and 35%, respectively), both databases performed comparably for plants and macro-fungi (~81% and ~57%, respectively). Results illustrated that using a multi-locus barcode approach increased identification success. This study outlines the utility of the BLAST search tool in GenBank and the BOLD identification engine for taxonomic identifications and identifies some precautions needed when using public sequence repositories in applied scientific disciplines.

Raw tracking data files used to produce the example data analysis. Contains tracking data from two flocks over several weeks. Data are focused on flocks foraging at a food source. Paper published in Methods in Ecology and Evolution, https://doi.org/10.1111/2041-210X.13005

Carbon Footprint Food Scanner Market Outlook

According to our latest research, the global carbon footprint food scanner market size reached USD 1.24 billion in 2024, reflecting robust growth driven by heightened consumer and regulatory focus on sustainability. The market is projected to grow at a CAGR of 18.7% from 2025 to 2033, with the total market size expected to reach USD 6.08 billion by 2033. This surge is underpinned by increasing awareness of climate change impacts, advancements in food technology, and the integration of digital solutions across the food supply chain. As per our latest research, the adoption of carbon footprint food scanners is being accelerated by both consumer demand for transparency and corporate commitments to environmental stewardship.

The primary growth factor for the carbon footprint food scanner market is the rising demand for sustainable food choices among consumers. As individuals become more conscious of their environmental impact, there has been a significant shift toward products and services that provide transparency regarding carbon emissions. Food scanners that quantify and display the carbon footprint of products at the point of purchase are gaining traction, particularly among millennials and Gen Z consumers. This trend is further amplified by the proliferation of eco-labeling initiatives and the integration of environmental scoring in retail environments. The growing prevalence of mobile applications and handheld devices makes it easier for consumers to access real-time carbon data, thereby supporting informed and sustainable purchasing decisions.

Another critical driver is the increasing regulatory pressure on food manufacturers and retailers to disclose and reduce their carbon emissions. Governments and international bodies are enacting stricter guidelines on carbon reporting, particularly within the food and beverage sector, which is a significant contributor to global greenhouse gas emissions. The adoption of carbon footprint food scanners helps businesses comply with these regulations by providing accurate, traceable data on the carbon intensity of their products. This not only ensures compliance but also enhances brand reputation and customer loyalty by demonstrating a commitment to sustainability. As a result, companies are investing heavily in advanced scanning technologies and digital platforms that facilitate seamless integration with existing supply chain management systems.

Technological innovation is also a major catalyst for market expansion. The evolution of barcode/QR code scanning, image recognition, and database integration technologies has made carbon footprint assessment more accessible, accurate, and scalable. Advanced algorithms and AI-driven analytics enable real-time analysis of a product's lifecycle emissions, from raw material sourcing to end-user consumption. This technological leap has broadened the application of food scanners beyond retail, extending to food service providers, manufacturers, and even household users. Integration with cloud-based platforms and IoT devices further enhances the capability of these solutions, enabling continuous monitoring and reporting of carbon footprints across the entire food value chain. This convergence of digital innovation and sustainability is expected to drive sustained growth in the carbon footprint food scanner market.

Regionally, Europe currently leads the market due to its stringent environmental regulations, high consumer awareness, and proactive industry initiatives. North America follows closely, with significant adoption among both businesses and consumers, while Asia Pacific is emerging as a high-growth region driven by rapid urbanization and increasing environmental consciousness. Latin America and the Middle East & Africa are also witnessing growing interest, albeit from a smaller base, as sustainability becomes a priority for both governments and multinational food companies operating in these regions. Overall, the global landscape is characterized by a dynamic interplay of regulatory, technological, and consumer-driven forces that are shaping the future of the carbon footprint food scanner market.

The global industrial barcode reader market, valued at $268 million in 2025, is projected to experience steady growth, driven primarily by the increasing automation in logistics and warehousing, and the expanding adoption of barcode technology across diverse manufacturing sectors. The market's Compound Annual Growth Rate (CAGR) of 2.9% from 2025 to 2033 reflects a consistent demand for efficient inventory management, supply chain optimization, and quality control processes. Growth is fueled by the rising need for real-time data capture and traceability, particularly in industries like pharmaceuticals, food and beverage, and automotive, where stringent regulatory compliance necessitates accurate and reliable data recording. The increasing preference for handheld barcode readers, offering flexibility and mobility, contributes significantly to market expansion. However, factors like the high initial investment costs associated with implementing barcode scanning systems and the potential for technological obsolescence could act as restraints on growth. Future market expansion is anticipated to be influenced by technological advancements, such as the integration of advanced imaging technologies and the development of ruggedized devices designed for harsh industrial environments. The market segmentation, with handheld and stationary readers catering to varied application needs in logistics, manufacturing, and other sectors, further underscores its diversified nature and future potential. The competitive landscape is characterized by the presence of both established players and emerging companies, each striving to offer innovative solutions and enhance their market share. Major companies like Datalogic, Zebra Technologies, Honeywell, and Cognex are leading the way through strategic partnerships, product diversification, and geographical expansion. The continuous evolution of barcode reading technologies, including advancements in 2D barcode scanning and integration with cloud-based platforms, is likely to shape future market dynamics. The Asia-Pacific region, particularly China and India, is expected to exhibit significant growth, driven by increasing industrialization and rising investments in manufacturing and logistics infrastructure. North America and Europe will continue to hold substantial market shares due to the established adoption of barcode technology and robust industrial automation practices. Overall, the industrial barcode reader market is poised for continued expansion, driven by a confluence of factors indicating a strong and enduring need for efficient data capture and management within various industries.

Intraguild predation (IGP) – predation between generalist predators (IGPredator and IGPrey) that potentially compete for a shared prey resource – is a common interaction module in terrestrial food webs. Understanding temporal variation in webs with widespread IGP is relevant to testing food web theory. We investigated temporal constancy in the structure of such a system: the spider-focused food web of the forest floor. Multiplex PCR was used to detect prey DNA in 3,300 adult spiders collected from the floor of a deciduous forest during spring, summer, and fall over four years. Because only spiders were defined as consumers, the web was tripartite, with 11 consumer nodes (spider families) and 22 resource nodes: 11 non-spider arthropod taxa (order- or family-level) and the 11 spider families. Most (99%) spider-spider predation was on spider IGPrey, and ~90% of these interactions were restricted to spider families within the same broadly defined foraging mode (cursorial or web-spinning spiders). Bootstrapped-derived confidence intervals (BCI’s) for two indices of web structure, restricted connectance and interaction evenness, overlapped broadly across years and seasons. A third index, % IGPrey (% IGPrey among all prey of spiders), was similar across years (~50%) but varied seasonally, with a summer rate (65%) ~1.8x higher than spring and fall. This seasonal pattern was consistent across years. Our results suggest that extensive spider predation on spider IGPrey that exhibits consistent seasonal variation in frequency, and that occurs primarily within two broadly defined spider-spider interaction pathways, must be incorporated into models of the dynamics of forest-floor food webs. Methods Study system We collected spiders and potential non-spider prey from the oak-dominated (Quercus alba and Q. rubra) Swallow Cliff Woods (41° 40.519’ N, 87° 51.437’ W) within the 320-ha Swallow Cliff nature preserve in Palos Township, Illinois (USA). The preserve, which is within the Chicago metropolitan region, is managed by the Cook County Forest Preserve District. Forests in this region are actively managed for several invasive plants (23), and the forest floor at Swallow Cliffs contains a thick leaf-litter layer with an abundant and diverse arthropod community. Collecting spiders and non-spider prey Our goal was to search the ground layer and low understory as thoroughly as possible, so that we would collect enough spiders from less-abundant families to yield the same number of spiders per family analyzed for prey DNA. We did not estimate spider densities. All collections were made between 1000 and 1600 hours. We collected from a different location each day. The size of the area searched each day was not measured and varied with the number of searchers. Collecting areas were widely distributed throughout Swallow Cliff Woods, but we did not subdivide the Woods into sampling regions. Most terrain was upland forest, but some collections were taken from a few scattered wet/marshy areas. The number of collecting days in each season was spring (31), summer (33), and fall (29) over the years 2009, 2010, 2011 and 2012; the number of days per year was 33, 12, 34 and 14, respectively. On each collecting day, we used both litter sifting and simple searching to capture spiders from several microhabitats. For litter sifting, we placed litter collected by hand into a flat tray (58 cm x 17 cm x 15 cm) with a screen bottom. This tray was shaken over a second tray of the same size with a solid bottom, allowing arthropods to fall through the screen to be collected by hand or aspirator. Sifted litter was returned to its original location. Spiders were also collected by hand from the litter surface, open areas in the litter, logs, low vegetation up to ~1m, and tree trunks up to ~2m. Individual spiders were placed in separate labelled vials. Of the spiders that were eventually analyzed for prey DNA (see below), 81% were captured from either leaf litter (70%) or adjacent bare ground/logs (11%). Thus, most spiders were collected from the litter layer broadly defined. The litter layer is a fairly distinct subsystem with respect to rates of migration of arthropod predators and prey (24). Nevertheless, we did not limit our definition of the “forest floor” to the litter layer because many spiders spin webs in vegetation close to the ground. Also, some cursorial species move back and forth between the ground and lower understory vegetation and tree trunks (for example, 84% of the Corinnidae, a guild of “foliage runners” (25), were collected from leaf litter). Therefore, we also analyzed spiders that had been collected from low vegetation (10%) and tree trunks (9%). All specimens were placed on ice within one hour of capture. On the same day, spiders collected for detection of consumed prey using PCR were taken to the laboratory where they were weighed and stored at -20◦C in a 1.5-mL microcentrifuge tube containing 95% ethanol (EtOH). Spiders and non-spider prey (see below) intended for primer development or assay optimization (see below for details) were kept alive, weighed, placed individually into 60-mL glass vials, and provided with water ad libitum at room temperature. Spiders were identified to family and genus using identification guides (26-29). Voucher specimens (one adult male and female) for each genus (when available) were archived at The Field Museum (Chicago, Illinois). Over the four years, ~14,000 spiders (juveniles and adults) from 20 families were collected. Presence of prey DNA was tested for adult spiders from 11 abundant families (those with at least 300 adults) that live primarily on the forest floor. Spiders from six of these families (Corinnidae, Gnaphosidae, Lycosidae, Pisauridae, Salticidae, and Thomisidae) do not spin webs to capture prey (“cursorial” spiders). The other five families (Agelenidae, Dictynidae, Hahniidae, Linyphiidae, and Theridiidae) are “web spinners.” This dichotomy reflects basic differences in foraging behavior (16, 17), but the distinction is not absolute. The web spinners in our food web include genera of spiders that also forage for prey off their web (18). Non-spider arthropod prey were also collected for primer development. They were not sampled quantitatively, but were simply selected due to their apparent abundance in leaf litter and/or activity just above the litter layer, and their likely occurrence in the diets of at least one spider family (15-17, 30). Non-spider nodes of the food web were broadly defined taxonomically (at the Order level except for Gryllidae): flies (Diptera), moths/butterflies (Lepidoptera), springtails (Collembola), ants/bees/wasps (Hymenoptera), jumping bristletails (Archaeognatha), crickets (Gryllidae), pseudoscorpions (Pseudoscorpiones), harvestmen (Opiliones), beetles (Coleoptera), earwigs (Dermaptera), and pillbugs (Isopoda). Molecular techniques Primer development and optimization We utilized multiplex PCR to sequence DNA from at least ten spiders from each family and at least ten specimens from each non-spider prey taxon. Each spider was first starved for at least ten days to eliminate any gut-content DNA that may have been present. Specimens were then homogenized in 180 μL of phosphate-buffered saline (PBS) (Hoefer, San Francisco, CA). DNA was then extracted with a Qiagen DNEasy Tissue Kit (Valencia, CA) using the manufacturer’s protocol. Upon completion of DNA extraction, the 200μL of eluate was well-mixed, separated into 20μL aliquots, and stored at -20°C until analysis. The general arthropod primers LCO-1490 and HCO-2198 (31) were used to amplify DNA from the mitochondrial genome’s cytochrome oxidase I (COI) region. Eluate from DNA extractions was amplified and sequenced by The Field Museum (Chicago, IL) or Research Resources Center (RRC) at the University of Illinois, Chicago. Sequences were used to conduct BLASTN searches following the protocol developed by (32) using the databases GenBank and BOLD (the Barcode of Life Database). Following (33), database sequences were used only if they showed ≥97% match to submitted sequences. Sequences were aligned using the CLUSTALW or AMPLICON programs. Primers were designed with the assistance of the IDT (Integrated DNA Technologies, Coralville, IA) program PrimerQuest and tested for melting temperature and CG content using Sci-Tools OligoAnalyzer (IDT). Spider gut-content testing After a PCR assay was developed and optimized for a particular prey taxon (spider family or non-spider arthropod), frozen field-caught adult spiders were tested for the presence of the target-prey DNA. Spiders were thawed to room temperature and underwent DNA extraction and PCR amplification as described above. The entire spider was homogenized, except for the largest individuals, for which legs were removed to increase the prey/predator DNA ratio; coxae were left attached to the body when possible because spider guts often extend into the coxae (17). The homogenate was then mixed and 4uL were added to a well (on a 96-well plate) that contained 21 uL of Master Mix. Every run also included positive, negative, and blank controls to ensure that target DNA was amplified and that no contamination existed on the run. Positive controls consisted of DNA specific to the target taxon in question, negative controls contained the PCR Master Mix without DNA template, and blank controls were created from MBG water. A sample was considered positive for target-prey DNA within the spider’s gut if the Ct value of the amplification curve was above the background threshold, if the shape of the curve was sigmoidal, and if the positive and negative controls were acceptable. Samples that did not show amplification were re-analyzed using arthropod-general primers (31) before identifying them as negative results; questionable samples (low amplification or a non-sigmoidal shape) were re-tested. For constructing the food web, adult

The Edible Barcode market is an innovative intersection of food safety, technology, and consumer convenience, rapidly transforming how industries approach product labeling and information dissemination. With the need for increased traceability, transparency, and consumer engagement, this market has gained significan

The global label design and printing software market is experiencing robust growth, driven by the increasing demand for efficient and customized labeling solutions across diverse industries. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is fueled by several key factors, including the rising adoption of cloud-based solutions, the increasing need for automation in labeling processes, and the growing importance of compliance regulations across various sectors like food and pharmaceuticals. The proliferation of e-commerce and the resultant surge in product shipments have further amplified the need for accurate and efficient label generation. Furthermore, the integration of advanced features such as barcode generation, serialization, and variable data printing is enhancing the overall functionality and appeal of these software solutions. This continuous innovation is attracting a diverse range of users, from small businesses to large enterprises, making the market highly competitive. Leading players such as Canon, Epson, and Xerox are continuously investing in research and development to enhance their product offerings and expand their market share. The market is segmented based on software type (standalone vs. integrated), deployment mode (cloud-based vs. on-premise), and industry vertical (food & beverage, healthcare, manufacturing, etc.). While the market faces certain restraints, such as high initial investment costs and the need for specialized technical expertise, the overall positive growth trajectory is expected to continue, propelled by the aforementioned drivers and increasing awareness of the benefits of efficient label design and printing processes. The forecast period from 2025 to 2033 presents significant opportunities for market participants to capitalize on the expanding market and emerging technological advancements.

This dataset represents the list of CA WIC authorized food items identified by food category and subcategory. Each item is uniquely identified by a Universal Product Code (UPC) or Price Look-Up code (PLU) for WIC electronic benefit transfer (EBT). The WIC Authorized vendors use the CA Authorized Product List (APL) to transact WIC food items at cash registers. The APL plays a crucial role in supporting WIC EBT purchases. WIC EBT requires vendor systems to maintain the APL to match the scanned food items' UPCs to ensure they are on the APL. The food items identified by UPC and PLU can be found in different tabs of the data file below.

The Women, Infants and Children (WIC) Program is a federally-funded health and nutrition program that provides assistance to pregnant women, new mothers, infants, and children under age five. WIC helps California families by providing food benefits to individual participants based on their nutritional need and risk assessment. The food benefits can be used to purchase healthy supplemental foods from approximately 3,800 WIC authorized vendor stores throughout the State. WIC also provides nutritional education, breastfeeding support, healthcare referrals, and other community services. Participants must meet income guidelines and other criteria. Currently, 84 WIC agencies provide services monthly to approximately one million participants at several hundred sites in local communities throughout the State.