POS Transactions: DB: Mexico data was reported at 11,311.000 Number in Mar 2019. This records an increase from the previous number of 9,648.000 Number for Feb 2019. POS Transactions: DB: Mexico data is updated monthly, averaging 5,581.500 Number from Apr 2011 (Median) to Mar 2019, with 96 observations. The data reached an all-time high of 50,169.000 Number in May 2018 and a record low of 1,743.000 Number in Dec 2011. POS Transactions: DB: Mexico data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA019: Point of Sales Transactions: Development Banks.

The dataset contains data from a pilot study conducted to assess the presence of advertising and promotion of cigarettes, e-cigarettes, and HTPs in Warsaw, Poland, in points-of-sale located near to schools that children and youth are likely to visit.

Mexico POS Transactions: DB: Baja California Sur data was reported at 73.000 Number in Mar 2019. This records a decrease from the previous number of 82.000 Number for Feb 2019. Mexico POS Transactions: DB: Baja California Sur data is updated monthly, averaging 5.000 Number from Apr 2011 (Median) to Mar 2019, with 96 observations. The data reached an all-time high of 122.000 Number in Dec 2018 and a record low of 0.000 Number in Feb 2015. Mexico POS Transactions: DB: Baja California Sur data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA019: Point of Sales Transactions: Development Banks.

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

Mexico POS Transactions: DB: Sinaloa data was reported at 520.000 Number in Mar 2019. This records a decrease from the previous number of 562.000 Number for Feb 2019. Mexico POS Transactions: DB: Sinaloa data is updated monthly, averaging 74.000 Number from Apr 2011 (Median) to Mar 2019, with 96 observations. The data reached an all-time high of 1,231.000 Number in Dec 2011 and a record low of 12.000 Number in Feb 2013. Mexico POS Transactions: DB: Sinaloa data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA019: Point of Sales Transactions: Development Banks.

221638 Global exporters importers export import shipment records of Pos system with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Comprehensive dataset of 1 Desalination plants in pos, Indonesia as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Mexico POS Transactions: DB: Nayarit data was reported at 0.000 Number in Mar 2019. This stayed constant from the previous number of 0.000 Number for Feb 2019. Mexico POS Transactions: DB: Nayarit data is updated monthly, averaging 0.000 Number from Apr 2011 (Median) to Mar 2019, with 96 observations. The data reached an all-time high of 0.000 Number in Mar 2019 and a record low of 0.000 Number in Mar 2019. Mexico POS Transactions: DB: Nayarit data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA019: Point of Sales Transactions: Development Banks.

Gain access to the most extensive collection of Japanese consumer purchase survey data, monitoring the buying habits of over 50,000 respondents from publicly traded retailers. Unlock an unparalleled database of Japanese consumer purchase information, capturing the shopping activities of more than 50,000 individuals across listed retail companies. Tap into the largest repository of Japanese consumer purchase survey insights, tracking the product purchases of 50,000+ respondents at publicly listed retailers. Utilize the broadest array of Japanese consumer purchase survey statistics, detailing the buying patterns of over 50,000 participants from publicly traded retail outlets.

Dataset

This dataset was created by Alvamau

Released under Database: Open Database, Contents: Database Contents

Contents

4315 Global import shipment records of Pos System with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

9052 Global export shipment records of Pos Systems with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Mexico POS Transactions: DB: Zacatecas data was reported at 44.000 Number in Mar 2019. This records a decrease from the previous number of 57.000 Number for Feb 2019. Mexico POS Transactions: DB: Zacatecas data is updated monthly, averaging 0.000 Number from Apr 2011 (Median) to Mar 2019, with 96 observations. The data reached an all-time high of 365.000 Number in May 2018 and a record low of 0.000 Number in Jul 2016. Mexico POS Transactions: DB: Zacatecas data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA019: Point of Sales Transactions: Development Banks.

Data obtained from computational DFT calculations on Hexagonal POs is provided. Available data include crystal structure, bandgap energy, stability, density of states, and calculation input/output files.

Data obtained from computational DFT calculations on Orthorhombic POs is provided. Available data include crystal structure, bandgap energy, stability, density of states, and calculation input/output files.

The presented database is a set of hydrological, meteorological, environmental and geometric values for Russia Federation for the period from 2008 to 2020.

Database consist of next items:

Point geometry for hydrological observation stations from Roshydromet network across Russia

Geometry of the catchment for correspond observation station point

Daily hydrological values

Water level

In relative representation (sm)

In meters of Baltic system (m)

Water discharge

as an observed value (qms/s)

as a layer (mm/day)

Daily meteorological values

Maximum and minimum daily temperatures (°C) from ERA5 and ERA5-Land

Total precipitation (mm/day) from ERA5, ERA5-Land, IMERG v06, GPCP v3.2 and MSWEP

Different kind of evaporation (mm/day) corresponded to each variable calculated in GLEAM model

Set of hydro-environmental characteristics derived from HydroATLAS database

Each variable derived from the grid data was calculated for each watershed, taking into account the intersection weights of the watershed contour geometry and grid cells.

Coordinates of hydrological stations were obtained from resource of Federal Agency for Water Resources of Russia Federation—AIS GMVO

To calculate the contours of the catchment areas, a script was developed that builds the contours in accordance with the rasters of flow directions from MERIT Hydro. To assess the quality of the contour construction, the obtained value of the catchment area was compared with the archival value from the corresponded table from AIS GMVO. The average error in determining the area for 2080 catchments is approximately 2%

To derive values for different hydro-environmental values from HydroATLAS were developed approach which calculate aggregated values for catchment, leaning on type of variable: qualitative (Land cover classes, Lithological classes etc.) Or quantitive (Air temperature, Snow cover extent etc.). Every quantitive variable were calculated as mode value for intersected sub-basins and target catchment, e.g. most popular attribute from sub-basins will describe whole catchment which are they relating. Quantitative values were calculated as mean value of attribute from each sub-basin. More detail could be found in publication.

Files are distributed as follows:

Each file has some connection with the unique identifier of the hydrological observation post. Files in netcdf format (hydrological and meteorological series) are named in response to identifier.

Every file which describe geometry (point, polygon, static attributes) has and column named gauge_id with same correspondence.

attributes/static_data.csv – results from HydroATLAS aggregation

geometry/russia_gauges.gpkg – coordinates of hydrological observation stations

        gauge_id
        name_ru
        name_en
        geometry




        0
        49001
        р. Ковда – пос. Софпорог
        r.Kovda - pos. Sofporog
        POINT (31.41892 65.79876)


        1
        49014
        р. Корпи-Йоки – пос. Пяозерский
        r.Korpi-Joki - pos. Pjaozerskij
        POINT (31.05794 65.77917)


        2
        49017
        р. Тумча – пос. Алакуртти
        r.Tumcha - pos. Alakurtti
        POINT (30.33082 66.95957)

geometry/russia_ws.gpkg – catchments polygon for each hydrological observation stations

        gauge_id
        name_ru
        name_en
        new_area
        ais_dif
        geometry




        0
        9002
        р. Енисей – г. Кызыл
        r.Enisej - g.Kyzyl
        115263.989
        0.230
        POLYGON ((96.87792 53.72792, 96.87792 53.72708...


        1
        9022
        р. Енисей – пос. Никитино
        r.Enisej - pos. Nikitino
        184499.118
        1.373
        POLYGON ((96.87792 53.72708, 96.88042 53.72708...


        2
        9053
        р. Енисей – пос. Базаиха
        r.Enisej - pos.Bazaiha
        302690.417
        0.897
        POLYGON ((92.38292 56.11042, 92.38292 56.10958...

Column ais_diff is corresponded to % error in area definition

nc_all_q

netcdf files for hydrological observation stations which has no missing values on discharge for 2008-2020 period

nc_all_h

netcdf files for hydrological observation stations which has no missing values on level for 2008-2020 period

nc_all_q_h

netcdf files for hydrological observation stations which has no missing values on discharge and level for 2008-2020 period

nc_concat

data for all available geometry provided in dataset

More details on processing scripts which were used for development of this database can be found in folder of GitHub repository where I store results for my PhD dissertation

05.04.2023 – Significant data changes. Removed catchments and related files that have more than ±15% absolute error in calculated area relative to AIS GMVO information. Now these are data for 1886 catchments across the Russia.

17.05.2023 – Significant data changes. Major review of parsing algorithm for AIS GMVO data. Fixed the way of how 0.0xx values were read. Use previous versions with caution.

11.10.2023 – Significant data changes. Added 278 catchments for CIS region from GRDC resource. Calculate meteorological and environmental attributes for each catchment. New folder /nc_all_q_h with no missing observations on discharge and level. Now these are data for 2164 catchments across CIS.

112 Global import shipment records of Pos System with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Data obtained from computational DFT calculations on Tetragonal POs is provided. Available data include crystal structure, bandgap energy, stability, density of states, and calculation input/output files.

This COVADIS data standard concerns local planning documents (LDPs) and land use plans (POSs that are PLU). This data standard provides a technical framework describing in detail how to dematerialise these planning documents into a spatial database that can be used by a GIS tool and interoperable. This standard of data concerns both the graphic zoning plans, the superimposed requirements and the regulations applying to each type of area.This standard of COVADIS data was developed on the basis of the specifications for the dematerialisation of urban planning documents updated in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The recommendations of these two documents are consistent even if their purpose is not the same. The COVADIS data standard provides definitions and a structure for organising and storing existing PLU/POS spatial data in an infrastructure in digital form, while the CNIG specification serves to frame the digitisation of such data. The ‘Data Structure’ section presented in this COVADIS standard provides additional recommendations for the storage of data files (see Part C). These are choices specific to the MAA and MEDDE data infrastructure that do not apply outside their context. Communal maps are the subject of another COVADIS data standard.

This COVADIS data standard concerns local planning documents (LDPs) and land use plans (POSs that are PLU). This data standard provides a technical framework describing in detail how to dematerialise these planning documents into a spatial database that can be used by a GIS tool and interoperable. This standard of data concerns both the graphic zoning plans, the superimposed requirements and the regulations applying to each type of area.This standard of COVADIS data was developed on the basis of the specifications for the dematerialisation of urban planning documents updated in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The recommendations of these two documents are consistent even if their purpose is not the same. The COVADIS data standard provides definitions and a structure for organising and storing existing PLU/POS spatial data in an infrastructure in digital form, while the CNIG specification serves to frame the digitisation of such data. The ‘Data Structure’ section presented in this COVADIS standard provides additional recommendations for the storage of data files (see Part C). These are choices specific to the MAA and MEDDE data infrastructure that do not apply outside their context. Communal maps are the subject of another COVADIS data standard.