Medical Doctors in the United States increased to 2.77 per 1000 people in 2019 from 2.74 per 1000 people in 2018. This dataset includes a chart with historical data for the United States Medical Doctors.

The dataset is related to student data, from an educational research study focusing on student demographics, academic performance, and related factors. Here’s a general description of what each column likely represents:

Sex: The gender of the student (e.g., Male, Female). Age: The age of the student. Name: The name of the student. State: The state where the student resides or where the educational institution is located. Address: Indicates whether the student lives in an urban or rural area. Famsize: Family size category (e.g., LE3 for families with less than or equal to 3 members, GT3 for more than 3). Pstatus: Parental cohabitation status (e.g., 'T' for living together, 'A' for living apart). Medu: Mother's education level (e.g., Graduate, College). Fedu: Father's education level (similar categories to Medu). Mjob: Mother's job type. Fjob: Father's job type. Guardian: The primary guardian of the student. Math_Score: Score obtained by the student in Mathematics. Reading_Score: Score obtained by the student in Reading. Writing_Score: Score obtained by the student in Writing. Attendance_Rate: The percentage rate of the student’s attendance. Suspensions: Number of times the student has been suspended. Expulsions: Number of times the student has been expelled. Teacher_Support: Level of support the student receives from teachers (e.g., Low, Medium, High). Counseling: Indicates whether the student receives counseling services (Yes or No). Social_Worker_Visits: Number of times a social worker has visited the student. Parental_Involvement: The level of parental involvement in the student's academic life (e.g., Low, Medium, High). GPA: The student’s Grade Point Average, a standard measure of academic achievement in schools.

This dataset provides a comprehensive look at various factors that might influence a student's educational outcomes, including demographic factors, academic performance metrics, and support structures both at home and within the educational system. It can be used for statistical analysis to understand and improve student success rates, or for targeted interventions based on specific identified needs.

Database is provided by ASL Marketing and covers the United States of America. With ASL Marketing Reaching GenZ has never been easier. Current high school student data customized by: Class year Date of Birth Gender GPA Geo Household Income Ethnicity Hobbies College-bound Interests College Intent Email

Improve AI/ML models with Macgence's USA medical speech dataset. High-quality group conversations between doctors and patients for precise analytics and innovation!

"Facilitate marketing campaigns with the healthcare email list from Infotanks Media that includes doctors, healthcare professionals, NPI numbers, physician specialties, and more. Buy targeted email lists of healthcare professionals and connect with doctors, specialists, and other healthcare professionals to promote your products and services. Hyper personalize campaigns to increase engagement for better chances of conversion. Reach out to our data experts today! Access 1.2 million physician contact database with 150+ specialities including chiropractors, cardiologists, psychiatrists, and radiologists among others. Get ready to integrate healthcare email lists from Infotanks Media to start email marketing campaigns through any CRM and ESP. Contact us right now! Ensure guaranteed lead generation with segmented email marketing strategies for specialists, departments, and more. Make the best use of target marketing to progress and move closer to your business goals with email listing services for healthcare professionals. Infotanks Media provides 100% verified healthcare email lists with the highest email deliverability guarantee of 95%. Get a custom quote today as per your requirements. Enhance your marketing campaigns with healthcare email lists from 170+ countries to build your global outreach. Request your free sample today! Personalize your business communication and interactions to maximize conversion rates with high quality contact data. Grow your business network in your target markets from anywhere in the world with a guaranteed 95% contact accuracy of the healthcare email lists from Infotanks Media. Contact data experts at Infotanks Media from the healthcare industry to get a quick sample for free. Write to us or call today!

Hyper target within and outside your desired markets with GDPR and CAN-SPAM compliant healthcare email lists that get integrated into your CRM and ESPs. Balance out the sales and marketing efforts by aligning goals using email lists from the healthcare industry. Build strong business relationships with potential clients through personalized campaigns. Call Infotanks Media for a free consultation. Explore new geographies and target markets with a focused approach using healthcare email lists. Align your sales teams and marketing teams through personalized email marketing campaigns to ensure they accomplish business goals together. Add value and grow revenue to take your business to the next level of success. Double up your business and revenue growth with email lists of healthcare professionals. Send segmented campaigns to monitor behaviors and understand the purchasing habits of your potential clients. Send follow up nurturing email marketing campaigns to attract your potential clients to become converted customers. Close deals sooner with detailed information of your prospects using the healthcare email list from Infotanks Media. Reach healthcare professionals on their preferred platform of communication with the email list of healthcare professionals. Identify, capture, explore, and grow in your target markets anywhere in the world with a fully verified, validated, and compliant email database of healthcare professionals. Move beyond the traditional approach and automate sales cycles with buying triggers sent through email marketing campaigns. Use the healthcare email list from Infotanks Media to engage with your targeted potential clients and get them to respond. Increase email marketing campaign response rate to convert better! Reach out to Infotanks Media to customize your healthcare email lists. Call today!"

United States PPI: ME: GP: ID: Parts & Accessories data was reported at 146.497 Sep2018=100 in Mar 2025. This records an increase from the previous number of 146.457 Sep2018=100 for Feb 2025. United States PPI: ME: GP: ID: Parts & Accessories data is updated monthly, averaging 122.373 Sep2018=100 from Sep 2018 (Median) to Mar 2025, with 79 observations. The data reached an all-time high of 146.497 Sep2018=100 in Mar 2025 and a record low of 100.000 Sep2018=100 in Sep 2018. United States PPI: ME: GP: ID: Parts & Accessories data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I066: Producer Price Index: by Commodities.

1. Overview This dataset is a collection of multimodal high quality image sets of medical data that are ready to use for optimizing the accuracy of computer vision models. All of the contents are sourced from Pixta AI's partner network with high quality & full data compliance.

2. Data subject The datasets consist of various models

3. X-ray datasets

4. CT datasets

5. MRI datasets

6. Mammography datasets

7. Segmentation datasets

8. Classification datasets

9. Regression datasets

10. Use case The dataset could be used for various Healthcare & Medical models:

11. Medical Image Analysis

12. Remote Diagnosis

13. Medical Record Keeping ... Each data set is supported by both AI and expert doctors review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

14. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at https://www.pixta.ai/ or contact via our email admin.bi@pixta.co.jp.

About this Dataset

Search Criteria- - Search as a guest - What type of care are you searching for? - Medical - What state do you want to search with? First we need CA, NY, NJ, PA, MA, Washington DC, and MD. Please give us this as a field so that we know which doctor is in what state - What type of plan do you want to search with? Please do Medical Networks on-Exchange, Medical (Individuals & Families), and Medical (Employer-Sponsored) - Plan/Network -ONLY ONE- Anthem Gold Advantage PPO - Need to search by COUNTIES of that States - Search by Care Provider - Behavioral Health REMARK THIS ONE- When you conduct the search I've already identified, it brings back a list of providers. Then there are additional filters at the top. From the "specialty" filter, Need to exclude these, and only include the remaining? • Applied Behavioral Analysis • Medication Assisted Treatment • Methadone • NP/Nurses • VA • Behavioral Health Facility
Fields- Plan Type, Plan Network, Name, Specialty, Full Address, State, Website, Email, Phone, Area of expertise

Category

Health & Medicine

Keywords

US Medicine,medical,doctors,California Doctors,california

Row Count

1860

Price

$30.00

This data release provides preliminary estimates of annual agricultural use of pesticide compounds in counties of the conterminous United States, for the year 2019, compiled by means of methods described in Thelin and Stone (2013) and Baker and Stone (2015). For all States except California, U.S. Department of Agriculture county-level data for harvested-crop acreage were used in conjunction with proprietary Crop Reporting District-level pesticide-use data to estimate county-level pesticide use. Where Crop Reporting District data were not available or were incomplete, estimated pesticide-use values were calculated with two different methods, resulting in a low and a high estimate based on different assumptions about missing survey data (Thelin and Stone, 2013). Pesticide-use data for California were obtained from the California Department of Pesticide Regulation Pesticide Use Reporting (DPR–PUR) database (California Department of Pesticide Regulation, written commun., 2020). The California county data were appended after the estimation process was completed for the rest of the Nation. Preliminary estimates in this dataset may be revised upon availability of updated crop acreages in the 2022 Agricultural Census, expected to be published by the U.S. Department of Agriculture in 2024. Estimates of annual agricultural pesticide use are provided as downloadable, tab-delimited files, organized by compound, year, state Federal Information Processing Standard (FIPS) code, county FIPS code, and amount in kilograms. Tables of annual agricultural pesticide-use estimates beginning in 1992 are available for download on the Pesticide National Synthesis Project webpage: https://doi.org/doi:10.5066/F7NP22KM. Beginning in 2019, estimates are reported for a reduced number of compounds. References cited: Baker, N.T., and Stone, W.W., 2015, Estimated annual agricultural pesticide use for counties of the conterminous United States, 2008–12: U.S. Geological Survey Data Series 907, 9 p., accessed July 12, 2015, at https://doi.org/10.3133/ds907. Thelin, G.P., and Stone, W.W., 2013, Estimation of annual agricultural pesticide use for counties of the conterminous United States, 1992–2009: U.S. Geological Survey Scientific Investigations Report 2013–5009, 54 p., accessed July 12, 2015, at http://pubs.usgs.gov/sir/2013/5009/.

The United States recorded a Government Debt to GDP of 124.30 percent of the country's Gross Domestic Product in 2024. This dataset provides - United States Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This geodatabase includes spatial datasets that represent the Basin and Range basin-fill aquifers in the States of Arizona, California, Idaho, Nevada, New Mexico, Oregon, and Utah. Included are: (1) polygon extents; datasets that represent the aquifer system extent, the entire extent subdivided into subareas or subunits, and any polygon extents of special interest (outcrop areas, no data available, areas underlying other aquifers, anomalies, for example), (2) contours: thickness contours used to generate the surface rasters in subarea 4 (Arizona), (3) modified source raster datasets for subareas 1 and 3, (4) corrected altitudes of top and bottom surface rasters of the entire aquifer. The thickness contours and modified surface rasters are supplied for reference. The extent of the Basin and Range basin-fill aquifer is from the linework of the Basin and Range aquifer extent maps in U.S. Geological Survey Hydrologic Atlas 730 Chapters B and C, and a digital version of the aquifer extent presented in the Groundwater Atlas of the United States (the U.S. Geological Survey Hydrologic Atlas. The Basin and Range basin-fill aquifer has no aquifer subunits, but is defined by five subareas: 1. Subarea 1 is the area that overlies the Basin and Range Carbonate aquifer, which was the subject of U.S. Geological Survey Scientific Investigations Report 2010-5193 (USGS SIR 2010-5193). 2. Subarea 2 is the area of a different aquifer system, which is set to null for use within the Basin and Range basin-fill aquifer from U.S. Geological Survey Principal Aquifers, 2003 (USGS Circular 1323, Figure 2) 3. Subarea 3 is the area of the Basin and Range basin-fill aquifer that was the subject of U.S. Geological Survey Geophysical Map 1012 (USGS GP-1012) and not covered by USGS SIR 2010-5193 or the Basin and Range basin-fill aquifer in Arizona, Arizona Geological Survey, Digital Geological Map 52 (AZGS DGM-52). Top of aquifer is land surface. USGS GP-1012 dataset is depth from land surface to basin bottom. 4. Subarea 4 is the area of the 01BSNRGB aquifer in Arizona, (AZGS DGM-52) 5. Subarea 5 areas are in the Basin and Range basin-fill extent areas that do not have top/bot defined. The resultant top and bottom surface rasters for each subarea were merged into surface rasters of the top and bottom of the entire Basin and Range basin-fill aquifer within a GIS using tools that create hydrologically correct surfaces from contour data, deriving the altitude from the thickness (depth from the land surface), and merging the subareas into a single surface. The primary tools were a version of "Topo to Raster", and "Mosaic to New Raster" used in ArcGIS, ArcMap, Esri 2014.

Coastal managers and ocean engineers rely heavily on projected average and extreme wave conditions for planning and design purposes, but when working on a local or regional scale, are faced with much uncertainty as changes in the global climate impart spatially varying trends. Future storm conditions are likely to evolve in a fashion that is unlike past conditions and is ultimately dependent on the complicated interaction between the Earth’s atmosphere and ocean systems. Despite a lack of available data and tools to address future impacts, consideration of climate change is increasingly becoming a requirement for organizations considering future nearshore and coastal vulnerabilities. To address this need, the USGS used winds from four different atmosphere-ocean coupled general circulation models (AOGCMs) or Global Climate Models (GCMs) and the WaveWatchIII numerical wave model to compute historical and future wave conditions under the influence of two climate scenarios. The GCMs respond to specified, time-varying concentrations of various atmospheric constituents (such as greenhouse gases) and include an interactive representation of the atmosphere, ocean, land, and sea ice. The two climate scenarios are derived from the Coupled Model Inter-Comparison Project, Phase 5 (CMIP5; World Climate Research Programme, 2013) and represent one medium-emission mitigation scenario (RCP4.5; Representative Concentration Pathways) and one high-emissions scenario (RCP8.5). The historical time-period spans the years 1976 through 2005, whereas the two future time-periods encompass the mid (years 2026 through 2045) and end of the 21st century (years 2081 through 2099/2100). Continuous time-series of dynamically downscaled hourly wave parameters (significant wave heights, peak wave periods, and wave directions) and three-hourly winds (wind speed and wind direction) are available for download at discrete deep-water locations along four U.S. coastal regions: • Pacific Islands • West Coast • East Coast • Alaska Coasts The Alaskan region includes a total of 25 model output points. Six output points surround the Arctic coast, eight surround the Aleutian Islands, four are within the shallow region of the Bering Sea, and the remaining seven are within the Gulf of Alaska. The U.S. West Coast region stretches from the U.S.- Mexico border to the U.S.- Canada border and includes open coast areas of California, Oregon, and Washington. The West Coast region includes fifteen model output points. Eight model output points are co-located with observation buoys and are identified by National Oceanic and Atmospheric Administration National Data Buoy Center (NDBC, http://www.ndbc.noaa.gov/) station numbers (N46229, N46213, N46214, N46042, N46028, N46069, N46219, N46047). The U.S. East and Gulf Coasts encompass fifteen coastal states stretching from the Gulf Coast States and Florida in the south to the U.S.-Canada border north of Maine. The region includes seventeen model output points; seven are co-located with NDBC observation buoys (N44011, N44014, N41001, N41002, N41010, N42001, N42055). Data summaries for the U.S. East and Gulf Coast regions are provided from the 1.25° x 1.00° global (NWW3) wave model grid (described in Data and Methods section below). Data summaries for the U.S. West Coast region are available from the NWW3 grid and from the finer resolution 0.25° x 0.25° Eastern North Pacific (ENP) grid nested within the NWW3 grid. Data summaries for the southern coast of Alaska are also available from the ENP grid. In cases where model data exist for both the NWW3 and ENP grids, both sets of data are available for download (http://dx.doi.org/10.5066/F7D798GR). The data and cursory overviews of changing conditions along the coasts are summarized in Storlazzi and others (2015) and Erikson and others (2016). References Cited: Erikson, L.H., Hegermiller, C.A., Barnard, P.L., and Storlazzi, C.D., 2016, Wave projections for United States mainland coasts: U.S. Geological Survey pamphlet to accompany data release, https://doi.org/10.5066/F7D798GR. Erikson, L.H., Hegermiller, C.A., Barnard, P.L., Ruggiero, P., and van Ormondt, M., 2015b, Projected wave conditions in the Eastern North Pacific under the influence of two CMIP5 climate scenarios: Journal of Ocean Modelling, v. 96, p. 171–185, https://doi.org/10.1016/j.ocemod.2015.07.004. Erikson, L.H., Hemer, M.A., Lionello, P., Mendez, F.J., Mori, N., Semedo, A., Wang, X.L., and Wolf, J., 2015a, Projection of wave conditions in response to climate change: A community approach to global and regional wave downscaling: Proceedings Coastal Sediments 2015, 13 p., https://doi.org/10.1142/9789814689977_0243. Meinshausen, M., Smith, S.J., Calvin, K., Daniel, J.S., Kainuma, M.L.T., Lamarque, J-F., Matsumoto, K., Montzka, S.A., Raper, S.C.B., Riahi, K., Thomson, A., Velders, G.J.M., and van Vuuren, D.P.P., 2011, The RCP greenhouse gas concentrations and their extensions from 1765 to 2300: Climate Change, v. 109, p. 213–241, https://doi.org/10.1007/s10584-011-0156-z. Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., and Wilbanks, T.J., 2010, The next generation of scenarios for climate change research and assessment: Nature, v. 463, p. 747–756, https://doi.org/10.1038/nature08823. Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., and Rafai, P., 2011, RCP 8.5: Exploring the consequence of high emission trajectories: Climatic Change, v. 109, p. 33–57, https://doi.org/10.1007/s10584-011-0149-y. Storlazzi, C.D., Shope, J.B., Erikson, L.H., Hegermiller, C.A., and Barnard, P.L., 2015, Future wave and wind projections for United States and United States-affiliated Pacific Islands: U.S. Geological Survey Open-File Report 2015–1001, 426 p., https://doi.org/10.3133/ofr20151001. Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2012, An overview of CMIP5 and the experiment design: Bulletin of the American Meteorological Society, v. 93, p. 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1. Thomson, A.M., Calvin, K.V., Smith, S.J., Kyle, G.P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M.A., Clarke, L.E., Edmonds, J.A., 2011, RCP4.5: A pathway for stabilization of radiative forcing by 2100: Climatic Change, v. 109, p. 77–94, https://doi.org/10.1007/s10584-011-0151-4. van Vuuren, D.P., Edmonds, J.A., Kainuma, M., Riahi, K., Thomson, A.M., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J., and Rose, S., 2011, The representative concentration pathways: an overview: Climatic Change, v. 109, p. 5–31, https://doi.org/10.1007/s10584-011-0148-z.

This data release provides state-level estimates of annual agricultural use of pesticide compounds by major crop or crop group for states in the conterminous United States, for years 1992-2019, compiled from data used to make county-level estimates by means of methods described in Thelin and Stone (2013) and Baker and Stone (2015). The source of these data is the same as the published county-level pesticide-use estimates for 1992-2009 (Stone, 2013), estimates for 2008-2012 (Baker and Stone, 2015), estimates for 2013-17 (Wieben, 2019), and preliminary estimates for 2018 and 2019 (Wieben, 2021a, Wieben, 2021b, respectively). County-level by-crop estimates are not published because of the increased uncertainty in estimating the geographic distribution of compounds applied to specific crops. High-acreage crops (corn, soybeans, wheat, cotton, rice, and alfalfa) are individually aggregated to state level while low-acreage crops are combined into groups (vegetables and fruit, orchards and grapes, pasture and hay, and other crops) prior to aggregating to the state level. This data release contains two tables of state-level annual agricultural pesticide-use estimates by crop or crop group (one for low estimates and one for high estimates) and associated metadata. These data were used to produce annual time-series charts for individual pesticide by crop or crop group for 1992-2019 available on the Pesticide National Synthesis Project webpage: https://doi.org/doi:10.5066/F7NP22KM. Beginning in 2019, estimates are reported for a reduced number of compounds. References cited: Baker, N.T., and Stone, W.W., 2015, Estimated annual agricultural pesticide use for counties of the conterminous United States, 2008-12: U.S. Geological Survey Data Series 907, 9 p., accessed July 12, 2015, at http://doi.org/10.3133/ds907. Stone, W.W., 2013, Estimated annual agricultural pesticide use for counties of the conterminous United States, 1992-2009: U.S. Geological Survey Data Series 752, 1 p. pamphlet, 14 tables, accessed July 12, 2015, at http://pubs.usgs.gov/ds/752/. Thelin, G.P., and Stone, W.W., 2013, Estimation of annual agricultural pesticide use for counties of the conterminous United States, 1992-2009: U.S. Geological Survey Scientific Investigations Report 2013-5009, 54 p., accessed July 12, 2015, at http://pubs.usgs.gov/sir/2013/5009/. Wieben, C.M., 2019, Estimated annual agricultural pesticide use for counties of the conterminous United States, 2013-17 (ver. 2.0, May 2020): U.S. Geological Survey data release, accessed January 15, 2021, at https://doi.org/10.5066/P9F2SRYH. Wieben, C.M., 2021a, Preliminary estimated annual agricultural pesticide use for counties of the conterminous United States, 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P920L09S. Wieben, C.M., 2021b, Preliminary estimated annual agricultural pesticide use for counties of the conterminous United States, 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9EDTHQL.

The Gross Domestic Product per capita in the United States was last recorded at 66682.61 US dollars in 2024. The GDP per Capita in the United States is equivalent to 528 percent of the world's average. This dataset provides - United States GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States PPI: Flow: sa: Stage4: ID: GP: Transportation of Passengers data was reported at 115.200 Nov2009=100 in Jun 2018. This records a decrease from the previous number of 115.900 Nov2009=100 for May 2018. United States PPI: Flow: sa: Stage4: ID: GP: Transportation of Passengers data is updated monthly, averaging 114.150 Nov2009=100 from Nov 2009 (Median) to Jun 2018, with 104 observations. The data reached an all-time high of 122.000 Nov2009=100 in Dec 2013 and a record low of 101.500 Nov2009=100 in Nov 2009. United States PPI: Flow: sa: Stage4: ID: GP: Transportation of Passengers data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I022: Producer Price Index: FD-ID System: Intermediate Demand: Production Flow: Seasonally Adjusted.

United States PPI: Flow: sa: Stage3: ID: GP: Energy data was reported at 108.100 Nov2009=100 in Jun 2018. This records an increase from the previous number of 104.500 Nov2009=100 for May 2018. United States PPI: Flow: sa: Stage3: ID: GP: Energy data is updated monthly, averaging 105.850 Nov2009=100 from Nov 2009 (Median) to Jun 2018, with 104 observations. The data reached an all-time high of 120.600 Nov2009=100 in Feb 2014 and a record low of 76.400 Nov2009=100 in Apr 2016. United States PPI: Flow: sa: Stage3: ID: GP: Energy data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I022: Producer Price Index: FD-ID System: Intermediate Demand: Production Flow: Seasonally Adjusted.

Health professionals, especially primary care physicians, are in high demand in many parts of the U.S. Some areas are experiencing health professional shortages. This map shows the ratio of population to primary care physicians in the U.S. Areas in dark red show where there are less primary care physicians per person.The data comes from County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. The layer used in the map comes from ArcGIS Living Atlas of the World, and the full documentation for the layer can be found here.County data are suppressed if, for both years of available data, the population reported by agencies is less than 50% of the population reported in Census or less than 80% of agencies measuring crimes reported data.

The Gross Domestic Product (GDP) in the United States expanded 2 percent in the first quarter of 2025 over the same quarter of the previous year. This dataset provides the latest reported value for - United States GDP Annual Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.