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.

Physicians (per 1,000 people) in United States was reported at 3.608 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Physicians - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

This statistic depicts the annual compensation among family practice physicians in the U.S. according to different sources and organizations. As of 2018, Sullivan Cotter Medical Group reported an annual compensation for family practitioners of some 267 thousand U.S. dollars, while Compdata came to some 235 thousand dollars annually.

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

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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!"

Graph and download economic data for All Employees, Offices of Physicians (CES6562110001) from Jan 1972 to May 2025 about physicians, health, establishment survey, education, services, employment, and USA.

Indonesia BioNER Dataset

This dataset taken from online health consultation platform Alodokter.com which has been annotated by two medical doctors. Data were annotated using IOB in CoNLL format. Dataset contains 2600 medical answers by doctors from 2017-2020. Two medical experts were assigned to annotate the data into two entity types: DISORDERS and ANATOMY. The topics of answers are: diarrhea, HIV-AIDS, nephrolithiasis and TBC, which marked as high-risk dataset from WHO. This… See the full description on the dataset page: https://huggingface.co/datasets/abid/indonesia-bioner-dataset.

About this Dataset

Carefully curated list of “Surgeon / Doctor / Dentist” in USA. We strive to keep our customers satisfied, so they no longer have to worry about finding quality leads. Which is why we offer a 100% data guarantee. If there is any information missing or incorrect we will replace it for you. Contact us immediately. What you will find below: - Contact Name - Company - Email - Contact no. & more

Category

Health & Medicine

Keywords

USA Surgeon,USA Doctors,US Surgeons,US Doctors,US Dentist

Row Count

8768

Price

$4999.00

The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com. All copyrights of the data belong to healthcaremagic.com and icliniq.com.

According to a survey carried out in the United States in 2023, willingness to share health data dropped when compared to the same survey question asked in 2020 and 2022. In 2023, 64 percent of adults would share health data with a doctor or clinician, while in 2020, 72 percent of respondents were willing to share health data with doctors or clinicians.

United States Trade Balance: CAFTA-DR data was reported at 335.700 USD mn in May 2018. This records a decrease from the previous number of 655.300 USD mn for Apr 2018. United States Trade Balance: CAFTA-DR data is updated monthly, averaging 205.300 USD mn from Jan 2009 (Median) to May 2018, with 113 observations. The data reached an all-time high of 907.300 USD mn in Nov 2017 and a record low of -340.000 USD mn in May 2012. United States Trade Balance: CAFTA-DR data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA009: Trade Statistics: Census Basis: By Region. Dominican Republic-Central America Foreign Trade Agreement includes Canada, Costa Rica, Dominican Republic, El Salvador, Guatemala, Honduras, Mexico and Nicaragua.

About 33 percent of U.S. physicians spent 17-24 minutes with their patients, according to a survey conducted in 2018. Physicians are often constrained in their time directly working with patients, which could have an impact on patient care outcomes. Studies found out that physicians spend almost half of their time in office on data entry and other desk work. More sophisticated, network-enabled EHR (electronic health records) systems for physicians could probably be a step towards more time directly with patients.

U.S. physicians

Physicians work in a variety of fields and across direct patient care and research. Within the last 50 years, the total number of active physicians has increased dramatically throughout the United States. Among all U.S. states, including the District of Columbia, the District of Columbia had the highest rate of all U.S. states of active physicians.

Physician time

In a recent study, physicians were asked about the time they spend with their patients. According to the results, a majority of physicians said that they felt their time with patients was limited. In 2018, most physicians saw 11-20 patients per day. Some reports have estimated that for every hour of direct patient contact, physicians spend an additional 2 hours working on reporting and desk work. Recent physician surveys have also indicated that one of the primary reasons for physician burn-out is having too many bureaucratic tasks.

About this Dataset

Carefully curated list of “Surgeon / Doctor / Dentist” in New York. We strive to keep our customers satisfied, so they no longer have to worry about finding quality leads. Which is why we offer a 100% data guarantee. If there is any information missing or incorrect we will replace it for you. Contact us immediately. What you will find below: - Contact Name - Company - Email - Contact no. & more

Category

Health & Medicine

Keywords

New York Surgeon,New York Doctors,New York Dentist,US Surgeon,US Doctors

Row Count

502

Price

$179.00

As of 2023, 76 percent of respondents surveyed in the United States would want to share their wearable device's data by opening the app in the device and reviewing the health data with the doctor in person during an appointment. Another method just under three-quarters would be willing to carry out is answering questions about health data while completing intake paperwork before an appointment. Less preferred methods included automatic data sharing and sending screenshots of health data to the doctor.

Sixty-seven maps from Indian Land Cessions in the United States, compiled by Charles C. Royce and published as the second part of the two-part Eighteenth Annual Report of the Bureau of American Ethnology to the Secretary of the Smithsonian Institution, 1896-1897 have been scanned, georeferenced in JPEG2000 format, and digitized to create this feature class of cession maps. The mapped cessions and reservations included in the 67 maps correspond to entries in the Schedule of Indian Land Cessions, indicating the number and location of each cession by or reservation for the Indian tribes from the organization of the Federal Government to and including 1894, together with descriptions of the tracts so ceded or reserved, the date of the treaty, law or executive order governing the same, the name of the tribe or tribes affected thereby, and historical data and references bearing thereon, as set forth in the subtitle of the Schedule. Go to this URL for full metadata: https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.TRIBALCEDEDLANDS.xml Each Royce map was georeferenced against one or more of the following USGS 1:2,000,000 National Atlas Feature Classes contained in \NatlAtlas_USGS.gdb: cities_2mm, hydro_ln_2mm, hydro_pl_2mm, plss_2mm, states_2mm. Cessions were digitized as a file geodatabase (GDB) polygon feature class, projected as NAD83 USA_Contiguous_Lambert_Conformal_Conic, which is the same projection used to georeference the maps. The feature class was later reprojected to WGS 1984 Web Mercator (auxiliary sphere) to optimize it for the Tribal Connections Map Viewer. Polygon boundaries were digitized as to not deviate from the drawn polygon edge to the extent that space could be seen between the digitized polygon and the mapped polygon at a viewable scale. Topology was maintained between coincident edges of adjacent polygons. The cession map number assigned by Royce was entered into the feature class as a field attribute. The Map Cession ID serves as the link referencing relationship classes and joining additional attribute information to 752 polygon features, to include the following: 1. Data transcribed from Royce's Schedule of Indian Land Cessions: a. Date(s), in the case of treaties, the date the treaty was signed, not the date of the proclamation; b. Tribe(s), the tribal name(s) used in the treaty and/or the Schedule; and c. Map Name(s), the name of the map(s) on which a cession number appears; 2. URLs for the corresponding entry in the Schedule of Indian Land Cessions (Internet Archive) for each unique combination of a Date and reference to a Map Cession ID (historical references in the Schedule are included); 3. URLs for the corresponding treaty text, including the treaties catalogued by Charles J. Kappler in Indian Affairs: Laws and Treaties (HathiTrust Digital Library), executive order or other federal statute (Library of Congress and University of Georgia) identified in each entry with a reference to a Map Cession ID or IDs; 4. URLs for the image of the Royce map(s) (Library of Congress) on which a given cession number appears; 5. The name(s) of the Indian tribe or tribes related to each mapped cession, including the name as it appeared in the Schedule or the corresponding primary text, as well as the name of the present-day Indian tribe or tribes; and 6. The present-day states and counties included wholly or partially within a Map Cession boundary. During the 2017-2018 revision of the attribute data, it was noted that 7 of the Cession Map IDs are missing spatial representation in the Feature Class. The missing data is associated with the following Cession Map IDs: 47 (Illinois 1), 65 (Tennessee and Bordering States), 128 (Georgia), 129 (Georgia), 130 (Georgia), 543 (Indian Territory 3), and 690 (Iowa 2), which will be updated in the future. This dataset revises and expands the dataset published in 2015 by the U.S. Forest Service and made available through the Tribal Connections viewer, the Forest Service Geodata Clearinghouse, and Data.gov. The 2018 dataset is a result of collaboration between the Department of Agriculture, U.S. Forest Service, Office of Tribal Relations (OTR); the Department of the Interior, National Park Service, National NAGPRA Program; the U.S. Environmental Protection Agency, Office of International and Tribal Affairs, American Indian Environmental Office; and Dr. Claudio Saunt of the University of Georgia. The Forest Service and Dr. Saunt independently digitized and georeferenced the Royce cession maps and developed online map viewers to display Native American land cessions and reservations. Dr. Saunt subsequently undertook additional research to link Schedule entries, treaty texts, federal statutes and executive orders to cession and reservation polygons, which he agreed to share with the U.S. Forest Service. OTR revised the data, linking the Schedule entries, treaty texts, federal statues and executive orders to all 1,172 entries in the attribute table. The 2018 dataset has incorporated data made available by the National NAGPRA Program, specifically the Indian tribe or tribes related to each mapped cession, including the name as it appeared in the Schedule or the corresponding primary text and the name of the present-day Indian tribe or tribes, as well as the present-day states and counties included wholly or partially within a Map Cession boundary. This data replaces in its entirety the National NAGPRA data included in the dataset published in 2015. The 2015 dataset incorporated data presented in state tables compiled from the Schedule of Indian Land Cessions by the National NAGPRA Program. In recent years the National NAGPRA Program has been working to ensure the accuracy of this data, including the reevaluation of the present-day Indian tribes and the provision of references for their determinations. Changes made by the OTR have not been reviewed or approved by the National NAGPRA Program. The Forest Service will continue to collaborate with other federal agencies and work to improve the accuracy of the data included in this dataset. Errors identified since the dataset was published in 2015 have been corrected, and we request that you notify us of any additional errors we may have missed or that have been introduced. Please contact Rebecca Hill, Policy Analyst, U.S. Forest Service, Office of Tribal Relations, at rebeccahill@fs.usda.gov with any questions or concerns with regard to the data included in this dataset.

This dataset provides information about the number of properties, residents, and average property values for Bless US Drive cross streets in Wentzville, MO.

Original provider: MarAlliance

Dataset credits: Data provider MarAlliance - Marine Megafauna Program Originating data center Satellite Tracking and Analysis Tool (STAT) Project partner Partners for this project include the Comision Nacional de Areas Naturales Protegidas of Mexico(Dr. Francisco Remolina), The Department of Fisheries in Belize (Beverly Wade and James Azueta), the Centro de Investigaciones de Ecosistemas Costeros of Cuba (Dr. Fabian Pina), Deep Blue in Utila Honduras (Steve Fox), University of Southern Mississippi in Louisiana, USA (Dr. Eric Hoffmayer)and National Oceanographic and Atmospheric Administration Flower Garden Banks National Marine Sanctuary in the US (Emma Hickerson). Project sponsor or sponsor description This Project is supported by the Summit Foundation, and several donors who wish to remain anonymous.

Abstract: Whale sharks (Rhincodon typus) and manta rays (Manta spp.)represent some of the most iconic species of fish worldwide and yet only recently are their patterns of movement becoming known. Whale shark and manta site fidelity and movements in relation to ephemeral food sources and a host of environmental factors are being elucidated through a long term and multi-partner project named the MarineMeganet. The Western Caribbean and Gulf of Mexico possess several aggregations of whale sharks and a large aggregation of manta rays as well as a range of other species of ocean giants that gather seasonally to feed. Although there are no targeted fisheries for the world's largest fish and ray in this region, their large and predictable aggregations remain at risk from ship strikes, uncontrolled tourism and, in the case of the whale shark, the capture of its food source (eg fish that produce the spawn that whale sharks feed on).

To better understand aggregation dynamics of whale sharks and manta rays we are investigating their patterns of movement in relation to ephemeral food pulses and anthropogenic threats by deploying satellite location only tags that will provide near real time tracking information to inform the management and conservation of these species.

Los tiburónes ballena (Rhincodon typus) y las manta rayas (Manta spp.) representan algunas de las especies más emblemáticas de peces en todo el mundo y sin embargo sólo recientemente sus patrones de movimiento han sido revelado. La fidelidad al sitio y los movimientos en lo referente a fuentes alimenticias efímeras y una serie de factores ambientales se están esclareciendose a través de un largo plazo y el proyecto denominado el MarineMeganet. El oeste del Caribe y el Golfo de México poseen varias agrupaciones de tiburones ballena y una gran acumulación de manta rayas, así como una variedad de otras especies de gigantes del océano que se reúnen por temporadas para alimentarse. Aunque no hay ninguna pesca regional enfocada hacia estos planktivoros grandes, sus agregaciones grandes y predecibles permanecen en riesgo de huelgas de la nave, turismo descontrolado y, en el caso del tiburón ballena, la captura de su fuente de alimento (por ejemplo los peces que producen la semilla que se alimentan de tiburones ballena).

Para mejor comprender la dinámica de la agregación de tiburones ballena y de las manta rayas estamos investigando sus patrones de movimiento en relación con pulsos de alimentos efímeras y amenazas antropogénicas mediante la colocación de etiquetas satélites que proporcionarán información para la gestión y conservación de estas especies de seguimiento en tiempo casi-real.

Supplemental information: Visit STAT's project page for additional information.

This dataset is a summarized representation of the telemetry locations aggregated per species per 1-degree cell.

Hugging Face Hub dataset tl;dr summaries

Would it be nice to have a tl;dr summary for datasets on the Hub? This dataset (how meta!) consists of tl;dr summaries for the 500 most liked datasets on the Hub. Please add your thoughts to this discussion (I will also consider a like of the dataset as an upvote)

  Examples

A sample of 15 summaries, alongside their full cards. You can use the datasets server to explore more examples.

  OpenAssistant/oasst1

Downloads: 5259… See the full description on the dataset page: https://huggingface.co/datasets/davanstrien/dataset-tldr.

Key information about US Number of Registered Vehicles

• US Number of Registered Vehicles was reported at 283,400,986 Unit in Dec 2022.
• This records an increase from the previous number of 282,354,993 Unit for Dec 2021.
• US Number of Registered Vehicles data is updated yearly, averaging 93,949,852 Unit from Dec 1910 to 2022, with 113 observations.
• The data reached an all-time high of 283,400,986 Unit in 2022 and a record low of 468,500 Unit in 1910.
• US Number of Registered Vehicles data remains active status in CEIC and is reported by CEIC Data.
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: No of Registered Vehicles: Annual.

United States PPI: Final: excl Government data was reported at 115.100 Apr2010=100 in Jun 2018. This records an increase from the previous number of 114.600 Apr2010=100 for May 2018. United States PPI: Final: excl Government data is updated monthly, averaging 108.200 Apr2010=100 from Apr 2010 (Median) to Jun 2018, with 99 observations. The data reached an all-time high of 115.100 Apr2010=100 in Jun 2018 and a record low of 99.900 Apr2010=100 in Jun 2010. United States PPI: Final: excl Government 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.I019: Producer Price Index: FD-ID System: Final Demand.