This statistic shows the number of pharmacists in the United States from 2001 to 2016. In 2001, there were ******* physical therapists employed in the United States. In 2016, there were ******* physical therapists employed.

Comprehensive dataset of 17,373 Physical therapists in California, United States 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.

Comprehensive dataset of 2,190 Physical therapists in Iowa, United States 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.

Explore the progression of average salaries for graduates in Physical Therapist (Equivalent To Us Health Science, With Concentration In Physical Therapy) from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Physical Therapist (Equivalent To Us Health Science, With Concentration In Physical Therapy) relative to other fields. This data is essential for students assessing the return on investment of their education in Physical Therapist (Equivalent To Us Health Science, With Concentration In Physical Therapy), providing a clear picture of financial prospects post-graduation.

Comprehensive dataset of 4,610 Physical therapists in Maryland, United States 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.

This dataset provides information on 4,492 in Ohio, United States as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Home Health Care Agency Ratings

Quality Measurements, Types of Services and More

By US Open Data Portal, data.gov [source]

About this dataset

This dataset provides a list of all Home Health Agencies registered with Medicare. Contained within this dataset is information on each agency's address, phone number, type of ownership, quality measure ratings and other associated data points. With this valuable insight into the operations of each Home Health Care Agency, you can make informed decisions about your care needs. Learn more about the services offered at each agency and how they are rated according to their quality measure ratings. From dedicated nursing care services to speech pathology to medical social services - get all the information you need with this comprehensive look at U.S.-based Home Health Care Agencies!

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How to use the dataset

Are you looking to learn more about Home Health Care Agencies registered with Medicare? This dataset can provide quality measure ratings, addresses, phone numbers, types of services offered and other information that may be helpful when researching Home Health Care Agencies.

This guide will explain how to use the data in this dataset to gain a better understanding of Home Health Care Agencies registered with Medicare.

First, you will need to become familiar with the columns in the dataset. A list of all columns and their associated descriptions is provided above for your reference. Once you understand each column’s purpose, it will be easier for you to decide what metrics or variables are most important for your own research.

Next, use this data to compare various facets between different Home Health Care Agencies such as type of ownership, services offered and quality measure ratings like star rating or CMS certification number (from 0-5 stars). Collecting information from multiple sources such as public reviews or customer feedback can help supplement these numerical metrics in order to paint a more accurate picture about each agency's performance and customer satisfaction level.

Finally once you have collected enough data points on one particular agency or a comparison between multiple agencies then conduct more analysis using statistical methods like correlation matrices in order to determine any patterns that exist within the data set which may reveal valuable insights into topic of research at hand

Research Ideas

• Using the data to compare quality of care ratings between agencies, so people can make better informed decisions about which agency to hire for home health services.
• Analyzing the costs associated with different types of home health care services, such as nursing care and physical therapy, in order to determine where money could be saved in health care budgets.
• Evaluating the performance of certain agencies by analyzing the number of episodes billed to Medicare compared to their national averages, allowing agencies with lower numbers of billing episodes to be identified and monitored more closely if necessary

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: csv-1.csv | Column name | Description | |:----------------------------------------...

A Gold Standard Corpus for Activity Information

Dataset Title: A Gold Standard Corpus for Activity Information (GoSCAI)

Dataset Curators: The Epidemiology & Biostatistics Section of the NIH Clinical Center Rehabilitation Medicine Department

Dataset Version: 1.0 (May 16, 2025)

Dataset Citation and DOI: NIH CC RMD Epidemiology & Biostatistics Section. (2025). A Gold Standard Corpus for Activity Information (GoSCAI) [Data set]. Zenodo. doi: 10.5281/zenodo.15528545

EXECUTIVE SUMMARY

This data statement is for a gold standard corpus of de-identified clinical notes that have been annotated for human functioning information based on the framework of the WHO's International Classification of Functioning, Disability and Health (ICF). The corpus includes 484 notes from a single institution within the United States written in English in a clinical setting. This dataset was curated for the purpose of training natural language processing models to automatically identify, extract, and classify information on human functioning at the whole-person, or activity, level.

CURATION RATIONALE

This dataset is curated to be a publicly available resource for the development and evaluation of methods for the automatic extraction and classification of activity-level functioning information as defined in the ICF. The goals of data curation are to 1) create a corpus of a size that can be manually deidentified and annotated, 2) maximize the density and diversity of functioning information of interest, and 3) allow public dissemination of the data.

LANGUAGE VARIETIES

Language Region: en-US

Prose Description: English as written by native and bilingual English speakers in a clinical setting

LANGUAGE USER DEMOGRAPHIC

The language users represented in this dataset are medical and clinical professionals who work in a research hospital setting. These individuals hold professional degrees corresponding to their respective specialties. Specific demographic characteristics of the language users such as age, gender, or race/ethnicity were not collected.

ANNOTATOR DEMOGRAPHIC

The annotator group consisted of five people, 33 to 76 years old, including four females and one male. Socioeconomically, they came from the middle and upper-middle income classes. Regarding first language, three annotators had English as their first language, one had Chinese, and one had Spanish. Proficiency in English, the language of the data being annotated, was native for three of the annotators and bilingual for the other two. The annotation team included clinical rehabilitation domain experts with backgrounds in occupational therapy, physical therapy, and individuals with public health and data science expertise. Prior to annotation, all annotators were trained on the specific annotation process using established guidelines for the given domain, and annotators were required to achieve a specified proficiency level prior to annotating notes in this corpus.

LINGUISTIC SITUATION AND TEXT CHARACTERISTICS

The notes in the dataset were written as part of clinical care within a U.S. research hospital between May 2008 and November 2019. These notes were written by health professionals asynchronously following the patient encounter to document the interaction and support continuity of care. The intended audience of these notes were clinicians involved in the patients' care. The included notes come from nine disciplines - neuropsychology, occupational therapy, physical medicine (physiatry), physical therapy, psychiatry, recreational therapy, social work, speech language pathology, and vocational rehabilitation. The notes were curated to support research on natural language processing for functioning information between 2018 and 2024.

PREPROCESSING AND DATA FORMATTING

The final corpus was derived from a set of clinical notes extracted from the hospital electronic medical record (EMR) for the purpose of clinical research. The original data include character-based digital content originally. We work in ASCII 8 or UNICODE encoding, and therefore part of our pre-processing includes running encoding detection and transformation from encodings such as Windows-1252 or ISO-8859 format to our preferred format.

On the larger corpus, we applied sampling to match our curation rationale. Given the resource constraints of manual annotation, we set out to create a dataset of 500 clinical notes, which would exclude notes over 10,000 characters in length.

To promote density and diversity, we used five note characteristics as sampling criteria. We used the text length as expressed in number of characters. Next, we considered the discipline group as derived from note type metadata and describes which discipline a note originated from: occupational and vocational therapy (OT/VOC), physical therapy (PT), recreation therapy (RT), speech and language pathology (SLP), social work (SW), or miscellaneous (MISC, including psychiatry, neurology and physiatry). These disciplines were selected for collecting the larger corpus because their notes are likely to include functioning information. Existing information extraction tools were used to obtain annotation counts in four areas of functioning and provided a note’s annotation count, annotation density (annotation count divided by text length), and domain count (number of domains with at least 1 annotation).

We used stratified sampling across the 6 discipline groups to ensure discipline diversity in the corpus. Because of low availability, 50 notes were sampled from SLP with relaxed criteria, and 90 notes each from the 5 other discipline groups with stricter criteria. Sampled SLP notes were those with the highest annotation density that had an annotation count of at least 5 and a domain count of at least 2. Other notes were sampled by highest annotation count and lowest text length, with a minimum annotation count of 15 and minimum domain count of 3.

The notes in the resulting sample included certain types of PHI and PII. To prepare for public dissemination, all sensitive or potentially identifying information was manually annotated in the notes and replaced with substituted content to ensure readability and enough context needed for machine learning without exposing any sensitive information. This de-identification effort was manually reviewed to ensure no PII or PHI exposure and correct any resulting readability issues. Notes about pediatric patients were excluded. No intent was made to sample multiple notes from the same patient. No metadata is provided to group notes other than by note type, discipline, or discipline group. The dataset is not organized beyond the provided metadata, but publications about models trained on this dataset should include information on the train/test splits used.

All notes were sentence-segmented and tokenized using the spaCy en_core_web_lg model with additional rules for sentence segmentation customized to the dataset. Notes are stored in an XML format readable by the GATE annotation software (https://gate.ac.uk/family/developer.html), which stores annotations separately in annotation sets.

CAPTURE QUALITY

As the clinical notes were extracted directly from the EMR in text format, the capture quality was determined to be high. The clinical notes did not have to be converted from other data formats, which means this dataset is free from noise introduced by conversion processes such as optical character recognition.

LIMITATIONS

Because of the effort required to manually deidentify and annotate notes, this corpus is limited in terms of size and representation. The curation decisions skewed note selection towards specific disciplines and note types to increase the likelihood of encountering information on functioning. Some subtypes of functioning occur infrequently in the data, or not at all. The deidentification of notes was done in a manner to preserve natural language as it would occur in the notes, but some information is lost, e.g. on rare diseases.

METADATA

Information on the manual annotation process is provided in the annotation guidelines for each of the four domains:

- Communication & Cognition (https://zenodo.org/records/13910167)

- Mobility (https://zenodo.org/records/11074838)

- Self-Care & Domestic Life (SCDL) (https://zenodo.org/records/11210183)

- Interpersonal Interactions & Relationships (IPIR) (https://zenodo.org/records/13774684)

Inter-annotator agreement was established on development datasets described in the annotation guidelines prior to the annotation of this gold standard corpus.

The gold standard corpus consists of 484 documents, which include 35,147 sentences in total. The distribution of annotated information is provided in the table below.

The purpose of this project was to implement a process for learner-driven, formative, prospective, ad-hoc, entrustment assessment in Doctor of Physical Therapy clinical education. Our goals were to develop an innovative entrustment assessment tool, and then explore whether the tool detected (1) differences between learners at different stages of development and (2) differences within learners across the course of a clinical education experience. We also investigated whether there was a relationship between the number of assessments and change in performance.

AI-Generated Physical Therapy Routine Market Outlook

As per our latest research, the global AI-Generated Physical Therapy Routine market size stood at USD 1.42 billion in 2024 and is expected to reach USD 6.04 billion by 2033, registering a robust CAGR of 17.5% during the forecast period from 2025 to 2033. The significant growth in this market is primarily driven by the rising demand for personalized rehabilitation solutions, the increasing prevalence of chronic musculoskeletal and neurological disorders, and the rapid integration of artificial intelligence in healthcare delivery systems.

One of the primary growth factors propelling the AI-Generated Physical Therapy Routine market is the escalating need for individualized and adaptive rehabilitation programs. Traditional physical therapy often struggles to provide ongoing, real-time adjustments tailored to each patient's progress and unique needs. AI-driven solutions leverage advanced algorithms and machine learning to analyze patient data, monitor performance, and continuously optimize therapy routines. This not only enhances patient outcomes but also improves engagement and adherence to therapy regimens. As healthcare providers and patients increasingly recognize the benefits of these intelligent systems, adoption rates are accelerating across both developed and emerging economies, further fueling market expansion.

Another crucial driver is the global surge in chronic conditions such as arthritis, stroke, and sports-related injuries, which require long-term physical therapy interventions. The aging population, particularly in regions like North America, Europe, and parts of Asia Pacific, is contributing to a higher incidence of mobility-related ailments. AI-generated physical therapy routines offer scalable, cost-effective solutions that can be deployed in a variety of settings, from hospitals and clinics to home care environments. This versatility is particularly valuable as healthcare systems worldwide face mounting pressure to deliver high-quality care while managing costs and resource constraints. The ability of AI platforms to provide remote monitoring, automate progress tracking, and deliver evidence-based recommendations is transforming the rehabilitation landscape and supporting sustained market growth.

Furthermore, advancements in AI technologies, including natural language processing, computer vision, and wearable sensor integration, are enhancing the accuracy and effectiveness of digital physical therapy solutions. These innovations enable seamless data collection, real-time feedback, and personalized exercise adjustments, making therapy more interactive and dynamic. Strategic partnerships between technology providers, healthcare institutions, and research organizations are accelerating product development and market entry. As regulatory frameworks evolve to accommodate digital health solutions, the pathway for AI-generated physical therapy routines is becoming clearer, encouraging further investment and innovation in this burgeoning sector.

From a regional perspective, North America currently dominates the AI-Generated Physical Therapy Routine market, accounting for over 38% of global revenue in 2024, followed by Europe and Asia Pacific. The high adoption rate of digital health technologies, robust healthcare infrastructure, and supportive regulatory environment in these regions are key contributors to their market leadership. Asia Pacific is expected to witness the fastest growth during the forecast period, with increasing investments in healthcare digitization and a large, underserved patient population. Latin America and the Middle East & Africa are also emerging as promising markets, driven by rising awareness and gradual improvements in healthcare access and technology adoption.

Component Analysis

The AI-Generated Physical Therapy Routine market is segmented by component into software, hardware, and services, each playing a pivotal role in the ecosystem. The software segment leads the market, accounting for the la

Results of the binary logistic regression analysis with simultaneous entry.

Objectives: Physical therapists (PTs) are integral team members in fall prevention in clinical settings; however, few studies have investigated PTs' engagement in pro-bono community-based falls prevention. Therefore, we aimed to describe the characteristics of PTs and physical therapist assistants (PTAs) in the United States who conduct community-based fall screenings, the reach of screenings, their knowledge and utilization of the Centers for Disease Control and Prevention's fall-risk screening toolkit (STEADI, Stopping Elderly Accidents, Deaths, and Injuries), and therapists' knowledge and referrals to evidence-based programs (EBPs) and community resources.Methods: A cross-sectional survey distributed to a convenience sample of PTs/PTAs in the United States through news-blasts, and social media.Results: Four hundred and forty-four therapists who worked with older adults completed the survey. Approximately 40% of the respondents (n = 180) conduct screenings, most frequently annually. People who screen tend to be PTs with >20 years of experience, work in outpatient/wellness or academia, and be involved in the least amount of direct patient care. The majority (n = 344, 77.5%) of survey respondents were somewhat to very familiar with the STEADI, and ~84% (n = 114) of respondents who were very familiar with the STEADI (n = 136) use the toolkit to conduct community-based, pro-bono fall risk screenings. Twenty-six percent (n = 14) out of the 53 PTAs who responded to the survey conduct falls screenings in the community. Of the PTs/PTAs who conduct community-based fall screenings (n = 180), ~ 75% (n = 136) are aware of and refer older adults to EBPs. Over half also refer to Silver Sneakers and/or senior centers.Discussion: PTs and PTAs are key partners in evidence-based multifactorial fall prevention in the community. Data helps inform community organizations that most PTs who engage in community-based fall risk screening utilize the STEADI toolkit and refer to community-based programs. Community organizations seeking PT partners to engage in fall risk screenings and promote referrals to local resources or EBPs will likely have the most success collaborating with local physical therapy education programs or physical therapy clinic managers.

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Rehabilitation Equipment Market Size 2024-2028

The rehabilitation equipment market size is forecast to increase by USD 3.67 billion, at a CAGR of 4.44% between 2023 and 2028.

The market is driven by the high prevalence of Cardiovascular Diseases (CVD) and the ongoing technological advancements in rehabilitation equipment. The increasing incidence of CVDs necessitates the need for advanced rehabilitation solutions, creating a significant demand for this market. Technological innovations, such as the integration of Internet Of Things (IoT) and AI in rehabilitation devices, enable personalized treatment plans and remote monitoring, enhancing patient outcomes and convenience. However, cost barriers remain a significant challenge in the market. The high cost of advanced rehabilitation equipment and the lack of insurance coverage for these devices limit their accessibility to a large patient population.
This obstacle necessitates strategic collaborations between manufacturers, insurers, and healthcare providers to make these solutions more affordable and accessible to patients. Companies that successfully navigate this challenge and offer cost-effective, innovative rehabilitation solutions will gain a competitive edge in this market.

What will be the Size of the Rehabilitation Equipment Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, integrating advanced technologies to cater to the diverse needs of various sectors. Adaptive seating solutions, for instance, are now enhanced with smart technology to provide personalized support. Balance assessment tools, once static, now incorporate virtual reality therapy for immersive and effective evaluations. Respiratory rehabilitation equipment, a crucial component, is being revolutionized through functional electrical stimulation and rehabilitation robotics. Muscle stimulation devices, too, are advancing with biofeedback technology to optimize therapy sessions. Tele-rehabilitation platforms and remote patient monitoring systems are increasingly popular, enabling in-home rehabilitation and improving accessibility for patients. Wearable sensors and adaptive clothing are transforming patient care by providing real-time data and enhancing comfort.

Rehabilitation software and neurological rehabilitation tools are being integrated to create comprehensive solutions, while pressure mapping systems and ergonomic workstations ensure optimal patient positioning. Home healthcare equipment and mobility assistive devices are also adopting these innovations to cater to the evolving needs of the market. The integration of these technologies creates a dynamic market landscape, with continuous advancements shaping the future of rehabilitation care.

How is this Rehabilitation Equipment Industry segmented?

The rehabilitation equipment industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Physical rehabilitation and training
  Strength/endurance and pain reduction
  Occupational rehabilitation and training


End-user

  Hospitals and clinics
  Rehabilitation centers
  Home care
  Physiotherapy centers


Geography

  North America

    US
    Canada


  Europe

    Germany
    UK


  APAC

    China


  Rest of World (ROW)

By Application Insights

The physical rehabilitation and training segment is estimated to witness significant growth during the forecast period.

The market comprises a diverse array of tools and technologies designed for physical therapy and training applications. These solutions cater to individuals recovering from injuries, surgeries, or strokes, as well as athletes and fitness enthusiasts seeking performance enhancement. The market's growth is driven by the increasing prevalence of chronic diseases, an aging population, and heightened awareness of the benefits of rehabilitation and physical therapy. Key components of the market include adaptive equipment design, gait training systems, virtual reality therapy, smart home technology, occupational therapy supplies, adaptive clothing, therapeutic exercise equipment, speech therapy devices, balance assessment tools, respiratory rehabilitation equipment, muscle stimulation devices, remote patient monitoring, tele-rehabilitation platforms, wearable sensors, adaptive seating, rehabilitation software, neurological rehabilitation tools, patient monitoring systems, wheelchair accessibility, biofeedback technology, in-home rehabilitation, functional electrical stimulation, rehabilitation robotics, pressure mapping systems, ergonomic workstations, home healthcare equipment, ph

This dataset provides information on 574 in Wyoming, United States as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Snapshot img

Cardiac Rehabilitation Market Size 2024-2028

The cardiac rehabilitation market size is forecast to increase by USD 1.97 billion, at a CAGR of 6.59% between 2023 and 2028.

The market is driven by the high prevalence of cardiovascular diseases, making it a significant market with immense potential. The increasing burden of cardiovascular diseases worldwide necessitates the need for effective rehabilitation programs to improve patient outcomes and reduce healthcare costs. However, this market faces challenges, including the rising adoption of telerehabilitation and cost barriers. Telerehabilitation, a remote form of cardiac rehabilitation, is gaining popularity due to its convenience and accessibility. Patients can participate in rehabilitation programs from the comfort of their homes, reducing travel time and costs. This trend is expected to increase as technology advances and telehealth becomes more accessible.
However, it also poses challenges, such as ensuring patient engagement and adherence to the program, and addressing the need for proper equipment and technology. Cost barriers are another significant challenge in the market. Despite the proven benefits of cardiac rehabilitation, many patients do not participate due to the high costs associated with traditional in-person programs. This issue is further compounded by the lack of insurance coverage and reimbursement policies for these programs. To address this challenge, companies can explore innovative pricing models, such as pay-per-use or subscription-based pricing, and collaborate with insurance providers to increase coverage and reimbursement for cardiac rehabilitation services.
By addressing these challenges, companies can capitalize on the growing demand for cardiac rehabilitation services and improve patient outcomes while reducing healthcare costs.

What will be the Size of the Cardiac Rehabilitation Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free Sample

The market continues to evolve, driven by the ongoing need for secondary prevention and improved patient outcomes. This dynamic market encompasses various sectors, including lipid profile monitoring, patient compliance, exercise prescription, and telehealth platforms. These elements are integral to effective cardiac rehabilitation programs, which aim to optimize exercise intensity and physical activity guidelines for individuals with cardiac conditions. Lifestyle modification plays a crucial role in cardiac rehabilitation, with a focus on managing diabetes, metabolic syndrome, angina pectoris, and other related conditions. Telehealth platforms facilitate remote patient monitoring, enabling continuous assessment of heart function, cardiac output, ejection fraction, and blood pressure.

Psychological support and patient education are also essential components, addressing the emotional and informational needs of patients undergoing rehabilitation. Remote patient monitoring, including left ventricular function assessment, holter monitoring, and stress testing protocols, allows for disease progression monitoring and risk stratification. Graded exercise testing and recovery monitoring are essential for optimizing exercise prescription and ensuring safe and effective rehabilitation programs. Pulmonary rehabilitation and heart failure management are additional applications that contribute to the market's ongoing growth and development. The integration of technology, such as VO2 max assessment and echocardiography results, further enhances the capabilities of cardiac rehabilitation programs.

The market's continuous dynamism reflects the evolving needs of patients and the advancements in medical technology, ensuring that cardiac rehabilitation remains a vital component of healthcare services.

How is this Cardiac Rehabilitation Industry segmented?

The cardiac rehabilitation industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Rehab centers
  Hospitals
  Clinics
  Others


Type

  Phase I
  Phase II
  Phase III


Technology Specificity

  Wearable Devices
  Tele-Rehabilitation
  Mobile Apps


Application

  Coronary Artery Disease
  Heart Failure
  Post-Surgery Recovery


Geography

  North America

    US
    Mexico


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    Australia
    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The rehab centers segment is estimated to witness significant growth during the forecast period.

Cardiac rehabilitation r

This dataset provides information on 2,579 in Connecticut, United States as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 4,108 in Tennessee, United States as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 6,350 in New Jersey, United States as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 925 in New Mexico, United States as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 181 in Wyoming, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.