FDA is disclosing the final inspection classification for inspections related to currently marketed FDA-regulated products. The disclosure of this information is not intended to interfere with planned enforcement actions, therefore some information may be withheld from posting until such action is taken.

This information is derived from inspections of restaurants and other food establishments in Chicago from January 1, 2010 to the present. Inspections are performed by staff from the Chicago Department of Public Health’s Food Protection Program using a standardized procedure. The results of the inspection are inputted into a database, then reviewed and approved by a State of Illinois Licensed Environmental Health Practitioner (LEHP). For descriptions of the data elements included in this set, please click here.

Note about 7/1/2018 change to food inspection procedures that affects the data in this dataset: http://bit.ly/2yWd2JB

Disclaimer: Attempts have been made to minimize any and all duplicate inspection reports. However, the dataset may still contain such duplicates and the appropriate precautions should be exercised when viewing or analyzing these data. The result of the inspections (pass, pass with conditions or fail) as well as the violations noted are based on the findings identified and reported by the inspector at the time of the inspection, and may not reflect the findings noted at other times. For more information about Food Inspections, go to https://www.chicago.gov/city/en/depts/cdph/provdrs/food_safety.html.

The INSPTRAX System tracks Air, RCRA, and Water inspection targeting, planning and tracking information. It is used by the the Air, RCRA, and Water programs to input annual inspection targets, then used by the Region to send a list of draft and final targets to Region 7 four states (Iowa, Kansas, Missouri, Nebraska). It is then used to generate quarterly inspection schedules for conducting the actural inspection activities by our ENSV inspectors and finally used to track all inspection activities.

This dataset tracks the updates made on the dataset "Inspection Database" as a repository for previous versions of the data and metadata.

The CDRH Inspections Database provides information about medical device inspections that were the responsibility of CDRH from 2008 to the present.

Additional fields such as Category and Structure Type are based off fields needed in the Incident Status Summary (ICS 209).

Please review the DINS database dictionary for additional information.

This is a public use data file on Delaware restaurant inspections for the past two years. It includes a list of restaurants with violations reported during the inspection of the establishment. There may be more than one record for a specific restaurant for a specific inspection date. Multiple records for the same restaurant and inspection date indicates multiple violations.

Sidewalk Management System is used to track and organize inspections, violations and the status of New York City sidewalks Identifies locations where DOT inspectors performed sidewalk inspections for defects. For more information please visit NYC DOT website: www.nyc.gov/sidewalks

This map includes 5 data sets of CMOM items to inspect. All data sets are due to be completed by 12/31/2015. Layers represent:Manholes: All manholes related to InfoMaster CCTV lists (Performance 1-5, Risk 1-2, and P1 Bad Point Repair). Manholes inspected and entered in the Manhole Inspection database since 2008 are marked done.CCTV All VCP Large: All VCP lines (exclude interceptors, lines in CIP, and lines already inspected since 2007), only 8" and larger to be targeted by QuickCam. Lines inspected through WinCan or QuickCam work orders entered in Cityworks are marked done.CCTV All VCP Small: All VCP lines (exclude interceptors, lines in CIP, and lines already inspected since 2007), only 6" and smaller to be targeted by CCTV crew. Lines inspected through WinCan or QuickCam work orders entered in Cityworks are marked done.CCTV InfoMaster VCP: All VCP lines in InfoMaster CCTV Lists (Performance 1-5, Risk 1-2, and P1 Bad Point Repair, exclude interceptors and lines in CIP). Lines inspected through WinCan or QuickCam work orders entered in Cityworks are marked done.CCTV InfoMaster PVC: All VCP lines in InfoMaster CCTV Lists (Performance 1-5, Risk 1-2, and P1 Bad Point Repair, exclude interceptors and lines in CIP). Lines inspected through WinCan or QuickCam work orders entered in Cityworks are marked done. Only 30% of these lines are due for inspection by 12/31/2015

Comprehensive dataset of 8 Sanitary inspections in Indiana, United States as of June, 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.

These documents give information that will help you understand regulated inspections processes. It gives you access to information about each type of drug and health product inspection done by Health Canada, in Canada and abroad.

This data set is sunset and will not be updating any more. Please go to this link for updated information: https://inspections.myhealthdepartment.com/dallas

This data set is intended to communicate the name of establishment, the physical location of the establishment, the date the inspection was conducted, the overall score for the inspection, and the point deduction for the individual violations.

Disclaimer: The inspection data represents a specific period in time. It does not represent the ownership of the establishment or the full history of the establishment.

🇬🇧 영국

The photomask inspection equipment market is experiencing robust growth, driven by the increasing demand for advanced semiconductor devices and the miniaturization of integrated circuits. The market, estimated at $2 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 7% through 2033, reaching approximately $3.5 billion. Key drivers include the escalating need for higher resolution and precision in photomask manufacturing to support the production of cutting-edge chips for applications like 5G, AI, and high-performance computing. The rising adoption of advanced inspection techniques like Die-to-Die (DD) and Die-to-Database (DB) methods contributes significantly to market expansion. While the market faces certain restraints, such as the high cost of equipment and the complexities associated with advanced inspection technologies, ongoing technological advancements and the relentless pursuit of smaller, more efficient chips are likely to outweigh these challenges. Leading players like KLA-Tencor, Applied Materials, and ASML are at the forefront of innovation, constantly developing new solutions to meet the evolving needs of semiconductor manufacturers and mask shops. The market's regional distribution reflects the established semiconductor manufacturing hubs, with North America and Asia Pacific holding significant market shares. The strong growth trajectory is expected to continue, fueled by sustained investment in R&D and the ever-increasing demand for sophisticated electronic devices. The segmentation of the photomask inspection equipment market highlights the preference for both Die-to-Die (DD) and Die-to-Database (DB) methods. While DD methods offer faster inspection times, DB methods provide superior accuracy and defect detection capabilities. The choice between these methods depends on the specific requirements of the semiconductor manufacturing process and the desired level of defect detection. Furthermore, the market is geographically diverse, with established players in North America and Europe continuing to hold strong positions. However, the rapid growth of the semiconductor industry in Asia Pacific, particularly in China and South Korea, is driving significant market expansion in this region. This regional diversification presents both opportunities and challenges for manufacturers, requiring strategic adaptation to local market dynamics and regulatory landscapes. The historical period (2019-2024) likely showed a steady growth rate setting the stage for the forecasted expansion.

Comprehensive dataset of 14 Sanitary inspections in Michigan, United States as of June, 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 8 Sanitary inspections in New York, 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.

The data set is refreshed on a weekly basis on Fridays at 2:45 AM. The website will reflect the last time the data set was updated and the total count of rows. The grid on the “Data” tab will display the up to date data. However, in certain situations there is a delay in the refresh of the downloadable data file. Sometimes the downloadable file does not reflect the updates to the data in the portal. After a delay (duration has been variable; up to 30 minutes), the file will be updated on the server and then downloads will include the updated data.

Canada regularly inspects registrants that sell or distribute pest control products. These registrants are usually companies that own the pesticide registrations. Every year, PMRA inspect about 10% of companies. Inspections usually take 4 to 8 weeks; an additional 4 weeks may be needed if samples have been sent to the federal laboratory for testing. A list of all inspections on pesticide registrants conducted by Health Canada since April 1, 2016 will be included within the database. Information published will include registrant information (i.e. name, address), date of the inspection, inspection rating, inspection observations, and whether corrective actions are requested by Health Canada. Definitions Compliant rating: A compliant rating indicates that the company meets the regulations. A compliant rating that contains observations could indicate: - there were technical deficiencies - the risk to health or the environment is low For a compliant rating with observations, corrective actions are often required. Non-compliant rating: A non-compliant rating contains observations that could indicate: - there were deficiencies - the risk to health or the environment is moderate or significant Immediate corrective action is required within a definite time frame. (Current to August 13th , 2020.)

This is a dataset of information from the results of inspections and /or enforcement of violations related to commercial pool and spa for public health and safety by Austin Public Health - Environmental Health Services Division. Data is updated weekly. Data file includes three years of data.

The CAPA Apple Quality Grading Multi-Spectral Image Database consists of multispectral (450nm, 500nm, 750nm, and 800nm) images of health and defected apples of bi-color, manual segmentations of defected regions, and expert evaluations of the apples into 4 quality categories. The defect types consist of bruise, rot, flesh damage, frost damage, russet, etc. The database can be used for academic or research purposes with the aim of computer vision based apple quality inspection.

The CAPA Apple Quality Grading Multi-Spectral Image Database is a propriety of ULG (Gembloux Agro-Bio Tech) - Belgium, and cannot be used without the consent of the ULG (Gembloux Agro-Bio Tech), Belgium. For consent, contact Devrim Unay, İzmir University of Economics, Turkey: unaydevrim@gmail.com OR Marie-France Destain, Gembloux Agro-Bio Tech, Belgium: mfdestain@ulg.ac.be

In disseminating results using this database, 1. the author should indicate in the manuscript that it was acquired by ULG (Gembloux Agro-Bio Tech), Belgium. 2. cite the following article Kleynen, O., Leemans, V., & Destain, M.-F. (2005). Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering, 69(1), 41-49.

Relevant publications: Kleynen et al., 2003 O. Kleynen, V. Leemans and M.F. Destain, Selection of the most efficient wavelength bands for ‘Jonagold’ apple sorting. Postharv. Biol. Technol., 30 (2003), pp. 221–232. Leemans and Destain, 2004 V. Leemans and M.F. Destain, A real-time grading method of apples based on features extracted from defects. J. Food Eng., 61 (2004), pp. 83–89. Leemans et al., 2002 V. Leemans, H. Magein and M.F. Destain, On-line fruit grading according to their external quality using machine vision. Biosyst. Eng., 83 (2002), pp. 397–404. Unay and Gosselin, 2006 D. Unay and B. Gosselin, Automatic defect detection of ‘Jonagold’ apples on multi-spectral images: A comparative study. Postharv. Biol. Technol., 42 (2006), pp. 271–279. Unay and Gosselin, 2007 D. Unay and B. Gosselin, Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition. J. Food Eng., 78 (2007), pp. 597–605. Unay et al., 2011 Unay, D., Gosselin, B., Kleynen, O, Leemans, V., Destain, M.-F., Debeir, O, “Automatic Grading of Bi-Colored Apples by Multispectral Machine Vision”, Computers and Electronics in Agriculture, 75(1), 204-212, 2011.