Micrographics Data

[Norine Wiltshire]

Micro Data Linking (MDL) is a statistical method for combining data available for individual entities. Entities may be enterprises or persons or, also, a combination of the two (for example, linked employer-employee data).

In official statistics, it has become a strategic approach for exploring new informative needs (for example, globalization and green transition) without increasing the statistical burden on respondents.

MDL may use traditional statistical sources - for example, data collected with surveys already conducted in the Member States under the European regulation - but also new data collections, such as administrative sources available at the national level or, as a promising future perspective, the vast amount of big data emerging from private-sector data sources.

During recent years, Eurostat has been carrying out in collaboration with Member States a number of microdata linking projects in business statistics. Grants proposals have been launched to develop methodological guidance and national competences. These initiatives have provided encouraging results. Nevertheless, next steps should develop in the direction of increasing the number of Member States involved in these initiatives.

Overall, these experiences at the country level highlights the importance of coordination among countries to develop harmonized and comparable outputs that can increase international comparability and, thus, support policy decision at the EU level. This approach also implies the need to share the organizational aspects of every step, together with the final objectives of the specific project undertaken.

UCI provides high-contrast black-and-white or grayscale scanned raster images from original or blueprint drawings. In the process, UCI edits raster images, performing the cleanup of illegible text, unclear line work and other imperfections due to poor source materials or scanner limitations. UCI can add additional information to the images, such as building changes or modifications that would be deemed vital in the archiving of building and engineering data.

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Background: Although Mohs micrographic surgery (MMS), narrow margin excision (NME), and wide margin excision (WME) are commonly used to treat melanoma of the face, there is a paucity of data comparing mortality outcomes for each method.

Materials and methods: A retrospective review of Surveillance, Epidemiology, and End Results registries for patients diagnosed with melanoma of the face between 2003 and 2012 was conducted.

To generate thumbnails for the micrographs, I had to sum over the frames of the raw TIFF files and split the image into squares and sum over those before it was remotely human readable. given that any individual frame is so sparse in information (at least to the eye), what does this mean for the accuracy of motion correction, including full frame motion correction? Do we have any idea if this accurately describes stage drift? Also, my understanding is that full frame motion correction essentially does translational cross correlation between frames to determine the likely stage drift. Is this the case?

Similarly, do patch motion correction and local correction simply apply a similar process of translational cross correlation to smaller patches or boxes around picked particles to predict trajectories? I also noticed that the plots produced by local motion correction contain grey lines that are separate from the trajectories plotted in red, and I have yet to find an explanation for what they represent. I was also wandering what biases are involved in predicting the trajectory (for example, biases for smoothness).

Thanks for your patient explaination!
I think I get it. All operations like binning, contrast normalization and so on are just for making visualization result more human readable. Just for plotting, not really processing the data. Since it is so, parameters in contrast normalization code and some other questions are not so important.
Your explaination really helps! Thanks a lot!

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Technology and hardware obsolescence are significant drivers of the current wave of Microfilm transformation. As a result, many large organisations are evaluating their archives stored on microfilm. This is crucial because the ink on Microfilm can fade over time if not stored properly, making data retrieval challenging.

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These can be designed from scratch or modified from existing. There is a card library from which you can create more interfaces from which to extract data. They are catered to typical operations and usually have some settings from which you can fine tune them.

You also have the option to run data reports using templates for Microsoft Power BI. This requires a data extraction to be made. You can even schedule a data extraction to run automatically at convenient time. Once the data extraction is complete, it can be accessed in Microsoft Power BI and displayed in a convenient dashboard.

However you extract the information, the purpose is to make informed decisions that are data driven. This is the responsibility of the executive. If you do not have the skills to get the information you need, you will have to work with your team to make sure you get the right information at the right time. Appointing a BIM Manager or Document Controller on ACC would be a good place to start. They can prepare the data for you, so you just need to access the information.

If you need help adopting the AEC Collection or the Autodesk Construction Cloud in your practice or if you are looking to invest in hardware, please contact Micrographics so that we may be of assistance.

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All-in-one software for digitization projects with import of existing metadata, image quality control, image processing, indexing, content indexing, output in various data formats, export of metadata in standard formats. Can be used with Zeutschel scanners and third-party scanning systems.

Importance: Mohs micrographic surgery (MMS) is a skin cancer treatment that uses staged excisions based on margin status. Wide surgeon-level variation exists in the mean number of staged resections used to treat a tumor, resulting in a cost disparity and question of appropriateness. Objective: To evaluate the effectiveness of a behavioral intervention aimed at reducing extreme overuse in MMS, as defined by the specialty society, by confidentially sharing stages-per-case performance data with individual surgeons benchmarked to their peers nationally. Design, Setting, and Participants: This nonrandomized controlled intervention study included 2329 US surgeons who performed MMS procedures from January 1, 2016, to March 31, 2018. Physicians were identified using a 100% capture of Medicare Part B claims. The intervention group included physicians affiliated with the American College of Mohs Surgery, and the control group included physicians not affiliated with the American College of Mohs Surgery. Interventions: Individualized performance reports were delivered to all outlier surgeons, defined by the specialty society as those with mean stages per case 2 SDs above the mean, and inlier surgeons in the intervention group. Main Outcomes and Measures: The primary outcome was surgeon-level change in mean stages per case between the prenotification (January 2016 to January 2017) and postnotification (March 2017 to March 2018) periods. A multivariable linear regression model was used to evaluate the association of notification with this surgeon-level outcome. The surgeon-level metric of mean stages per case was not risk adjusted. The mean Medicare cost savings associated with changes in practice patterns were calculated. Results: Of the 2329 included surgeons, 1643 (70.5%) were male and 2120 (91.0%) practiced in metropolitan areas. In the intervention group (n = 1045), 53 surgeons (5.1%) were outliers; in the control group (n = 1284), 87 surgeons (6.8%) were outliers. Among the outliers in the intervention group, 44 (83%) demonstrated a reduction in mean stages per case compared with 60 outliers in the control group (69%; difference, 14%; 95% CI of difference, -1 to 27; P =.07). There was a mean stages-per-case reduction of 12.6% among outliers in the intervention group compared with 9.0% among outliers in the control group, and outliers in the intervention group had an adjusted postintervention differential decrease of 0.14 stages per case (95% CI, -0.19 to -0.09; P =.002). The total administrative cost of the intervention program was $150000, and the estimated reduction in Medicare spending was $11.1 million. Conclusions and Relevance: Sharing personalized practice pattern data with physicians benchmarked to their peers can reduce overuse of MMS among outlier physicians..

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