Cary, NC October 21-24
Agenda
Oct 21 Monday
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12:15 PM-7:30 PM
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Transportation provided between Embassy Suites and buildings on SAS campus
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12:30 PM-6:00 PM
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Registration Desk and Workshop Check-in Open
Room
Executive Briefing Center
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1:00 PM-2:15 PM
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Workshops
For an additional fee of $100 per workshop, you can add pre-conference workshops to your Discovery Summit agenda. Seats are limited, so be sure to secure your spot as soon as possible. Available to add at time of registration.Room
Executive Briefing Center 7, 8, 9, 150
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2:45 PM-4:00 PM
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Workshops
For an additional fee of $100 per workshop, you can add pre-conference workshops to your Discovery Summit agenda. Seats are limited, so be sure to secure your spot as soon as possible. Available to add at time of registration.Room
Executive Briefing Center 7, 8, 9, 150
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4:15 PM-4:45 PM
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Paper Presenter and Host Meeting
Room
Executive Briefing Center 7
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5:30 PM-7:30 PM
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Welcome Reception & Dinner
We’re so glad you’re here! Reconnect with colleagues from across the globe as you enjoy a meal in the Marketplace Café and Terrace at SAS world headquarters.Room
Executive Briefing Center Marketplace Café and Terrace
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Oct 22 Tuesday
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8:15 AM-5:00 PM
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Transportation provided between Embassy Suites and buildings on SAS campus
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9:00 AM-10:15 AM Plenary
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Building Your JMP Ecosystem
Platform Presets to Powerful Custom ApplicationsThis plenary session explores the many ways of using JMP, JMP Pro, and JMP Live to build customized solutions for your organization’s exact needs, all the way from simple out-of-the-box customizations using tools like Data Connectors, Workflow Builder, and Platform Presets, to building sophisticated custom applications using the JMP Scripting Language. We showcase a variety of real-world examples, including some of the innovative applications and extensions available through the upcoming JMP Marketplace.
Whether you’re a novice user or an advanced scripter, this session promises valuable insights and practical tips. Live demos of custom add-ins, data connectors, and utility functions, as well as packages of Platform Presets, highlight JMP’s ability to deliver tailored solutions effortlessly. Don’t miss this opportunity to unlock the full potential of JMP and elevate your organization’s data analysis and graphing capabilities.
Presenters
Room
Building V Auditorium
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10:30 AM-5:00 PM
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JMP Lab
Participate in testing features, provide critical user feedback, and experience new innovations first-hand at JMP Lab. You will have the opportunity to directly influence the development of JMP, helping to enhance its functionality, ease of use, and the overall user experience.Room
Executive Briefing Center 6
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10:30 AM-11:15 AM
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Discovery Expo
Meet and network with JMP R&D experts – the minds behind the software. You’ll also have the chance to talk to User Enablement and meet with JMP Education, who can answer your questions about training options, course materials, and other Learn JMP resources.
Room
Executive Briefing Center Networking Hall
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11:30 AM-12:15 PM
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Hop to It: Crafting Paper Frogs with Easy DOE
Experience a lively session that combines the power of JMP's Easy DOE platform with the creative energy of a father-daughter team. Our presenters – an R&D manager and his 9-year-old daughter – guide you through an engaging experiment using Easy DOE to create different variations of paper frogs.
With Easy DOE's user-friendly approach in JMP 18, you learn how to effortlessly set up and run designed experiments to explore how different paper frogs perform under various conditions. This session highlights some of Easy DOE's latest features in JMP 18, including user-friendly options for factor specification and model selection.
Join us to see how the family team uses Easy DOE to analyze results and uncover patterns in their paper frog designs. Whether you're new to DOE or looking to expand your expertise, this session offers practical insights and strategies for your own projects.
Room
Executive Briefing Center 150Skill Level
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I Know the Jaguar by His Paw: Distinguishing Jaguar and Puma Tracks in JMP
In the neotropics of Central and South America, the sympatric species of jaguar (Panthera onca) and puma (Puma concolor) are threatened due to human activity. Being able to identify the absence or presence of each species within a given region is fundamental in developing effective conservation strategies. While these cryptic and elusive species are rarely observed in the wild, the tracks they leave behind are indicators of their presence and distribution. However, jaguar and puma tracks cannot be easily distinguished visually.
In this talk, we unveil a new, intuitive field tool in JMP, allowing conservation biologists to rapidly differentiate between jaguars and pumas. First, we demonstrate how to extract features with the JMP FIT (Footprint Identification Technology) add-in and apply linear discriminant analysis. Next, we show how to extract the shape of a track with a statistical shape analysis in JMP. Finally, we predict the species using a neural network model of the shape coordinates, visualizing and exploring the output though a novel, interactive shape profiler tool.
Presenters
Room
Executive Briefing Center 7Skill Level
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DOE and Consumer Research: Tackling Consumer Preference Variability
One of the great things about humans is that we are all unique. Diversity has benefits all around us, but variation in preferences with regard to consumer goods makes it challenging to predict what will delight the greatest number of people. The use of design of experiments (DOE), facilitated by JMP, is critical to designing formulations with the most appeal.
The objective of this presentation is to interactively present methods to optimize formulations for the greatest consumer liking. Two DOE approaches from a real food formulation case study will be presented: an 18-run definitive screening design (DSD) and an 18-run space-filling design modeled using SVEM and neural networks.
To prepare data for modeling, multidimensional scaling is demonstrated to remove anomalous participants’ data. Participant clusters built using hierarchical clustering are used when fitting models using fit definitive screening and SVEM neural networks. The power of the JMP profiler will highlight how consumer preferences differ by cluster. Text Explorer is used to show how to verify insights gained through modeling by exploring verbatim comments by study participants. Lastly, insights gained from each experimental design and modeling approach are compared along with limitations of each. Attendees are presented with a more information-rich alternative to traditional DOE and consumer testing strategies.
Presenter
Room
Executive Briefing Center 8Skill Level
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Configuring, Connecting, and Collaborating with Data Connectors in JMP 18
Getting data into JMP is a core part of the analytic workflow, so JMP 18 introduces new data connectors to broaden the range of third-party data sources that JMP can access. JMP 18 allows more detailed configuration of ODBC data sources and makes it easier to share those configurations with colleagues across platforms and publish to JMP Live. We discuss data connectors, not only as they exist in JMP 18, but also plans for extending them in future versions of JMP.
Presenters
Room
Executive Briefing Center 9Skill Level
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12:15 PM-1:30 PM
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Lunch
Room
Executive Briefing Center Marketplace Café
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1:30 PM-2:00 PM
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Discovery Expo
Meet and network with JMP R&D experts – the minds behind the software. You’ll also have the chance to talk to User Enablement and meet with JMP Education, who can answer your questions about training options, course materials, and other Learn JMP resources.Room
Executive Briefing Center Networking Hall
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2:00 PM-2:45 PM
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Remote, Hybrid, In-Office, Anywhere: Trends in Workplace Flexibility
The debate over returning to the office vs working remotely (whether that be home or anywhere else) has caused lively discussions over the past several years. Many companies have called for the full return of employees to the office while others have maintained hybrid or fully remote options. Both authors have transitioned from full-time in-office jobs to full or partial remote work and have found a balance that suits their individual styles. What about you? How has your workplace situation changed and where do you stand in the debate?
A growing body of research from the Working from Home Research Project and the Survey of Working Arrangements and Attitudes has received significant media interest. Additionally, the Bureau of Labor Statistics and the Brookings Institute have also published data on trends and preferences for remote work.
In this paper, we highlight the use of Multiple File Import, Workflow Builder, Graph Builder and JMP Live (including new features in JMP 18) to prepare, segment, visualize, and share the survey data. We also conduct our own informal survey in the JMP Community to show how the Community’s remote work trends and preferences compare to the larger public data sets.
Presenters
Room
Executive Briefing Center 150Skill Level
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Modeling Antibiotic Tolerance in Chronic Lung Infection
The airway environment in individuals with muco-obstructive airway diseases (MADs) is characterized by dehydrated mucus due to hyperabsorption of airway surface liquid and impaired mucociliary clearance. As MADs progress, pathological mucus becomes increasingly viscous due to mucin overproduction and host-derived extracellular DNA (eDNA) accumulation. Pseudomonas aeruginosa, a major pathogen in MADs, colonizes this mucus niche persistently. Despite inhaled antibiotic therapies and the absence of antibiotic resistance, antipseudomonal treatment failure remains a clinical challenge.
We used JMP's data visualization and statistical modeling to investigate how mucin and eDNA concentrations – dominant polymers in respiratory mucus – affect P. aeruginosa’s antibiotic tolerance to understand antibiotic recalcitrance. Our findings reveal that polymer concentration and molecular weight impact P. aeruginosa survival after antibiotic exposure. Surprisingly, polymer-driven tolerance is not solely linked to reduced antibiotic diffusion. Additionally, we established an in vitro model that mirrors ex vivo antibiotic tolerance observed in expectorated sputum across different MAD etiologies, ages, and disease severities, highlighting the intrinsic variability in host-evolved P. aeruginosa populations.
Presenters
Room
Executive Briefing Center 7Skill Level
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Golf is Hard, but Designing Experiments is Easy
Once you’ve learned how easy it is to design an experiment in JMP, you never look at the world around you the same. Everything becomes an opportunity for an experiment! This presentation uses a practical example to demonstrate the process of design of experiments (DOE), including designing the experiment, modeling the results, and optimizing the inputs to provide the most desirable output.
Attendees at last year’s Discovery conference were treated to an evening of unique fun: hitting glow-in-the-dark golf balls on the driving range at Indian Wells Golf Resort. The driving range has Toptracer technology that monitors each shot. Total distance, carry, ball speed, launch angle, and curve are some of the variables reported with each shot. A driving range that provides so much data provided a perfect opportunity to design an experiment using JMP!
After an evening with fellow JMP users and friends, an experiment was designed using the Custom Designer in JMP. The design took only minutes to create. Input variables based on the golf stance setup were used in the design. These included variables such as grip, club head alignment, stance width, and ball location. The designed experiment was executed on the driving range, a model was created, and optimum settings to create the longest and straightest shot were discovered. The modeling and optimization were completed in minutes, while still on the driving range! This allowed for confirmation runs to immediately be performed. The benefits were later transferred the golf course as well.
Presenter
Room
Executive Briefing Center 8Skill Level
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Topographical Air Gap Analysis of a Multilayer Glass Substrate Stack
A detailed visualization of air gap measurements between stacked glass substrates was created using JMP software. This visualization was generated through a meticulous measurement process using low coherence interferometry, which accurately measures the gaps without contacting the substrates. By plotting these measurements on an XY plot (with quantile grouping), JMP provides a clear spatial representation of air gap variations across the substrates' surface.
The surface plot generated in JMP further enhances understanding by showing a topographical view of the substrate surface. This visualization is crucial as it highlights a significant bow at the center of the stack, a defect that leads to stress upon adhesion and subsequent delamination further along the production line.
Utilizing JMP's analytical tools, we conducted a root cause analysis. The visual output from the software pinpointed the central bow, enabling prompt corrective actions. This process showcases the power of JMP in transforming complex data into actionable insights, ensuring the manufacturing integrity of the stacked glass substrates. This presentation also demonstrates how JMP's new workflow add-in enables nonscript-oriented users to showcase data independant of any scripting knowledge.
Presenter
Room
Executive Briefing Center 9Skill Level
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3:00 PM-4:15 PM
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JMP Product Roadmap Sessions
Learn what you can do now, what’s coming in JMP 19, and what’s planned for the future from JMP product managers and developers.Room
Executive Briefing Center 7, 8, 9, 150
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4:15 PM-5:00 PM
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Discovery Expo
Meet and network with JMP R&D experts – the minds behind the software. You’ll also have the chance to talk to User Enablement and meet with JMP Education, who can answer your questions about training options, course materials, and other Learn JMP resources.Room
Executive Briefing Center Networking Hall
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6:30 PM-8:30 PM
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Dinner Adventure
Plan on fresh air, great food, and warm hospitality at a Raleigh landmark. The 60-some year old Angus Barn is family-owned and operated, still offering fine dining in rustic elegance.Room
Angus Barn Pavilion
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8:00 PM-8:30 PM
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Transportation departs from Angus Barn Pavilion to Embassy Suites
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Oct 23 Wednesday
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8:15 AM-7:30 PM
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Transportation provided between Embassy Suites and buildings on SAS Campus
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9:00 AM-10:15 AM Plenary
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Analytics Champions
Trials and Triumphs in Pursuit of Enterprise AnalyticsAs practitioners of applied statistics – regardless of industry, background, or skill level – we all know the power that data holds as a strategic asset. Despite overwhelming consensus around the promise of statistics, however, many organizations are struggling to go beyond buzzwords like “AI” and “digital transformation” to actually build a culture of data literacy at an enterprise scale. In this session, we hear from a variety of analytics advocates from organizations at different levels of analytic maturity. Whether it is a lack of investment in training or leadership that fails to see the value of statistical enablement, we can learn from peers who have been there before – and found ways to overcome some of the organizational barriers standing in the way of data transformation.
Presenter
Room
Building V Auditorium
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10:30 AM-5:30 PM
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JMP Lab
Participate in testing features, provide critical user feedback, and experience new innovations first-hand at JMP Lab. You will have the opportunity to directly influence the development of JMP, helping to enhance its functionality, ease of use, and the overall user experience.Room
Executive Briefing Center 6
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10:30 AM-11:15 AM
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Discovery Expo
Meet and network with JMP R&D experts – the minds behind the software. You’ll also have the chance to talk to User Enablement and meet with JMP Education, who can answer your questions about training options, course materials, and other Learn JMP resources.Room
Executive Briefing Center Networking Hall
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11:30 AM-12:15 PM
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How I Used JMP on a Server with Add-ins to Save My World
We have several JMP add-ins that are used by 100+ users. The add-ins require JMP, an Oracle ODBC client, and custom menus. Initially, we deployed the add-ins to individuals' PCs, which required installing JMP and Oracle, as well as configuring ODBC drivers and add-in menus on each PC. It was a maintenance nightmare, because every week, someone's hard disk failed or their PC was reimaged.
To reduce this burden, we opted to use Citrix servers to run JMP and the add-ins. We are in the process of switching from Citrix to Microsoft Remote Desktop Protocol (RDP). The benefits include:
- Only needing to set up things once.
- Excellent performance for our overseas users.
- Additional level of security.
This talk provides details on server setup, security, configuring Oracle, configuring JMP add-ins, code maintenance, and how users save files from Citrix or RDP.
Presenter
Room
Executive Briefing Center 150Skill Level
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Classify and Parameterize Time Varying Curves with Functional Data Explorer
There is often a need to classify time-varying curves into different categories. For example, cell growth curves may have different shape types (e.g., diauxic growth) that characterize the growth behavior of cells. These different curve types make it difficult to automatically and accurately pull characterizing parameters (e.g., lag to start-of-growth, growth rates, final growth level, etc.) out of the curves.
JMP Pro's Functional Data Explorer (FDE) can be used to classify these curves into certain types. Once classified, FDE filters noise out of the curves, greatly simplifying the assessment of critical growth parameters.
Real-world data is used to demonstrate how FDE in JMP Pro can perform these operations.
Presenter
Room
Executive Briefing Center 7Skill Level
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Using Functional DOE to Model Complex Continuous Cell Culture Processes
Cell culture plays a crucial role in the production of biologics. When introducing process changes as part of a design of experiment (DOE), accurately modeling the behavior of the cell culture process is challenging as the process involves multiple interdependent growth and production phases, only some of which may be impacted by process changes. Traditional parametric non-linear models struggle to effectively capture this complexity, while non-parametric models alone can be disjointed and difficult to correlate with DOE parameters.
To address this issue, functional DOE simplifies the complexity into principal components and correlates the changes with DOE parameters. This approach enables the creation of a prediction profiler, which can optimize cell culture parameters from small scale data and use them to predict behavior during larger-scale production. The entire process can be performed within the Functional Data Explorer Platform in JMP Pro and can provide a more efficient approach for optimizing cell culture processes.
Presenter
Room
Executive Briefing Center 8Skill Level
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Assessment of Dielectric Reliability in Semiconductor Manufacturing
Reliability assessment of devices and interconnects in semiconductor technologies is typically done for technology certification and periodic monitoring, using relatively small (single digit to tens) sample sizes per condition. Volume manufacturing data can be used over time to assess dielectric reliability by ramped voltage-breakdown measurements on scribe-lane test structures. Over time, this can provide a detailed view of dielectric behavior, including a mixture of intrinsic and extrinsic mechanisms affecting dielectric integrity. In particular, low failure-rate outliers or tails can be detected and addressed, which may otherwise pose field-quality risks.
For practical reasons, the ramp may be stopped at a target voltage to reduce test time and avoid damage to probe cards and needles, which may result in a small number of data points being censored. Fitting large data sets with a small number of censored data points can lead to convergence challenges, resulting in incorrect fitting parameters and lack of confidence intervals, as well as posing significant computational challenges.
This work explores these challenges with the JMP Life Distribution platform and examines alternatives and solutions to allow correct analysis, fitting, and extrapolation.
Presenters
Room
Executive Briefing Center 9Skill Level
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12:15 PM-1:30 PM
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Lunch
Room
Executive Briefing Center Marketplace Café
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1:30 PM-2:00 PM
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Discovery Expo
Meet and network with JMP R&D experts – the minds behind the software. You’ll also have the chance to talk to User Enablement and meet with JMP Education, who can answer your questions about training options, course materials, and other Learn JMP resources.Room
Executive Briefing Center Networking Hall
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2:00 PM-2:45 PM
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Double the Pleasure, Double the Fun! Reliability Under Two Failure Modes
Life got you down? Do you have two failure modes and you're not sure how to make reliability predictions? There is a path to success! Using a straightforward method, an Arrhenius data set of transistor lifetimes with two independent, lognormal failure mechanisms are modeled in JMP. The upper confidence bound on the probability of failure at use conditions is also estimated.
But what about future testing? You may need to test similar parts in the next qualification. How should you design your life tests when there is more than one failure mode? Again, there is a solution! A graphical method for planning life tests with two independent, lognormal failure mechanisms is demonstrated. Reliability estimates from simulated bimodal data are shown with the contour profiler, helping you navigate this difficulty. This simple graphical approach allows the practitioner to choose test conditions that have the best chance of meeting the desired reliability goal.
Presenter
Room
Executive Briefing Center 150Skill Level
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Machine Learning-Assisted Experimental Design for Formulation Optimization
Design of experiments (DOE) is a statistical method that guides the execution of experiments, analyzes them to detect the relevant variables, and optimizes the process or phenomenon under investigation. The use of DOE in product development can result in products that are easier and cheaper to manufacture, have enhanced performance and reliability, and require shorter product design and development times.
Nowadays, machine learning (ML) is widely adopted as a data analytics tool due to increasing availability of large and complex sets of data. However, not all applications can afford to have big data. For example, in pharma and chemical industries, experimental data set is typically small due to cost constraints and the time needed to generate the valuable data. Nevertheless, incorporating machine learning into experimental design has proved to be an effective way for optimizing formulation in a small data set that can be collected cheaper and faster.
There are three parts in this presentation. First, the literature relevant to machine learning-assisted experimental design is briefly summarized. Next, an adhesive case is presented to illustrate the efficiency of combining experimental design and machine learning to reduce the number of experiments needed for identifying the design space with an optimized catalyst package. In the third part, which pertains to an industrial sealant application, we use response surface data to compare the prediction error of the RSM model with models from various machine learning algorithms (RF, SVR, Lasso, SVEM, and XGBoost) using validation data runs within and outside the design space.
Presenter
Room
Executive Briefing Center 7Skill Level
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Using Point-and-Click and Basic Code-Cracking Skills to Import Tricky Data
During my time working as a scientist in R&D, there were times I wanted to augment my analyses with data that was not always in a straightforward file type that could be imported efficiently into JMP. For example, think of multiple PDF pages with chemical composition information – each with slightly different formatting. Or think about multiple web pages with data for different regions – with a different URL for each page. Another example would be including experimental pictures in a data table – and matching each of the hundreds of pictures with the corresponding file names.
Although JMP supports bringing in data from PDF formats and websites, as well as allowing you to bring in pictures into a data table, it is not always obvious how to do this efficiently when you have lots of pages or images to import. If you are not a proficient scripter (or don't know someone who is) you may end up doing each import manually…or simply forgoing the information and moving on.
The good news is that even without being able to write a script from scratch, you can use basic JSL decoding skills to bring in data from these kinds of sources efficiently. In this paper, we show a few examples of how we used JMP’s point-and-click tools, along with basic JSL deciphering skills, to automate the import of images, data from PDFs and web pages with scale.
Presenters
Room
Executive Briefing Center 8Skill Level
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Using DOE to Improve the Performance of a High-Speed Dynamic Seal
At Francis Medical, the cannula of our disposable delivery device is inserted into the urethra and then a small round catheter exits the cannula into the prostate where steam exiting from the catheter is used to ablate tissue. Due to hydrostatic pressure from the bladder, the dynamic seal between the cannula and catheter is the only barrier for a possible fluid ingress pathway into the delivery device, which is undesirable.
Testing the dynamic seal on the bench is accomplished by pressurizing the cannula using a pneumatic pressure decay tester. While the output of this tester is continuous, the distribution is bimodal and is best modeled as a binary output: either the seal leaks or the seal does not leak. In JMP, there is no straightforward method to calculate the power and sample size to allow for comparison of different design of experiment (DOE) studies for an attribute output.
In this paper, we outline the process methodology we used to determine the number of factors to test and how many runs to complete, including a simulated power analysis. In addition, we discovered a unique way to condition the samples prior to pressure decay testing to create more dynamic seal failures than achieved through historical testing. In the end, the results of the experiment allowed us to make definitive design decisions with confidence and improve the dynamic seal performance by a factor of 25 from the current design.
Room
Executive Briefing Center 9Skill Level
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3:00 PM-3:45 PM
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Modeling Coral Reef Resilience in the Republic of Palau
Coral reefs across the planet are threatened by the rising seawater temperatures driven by climate change. Although many corals indeed "bleach," and consequently perish, as a result of prolonged exposure to abnormally high temperatures, some species (or even genotypes within a single species) maintain a marked level of climate resilience. Historically, we have identified these "super corals" in post-hoc fashion: searching through the proverbial rubble of a highly impacted reef to find the survivors.
For coral reef restoration and other purposes, a more targeted, proactive means of identifying climate-resilient corals would be preferred to this "needle in a haystack" approach. To this end, I showcase a rich coral eco-physiological data set acquired during a month-long research expedition to the most remote corners of the Micronesian nation of Palau. After some rudimentary data processing and visualization, I show, using JMP Pro 17, how predictive models of coral resilience can be built relatively easily.
I then demonstrate how GUIs derived from the models' prediction profilers can be embedded on web pages so that they can be used by scientists as a planning tool. Specifically, the model-based profilers allow researchers to predict the environmental conditions (e.g., depth, type of coral reef, salinity) at which they are most likely to find resilient corals during their bioprospecting surveys. This analytical tool will therefore aid marine biologists in locating corals with high climate tolerance that should be propagated in efforts to restore degraded reefs.
Presenter
Room
Executive Briefing Center 150Skill Level
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Decoding Mountain Bike Suspension: Graphs and Reviews
Full-suspension mountain bikes display a wide diversity of kinematic designs to control the motion of the rear wheel. Differences between these designs are present in functional curves such as leverage ratio, anti-rise, anti-squat, pedal kickback, and axle path. Due to the complex nature of these curves, comparing and understanding how different curve shapes will impact riding behavior is difficult.
In this paper, we begin by extracting the characteristic curves from specialized software as graphs for various models of mountain bikes. We then demonstrate JMP Scripting to import these images and perform basic image analysis, extracting the functional curves from these graphs. Additionally, we show Python scripting within JMP to collect reviews of the corresponding models from popular bike review websites. Then, using both the Functional Data Explorer and Text Explorer in JMP Pro, we examine the patterns in suspension curves that correlate with changes in the sentiment of the reviews examined.
Presenters
Room
Executive Briefing Center 7Skill Level
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To DOE or Not to DOE? That’s Not Even a Question!
From grade school science fairs to undergraduate chemistry labs and beyond, our future researchers, scientists, and engineers are often not formally taught design of experiments (DOE) methods. Instead, they are typically told to use the “one-factor-at-a-time” (OFAT) approach. But OFAT has major disadvantages and will always be less efficient than DOE, requiring more time and effort for worse results. Clearly, “To DOE or not to DOE?” should not even be a question!
Join us to hear how a high school dirt biker learned that lesson by harnessing DOE to take his race performance to the next level. We show how, using JMP’s Easy DOE platform, he was able to realize the advantages of DOE without needing in-depth DOE knowledge. Then we advance beyond Easy DOE to JMP's Custom and Definitive Screening Designs, for flexibly accommodating constraints and special cases.
Come for the fast dirt bikes and stay to see that OFAT just can’t keep up and that DOE is always the right answer!
Presenters
Room
Executive Briefing Center 8Skill Level
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Streamlining Manufacturing Excursion Investigations with JMP
Excursions can lead to significant costs at a manufacturing facility. In the semiconductor industry, production downtime, scrapped and low-yield wafers, and decreased output can result in substantial revenue losses. Engineers tasked with investigating these excursions must quickly uncover actionable insights for data-driven decisions that minimize downtime and improve product quality.
JMP's Analytic Workflow can help you outline the steps and tools needed to efficiently investigate excursions. Explore how Query Builder facilitates data access from databases and how JMP's powerful Tables menu aids in data manipulation. Discover optimal table formats for visualizing and analyzing wafer map data. Utilize exploratory and analytical techniques to discover hidden relationships across manufacturing process steps. Enhance your analysis with JMP Pro, leveraging features like image analysis in the new Torch Deep Learning Add-In for JMP Pro 18 to gain advanced insights.
Automate and rerun your entire analysis using Workflow Builder in JMP, ensuring speed, repeatability, flexibility, and analytical power without the need for coding. Learn how leveraging these tools and techniques in JMP and JMP Pro can lead to efficient resolution and substantial cost savings in a fast-paced manufacturing environment.
Presenter
Room
Executive Briefing Center 9Skill Level
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4:00 PM-4:30 PM
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Developing a Unit Conversion Add-In Using JMP 18 Python Integration
The units of physical quantities are extremely important to scientists and engineers. They communicate a lot but, unfortunately, can lead to huge and costly mistakes. An add-in has been developed to help manage units within JMP and reduce these errors. This add-in uses the new JMP 18 Python integration to easily do what would have been difficult to achieve in JMP 17. This presentation shows the capabilities of this add-in, as well as how it was developed, including Python use, source control, testing, documentation, and build systems.
Presenter
Room
Ped 1Skill Level
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Evaluating Racial Equity Among the Deceased Organ Donation Process
This study aims to characterize racial inequities at each stage of the deceased organ donation process and identify intervention opportunities for organ procurement organizations (OPOs) to facilitate more lifesaving transplants.
We performed a retrospective cohort study using the Organ Retrieval and Collection of Health Information for Donation (ORCHID) database to identify racial inequities across the four stages of the deceased donation process: approach, authorization, procurement, and transplantation. We represented unadjusted estimates as risk ratios with a Wald test to calculate two-sided p-values. Then, we performed nominal logistic regression to produce adjusted odds ratios. Leveraging the model screening function on JMP Pro 17.2, we used a boosted neural network to estimate the main and total effects of model variables on progression to donation.
Whereas the approach, procurement, and transplantation rates are higher among non-white individuals referred for donation, the disproportionately high authorization rate among white donor families creates a racial disparity in transplantation opportunities. The odds of white families being approached is 54% higher than that of non-white families, and the odds of authorizing donation is 3.07 times that of non-white families. However, the boosted neural network reveals that the main and total effects of donor race within an adjusted model are negligible.
Racial disparities in transplantation are primarily attributable to lower authorization rates among non-white donor families. Thus, OPOs must develop culturally competent approach strategies to increase engagement with and education among non-white communities.
Presenters
Room
Ped 2Skill Level
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Plate Map Dashboard: Unveiling Data Patterns with Multiview Visualization
Microtiter plate maps are a standard tool in laboratory experiments, allowing scientists to investigate physical, chemical, and/or biological reactions of test articles in various assays. Traditional data visualization methods of microtiter plates are often inefficient when conveying relationships unique to plate data, to include capturing both the spatial and temporal sources of variability.
To address this problem, we created a JMP dashboard to visualize plate maps, providing users with unique insights into the spatial distribution and elements of their data. The dashboard facilitates easy visualization and exploratory data analysis through multiple interactive views of heat maps, scatter plots, and dose response curves on data simulated to highlight typical issues encountered in plate experiments. Users can dynamically switch between views, customize visualizations, and interact with individual or groups of data points that warrant probing.
Additionally, the dashboard supports data filtering and annotation through row labeling, enhancing the interpretability and utility of plate map visualizations. The dashboard also serves as an effective tool in communicating data quality and potential areas of concern, such as plate and/or lab variability, or other process errors that may exist in the data. By facilitating flexible exploration and analysis of complex data sets, the dashboard empowers users to gain deeper insights from their plate-based experiments, accelerating scientific discovery and knowledge.
Presenters
Room
Ped 3Skill Level
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My Favorite Graph Builder Tips
The Town of Cary is home to JMP, as well as many of us who work there. Cary offers many recreational activities, placing an emphasis on environmental endeavors for which the town continuously collects data. Using Graph Builder, we explore data ranging from Cary’s food waste and recycling collection, electric vehicle charging stations, recreational parks, and bike routes. During our exploration, we demonstrate ways to efficiently and effectively use Graph Builder’s features to create visualizations that help construct a better understanding of Cary. For this session, I share a handful of my favorite ways to use Graph Builder that I hope others find helpful in their own analyses!
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Ped 4Skill Level
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Narrowing Down Choice: Using Bayesian Methods to Determine the Right Yeast
In Novonesis’ pursuit to understand the performance of industrial yeast products in specific customer conditions, our application research and development groups excel in generating insightful data sets for screening top yeast candidates at customer scale. Scientists regularly employ statistics to address complex questions related to both broad and specific customer scenarios.
The introduction of Bayesian methods into Python libraries has significantly enhanced our ability to compute models and facilitate data-based decisions based on probabilities, even when traditional statistically significant results are not available. Additionally, the new capabilities in JMP 18 enable seamless integration with Python, empowering our scientists to generate large data sets and address customer-specific inquiries without leaving JMP. By leveraging application research data, customer trial data, and prior information, we are embarking on a journey to characterize our decisions based on informed data and Bayesian statistics, rather than relying on statistically insignificant trends. This approach aims to provide a more comprehensive understanding and quantification of performance, ultimately leading to more informed decision making.
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Ped 5Skill Level
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LEGO: Predicting New Sets That Have a Better Return on Investment Than Gold
LEGO has had a long history of manufacturing toys, particularly in the development of their branded interlocking plastic bricks. Over the years, this history has created generations of block-building enthusiasts who grew up with LEGO and continue to collect new sets into adulthood. It's no wonder that LEGO has become a semi-mainstream investment, as a study published in Science Direct in 2022 reported these sets have a better rate of return than even gold. Is it possible to predict which sets are worth purchasing over others?
In our study, we calculated return on investment (ROI) for each set based on the original USD MSRP of the set vs the current price it fetches. We then compared the LEGO set ROI against ROI for gold calculated from the average yearly opening/closing stock price for gold since 2014. The model then determined if a LEGO should be purchased if the set's ROI was greater than gold's.
Of five tested, Tree Ensemble was the winning model, as the model predicted it performed 1.8 times better than a random selection in the top 30% of LEGO sets. It indicated that Theme Group had the highest variable importance.
Our research proved that many LEGO sets have a higher ROI than gold; our model can even predict which sets will have higher ROI. Collectors can use our model to determine if a purchase will be a worthwhile investment, and even novices can feel confident that they are making a valuable purchase.
Room
Ped 6Skill Level
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Using Easy DOE to Evaluate Optimal Conditions for Organometallic Catalysis
Often involving sensitive reagents and complex, unstable products, the synthesis of organometallic catalysts can be challenging. Relatively weak bonds between metal centers and coordinating groups mean that aquo and dioxygen ligands can interrupt the desired molecular structure, frequently necessitating oxygen- and water-free working conditions. Following synthesis and characterization, the optimal conditions for these catalysts must be found. Costly and unsustainable metals such as rhodium, iridium, and palladium often form the centers of catalysts, and thus their consumption must be minimized. In this work, the relevance of Easy DOE to the optimization and analysis of three iridium catalysts is discussed in two groups of variables.
The first set of conditions informs what the best working conditions of the catalysts are and helps outline its capabilities. Variables that are tested are the catalyst substituent (crown ether, methoxyethyl, or methyl), the substrate, the addition sodium or lithium salts, and the addition of water. The second set of conditions form an evaluation of environmental friendliness, nodding to Anastas and Warner’s criteria for green chemistry. Variables that are tested are the solvent (traditional solvents such as dichloromethane vs. greener choices such as acetonitrile), the pressure of hydrogen, and the sensitivity to oxygen. Easy DOE is employed to design these runs, slimming the input required to obtain meaningful data. Together, these two sets of conditions give a picture of the chemical environment that best suits the catalysts, as well as how to tune this chemistry for the greener.
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Ped 7Skill Level
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Using DOEs and MCSs in Structural Assessments of Subsea Equipment
A marine drilling riser system is used in offshore exploration as a conduit connecting the drilling vessel with the subsea well. It is a complex structural subsea piping system commonly constructed by 75-90-feet long joints, typically sequentially assembled until they reach the wellhead, sometimes at water depth exceeding 10,000 feet.
In a recertification project of a riser system meant to ensure compliance with regulatory requirements, inspection findings strongly indicated that the system had been exposed to an accelerated corrosion process. Corrosion rates for carbon steel in a seawater submerged application are normally measured to 0.1-0.4 mm per year. The inspection data showed localized corrosion rates exceeding 4 mm per year. Thirty riser joints were completely disassembled and inspected. However, 65 riser joints were inaccessible as they were located offshore and already in service. To quantify the operational risks and estimate the probability of non-compliance with the governing code, it became urgently necessary to extrapolate the corrosion data from the 30 inspected units to the inaccessible 65 units.
Data distributions from the sample of 30 riser joints was used to run Monte Carlo simulations, using transfer equations modelled through a Fast Flexible Filling Design DOE in which the responses were generated through deterministic computer simulations. While the results of the simulations showed that the risk of non-compliance was unacceptable if the system was utilized to its design limits, even a slight reduction of the pressure level in the pipes reduced the risk of non-compliance to acceptable levels.
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Ped 8Skill Level
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4:45 PM-5:15 PM
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Furever Homes: Discovering Insights With JMP to Reduce Shelter Dog Returns
Animal shelters strive to permanently place dogs in homes with individuals and families. Yet, one long-term retrospective study estimates the percentage of dogs returned to shelters after adoption is approximately 9 percent. Returning shelter dogs impacts the resources available to care for additional animals. Identifying the root causes of returns to the shelter can inform programming targeted at reducing this phenomenon.
Using a data set for a large shelter system in New York, comprised of 3,465 first-time shelter dogs tracked for six months following intake, we identified potential areas for interventions to reduce return rates of adopted dogs through multiple models. Logistic regression in combination with stepwise variable selection was the model reported in this research project to explore the relationship between the probability of a dog being returned to a large number of covariates. Machine learning models were also fit and compared to the logistic regression model yielding similar but more nuanced results.
We found that a dog’s age, tendency for aggressive behavior, breed, length of stay, and shelter geography are related to the probability that a dog will be returned. Additionally, we found that transporting dogs between shelters was not related to return probability. JMP’s easy-to-use, interactive analysis and visualizations helped the client, who had little statistics training, understand and ultimately execute the analysis on her own. These findings will guide the allocation of resources to interventions, such as educational materials or training programs, that may help reduce overall return rates.
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Ped 1Skill Level
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Missing Marks Delay the Dig: JMP Visualizations in a Root Cause Analysis
The Virginia Underground Utilities Damage Prevention Act requires excavators, both contractors and home owners, to request the marking of underground utilities before a project begins. Examples of excavation projects include landscaping, digging for a foundation, and fiber optic cable install. Failure to obtain utility location markings can result in serious consequences including fines, personal injury, and, in extreme cases, death. Virginia811 (VA811) administers the utility location process by taking requests, routing them to utility members, and recording responses. Complete responses must be obtained within the response window from all members (gas, power, water, sewer, telecom, etc.) serving a site for excavation to begin. In recent years, the VA811 system has experienced a 150% increase, from 4% to 10%, in the number of delayed tickets due to “no-shows” or non-responses from members. Stakeholders are understandably upset with the no show impact on project schedule and cost. Simple statistical analyses and data visualizations were used to perform a root cause investigation presented to the VA811 Advisory Board. These visualizations from the JMP Graph Builder include paneled bar graphs, scatterplots with regression lines, forest plots, and maps in the form of small multiples which show relationships over the years as well as seasonal trends within the years. The displays helped dispel myths and prompt conversations among stakeholders who possessed very little data acumen, ultimately leading to pilot solutions, discovery of process “blind spots”, and further root cause investigations.
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Ped 2Skill Level
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Predicting the Next NFL Passing Yards Leader: A Data Visualization Journey
Tom Brady is generally considered the best quarterback in NFL history. His record for most career passing yards is one of the many impressive records he has. To achieve this, he consistently maintained a high passing yards total per year, had the most passes attempted and completed, avoided season-impacting injuries, and had a long career overall.
Records are made to be broken.
The next quarterback to break this record might already be playing the game. But who? And when?
This presentation takes us on a data exploration and visualization journey to show how Tom Brady became such a dominant force in the NFL. We then identify which current quarterbacks have the greatest chance at challenging this record and when it might occur.
We cover the entire analytic workflow: a JSL script that scrapes data from a website; a data clean-up workflow that prepares the data for analysis; data exploration and visualization; and finally, a published JMP Live report that will be updated throughout the season.
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Ped 3Skill Level
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The Story of Whole Body Odor Protection
In this poster, we want to showcase how using the power of qualitative and analytical tools in JMP allowed us to uncover the biggest concerns for people relating to body odor.
What we discovered is that people are concerned about more than just underarm odor. Did you know that >40% of people in North America are concerned with body odor from other parts of their body? And that they are doing a lot of things to prevent and combat it?
Join us for a fun and interactive JMP talk to learn how you might relate to this problem!
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Ped 4Skill Level
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Real-Time Monitoring to Large Data Modeling for Bioprocessing Excellence
In our organization, we leverage JMP products across various facets of our operations, showcasing their versatility and efficacy. Primarily, JMP Live serves as a powerful business intelligence tool, enabling us to visualize fermentation kinetics and financial data with clarity and precision. By harnessing JMP's intuitive interface, we empower decision makers to glean insights and make data-driven decisions in real time.
Furthermore, JMP's analytical capabilities are instrumental in our data analysis endeavors. We seamlessly integrate SQL queries into a JMP add-in, facilitating swift access to extensive data sets from our data warehouse. This streamlined approach expedites our analytical processes, allowing us to extract meaningful insights efficiently.
In addition to data analysis, we utilize JMP's advanced modeling capabilities to enhance our predictive analytics efforts. For example, by employing Gompertz models in JMP, we predict CO2 off-gassing based on NIR bioethanol kinetics, a critical factor in our production processes. Control charting and comparison against production data enable us to quantify performance metrics, such as the percentage of theoretical yield, aiding in process optimization and quality control.
Through these applications, JMP products play a pivotal role in data-driven decision making, process optimization, and quality assurance initiatives within our organization. As we continue to innovate and refine our methodologies, JMP remains an indispensable ally in our pursuit of operational excellence and continuous improvement.
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Ped 5Skill Level
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Make JMP Better by Reporting Your Crashes and Telemetry
If you’ve ever had the misfortune to encounter a JMP crash, you have probably encountered a prompt asking you to send JMP your crash files. (Side note: please send us your crashes and provide your email if you’d like to work with JMP Technical Support.) If you send your files, they are combined with those from internal testers and customers all over the world and are fed into the JMP Crash System. The JMP DevOps team uses our own JMP software to analyze the JMP crash files that you've reported and then works with JMP Development to define connections between crash signatures and known bugs.
I outline the process we go through to use JMP and other tools to analyze JMP crashes. It is now a relatively simple exercise for us to match up a new crash report to a known issue or identify it as a novel case that needs further investigation. However, solving novel mystery crashes isn’t such a straightforward process. This poster demonstrates how you can help us solve more crashes by providing detailed information with your crash reports and explains how this helps the JMP development process. In addition to crash details, JMP 18 also allows you to send anonymous telemetry about your JMP feature usage. We use this information to better understand customer usage patterns, as well as hardware and operating system distributions.
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Ped 6Skill Level
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Using Graph Builder to Improve Data Quality in Fiber Photometry Data Sets
Fiber photometry is a cutting-edge technique that captures real-time in-vivo brain activity in laboratory animals. Often aligned with video-tracked behavioral data, fiber photometry allows scientists to directly pair observed behavior and brain signaling. While fiber photometry is useful for novel behavioral experiments, each experimental step (data collection, processing, analysis) introduces the possibility of error. As such, ensuring research is reproducible is critical, but the size of the data sets (up to 0.25 gigabytes) makes quality control a cumbersome task.
JMP Graph Builder provides an excellent interface for dynamically and efficiently identifying quality control issues. In a project using fiber photometry to understand how norepinephrine signaling accompanies fear behavior in rodents, we used JMP to detect misalignment between the Ethovision XT-tracked behavior data and the fiber photometry data, to provide easy identification of behavioral tracking disruptions and to ensure that expected patterns were present. These quality control checks allowed for timely understanding of, intervention to, and correction to the data set, thus promoting research integrity. Additionally, the quality control plots gave scientists a novel and insightful way to understand their experiments.
Graph Builder provided data quality checks for a sound analysis via JMP’s modeling platform. The scientists were also able to use JMP scripts and were motivated to learn how to use JMP themselves from this process. In this talk, we showcase the problem of quality control for large, complicated data sets, as exemplified through fiber photometry, and how JMP’s graphing capabilities allowed us to ensure quality data and reproducible research.
Presenters
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Kathryn Konrad Social and Scientific Systems, a DLH Holdings Corp. Company
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Katherine Allen-Moyer Social and Scientific Systems Inc., a DLH Holdings Corp Company
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Keith R. Shockley Biostatistics and Computational Biology Branch, NIEHS
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Helen Cunny Division of Translational Toxicology, NIEHS
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Leslie R. Wilson Neurobehavioral Core, NIEHS
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Jesse D. Cushman Neurobehavioral Core, NIEHS
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Ped 7Skill Level
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Design of Experiments for Complex Biochemical Systems
Cell-free expression (CFE) systems are a suite of methods that reconstitute complex cellular functions like transcription, translation, and metabolism outside the confines of a living cell. CFE systems have numerous biotechnological uses in sensing, biomanufacturing, medicine, basic research, and education. Most CFE systems are made from combining cellular lysates with a complex blend of excipients that improve activity. While the number of excipients makes exploring the combinatorial spaces challenging, high-throughput experimentation with acoustic liquid handling makes it feasible to optimize formulations if paired with an appropriate statistical framework.
Here we describe our use of design of experiments (DOE) to optimize excipient combinations for specific use cases of CFE. We pair our DOE with functional data analysis (FDA) to collapse activity over time measurements to metrics readily used for analysis. Initial formulation DOE examples range from five to 14 components. We further describe our efforts to push to higher scales, attempting mixture-process DOE designs with as many as 42 components using an experimental set-up that allows for 1,536 formulations to be tested at once.
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Ped 8Skill Level
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5:30 PM-7:30 PM
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Networking Dinner
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Building A Terrace Café
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Oct 24 Thursday
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9:00 AM-4:00 PM
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Transportation provided between Embassy Suites and buildings on SAS Campus
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9:30 AM-10:15 AM
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Using Augment Design to Resolve a DOE Blocking Mistake
DOE plays an important role in AMAT End-End continuous process, design improvement and optimization. Unfortunately, most DOE practitioners may randomly pick any DOE design with JMP that they are familiar with while lacking a DOE statistics foundation, resulting in poor predictive models. As one of the most powerful Resolution IV algorithms, DSD allows us to study both main and interaction effects of a large number of predictors in a relatively small DOE run size. However, DSD cannot tolerate any Orthogonality Violations such as GRR Noise, SPC Time Noise, Design Constraint or Recursive Stepwise Algorithm.
We studied a special case regarding DSD Blocking Design. Instead of assigning the two operation systems as Predictor factor, Blocking factor has been assigned. The DOE experimental operators did not pay full attention and changed the run order, not following the Blocking plan. After completing the first half of nine runs (1st Blocking), the design process owner found Blocking mistakes and stopped the DOE runs immediately, which induced poor design evaluation. After discussion, we found five alternative resolutions to improve the Orthogonal design structure. We conducted detailed Design Diagnostics on each alternative DOE proposal considering the DOE schedule and cost constraints. Among the five proposals, Augment DOE, adding nine new Augment points (all at corners) is the best to recover the most Orthogonal risks at the highest Return of Investment Ratio. Through this DOE Blocking case study, we have further upgraded our JMP DOE knowledge with effective communication through this crisis.
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Executive Briefing Center 150Skill Level
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Using Functional Data Analysis: A Case Study
Many instruments and sensors generate data over a continuum. Analyzing and understanding this “curve” data can be difficult at best, especially when you are trying to analyze data over the whole continuum. Many chemometric techniques are options, but for analyzing curve data, they are basically analyzing the data point by point instead of the whole curve or group of curves at once.
Functional data analysis is one of the newer methods that has come the forefront for analyzing curve data. This presentation uses case studies on chromatographic and spectral data that show the utility of analyzing data over the continuum in conjunction with more traditional chemometric methods. Some of the newer chemometric analysis techniques in JMP Pro 18 Functional Data Explorer are highlighted.
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Executive Briefing Center 7Skill Level
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Scripting an Interactive Tool for Exploration of Historical Throughput Data
Semiconductor factory capacity modeling involves maintenance of throughput values (as parts per hour [PPH]) for thousands of process recipes. Processing data is stored for each run, providing a rich pool of historical data that can be used to determine PPH values. In capacity modeling, inefficiencies that degrade capacity are isolated from throughput, so modeled PPH should represent the highest capable performance under continuous operation. These requirements complicate the process of sifting through historical data and special care is needed by process experts to identify non-steady-state runs that should be excluded from the analysis.
Using JSL, an interactive tool was created to assist in analysis of historical throughput data to evaluate, select, and document PPHs by recipe for use in capacity modeling. The script constructs a fully interactive, self-contained dashboard application that utilizes JSL-enhanced platforms and custom controls to assist in exploring sources of variation and isolation of steady-state runs. PPH value selection is made and documented directly in the dashboard, and finally, a summary of the selected values and distributions can be exported to PowerPoint for a complete analysis report.
This presentation includes a demonstration of the dashboard tool and walks through parts of the script.
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Executive Briefing Center 8Skill Level
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Improve Your Gauge Throughput: Combining Custom DOE with a Gauge R&R
Improving gauge throughput for production demand while maintaining low measurement variation can be challenging. Often, throughput and measurement variation are competing variables and understanding the relationship between them can be difficult without utilizing a design of experiment (DOE).
In this case study, we show how JMP’s Custom DOE platform can be coupled with a measurement systems analysis (gauge repeatability and reproducibility) to increase throughput while maintaining the accuracy and precision of the gauge. Utilizing JMP's DOE platform, the development time was reduced by an estimated 50% as opposed to a one-variable-at-a-time approach. We demonstrate the physical constraints of the gauge and how these constraints are added into the Custom DOE. We then model the DOE results including optimizing the design space and experimentally validating the 60% increase to the gauge throughput.
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Executive Briefing Center 9Skill Level
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9:30 AM-1:30 PM
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JMP Lab
Participate in testing features, provide critical user feedback, and experience new innovations first-hand at JMP Lab. You will have the opportunity to directly influence the development of JMP, helping to enhance its functionality, ease of use, and the overall user experience.Room
Executive Briefing Center 6
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10:15 AM-11:00 AM
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Discovery Expo
Meet and network with JMP R&D experts – the minds behind the software. You’ll also have the chance to talk to User Enablement and meet with JMP Education, who can answer your questions about training options, course materials, and other Learn JMP resources.Room
Executive Briefing Center Networking Hall
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11:00 AM-11:45 AM
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Revolutionizing Semiconductor Manufacturing Tests with Predictive Modeling
The semiconductor manufacturing industry stands on the brink of a transformative era, powered by advanced analytical techniques. This presentation delves into the application of predictive modeling and diagnostic analysis within JMP software to significantly enhance manufacturing outcomes, particularly during the crucial early sort and class test phases. By leveraging comprehensive parametric data collected across various stages of the semiconductor production process, we embark on a journey to refine the prediction of unit-level pass/fail outcomes and unearth the underlying causes of potential defects.
Our study highlights the strategic use of JMP’s predictive modeling capabilities to accurately forecast the final system-level test status of semiconductor products. This approach not only allows for early detection of issues but also facilitates the implementation of corrective measures in a timely manner, thus ensuring higher yield rates and superior product quality. In parallel, diagnostic analysis within JMP offers a deep dive into the data, enabling manufacturers to identify and address root causes of failures across the intricate web of production processes.
This presentation showcases real-world applications of these JMP features, demonstrating their pivotal role in streamlining semiconductor manufacturing workflows. See how predictive modeling and diagnostic analysis can be effectively employed to optimize production outcomes, reduce costs, and enhance product reliability. Join us in exploring the cutting-edge analytical strategies that promise to redefine the future of semiconductor manufacturing.
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Executive Briefing Center 150Skill Level
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Imagine That: Predicting Chemical Formulation Targets from Images
At Syngenta, image classification is key to the modeling of samples within an automated chemical formulation system that has produced more than 100,000 pictures. The images are used to determine the next steps in the development of products but their interpretation by eye alone is very time-consuming and subjective to the evaluator.
The Torch Deep Learning Add-In for JMP Pro uses the power of convolutional neural networks and GPU computing to dramatically simplify the analysis and give consistent results across an agreed set of control images. We show how easy it is in the add-in to import and predict a rather complicated set of pictures and return meaningful, interactive output in minutes for tasks that normally take hours or even days with manual processing and Python coding.
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Executive Briefing Center 7Skill Level
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Case Study: An Investigation Root Cause Determined Using JMP Graph Builder
JMP is a powerful tool when used to assess large data sets. In this case study, an investigation was opened to determine the root cause of a set of instrumentation failures. Using JMP, the team was able to rule items in or out, perform trending, and visualize the data. As a result, the investigation was resolved and the root cause was identified.
This presentation includes information about the investigation and a brief demonstration of how we used the 96 well map feature in Graph Builder, paired with our knowledge of the instrumentation, to determine the root cause.
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Executive Briefing Center 8Skill Level
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JMP Add-In Builder for Automating Microbial Growth Characterization
Growth curve assays using micro titer plate readers are commonly used in the biopharmaceutical industry for screening media components. These assays can generate large amounts of data very quickly; thus, automated approaches to the de-bottleneck the data analysis step are critical to enable faster decision making. At Kerry, retrieving, cleaning, and formatting the growth assay data is a manual process that is time-consuming and error-prone.
In this work, an automated data formatting and clean-up approach using JMP’s workflow builder is being evaluated to reduce the data analysis time and automate redundant analyses. JMP's add-in developer is being explored for capturing automated scripts into easy-of-use add-ins for JMP beginners and experts alike. If successful, add-ins will be made publicly available so other researchers performing microtiter plate assays can benefit with easy data retrieval, clean-up, and analysis.
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Executive Briefing Center 9Skill Level
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11:45 AM-12:15 PM
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Discovery Expo
Meet and network with JMP R&D experts – the minds behind the software. You’ll also have the chance to talk to User Enablement and meet with JMP Education, who can answer your questions about training options, course materials, and other Learn JMP resources.Room
Executive Briefing Center Networking Hall
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12:15 PM-1:30 PM
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Lunch
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Executive Briefing Center Marketplace Café
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1:30 PM-2:45 PM Plenary
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Imagining the Future
A Reframing of How We Think of Science, Technology, and InnovationAfter a week of Discovery, we’ll close the conference with a very special keynote...one designed to tie what we’ve just learned this week with new ideas about the future. Our featured speaker will take us from black holes to science policy to social justice. How will she do that? By pulling from her rich history and sharing her vision for a different, better future for everyone.
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Executive Briefing Center 250
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2:45 PM-3:30 PM
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Last-Chance Networking Reception
We hate to say goodbye as much as you do. That’s why we always build in last-chance networking opportunities. Finish those conversations, make one more new-feature request, and exchange phone numbers because, really, statistical discovery should never end.
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Executive Briefing Center Networking Hall
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Times subject to change