How to Overcome Legacy Obstacles and Implement a Cloud-First Strategy

Transformative change means rethinking the scientific computing workflow.

The need to embrace and enhance data science within the Life Sciences has never been greater. Yet, many Life Sciences organizations performing drug discovery face significant obstacles when transforming their legacy workflows.

Multiple factors contribute to the friction between the way Life Science research has traditionally been run and the way it needs to run moving forward. Companies that overcome these obstacles will be better equipped to capitalize on tomorrow’s research advances.

5 Obstacles to the Cloud-First Data Strategy and How to Address Them

Life Science research organizations are right to dedicate resources towards maximizing research efficiency and improving outcomes. Enabling the full-scale Cloud transformation of a biopharma research lab requires identifying and addressing the following five obstacles.

1. Cultivating a Talent Pool of Data Scientists

Life Science researchers use a highly developed skill set to discover new drugs, analyze clinical trial data, and perform biostatistics on the results. These skills do not always overlap with the demands of next-generation data science infrastructure. Life Science research firms that want to capitalize on emerging data science opportunities will need to cultivate data science talent they can rely on.

Aligning data scientists with therapy areas and enabling them to build a nuanced understanding of drug development is key to long-term success. Biopharmaceutical firms need to embed data scientists in the planning and organization of clinical studies as early as possible and partner them with biostatisticians to build productive long-term relationships.

2. Rethinking Clinical Trials and Collaborations

Life Science firms that begin taking a data science-informed approach to clinical studies in early drug development will have to ask difficult questions about past methodologies:

Do current trial designs meet the needs of a diverse population?
Are we including all relevant stakeholders in the process?
Could decentralized or hybrid trials drive research goals in a more efficient way?
Could we enhance patient outcomes and experiences using the tools we have available?
Will manufacturers accept and build the required capabilities quickly enough?
How can we support a global ecosystem for real-world data that generates higher-quality insights than what was possible in the past?
How can we use technology to make non-data personnel more capable in a cloud-first environment?
How can we make them data-enabled?

All of these questions focus on the ability for data science-backed cloud technology to enable new clinical workflows. Optimizing drug discovery requires addressing inefficiencies in clinical trial methodology.

3. Speeding Up the Process of Achieving Data Interoperability

Data silos are among the main challenges that Life Science researchers face with legacy systems. Many Life Science organizations lack a company-wide understanding of the total amount of data and insights they have available. So much data is locked in organizational silos that merely taking stock of existing data assets is not possible.

The process of cleaning and preparing data to fuel AI-powered data science models is difficult and time-consuming. Transforming terabyte-sized databases with millions of people records into curated, AI-ready databases manually is slow, expensive, and prone to human error.

4. Building Infrastructure for Training Data Models

Transforming legacy operations into fast, accurate AI-powered ones requires transparent access to many different data sources. Setting up the infrastructure necessary takes time and resources. Additionally, it can introduce complexity when identifying how to manage multiple different data architectures. Data quality itself may be inconsistent between sources.

Building a scalable pipeline for training AI data models requires scalable cloud technology that can work with large training datasets quickly. Without reputable third-party infrastructure in place, the process of training data models can take months.

5. Protecting Trade Secrets and Patient Data

Life Science research often relies on sensitive technologies and proprietary compounds that constitute trade secrets for the company in question. Protecting intellectual property has always been a critical challenge in the biopharmaceutical industry, and today’s cybersecurity landscape only makes it more important.

Clinical trial data, test results, and confidential patient information must be protected in compliance with privacy regulations. Life Science research organizations need to develop centralized policies that control the distribution of sensitive data to internal users and implement automated approval process workflows for granting access to sensitive data.

Endpoint security solutions help ensure sensitive data is only downloadable to approved devices and shared according to protocol. This enables Life Science researchers to share information with partners and supply chain vendors without compromising confidentiality.

A Robust Cloud-First Strategy is Your Key to Life Science Modernization

Deploying emergent technologies in the Life Science industry can lead to optimal research outcomes and better use of company resources. Developing a cloud computing strategy that either supplements or replaces aspects of your legacy system requires input and buy-in from every company stakeholder it impacts. Consult with the expert Life Science research consultants at RCH Solutions to find out how your research team can capitalize on the digital transformation taking place in Life Science.

AI Ecosystems, Edge, and the Potential for Quantum Computing in Research Science

Key Takeaways from NVIDIA’s GTC Conference Keynote

I recently attended NVIDIA’s GTC conference. Billed as the “number one AI conference for innovators, technologists, and creatives,” the keynote by NVIDIA’s always dynamic CEO, Jensen Huang, did not disappoint.

Over the course of his lively talk, Huang detailed how NVIDIA’s DGX line, which RCH has been selling and supporting since shortly after the inception of DGX, continues to mature as a full-blown AI enabler.

How? Scale, essentially.

More specifically, though, NVIDIA’s increasing lineup of available software and models will facilitate innovation by removing much of the software infrastructure work and providing frameworks and baselines on which to build.

In other words, one will not be stuck reinventing the wheel when implementing AI (a powerful and somewhat ironic analogy when you consider the impact of both technologies—the wheel and artificial intelligence—on human civilization).

The result, just as RCH promotes in Scientific Compute, is that the workstation, server, and cluster look the same to the users so that scaling is essentially seamless.

While cynics could see what they’re doing as a form of vendor lock, I’m looking at it as prosperity via an ecosystem. Similar to the way I, and millions of other people around the world, are vendor-locked into Apple because we enjoy the “Apple ecosystem”, NVIDIA’s vision will enable the company to transcend its role as simply an emerging technology provider (which to be clear, is no small feat in and of itself) to become a facilitator of a complete AI ecosystem. In such a situation, like Apple, the components are connected or work together seamlessly to create a next-level friction-free experience for the user.

From my perspective, the potential benefit of that outcome—particularly within drug research/early development where the barriers to optimizing AI are high—is enormous.

The Value of an AI Ecosystem in Drug Discovery

The Cliff’s Notes version of how NVIDIA plans to operationalize its vision (and my take on it), is this:

Application Sharing: NVIDIA touted Omniverse as a collaborative platform — “universal” sharing of applications and 3D.
Data Centralization: The software-defined data center (BlueField-2 & 3 / DPU) was also quite compelling, though in the world of R&D we live in at RCH, it’s really more about Science and Analytics than Infrastructure. Nonetheless, I think we have to acknowledge the potential here.
Virtualization: GPU virtualization was also impressive (though like BlueField, this is not new but evolved). In my mind, I wrestle with virtualization for density when it comes to Scientific Compute, but we (collectively) need to put more thought into this.
Processing: NVIDIA is pushing its own CPU as the final component in the mix, which is an ARM-based processor. ARM is clearly going to be a force moving forward, and Intel x86_64 is aging … but we also have to acknowledge that this will be an evolution and not a flash-cut.

What’s interesting is how this approach could play to enhance in-silico Science.

Our world is Cloud-first. Candidly, I’m a proponent of that for what I see as legitimate reasons (you can read more about that here). But like any business, Public Cloud vendors need to cater to a wide audience to better the chances of commercial success. While this philosophy leads to many beneficial services, it can also be a blocker for specialized/niche needs, like those in drug R&D.

To this end, Edge Computing (for those still catching up, a high-bandwidth and very low latency specialty compute strategy in which co-location centers are topologically close to the Cloud), is a solution.

More interestingly, though, is how this strategy could help Life Sciences companies dip their toes into the still unexplored waters of Quantum Computing through cuQuantum … Quantum (qubit) simulation on GPU … for early research and discovery.

I can’t yet say how well this works in application, but the idea that we could use a simulator to test Quantum Compute code, as well as train people in this discipline, has the potential to be downright disruptive. Talking to those in the Quantum Compute industry, there are anywhere from 10 – 35 people in the world who can code in this manner (today). I see this simulator as a more cost-effective way to explore technology, and even potentially grow into a development platform for more user-friendly OS-type services for Quantum.

A Solution for Reducing the Pain of Data Movement

In summary, what NVIDIA is proposing may simplify the path to a more synergistic computing paradigm by enabling teams to remain—or become—Cloud-first without sacrificing speed or performance.

Further, while the Public Cloud is fantastic, nothing is perfect. The Edge, enabled by innovations like what NVIDIA is introducing, could become a model that aims to offer the upside of On-prem for the niche while reducing the sometimes-maligned task of data movement.

While only time will tell for sure how well NVIDIA’s tools will solve Scientific Computing challenges such as these, I have a feeling that Jensen and his team—like our most ancient of ancestors who first carved stone into a circle—just may be on to something here.

Containerization: The New Standard for Reproducible Scientific Computing

Containers resolve deployment and reproducibility issues in Life Science computing.

Bioinformatics software and scientific computing applications are crucial parts of the Life Science workflow. Researchers increasingly depend on third-party software to generate insights and advance their research goals.

These third-party software applications typically undergo frequent changes and updates. While these updates may improve functionalities, they can also impede scientific progress in other ways.

Research pipelines that rely on computationally intensive methodologies are often not easily reproducible. This is a significant challenge for scientific advancement in the Life Sciences, where replicating experimental results – and the insights gleaned from analyzing those results – is key to scientific progress.

The Reproducibility Problem Explained

For Life Science researchers, reproducibility falls into four major categories:

Researchers are facing challenges in some of these categories more than others. Improving training and policy can help make direct and analytic replication more accessible. Systemic and conceptual replication is significantly harder to address effectively.

These challenges are not new. They have been impacting research efficiency for years. In 2016, Nature published a study showing that out of 1,500 life science researchers, more than 70% failed to reproduce another scientist’s experiments.

There are multiple factors responsible for the ongoing “reproducibility crisis” facing the life sciences. One of the most important challenges scientists need to overcome is the inability to easily assemble software tools and their associated libraries into research pipelines.

This problem doesn’t fall neatly into one of the categories above, but it impacts each one of them differently. Computational reproducibility forms the foundation that direct, analytic, systemic, and conceptual replication techniques all rely on.

Challenges to Computational Reproducibility

Advances in computational technology have enabled scientists to generate large, complex data sets during research. Analyzing and interpreting this data often depends heavily on specific software tools, libraries, and computational workflows.

It is not enough to reproduce a biotech experiment on its own. Researchers must also reproduce the original analysis, using the computational techniques that previous researchers used, and do so in the same computing environment. Every step of the research pipeline has to conform with the original study in order to truly test whether a result is reproducible or not.

This is where advances in bioinformatic technology present a bottleneck to scientific reproducibility. Researchers cannot always assume they will have access to (or expertise in) the technologies used by the scientists whose work they wish to reproduce. As a result, achieving computational reproducibility turns into a difficult, expensive, and time-consuming experience – if it’s feasible at all.

How Containerization Enables Reproducibility

Put simply, a container consists of an entire runtime environment: an application, plus all its dependencies, libraries, and other binaries, and configuration files needed to run it, bundled into one package. By containerizing the application platform and its dependencies, differences in OS distributions and underlying infrastructure are abstracted away.

If a researcher publishes experimental results and provides a containerized copy of the application used to analyze those results, other scientists can immediately reproduce those results with the same data. Likewise, future generations of scientists will be able to do the same regardless of upcoming changes to computing infrastructure.

Containerized experimental analyses enable life scientists to benefit from the work of their peers and contribute their own in a meaningful way. Packaging complex computational methodologies into a unique, reproducible container ensure that any scientist can achieve the same results with the same data.

Bringing Containerization to the Life Science Research Workflow

Life Science researchers will only enjoy the true benefits of containerization once the process itself is automatic and straightforward. Biotech and pharmaceutical research organizations cannot expect their researchers to manage software dependencies, isolate analyses away from local computational environments, and virtualize entire scientific processes for portability while also doing cutting-edge scientific research.

Scientists need to be able to focus on the research they do best while resting assured that their discoveries and insights will be recorded in a reproducible way. Choosing the right technology stack for reproducibility is a job for an experienced biotech IT consultant with expertise in developing R&D workflows for the biotech and pharmaceutical industries.

RCH Solutions helps Life Science researchers develop and implement container strategies that enable scalable reproducibility. If you’re interested in exploring how a container strategy can support your lab’s ability to grow, contact our team to learn more.

AWS Certification Makes All the Difference in Cloud Service Offerings

Certified AWS engineers bring critical expertise to research workflows and data architecture.

Organizations of every kind increasingly measure their success by their ability to handle data.

Whether conducting scientific research or market research, the efficiency of your data infrastructure is key. It will either give you a leading competitive edge or become an expensive production bottleneck.

For many executives and IT professionals, Amazon’s AWS service is the go-to Cloud computing solution. Amazon isn’t the only vendor on the market, but it is the most popular one, even if Microsoft, Azure, and Google Cloud aren’t far behind.

Both Research teams and IT professionals looking to increase their data capacities are always looking for good tech talent. In a world of uncertainties, official certification can make all of the difference when it comes to deploying new technologies.

AWS Certifications: What They Mean for Organizations

Amazon offers 11 globally recognized certifications for its industry-leading Cloud technologies. Studies show that professionals who pursue AWS certification are faster, more productive troubleshooters than non-certified employees.

One of the highest levels of certification that an AWS professional can obtain is the AWS Solutions Architect – Professional certification. This represents a technical professional who can design and deploy entire Cloud system frameworks from the ground up, creating efficient data flows and solving difficult problems along the way.

Professional Architect certification holders have earned this distinction by demonstrating the following:

While everything in the AWS certification system relies on Amazon technology, the fundamental processes involved are essentially vendor agnostic. Every growing organization needs to migrate complex applications between platforms while controlling costs and improving data efficiency – AWS is just one tool of many that can get the job done.

This is especially important for research organizations that work in complex Cloud environments. Being able to envision an efficient, scalable Cloud architecture solution and then deploy that solution in a cost-effective way is clearly valuable to high-pressure research environments.

Meet The AWS-Certified Solutions Architects on the RCH Team

At RCH Solutions, we pride ourselves on leveraging the best talent and providing best-in-class Cloud support for our customers. When we have AWS problems to solve, they go to our resident experts, Mohammad Taaha and Yogesh Phulke, both who have obtained AWS Solutions Architect certification.

Mohammad has been with us since 2018. Coming from the University of Massachusetts, he served as a Cloud Engineer responsible for some of our most exciting projects:

Creating extensive solutions for AWS EC2 with multiple frameworks (EBS, ELB, SSL, Security Groups and IAM), as well as RDS, CloudFormation, Route 53, CloudWatch, CloudFront, CloudTrail, S3, Glue, and, Direct Connect.
Deploying a successful high-performance computing (HPC) cluster on AWS for a Life-Sciences customer, using Parallel Cluster running SGE scheduler for the purpose.
Automating operational tasks including software configuration, server scaling and deployments, and database setups in multiple AWS Cloud environments with the use of modern application and configuration management tools (e.g. CloudFormation and Ansible).
Working closely with clients to design networks, systems, and storage environments that effectively reflect their business needs, security, and service level requirements.
Architecting and migrating data from on-premises solutions (Isilon) to AWS (S3 & Glacier) using industry-standard tools (Storage Gateway, Snowball, CLI tools, Datasync, among others).
Designing and deploying plans to remediated accounts affected by IP overlap after a recent merger.

All of these tasks have served to boost the efficiency of data-oriented processes for clients and make them better-able to capitalize on new technologies and workflows.

AWS Isn’t the Only Vendor Out There

Though It’s natural to focus on Amazon AWS thanks to its position as the industry leader, RCH Solutions is vendor agnostic, which means we support a range of Cloud service providers and our team has competencies in all of the major Cloud technologies on the market. If your organization is better served by Microsoft Azure, Google Cloud, or any other vendor, you can rest assured RCH Solutions can support your Cloud computing efforts.

Storage Wars: Cloud vs. On-Prem

Essential Questions to Ask When Evaluating Your Options

Cloud computing is having a fundamental impact on the biotech industry. Tasks that were extremely time consuming or simply not possible even a decade ago can now be performed quickly and efficiently in the Cloud.

Take big data storage and analysis. Amazon Web Service, Microsoft Azure, and Google Cloud – to name just the three biggest – offer storage and Cloud computing services that allow companies to store massive data sets and provide the computing power required to analyze it.

Affordable access to these powerful tools shortens timelines and allows even small companies to perform tasks that, until recently, were limited to deep-pocket companies that could afford to buy the hardware needed.

Cloud computing solutions also transfer the power of selection and implementation into the hands of the functional areas. IT is no longer the rate-limiting step in implementation; cloud pay-to-play solutions can be turned on just as quickly as credit card information can be transmitted.

And there are capital considerations as well. The difference is upfront capital expense (CapEx) in on-prem storage, vs. operational expense (OpEx) for the cloud. Not having to come up with large sums of money immediately is an advantage of the Cloud.

But there is a flip side. In the biotech/biopharma world, compliance with regulatory requirements, such as 21 CFR Part 11, rank high on the list of issues, and Cloud-based systems might not afford the necessary protection. Security is another important consideration. After all, Cloud computing means sharing your company’s sensitive information to a third-party service provider.

Not to mention, the many benefits to on-premise options, including the ability to tailor your environment to meet very specific company needs.

For these reasons, conversations centered on the implementation or better execution of Cloud solutions permeate research and IT teams, especially as the working world shifts toward higher adoption of virtual work and collaboration practices.

If you’re exploring which Cloud vs. on-prem solutions are right for your work and team, consider the following critical considerations before making any moves:

Business objective. What is the main objective of migrating your business to the cloud, and how will the cloud support your broader R&D or data goals?
Impact. How will a migration impact your organization’s ability to maintain productivity, and can you afford outages if needed?
Readiness. Are you prepared to support a cloud infrastructure? What steps must you take now to ensure compatibility between current on-premise deployments and cloud?
Workflow. What applications make sense to keep on-premise and which would be ripe for the cloud? One size (or in this case, storage strategy) does not fit all.
Capital. Have you assessed costs, including expenses related to the dedicated human resources necessary to support the migration?
Time. Have you thought about realistic timelines and possible roadblocks that could increase migration times?
Risk Mitigation. What are some known risks or cons that may make you, or your organization, hesitant, and how will your CSP support efforts to reduce risk through all phases of your relationship?
Security. Will your data be secure? What security protocols can you trust your cloud service provider to follow to ensure you realize the many benefits of the cloud without sacrificing security?
Compliance. Will your cloud service provider meet client compliance?
Business Continuity and Disaster Recovery. How will your cloud service provider accommodate and plan for the unknown … a requirement we know all too well following COVID-19.

And this list could go on.

You can find more information about how RCH Solutions can help develop your Cloud strategy here.

Looking for support for your AI Initiatives?

How AWS is Supporting the Life Sciences—re:Invent 2019

Last week we attended AWS re:Invent 2019, the premier event for any organization stepping—or running—into the Cloud as part of their IT strategy. It was our 4th time attending and, as expected, it didn’t disappoint.

re:Invent was jam-packed with engaging speakers covering important topics, opportunities to connect with existing customers and partners, and of course, the chance to snag some ever-coveted Amazon swag. But, what struck me most, was the growing and noticeable presence of Life Sciences attendees. From large pharma to start-ups, the Life Sciences were better represented than ever before. It’s a clear sign that we’ve reached an inflection point within our industry, and the Cloud’s role within an effective a Bio-IT strategy has been cemented.

Equally as important, AWS showed their interest in continuing to enhance their solution-set to meet the specific needs of medical research and discovery.

Here are seven quick takeaways from the event that demonstrate how AWS is moving the needle to better support Cloud computing in the Life Sciences:

1. Linux Deployment It’s estimated that Linux deployments in AWS are over 4X that of Windows in the Cloud. The reasons are many but those that pertain to Life Sciences include availability, reliability, scalability, and scalability.

2. Quantum Computing Amazon Braket is the new Quantum Computing Service, which can help the acceleration of significant breakthroughs in science.

3. Elastic Kubernetes Service (EKS) Running Kubernetes pods on AWS Fargate, the serverless compute engine built for containers on AWS, makes it easier than ever to build and run your Kubernetes apps in the Cloud.

4. SageMaker AWS has added over 50 new enhancements, including those for the Deep Graph Library (DGL). With DGL, you can improve the prediction accuracy of recommendations, fraud detection, and Drug Discovery systems using Graph Neural Networks (GNNs).

5. New EC2 Gravitron 2 Powered ARM Instances and Inf1 ML Inference Optimized Instances. These are ideal for scientific computing and high-performance machine learning workloads. These are extremely promising, as they provide high performance and the lowest cost machine learning in the Cloud.

6. Amazon S3 Access Points Easily manage access for shared datasets on S3, with the ability to create hundreds of access points per bucket, each with a name and permissions customized for the application.

7. Amazon Redshift Update Next-Generation Compute Instances and Managed Analytics-Optimized Storage should streamline the process to manage data workflows and findings. This enables you to save data transformation and enrichment you have done in Amazon Redshift into your Amazon S3 data lake in an open format.You can then analyze your data with Redshift Spectrum and other AWS services such as Amazon Athena, Amazon EMR, and Amazon SageMaker. See something I missed? Share your top takeaways from AWS re:Invent below.

Keeping Cloud Costs Manageable

Today, the Cloud is a critical component of most Bio-IT strategies. The benefits of incorporating the Cloud into your workflows are many and include the ability to open and accelerate access to critical research data and foster greater processing capabilities at scale. There’s also significant opportunity for cost savings. Instead of investing in costly on-prem servers and data centers to potentially use only a fraction of its capacity, using the “pay-as-you-need” model based on your consumption of compute resources ultimately helps you achieve a lower cost.

However, what many IT teams don’t accurately account for is the potential for the Cloud to become a significant cost-center in-and-of-itself. Excess users, unused databases, and duplicate workflows—these are all very common yet costly contributors of unnecessary expense within your Cloud environment. Without the right strategies, processes, and controls in place to guard against missteps such as these, departments may find their costs growing out of control and see the need, as one of our customers said best, to “put their Cloud on a diet.”

If your Cloud environment is starting to feel a little tight around the middle, keep these tips in mind:

1. Define clear and measurable Cloud goals to set a realistic budget.

Ideally, goal-setting is done in advance of implementation, but it’s never too late to solidify a strategy for better outcomes.

By defining (or redefining) your goals clearly, your team can carefully budget for your Cloud needs. This begins with an accurate estimation of total Cloud users (think groups and individuals across the full span of the workflow) since services are offered on a per-user or a usage basis. Budgets should accommodate a fluctuation in users, and ongoing monitoring to remove old or unwanted accounts should be performed regularly. Ironically, underestimating an organization’s demand for a service can lead to exceeding the budget. Luckily, most Cloud providers offer helpful tools for accurate budgeting purposes.

2. Understand how the Cloud influences workflow.

The Cloud is only as effective—and efficient—as you set it up to be. Be sure to address processes and workflows not only within your Cloud but also happening around the Cloud to streamline processes or services and avoid duplication.

Here are a few ways to do that when working with some of the most notable Cloud providers:

Finally, it’s always a good idea to integrate services across management platforms to maintain consistency and deploy your application in multiple regions with lower latency to local customers. The latter provides disaster recovery from region-wide outages and enables low-latency global access to applications and data.

3. Work with a vendor who is familiar with the unique needs in Life Sciences

There are several benefits of working with a vendor who brings specific experience in implementing or optimizing Cloud computing workflows within the Life Sciences.

The most significant is helping organizations still determining how the Cloud will work for their needs, set goals, and adapt workflows based on best practices and the unique demand of their business.

For these teams, they typically see a dramatic increase in agility for the organization, since the cost and time it takes to experiment and develop is significantly lower.

4. Focus On Outcomes

The bottom line: Cloud computing allows you to focus on outcomes, rather than racking, cabling, and powering servers, and offers the potential to significantly increase the pace of innovation and discovery within R&D teams. However, if not implemented or appropriately optimized, it can introduce an entirely new set of challenges—and cost—many organizations are not prepared to face. Partnering with a vendor who brings first-hand experience supporting organizations as they navigate this new and rapidly evolving territory—and being involved in defining and executing solution sets—is invaluable.

Cookie	Duration	Description
cookielawinfo-checkbox-analytics	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional	11 months	The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy	11 months	The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.