Optimizing Lab Operations in a Data-Driven Era

Navigating the Digital Lab: Smarter Data Management The current digital lab landscape,innovations, and considerations OPTIMIZE WORKFLOWS with Laboratory Software LEVERAGE Advanced Analytics and AI ENSURE Data Integrity Across Systems SOFTWARE & DATA MANAGEMENT RESOURCE GUIDE 2 Lab Manager Software & Data Management Resource Guide Optimizing Lab Operations in a Data‑Driven Era .............3 Building Your Lab’s Digital Backbone...............................4 Implementing a Scalable, Quality‑Enabled LIMS .................... 5 Transform Lab Operations with Smart Scheduling and Management Tools.........................................................8 Leveraging ELNs and LIMS to Optimize Asset Management ..... 9 The Benefits of Electronic Laboratory Notebooks for Lab Quality and Compliance .......................................12 Lab Scheduling Solutions to Ensure Organizational Success .....15 The Five Attributes of Effective Laboratory Risk Management Software....................................................19 Advancing Lab Operations with Analytics and AI.......... 22 From Observations to Action: Turning Data into Actionable Insight.......................................................... 23 Looking Forward: How AIPowered Integrated Data Analytics Could Strengthen LIMS ............................... 26 Data Management...................................................... 29 The Relationship Between Data Integrity and Security ............ 30 The Role of ELNs in Lab Data Security ............................... 33 Maintaining Data Integrity for Western Blotting Experiments.... 36 An Audit Readiness Guide to Data Integrity and Security ....... 39 Table of Contents 3 Lab Manager Software & Data Management Resource Guide Introduction With this resource guide, labs will gain invaluable insights into optimizing their operations, safeguarding data, and staying ahead in an increasingly digital world. Optimizing Lab Operations in a Data‑Driven Era Essential tools, strategies, and best practices In the rapidly evolving world of bioinformatics and pharmaceutical research, there is a need for robust, adaptable platforms and cutting-edge technology. This resource guide explores the essential tools, strategies, and best practices for modern labs to thrive in an increasingly data-driven landscape. From foundational research platforms to advanced data management techniques, this guide covers key aspects of lab operations that ensure efficiency, compliance, and success. This guide delves into the importance of resilient platforms that enable seamless data management, analysis, and interpretation. It covers practical software applications that streamline lab operations, including smart scheduling, asset management, and risk management—tools that help labs reduce costs and ensure safety and compliance. Readers will also learn how analytics and artificial intelligence (AI) can transform vast amounts of data into actionable insights, empowering labs to make informed decisions and increase operational efficiency. The final section addresses the critical role of data integrity and security, emphasizing the need to protect research data from manipulation and breaches while adhering to regulatory standards. Chapter 1 Building Your Lab’s Digital Backbone As labs increasingly rely on data-driven methodologies, implementing resilient and adaptable digital systems is no longer optional. From managing vast biological datasets to maintaining quality control, the backbone of any successful lab lies in the strength and reliability of its systems. For bioinformatics labs, this means establishing robust platforms that support the management, analysis, and interpretation of complex datasets. For regulated labs, the focus is on ensuring seamless operations while maintaining compliance. This chapter explores the complexities of selecting and implementing laboratory information management systems (LIMS) and quality management software (QMS), with insights from industry experts. You’ll also discover key strategies for developing bioinformatics platforms that are scalable, secure, and tailored to the evolving needs of modern labs. 5 Lab Manager Software & Data Management Resource Guide Implementing a Scalable, Quality‑Enabled LIMS Selecting the best software from the start can save time and headache down the road By Tara Cepull, MA and Holden Galusha Many modern LIMS now incorporate features of QMS systems, such as audit trails and reporting functions, to consolidate sample tracking and product quality into one platform. This is a boon for many labs: fewer programs to learn and train on, less time spent on data entry, and more time for science. For other labs—particularly highly regulated ones—a hybrid LIMS/QMS may not suffice, and dedicated platforms for each will be needed. Since shopping software is far from the first priority for an up-and-coming lab, many lab managers are left unaware of what solutions exist that can scale alongside their lab and, inadvertently, select suboptimal platforms. 6 Lab Manager Software & Data Management Resource Guide A new lab has many options for both LIMS and QMS programs, ranging from “skeleton” platforms to “all the bells and whistles,” according to Jose Cobar, MS, MLS(ASCP), CLS, lab manager of Sutter Health Novato Community Hospital. But in the end, he says, the decision often boils down to the cost. Lab staff want a tightly integrated system while lab leadership can’t rationalize the price tag of a QMS-enabled LIMS. As a result, Cobar has most often seen a “hodgepodge” approach adopted: because new labs typically don’t have the funds or time to implement a thoroughly researched and comprehensive program that will grow with their lab, they choose disparate programs for quality management and sample tracking and then “Frankenstein a system together” with those components. A Frankenstein system may suffice for a while. But, eventually, the lab will outgrow it. The challenge then becomes organizational inertia. It is far harder to migrate to a better product than it is to tolerate a subpar system. Cobar describes this as the laboratory’s “original sin”: once ingrained, it can take a “Herculean effort” to change systems given the logistical issues it creates, which is something to keep in mind as initial systems choices are made. The case for choosing scalable software from the start Cobar understands that business needs may necessitate quickness, spurring pressure to make swift decisions or cut corners. However, as a clinical lab manager, he is responsible for ensuring quality in his lab; he emphasizes that one laboratory error can result in tragedy. Additionally, errors can warrant the recall of patient diagnostic reports, which may result in significant negative financial impacts for the lab. Cobar has found that framing the argument for implementing systems carefully in terms of financial impact resonates with lab owners, who may be the ones pushing for speed. He emphasizes, “A good quality program reduces harm and financial issues, too . . . if it’s done right: no recall of reports [and] no injury [to] the brand.” The takeaway: Lab leaders should select scalable LIMS that offer quality management features suitable for their lab. If no such LIMS fits the bill, leaders should consider implementing distinct LIMS and QMS platforms. By implementing a long-term solution from the beginning, labs can avoid operational inefficiencies and costly migrations later. The goal of any bioinformatics platform is to function as a centralized infrastructure of tools and resources that support analytical and computational processes to manage and evaluate biological data. These platforms must be robust, scalable, and perform consistently. Bioinformatics platforms can vary widely in design and scope, but there are some common implementation and maintenance strategies that will help keep your platform robust. Building Resilience: Implementing a Robust Bioinformatics Platform How to ensure success when developing a reliable and scalable biological data environment Having a well-defined concept to build upon is crucial for understanding where the project is heading. Factors to consider: • What is the main purpose and function of the platform in your research? • Are there features, software, operating systems, or pipelines required for specific data types? • What are the inputs and outputs? • How will data be stored? Define launch objectives and system requirements A knowledgeable, well-trained user base with access to accurate documentation will increase both user productivity and confidence in their results. Factors to consider: • How will new users be trained and onboarded? • How will training materials be stored, accessed, and reviewed? • Will there be a dedicated support team or help desk? • How will users provide feedback on bugs and errors? Provide documentation, user training, and feedback mechanisms Testing will ensure that the data is valid, and the retrieval and analysis results are consistent between runs. Factors to consider: • How will the system and software be benchmarked and analyzed? • Which quality assurance tests will be performed prior to launch? • How often will maintenance and performance be tested? Perform system testing and quality assurance Verification of the user authentication process can help determine any user-based security concerns. Factors to consider: • Is the platform open to the Internet? • What security measures (e.g., firewalls, data encryption, etc.) will be implemented? • How will security and user activity be monitored? Validate security protocols and system monitoring Remember to consider scaleup and flexibility. As user count, computational demands, and storage requirements grow over time, the platform must be able to scale effectively. This can be achieved through a variety of methods, such as scalable storage solutions, database optimization, and incorporating load-balancing mechanisms for systems and servers. Consider how the platform can remain flexible, when infrastructure scaling will be necessary, and how upgrades to the existing infrastructure will be addressed. Chapter 2 Transform Lab Operations with Smart Scheduling and Management Tools Running a successful lab requires more than scientific expertise—it demands efficient management of resources, people, and processes. Whether you’re coordinating team meetings, maintaining equipment, or managing safety protocols, the right tools can transform a lab into a well-oiled machine. By focusing on three core pillars—scheduling, asset management, and risk management—labs can enhance productivity, reduce costs, and ensure regulatory compliance. Robust scheduling systems streamline communication and prevent logistical bottlenecks, helping labs stay on track with their research goals. Asset management solutions provide data-driven insights to optimize equipment usage and lifecycle management, enabling smarter decision-making and long-term cost savings. Meanwhile, effective risk management software safeguards labs by minimizing safety incidents and ensuring compliance with environmental health and safety (EHS) regulations. This chapter dives into practical tools and strategies for improving lab operations, from choosing meeting and equipment scheduling platforms to implementing advanced asset and risk management systems. With the right solutions, labs can achieve efficiency, scalability, and success in their operations. 9 Lab Manager Software & Data Management Resource Guide Leveraging ELNs and LIMS to Optimize Asset Management Lab notebooks and information management systems can improve asset traceability, scheduling, and maintenance By Melody Nelson For laboratory leaders embarking on real-time asset management, electronic laboratory notebooks (ELNs) and laboratory information management systems (LIMS) can both play effective roles. These programs can allow for a complete drill-down into which equipment, specimens, and even benches were chosen for the respective work. As is often in laboratories, expanded access for insights can be accompanied by a considerable lift. However, the downstream benefits can certainly outweigh the operational maintenance. Improving asset traceability with ELNs Fueling the demand is ELNs’ FAIR (findable, accessible, interoperable, reusable) development that creates possibilities for sharing documentation across disciplines. ELNs 10 Lab Manager Software & Data Management Resource Guide are highly customizable in their organizational structure. Institutions can organize data by researcher, project, or team. Further, a review and auditing hierarchy can be defined with real-time approval tracking. This allows cross-disciplinary teams to collaborate within excellent security features as well as an expanding list of integrated software and applications, like research literature search engines. In larger institutions, laboratorians may be stretched across multiple laboratories and campuses, making item tracking and flexible sample management challenging. ELNs have begun incorporating inventory modules to assist teams in understanding everything from which samples have been used in studies to how many racks are available in the freezer and where another centrifuge can be found. This level of granularity is accompanied by an initial massive expense of time. In fact, many organizations elect to hire an individual to complete the initial laboratory walkthroughs and document all equipment within the ELN software. Legacy bulk imports may be a feature of specific ELNs; however, this often requires a previous spreadsheet or database. Ideally, an ELN’s inventory management feature should be organized in a nested approach where rooms and major storage areas are identified, and containers or samples are digitally filed under and within those top-level areas. When doing so, standardized nomenclature of assets (e.g., templated asset labels) can help staff identify what they need. Many ELNs allow users to attach photographs of the assets for easy recognition. Once the inventory is developed in the ELN, users can select containers, materials, and samples in the experiment’s list of materials, effectively linking them to the experiment write-up. This not only enhances traceability and reproducibility of the work but also allows quality auditors to search experiments by keyword or tag if they believe an asset is in question. Further, for specimen tracking, specimens can be moved from freezers to a bench and updated immediately in the inventory to alert others that the specimen is in use and display its current location. Optimizing asset management with a LIMS The benefits of ELNs lead to the question of whether ELNs can be linked to laboratory information management systems (LIMS). While complementary, LIMS differ from ELNs by addressing the operational workflows of a laboratory and overall data organization for reporting capabilities. In terms of asset management, LIMS software creates a high-level inventory of instrumentation and can track consumption, creation, and expiration of kits, reagents, and chemicals. Laboratory instrumentation data such as serial number and model, contract type and dates, installation dates, guides, and training documents can all be stored. Similar to an ELN, LIMS can identify if users have made changes to any of the equipment logs and when those changes were added while keeping an audit of the previous information. Increased customizability with LIMS The ability to know which bench or experiment the equipment has been tagged for may prove difficult depending on the LIMS you’re using. If offered by the LIMS, asset management and tracking could be separated into specific modules for separate selection or packaged purchasing. For this reason, LIMS are unique and highly customized to specific organizations. While they can facilitate collaboration with external partners and software, compatibility with other software, data security, and user costs are often barriers to large-scale, cross-disciplinary installations. In short, as customizability grows, complexity grows with it—labs can reap certain benefits with the flexibility of a LIMS that other informatics platforms can’t match, but the initial setup and maintenance of the system will be more demanding than out-of-the-box platforms. Schedule asset usage and maintenance with a LIMS Many LIMS offer equipment schedulers that allow authorized personnel to reserve equipment to prevent wasted time “ELNs have begun incorporating inventory modules to assist teams in understanding everything from which samples have been used in studies to how many racks are available in the freezer and where another centrifuge can be found.” 11 Lab Manager Software & Data Management Resource Guide when two people have inadvertently planned to use the same instrument at the same time. This calendar format can also be useful for maintenance teams or service/calibration needs as instruments can be flagged as unavailable. If used for such events, the logs from the maintenance service providers can also be stored within the LIMS software for easy reference. Likewise, preventative maintenance and equipment cleaning reminders can be preprogrammed as tasks to the calendar or as auto-messaging to ensure that staff don’t miss important dates. Generate reports and connect assets Report generation and instrument connectivity remain the strongest advantages of LIMS software. For assets, this allows for event log tracking when malfunctions occur and a method of reviewing instrument performance over time. It also sends interface messages between the systems, enabling an eagle-eye view of the connected instrument’s operational status and error messages. Trend analysis can prevent determinantal equipment failures and promote longitudinal data review for small shifts in performance that could lead to variation in sample results. As technology scales alongside laboratory complexity, the options for LIMS and ELNs will continue to diversify. ELNs, while typically cheaper than LIMS and flexible across devices, are also limited in their ability to operationalize other laboratory processes. LIMS, while more holistic in the offerings, require the larger-scale migration of operational processes if a system switch is required, which is very costly. For some organizations, the choice may be one or the other. However, organizations that want the benefits of both, can leverage all-in-one data integration software that combines data feeds from ELNs and LIMS into one platform. Whatever the result, leaders should understand the documentation and needs of their laboratory staff, account for the time requirements involved in mapping inventory, and ensure that the current inventory lists are understood in entirety prior to undertaking any change to asset management processes. Solutions for core facilities Core facilities centralize services, equipment, and expertise for greater efficiency and resource utilization. Core facility managers have multiple responsibilities, ranging from coordinating incoming samples and ensuring appropriate analysis and data interpretation, to scheduling, billing, and equipment maintenance, to name a few. Core facility management software solutions are designed to support busy managers in maintaining organized, efficient operations. Core facility management software is similar to a LIMS with added financial management capabilities. This type of software can aid managers in obtaining funding information, ensuring compensation for completed work, and record keeping in the event of an audit. 12 Lab Manager Software & Data Management Resource Guide The Benefits of Electronic Laboratory Notebooks for Lab Quality and Compliance Using an ELN to document lab processes will help your organization remain compliant and maintain data integrity By Morgana Moretti, PhD As laboratories strive for higher quality, companies are increasingly adopting electronic laboratory notebooks (ELNs) to record and access lab data. Compared to paper notebooks, these systems can more efficiently meet the volume, complexity, accessibility, and security requirements necessary for the success of laboratory operations. 13 Lab Manager Software & Data Management Resource Guide This article explores the benefits of using an ELN in your lab and discusses how this tool can help you maintain quality and compliance. Organization and transparency A major benefit of using an ELN is that it can organize data into a single source. A centralized and structured data management system like an ELN not only helps with organization, but also fosters transparency among laboratory professionals and allows for the traceability and reproducibility of data. ELNs provide a permanent record of the laboratory’s activities and come with a built-in search engine that makes it much easier for lab staff and managers to access the information they need. If a lab member is looking for an experiment performed six years ago, there is no need to look page-bypage through handwritten protocols, notes, or results; a quick search will give them all the necessary information. Recording all routine experimental information in an ELN avoids data loss, transcription errors, and records stored haphazardly. ELNs also resolve the issue of poor handwriting and unclear notes, which can cause long-term problems. In addition, by using an ELN, organizations can eliminate the unnecessary use of paper, which is always good for the environment. Data sharing Another advantage of using an ELN is its ability to support collaboration between lab members. With an ELN, everyone can access relevant information simultaneously without worrying about conflicting document versions. Importantly, researchers can access their records remotely, which allows geographically separated stakeholders to collaborate. This benefits companies seeking to adopt flexible work models and relieves researchers of the challenges that arise when physical access to facilities is restricted, as seen during the COVID-19 pandemic. Implementing an ELN platform also mitigates the need to transport physical laboratory notebooks between locations, reducing the risk of cross-contamination and data loss. Furthermore, lab members can share documents securely via email or file-sharing platforms like Dropbox or Google Drive. With improved data accessibility, scientists across departments and international locations can collaborate just as easily as a single team. Time and cost savings Companies that implement ELNs can save time and money by removing the need to print multiple copies of documents and manually fill out forms. A survey of ELN users compared the time they needed to perform tasks before and after ELN implementation in their labs. The survey showed that, on average, scientists saved nine hours per week using an ELN. For some people, time savings amounted to 17 hours per week. Most researchers reported that using an ELN reduced the time invested in preparation for meetings and writing emails. Workflow automation ELNs provide workflow automation capabilities that make it easier for teams to manage their tasks. For example, some ELN platforms connect with legacy systems and third-party apps like HubSpot, Google Sheets, and Shopify to unify lab data in one place. Users can also take advantage of pre-populated protocols or standard operating procedure templates and set automation rules to prepare their projects quickly. Some ELNs have inventory tracking system integration that allows lab inventory to be managed within the same software that stores data. Labs can use this feature to keep track of materials used in production processes and automate tasks related to stock control, such as order processing and shipping. This means increased efficiency, reduced costs, and improved customer service. “ELNs provide a permanent record of the laboratory’s activities and come with a built-in search engine that makes it much easier for lab staff and managers to access the information they need.” 14 Lab Manager Software & Data Management Resource Guide Data security and compliance ELNs offer unparalleled security compared to paper notebooks. While traditional paper notebooks can be lost or stolen, ELNs have backup methods that prevent information from being lost. For example, cloud storage enables frequent automatic backups. In addition, ELNs offer several ways to export data, allowing lab managers to keep an updated copy of all information on their ELN. Usually, all information transferred and processed from ELNs is encrypted. Moreover, password brute-force detection on hosting accounts and multi-factor authentication protect ELN data. In most commercial ELNs, user rights and roles restrict view and edit access to only the relevant lab members. The specificity of information and how it is accessed, shared, and exported can be controlled, preventing unauthorized access to sensitive data. Compliance features, such as audit trails, allow users to easily track changes made over time as well as who made them. These features improve security while meeting industry regulations like the Health Insurance Portability and Accountability Act or the General Data Protection Regulation. Many ELNs also provide compliance with FDA 21 CFR Part 11. ELNs improve productivity without sacrificing security The use cases for ELNs across industries are varied but universally beneficial due to their ability to save time while increasing the accuracy of captured information. By using an ELN solution rather than paper lab notebooks or manual filing systems, organizations can improve their productivity without sacrificing security or compromising data integrity. From built-in features that optimize search to templates tailored to each facility’s needs, ELNs empower labs to keep their workflows running smoothly as they follow the best research practices. Using these features, organizations can maintain high operational efficiency, and lab managers can ensure that all processes meet necessary regulations. 15 Lab Manager Software & Data Management Resource Guide Lab Scheduling Solutions to Ensure Organizational Success Implementing robust scheduling for the “three Ps of business” can streamline communication and boost productivity in your lab By Holden Galusha Popularized by businessman and television personality Marcus Lemonis, the “three Ps of business”—people, process, and product—are the factors that, if one succeeds at individually, will translate to overall business success. This principle applies to laboratories as well. By continually improving in these three dimensions, you can be on the path to meeting your ultimate research goals while cutting costs and boosting efficiency. An easy way to improve across all three dimensions is robust scheduling. There are a wide variety of platforms available to schedule and streamline lab processes under all three Ps. Implementing the right scheduling solutions can have a profound impact on productivity, communication, and overall lab operations. These solutions help ensure that tasks are completed on time, resources are utilized effectively, and everyone is on the same page. While it may require some 16 Lab Manager Software & Data Management Resource Guide initial setup and training, the long-term benefits of scheduling software outweigh any short-term inconveniences. Here are the benefits of implementing such solutions and some questions to consider while searching for the right one: People: Streamline communication with meeting scheduling software Of the three Ps, scheduling software for people may be the most impactful. Efficient communication is critical to organizational success. There are a plethora of solutions available to streamline communication and yet, many organizations still rely on email to coordinate meetings or relay information that is better suited for a face-to-face discussion. There are numerous benefits to meeting scheduling software. For instance, these programs make it easy for attendees to RSVP and for the organizer to see who can and cannot attend. Trying to coordinate a meeting of more than a couple of individuals over email quickly becomes unwieldy as the thread lengthens with responses. Many meeting scheduling programs allow you to reserve conference rooms as well, which cuts down on conflicts between teams who inadvertently try to meet in the same place at the same time. Finally, many meeting scheduling applications have companion web apps and mobile apps that make it easy for attendees to be notified if the details of a meeting have changed. Questions to ask when selecting meeting scheduling software: • How does this software accommodate remote workers? Will any of your team be attending remotely? If so, how will they be joining? The more integrated your calendar and video conference platforms are, the easier it will be to hold meetings with hybrid teams. • Will I need to book conference rooms? Depending on the size of your organization, you may need software that allows you to reserve rooms to prevent scheduling conflicts with other teams. Process: Decrease overhead with operations scheduling software Administrative and operational processes, such as inventory management and asset procurement, are prone to becoming efficiency drains. Because these processes don’t directly contribute to a lab’s bottom line of research, they may never be reevaluated and optimized after initial implementation. An easy way to streamline these processes is by implementing scheduling software to ensure that routine operations, such as ordering consumables or booking preventative maintenance visits, do not fall between the cracks. Scheduling these processes means that they will be done promptly and in accordance with the needs of the lab, which will curb issues and interruptions that lessen the lab’s productivity. Additionally, scheduling routine maintenance will heighten the ROI of your equipment assets by maximizing their lifespans. While routine maintenance costs more upfront, it often saves money in the long run, preventing unexpected breakdowns and extending the useful life of the equipment. It also ensures that instruments are calibrated and performing optimally, which is crucial for accurate and reliable research results. 17 Lab Manager Software & Data Management Resource Guide Questions to ask when selecting process scheduling software: • Is my inventory complex enough to warrant dedicated inventory management software? Unless your lab is no larger than a couple instruments, then your inventory is likely complex enough to justify dedicated management software. Using spreadsheets to manage inventory is inefficient and does not scale smoothly. • How many people will need to access this software? Inventory management platforms are usually Software as a Service (SaaS) models. SaaS pricing is typically based on the number of users. Tally how many people will be using the software to ensure that the final price fits your budget. • What kind of user management/access control features does the software have? For security reasons, it is prudent that you determine who gets access to what in the software. The more complex the hierarchy of your lab is, the more sophisticated you will want the access control customization to be. Product: Ensure smooth research with equipment booking software Not having a dedicated platform for booking equipment that’s shared amongst multiple researchers or teams can cause logistical issues and frustration for those involved. For instance, taking the time to prep an experiment and then approach the analyzer only to find it in the middle of processing samples for another researcher potentially means that all that prep time has been wasted and the organization has lost money. Equipment booking software is an easy way to curb these issues. Questions to ask when selecting equipment booking software: • How many shared instruments are in my lab(s)? Some equipment booking platforms offer features that allow you to easily collect asset utilization data. Such data can be useful for identifying what is being used, what is not being used, what needs to be replaced, etc. • Will I need custom booking rules or user access management? Like inventory management software, you may want to limit who has access to certain instruments and at what times they do. While it may seem like a chore to add even more software to your suite, the right scheduling program can ease communication, augment your asset utilization data, and more. Over time these benefits can save you time and help you identify ways to make your lab a leaner operation. By investing in scheduling solutions for people, processes, and products, labs can unlock significant efficiency gains and set themselves up for long-term success in achieving their research goals. Getting Started with DataDriven Asset Management Decide the scope and platform of an asset management solution with these steps Getting started ‰ Determine what asset management means for your organization. What is an asset, and how broad is the program? ‰ Decide why you want to manage your assets in a controlled manner. Are you driven by operational excellence improvements, cost controls, or regulatory factors? ‰ Consider what asset management classes are important. Will you want to manage IT, fixed assets, and/or financial transactions? ‰ Evaluate the data you currently track. What data makes sense to continue collecting, and what can be discontinued? ‰ What data you can obtain easily—manually or electronically? Raise questions as to what data would be useful to help manage the asset fleet more easily. ‰ Determine which data make sense to collect and manage ‰ Know your budget Asset management platforms The benefits of asset management software Asset management software is another step to improve effective lab operations. Data indicating how often a piece of equipment is used, its condition, and service history are highly valuable and help guide future purchasing decisions. Asset management software compiles this information, and other data, to produce an uptodate status report on laboratory instruments. It is also a valuable tool to support compliance efforts, as maintenance notifications and archived service and calibration records simplify inspections. Spreadsheets and databases: Basic electronic systems, like spreadsheets or simple databases, are often good places to start as they have builtin applications to visualize data. Asset management systems: Software and hardware products are available that monitor various types of data including calibration/ qualification/maintenance, instrument usage, and facility monitoring data. Fully integrated asset management platforms: Integrated enterprise systems provide data capture, visualization, and trending for all aspects of the laboratory environment. 19 Lab Manager Software & Data Management Resource Guide The Five Attributes of Effective Laboratory Risk Management Software A good RMS should be scalable, customizable, multi-dimensional, intuitive, and comprehensive By Holden Galusha Laboratory risk management software (RMS) is used by EHS professionals, lab managers, and others involved with safety to address risk-, safety-, and health-related tasks effectively. Such activities may include chemical inventory management, maximum allowable quantity (MAQ ) tracking, and more. There are numerous RMS options available and sifting through them to find the platform that best suits your needs can be challenging. While the specifics of what constitutes a good RMS platform depend on each lab’s unique needs, there are five attributes to keep in mind when searching. 20 Lab Manager Software & Data Management Resource Guide Attributes of an effective laboratory RMS Scalable A RMS should grow as your organization grows. This means more than just adding new user accounts. User accounts should be managed to reflect real-life staff hierarchies. For instance, lab managers should be able to change the permissions and view the activity of their direct reports. Bench staff should only be able to access the modules directly relevant to their duties. The key to effective, scalable user management lies in the software’s organizational capabilities. The same holds true for other features. Along with scaling user accounts, the RMS should also accommodate a growing chemical inventory, additional software modules, and documentation effectively. Additionally, with scaling features comes a scaling price. Each RMS provider uses a different pricing model. One of the most common models is subscription-based, with which organizations pay a monthly fee based on their number of users or labs, gross revenue, or other factors. This software as a service model may be used in tandem with one-time fees, like paying for certain modules, customizations, etc. When examining which software to buy, consider how well the options will scale to meet your needs and if its pricing model suits your budget. Configurable and customizable How specialized are your needs for risk and safety management software? In many cases, using a RMS with default settings will suffice. But each lab has unique needs, so the more flexible a RMS is, the better it will accommodate your workflow. It is important to note that configurability and customizability are different things. Typically, configuration is tweaking settings built into the software by default to change its appearance or behavior at a fairly shallow level. The underlying functionality largely remains the same. Customization, on the other hand, is changing the code behind the software to extend or modify its functionality. Many RMS vendors offer customization options in which you can describe a feature you need, and they will program and implement that feature for a fee. If you cannot find a specific feature present in any RMS by default, consider going with a RMS that offers customization so they can add in the feature you need, provided it’s technologically feasible. Multi-dimensional It’s common for those in new, small labs to manage all things related to operations, risk and safety included, with a spreadsheet. While this may work for a short while, it is important to have a plan in place to migrate from spreadsheets to a dedicated RMS solution as the organization grows. Spreadsheets are only two-dimensional: columns and rows. This works for many applications but is not very sophisticated. The limitations in scalability and ease of use will quickly become obvious as your lab grows. For instance, data entry errors are much easier to make on a spreadsheet because all of the data is editable on the same screen. RMS silos editing into separate pages, which cuts down on wrong data entry. Additionally, many spreadsheet programs aren’t designed for effective multi-user access like a RMS is. Risk management programs are built with the knowledge that more than one person will be using it simultaneously, so it will minimize users from accidentally overwriting each other’s data entry. Spreadsheets, even cloud-based ones that allow multiple simultaneous editors, don’t have the same safeguards in place to prevent people from overwriting each other’s entries. It should be noted that while spreadsheets aren’t suited for day-to-day risk management work, they are still very useful for analyzing data exported from the RMS. 21 Lab Manager Software & Data Management Resource Guide When evaluating different RMS options, consider how your facility is set up and search for RMS platforms that mimic this structure. Example questions to ask yourself may include: ) Should “labs” refer to different groups of people or physical spaces in the building? ) What hazard categories must be included, and how should they be represented? A key takeaway is that the more closely software mirrors real life, the more intuitive the software will be—and intuitive usage is vital to convincing others to use new software. Intuitive For new software to be effective, all staff must be on board with it. This is especially important in regard to safety and risk software. If only some of the staff are using the software, communication gaps will appear and urgent issues or risks may go unaddressed, which could result in more safety incidents. To encourage adoption of the new RMS, it is essential that it be easy to learn and use. Humans naturally take the route of least resistance. If the new RMS makes their job more complex—or, at the very least, less convenient—they will resist using it. A good user experience should be one of your top priorities in a RMS. However, what is intuitive for one person may not be intuitive for another. As such, it’s important to test out multiple RMS platforms with the input of others who will be using it regularly. Recruit some colleagues and direct reports to sit in on demos with vendors and, if possible, try the software themselves. Use their feedback to make an informed decision on which RMS platform is the easiest to learn and offers the best user experience (UX). Additionally, take time to explore how the UX can be tweaked on an individual account basis. How extensive are the user interface customization options? What are the different options for displaying data, organizing information, etc.? Even a feature as simple as a dark mode (light text on a dark background, which is easier on the eyes) can positively impact user adoption. Depending on the needs of your staff, it may be worthwhile to see if the software has accessibility features, such as a high-contrast user interface toggle to maximize legibility for those with low vision. Comprehensive Your RMS should be more than just incident logs. Ideally, it should manage chemical inventory, track hazards, serve as a repository for safety and risk documentation, help identify which areas of the facility are approaching MAQ limits, and more. However, it is still important to prioritize features when searching for a RMS. There is a computer science concept known as the Unix philosophy. The Unix philosophy, which originates with Ken Thompson, computer scientist and pioneer of the Unix operating system, states that every program should do one thing and do it well. While this may not be completely feasible in the case of RMS platforms, as an effective RMS necessitates handling multiple functions, the general idea is still valuable to keep in mind as you search. You likely won’t be able to find one program that does everything related to EHS to your satisfaction, so identify which features are most essential and shop accordingly. For your other wanted features, you may be able to find different software to accommodate them or hire the RMS vendor to develop those features for you. Ultimately, the right RMS will help you build a complete picture of your lab’s safety status and equip you with the tools needed to act on that information swiftly and effectively. Chapter 3 Advancing Lab Operations with Analytics and AI The ability to interpret data and translate it into actionable insights has never been more critical for laboratory success. Laboratories generate massive amounts of data daily, yet much of it remains untapped, uncontextualized, or underutilized. By understanding the four types of data analytics—descriptive, diagnostic, predictive, and prescriptive—lab managers can transform raw data into meaningful insights, paving the way for informed decision-making and operational efficiency. This chapter explores the journey from data collection to actionable insight statements, offering practical steps to clean, contextualize, and utilize data effectively. It also highlights the transformative potential of emerging technologies, such as AI-powered analytics, cloud computing, and explainable AI, to strengthen LIMS. These advancements promise to elevate lab operations by increasing efficiency, enhancing data transparency, and democratizing access to predictive tools. 23 Lab Manager Software & Data Management Resource Guide From Observations to Action: Turning Data into Actionable Insight Understanding the four types of data analytics, contextualizing data, and deriving insight By Melody Nelson Laboratories generate massive amounts of data. However, data is meaningless without context, awareness of operational constraints, or simply accounting for human variability and errors. As laboratory managers, if we seek data-based decision-making, we must understand how to decipher data, apply findings, and communicate action steps in a meaningful way. Understanding the four types of data analytics, how to contextualize data, and derive actionable insight statements are the key ingredients to making data-driven decisions. The four types of data analytics Data analytics is the process of leveraging raw data to answer a question, find insights into operational workflows, or observe trends.1,2 The four major types of data analytics 24 Lab Manager Software & Data Management Resource Guide are descriptive, diagnostic, predictive, and prescriptive.3 As laboratory data sophistication will be unique to your lab, this article will focus on descriptive, diagnostic, and predictive aspects of raw data. Aspects of these raw data forms can often be managed manually and are accessible strategies for most managers. There is no one-size-fits-all approach when drilling down into an observation and filtering the critical pieces of data needed to better understand what is occurring. You may choose to frame a structured inquiry around one or all types of data. It is advantageous, however, to proactively identify the questions you are trying to answer by the data instead of making generalized, wide data requests. Also, you should draft your intent and next steps. This safeguards that the data will not be underutilized once accessed. Specificity might seem tedious, but it will ensure you get raw data that has meaningful potential. Type of data analytics Sample data inquiry statements Descriptive Did the first shift see an increase in contamination? Diagnostic Did contamination occur in one unit more than another? Predictive Did an increase in contamination spike at specific points in time? Cleaning and contextualizing data Raw data could be referred to as source data. It’s a direct, unprocessed export. This means it will need to be cleaned before it can resemble something actionable. Many organizations utilize data visualizations, such as dashboards, with descriptive charts or graphs. It’s important to remember that the sleek colors and flashy graphics are populated by raw data. Cleaning raw data and placing it in the right context are crucial steps to ensure that all visualizations are unaffected by junk or misplaced data entries. Clean data should not include inaccuracy, manual entry errors, be outdated, or contain incomplete documentation.4 Unfortunately, in the laboratory, we are often faced with process variability that leads to junk data creation. Those guidelines also hold true for setting parameters. Consider turn-around time monitors. While a simple example, depending on which point in the process you wish to include, the metric may not be represented if the original parameters excluded your data interest. For example, if you consider turn-around as from the time of sample collection, but the dashboard is measuring from the time of receipt in the lab, this would be an inaccurate representation. It should never be assumed that a data analyst, who may or may not have a laboratory background, understands the context of your data, or classifies your data outputs in the same manner you would. Analytics are programmed to work in the way they are intended; junk or poorly contextualized data can mislead leaders into poorly informed outcomes.4 Thus, access to raw data is important because it allows leaders to trace back decisions and ascertain whether original data cleaning and context were approached accurately. While data can be scrubbed until it shines, context is what places data in the right position to generate actionable insights. As leaders, it’s important to consider any environmental factors, operational workflows, or process deviations that may be impacting your data. Further, recognizing where convenience has replaced or impaired standardization is critical. For example, staff may choose to type comments instead of utilizing standard, pre-built ones. In some cases, it may be necessary to fix an operational process or procedural guidance prior to utilizing any data outputs. Your first data set may highlight staff selection variation requiring a standardizing clarification before any data-driven changes are made. Developing actionable insight statements With clean, contextualized data in hand, you’re ready to develop an actionable insight statement and share your findings with staff and stakeholders. An insight statement summarizes what you discovered during data analysis and how your discovery provides an understanding of why something is happening.3 Additionally, to be actionable, interventions and initiatives that are generated by the data outcomes should be included. You can even incorporate your expected measurable outcomes from the data-driven approach and how you plan on monitoring success. Preparing actionable insight statements is a great exercise to get ready for the change management discussions with your staff and institution. Leveraging actionable insight statements as an elevator pitch for change is only the first step. Long-lasting transformation and sustainable data-driven culture depend on focused change management and monitoring efforts.5 As a leader, 25 Lab Manager Software & Data Management Resource Guide you must support, inform, and help individuals move toward proposed data-driven changes. For any new process, training and communication with internal and external personnel will be essential. You may need to discuss a go-live date for standardized efforts or drop-dead date for old procedures. You may also need to secure leadership alliances prior to making changes or create business readiness documentation. Likewise, you should develop improvement plans according to new data reviews at scheduled intervals. Key takeaways While prioritizing staff observations can prove difficult in the quagmire of leadership tasks, having an open door affords leaders a valuable perspective on the laboratory’s performance. If you decide to move toward data-driven decision-making, it is important to make clean data the expectation, seek standardization where you can find it, and create accessible visualizations once you know your data is clean and standards are in place. Also, proactively mapping out actionable insight statements, change management strategies, and monitoring steps will enable you to create sustainable change in your laboratory and institution. References: 1. Andreasiodmok. “Human Centered policy? Blending ‘big data’ and ‘thick data’ in national policy”. Gov.UK. January 17, 2020. 2. Bay Atlantic University. “Characteristics of Big Data: Types, & Examples”. June 1, 2020. 3. Cote, Catherine. “4 Types of Data Analytics to Improve Decision Making”. Harvard Business School. October 2021. 4. Tableau. “Dirty Data is costing you: How to solve common data preparation issues”. Accessed January 2023. 5. Miller, K. “5 Critical Steps in the Change Management Process”. Harvard Business School Online. March 19, 2020. 26 Lab Manager Software & Data Management Resource Guide Looking Forward: How AIPowered Integrated Data Analytics Could Strengthen LIMS Analytic capabilities, cloud computing, and explainable AI are poised to create the next step in LIMS evolution By Gail Dutton Data analytics is being increasingly integrated into lab information management systems (LIMS), strengthening existing capabilities and adding others. Empowered by artificial intelligence (AI) and machine learning (ML), the latest LIMS iterations claim to increase accuracy and the speed of analysis. Future advancements are poised to incorporate generative AI (genAI) to provide what proponents hope eventually may become the equivalent of a second set of expert eyes to analyze and contextualize the results. Following are some key ways that LIMS are poised to evolve thanks to transparent AI, genAI, and cloud computing. 27 Lab Manager Software & Data Management Resource Guide Boosting analytic capabilities Embedded analytics is a key feature of modern LIMS that relies on findable, accessible, interoperable, and reusable (FAIR) data. This minimizes the need to export data from LIMS to other analytic systems, thus saving time. Embedding AI in LIMS allows users to act on decisions made through AI and for the LIMS to react accordingly. Embedding AI within LIMS makes it part of an everyday process. That, in turn, improves overall lab efficiency. Incorporating AI into data analytics allows existing tools to analyze more data and identify less obvious correlations that less sophisticated approaches may miss. Another increasingly important feature, predictive analytics, takes insights a step further by using historical data and ML to predict outcomes and trends. In enabling labs to anticipate problems, optimize resource allocation, and make informed decisions quickly, these predictions can create cost savings and increase productivity. “The newest predictive models can also provide real-time analysis,” says Roosbeh Sadeghian, PhD, associate professor of data analytics, at Harrisburg University. For lab managers, predictive analytics can identify pending bottlenecks before they occur and predict future resource needs, for example. Industry-wide, prediction capabilities will continue to improve, Sadeghian says. Citing drug discovery, an area that many researchers are working to elevate with AI, he continues, “We have lots of tools in the ML and AI area and many [compound] libraries that can identify potential drug candidates and even predict their likelihood of success.” The evolution of AI-enabled analytics is supplemented by the growth of a supporting technology: cloud computing. Cloud-based, AI-enabled analytics is growing “The cloud isn’t new,” Andre L’Huillier, PhD, assistant professor, computational social science, at Harrisburg University, acknowledges, but it is bringing qualitative changes to data analysis. “The idea of having everything very accessible, and then having these intelligent systems that integrate all of those aspects, is where we’re starting to get to new places.” Combining the analytic capabilities of AI-powered LIMS with the benefits of cloud computing democratizes access to these insights. After all, running AI models demands computational power. Many (if not most) labs will not have the budget, space, or in-house talent to run on-premises AI models. With a cloud LIMS, computation and storage needs can scale naturally with the lab. Overhead like security, upgrades, and maintenance will also be handled by the LIMS vendor. Of course, all the benefits of AI remain hindered without transparency into how these models arrive at their output. This is why AI researchers are seeking to fine-tune explainable AI. Soon, explainable AI Historically, AI has arrived at their output directly from data, out of view of the developers. As IBM points out, “Not even the engineers or data scientists who create the [AI] algorithm can understand or explain what exactly is happening inside them, or how the AI algorithm arrived at a specific result.”1 Explainable AI, when it is commercialized, promises to add the transparency that earlier versions of AI sorely lacked. With explainable AI, lab managers will be able to see the reasoning and biases an algorithm used to reach its conclusions—what data was considered and how it was weighted, for example—to ensure the system is working properly, “With explainable AI, lab managers will be able to see the reasoning and biases an algorithm used to reach its conclusions—what data was considered and how it was weighted, for example—to ensure the system is working properly, meets regulatory standards, or challenge data outcomes.” 28 Lab Manager Software & Data Management Resource Guide meets regulatory standards, or challenge data outcomes. Such transparency is instrumental in enabling users to trust AI’s conclusions and facilitates clear communication of those findings to others. Current explainable AI doesn’t yet provide full transparency, L’Huillier cautions. It does, however, provide insights at the foundational level for AI-based models. For example, because ML algorithms continue learning after they are trained, their conclusions may begin to drift as they are exposed to more and more data. Over time, that may affect the AI algorithm’s conclusions. Explainable AI lets even non-technical users check for drift and ensure that the key variables still carry their original weight. In traditional modeling, this would be like adjusting the coefficients or the weighting of various elements. When explainable AI is ready for real-world applications in the lab, it may open the door to the widespread adoption of genAI. The role of genAI GenAI is unique from conventional AI in that it is designed to create new content by synthesizing data, rather than only analyzing existing data. With platforms like ChatGPT, GenAI is evolving from a curiosity to a platform that can support many types of tasks—including analysis. Consequently, generative AI will likely become a key feature in data analytics tools. With generative AI, scientists may be able to query their data in natural language, making the analysis process faster and more intuitive. Of course, explainable AI will be necessary for genAI analysis solutions to be fully adopted. Current genAI is prone to hallucinations, running against the reliability and consistency required for scientific work. What lies next? The next challenge in advancing AI-enabled LIMS will be acceptance, followed by technical harmonization, Sadeghian says. “It’s a bit tricky” to convince people that AI can help improve their work. That leaves an innovation gap that slows the uptake of many technologies. Nonetheless, AI is becoming a valued tool in data analytics for life sciences labs. By leveraging AI, business intelligence, and genAI technologies, we are driving more efficient, accurate, and insightful lab operations. Chapter 4 Data Management Ensuring the integrity and security of data is not just a technical necessity but a critical factor for maintaining the credibility of scientific findings, upholding regulatory compliance, and protecting the overall reputation of institutions. The interplay between data integrity and security is vital—accurate, trustworthy data enables organizations to identify potential threats, conduct effective audits, and comply with regulatory standards. Beyond safeguarding publication integrity, data integrity ensures that experimental results accurately reflect reality. Misleading data can skew scientific progress, making it imperative for journals to enforce stricter prepublication requirements, especially in areas like western blotting. Furthermore, compliance with regulations such as the FDA’s 21 CFR Part 11, which governs the use of electronic records, is critical for labs working in fields like pharmaceuticals and biotechnology, where breaches can lead to severe consequences such as product recalls and plant closures. In this chapter, we will explore how labs can balance data integrity with security to safeguard their research, enhance regulatory compliance, and protect their bottom line. 30 Lab Manager Software & Data Management Resource Guide The Relationship Between Data Integrity and Security Data integrity and security have a dynamic that can uphold product quality and protect privacy By Holden Galusha High-quality data is the primary value proposition of research labs. To protect their bottom line, labs must implement mechanisms to ensure the authenticity and accuracy of their data. Protecting data integrity involves employing systems that secure sensitive information and prevent unauthorized access—objectives that are all accomplished with effective data security practices. By prioritizing the relationship between data integrity and security, labs can uphold product quality, ensure client privacy, and protect their value offering. The interplay of data security and integrity Data security and integrity are inherently coupled. Without robust security measures in place, the integrity of data 31 Lab Manager Software & Data Management Resource Guide is constantly at risk, subject to manipulation by user error, sabotage by threat actors, and destruction by local disasters. Data security is not a binary state in which a lab is either “secured” or “unsecured”; it is a broad set of protocols that work to minimize risk. Data security is a collection of complementary practices, not a single, one-size-fits-all solution. In turn, data integrity can aid in data security. With accurate, trustworthy data sets, organizations can identify threats and anomalies more effectively, perform more accurate security audits, and ensure regulatory compliance. Essential security practices for data integrity There are a variety of measures that labs can enact to ensure the security, and thereby integrity, of their data. A data security solution should be holistic, covering every aspect of the lab and the unique flows of every data stream. While this list isn’t comprehensive, some fundamental security practices for every lab include data encryption, access control, and regular backups. ) Data encryption: A secure solution will encrypt data both in transit and at rest. When relaying data from the lab to the cloud service provider, the data should be sent only over a secure communication protocol, namely, HTTPS, which is the standard HTTP web protocol wrapped in an encrypted SSL connection. With HTTPS, data intercepted by third parties cannot be read. Data should also be encrypted when in storage in the event that a threat actor gains access to the hardware that the data is stored on. By protecting data with endtoend encryption, sensitive data stored in an ELN, LIMS, or other program will not be easily compromised by external threat actors. ) User access control: Users should only receive the minimum permissions possible to execute their role, a concept known as least privilege. By practicing least privilege, organizations can minimize attack surface and mitigate errors—malicious insiders will have fewer opportunities to steal or sabotage sensitive data. ) Backups: In the event of a worsecase scenario in which production data is lost or corrupted, data backups allow labs to restore the data and resume operations with minimized downtime. Backups are a core component of overall security. The relationship between data integrity and security forms the backbone of a lab’s operational excellence and credibility. High-quality data is the lifeblood of research labs, and protecting that data begets the implementation of various security measures. Securing data is not just a necessity, but a strategic imperative, and labs that do that successfully will protect their bottom line and enhance their reputation. Transform your lab’s performance and outcomes! Attendees will explore the latest advancements in AI-driven data analysis, automation, and lab management software. Expert speakers will share insights on integrating AI and software solutions to enhance efficiency, accuracy, and decision-making in the lab. Learn how to streamline workflows, optimize resource management, and stay ahead in the rapidly evolving landscape of lab technology. 2025 REGISTER NOW SEPTEMBER 9, 2025 Managing data integrity has always been difficult. Technology evolves, more complex data management systems appear, and workers interface with systems in new ways. It has taken decades to develop and finalize regulations to ensure data integrity based on constantly changing conditions. Today, regulations require conformance to the principals of ALCOA (attributable, legible, contemporaneous, original, and accurate). But even ALCOA continues to evolve. Additional concepts have created ALCOA+, and more are likely to come, making the process even more confusing and challenging. Perhaps a new acronym can provide additional clarity to this difficult topic: TRUST (tangible, reliable, unique, sustainable, and tested). Here, we’ll explain how TRUST along with ALCOA can help ensure that your data systems meet integrity criteria. TANGIBLE • Regulatory agencies focus on documented evidence for data integrity and non-compliance is often cited for missing documentation or audit trails • Real-time review of electronic data is required with review/approval signatures within data records • Keep in mind that older equipment may not meet current regulatory standards, and previous workarounds for Part 11 compliance are no longer acceptable RELIABLE • Systems, instruments, and connections must work together seamlessly • User Requirements (URS) drive success—develop them before purchasing equipment (postpurchases URS are about fixing issues, not meeting needs) • Strong URS lead to better system reliability and informed vendor selection UNIQUE • Systems can time/date stamp files to track changes • Disable removable storage such as USB ports or optical drives to prevent unauthorized copying • Evaluate remote work risks as unintentional unsecured networks can create vulnerabilities in data transfer • Review validation and SOPs for open systems, and update systems and risk assessments for remote and open system requirements SUSTAINABLE • Systems can time/date stamp files to track changes • Demonstrate the ability to recover data from different servers/locations • Account for the age and condition of removable media and off-site storage • For traceability, ensure validation data aligns with URS and functional specifications • When handling legacy systems, plan for proper long-term storage of workstations, applications, and data, and consider data integrity when updating or replacing aging equipment TESTED • Test scripts identify errors before they occur, not just for compliance • Stress testing pushes the system beyond normal operating conditions to identify potential failures • Create environmental stress by testing systems in conditions exceeding normal usage (e.g., 40°C vs. room temperature) • Simulate high data collection rates to ensure the system can handle the load without errors • Stress test CPU, memory, graphics, and storage for potential errors in data collection Evaluating and managing your programs based on the concepts of TRUST should provide an additional framework that, along with ALCOA, can help effectively manage the integrity of your laboratory’s data. MANAGING THE INTEGRITY OF DATA Lab managers must refocus their efforts on managing data due to increased regulatory agency activity 33 Lab Manager Software & Data Management Resource Guide The Role of ELNs in Lab Data Security Electronic lab notebooks enable reproducible research with secure data By Gail Dutton It’s easy to imagine the data in your lab is safe from outside threats like viruses and hackers because it is protected by a layer of enterprise-level security. The top electronic lab notebooks (ELNs) limit that risk by taking a zero-trust approach in which all users are authorized, authenticated, and continuously validated before they can access the applications and data. They also provide user access controls, traceability, and data encryption. In that way, they provide the security and integrity that is vital for reliable data analysis and data-based conclusions, as well as hosting other experimental and sample data such as observations, formulations, protocols, reagents, etc. Industry standards and best practices One of the overarching protections ELNs provide is compliance with industry standards and best practices, as well as with relevant government regulations. In the US, this means the Federal Risk and Authorization Management Program (FedRAMP). This program standardizes a risk-based data 34 Lab Manager Software & Data Management Resource Guide security approach for cloud service providers throughout the federal government. FedRAMP deals with common security and privacy standards and has become the gold standard in the US. It applies to cloud-based service providers that want to work with the federal government or companies that work with the government. Also expect an ELN to comply with the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) standard 27001 for information security management. It advocates a robust, holistic risk management approach to current and future threats applying to people and technologies, as well as data security policies. Add-on modules enable some ELNs to comply with industry-specific regulations, but the onus is still on the user to meet relevant compliance standards. Life science labs, for example, can benefit from an ELN that helps them become compliant with 21 CFR Part 11 in the US and Annex 11 in the EU. An ELN should take a compliance-by-design approach in the software development lifecycle, providing features that help users meet industry standards and regulatory commitments. This can include an audit trail, electronic signatures, time stamps, and other security features that ensure any changes to data can be traced back to the individual who made the changes and when they were made. As projects advance, adherence to these regulations ensures regulators that the submitted data is reliable. Automatic backups protect data Ideally, ELN backups should occur automatically. Ideally as many times as possible in a short period of time. That means minute-by-minute saves as data is added or changed and nightly backups of the entire ELN to the cloud. Ideally, ELN data will be backed up to the cloud and cross-replicated to multiple data centers in different geographic locations. If there is an issue in one area the data is stored, it can be retrieved from another site. Note that data privacy and storage regulations shift depending on where a data center is located, so users should seek providers that have data centers located in their country. ELN cloud storage providers should host data centers in North America, Europe, Asia, and Oceania to reach as many users as possible. Lab data also should be encrypted. Industry best practices call for using Advanced Encryption Standard (AES) 256 for 256-bit encryption. It is outlined in FedRAMP and ISO 27001 and approved in the US for top-secret government information. Atop that, another protocol, Transport Layer Security (TLS), further protects data on the move. The latest version, 1.3, was released in 2018. This multi-tier encryption thwarts man-in-the-middle attacks, which, as the name implies, surreptitiously reroute or alter data in transit. With AES-256 encryption, even if TLS is breached, the data is still secure. It’s like having a lockbox inside an armored car. Cloud storage Because many ELNs provide their applications as a service (Software-as-a-Service, or SaaS), the threat of security risks caused by unpatched software is reduced. Security doesn’t depend upon whether lab personnel have time to perform software updates themselves. For onsite deployments, software updates should always be installed on the local IT infrastructure. In a virtual, cloud-based environment, a customer’s instance of the ELN can be independent of those of other clients. Each instance is like a safety deposit box inside a bank vault. Although many boxes share the vault, only specific users can access an individual box. This type of ELN deployment further increases the level of security. Archiving Data from an ELN has value to multiple business units outside the laboratory, and that value can persist for years. Different industries have different rules for data retention, but archiving data for at least five years is often the minimum requirement. “One of the overarching protections ELNs provide is compliance with industry standards and best practices, as well as with relevant government regulations.” 35 Lab Manager Software & Data Management Resource Guide Ideally, an ELN will not delete any data for the duration of a client’s account with the service. Therefore, users can see the evolution of the data as it is accessed, analyzed, augmented, and compiled into reports, as well as how, when, and by whom it was changed. Role-based access control ELNs also help lab managers control users’ access to data. For example, a lab intern may be able to read data but not change it, and scientists and their collaborators may access some projects but not others. Controlling access is important not only from a security aspect but also in terms of managing the risks of errors or modifications that may affect data integrity. The ability to segment access to data and reports provides the security labs need to allow collaborations with external organizations—including companies with which they sometimes may compete—while protecting proprietary information. Authentication methods can be both secure and user-friendly. Single sign-on authentication technologies let users access many digital platforms with one log-in credential while providing multi-factor authentication. Typically, a user may sign in with a username and password and authenticate themselves with a biometric identifier (like a face or fingerprint scan), a PIN, a one-time code sent to a smartphone, or a physical token. With that approach, users have one strong set of passwords they can remember. For added protection, a session timeout feature ensures that files aren’t left open indefinitely, thus limiting the ability of an unauthorized party to access or alter data surreptitiously if the user walks away. Human readable formats also make it easier for users to spot entries that may be amiss. Mobile security Mobile apps that function as a companion to browser-based ELNs are still relatively uncommon. By accessing their ELN from their phones or tablets, scientists can easily check protocols and make notes at the bench without having to manually transcribe those notes, thus minimizing potential errors. Because these notations are synchronized with the ELN in real time, data is current and reliable. The same security features that manage access to the browser version of the ELN—notably, multi-factor authentication—also should be in place for mobile apps. For example, a mobile authentication flow could require users to log into the ELN and generate a single-use, time-sensitive QR code that they then scan with their camera before accessing the ELN and signing into their appropriate sections. ELNs, whether in the cloud or on-premises, are foundational to the protection of lab data. They are evolving into the central hub for managing technologies used in the lab, reducing complexity for lab managers and lab users alike. 36 Lab Manager Software & Data Management Resource Guide Maintaining Data Integrity for Western Blotting Experiments Researchers must follow best practices for data normalization By Chelsea B. Pratt, PhD A number of recently retracted papers related to western blot anomalies have brought the scientific publication and peer-review process under greater scrutiny, which underscores the need for data integrity stewardship and greater stringency for manuscript submission approval. In 2023, Stanford University’s former president and prominent neuroscientist Marc Tessier-Lavigne resigned from his position amid a controversy surrounding allegations of research misconduct. Tessier-Lavigne’s resignation came after an eight-month investigation initiated in late November 2022, following reports by The Stanford Daily that raised concerns about potential image manipulation in his research papers dating back to 2008. Notable discrepancies included the reuse of previously published western blot images as different experiments and blot duplication across multiple experiments. Elisabeth Bik, a scientific integrity consultant and former Stanford staff scientist, has been at the forefront of raising concerns about certain papers authored by Tessier-Lavigne and others primarily revolving around potential 37 Lab Manager Software & Data Management Resource Guide image alterations, in particular the alteration or misrepresentation of western blot images. The wide availability of image-manipulation tools has been of concern to the scientific community for many years now. Crowd-sourced communities have organized websites like Retraction Watch and PubPeer to flag images that have the digital signatures of image manipulation. Editors themselves are increasingly using these tools, initially developed by researchers, during the prepublication review process to ensure overall data. However, data integrity extends far beyond protecting the publication record from fraudulent results. At its heart, data integrity is the idea that the data presented in a scientific paper accurately reflects the reality of what happened in the experiment. Experimental results can skew this reality if such experiments are not designed or executed properly. Advancing our scientific understanding depends on the accuracy of published information. It is therefore imperative for journals to have more stringent prepublication requirements, and for researchers to follow best practices for data normalization, particularly in western blotting, to prevent inaccurate data publication. The normalization concept and a comparison of primary methods As it pertains to western blotting, normalization can be defined as an analytical process that allows meaningful and mathematically faithful comparisons of different samples respective to a commonly shared internal control, which in turn minimizes variations. Data normalization using a loading control is required to demonstrate that a western blotting experiment was executed in an unbiased manner and that no systematic errors, such as inconsistent sample preparation, pipetting errors, and uneven gel-to-membrane transfers, have affected the experimental findings. Such experimental errors may potentially result in lane-to-lane differences in sample concentration and therefore need to be corrected before comparisons can be made among different experimental samples. Proper western blot normalization is required to show that the changes in band intensities correlate to the biological changes in test samples. To accurately evaluate changes in target protein expression levels using a western blot, both the target protein and loading control should be measured in their linear detection range. The general criteria for a protein to serve as a loading control is that it is: ubiquitous, abundant, and constitutively expressed. Typically, “housekeeping proteins” (HKPs), such as β-Actin, are used as internal normalization standards (i.e., loading controls) for western blots. HKPs are involved in basic cellular functions and are often highly expressed, whereas target proteins may be present in low abundance. This therefore requires a large amount of sample to be loaded to enable target protein detection. However, this also further increases the amount of HKP loaded; a discrepancy that is often not accounted for in western blot experiments, as researchers will assume that both signals are being collected inside their linear dynamic range without testing them explicitly. This practice can lead to challenges, such as the overloading of HKPs, resulting in oversaturated reference bands beyond their linear detection range. If this happens, HKPs are not serving their function as a loading control. Additionally, HKP expression levels themselves may vary under different experimental conditions and biological factors (e.g., cell cycle, cell density, tissue type, subject age, and response to treatment). Therefore, it is important to validate any HKP for consistent expression levels for the specific sample type and experimental conditions using a linear range determination test via serial dilution. This test should be performed to confirm a linear and proportionate signal response for both the HKP choice and the protein target of interest being studied. Once optimal protein loading concentration is determined, one or more rounds of optimizations may be required and normally start by adjusting the primary and secondary antibody dilution ratios. Total protein normalization (TPN) has been introduced as a method to overcome these linearity challenges in immunodetection. TPN involves the quantification of the total amount of protein loaded in each lane, which can be measured by collecting the signal from a total protein stain on the membrane. For this method to be practical as a western blot loading control, the stain must either be compatible with immunodetection (i.e., stain-free technology) or reversible with destaining steps (i.e., Ponceau S). These stains tend to be less sensitive than antibody-based immunodetection, reducing the likelihood of signal oversaturation. Additionally, TPN stains exhibit good linearity within the common loading range of 10–50 µg of cell lysate per lane. This approach enables the measurement of both low-abundance target proteins (using sensitive immunodetection) and high-abundance HKPs (using less sensitive total protein staining) within their respective linear dynamic ranges. Total protein signals are also far less likely to be affected by biological changes than any individual HKP signal. TPN also eliminates the need for stripping and re-probing steps associated with HKP normalization, which reduces potential variability in the reference signal. Due to 38 Lab Manager Software & Data Management Resource Guide the reliable linearity and inherently robust nature of the total protein signal, there is increasing advocacy for TPN as the method of choice for western blotting normalization. HKP TPN Antibodies for most common HKPs are required, but readily available. Antibodies are not required for normalization. HKP expression levels are usually very high, making detection fairly easy. HKP expression levels are inconsequential. HKPs are often ubiquitously expressed. HKP ubiquity is inconsequential. Control experiments are necessary, time-consuming, and require multiple control experiments. Control experiments are simple and only require a single control experiment. HKP signal intensities are a proxy representation of all proteins present in the sample. The total protein signal intensity is the actual signal of all proteins present in the sample. Stain-free total protein normalization The advent of stain-free imaging technology has significantly improved TPN as an alternative loading control for quantitative western blotting. This technology enhances the natural protein fluorescence by covalently binding to tryptophan residues in-gel via a rapid UV excitation, enabling imaging of total protein in gel and on a blot without time-consuming staining and destaining steps. It facilitates easy visualization of the experiment at every step of the electrophoresis, transfer, and incubation process, thereby allowing researchers to have high confidence that their blotting experiment is robust and reliable. Stain-free imaging, an integral part of Stain-Free TPN, allows for the elimination of the use of problematic HKPs as loading controls, particularly in the common loading range for cell lysates. By normalizing bands to total protein in each lane, researchers can obtain more reliable and truly quantitative western blot data. By utilizing Stain-Free total protein measurement as the loading control, researchers can ensure that both target proteins and loading controls are assessed within their linear dynamic ranges in western blot experiments (i.e., linear response). Moreover, Stain-Free imaging eliminates the challenges associated with using HKPs as loading controls. Stain-Free total protein measurement provides a more reliable control, particularly for the loading range typically employed for cell lysates, enabling users to obtain truly quantitative western blot data (i.e., scalar response) by normalizing bands to total protein in each lane. In the case of multiplex western blots, this also means one of your fluorescent channels can be used for an additional protein of interest instead, getting more data out of one blot. To simplify Stain-Free TPN, unique workflows have been developed for the visualization of the gel and the blot, and the imaging process does not interfere with downstream immunodetection steps. Meeting new publication guidelines Several prominent scientific journal publishers have recently updated their editorial guidelines for data publication, with a strong focus on reproducibility and quantitation criteria. The Journal of Biological Chemistry (JBC), for instance, provides specific submission guidelines for data collection and presentation, particularly addressing quantitative western blot publication. These guidelines offer recommendations on normalization methods and supporting data, and express concerns related to the use of HKPs, imaging technology, and antibody specificity. Adhering to best practices around data integrity has a myriad of benefits for researchers. Besides being ready to easily meet publication standards, researchers will decrease the time they spend troubleshooting failures, create new insights into how their experiments are working, and ultimately increase their confidence in their results. JBC guidelines: Quantitative blots How to meet requirements "Housekeeping proteins should not be used for normalization without evidence that experimental manipulations do not affect the expression." Use TPN instead of housekeeping proteins. "Methods including detection of enhanced chemiluminescence using X-ray film have a very limited dynamic range." Use an imaging system with at least four logs of dynamic range. "A description of the data supporting the specificity of all antibodies is required." Use fully validated antibodies. 39 Lab Manager Software & Data Management Resource Guide An Audit Readiness Guide to Data Integrity and Security Examining the scope of 21 CFR Part 11 and what you can do to remain compliant By Tara Cepull, MA With many recent instances of cybersecurity attacks and data breaches, it is more important than ever for laboratories to protect the integrity and security of their data. Maintaining properly secure and reliable data for your laboratory is not just important for your lab, its patients, and the general public, it is mandated by the government. 21 CFR Part 11 sets forth the federal requirements to ensure the integrity and security of electronic records. It protects public health and safety by mandating secure and accurate electronic data. If compliance with 21 CFR Part 11 is not achieved, you may receive a warning letter, citation, monetary penalty, injunction, or even criminal prosecution, among other potential punishments designed to right the non-compliant wrong(s). 40 Lab Manager Software & Data Management Resource Guide What is the scope of 21 CFR Part 11? Using 21 CFR Part 11 as a guideline for electronic records administration and maintenance alongside your lab’s quality management system, your lab’s electronic data transmission can remain compliant and serve you, your team, and your customers or patients well. The code applies to companies operating under the Food and Drug Administration’s (FDA) oversight that utilize electronic records and signatures, including research and clinical laboratories. According to 21 CFR 11.3(b)(6), electronic records are anything in digital format handled by a computer system, which includes patient records and spreadsheets, as well as audio and video files. 21 CFR 11.3(b)(7) defines electronic signatures as any symbol approved or used by the individual to constitute the equivalent of their handwritten signature. 21 CFR Part 11 is broken into three subsections to address the scope and offer general definitions of key terms utilized therein (Subpart A), establish requirements for electronic records (Subpart B), and instruct the use of electronic signatures (Subpart C). In general, the code serves as a means to ensure that all electronic records are reliable, accurate, and equivalent to paper records such that they can be a complete substitute. It is important to note that all computer systems maintained under 21 CFR Part 11 must be made available for FDA inspection; thus, maintaining continual compliance is of the utmost importance. Subpart A concerns itself with the general provisions and scope of 21 CFR Part 11, i.e., what the code does and does not apply to. Implementation as it pertains to records that must be submitted to the FDA are outlined, and key definitions for terms are provided as well. Subpart B, covering electronic records, instructs both closed and open systems; closed systems are those that are controlled by users in the system that also create or edit the electronic records, while open systems are controlled by individuals who are not responsible for the content of the electronic records. The same base requirements apply to both, but open systems are mandated to have additional security measures that can be achieved through efforts such as data encryption. Both open and closed systems are required to be validated to show that the data is accurate, reliable, and the system has consistent performance, as well as that the records can be retrieved with ease. Limiting personnel access to those necessary and performing authority and device checks, alongside using timestamps and performing regular operational checks, are required. Controls over documentation, including access and distribution are necessary, as are policies holding individuals accountable for actions signed off on through their electronic signatures. This section also touches briefly on electronic signatures, noting that they must include specific, pertinent information, including the person’s name, the date, the time, and the meaning of the signature (i.e., whether it indicates approval, review, authorship, etc.). Finally, to avoid falsification, the electronic signature must be directly linked with the record itself. Subpart C wholly concerns itself with electronic signatures and prescribes the requirements for its components and controls, including those for passwords and identification codes. Essentially, everyone’s signature must be unique, organizations must verify the signer’s identity prior to electronic signing, and signers must certify that their electronic signatures are the equivalent, legally, of their handwritten version. There are a number of controls that companies must be aware of to meet electronic signature compliance requirements outlined in the code’s Parts 11.2 and 11.3. The FDA provided a comprehensive, 12-page guidance document to assist with full compliance of all aspects of 21 CFR Part 11. How to maintain audit readiness and compliance with 21 CFR Part 11 Perhaps the first step for labs to maintain compliance and audit readiness is to avoid common pitfalls in data integrity and security. Some of these hazards include the inadequate validation of computer systems and/or software, insufficient data backup processes, and inadequate staff training. Continuous monitoring can be used to identify potential threats in data security and produce real-time documentation of non-compliant activities—such as unauthorized personnel gaining access to sensitive data—to address those compliance issues as soon as possible. Some software allows you to create rules “Perhaps the first step for labs to maintain compliance and audit readiness is to avoid common pitfalls in data integrity and security.” 41 Lab Manager Software & Data Management Resource Guide that trigger immediate notifications to authorized personnel when violations occur. Addressing other issues, such as insufficient data backup procedures, may require lab managers to work together with their organization’s information technology department to solve. How can my lab enhance its potential audit outcomes? A cloud-based software system may reduce the risk of breach as compared to onsite systems, especially for smaller labs. A benefit of cloud-based software is frequent updates, which ensures data integrity and security issues are patched quickly. However, the control, cost, and maintenance management associated with on-site software can be beneficial for larger labs. No matter the size of your laboratory, it is beneficial to employ two-factor authentication for all users and a mobile device management program so that security policies align with the code and achieve compliance. Validating security protocols is about the lab’s processes and tools, including software systems. Labs focused on this aspect of compliance can leverage their own testing and documentation to assist with validation efforts. Final thoughts As briefly touched above, there are serious ramifications for laboratories who are non-compliant with 21 CFR Part 11. Incurring fines and legal fees for non-compliance can jeopardize the financial security of the lab. Further, accreditation could be placed in jeopardy for a lab with rogue data and security policies; loss of accreditation, the exiting of critical lab partners, and even complete laboratory shutdown are potential negative outcomes associated with non-compliance. “A cloud-based software system may reduce the risk of breach as compared to onsite systems, especially for smaller labs.” What Every Lab Should Know About Data Integrity and Compliance Lack of data integrity is a major cause of compliance breaches, often cited in FDA warning letters due to incomplete data. In the pharmaceutical and biotechnology industries, ensuring the trustworthiness and reliability of electronic records, as outlined in FDA’s 21 CFR Part 11, is crucial to prevent risks such as import bans, product recalls, or plant closures. Here’s what steps your lab can take to support data integrity and compliance: Ensure data integrity ‰ Verify that original data remains visible within the system after alterations ‰ Limit the number of users who can alter data ‰ Implement automatic audit trails to log the date, time, and source of every entry or change ‰ Test and validate electronic record systems regularly Key record characteristics ‰ Ensure data is retrievable and identifiable ‰ Attribute all data to specific subjects or individuals ‰ Maintain audit trails showing who altered the data, why, and when ‰ Retain the ability to reconstruct trials and experiments Systems critical to data integrity ‰ Identify critical systems related to product distribution, approval, manufacturing, and quality assurance ‰ Evaluate the regulatory impact of systems, including erecords and esignatures Achieving FDA 21 CFR Part 11 compliance ‰ Implement workflows to manage the record lifecycle, including routing, approval, version control, and audit trails ‰ Develop SOPs to document user identity, accountability, and procedures ‰ Certify esignatures as legally binding equivalents of handwritten signatures ‰ Conduct software validation for commercial and custom systems ‰ Ensure qualification of personnel involved in system management and use ‰ Maintain archiving and retrieval systems Handling hybrid systems ‰ Link handwritten signatures to erecords with detailed identifiers (e.g., file name, size, creation date, hash value) ‰ Ensure hybrid systems maintain compliance with Part 11 Audit trail management ‰ Initiate audit trails: ‰ For data: From the moment data reaches durable media ‰ For textual documents: Upon approval and release ‰ Ensure audit trails are secure, computergenerated, and timestamped ‰ Retain audit trail documentation for at least as long as the associated erecords Understanding open vs. closed systems ‰ Closed systems: Controlled access by record owners (e.g., within a company’s firewall) ‰ Open systems: Uncontrolled access requiring additional measures to protect data integrity