Tag: Server SDMS

For life science organizations, evaluating and selecting a data management system is a critical process that can have a lasting impact on their research. Research data is among the most valuable assets that an organization possesses due to the enormous amount of time, effort, and resources spent in its collection and analysis. Whether from lab instruments, a clinical trial or an observational study, data is central to decision-making, and ultimately to scientific discovery. 

When evaluating data management systems, focus on scalability and flexibility.

Efficiently managing research data requires a data management system that is specifically tailored to the ever-changing needs of scientific research. Planning for the future needs of your organization is essential when choosing a data management system. Research organizations may need to plan for:

• Expanding their lab, research team or collaborators
• Shifting into new experiments, instruments, assays, therapeutic areas or types of data
• Dealing with exponential growth in the volume of data being captured
• Experiencing increased needs for compliance, security, privacy, data sharing, provenance tracking, reproducibility

The data management system you choose should be able to handle any of the scenarios above. By focusing on your future needs you can avoid being locked into a system that will not grow and adapt to new requirements. Under this guiding principle, Our recent webinar outlined six areas that are critical to choosing the right data management system for your organization.

When evaluating a data management system look for:

1. Robust security and compliance controls
   The first requirement for any data management system is that it meets the security and compliance needs of your organization and area of research. Without meeting these requirements the system will likely hit internal roadblocks and may be deemed insufficient for your organization and use case.
2. Ability to handle large volumes of diverse data
   Your data management system should be designed to efficiently bring together and integrate large volumes (millions of rows, hundreds of columns) of diverse data. A clinical trial may include demographics data, sample data, instrument data, and clinical data. Your system should be able to centralize and align all of these data types for efficient and accurate analysis.
3. Support for automated, high throughput data acquisition
   Large volumes of data necessitate automation for scalable and efficient workflows. However, data files are often imperfect and automating their import requires robust quality control functions to ensure the accuracy and integrity of data within the system.
4. Integration of disparate data and metadata
   Beyond acquiring large volumes of data using automation, the system must integrate and annotate data in scientifically relevant ways. For example, a clinical trial may need to align sample, assay, and clinical data via participants and visits. By using clinical ontologies, your system should be able to harmonize your data in meaningful ways.
5. Support for data analytics systems
   When deciding what system support is required for analytic tools, it is imperative that you include your research team and the larger organization. The system you choose should support a wide diversity of analytic tools. This includes both native tools within the system as well as third-party analysis software.
6. Actively developed and supported by the vendor
   Choosing a system that is actively being developed and supported by the vendor is key to the ability of the system to grow and adapt to the changing needs of your organization. Like scientific research, technology is consistently evolving and your vendor should be dedicated to the success of your organization and research goals.

Watch the webinar below to learn more:[vc_video link=”https://youtu.be/jIubcWkiPwo” align=”center”]

LabKey Server

• The Text Choice data type provides an easy way to define a set of expected text values for a field. (docs)
• Sample naming patterns are validated during sample type definition. Example names are visible to users creating new samples. (docs)
• Queries can be included in published studies. (docs)
•  Ontology insert and update forms now support type ahead filtering for quick input into ontology lookup fields. (docs)

Sample Manager

•  Sample Finder: Find samples based on source and parent properties, giving users the flexibility to locate samples based on relationships and lineage details. (docs)
•  New Storage Editor and Storage Designer roles, allowing admins to assign different users the ability to manage freezer storage and manage sample and assay definitions. (docs)
•  Redesigned main dashboard featuring storage information and prioritizing what users use most. (docs)

Biologics

•  ELN Improvements:
  • Notebook entry locking and protection: Users are notified if someone else is already editing a notebook entry and prevented from accidentally overwriting their work.
  • Find notebooks easily based on a variety of filtering criteria. (docs)
• Freezer Management for Biologics: Create a virtual match of your physical storage system, and then track sample locations, availability, freezer capacities, and more. (docs)
•  User-defined barcodes can also be included in sample definitions and search-by-barcode results. (docs)

Premium Resources

•  Premium Resource: Change the Encryption Key
•  Premium Resource: LabKey Data Finder: The DataFinder module has been enhanced to support partial word searching, custom publication facets and reordering of facets, and multiple data finders on a single server.

See the full Release Notes 22.3 (March 2022)

LabKey Server

•  File Watchers – Use file watchers to automate the import of sample and assay data.
•  Ontology Concept Picker – Improve data entry by guiding users to a specific part of the concept hierarchy.

Sample Manager

•  Study/Sample Integration – Add samples to studies and associate them with specific participants and timepoints.
•  Aliquots – Create aliquots of samples singly or in bulk.
•  Picklists – Create and manage picklists of samples to simplify operations on groups of samples.
•  Move Storage Units – Track movement of storage units within a freezer, or to another freezer.
•  Barcode/UniqueID Fields – A new field type, “UniqueID”, generates values when samples are added to a Sample Type or when the barcode field is added to an existing sample type.

Biologics

•  Electronic Lab Notebooks have been added to LabKey Biologics. Link directly to data in the registry and collaboratively author/review notebooks.
•  Hide Sequence Fields – Nucleotide and Protein Sequence values can be hidden from users who otherwise have access to read data in the system.

Premium Resources Highlights

•  Premium Resource: Plotly Dash Demo – Use Plotly Dash to visualize data in LabKey Server.
•  Premium Resource: Training Materials – Videos and Slide Decks to introduce new users to LabKey Server._________________

Click here to read the full 21.7 Release Notes

The Knight Cancer Center’s Precision Oncology program depends on the complete, correct, and integrated collection of clinical and research data. LabKey handles the data so that researchers can focus on treatments and patient care.

The status quo for cancer treatment is to assign a standard treatment based on the type or location of a patient’s cancer. Past performance of treatments is aggregated and evaluated based on general effectiveness for an average patient. This approach does not take into account the uniqueness of a patient’s tumor on a genomic, structural or microscopic level. 

Precision oncology by contrast uses a targeted approach that takes into account the individual characteristics of a patient and their cancer to create specific treatment recommendations tailored to the individual patient.   By collecting large amounts of data, oncologists are able to analyze fine-grained details about the performance of specific treatments and patient attributes to improve the outcome for the individual patient. 

Managing Precision Oncology Data

Managing the vast amount of data required for precision oncology can be a challenging task for researchers. Having a myriad of spreadsheets with multiple owners or trying to collect and analyze data that is siloed in disparate systems can be detrimental to gaining a complete understanding of a patient and their cancer.

LabKey provides research data management tools that are highly tuned to bring together the diverse datasets and offer alignment and reporting.  With LabKey, the OHSU Data Management Systems Team makes the data Findable, Accessible, Interoperable, and Reusable to facilitate clinical decision-making and research discovery/hypothesis testing and generation. 

LabKey Server for Precision Oncology Data Management at OHSU

LabKey Server is at the heart of the Knight Cancer Institute’s data management system. Led by Patrick Leyshock Ph.D., the Data Management System Operations Team at the Knight Cancer Institute is responsible for designing, implementing, and maintaining both technologies and work processes for precision oncology data management. The presentation below focuses on the Knight Cancer Institute’s LabKey-based precision oncology data management system and covers multiple aspects of the system, including its architecture, workflows, and user types.

LabKey Server

• Ontologies Integration – Control vocabularies and semantics using ontology integration.
• • Browse the concepts in your ontologies and add concept annotations to data. 
  • Use an Ontology Lookup to map entered information to preferred vocabularies.
  • Additional SQL functions and annotations.
• Option to merge changes when importing dataset data. (Also available in 20.11.3)
• Dataset audit logs provide a detailed view of what data has changed
• New Assay Type Selection Interface – Simplified method for selecting a Standard assay (recommended and most common), vs. one of the Specialty assays. 

Sample Manager

• Freezer management has been added to Sample Manager. Key features include:
• • Match digital storage to physical storage in your lab with configurable freezers.
  • Easily store and locate samples from anywhere in the application. 
  • Track locations and chain of custody of samples. 
  • Check in and out, record amount used, and increment freeze/thaw counts. 
  • Easily migrate from another system by importing sample data simultaneously with location data
• Improved sample import includes removal of previous size limits on data import, background sample imports, and in-app notifications when background imports complete.

Biologics

•  A new home page highlights samples and assay data.
•  A new navigation menu has been introduced and is available throughout the application.
•  Improved user experience for creation and management of Sample Types and Assay Designs.
•  In-app Notifications are provided when background imports are completed. 

Premium Resources Highlights

•  Premium Resource: Custom Home Page Examples – Create home pages for your LabKey Server with custom links and buttons.
•  Premium Resource: ReactJS Development Resources – Improved examples and guidance for using the @labkey/build system.

Release Notes

See the full Release Notes 21.3 (March 2021)

Decentralized clinical trials (DCTs) expand options for trial participation by populations who are unable to leave their homes as easily or frequently as is required for traditional clinical trials. They can also help clinical trials do a more thorough job of accessing diverse populations by reducing geographical barriers, increasing trial enrollment, and improving retention. DCTs can also assist in capturing participant-centric data outside of the medical setting such as medication adherence, exercise, quality-of-life metrics, and other pertinent data. Although decentralized clinical trials have recently taken the spotlight as a remedy to challenges brought on by the COVID-19 pandemic, they will likely continue to be a widely used tool in clinical trial studies. 

Mobile Apps for Decentralized Clinical Trials

Mobile apps in particular have the potential to increase the efficiency and reach of prospective studies. One notable example is FDA MyStudies. The FDA selected Harvard Pilgrim Health Care Institute (HPHCI) to lead the development of this mobile application to facilitate the collection of real-world data directly from patients to support decentralized clinical trials, observational studies, and registries. Harvard Pilgrim selected LabKey as their development partner tasked with building a secure back-end storage environment for collected data. LabKey was selected due to the platform’s flexible, science-specific architecture. With the project’s long-term goal of expanding the use of real-world data across research programs, the application framework needed to support a broad range of potential healthcare topics through configuration as opposed to requiring development for each new project. LabKey Server also stood out as an ideal solution because of the platform’s ability to handle PHI/PII data in a manner compliant with HIPAA and FISMA regulations. 

The primary goal of the project was to build an open-source reusable platform consisting of a mobile device application and patient data storage environment that fulfills the FDA’s regulatory needs regarding data security and traceability. Creating a platform that meets regulatory data security and privacy standards while remaining extensible to different types of studies and patient cohorts was central to the requirements and design of the platform. The FDA MyStudies platform can be used and rebranded for studies in other various therapeutic areas and has most recently been modified to enable contactless patient informed consent during the COVID-19 pandemic.

For more information on FDA MyStudies for decentralized clinical trials, please see below:

FDA MyStudies Case Study

Publication: The FDA MyStudies app: a reusable platform for distributed clinical trials and real-world evidence studies

Since the start of the COVID-19 pandemic, the urgent need to gather, harmonize and share data has been felt across the scientific research community. To meet this demand, the NIAID Immune Response Consortium developed the HGRepo Informatics Framework in partnership with LabKey. This informatics system centralizes unstructured and de-identified clinical data collected from 14 domestic and international hospital sites. Researchers are using this data to study host genetics, host response, and microbial genetics as they relate to COVID-19. Downstream data from their research includes gene expression data, single-cell multi-omics data, humoral signature data, serology and more. 

Support from LabKey

LabKey supports NIAID by providing a platform to link all of the generated data back to the original clinical, demographics, and phenotype data. By helping integrate, track and make data accessible for collaborative research and integrated analysis, LabKey is helping researchers to quickly get the most insight from the consortium research data. In short, LabKey Server provided a central data repository to provide standardization and structure to clinical data including:

• Definition of Dataset and Sample Properties
• Controlled Vocabularies 
• Categorical Data Groupings
• Reports and Views
• Automated update of links to Assay Data
• Calculated timeline durations for samples
• NIH SAML Authentication
• Folder & Role Permissions

Watch the HGRepo Presentation at LKUC 2020

At the 2020 LabKey User Conference,  Sandhya Xirasagar Ph.D., Lead, Clinical and Laboratory Informatics Section with NIAID and  Jason Barnett, Contract Lead, Medical Science and Computing, Inc. presented on the HGRepo Informatics Framework. Their presentation covers the informatics framework including the background and requirements for the scientific data management system, a demo of HGRepo and the future vision for the system. Watch below:

High throughput plate-based immunoassays such as ELISA and ELISpot are important tools in drug discovery, clinical trials, bioanalytical labs and immunological research. With the advancement of these assays into microplates and their relatively inexpensive nature, both of these assays are widely used globally to evaluate immune responses to a variety of antigens.

Because of their low price point and technology requirements, ELISA and ELISpot assays are often performed in a high-throughput manner which produces a large amount of data in a short time frame. To support the reproducibility of experiment results, teams need lab data management tools that are scalable enough to manage this high volume data in a controlled manner without causing research bottlenecks. LabKey Server provides support for efficient ELISA and ELISpot data management by:

Streamlining Data Management in the Laboratory

LabKey Server helps streamline the management of immunoassay and other laboratory data by:

• Automatically importing instrument files upon availability using LabKey file watchers
• Providing automated cleaning, validation, and transformation of data from laboratory instruments, databases and software
• Structuring experiment results in data grids that can easily be sorted, filtered, and queried

Tracking Data Provenance

LabKey provides scientists with mechanisms for capturing data provenance by:

• Offering users easy-to-understand graphical plate mapping tools
• Allowing users to add metadata about the antigen or immune-responder
• Providing the ability to calculate background and normalize data when appropriate
• Visualizing calibration curves or normalized spot counts for dependable analysis

Facilitating Lab Data Integration & Analysis

LabKey helps scientists integrate, analyze and share their ELISA and ELISpot data by:

• Allowing users to easily integrate their plate-based data with other related data
• Providing built-in visualization tools for live or snapshotted analyses
• Offering a mature permissions strategy for securely sharing data and analyses with collaborators

LabKey Server helps research teams efficiently manage lab data at scale.

Click here to request a demo.

Browse Human Immune System Data

The Human Immunology Project Consortium (HIPC) program was established by the NIAID Division of Allergy, Immunology, and Transplantation (DAIT) as part of the overall NIAID focus on human immunology. To support the data management, analysis, and research efforts of HIPC, ImmuneSpace was developed. This project is led by Dr. Gottardo of the Fred Hutchinson Cancer Research Center. 

Immune system related data, collected from over 100 studies at HIPC research centers, is available to the public in ImmuneSpace, developed in collaboration with LabKey. Well-characterized human cohorts are studied using a wide variety of tools including:

• Multiplex transcriptional, cytokine, and proteomic assays
• Multiparameter phenotyping of leukocyte subsets
• Assessment of leukocyte functional status

LabKey Server Support for HIPC

LabKey Server provides the underlying system to centralize data for securely publishing, aligning, and harmonizing datasets across what would otherwise be siloed study repositories. Shared datasets and extensible schemas help bring diverse data together in a way that it can be treated as a global data bank.

The research network data centralization solution offered by LabKey server is specifically designed to: 

• Bring together data from disparate studies, making the data centrally available 
• Align data structures to minimize manual processing and facilitate integration
• Provide quality control tools to validate and standardize data from contributors
• Create secure workspaces to curate data prior to sharing with a wider audience
• Facilitate exploration and analysis of of complete integrated datasets
• Visualize, analyze, and report on aggregated data using built-in plotting and reporting tools. For example, LabKey Server’s powerful integration with the R statistical programming environment supports presenting R script results as live reports within LabKey Server.

Explore the new HIPC Interface

The ImmuneSpace platform recently launched a new Data Finder interface which makes it easy for users to explore and analyze datasets from hundreds of studies. Developed in ReactJS, with consulting support from LabKey developers and designers, plus extensions to the APIs, this modern interface presents an intuitive browsing experience. LabKey’s integrations with analysis and visualization tools including RStudio help power unique solutions like the ImmuneSpace DataFinder. 

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Filter by patient characteristics, study focus, and even by the type and timeframe of data available. Dashboard graphics update in real time to guide you as you make selections.

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Identify your own cross-study participant group of interest and save it for deeper analysis.  Use the group as a basis for visualizations and analysis.

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Investigating hypotheses across this existing landscape of collected data can unlock new insights for future study and help you connect with research similar to your own. Filtered study details can be scanned in a card format, or sorted in any way you choose.

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Click Here to get started by registering for a free account.