Optimize Research Data Management with Labguru

RDM Labguru
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What is Research Data Management (RDM)?

Research Data Management refers to the methods of organization, storage, processing, and caring for information that is produced from a research project or used during a research project. The term is often mentioned nowadays in relation to open science — the general movement in the world of research to make scientific data open, accessible, and reusable. 

Why Should You Invest In Research Data Management?

  • Prevent data loss and human error
— using protected databases and storing information in an organized manner significantly reduces the risk for loss, and tracking experiments, supply quantities and equipment usage prevents unnecessary repetitions and mistakes.

      • Improve research processes — being particular about your data management leads to establishing good research procedures

      • Ensure continuity — having all your information in an easily accessible and searchable format enables different teams to collaborate along the process of research or production, and enables laboratories to continue research done by former lab members

      • Meet requirements for publication — many journals require raw data to be submitted along with the manuscript, and gathering relevant is much easier when the data is properly structured and organized

      • Meet requirements for funds — many grant institutions require researchers to provide an RDM plan detailing their methods of research data management

      • Meet regulatory requirements — identifying, formatting, protecting, and structuring your data leads to better protection against compliance infractions and improves your ability to comply with regulations.
RDM Labguru 2

Labguru Research Management System is FAIR

As scientific research advances, laboratories of all fields have to deal with growing amounts of complex data. Managing all this information can seem a daunting task. Labguru helps you to simplify and centralize all your RDM processes.

The guidelines for proper RDM vary between research communities, but most of them address the same issues. The FAIR guidelines, published in 2016 in Scientific Data, summarize these core principles: data has to be Findable, Accessible, Interoperable, and Reusable. Here’s how Labguru helps you meet these requirements:



      • All data is stored in one Place, structured in a comprehensive and intuitive folder layout according to projects and experiments.
      • Easily search for necessary information.
      • View the full history of each notebook entry, and trace any data back to samples and different stages of experiments.
      • Information is backed up to three different locations to ensure full disaster recovery, and all communications within the application and between Labguru and the servers are encrypted for maximum protection. 
      •  Historical versions of data can be recovered per your request, in high accuracy (to the level of a specific minute).


      • Labguru is a cloud-based ELN that allows access information anytime, anywhere, using any computer connected to the internet.
      • Ensures a seamless workflow between different teams as everyone has access to project data: create shared experiments, comment and discuss, sign, approve or reject experiments, as well as export raw or processed data, create back-ups, and share results. 
      • Labguru functions as a central information hub. Laboratories can use it to create an open protocol repository and a supply and sample library.


      • Built-in data processing, analysis, and storage. It prevents the creation of siloed databases on different systems and allows you to analyze data directly from within your research software, as well as process information from various sources and formats. 
      • Easily integrate with other systems, if necessary — the Labguru API feature lets users import and export data, connect and gather data from lab instruments, connect to third-party databases and catalogs, automate lab processes, and analyze data using external analysis software.
      • Set standardized methods of data collection and recording, so that it can be exchanged and integrated with other data and be understood and processed in different contexts.


      • Labguru offers customizable protocol templates that can be used to reproduce experiments.

      • A series of experiments that use the same protocol can be tied together in a dataset, and the progress between them can be easily viewed and analyzed. 

Key Research Data Management Features 

BI and Insights

  • Labguru offers Dashboards, a research data management tool that helps you analyze results and make data-driven decisions. Dashboards centralizes experimental data from different devices, allowing you to create and present visualized reports of data based on pre-designed SQL queries. The reports can be presented within the Labguru research management system or separately on a wall screen.

  • With this tool, you can transform raw data into clear insight about the state of your research and laboratory management, helping you move forward in a calculated way.

Workflow Editor

Lab Automation

Many modern laboratories are looking toward artificial intelligence and predictive analysis, as these hold great promise for accelerated research. However, in order to enable such descriptive analysis, the experimental data must be of high-quality and properly structured. The more information is gathered in labs, the more time and effort it requires to prepare it for predictive analysis. Save this time and effort and simplify data management by using our process automation tool: design steps that happen automatically, including data analysis scripts, triggered by lab members’ actions.

In-app Editing

Labguru makes all your data accessible and easy to edit — All types of data files (Prism, Flowjo, etc.) can be opened and edited directly from Labguru, allowing you to work on research data while keeping it tied to the context of a project in your research software.

Easy Edit
The Form Element

Customize Protocols

Ensure reproducibility and a standardized data capture strategy with personalized protocol templates. Design procedures that are reusable and interoperable with text and number input, checkboxes, drop-down lists, and more. 


Organize and structure your data: create sets of experimental results, organized in a table layout, and use them to easily run analysis and establish theories. The datasets feature is an excellent tool for linking items in the system and getting the bigger picture — for example, create a dataset based on a protocol so that each time it is used in a new experiment, a new line is added to the dataset, allowing you to view progress and change between experiments.


If you’re looking to boost research data management at your laboratory, Labguru is the perfect solution for you. Our support team is here to help you implement Labguru and customize it to your lab’s needs.  To learn more, click here:

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