Project Management: Less is More

Google Drive, Box, DropBox, Egnyte, OneNote, EndNote, and SharePoint are great solutions for storing the raw files from your lab instruments. But when it comes to managing lab projects and accessing your results, whether for R&D, process development, analytics, or diagnostics, this software can quickly hit the limits of its capabilities. As a result, folder creep – unlimited levels of folders and files, with no end in sight – and digital chaos – different versions of files stored in multiple locations, decentralized data, with poor to no visibility to desired data points – can take over. 

Instead, we want digital clarity – structured project management that allows you to easily collect, access, manage, and analyze your data. Our experience has shown that a hierarchy with 3 layers is the best way to achieve digital clarity, offering the ultimate structure for collecting, managing, and analyzing data in a standardized manner.

Project Structure and Hierarchy

3-Layer Hierarchy allows you to organize, standardize, and achieve leaner operations in the lab. With simple adjustments to how you name your internal programs/projects, you can become extremely efficient and eliminate unnecessary clutter in your file management.

Here’s an example of how we achieve this at eLabNext:

• Projects > Study > Experiment
• Project Group

The electronic lab notebook allows you to track results and share data recorded in experiments. Experiments allow you to organize your research data in structured reports, share information with other lab members, and organize it into projects and studies. Data is added to experiments through dedicated sections: users can log data in open-format text sections, add a pre-generated protocol from a template, link a list of samples from the lab inventory, collect results in Excel sheets, make freehand drawings, or upload images and files to their experiments. 

Sharing & Collaboration

Organizing your lab’s operations via the Project > Study > Experiment architecture unlocks the option to collaborate with your colleagues, including sharing Experiment Templates and copying/moving your experiments and reports to create a more free shareable ecosystem.

Here’s how it works in eLabNext: 

• Collaborate
• Share
• Signature and Witness Signature

Data Structures & Data Accessibility

You can’t search if you’re unsure what you’re looking for. Searching for and accessing data will be a challenge if your Projects/Studies/Experiments aren’t named accurately and the sections within them are not structured. 

Here are some tips for making your records searchable:

Maintain Naming Convention

• Develop a healthy and sustainable naming system for your projects, studies, and experiments/reports (such as Project X – Study Y – Report 001 – ABC)

•  Ensure that the coding and naming system allows you to grow into it with new projects and related content that gets created.

Develop a Structure

• Develop a structure that you can grow into, for example, based on your departments, R&D targets, and a 2- to 3-year game plan. 

• When developing your internal structures, think ahead to make it easier for you to access your data.

• Templatize your most common experiments with defined sections, such as:

Intro
Purpose
Supplies Used
Samples Used
Protocol Used
Data Analysis (Excel)
Equipment Used
Experimental Methods and Discussions
File Attachments
Images
Notes and Comments
Results
Conclusions

Leverage a Timeline

• Leverage the timeline to search by users and sections. Your sections need to be organized so that you can access them on the front end and use the API to pull data into Data Analytics software.

Access Full Experiment Lists

• If you’d like more granularity, utilize the Admin Mode in Experiment List to search for by keywords and access all of the desired experiments.

Looking Ahead: Getting AI-Ready

While these practices might seem like you are just getting organized, this standardization effort will set you up for proper AI use, to build internal LLMs and predictive modeling. The more structured your projects are, the more metadata fields you fill with valuable data and the bigger your arsenal of tools for AI implementation and data access.

If you’re interested in learning more about data standardization and how this will set you apart in the decades to come, contact us today!