The Scientist’s Survival Guide:
Overcoming the endless search for scientific data
Tired of the never-ending search for the scientific data you need for your experiments and workflows? Read on to hear more about the top challenges scientists can face when it comes to data searchability, and how the right platform overcomes them.
It’s the middle of your workday, and you’re on the brink of a major research breakthrough. But there’s a hitch—managing data from an array of systems and instruments has you stuck in a data maze, with no way out. Hours pass as you sift through fragmented datasets, struggling to compile the critical information necessary to proceed.
For many researchers, this frustrating scenario is an unfortunate reality. It’s one of the key challenges addressed in our #savethescientist video series, which tackles the most pervasive issues facing modern research scientists. You’ll find the first episode, on ‘The Endless Search’, below.
So how can you overcome this daily data dilemma? Let’s take a closer look at the key obstacles impacting data management and searchability in the lab, then outline some practical solutions for scientists like you to break free from the endless search and achieve lightning-fast discovery.
Why is searching for scientific data so painful for scientists?
Most scientists would agree that their current approach to searching for research data takes too much time and requires too much manual effort. But why is this the case? Why does the endless data search continue to hinder today’s labs?
Data siloes across instruments
The first cause is persistent data siloes across scientific instruments. For example, an experiment involves a qPCR to generate data on gene expression, a flow cytometer to provide data on cell populations, and a mass spectrometer to analyze proteomic profiles. The scientist and their team must then review outputs from each of these systems, which come in multiple formats, and compile them together in a unified spreadsheet. This process takes hours of time that could be better spent on experimentation and analysis.
Questions to consider
- How many different instruments do you utilize in your lab?
- How do you currently retrieve and compile data from each of them?
Inconsistent data documentation
The second challenge is the absence of a unified data standard across all systems, instruments, and teams. Without a data standard, it is not uncommon for data points to be labeled differently or incorrectly—especially in larger organizations that have numerous team members working on a given project. A lack of standardization makes it even more difficult to find the information you need, and can even leave you with incomplete or duplicative results.
For example, one scientist might label a dataset from a particular project July24GeneExp, but another places their data from the same project into a folder called GeneResults_July24. When a third scientist goes back to reference this data, the information she needs isn’t in a centralized location, and she may not even realize multiple folders exist.
Questions to consider
- Is there a standard for labeling projects in your organization in a shared location?
- How do you ensure everyone adheres to this standard?
Inadequate search capabilities
A natural byproduct of fragmented systems, insufficient interoperability, and the absence of a centralized data repository is poor searchability. Rather than simply searching by keyword, scientists may be left to manually sort through files and track down the information you need.
Finding current data sets is hard enough, but hunting down historical data can feel all-but-impossible. For example, referencing a year-old experiment stored in an archive might require a scientist to look back through emails, handwritten notes, and other sources to find the correct file path. They may even have to talk to IT to access those files if the project precedes a certain date.
Questions to consider
- Where are your old experiments stored?
- How do you access them?
Time-consuming data retrieval
Even if a scientist knows exactly where all the data they need is located, they often encounter hurdles in actually tracking it down and using it.
For example, data retrieval can require a scientist to log into multiple systems or contact colleagues for access to specific datasets. Often, the results are returned in a Word document or Excel sheet that he must manually integrate with the rest of his findings. And that doesn’t even account for archived files that require special permissions from IT to access. All of this creates additional steps and delays, preventing scientific teams from executing experiments and generating analyses as quickly as possible.
Questions to consider
- How many different systems are used in your lab?
- How do you get the data you need from each of them?
How does a proper lab management system solve the problem?
With layers of inefficiency related to data syntax, systems, and processes, it’s easy to see why many research scientists spend more time searching for data than on advanced scientific tasks.
Solving the endless search requires more than a shared folder and file naming convention, more than a passive data repository, and more than a standard laboratory information management system (LIMS). Here are four key things to look for in a lab management system that is capable of saving you from the endless data search and freeing more of your time for research.
A proper lab management system should integrate seamlessly with lab instruments, automating the collection of real-time and historical data from all of them and making that information unified and easily accessible for scientists. This circumvents the need for manual data retrieval and reformatting, allowing the scientist to focus on more scientific tasks and fewer operational ones. Furthermore, it eliminates duplicative data and discrepancies in formatting, leaving less room for error.
The right platform will empower scientists with full, granular searchability that allows them to readily find the information they need with a simple query—whether they are searching current experiments or historical archives. Scientists shouldn’t have to put too much thought into crafting their search queries; an intuitive search should return precisely the results they are looking for, requiring an inherent understanding of the scientific relationships between information.
A true scientific informatics platform should also serve as the steward of standardization, ensuring that data across instruments, experiments, and workflows from the entire organization is unified in a consistent and usable format. Eliminating fragmentation in both data and file types allows scientists to get started on analysis immediately, without time-consuming data preparation and reconciliation.
Perhaps most importantly, a proper platform will allow scientists to do all their data-related tasks—from finding individual datasets to analyzing that data—in the same place where their experiments reside. Instead of multiple logins and learning curves scientists can log in once and do any research related activity.
The de facto approach to scientific data preparation creates a gap between a scientist’s data and their experiments, workflows, and analyses. A true platform eliminates this gap in a single, frictionless experience that is designed for the scientist from the ground up.
#savethescientist with built-in scientific data management
The pace of scientific research is hastening, and faster is always better when it comes to the discovery of lifesaving therapeutics. However, until we save the scientist from the burden of scientific data management, we cannot truly accelerate scientific discovery.
As the only unified lab informatics platform made for scientists, Sapio empowers scientists with full data searchability right within their core lab system. For organizations with more extensive data requirements, Sapio Jarvis enables scientists to access and utilize a living knowledge graph that is fully searchable, scientifically contextualized, and ready for analysis and visualization. With Sapio, a search query may not even be necessary—Sapio ELaiN allows scientists to seamlessly interact with and ask questions of their data in an AI-powered, chat-based interface.
Ready to stop the endless data search in your organization?
