5 Steps to Make Your Scientific Data More Valuable

Research activities these days generate an unmanageable amount of scientific data, making it increasingly time consuming and costly to leverage in any meaningful way.

 

Here are 5 steps to make your scientific data more valuable :

1. Put your unstructured data in one place.

Your primary experimental results are captured in an ELN and the processes are tracked by a LIMS system. But these systems are disconnected from files that constitute the experimental data with files usually stored elsewhere on a file share. There are multiple systems and multiple people, formatting and organizing the data in different ways. We call this unstructured data. You need to find a system that can automatically pull unstructured data from all of these sources and safely store it in one secure place.

2.  Automate the organisation of your data.

All of this unstructured data limits your ability to collaborate around a scientific topic or assess the quality and completeness of your data quality. It also limits the capability to re-use data previously produced. In a nutshell, it slows you down. You need automatically assigned metadata and semantic understanding of scientific entities and chemistry, alongside other systems (file systems, ELN, LIMS, document management systems, etc.), to integrate and organise your data so that you never lose a file again.

3. Use a scientific search engine.

With the right tool, you can qualify a ton of files in seconds and you won’t know what to do with all the time it saves you. You want to be able to search all the experimental data related to any specific experiment, project, disease, or tissue, etc., recognising your terms and their relationship with each other. You want to see all molecules involved in your data using structural searches or all species studied using synonyms coming from major ontologies.

Combine structural and text searches in Inquiro

4. Represent your data visually.

With this volume of data, you need to find a graphical tool that lets you visually navigate between data files and easily identify correlations in your data. For example, with Inquiro, you can see how all documents that relate to a particular substance are spread over specific tissue and in which disease context. A high level overview of the data set can also allow you to assess the completeness of the data. You can then answer questions like: do I have all the data for this experiment?

Tissues and associated diseases study link to the substance tykerb

5.  Integrate with analytic tools.

Bring all files that match your criteria into other tools:

  • to automate the metadata based pipe line with tools like Pipeline Pilot, for example, you can programmatically select all chromatogram at a certain format for a project wherever they are and send them to the right tool, and
  • to visualise your data in highly customised views with a tool like Spotfire.

A pipeline of such well qualified data will allow you to spend less time looking for your data and more time extracting insight.

If you’d like to talk to us about how we can help you make your scientific data more valuable, please follow the contact link on the top bar to arrange a call with one of our data management experts

Erwan David, Chief technical officier

Erwan David, Co Founder.