Research is both a solitary and collaborative process. To be able to make a meaningful contribution, scientists spend years researching on a niche subject, often independently. Yet, science is a global collaborative effort. Collaborations enable some of the smartest people on the planet to work together, to solve critical problems like pandemics, climate change, pollution, hunger and diseases. Science is arguably the greatest example of the metaphor “Standing on the shoulders of giants”.
It would be safe to assume that the scientists working on major problems are using state of the art tooling.
The answer is NO! The most widely used tool for research documentation are paper notebooks followed by text editors like Google Docs. And for communication? Yes, you guessed it right: emails and post-its. These tools are highly inefficient for collaboration.
Text editors don't encourage a documentation format, which makes it very difficult to understand another scientist's work. In fact, understanding one's own work from a few months ago can be tricky. Research in life sciences is technically demanding and it is quite cumbersome to discuss, troubleshoot and stay updated about your team's progress via emails.
This begs the next question - What are the consequences of using these inefficient tools?
So far we have established:
Then, what is happening? Are there no good tools for collaboration? The answer is a Yes & No. There are tools (Electronic Lab Notebooks and Laboratory Information Management Systems) and practices which set out to improve collaboration but they have ALL fallen short. Our research has shown that less than 10% of the users invest in R&D software. The reason for failing is simple - These tools were designed with a very limited understanding of the R&D workflow or the user.
For the last 7 years, we have been working closely with scientists globally to build the next GitHub/JIRA/Amazon for R&D.
Our plan is to:
Because no one likes it but everybody needs it. Our lives literally depend on it. Capturing ALL information is critical - data, metadata, dependencies, tacit and explicit knowledge.
Once we understand each other’s work, we need to make working together easier. Science is a domain where we move faster together.
The increase in collaborations and documentations will inevitably lead to an explosion in data. Manual analysis and navigation of all this data will be prohibitive, so we are developing an AI solution that can step in to make sense of all this information.
We will exponentially accelerate research by addressing all current problems in collaboration. Problems like hunger, cancer and aging, currently considered moonshots, will be solved.
Now, we have completed Step 1 and Step 2 by building out an extensive cloud-based R&D management platform. Feedback from early users is very encouraging and we are preparing to launch the platform to a wider audience. Step 3 is near completion too and more details about the functionality and launch dates will be shared in the future blogs. Completion of the first three steps and ensuring wide adoption should lay the foundation for successful completion of Step 4.
This blog is a part of a series where the Descign team discusses the problem they are trying to solve and their unique approach and insights. The goal of this blog is not to generate inbound leads but to engage with the scientific community, especially people interested in Bio-IT/Research Informatics/Research tooling. So please connect with us on Twitter & LinkedIn by clicking on the icons below or simply drop us a message on email@example.com. We have a very ambitious Mission of building the world's largest research ecosystem and we will be happy to receive all the help we can.
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