Research Data Management (RDM) refers to the process of managing data used or created during or after a research project. This includes creating, sharing, storing, organizing and processing the data. The term usually heard in the context of academic research, since researchers need to submit an RDM plan when applying for funding — funders want to see that data is properly structured and stored according to open science provisions.
However, prope RDMis important for researchers in all fields, in both academia and industry. Making a good RDM plan increases efficiency because it makes you work in a more organized way and ensures continuity by preserving research data for future generations of scientists.
Many laboratories use paper lab notebooks to record their manuscripts and results. This poses risks of loss, damage and theft, and has no convenient sharing, searching and analyzing options. Therefore, many researchers are opting for digital RDM solutions. A digital RDM tool can prevent data loss and human error, ensure research continuity and collaboration, and increase efficiency by helping you work in a more organized way.
There are many options offered to scientists, and choosing one can be hard. In this article, we will present 4 questions that you should ask in order to achieve ideal research data management.
If you’re considering switching to a digital RDM solution, one of your main concerns should be security. Ask the following questions:
Consider how you will share data, from manuscripts to experiment results, with teammates, peers and supervisors. Here’s what you should check for:
Switching to a digital RDM tool is a good opportunity to create a standardized organization method that will help you find things more easily, see the big picture and derive conclusions about your progress.
Ask yourself where your data comes from, and which other softwares do you need in order to process it. Make sure that your research management system of choice can connect using an API to your lab instruments in order to gather data, to your external analysis software if you use one, and to any external databases and catalogs that you need.
Some research programs include built-in data processing and analysis. Check for this feature — if you can perform analysis directly from within your research software and keep it tied together with the rest of your research, you can prevent information loss caused by integrating between different systems, and avoid paying different vendors.
Labguru is a cloud-based digital research software that helps you organize, process, protect and analyse complex research data. The software includes electronic lab notebook, lab storage. equipment and inventory management, as well as informatics and analysis. Using Labguru, you can make sure all your data is centralized in one place and FAIR (findeable, accessible, interoperable and reusable). To learn more about how Labguru can help you with research data management, click here: