Before beginning a new research project, it is essential to define a data management plan that outlines practices used to collect, organize, store, and back up the generated data.
A data management plan or DMP is a formal document that explains how you will handle your data both during your researches and after the project is completed. It also outlines the rights and responsibilities of all project participants as to their roles in the management and retention of research data generated during the project.
A “well conceived” template provides a reliable way to begin each new project and ensure that it starts with the right collection of settings or features.
Find after a checklist containing the main questions that researchers may want to cover to ensure an efficient data management.
1/ Project and data summary
It is really essential to have a clear definition for the project.
What is the purpose of the project and its relation to data collection/generation? What types or formats of data are deposited? (numerical data, text sequences, processed or generated data..) To whom data might be useful? Are you re-using data that someone else produced? What is the origin of the data? What is the expected size of the data? How long is the data collected and how often will it change? Who will be responsible for managing the data? Who will ensure that the data management plan will be carried out?
2/ Data organization
Good research data organization is the key leading to the integration of subsequent data for lead discovery or repositioning investigations. Here are some rules to consider in data organization
Make research data findable
Are the data produced and/or used in the project discoverable with metadata, identifiable and locatable? Which methodology and standards will be applied? What metadata will be created? Will search keywords or key-data be provided that optimize possibilities for re-use? What tools or software will be required to read or view the data?
Choose the way to share data
How taking into account the need to share and protect scientific data? Which data produced and/or used in the project will be available? To whom? If certain datasets cannot be shared or need to be shared under restrictions, how will access be provided? How will you implement permissions and restrictions? Who will have the right to manage access? Who will held intellectual property rights for the data and other information created by the project?
Make research data interoperable and reusable
Will the data produced by and/or used in the project be useable by third parties? Will the data allow exchanges and re-use between partners? Will data be exportable or compliant with available software applications or recombinant with different datasets from different origins? Will data be analyzable and how?
3/ Data storage and preservation
The main challenge remains to store and back-up the huge amount of data generated and collected.
How data will be curated or preserved in long term? Where (physically) will you store the data during the project’s life time? Will you manage your own storage and backup? How regularly will back-ups be made? Who will be responsible for backup? What provisions will be in place for data security? (data recovery procedures, secure storage) How data will be transferred or transmitted if this is required?
As a conclusion, good data management is essential to ensure that data can be preserved and remain accessible in the long-term, so it can be re-used and understood by future partners. A data management plan is an easy-to-follow road map that guides researchers and explains how data are treated throughout the life of the project and after the project is completed. Drugdesigntech helps researchers to establish a data management plan and offers services to fully customize data storage, knowledge management, access and integration to fit organization’s needs.