Biological data management is a critical component of modern biological research. It involves the acquisition, modeling, storage, integration, analysis, and interpretation of various types of data, including analog signals, digital images, sequences, spreadsheets, taxonomies, structured records, and unstructured text data. This type of data management is essential for researchers to gain a “low-level view” of their data parameters and results. It also allows for the expansion of the number of different types of experiments that can be performed.
Collaborative networks present unique challenges when it comes to managing experimental data. This is due to the wide range of methods used in biological research, such as microscopy, enzymology, biophysical techniques and whole cell experiments. A successful data management system must meet the “data entry, information output” paradigm. To achieve this goal, it is important to review existing literature on the subject and gain experience in developing data management systems such as UniTrack and LabDB. More detailed analyses often require significant data processing and accuracy checks.
This is why “unified data management systems” have been developed with panels that allow for easy access to data (Figs.). However, there are still social engineering threads that need to be addressed so that scientists understand why they are being asked to preserve their metadata. Automated importation of protein production and crystallization data from local LIMS or equipment databases is also possible. Data management is essential for efficient biological research. As centers continue to produce more experimental samples quickly, the process of entering results into databases becomes a speed-limiting step.
By understanding the benefits of data management in biology, researchers can ensure that their experiments are conducted in an efficient and accurate manner.
