How to Manage Data to Manage Your Scientific Career?

 

Data Management in the Scientific Research

 

Data in every field of career has become the real asset for the professionals. In case of scientific research, data is itself God, and everything depends in this career majorly on real, prolific data. When a researcher works on some experiments, which may take an hour, a day or a month, the success comes when he or she gets productive positive data. Though, in the fundamental scientific research, chance of failure is too high compared to other kind of research. This is quite normal, because a true scientific information which has been generated or revealed through systematic experiment is generally universal and you know, universal stuff is not produced in bunch. Therefore, generating quality data in fundamental science is a laborious job and it is mostly tedious to store it in a proper way. Most of the time a new researcher or a PhD student of science does not know how to generate and harvest the data with proper management that saves time and how to represent the acquired data in a meaningful way with best interpretation which leads them to spend more time in a single experiment. Sometimes we see, high quality data suffers to be published in good journals just due to lack of proper interpretation and representation. These two things are keys for a researcher to become productive in the scientific career. Here some skills are pointed out which may help in data management.

1. Collect the data in multiple sets

Most of the young researchers conduct one experiment in a single set to avoid labor, and if that experiment fails by chance due to some technical faults, then he or she has to set it again. Thus both the time and energy have been spent with no productivity which leads to mental exhaustion of the researcher. To avoid this, the simplest way is to set the experiment in multiple sets, at least in two sets if the experimental set is too big to handle. That provides more chances to conduct it successfully and if both experiments become successful then number of data will be increased for each point which will make the result statistically more valid and there will be no need to repeat the same experiment later.  

2. Always collect the data in a fresh mind

It is not a good idea to collect the experimental data with an exhausted mind or in a bad mood. If someone will do that, there will be a huge chance to make an error in data collection, even after conducting a successful experiment. Therefore, it is always brilliant to collect data when your mind is calm and fresh.

3. Take pictures of the experimental set-up, if it is big (or even it is not big)

Most of the people have a mobile phone with in-built camera now a day and PhD students or researchers are expected to have. The best way to keep a history of the experiment is to take snapshots of experimental set-up and file those digitally.  This will help the researcher to remember the scene and exact day on which he or she did the experiment and this visualization may help to modify the specific part of the experiment in future. Sometimes, in technical reports or paper the presentation of experimental set-up is mandatory, this habit will also help in such case.

4. Always keep multiple copies of the data

Whatever data is being collected should be stored in multiple copies and all these copies of data sheets should be kept in different places digitally-which may be office desktop, personal laptop, mail attachment (best option), cloud, hard-drive, pen-drive, mobile phone’s store etc.  These data should be kept with proper file names in an organized way.

5. Data interpretation decides the success of your work

When collected data are used to prove a pre-decided hypothesis in science (what is usual in most of the cases), then biasness comes to destroy the researcher’s confidence. Therefore, analysis and interpretation of data should be done with an unbiased open mind. In many cases data shows whether the hypothesis is valid or not, and sometimes data corrects the hypothesis. In case of large data set, numerous information can be revealed which can be interpreted in various ways. One has to decide before interpretation, that what the exact focus of the experiment is, what is the aim of the data and how it can help to prove the hypothesis. It is always necessary to check whether the analysis of the data is statistically valid or not. If it is not valid the interpretation cannot be correct at all.  Therefore, valid and best interpretation of the data results in good publication and earn reputation.

6. Nice data has to be presented in nicer way

Sometimes very good and validated data are rejected by the reviewers because of lack of proper representation. It is always better to present one’s data in an attractive way. Smart and handsome graphs, plots and diagrams can make one ordinary data attractive and a nice data nicer. A graphical or schematic representation or visualization makes one convinced with a theory. Now a days, several softwares has been evolved and marketed to build gorgeous and beautiful visualization of complex data. To use that there is no need to become a data scientist or a professional data analyst or a coding master. One need to learn some basic computational skills and knowledge of statistics to use the automated algorithms for better data analysis and visualization. Sigma Plot, Prism, MetaboAnalyst, CytoScapes, Biorender these are very popular tools for general data and biological data. Apart from those, numbers of online tools are available for data analysis and representation, even skill in Microsoft Excel also makes one a good data presenter.

All the above points are some general points to practice to make one’s research hassle free, which I have learnt with my experiences. I hope these help the young enthusiasts who will be generating numerous high quality scientific data to capture the better future.