There has been a growing concern among intellectuals in many scientific fields, from academic researchers to pharmaceutical scientists, about the lack of practical application of their published test results towards solving real-world problems. Although they are given enough funding to operate laboratories with well-calibrated analytical instruments and high-tech equipment, these scholars still struggle to produce valid data from their in-house projects.
This crisis has severe consequences for scientists who follow the Scientific Method, which is still the foundation of research and development efforts. They begin by forming a theory that could be tested under specific circumstances, as in the variables that will be altered to see if the data changes. One case study from Bayer Healthcare involved a thorough review of 67 famous projects completed by the scientific community. To their dismay, only around 25% of the studies could be repeated in a different environment outside the lab facility.
Identifying the Problem in Data-Driven Theories for Publications
In November, another investigation of cognitive science news outlets proved that roughly 50% of psychology papers stated a hypothesis that can be replicated by someone who did not participate in the original studies. There were many flaws in testing scientific procedures, possibly due to insufficient sampling of data (a narrow demographic population) and confirmation bias (seeking out information to validate one’s assumptions).
Similar findings were reported in other fields including medicine, healthcare, life sciences, and economics. Scientists are now losing credibility in the public’s eye because their results are full of contradictions that can be easily countered. So what is the root cause of these incidents? There are several factors that contribute to how research is conducted in accordance with Big Data. Scientists have cut corners by accepting data-driven hypotheses, leading to invalid statistical analyses of natural phenomenon.
How Data Catalogs Improve the Management of Scientific Entries
One huge problem the science community has is keeping empirical data confined in the facility. The lack of sharing data for broad interpretation by analysts is restricting who gets to benefit from new scientific discoveries. Big data’s potential is not being fully incorporated into many fields of science. Academic researchers are often reluctant to present their findings, worried that someone else will take credit for their work. It really comes down to the scarce resources and low employment prospects that drive scientists away from the innovation brought upon by Big Data.
A good way for scientists to organize their findings is through using a data catalog, a service that stores required data sources on software like SQL, so users are granted access to the data from any location recognized by the system. Data scientists often rely on this metadata for their base tables and indexes. Reporting tools help address data compliance regulations and enable cloud apps to compartmentalize data by sorting APIs from views and stored procedures.
Big Data’s Effect on Data Collection During the Experimental Phase
In a traditional experiment, the scientist proposes a hypothesis with assistance from a statistician, who knows about proper data collection methods. Instead of devising a step-by-step procedure, scientists now have access to technology that obtains the data for them, at a rate of 2.5 exabytes per day. There’s no more trial and error in the testing phase since statisticians don’t need to compute distribution probabilities anymore. But it does conflate with the lengthy process of performing an experiment.
In fact, inferences made about the data before creating the statement of purpose would cause the latter to be invalid. It takes multiple runs to verify data trends or else external factors will affect the results. Another issue lies in the lack of focus on data gathering techniques. The lack of data scientists has limited the flexibility of researchers in moving beyond a single subject of study. In other words, researchers need improved tools for storing their data, such as software upgrades in LIMS.
