How can you maintain your R&D literature review as new research emerges?

Results are the most important thing in R&D work. Much will depend on what work needs to be done and what we want to achieve. In scientific work, the most important will be current scientific articles and long-term awareness building based on work experience, reports and contacts. When working on the product, it will be important to build situational awareness of the available technical level and competition. The search itself can be accelerated by defining keywords and then deepening your knowledge in the found areas of interest. We cannot forget that R&D work is creative work. Even the best literature will not create a new solution and innovation. Too much information can be harmful, and quick verification is more important.

Credible, authoritative, and peer reviewed will not always result in unbiased and / or reproducible conclusions. Just because someone uses the scientific method to obtain results does not mean the results are correct.

When searching for literature and evaluating it, the most important thing is to achieve high self-awareness. Of course, we can limit ourselves to the scientific literature on high IF from the last 3 years. However, this approach is wrong. Most of the information obtained will be unsuitable for assimilation and application, and after 5 years most of it is no longer worth much. To properly use literature for R&D purposes, "digging" is necessary. Have you found an interesting technical solution? look up everything about her. Technology (patent applications, scientific articles, press annotations, even Wikipedia will be good), Creators (check who created the solution and what their sources and achievements are) and much more.

Creating a system to track the papers you've read is critical. Zotero and Mendeley are two go-to "itunes for pdfs" that many academics use. Both tools also have excellent citation support (bibtex and MS Word). When you add a paper, take the time to make sure the generated bibliographic entry is fully correct. You will thank yourself when you go to write a paper and don't have to do any work for citation management.

We live in times where access to information is very simple. Unfortunately, most of the easily available information is junk and our time is limited. When storing and collecting literature, it is necessary to properly categorize it and archive it in such a way that we can quickly return to the facts that interest us (e.g. relevant to the topic we are currently working on or the task we want to perform). The methods of creating such resources are different and will result from the organization of individual/team work and the personal dispositions of the creators. We should not forget that this is still just creating tools to perform the actual R&D task.

At this point, it may sound as if the scientists who have worked in our teams, within or outside the conversational distance, could produce their perfect views about their external research environment. Yet, most of them have made personal mistakes and attempted to camouflage their real bios with the unmasked unprofessional attitude towards their colleagues. The best research feedback is obtained during the conference presentations, while having that special senior insider with the imaginary spotlight has never produced any know-how in the long run.

Most scientists who have contributed volumes in their respective R&D fields have kept their best ideas to themselves. Their real scores can never be found in their CVs nor in their publications despite their clear research direction. Therefore, it is better to grow one's own ideas in private contemplation, then to rely on data science and R&D field analysis. No organisation will support this, because it cannot be integrated into their management framework. However, that has been and still is the only scientist's advantage over the global mediocrity.