it is challenging - looking for and creating your own systematic review is probably one of the better ways to be sure you know what literature exists and then considering that literature in a more manageable way

Thank you, Philip Mccallion very much,

That is a good way but with hundreds of items and even thousands that becomes hard to manage manually. As far as IT tools usage is concerned, for example some softwares for textual analysis or artificial intelligence (AI) applications, I am not sure how well it can analyse the contents and believe the human is a human to the work. How do you percieve the use of AI or other IT tools, softwares? How accurate is it and reliable in the real sense compared to when reading articles by our own?

Thanks

You can prepare and excel file including the most relevant information such as the size, concentration, dose, effect or result etc. You can update the information once every month and add latest information. This way you can compile all important information.

Note that for every research work you must be focus, original and not only writing what others have written but also contributing your own understanding to the research topic. You must not be 'jack of all trade', but only master the areas that interest you and you can defend and add to your knowledge. Research is all about your own knowledge so you can criticise what someone wrote with your own originality peculiar to your own country of research instead of generalisation in your scope/problem/objective of study.

I understand what you mean, @Jasmin. It happens to me because I do transdisciplinary research.

There are two type of tools that help a lot.

One of them is "find trusted sources, validate them and follow them". This is true not only for researchers but also for publications. You can suscribe to feed lists to receive automatic updates. Also, you can belong to communities of practice or interest on the web and participate in them by asking for (and providing) resources and opinions. You must find and choose communities that are relevant and respected in the field or topics that you are interested in.

The second tool is iteration of small cycles of research. When one knows that our knowledge is limited and partial, it pays to "divide" the research work in short cycles. At the end of each cycle you will have acquired knowledge from your own work and from others. And you will have the opportunity to reframe or reorient your research in the next cycle. This means that you can reduce the "price" that you pay for "not knowing" because it gives you an increased opportunity to adapt to change- either in the knowledge base or in the context of your research. This is especially important when you are working under conditions that you can not control fully, such as field work, "in vivo" health research and research on complex problems in "real" life situations.

For example, "grounded theory" allows you to develop rigorous scientific theories using iterations. Agile methods allow for iterations in development.

You can manage this this by looking at the relevance of the literature available and the time frame of the literature. If the year has no direct relevance to the research direction then you might leave out the literature.

Sometimes you can´t do that @Francis, because you may need to read older works in order to fully understand current ones.

The amount of reading that you have to do depends on the agreement about accepted knowledge in the field. Social sciences topics require at least 10 times more reading than natural sciences topics.

The degree of maturity of scientific field is also a relevant factor. In new ones, important concepts and models are still being developed and you have to keep close watch because they may undergo huge changes in the lifespan of even small R&D projects.

I presume that "keeping up" in some disciplines is easier than in others. For the sciences, my impression as an outsider looking in, is that article abstracts usually would give people enough of an idea of current developments and results, and give a broad overview of the state of the field, with the abstracts also helping them to look more closely at a manageable select set. In the humanities, it's not so easy since the value of an a work often lies in the fine details and dialectical manoeuvres, and humanists tend not to have precise methodologies in common or work on shared research problems (and even the word 'research' often seems out of place for what they do).

When I was a grad student in philosophy in the late 1970s (pre-internet days), I was able to keep up, in a way satisfactory to my needs (both general and specific), by spending an afternoon or two a week perusing periodicals in the library: reading a few articles in full, but mostly skimming, or just looking at introductions and conclusions. Those days are gone. Narrow specialization is gaining the upper hand, and even there it's harder to keep up because of the sheer volume. I know that digital humanities and data mining is allowing scholars in some humanities disciplines (e.g. English literature and literary history) to study phenomena and pursue projects at far greater levels of generality than would once have been thought possible, and "keeping up" has become mostly access to and management of data. However, except for some sorts of digitizable historical studies (which per se wouldn't really be philosophy), I can't yet see how philosophy will find a way ahead.