Scientific Information - Science topic

The progressive digitization of data and archived documents, digitization of data transfer processes, Internetization of communications, economic processes but also of research and analytical processes is becoming a typical feature of today's developing developed economies. Currently, another technological revolution is taking place, described as the fourth and in some aspects it is already the fifth technological revolution. Particularly rapidly developing and finding more and more applications are technologies categorized as Industry 4.0/5.0. These technologies, which support research and analytical processes carried out in various institutions and business entities, include Big Data Analytics and artificial intelligence. The computational capabilities of microprocessors, which are becoming more and more perfect and processing data faster and faster, are successively increasing. The processing of ever-larger sets of data and information is growing. Databases of data and information extracted from the Internet and processed in the course of conducting specific research and analysis processes are being created. In connection with this, the possibilities for the application of Big Data Analytics supported by artificial intelligence technology in terms of improving research techniques, in terms of increasing the efficiency of the research and analytical processes used so far, in terms of improving the scientific research being conducted, are also growing rapidly.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think on this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

I have described the key issues of opportunities and threats to the development of artificial intelligence technologies in my article below:

I invite you to collaborate with me on scientific research,

Best wishes,

Dariusz Prokopowicz

In the age of digitalisation, where science generates unprecedented amounts of data, big data analytics and data science, supported by generative artificial intelligence, are becoming key tools to support the research process. They enable researchers not only to process and analyse this data effectively, but also to discover hidden patterns and trends that would be inaccessible using traditional methods. Thanks to machine learning algorithms, researchers can identify complex relationships, formulate new hypotheses and generate innovative theories, which significantly accelerates scientific progress. Generative artificial intelligence, which is capable of creating new content based on existing data, opens up new possibilities for automating analysis, generating hypotheses and supporting the publication of research results, allowing scientists to focus on interpreting and formulating conclusions. However, to fully utilise the potential of these technologies, it is necessary to continuously develop methodologies and algorithms, as well as to consider the ethical aspects of their application, which emphasises the key role of scientific research in this field.

The research and observations that I conduct show that artificial intelligence technology has been developing rapidly in recent years and is finding new applications, with new opportunities and threats emerging. I have described the main determinants, including the potential opportunities and threats to the development of artificial intelligence technology, in my article below:

I have described the issue of Industry 4.0/5.0 technology applications, including Big Data Analytics, with the aim of improving data and information transfer and processing systems, in the following articles:

And what is your opinion on this topic?

What is your opinion on this matter?

Please answer,

I invite everyone to the discussion,

Thank you very much,

Best wishes,

I invite you to scientific cooperation,

Dariusz Prokopowicz

'Research is a fruitful journey that brings me closer to the world and its people, gives me another layer to believe in a creator, offers me material wealth and recognition, swallows my loneliness, fears and inadequacies; a soothing activity that makes life extremely meaningful to me.'

-Dickson Adom

Recently, rapid development of ICT and Industry 4.0/5.0 technologies is taking place, including Big Data, Internet of Things, cloud computing, digital twins, multi-criteria simulation models, machine learning, deep learning and generative artificial intelligence, among others. Developments in generative artificial intelligence technology are being made through the use of artificial neural networks, among others. New applications of generative artificial intelligence are determined by the previously carried out process of GAI system training, i.e. teaching the implementation of specific skills, performing complex tasks, performing new functions, solving specific problems intelligently using deep learning technology. Increasingly, generative artificial intelligence technology is being trained to intelligently perform complex research and analysis processes. Among other things, this kind of application of generative artificial intelligence is the implementation of this technology for business analytics carried out using large sets of data and information, i.e. analytics carried out on computerized business intelligence and Big Data Analytics platforms. This type of analytics is being applied in various fields of knowledge, various sectors of the economy, various companies, enterprises, financial and public institutions. This type of analytics is also increasingly used in improving research processes and increasing the efficiency of complex analytical processes carried out as part of ongoing research in various scientific disciplines. Since OpenAI's ChatGPT chatbot was made available on the Internet, i.e. since November 2022, more similar intelligent chatbots created by other leading technology companies have been successively appearing. The intelligent chatbots made available on the Internet are used, among other things, in the development of the results of scientific research conducted, in the execution of certain stages of analytical processes, in the processing of results obtained from scientific research conducted, etc. The increase in the application of intelligent chatbots in research and analytical processes is due to the simplicity of operation of these chatbots, their availability on the Internet in the formula of open access, the ability of these tools to implement complex research processes, multi-criteria analysis, intelligent problem solving. On the other hand, the possibilities of applying the aforementioned chatbots in the processes of conducted scientific research are still severely limited due to the many imperfections of the databases on which certain generative artificial intelligence systems were trained. It still happens that the databases of data and information on which the said GAI systems were trained contain data and information in many respects outdated, incomplete, in the course of the "work" of these tools certain data and information can be "creatively" combined so that in the results of the work of a certain intelligent chatbot there are often "fictitious facts", ie. generated new and factually inconsistent content, factual errors, misrepresentations, falsehoods, which can be presented and described within the phraseologically, syntactically, stylistically correct essays, papers, articles, etc., written by generative artificial intelligence. Besides, the textual and other studies created by these tools often do not show all the data sources, all the source publications, all the materials that the chatbot used in drawing certain data and information while creating the commissioned human textual, graphic work, etc. Besides, even if the sources of data and information are partially shown, they are often shown in an incomplete way, inconsistent with the current standards for showing and compiling source and bibliographic footnotes. Perhaps, in the future, the aforementioned, used currently made available on the Internet intelligent chatbots will be sufficiently improved, corrected, supplemented so that they can be used by researchers and scientists in specific research, analytical processes within the framework of ongoing scientific research to a fuller extent and without the currently existing risks. Therefore, the development of intelligent chatbots available on the Internet based on generative artificial intelligence currently both negatively and positively can affect the development of science, the development of scientific research, the analysis of data from conducted research, the description of results obtained from conducted research, the writing and publishing of scientific texts, etc. Whether serious risks are generated or rather positive aspects prevail with the application of currently available intelligent chatbots on the Internet in certain aspects of the research and analysis processes carried out as part of the scientific research conducted depends on a number of factors. On the one hand, it depends on whether the technology company developing the said intelligent chatbots keeps improving them, enhancing them and expanding them with new functions and skills. On the other hand, it also depends on whether such cultivated specific research and analytical tools are used prudently by researchers and scientists with knowledge of the drawbacks and limitations associated with the use of these tools.

I described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

How different is International Scientific Indexing (ISI) from Institute of Scientific Information (ISI)?

Authors/ researchers get confused on this two entities that index scientific articles.

Scientific cooperation can develop on the scale of specific scientific and research institutions, scientific and teaching institutions, research and development centers, research and implementation laboratories, educational institutions, research centers and laboratories of companies and enterprises, government agencies dealing with science and scientific research, local government institutions and non-governmental organizations whose activities are based on the results of scientific research, and so on. Scientific cooperation can develop on a national and/or international scale. Scientific cooperation can develop in one or more scientific disciplines, i.e., interdisciplinary. Scientific cooperation may develop, among other things, in terms of conducting exchanges of experience in scientific research, joint team research, joint team publication of scientific research results, etc. Online indexing databases of scientific institutions, indexing of scientific publications, indexing of scientific persons, researchers and scientists, etc. can be helpful in establishing scientific cooperation. Besides, Internet portals that enable remote through the Internet to exchange scientific experiences, discuss scientific topics, etc. can also be helpful in developing scientific cooperation. An example of this kind of scientific portal is this Research Gate portal, where we can hold discussions on scientific topics, ask questions and answer questions in the discussion forum. In this way, new scientific cooperation can also be initiated, which I hereby encourage.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion about it?

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Can the conduct of analysis and scientific research be significantly improved, increase efficiency, significantly shorten the execution of the process of research work through the use of Big Data Analytics and artificial intelligence applied to the processing of large data sets and realized by the most powerful quantum computers?

What are the analytical capabilities of processing large data sets extracted from the Internet and realized by the most powerful quantum computers, which also apply Industry 4.0/5.0 technologies, including generative artificial intelligence and Big Data Analytics technologies?

Can the scale of data processing carried out by the most powerful quantum computers be comparable to the data processing that is carried out in the billions of neurons of the human brain?

In recent years, the digitization of data and archived documents, digitization of data transfer processes, etc., has been progressing rapidly.

The progressive digitization of data and archived documents, digitization of data transfer processes, Internetization of communications, economic processes but also of research and analytical processes is becoming a typical feature of today's developing developed economies. Accordingly, developed economies in which information and computer technologies are developing rapidly and finding numerous applications in various economic sectors are called information economies. The societies operating in these economies are referred to as information societies. Increasingly, in discussions of this issue, there is a statement that another technological revolution is currently taking place, described as the fourth and in some aspects it is already the fifth technological revolution. Particularly rapidly developing and finding more and more applications are technologies classified as Industry 4.0/5.0. These technologies, which support research and analytical processes carried out in various institutions and business entities, include Big Data Analytics and artificial intelligence, including generative artificial intelligence with artificial neural network technology also applied and subjected to deep learning processes. As a result, the computational capabilities of microprocessors, which are becoming more and more perfect and processing data faster and faster, are gradually increasing. There is a rapid increase in the processing of ever larger sets of data and information. The number of companies, enterprises, public, financial and scientific institutions that create large data sets, massive databases of data and information generated in the course of a specific entity's activities and obtained from the Internet and processed in the course of conducting specific research and analytical processes is growing. In view of the above, the opportunities for the application of Big Data Analytics backed by artificial intelligence technology in terms of improving research techniques, in terms of increasing the efficiency of the research and analytical processes used so far, in terms of improving the scientific research conducted, are also growing rapidly. By using the combined technologies of Big Data Analytics, other technologies of Industry 4.0/5.0, including artificial intelligence and quantum computers in the processing of large data sets, the analytical capabilities of data processing and thus also conducting analysis and scientific research can be significantly increased.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion about it?

What do you think about this topic?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

The materials science dissertation did not conduct experimental studies. What is the name and how to search on the Internet for this type of dissertation, in which, for example, they simply applied some method or developed some approach, but did not directly experiment, for example, to change the structure, did not create any materials, substances and etc. among the sciences where it is usually required to do this (materials science, chemistry)? Give examples of such dissertations. Thank you!

What is the difference between a literary review and a regular report that is made for a speech, which is usually given at school? Give comparisons between the report and the literary review, where the text is more like an abstract, and where is it like a literary review? How to distinguish a report from a review? How should the text in the report and review look like? Thank you!

What is the difference between a literary review and an abstract? Can you give examples of comparing an abstract and a literature review? Thank you!

Key Highlights Include:

Scientific community is now facing a new threat of unethical publications in predatory journals.

How someone can identify the predatory journal?

There’s has always been confusion between SCI, SCIE and SCImago journals. Here’s how you differentiate them:

Why are there still pseudo-scientific conspiracy theories that undermine obviously confirmed facts and scientific knowledge in the present era of publicly available large amounts of scientific knowledge?

Why in the present age of computerization, the digitization of knowledge resources and the huge scientific knowledge available on the Internet are still created pseudoscientific conspiracy theories, sometimes absurd claims of the type that the Earth is flat, that evolution is a fiction, that some people are aliens from outside the Earth etc.? For what reason and for what purpose are these types of irrational pseudoscience theories created?

Please reply

I invite you to the discussion

Thank you very much

Best wishes

From the history of science development, many examples of scientific discoveries can be given, which gave humanity significant for the development of civilization and prosperity, various inventions, the effects of scientific discoveries in the form of new materials, technologies, etc. that have found practical application in various areas of production of economic goods that have become in common use by people, they have increased prosperity and made life easier.

However, the answer to this question in the context of the future of the next several dozen or more years, ie in the perspective of the development of science in the 21st century, is already determined by new factors. To these factors should be added global problems and their negative effects such as progressive global warming, increase in greenhouse gas emissions, increased risk of various climatic cataclysms, increased environmental pollution, declining resources of raw materials, arable land and clean water, the need to develop renewable sources of energy, electromobility, recycling, etc., ie the need to develop business processes according to the model of sustainable pro-ecological development, according to the green economy concept.

Whether thanks to the development of science people become happy, it depends mainly on the results of scientific research, scientific discoveries, new concepts created, innovative solutions in specific fields of knowledge regarding their applications in various areas of human life, but also for life on Earth and the natural environment Earth.

Considering the impact of the development of civilization on the natural environment of the planet Earth, progressive global warming, increasing climatic disasters, also the issues of pro-ecological applications of scientific discoveries are important, because in the 21st century it will be possible to save the planet Earth through a climate catastrophe. If this plan can be achieved in the 21st century, then life on Earth will be saved, and thus people in later generations will be happier.

In connection with the above, I am asking you the following question:

Will the development of science improve the living conditions of people in the future?

Please reply

Best wishes

ISI mean or stand for what?

As to add an imact factor or to calculate the imact factor of the Journal different agencies are doing it.

ISI. Internation scientifc indexing.

ISI. International Scientifc Indexing.

ISI. Institue of Scientific Information which is now with clarivatic analytic.

Dear colleagues,

I need to find list of an aggregative impact factor of scientific field. For the standard impact factor of separate journals it is very easy, there are a lot of good databases. However I can not found list of an aggregative impact factor of scientific field. Can anyone help me to solve this problem?

A "googwik scientist" is a special case of an amateur of science who, as a source, uses almost exclusively Google and Wikipedia.<br /><br />

Since choosing a reference from Google, a user is hers/his own judge, and since Wikipedia is unreliable, a product of a consensus of "all" and not only of the experts, such research promotes multiplication of "referenced" mistakes.

Is “Googwik scientist” an immoral person?

Sometimes even the references support nonsense. Scientists also use Google and Wikipedia, but since they already know well the background of the studied problem, they read more critically the variety of sources available on Internet and can in fact profit from them. People who are not experts, on the contrary, are bound to accept unscientific or unfounded information as scientific and be victims of the illusion that they are using reliable sources, and falsely believe to have acquired knowledge.<br /><br />

Is "googwik science" good as a means to increase general knowledge of all and contribute to the advancement of science in general, since it offers, as knowledge, both - correct information, that may be falsely interpreted, or even false information?

In short my question is:

When trying to increase our knowledge (and also when discussing with other people about some particular subject) does an approach of avoiding to consult the primary sources (original works) and relaying only on the secondary sources (various review articles offered on Google or on Wikipedia sites) carry a risk of misinformation and false illusions of ‘knowledge’ - and leading to what I call “GoogWik Science”?

Does science profit from "googwik science" or does "googwik science" in fact damages science by introducing science for all - which in reality permits false interpretation and thereby neither helps to increase knowledge nor to promote science?

Finally: Is “Googwik scientist” an immoral person?

Recently, there is an increasing discussion concerning the price for scientific literature. At university, it is no problem to get adequate information for free but in non-university villages, it might be impossible to get the current papers of scientific work for a adequate price. What is your experience?

What is the mechanism used by the scientific websites to calculate the citations for the papers of each author? are they the same depending on the same database across all the major websites like "RESEARCHERID, GOOGLE SCHOLAR, RESEARCH GATE"? and if so, why sometimes you have deferent results in each?.

There are information that we know that we don't know, and there are info.that we don't know we don't know. So, the second type is what bothers me! Everyday I see/read info.That I didn't know I didn't know, and this info.is essential to some degree..So I was wondering what scientific journals are dedicated to the scientific method, scientific thought, and metacognition so one can approve his technique and don't miss important info.

In some cases the scholar need to publish his paper in short time, for need as thesis submission term. So I need to send my paper to good and fast impact factor journal.

How to prepare a good high quality research proposal for a grant application?

C and E news covered an article on such a tool

Skills investigated with the use and management of scientific information in the process of doctoral training. Fundamentally advanced skills in critical analysis of information, use of collaborative tools for researchers, quality of information, publication of research results

Most of us assume, what we experience or thought as facts of nature. Let me explain this based on my experience. I grow up in a remote village in India. One of the most fascinating thing I saw around the age of 10 was a Magnet, which was taken out of a dynamo of a bicycle and my fiend showed me in the school. I wanted one and nagged my parents, and finally they barrowed one form a bicycle repair shop for few days for me to play with it. One of the fascinating thing was, how small gains of iron stick to it.

After few years, in the science class I learned about the Universal gravity. If was unbelievable and fascinating again to learn that I am a miniscule gain of iron stuck to the huge magnet – The Earth. Up until that time, I was of the impression that the Earth is round disk and one could fall-off, if he runs to the edge of the Earth.

My tacit assumptions were shaped by my experiences and my friends talking about the edge of the disk shaped earth and falling off the Edge etc. If I were not a boy and not thought from the science book, I might have refused to accept that the Earth is a huge Magnet. Even it contradicted my perception of reality, I was forced to accept it, because I had no choice (but few adults supported my view, I might had refuse to accept it).

Similar kind of flawed tacit assumption was at the root of geocentric paradigm, which lead to one of the greatest scientific crisis known to mankind. The following web-page illustrates how complex it is to expose the insidious tacit assumption: http://www.real-software-components.com/forum_blogs/BriefSummaryOfTruths.html#Chronology

How is it possible to expose such insidious tacit assumption, if it exists in any modern scientific discipline in the 21st century? The scientist and researchers feel that they know everything and it is impossible to have such insidious flaw in their knowledge. They refuse to accept anything that doesn’t fit their perception of reality. Every other researcher and expert are in support of their perception of reality, so they have no reason to accept the error in tacit assumption: https://www.researchgate.net/publication/295525659_Tacit_assumptions_or_Implicit_assumptions_if_they_are_flawed_leads_to_paradoxical_paradigm_and_scientific_crisis

Best Regards,

Raju Chiluvuri

I heard (Süddeutsche Zeitung, February 6/7th 2016), that people like to sit in a "reverberation chamber", they are consuming only informations which confirm their opinions, because it feels good to be liked or confirmed.

The social networks tend to strenghten this behavior, especially with the help of new filter systems, which gain new connections by evaluating the personal data of the user.

Is this contradictory to objective thinking and having an open mind?

What about the filtering systems of RG?

Can we claim any conclusive findings solely on the basis of a single study that obtained a p-value less than 0.05? What can we really claim? Please state your views. Thank you.

Clearly, neuroscience is in trouble given some of the flawed experiments being published in high-profile journals such as Science and Nature (e.g. see recent scandal of Shigeaki Kato of the University of Tokyo).

In the old days (the 60s and early 70s) an investigator like Peter Schiller of MIT, for example, could plunge his recording electrode into any part of the brain and come up with something that was novel and even publishable. Sharing his philosophy of science with me one day, Peter Schiller declared, “I refuse to read the literature for it may bias the creative process. This is in keeping with the way David Hubel (a Nobel Prizer) does things.”

Today, a new student with a freshly minted PhD often has come up with a finding so that he can go on to do a post-doctorate in his field of study which will yield a professorship down the road. If this student adopts the mentality ‘I refuse to read the literature for it may bias my creativity’, we all know where such laziness will lead: to the creation of clever (as a fox) investigators who can pull the wool over the student’s eyes with the latest bewilderment.

The field is now populated with PhDs who do not have the skill-set to evaluate a scientific work that goes beyond their PhD studies. This propensity is further reinforced by the mantra ‘publish or perish’ where publish means putting out a novel piece of experimental work, which often requires more doing than thinking. In the USA this is done as fast as possible so one gets published before the thinking process by peers can even commence. The good old days of assuming that an article in Science or Nature can be cited automatically (with no critical evaluation) will end soon given the new climate (see: British House of Commons Science and Technology Committee, Peer Review in Scientific Publications, July, 2011).

A publication in Science or Nature today should be viewed as the beginning (not the end) of the peer-review process. The sooner that students with freshly minted PhDs realize this, the sooner will the field of Neuroscience start cleaning up the mess that it has produced for itself. And remember, we are responsible for having created investigators who deceive us.

There is a widely accepted notion that some fields are more productive than others regarding the number of publications. For example, a bioinformatics lab may produce 10 to 20 papers a year and a developmental biology lab 2 to 4. This will depend on the experimental complexity of the research. Is there any rigorous study about this?

Polish researcher, Piotr Wołkowski discussed the book by Lee Smolin published in 1999. Five physics problems of that time were listed, including quantum gravitation. As usually happens with revolutions, scientific progress is completely unpredictable. This is also applicable today, when from 2003 we had no idea about dark matter and energy, and a number of more trivial problems like high-temperature superconductivity. Probably, as I say in paper published in Lodz University proceedings, we need new mathematics. More: it is already somewhere present and it would be enough to know - where.

I'm a novice researcher. While reading new papers, I face many terms, statistical approaches, tools, and techniques that I don't know about. I wonder how authors knew all of this! I want to know how I should deal with such situation with every single paper. Should I stop to study these terms, tools ...etc.

Please advise.

Take for example the deeper meaning of differentiation: the local existence of a linear mapping that locally linearizes the nonlinear function. This is apparent in functions of one or many variables and also in vector functions. Take a look at the dynamical systems: when things are too difficult we always study the linearized version by the relevant Jacobian. Look at general relativity: all tensors are differential operators of differential geometry, i.e. locally linear concepts. Continue on quantum field theory: everything is inside a proper linear space and linearization is everywhere.

Can anyone find even one branch of modern science that is not linear or at least linearizable? I doubt it. So, the question is:

After so many centuries of linear science are we still satisfied with this paradigm?

Albert Szent-Györgyi wrote: "Discovery consists of seeing what everybody has seen and thinking what nobody has thought." How might we increase the value of our scientific efforts? Is it about better questions, better data or what?

I have been invited to take part in a conference on the role of evidence in creating science education policy. I am thinking of broadening the debate about the role of evidence in developing policy and to what extent such evidence should be 'scientific'. I have a number of questions I am working with such as, what is evidence? What evidence counts? And to what extent should it be scientific?