Data - Science topic

I am trying to calculate an ECM with panel data and thereby I have the following problem. I run the ecm command from the ecm package an error occurs saying “non-numeric matrix extent “. I found out that the reason is the creation of the panel data. I tried two different approaches to fix the problem.

At first I created the panel dataset with the pdata.frame command from the plm package.

p.dfA <- pdata.frame(data, index = c("id", "t"))

where “index” indicates the individual and time indexes for the panel dataset. The command converts the id and t variables into factor variables which later leads to the error in the ecm command.

Secondly, I created the panel dataset with the panel_data command from the panelr package.

p.dfB <- panel_data(data, id = "id", wave = "t")

where “id” is the name of the column (unquoted) that identifies participants/entities. A new column will be created called id, overwriting any column that already has that name. “wave” is the name of the column (unquoted) that identifies waves or periods. A new column will be created called wave, overwriting any column that already has that name.

This panel_data command also converts the id variable into a factor variable. So, the same error occurs in the ecm command.

If I transfer the factor variables back into numeric variables, I lose the panel structure of the dataset.

Could someone please explain to me how to run an ECM with panel data in R?

Thank you very much in advance!

Hey everyone,

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Advanced analytical systems, including complex forecasting models that enable multi-criteria, highly sophisticated, big data and information processing-based forecasts of the development of multi-faceted climatic, natural, social, economic and other processes are increasingly based on new Industry 4.0/5.0 technologies, including Big Data Analytics and machine learning, deep learning and generative artificial intelligence. The use of generative artificial intelligence technologies enables the application of complex data processing algorithms according to precisely defined assumptions and human-defined factors. The use of computerized, integrated business intelligence information systems allows real-time analysis on the basis of continuously updated data provided and the generation of reports, reports, expert opinions in accordance with the defined formulas for such studies. The use of digital twin technology allows computers to build simulations of complex, multi-faceted, prognosticated processes in accordance with defined scenarios of the potential possibility of these processes occurring in the future. In this regard, it is also important to determine the probability of occurrence in the future of several different defined and characterized scenarios of developments, specific processes, phenomena, etc. In this regard, Business Intelligence analytics should also make it possible to precisely determine the level of probability of the occurrence of a certain phenomenon, the operation of a process, the appearance of described effects, including those classified as opportunities and threats to the future development of the situation. Besides, Business Intelligence analytics should enable precise quantitative estimation of the scale of influence of positive and negative effects of the operation of certain processes, as well as factors acting on these processes and determinants conditioning the realization of certain scenarios of situation development. Cloud computing makes it possible, on the one hand, to update the database with new data and information from various institutions, think tanks, research institutes, companies and enterprises operating within a selected sector or industry of the economy, and, on the other hand, to enable simultaneous use of a database updated in this way by many beneficiaries, many business entities and/or, for example, also by many Internet users in a situation where the said database would be made available on the Internet. In a situation where Internet of Things technology is applied, it would be possible to access the said database from the level of various types of devices equipped with Internet access. The application of Blockchain technology makes it possible to increase the scale of cybersecurity of the transfer of data sent to the database and Big Data information as part of the updating of the collected data and as part of the use of the analytical system thus built by external entities. The use of machine learning and/or deep learning technologies in conjunction with artificial neural networks makes it possible to train an AI-based system to perform multi-criteria analysis, build multi-criteria simulation models, etc. in the way a human would. In order for such complex analytical systems that process large amounts of data and information to work efficiently it is a good solution to use state-of-the-art super quantum computers characterized by high computing power to process huge amounts of data in a short time. A center for multi-criteria analysis of large data sets built in this way can occupy quite a large floor space equipped with many servers. Due to the necessary cooling and ventilation system and security considerations, this kind of server room can be built underground. while due to the large amounts of electricity absorbed by this kind of big data analytics center, it is a good solution to build a power plant nearby to supply power to the said data center. If this kind of data analytics center is to be described as sustainable, in line with the trends of sustainable development and green transformation of the economy, so the power plant powering the data analytics center should generate electricity from renewable energy sources, e.g. from photovoltaic panels, windmills and/or other renewable and emission-free energy sources of such a situation, i.e., when a data analytics center that processes multi-criteria Big Data and Big Data Analytics information is powered by renewable and emission-free energy sources then it can be described as sustainable, pro-climate, pro-environment, green, etc. Besides, when the Big Data Analytics analytics center is equipped with advanced generative artificial intelligence technology and is powered by renewable and emission-free energy sources then the AI technology used can also be described as sustainable, pro-climate, pro-environment, green, etc. On the other hand, the Big Data Analytics center can be used to conduct multi-criteria analysis and build multi-faceted simulations of complex climatic, natural, economic, social processes, etc. with the aim of, for example. to develop scenarios of future development of processes observed up to now, to create simulations of continuation in the future of diagnosed historical trends, to develop different variants of scenarios of situation development according to the occurrence of certain determinants, to determine the probability of occurrence of said determinants, to estimate the scale of influence of external factors, the scale of potential materialization of certain categories of risk, the possibility of the occurrence of certain opportunities and threats, estimation of the level of probability of materialization of the various variants of scenarios, in which the potential continuation of the diagnosed trends was characterized for the processes under study, including the processes of sustainable development, green transformation of the economy, implementation of sustainable development goals, etc. Accordingly, the data analytical center built in this way can, on the one hand, be described as sustainable, since it is powered by renewable and emission-free energy sources. In addition to this, the data analytical center can also be helpful in building simulations of complex multi-criteria processes, including the continuation of certain trends of determinants influencing the said processes and the factors co-creating them, which concern the potential development of sustainable processes, e.g. economic, i.e. concerning sustainable economic development. Therefore, the data analytical center built in this way can be helpful, for example, in developing a complex, multifactor simulation of the progressive global warming process in subsequent years, the occurrence in the future of the negative effects of the deepening scale of climate change, the negative impact of these processes on the economy, but also to forecast and develop simulations of the future process of carrying out a pro-environmental and pro-climate transformation of the classic growth, brown, linear economy of excess to a sustainable, green, zero-carbon zero-growth and closed-loop economy. So, the sustainable data analytical center built in this way will be able to be defined as sustainable due to the supply of renewable and zero-carbon energy sources, but will also be helpful in developing simulations of future processes of green transformation of the economy carried out according to certain assumptions, defined determinants, estimated probability of occurrence of certain impact factors and conditions, etc. orz estimating costs, gains and losses, opportunities and threats, identifying risk factors, particular categories of risks and estimating the feasibility of the defined scenarios of the green transformation of the economy planned to be implemented. In this way, a sustainable data analytical center can also be of great help in the smooth and rapid implementation of the green transformation of the economy.

Kluczowe kwestie dotyczące problematyki zielonej transformacji gospodarki opisałem w poniższym artykule:

IMPLEMENTATION OF THE PRINCIPLES OF SUSTAINABLE ECONOMY DEVELOPMENT AS A KEY ELEMENT OF THE PRO-ECOLOGICAL TRANSFORMATION OF THE ECONOMY TOWARDS GREEN ECONOMY AND CIRCULAR ECONOMY

Zastosowania technologii Big Data w analizie sentymentu, analityce biznesowej i zarządzaniu ryzykiem opisałem w artykule mego współautorstwa:

APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTELLIGENCE SOFTWARE IN INTEGRATED RISK MANAGEMENT IN ORGANIZATION

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

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

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

In the domain of clinical research, where the stakes are as high as the complexities of the data, a new statistical aid emerges: bayer: https://github.com/cccnrc/bayer

This R package is not just an advancement in analytics - it’s a revolution in how researchers can approach data, infer significance, and derive conclusions

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At its heart, bayer is about making Bayesian analysis robust yet accessible. Born from the powerful synergy with the wonderful brms::brm() function, it simplifies the complex, making the potent Bayesian methods a tool for every researcher’s arsenal.

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bayer offers a seamless experience, from model specification to result interpretation, ensuring that researchers can focus on the science, not the syntax.

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Understanding the impact of variables is no longer a trudge through tables. bayer brings you rich visualizations, like the one above, providing a clear and intuitive understanding of posterior distributions and trace plots.

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Clinical trials, especially in rare diseases, often grapple with small sample sizes. `Bayer` rises to the challenge, effectively leveraging prior knowledge to bring out the significance that other methods miss.

Prior Knowledge as a Pillar

Every study builds on the shoulders of giants. `Bayer` respects this, allowing the integration of existing expertise and findings to refine models and enhance the precision of predictions.

From Zero to Bayesian Hero

The bayer package ensures that installation and application are as straightforward as possible. With just a few lines of R code, you’re on your way from data to decision:

# Installation devtools::install_github(“cccnrc/bayer”)# Example Usage: Bayesian Logistic Regression library(bayer) model_logistic <- bayer_logistic( data = mtcars, outcome = ‘am’, covariates = c( ‘mpg’, ‘cyl’, ‘vs’, ‘carb’ ) )

You then have plenty of functions to further analyze you model, take a look at bayer

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Many research institutions have included among the main types of threats and risks developing globally in 2023 the question of the increase in the scale of organized disinformation operating in online social media. The diagnosed increase in the scale of disinformation generated in online social media is related to the use of applications available on the Internet based on generative artificial intelligence technology. With the help of applications available on the Internet, it is possible without being a computer graphic designer and even without artistic skills to simply and easily create graphics, drawings, photos, images, videos, animations, etc., which can represent graphically professionally created “works” that can depict fictional events. Then, with the help of other applications equipped with generative artificial intelligence technology and advanced language models, i.e. with the help of intelligent chatbots, text can be created to describe specific “fictional events” depicted in the generated images. Accordingly, since the end of 2022, i.e. since the first such intelligent chatbot, i.e. the first versions of ChatGPT, were made available on the Internet, the number of memes, photos, comments, videos, posts, banners, etc. generated with the help of applications equipped with tools based on artificial intelligence technology has been growing rapidly, including the rapid increase in the scale of disinformation generated in this way. In order to limit the scale of the aforementioned disinformation developing in online media, on the one hand, technology companies running social media portals and other online information services are perfecting tools for identifying posts, entries, comments, banners, photos, videos, animations, etc. that contain specific, usually thematic types of disinformation. However, these solutions are not perfect, and the scales of disinformation operating in internecine social media are still high. On the other hand, specific institutions for combating disinformation are being established, NGOs and schools are conducting educational campaigns to make citizens aware of the high scale of disinformation developing on the Internet. In addition, proposed regulations such as the AIAct, which as a set of regulations on the proper use of tools equipped with artificial intelligence technology is expected to come into force in the next 2 years in the European Union may play an important role in reducing the scale of disinformation developing on the Internet.

I have 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 regards,

Dariusz Prokopowicz

Where can I get global data (freely accessible) for small and micro-enterprises across countries and time?

In the rapidly evolving world of SaaS, where convenience often trumps concerns, data privacy remains a pivotal issue. As we increasingly rely on these platforms for both personal and professional tasks, the lines around data ownership and privacy seem to blur. SaaS providers / vendors, with their vast capabilities for data processing and storage, find themselves at the center of this ongoing debate. How do we navigate the fine line between leveraging the undeniable benefits of SaaS and protecting our personal data? In your experience, what measures should both users and providers take to ensure data privacy and security? Do you believe the current legal and ethical frameworks are sufficient to protect user data in the SaaS model?

In order to reduce the growing scale of disinformation, including disinformation generated in social media through in the increasing scale of emerging fakenews, deepfakes, disinformation generated through the use of applications available on the Internet based on generative artificial intelligence technology, the just mentioned GAI technology can be used. Constantly improved, taught to carry out new types of activities, tasks and commands, intelligent chatbots and other applications based on generative artificial intelligence technology can be applied to identify instances of disinformation spread primarily in online social media. The aforementioned disinformation is particularly dangerous for children and adolescents, it can significantly affect the world view of the general public's awareness of certain issues, it can affect the formation of development trends of certain social processes, it can affect the results of parliamentary and presidential elections, it can also affect the level of sales of certain types of products and services, and so on. In the absence of a developed institutional system of media control institutions, including the new online media; lack of a developed system of control of the level of objectivity of content directed to citizens in advertising campaigns; lack of consideration of the issue of disinformation analysis by competition and consumer protection institutions; lack of or poorly functioning democracy protection institutions; lack of institutions that reliably take care of a high level of journalistic ethics and media independence, the scale of disinformation of citizens by various groups of influence, including public institutions and commercially operating business entities may be high and may generate high social costs. Accordingly, new technologies of Industry 4.0/5.0, including generative artificial intelligence (GAI) technologies, should be involved in order to reduce the scale of growing disinformation, including the generation of factoids, deepfakes, etc. in social media. The aforementioned GAI technologies can help identify fakenews pseudo-journalistic content, identify photos containing deepfakes, identify factually incorrect content contained in banners, spots and advertising videos published in various media as part of ongoing advertising and promotional campaigns aimed at activating sales of various products and services.

I described the applications of Big Data technologies in sentiment analysis, business analytics and risk management in an article of my co-authorship:

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 regards,

Dariusz Prokopowicz

This issue is particularly important, because it happens that the data entered into ChatGPT, the information contained in the texts entered for the purpose of automated rewriting, remains in the database that this chatbot uses in the situation of generating answers to questions asked by subsequent Internet users. The problem has become serious, as there have already been situations where sensitive data on specific individuals, institutions and business entities has been leaked in this way. On the other hand, many institutions and companies use ChatGPT in the preparation of reports, editing of certain documents. Also, pupils and students use ChatGPT and other similar intelligent chatbots to generate texts that act as credit papers and/or from which they then compose their theses. On the other hand, functions have been added to some existing anti-plagiarism applications to detect the fact that ChatGPT is being used in the course of students' writing credit papers and theses. In addition to this, the problem is also normative in nature, as it is necessary to adapt the legal norms of copyright law to the dynamic technological advances taking place in the development and application of generative artificial intelligence technology, so that the provisions of this law are not violated by users using ChatGPT or other similar intelligent chatbots. Among the important issues that could significantly reduce the scale of this problem would be the introduction of a mandatory requirement to mark all works, including texts, graphics, photos, videos, etc., that have been created with the help of the said intelligent chatbots, that they have been so created. On the other hand, it is necessary for the AI-equipped chatbots to be improved by their creators, by the technology companies developing these tools, in order to eliminate the possibility of ChatGPT "publishing" confidential, sensitive information from institutions and companies in response to questions, commands, tasks of developing a certain type of text by subsequent Internet users. In addition, the said intelligent chatbots should be improved in such a way that if in the course of automated text generation, including inspiration from other source texts, "quoting" whole sentences, substantial fragments of them, substantive content of other publications but without fully showing the sources, i.e. without a full bibliographic description of all the source publications that the chatbot generating subsequent texts used. In addition, the user of the aforementioned intelligent chatbots does not know to what extent the text they created with the help of these tools is plagiarized from other texts previously entered into them or from publications published on the Internet, including documents of companies and institutions, theses, scientific publications, industry articles, journalistic articles, etc.

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

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

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 is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Despite the training and updating of internal rules and regulations in many companies and enterprises regarding the proper use of intelligent chatbots, i.e., for example, the ChatGPT made available online by OpenAI and other similar intelligent applications that more technology companies are making available on the Internet, there are still situations where reckless employees enter sensitive data of the companies and enterprises where they are employed into these online tools. In such a situation, there is a high risk that the data and information entered into ChatGPT, Copilot or any other such chatbot may subsequently appear in a reply, an edited report, essay, article, etc. by this application on the smartphone, laptop, computer, etc. of another user of the said chatbot. In this way, another Internet user may accidentally or through a deliberate action of searching for specific data come into possession of particularly important, key, sensitive data for a business entity, public institution or financial institution, which may concern, for example, confidential strategic plans, i.e., information of great value to competitors or intelligence organizations of other countries. This kind of situation has already happened and occurred in some companies characterized by highly recognizable brands in specific markets for the sale of products or services. Such situations clearly indicate that it is necessary to improve internal procedures for data and information protection, improve issues of efficiency of data protection systems, early warning systems informing about the growing risk of loss of key company data, and improve systems for managing the risk of potential leakage of sensitive data and possible cybercriminal attack on internal company information systems. In addition, in parallel to improving the aforementioned systems that ensure a certain level of data and information security, internal regulations should be updated on an ongoing basis according to the scale of the risk, the development of new technologies and their implementation in the business entity, with regard to the issue of correct use by employees of chatbots available on the Internet. In parallel, training should be conducted, during which employees learn about both new opportunities and risks arising from the use of new applications and tools based on generative artificial intelligence technology made available on the Internet. Another solution to this problem may be to order the company to completely ban employees from using smart chatbots made available on the Internet. In such a situation, the company will be forced to create its own, operating as internal such applications and intelligent chatbots, which are not connected to the Internet and operate solely as integral modules of the company's internal information systems. This type of solution will probably involve the company incurring significant financial expenses as a result of creating its own such IT solutions. The costs can be significant and many small companies' financial barrier can be high. However, on the other hand, if the construction of internal IT systems equipped with their own intelligent chatbot solutions becomes an important element of competitive advantage over key direct competitors, the mentioned financial expenses will probably be considered in the category of financial resources allocated to investment and development projects that are important for the future of the company.

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

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

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 regards,

Dariusz Prokopowicz

We sometimes face difficulties in finding accessible data sources. Here are some data sources that are available freely for research:

1. Demographic and Health Survey (DHS) data (https://dhsprogram.com/data/available-datasets.cfm)

2. Global Tobacco Surveillance System Data (GTSSData) (https://nccd.cdc.gov/GTSSDataSurveyResources/Ancillary/DataReports.aspx?CAID=2)

3. Multiple Indicator Cluster Surveys (MICS) data by Unicef (https://mics.unicef.org/surveys)

In recent years, more and more companies and enterprises, before launching new product and service offerings as part of their market research, commission sentiment analysis of changes in public sentiment, changes in awareness of the company's brand, recognition of the company's mission and awareness of its offerings to specialized marketing research firms. This kind of sentiment analysis is carried out on computerized Big Data Analytics platforms, where a multi-criteria analytical process is carried out on a large set of data and information taken from multiple websites. In terms of source websites from which data is taken, information is dominated by news portals that publish news and journalistic articles on a specific issue, including the company, enterprise or institution commissioning this type of study. In addition to this, the key sources of online data include the pages of online forums and social media, where Internet users conduct discussions on various topics, including product and service offers of various companies, enterprises, financial or public institutions. In connection with the growing scale of e-commerce, including the sale of various types of products and services on the websites of online stores, online shopping portals, etc., as well as the growing importance of online advertising campaigns and promotional actions carried out on the Internet, the importance of the aforementioned analyses of Internet users' sentiment on specific topics is also growing, as playing a complementary role to other, more traditionally conducted market research. A key problem for this type of sentiment analysis is becoming the rapidly growing volume of data and information contained in posts, comments, posts, banners and advertising spots posted on social media, as well as the constantly emerging new social media. This problem is partly solved by the issue of increasing computing power and multi-criteria processing of large amounts of data thanks to the use of increasingly improved microprocessors and Big Data Analytics platforms. In addition, in recent times, the possibilities of advanced multi-criteria processing of large sets of data and information in increasingly shorter timeframes may significantly increase when generative artificial intelligence technology is involved in the aforementioned data processing.

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

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

I described the applications of Big Data technologies in sentiment analysis, business analytics and risk management in my co-authored article:

APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTELLIGENCE SOFTWARE IN INTEGRATED RISK MANAGEMENT IN ORGANIZATION

The use of Big Data Analytics platforms of ICT information technologies in sentiment analysis for selected issues related to Industry 4.0

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

The acceleration of the processes of digitization of the economy triggered by the development of the Covid-19 pandemic has resulted in a significant increase in computerization, Internetization, applications of ICT information technologies and Industry 4.0 to various economic processes. There is an increase in applications of specific Industry 4.0 technologies in many industries and sectors of the economy, i.e., such as Big Data Analytics, Data Science, cloud computing, machine learning, personal and industrial Internet of Things, artificial intelligence, Business Intelligence, autonomous robots, horizontal and vertical data system integration, multi-criteria simulation models, digital twins, additive manufacturing, Blockchain, cybersecurity instruments, Virtual and Augmented Reality, and other advanced Data Mining technologies. In my opinion, among others, in the fields of medical therapies, communications, logistics, new online media, life science, ecology, economics, finance, etc., and also in the field of predictive analytics, there is an increase in the applications of ICT information technologies and Industry 4.0/Industry 5.0. Artificial intelligence technologies are growing rapidly as they find applications in various industries and sectors of the economy. It is only up to human beings how and in what capacity artificial intelligence technology will be implemented in various manufacturing processes, analytical processes, etc., where large data sets are processed in the most efficient manner. In addition, various opportunities are opening up for the application of artificial intelligence in conjunction with other technologies of the current fourth industrial revolution referred to as Industry 4.0/5.0. It is expected that in the years to come, applications of artificial intelligence will continue to grow in various areas, fields of manufacturing processes, advanced data processing, in improving manufacturing processes, in supporting the management of various processes, and so on.

I have been studying this issue for years and have presented the results of my research in the article, among others:

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 is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Explore the integration of GraphQL subscriptions for efficient real-time communication in web development. Share insights on implementation, benefits, and potential challenges.

Seeking insights on strategies and technologies to promote ethical handling of data within social platforms, balancing user privacy and societal well-being. Open to perspectives from experts in data ethics and social sciences.

The future of blockchain-based internet solutions

Blockchain is defined as a decentralized and distributed database in the open source model in a peer-to-peer internet network without central computers and without a centralized data storage space, used to record individual transactions, payments or journal entries encoded using cryptographic algorithms.

In current applications, blockchain is usually a decentralized and dispersed register of financial transactions. It is also a decentralized transaction platform in a distributed network infrastructure. In this formula, blockchain is currently implemented into financial institutions.

Some banks are already trying to use blockchain in their operations. if they did not do it, other economic entities, including fintechs, implementing blockchain could become more competitive in this respect. However, cryptocurrencies and a secure record of transactions are not the only blockchain applications. Various potential blockchain applications are being considered in the future.

Perhaps these new, different applications already exist in specific companies, corporations, public institutions or research centers in individual countries. In view of the above, the current question is: In what applications, besides cryptocurrency, blockchain in your company, organization, country, etc.?

Please reply

I invite you to the discussion

Thank you very much

Best wishes

Dear all,

I am trying to do some criterion and construct validity exercises with Dutch data.

Do you know any data or studies which use the PVQ-RR or PVQ-40 or other detailed measurements?

Thanks,

Oscar

Well, ChatGPT is not perfect in terms of self-learning new content and perfecting the answers it gives, because it happens to give confirmation answers when there is information or data that is not factually correct in the question formulated by the Internet user. In this way, ChatGPT can learn new content in the process of learning new but also false information, fictitious data, in the framework of the 'discussions' held. Currently, various technology companies are planning to create, develop and implement computerised analytical systems based on artificial intelligence technology similar to ChatGPT, which will find application in various fields of big data analytics, will find application in various fields of business and research work, in various business entities and institutions operating in different sectors and industries of the economy. One of the directions of development of this kind of artificial intelligence technology and applications of this technology are plans to build a system of analysis of large data sets, a system of Big Data Analytics, analysis of information taken from the Internet, an analytical system based on artificial intelligence conducting analytics in real time, integrated with an Internet search engine, but an artificial intelligence system more perfect than ChatGPT, which will, through discussion with Internet users, improve data verification and will learn but only real information and data. Some of the technology companies are already working on this, i.e. on creating this kind of technological solutions and applications of artificial intelligence technology similar to ChatGPT. But presumably many technology start-ups that plan to create, develop and implement business specific technological innovations based on a specific generation of artificial intelligence technology similar to ChatGPPT are also considering undertaking research in this area and perhaps developing a start-up based on a business concept of which technological innovation 4.0, including the aforementioned artificial intelligence technologies, is a key determinant.

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 subject?

Please respond,

I invite you all to discuss,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

In large corporations characterized by a complex, multi-level organizational structure, the flow of information can be difficult. New ICT and Industry 4.0 information technologies are proving to be helpful in this regard, improving the efficiency of the flow of information flowing between departments and divisions in the corporation. One of the Industry 4.0 technologies that has recently found various new applications is artificial intelligence. Artificial intelligence technology is finding many new applications in recent years. The implementation of artificial intelligence, machine learning and other Industry 4.0 technologies into various business fields of companies, enterprises and financial institutions is associated with the increase in digitization and automation of processes carried out in business entities. For several decades, in order to refine and improve the flow of information in a corporation characterized by a complex organizational structure, integrated information systems are being implemented that informationally connect applications and programs operating within specific departments, divisions, plants, etc. in a large enterprise, company, corporation. Nowadays, a technology that can help optimize remote communication and information flow in a corporation is artificial intelligence. Artificial intelligence can help optimize information flow and data transfer within a corporation's intranet.

Besides, the technologies of Industry 4.0, including artificial intelligence, can help improve the cyber security techniques of data transfer, including that carried out in email communications.

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

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

I´m doing my research about determinants of houses prices in dublin. I need a DataBase to study. Where i can find that?

Greetings, fortunately recently my paper was published which were regarding open data, however, I intend to establish a new research project now and collaborate with other researchers, preferably from other countries, and intend to have a look at open data from a legal lens. What steps should I take to expand my academic reach and find coauthors for my future research project?

Thank you

Dear Researchers, I am looking for open-source Gravity/Magnetic data for interpretations via Oasis montaj Software and Voxi Earth Modeling. Please specify some sources where form the data is easily accessible.

Regards,

Ayaz

Greetings, I hope everyone is having a great day. Just wanted to share my joy of my paper being published in the field of open data and economics and would be happy to share the work which is on "https://doi.org/10.1007/s13132-023-01518-z".

Also, welcome any feedback or opinions from fellow colleagues and researchers.

Data generation (collection) is a key and critical component of a qualitative research project. The question is, how can one make sure that sufficient data have been generated/collected?

How far is it acceptable to do a review work using the data extracted from Dimensions database, especially for publication in a reputed journal?

Leading Internet technology companies that also have and are developing search engines in their range of Internet information services are working on developing technological solutions to implement ChatGPT-type artificial intelligence into these search engines. Currently, there are discussions and considerations about the social and ethical implications of such a potential combination of these technologies and offering this solution in open access on the Internet. The considerations relate to the possible level of risk of manipulation of the information message in the new media, the potential disinformation resulting from a specific algorithm model, the disinformation affecting the overall social consciousness of globalised societies of citizens, the possibility of a planned shaping of public opinion, etc. This raises another issue for consideration concerning the legitimacy of creating a control institution that will carry out ongoing monitoring of the level of objectivity, independence, ethics, etc. of the algorithms used as part of the technological solutions involving the implementation of artificial intelligence of the ChatGPT type in Internet search engines, including those search engines that top the rankings of Internet users' use of online tools that facilitate increasingly precise and efficient searches for specific information on the Internet. Therefore, if, however, such a system of institutional control on the part of the state is not established, if this kind of control system involving companies developing such technological solutions on the Internet does not function effectively and/or does not keep up with the technological progress that is taking place, there may be serious negative consequences in the form of an increase in the scale of disinformation realised in the new Internet media. How important this may be in the future is evident from what is currently happening in terms of the social media portal TikTok. On the one hand, it has been the fastest growing new social medium in recent months, with more than 1 billion users worldwide. On the other hand, an increasing number of countries are imposing restrictions or bans on the use of TikTok on computers, laptops, smartphones etc. used for professional purposes by employees of public institutions and/or commercial entities. It cannot be ruled out that new types of social media will emerge in the future, in which the above-mentioned technological solutions involving the implementation of ChatGPT-type artificial intelligence into online search engines will find application. Search engines that may be designed to be operated by Internet users on the basis of intuitive feedback and correlation on the basis of automated profiling of the search engine to a specific user or on the basis of multi-option, multi-criteria search controlled by the Internet user for specific, precisely searched information and/or data. New opportunities may arise when the artificial intelligence implemented in a search engine is applied to multi-criteria search for specific content, publications, persons, companies, institutions, etc. on social media sites and/or on web-based multi-publication indexing sites, web-based knowledge bases.

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

What is your opinion on the subject?

What do you think about this topic?

Please respond,

I invite you all to discuss,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Suppose I want to analyse 100 year of monthly mean flow data of a particular month.

These are few questions for your reference,

How much did you learn about managing your money from your parents?

· None

· Hardly at all

· Little

· Some

A lot

How often were you influenced by or did you discuss about finances with your parents?

· Never

· Once a year

· Every few months

· Twice a month

Weekly

What is your current investment amount in stocks/shares? (Portfolio value)

· 1 - 90,000

· 90,000–170,000

· 170,000–260,000

· 260,000–340,000

· More than 340,000

The above questions are allocated weights from 1 to 5.

I was exploring differential privacy (DP) which is an excellent technique to preserve the privacy of the data. However, I am wondering what will be the performance metrics to prove this between schemes with DP and schemes without DP.

Are there any performance metrics in which a comparison can be made between scheme with DP and scheme without DP?

Thanks in advance.

Hi,

I am trying to export the XPS data from CASA software to ASCII format but getting an error stating "Demo version: Data not saved". Please suggest how to do it.

Hi All!

I am looking for occurrence data for these species that aren't found in the typical places. If anyone has any data on these species that they would be willing to share for acknowledgements, citation, etc., please reach out to me via DM.

Thanks!

EV

For example, if in a few years it will be possible to create a kind of new generation of artificial intelligence equipped with artificial thought processes, artificial consciousness and integrate a language model with a database of data, knowledge, etc. derived from publications written by that person and documented statements, previously given interviews then perhaps in a few years it will be possible to talk to a kind of artificial consciousness simulating the consciousness of a specific person who has long since died and would answer questions simulating that person, e.g. the long-dead Albert Einstein. In this way, there could be language models available on the Internet based on generative artificial intelligence equipped with artificial thought processes, artificial consciousness, with the knowledge of a specific person with whom the Internet user could converse. If this kind of highly intelligent tools were created and offered as a service to talk to a specific Person, known and living many years ago, this kind of service could probably become very popular as a new Internet service. However, the question of ethics and possible copyright of works, publications, books written by a specific person many years ago and whose knowledge, data and information would be used by a generative artificial intelligence simulating the consciousness of this person and answering questions, participating in discussions with people, with Internet users, remains to be considered. Beyond this, however, there is a specific category of risk of disinformation within this kind of online service that could be created in the future. This risk of disinformation would occur if there were situations of responses given by artificial intelligence to questions posed by humans, which would contain content, information, data, wording, phrases, suggestions, etc., which would never be uttered by a specific person simulated by artificial intelligence. The level of this kind of risk of misinformation would be inversely proportional to and determined by the level of sophistication, perfection, etc. of the construction of this kind of new generation of artificial intelligence equipped with artificial thought processes, artificial consciousness and the integration of a linguistic model with a database of data, knowledge, etc. derived from publications written by this person and documented statements, previously given interviews, etc., and the perfection of the learning system to give increasingly perfect answers given to the questions asked and to learn by the generative artificial intelligence system to actively participate in discussions.

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 subject?

Please respond,

I invite you all to discuss,

Thank you very much,

Counting on your opinions, on getting to know your personal opinion, on an honest approach to discussing scientific issues and not the ready-made answers generated in ChatGPT, I deliberately used the phrase "in your opinion" in the question.

The above text is entirely my own work written by me on the basis of my research.

I have not used other sources or automatic text generation systems such as ChatGPT in writing this text.

Copyright by Dariusz Prokopowicz

Best wishes,

Dariusz Prokopowicz

I am trying to run a spatio-temporal autoregressive model (STAR). Therefore I need to create a spatial weight matrix W with N × T rows and N × T columns to weight country interdependencies based on yearly trade data. Could someone please tell me how I to create such a matrix in R or Stata?

For years, in databases, data warehouses and Big Data platforms, Internet technology companies have been collecting information on citizens, Internet users, customers using their online information services.

Continuous technological progress increases the possibilities of both obtaining, collecting and processing data on citizens in their role as potential customers, consumers of Internet offers and other media, Internet information services, offers of various types of products and services, advertising campaigns that also influence the general social awareness of citizens and the choices people make concerning various aspects of their lives. The new Industry 4.0 technologies currently being developed, including Big Data Analytics, cloud computing, Internet of Things, Blockchain, cyber security, digital twins, augmented reality, virtual reality and also machine learning, deep learning, neural networks and artificial intelligence will determine the rapid technological progress and development of applications of these technologies in the field of online marketing in the years to come as well. The robots being developed, which collect information on specific content from various websites and webpages, are able to pinpoint information written by internet users on their social media profiles. In this way, it is possible to obtain a large amount of information describing a specific Internet user and, on this basis, it is possible to build up a highly accurate characterisation of a specific Internet user and to create multi-faceted characteristics of customer segments for specific product and service offers. In this way, digital avatars of individual Internet users are built in the Big Data databases of Internet technology companies and/or large e-commerce platforms operating on the Internet, social media portals. The descriptive characteristics of such avatars are so detailed and contain so much information about Internet users that most of the people concerned do not even know how much information specific Internet-based technology companies, e-commerce platforms, social media portals, etc. have about them.

Geolocalisation added to 5G high-speed broadband and information technology and Industry 4.0 has, on the one hand, made it possible to develop analytics for identifying Internet users' shopping preferences, topics of interest, etc., depending on where, specifically geographically, they are at any given time with the smartphone on which they are using certain online information services. On the other hand, the combination of the aforementioned technologies in the various applications developed in the applications installed on the smartphone has made it possible, on the one hand, to increase the scale of data collection on Internet users, and, on the other hand, also to increase the efficiency of the processing of this data and its use in the marketing activities of companies and institutions and the implementation of these operations increasingly in real time in the cloud computing, the presentation of the results of the data processing operations carried out on Internet of Things devices, etc.

It is becoming increasingly common for us to experience situations in which, while walking with a smartphone past some physical shop, bank, company or institution offering certain services, we receive an SMS, banner or message on the Internet portal we have just used on our smartphone informing us of a new promotional offer of products or services of that particular shop, company, institution we have passed by.

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

The text above is my own, written by me on the basis of my research.

In writing this text, I did not use other sources or automatic text generation systems such as ChatGPT.

Copyright by Dariusz Prokopowicz

What do you think about this topic?

What is your opinion on this subject?

Please answer,

I invite you all to discuss,

Thank you very much,

Best regards,

Dariusz Prokopowicz

My team and I are trying to open a dialogue about designing a Continuum of Realism for synthetic data. We want to develop a meaningful way to talk about data in terms of the degree of realism that is necessary for a particular task. We feel the way to do this is by defining a continuum that shows that as data becomes more realistic, the analytic value increases, but so does the cost and risk of disclosure. Everyone seems to be interested in generating the most realistic data, but let's be honest, sometimes that's not the level of realism that we actually need. It is expensive and carries a high reidentification risk when working with PII. Sometimes we just need data to test our code, and we can't justify using this level of realism when the risk is so high. Have you also encountered this issue? Are you interested in helping us fulfill our mission? Ultimately we are trying to save money and protect consumer privacy. We would love to hear your thoughts!

Hi everyone,

I need to convert standard error (SE) into standard deviation (SD). The formula for that is

SE times the square root of the sample size

By 'sample size', does it mean the total sample size or sample sizes of individual groups? For example, the intervention group has 40 participants while the control group has 39 (so the total sample size is 79) So, when calculating SD for the intervention group, do I use 40 as the sample size or 79?

Thank you!

The economic development of a country is determined by a number of factors, which include the level of innovativeness of economic processes, the creation of new technological solutions in research and development centres, research institutes, laboratories of universities and business entities and their implementation into the economic processes of companies and enterprises. In the modern economy, the level of innovativeness of the economy is also shaped by the effectiveness of innovation policy, which influences the formation of innovative startups and their effective development. The economic activity of innovative startups generates a high investment risk and for the institution financing the development of startups this generates a high credit risk. As a result, many banks do not finance business ventures led by innovative startups. As part of the development of systemic financing programmes for the development of start-ups from national public funds or international innovation support funds, financial grants are organised, which can be provided as non-refundable financial assistance if a startup successfully develops certain business ventures according to the original plan entered in the application for external funding. Non-refundable grant programmes can thus activate the development of innovative business ventures carried out in specific areas, sectors and industries of the economy, including, for example, innovative green business ventures that pursue sustainable development goals and are part of green economy transformation trends. Institutions distributing non-returnable financial grants should constantly improve their systems of analysing the level of innovativeness of business ventures planned to be implemented by startups described in applications for funding as innovative. As part of improving systems for verifying the level of innovativeness of business ventures and the fulfilment of specific set goals, e.g. sustainable development goals, green economy transformation goals, etc., new Industry 4.0 technologies implemented in Business Intelligence analytical platforms can be used. Within the framework of Industry 4.0 technologies, which can be used to improve systems for verifying the level of innovativeness of business ventures, machine learning, deep learning, artificial intelligence (including e.g. ChatGPT), Business Intelligence analytical platforms with implemented Big Data Analytics, cloud computing, multi-criteria simulation models, etc., can be used. In view of the above, in the situation of having at one's disposal appropriate IT equipment, including computers equipped with new generation processors characterised by high computing power, it is possible to use artificial intelligence, e.g. ChatGPT and Big Data Analytics and other Industry 4.0 technologies to analyse the level of innovativeness of new economic projects that plan to develop new start-ups implementing innovative business solutions, technological, ecological, energy and other types of innovations.

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

What do you think?

What is your opinion on this subject?

Please respond,

I invite you all to discuss,

Thank you very much,

Warm regards,

Dariusz Prokopowicz

Dear researchers,

I am working on a project related to solar wind. I want to download a 1-minute resolution data from a BepiColombo spacecraft. However, I am struggling with that. Do you know any websites to download the data? OR, If you could help me to provide a BepiColombo data just for few days, it would be very helpful. I am expecting a valuable comments from a wonderful personalities.

Many thanks.

The spread of disinformation on social media used by setting up fake profiles and spreading fakenews on these media is becoming increasingly dangerous in terms of the security of not only specific companies and institutions but also the state. The various social media, including those dominating this segment of new online media, however, differ considerably in this respect. The problem is more acute in the case of those social media which are among the most popular and on which mainly young people function, whose world view can be more easily influenced by factual information and other disinformation techniques used on the Internet. Currently, among children and young people, the most popular social media include Tik Tok, Instagram and YouTube. Consequently, in recent months, the development of some social media sites such as Tik Tok is already being restricted by the governments of some countries by banning the use, installation of this application of this portal on smartphones, laptops and other devices used for official purposes by employees of public institutions. These actions are argued by the governments of these countries in order to maintain a certain level of cyber security and reduce the risk of surveillance, theft of data and sensitive, strategic and particularly security-sensitive information of individual institutions, companies and the state. In addition, there have already been more than a few cases of data leaks on other social media portals, telecoms, public institutions, local authorities and others based on hacking into the databases of specific institutions and companies. In Poland, however, the opposite is true. Not only does the organised political group PIS not restrict the use of Tik Tok by employees of public institutions, but it also motivates the use of this portal by politicians of the ruling PIS option to publish videos as part of the ongoing electoral campaign, which would increase the chances of winning parliamentary elections for the third time in autumn this year 2023. According to analysts researching the problem of growing disinformation on the Internet, in highly developed countries it is enough to create 100 000 avatars, i.e. non-existent fictitious persons, created as it were and seemingly functioning thanks to the Internet by creating profiles of these fictitious persons on social media portals referred to as fake profiles created and functioning on these portals, to seriously influence the world view, the general social awareness of Internet users, i.e. usually the majority of citizens in the country. On the other hand, in third world countries, in countries with undemocratic systems of power, all that is needed for this purpose is about 1,000 avatars of these fictitious people with stories modelled, for example, on famous people such as, in Poland, a well-known singer claiming that there is no pandemic and that vaccines are an instrument for increasing control of citizens by the state. The analysis of changes in the world view of Internet users, changes in trends concerning social opinion on specific issues, evaluations of specific product and service offers, brand recognition of companies and institutions can be conducted on the basis of sentiment analysis of changes in the opinion of Internet users using Big Data Analytics. Consequently, this type of analytics can be applied and of great help in detecting factual news disseminated as part of the deliberate spread of disinformation on social media.

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

What is your opinion on this topic?

What is your opinion on this subject?

Please respond,

I invite you all to discuss,

Thank you very much,

Warm regards,

Dariusz Prokopowicz

I am tiding up with the below problem, it's a pleasure to have your ideas.

I've written a coding program in two languages, Python and R, but each came to a completely different result. Before jumping to a conclusion, I declare that:

- Every word of the code in two languages has multiple checks and is correct and represents the same thing.

- The used packages in two languages are the same version.

So, what do you think?

The code is about applying deep neural networks for time series data.

Where can I get the original paper by Ackoff from 1989 titled "From data to wisdom" published in Journal of applied systems analysis?

The file that is publicly available under this title is a 2-page article from 1999.

I am currently using Fisher's exact test as some of my cell counts are <5. I have done this for lots of data in the same dataset which have generally been 2x3 or more (so reported Cramer's V as well), however now I am running 2x2, the Fisher's output is blank and I can't figure out why?! I have attached an example of the output - any help would be gratefully received!

Since statistics is the golden key to interpret data in almost all scientific & social branches !

I would like to fit some data sets. I read that LEVMW is the best program. does anyone has this program or a link for it?

Or are there other programms better than LEVMW?

Greetings respectable community of ResearchGate. I encountered some issues while gathering data from the World Bank Database, hence I would like to know if there are alternatives or other websites like the World Bank Database in which we can gather raw data.

The website can contain whatever form of indicators such as (developments, governance, competitiveness, economics, financial sector, etc.….) Thank you in advance for your assistance.

It is a dta file, can not be read with read.csv. I used haven to read it. However, the second row of column is real name, such as Checking Year and Checking Month , how can you extract it?

I had the same problem with the data collected from Thermo Scientific XPS. Here is the solution I found out, it may help other researchers.

Step 1: Open Avantage (on the XPS measurement computer), then go to "file"->"save as", a new window pops out.

📷(see the picture below)

Step 2: Name your .vgp file and click save. A new folder will be created, like Data Grid 2. DATA

Step 3: Open the software "DataSpace_BatchDump.exe". It can be found on the same computer with Avantage installed, in "C:\Program Files (x86)\Thermo\Avantage\Bin".

Step 4: Open the folder with .vgp/.vgd files in DataSpace_BatchDump, and click OK. Then, a new window pops out, find a location to export the files, like “C:\export”, then click ok twice. New .avg files will be saved in that location.

📷(see the picture below)

Step 5: Open CasaXPS, click “covert”, find “C:\export”. Type “.dth” and click ok. Then, the .vms files would be created.

📷(see the picture below)

Hello, i'm looking for a reliable insect pest database that shares information about the occurrence, geographic distribution, hosts of all insect pest over the world... I made a little research on my own but the results aren't quite reliable in my opinion from the databases i found (gbif, cabi..). I also believe that many technical reports revealing the occurrence of those insects are being published in governmental research centers of every country however they aren't accessible online. Is there a way to get access to those reports?

Hi Researchers,

I am looking for journals that publish scientifically valuable datasets, and research that advances the sharing and reuse of scientific data. Please let me know if you have any recommendations.

FYI, the dataset we are curating are bioinformatics image data.

Greetings everyone! could anyone kindly tell me where I can get data concerning the stock and bonds market?

Am looking for an email datasets from B2B sales. Is any data sources available?

I'm new to fsQCA and would like to conduct a fsQCA analysis in one of my dissertation studies. The majority of fsQCA methods begin with data collection, followed by the preparation of the data matrix and the truth table. Conditions and outcomes are represented in the data matrix. Conditions, as we know, are the responses to surveys or interviews. I'm curious where the "outcomes" come from. Do we ask participants to rate the outcomes on a scale, as they did in the conditions?

For example, in the data matrix attached, there are five conditions (LPI, TAB, WPP, PAP, and NR) and outcomes are indicated by PubINF.

I acknowledge this is a very basic question, but I look forward to receiving your response.

I tried using Gigasheet but it does not have many features that are available in excel. Suggest me some freely available sources where I can load my ~1.7 million rows and do some calculations like sort multiple columns, remove duplicates

TIA

Hello,

Does anyone know how to change the time length to 2 or 3s in Ansys Static Structural -> Model -> Static Structural -> Pressure -> Magnitude -> Function -> Tabular data? The current setup only allows me to go up to 1s.

Thanks!

Hi all,

I'm having trouble converting one particular variable in my dataset from string to numeric. I've tried manually transforming/recoding into a different variable and automatic recoding. I've also tried writing syntax (see below). The same syntax has worked for every other variable I needed to convert but this one. For all methods (manual recode, automatic recode, and writing a syntax), I end up with missing data.

recode variablename ('Occurred 0 times' = 0) ('Occurred 1 time' = 1) ('Occurred 2 times' = 2) ('Occurred 3+ times' = 3) into Nvariablename.

execute.

VALUE LABELS

Nvariablename

0 'Occurred 0 times'

1 'Occurred 1 time'

2 'Occurred 2 times'

3 'Occurred 3+ times'.

EXECUTE.

Thank you in advance for your help!

Dear researchers, we tried to download AOD data from aeronet for Nepal stations, however maximum data are missing. Are there any other appropriate website to download AOD data? Need your suggestions, thanks. :)

My lab wants to try to do as much of our pre-processing, processing, and analysis in R as possible, for ease of workflow and replicability. We use a lot of psychophysiological measures and have historically used MATLAB for our workflow with this type of data. We want to know if anyone has been successful in using R for these types of tasks.

Any good R packages?

I wanted to know the purity of MgSO4 and i went for XRF analysis for the same. I received data in the following format

[Quantitative Result]

---------------------------------------------------------------------------------

Analyte Result Proc-Calc Line Net Int. BG Int.

---------------------------------------------------------------------------------

S 61.3709 % Quant.-FP S Ka 1165.666 3.578

Mg 37.9584 % Quant.-FP MgKa 158.225 0.918

Ca 0.5466 % Quant.-FP CaKa 4.244 1.142

Si 0.1241 % Quant.-FP SiKa 0.862 0.148

can anyone please help me by explaining how to find purity of MgSO4 from this.

I welcome Answers and opinions.

Based on the literature review we get idea of moderators. But what we if want to introduce a new moderator in the literature.

1) What are the criteria for new moderator ?

2) How to theoretically support moderating variable ?

3) Is is necessary to adopt new moderating variable from same theory ?

Is there any special procedure to follow to get data?

For RWE studies is important to find the correct RWD sources, there fore I am looking for other sources of Japanese drugs codes, diagnosis codes, etc. Other than JMDC. It would be helpful if any of you could help me.

Hello;

We have two twenty years data sets, for a historical time span, and a future prediction. for both, statistical distributions are fitted for five-year intervals, and for historical and predicted data, the same statistical distribution (Johnson SB, Gen. Pareto, and Wakeby) اave been selected as the most appropriate distributions.

Similar statistical distributions have been obtained for all five-year intervals and for the entire twenty-year time series. We want to know what this similarity in data analysis means?

Best

Saeideh

Hello,

I have a huge number of data and I need to calculate such categorical statistical indices (e.g., POD, FAR, CSI, ETS) using python or R. I will be thankful for any kind of help.

Regards,

Dear colleagues

I've a CSV file (5 thousand lines) with information about the year,country,distance,product,quantity..

I can to open the file in notepad++ also..

Could you tell me please, how I can to construct the graph in excel or R studio with quantity or how to consider every quantity , which corresponds to the respective country?

Thank you very much