Archive for category: Expert Research Service

When many scientific authors first submit a manuscript to an English journal, they must first understand what the journal’s submission and review requirements are. When it comes to reviewing a manuscript, they must know the peer review. Regarding the response to the peer review comments, they already pay attention to the expert point of view. I have discussed it many times, but today I will discuss the basic question of peer review: what is peer review (peer review)?

How does peer review come about?
What is peer review? Simply put, peer review is the evaluation and evaluation of research by experts in a certain field. Many countries regard peer review as the focus of scientific research publication, and most journals also adopt peer review. It is generally believed that peer review can ensure the quality of published scientific research. When scientific research journals were first published, there was a review process before publication, but the format was different, and then gradually evolved into the form of peer review we know now. At the beginning, the job of deciding whether a paper should be published was entirely handled by journal editors, but by the beginning of the 20th century, science gradually became more sophisticated and researchers began to delve into narrow subject areas, so journal editors’ decision-making work became increasingly difficult. With the increase in the number of researchers, their demand for careers, funds, etc., began to produce a large number of papers, and the peer review mechanism began to take shape.

The role of peer review?
Peer review has subject experts to review the new research content submitted, so it can improve the credibility of the research. This procedure can help the journal editor decide whether to publish the research, and it can also allow experts to make suggestions. Those who understand the decision-making process of journal editors know that peer review cannot decide whether a paper is published or not. They can only provide advice to journal editors for decision-making.

Type of peer review?
According to journal needs, different journals adopt different methods of peer review, including single-blind, double-blind, and open peer review. Recently, some journals have started peer review after publication, hoping to eliminate bad science. Although the format of the review is different, the intention is to verify the science and ensure that the research has international influence after it is published.

How to deal with peer review?
All authors know or have experienced that the process of publishing a paper is not a step-by-step process. Paper submissions must prepare many related documents and write a submission letter according to the requirements of the journal. Once submitted, it will take as short as a few days or as long as a few weeks to receive a response from the journal. Very few papers Can be accepted for publication immediately without making any changes. Most of the papers need to be revised in several rounds based on the review comments until the journal editor thinks that the publication standards are met. Reviewers can suggest the extent of revision, such as simple minor revisions, or major revisions such as additional data supplement experiments. Journals will not accept papers that do not fully handle review comments. Therefore, authors must follow some guidelines for responding to peer review comments. Although authors do not necessarily agree to all review and revision comments, they must provide a valid rebuttal when responding. Contribution skills to increase the speed of paper publication.

Problems with the peer review system
Peer review has its advantages, of course, there are also some disadvantages, such as decision-making delays, review bias, plagiarism, and peer competition. In addition, although peer review does not involve any real money transactions, it contains many hidden costs. The most important cost is the time of peer reviewers and journal editors. Peer review is a free work, and reviewers completely voluntarily spend their time reviewing papers. Journal editors also need to spend time looking for suitable reviewers. Therefore, the academic community has different opinions on whether peer review is thankless work or responsibility.

The biggest goal of the peer review mechanism is to ensure that high-quality science is published. Therefore, when authors understand the importance of peer review in scientific publication, they should regard peer review as an excellent opportunity to improve the quality of the paper.

The background of big data is that the whole society is going digital, especially the development of social networks and various sensing devices. The development of cloud computing and search engines has made it possible to efficiently analyse big data. The core issue is how to quickly obtain valuable information from a large variety of data. Realizing corporate strategic operations through data analysis has become the norm, so what are the common errors in the data analysis process?

　Common errors in the process of data analysis:

　　1. The analysis goal is not clear

　　”Massive data does not actually produce massive wealth.” Because many data analysts do not have clear analysis goals, they are often confused in the massive data. Either the wrong data is collected, or the collected data is not complete, which will lead to the results of data analysis are not accurate enough.

　　However, if the target is locked at the beginning, what exactly do you want to analyze? If you think about results oriented, you will know what kind of data you need to support your analysis. In order to determine the source of the data, collection methods and analysis indicators.

　　2. Errors occur when collecting data

　　When the software or hardware that captures the data goes wrong, a certain error occurs. For example, if the usage log is not synchronized with the server, the user behavior information on the mobile application may be lost. Likewise, if we use hardware sensors like microphones, our recordings may capture background noise or other electrical signal interference.

　　3. The sample is not representative

　　When performing data analysis, there must be a credible data sample. This is the key to ensuring that the data analysis result is reliable. If the data sample is not representative, the final analysis result will be of no value. Therefore, for data samples, it is also required to be complete and comprehensive. Use single, non-representative data to replace all data for analysis. The analysis results obtained from such one-sided data may be completely wrong.

　　For example, Twitter users may be more educated and have higher incomes, and their age will be somewhat older. If such a biased sample is used to predict the box office of a movie whose target audience is young people, the analysis conclusion may not be reasonable. So make sure that the sample data you get is representative of the research population. Otherwise, your analysis and conclusions lack a solid foundation.

　　4. Correlation and causality confusion

　　Most data analysts assume that correlation directly affects causality when dealing with big data. Using big data to understand the correlation between two variables is usually a good practice method, but always using the “causal” analogy can lead to false predictions and invalid decisions. To achieve good results in data analysis, we must understand the fundamental difference between correlation and causality. Correlation often refers to observing changes in X and Y at the same time, while causality means that X leads to Y. In data analysis, these are two completely different things, but many data analysts often overlook the difference.

　　Correlationship in data science is not causation.If two relationships are related to each other, it does not mean that one caused the other.

　　5. Divorce from business reality

　　A professional data analyst must be very familiar with the industry situation, business process, and related knowledge of the project being analysed, because the result of data analysis is to solve the problems in the project or provide reference opinions for industry decision makers. If the business knowledge and data analysis work cannot be combined well, and the business reality is divorced and only concerned with the data, the analysis results obtained in this case will not have reference value.

　　6. Passionate about advanced analysis

　　Some data analysts will excessively pursue the so-called cutting-edge, advanced and fashionable analysis technology. When facing an analysis project, the first thing they think of is to choose a cutting-edge technology to solve it, rather than thinking from the real needs of the subject itself. Reasonable and cost-effective analysis technology. If you can get the same result in a simple way, there is no need to quote a complex data analysis model.