What is a Research Proposal?

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The research proposal is an integral part to the PhD application process. It should provide an outline of your proposed topic and begin to develop the framework you will use to conduct your research. It will be the primary resource you are assessed by when you make your application, and thus, needs to make a powerful first impression.

There are three main things that you need to demonstrate in your proposal. These are:
1) You are capable of independent critical thinking and analysis.

2) You can communicate your ideas clearly.

3) You understand what a PhD involves.

How to Make It Better


Despite needing to demonstrate these things, it is important to remember that your proposal does not need to be perfect; you are not expected to be an expert before you begin your PhD. What is expected is that you have thought about your topic, identified a potential research area and have started to develop a plan of how you might undertake your research. None of these needs to be perfect. They just need to demonstrate that with a bit of guidance, you will be able to achieve a PhD.

There is no specific format to a PhD proposal and often the format will be dependent on the institute you apply to. However, a good proposal will most likely include the following:

1) A clear title / research question


2) Introduction What is the research question / problem? What is your hypothesis, research aims and objectives? Why is this research important? What difference will your research make?


3) Brief literature review / Background  Put your research proposal into context with published literature. Identify any gap in the knowledge or questions which have to be answered. What will your research add to the research field?


4) Research timetable and methodology a Research timetable – what are the main stages of the project and what methods will you use to carry out your research? b. Explain what you expect from each year of your PhD. Openly discuss challenges you expect to face and how you might overcome them.


5) Summary and conclusions provide your readers with the main points and conclusion of your proposal.


6) Bibliography Include a list of key references, which demonstrate you have read around your subject area.

Tips


More doesn’t always mean better. The research proposal doesn’t have to be long; 1000-2000 words is a good guideline. However, what is more important is that it articulates clearly, coherently and concisely what you want to achieve during your PhD and how you will go about achieving it. The proposal should leave the readers interested, excited and keen to find out more about you and your ideas. Do this and you will have a great chance of submitting an impressive application!

What does peer review mean?

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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 8 best open source tools for data mining

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Data mining is also known as data exploration. It is a step in Knowledge-Discovery in Databases, a process of mining and analyzing large amounts of data and extracting information from it. Some of these applications include market segmentation-such as identifying the characteristics of a customer buying a specific product from a specific brand, fraud detection-identifying transaction patterns that may lead to online fraud, etc. In this article, we have compiled the 8 best open source tools for data mining.

1.weka

As an open data mining platform, WEKA has assembled a large number of machine learning algorithms that can undertake data mining tasks, including data preprocessing, classification, regression, clustering, association rules, and visualization on a new interactive interface.

2.Rapid Miner

RapidMiner is the world’s leading data mining solution, with advanced technology to a very large extent. Its data mining tasks cover a wide range, including various data arts, which can simplify the design and evaluation of the data mining process.

3. Orange

Orange is a component-based data mining and machine learning software package. Its functions are friendly, powerful, fast and multi-functional visual programming front end for browsing data analysis and visualization, and it is based on Python for script development. . It contains a complete series of components for data preprocessing, and provides data accounting, transition, modeling, model evaluation and exploration functions. It is developed by C++ and Python, and its graphics library is developed by the cross-platform Qt framework.

4. Knime

KNIME (Konstanz Information Miner) is a user-friendly, intelligent, and open source platform for data integration, data processing, data analysis and data exploration.

5. jHepWork

jHepWork is a complete set of object-oriented scientific data analysis framework. Jython macros are used to display one-dimensional and two-dimensional histogram data. The program includes many tools that can be used to interact with two-dimensional and three-dimensional scientific graphics.

6. Apache Mahout

Apache Mahout is a brand new open source project developed by the Apache Software Foundation (ASF). Its main goal is to create some scalable machine learning algorithms for developers to use for free under the Apache license. The project has reached its second year and currently only has one public release. Mahout contains many implementations, including clustering, classification, CP, and evolutionary programs. In addition, by using the Apache Hadoop library, Mahout can be effectively extended to the cloud.

7. ELKI

ELKI (Environment for Developing KDD-Applications Supported by Index-Structures) is mainly used to cluster and find outliers. ELKI is a data mining platform similar to weka, written in java, with a GUI graphical interface. Can be used to find outliers.

8. Rattle

Rattle (an easy-to-learn R analysis tool) provides statistical and visual summaries of data, converts data into easy-to-model forms, constructs unsupervised and supervised models from the data, presents the performance of the model graphically, and obtains new data set.

Common mistakes in data analysis

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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.

Heavy! Collection of blacklists and early warning journals of all units in China!!

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Recently, the First Hospital of Jilin University has compiled warning journals:

  1. Medicine
  2. International journal of clinical and experimental medicine
  3. PLoS one
  4. Scientific reports
  5. Oncology letters
  6. Experimental and therapeutic medicine
  7. Biochemical and biophysical research communications
  8. British journal of biomedical science
  9. Cancer radiotherapies
  10. International journal of molecular medicine
  11. International journal of osteopathic medicine
  12. Journal of genetic counseling
  13. Material science in semiconductor processing
  14. Journal of cellular Biochemistry
  15. Biomedicine and pharmacotherapy
  16. Journal of cellular physiology
  17. Life Sciences
  18. European review for medical and pharmacological sciences
  19. Cancer biomarkers
  20. International journal of clinical and experimental pathology
  21. Caner management and research
  22. American journal of cancer research
  23. American journal of translational research
  24. Biomed research international
  25. Bioscience reports
  26. International journal of biochemistry & cell biology
  27. International journal of oncology
  28. Journal of cancer
  29. Journal of cellular and molecular medicine
  30. Journal of clinical medicine
  31. Journal of experimental & clinical Cancer research
  32. Journal of international medical research,
  33. Molecular medicine reports
  34. Oncology research
  35. Oncotargets and therapy
  36. Theranostics
  37. World journal of Gastroenterology
  38. Artificial cells nanomedicine and biotechnology
  39. Experimental and molecular pathology
  40. Biofactors
  41. Brazilian journal of medical and biological research
  42. International journal of immunopathology and pharmacology
  43. Medical science monitor
  44. Bio-medical research Tokyo

On December 31, 2020, The Chinese Academy of Sciences officially released the “International Journal Early Warning List (Trial)” 

  1. Metal
  2. Coatings
  3. Materials
  4. Journal of nanoscience and nanotechnology
  5. Minerals
  6. Atmosphere
  7. Artificial cells nanomedicine and biotechnology
  8. Advances in civil engineering
  9. International journal of energy research
  10. Mathematical problems in engineering
  11. Sensors
  12. Energies
  13. Applied sciences-base1
  14. Polymers
  15. Electronics
  16. Processes
  17. Complexity
  18. Desalination and water treatment
  19. International journal of electrochemical science
  20. Catalysts
  21. Molecules
  22. Natural product research
  23. Sustainability
  24. Water
  25. Ieee access
  26. Agronomy-base1
  27. Journal of cellular biochemistry
  28. Journal of cellular physiology
  29. Bioscience reports
  30. Biomed research international
  31. Plant-base1
  32. Cells
  33. Boundary value problems
  34. Advances in difference equations
  35. Mathematics
  36. European review for medical and pharmacological sciences
  37. International journal of clinical and experimental pathology
  38. Medicine
  39. International journal of clinical and experimental medicine
  40. Biomedicine and pharmacotherapy
  41. Experimental and molecular pathology
  42. Brazilian journal of medical and biological research
  43. International journal of immunopathology and pharmacology
  44. Medical science monitor
  45. American journal of translational research
  46. Journal of biomaterials and tissue engineering
  47. Aging-us
  48. Life sciences
  49. Journal of clinical medicine
  50. International journal of environmental research and public health
  51. Acta medica mediterranea

The First Affiliated Hospital of Sun Yat-sen University does not support the journal catalog

1. European review for medical and pharmacologicalsciences

2. Cancer management and research

3. Bioscience reports

4. Cancer biomarkers

5. Journal of International medicalresearch

6. Journal of cellular biochemistry

7. Biochemical and biophysical researchcommunications

8. Biomedicine and pharmacotherapy

9. American journal of cancer research

10. Journal of cellular physiology

11. Life sciences

12. Journal of cellular and molecularmedicine

13. Theranostics

14. Journal of Experimental and clinicalcancer research

15. Journal of cancer

16. International journal of molecularmedicine

17. American journal of translationalresearch

18. Biomed research international

19. Journal of clinical medicine

20. Oncotarget

21. Medicine

22. Scientific reports

23. Tumor biology

24. International journal of biochemistryand cell bilogy

25. Biomedical research-INDIA

26. Cellular physiology and biochemistry

27. International journal of Clinical and experimentalmedicine

28. International journal of clinical andexperimental pathology

29. Experimental and therapeutic medicine

30. Molecular medicine reports

31. Medical science monitor

32. Oncology letters

33. International journal of oncology

34. World journal of gastroenterology

35. oncology research

36. Oncotargets and therapy

37. Plos one