To introduce participants to the details of communication and writing scientific papers.
To guide researchers in the writing of scientific paper to increase its acceptability for publication in a journal; and
To upgrade the pre-existing knowledge of writing skills in a scientific manner.
2. Before start writing
Be sure to select appropriate journal
Follow the instructions of journal
Keep research ethics in mind
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3. Essential information
1. Number of articles had already crossed 50 million by 2009.
2. Since then on an average 2.5 million new scientific papers are published
each year.
3. 34,400+ peer-reviewed journal in late 2014
4. No. of article increases at a rate of approximately 4% per year.
5. Number of researchers was 8.4 million in 2011 (Full time 6.3 million)
6. A researcher reads on an average 270 articles per year, depending on
discipline (more in medicine and science)
7. Average time spent on reading an article was 50 min in 1990 and 30 min
now.
8. If you read less than 270 articles per year, its likely that an average
researcher writes better than you do.
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4. "Hell is sitting on a hot stone reading your own scientific
publications"
- Erik Ursin
‘‘There are many exceptions in ecology. The author has summarized
them in four books’’
- Jens Borum, ecologist
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5. Scimago Journal and country rank (1996-2018)
Accessed date: July 1, 2019
Rank Country
Documents
Citable
documents
Citations
Self-
Citations
Citations per
Document
H
index
1 USA 12070144 10701848 297655815 134368758 24.66 2222
2 China 5901404 5785424 48833849 27480980 8.27 794
91 Nepal 14965 13382 166841 20487 11.15 129
239 Heard Island
and McDonald
Islands
2 2 9 0 4.50 1
Source: https://www.scimagojr.com/countryrank.php
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6. Scimago Journal & Country Rank (Among 31971 journal)
Accessed date: July 1, 2019
Rank
Title SJR
H
inde
x
Total
Docs.
(2018)
Total
Docs.
(3years)
Total
Refs.
(2018)
Total
Cites
(3years)
Citable
Docs.
(3years)
Cites /
Doc.
(2years)
Ref. /
Doc.
(2018)
1 CA - A Cancer
Journal for
Clinicians
72.57
6
144 45 127 3078 20088 103 206.85 68.40
14409 Journal of Nepal
Health Research
Council
0.253
Q3
9 23 92 0 55 88 0.26 0.00
17441
Kathmandu
University
medical journal
(KUMJ)
0.174
Q3
21 53 259 879 86 238 0.31 16.58
https://www.scimagojr.com/journalrank.php
7/2/2019 6
7. Scimago Journal & Country Rank (Among 31971 journal)
Accessed date: July 1, 2019
Rank
Title SJR
H
inde
x
Total
Docs.
(2018)
Total
Docs.
(3years)
Total
Refs.
(2018)
Total
Cites
(3years)
Citable
Docs.
(3years)
Cites /
Doc.
(2years)
Ref. /
Doc.
(2018)
1 CA - A Cancer
Journal for
Clinicians
72.57
6
144 45 127 3078 20088 103 206.85 68.40
14409 Journal of Nepal
Health Research
Council
0.253
Q3
9 23 92 0 55 88 0.26 0.00
17441
Kathmandu
University
medical journal
(KUMJ)
0.174
Q3
21 53 259 879 86 238 0.31 16.58
https://www.scimagojr.com/journalrank.php
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11. Difference between Introduction and Background
• Both an introduction, as well as, background is necessary and integral parts of a
document
• Introduction is like showing a trailer of a movie to entice a reader to go through
the entire document
• Background is to make a reader understand the reasons of conducting a study
and the incidents leading up to the study.
Example
• Intro: Known worldwide for it's magnificent Eiffel Tower, France has so much
more to offer in magnificence.
• Background: France is a European country situated between England and Spain.
The dominant language is, of course, French. The country also borders the
Pyrenees Mountains to the southwest, and Italy to the southeast.
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12. The difference is that introduction is like the universal set while
background is a subset of the universal set.
The introduction contains the background of the study as well as other
elements.
• statement of the problem
• goal and deliverables of the research
• justification/significance/rationale
• scope of the study
• aim and objectives
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14. Example
Source: Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of
real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol
2002,3:RESEARCH0034.
Background
Gene-expression analysis is increasingly important in many fields of biological research. Understanding patterns of expressed genes is expected to provide insight into
complex regulatory networks and will most probably lead to the identification of genes relevant to new biological processes, or implicated in disease. Two recently
developed methods to measure transcript abundance have gained much popularity and are frequently applied. Microarrays allow the parallel analysis of thousands of
genes in two differentially labeled RNA populations [1], while real-time RT-PCR provides the simultaneous measurement of gene expression in many different samples
for a limited number of genes, and is especially suitable when only a small number of cells are available [2,3,4]. Both techniques have the advantage of speed,
throughput and a high degree of potential automation compared to conventional quantification methods, such as northern-blot analysis, ribonuclease protection assay, or
competitive RT-PCR. Nevertheless, these new approaches require the same kind of normalization as the traditional methods of mRNA quantification.
Several variables need to be controlled for in gene-expression analysis, such as the amount of starting material, enzymatic efficiencies, and differences between tissues
or cells in overall transcriptional activity. Various strategies have been applied to normalize these variations. Under controlled conditions of reproducible extraction of
good-quality RNA, the gene transcript number is ideally standardized to the number of cells, but accurate enumeration of cells is often precluded, for example when
starting with solid tissue. Another frequently applied normalization scalar is the RNA mass quantity, especially in northern blot analysis. There are several arguments
against the use of mass quantity. The quality of RNA and related efficiency of the enzymatic reactions are not taken into account. Moreover, in some instances it is
impossible to quantify this parameter, for example, when only minimal amounts of RNA are available from microdissected tissues. Probably the strongest argument
against the use of total RNA mass for normalization is the fact that it consists predominantly of rRNA molecules, and is not always representative of the mRNA fraction.
This was recently evidenced by a significant imbalance between rRNA and mRNA content in approximately 7.5% of mammary adenocarcinomas [5]. Also, it has been
reported that rRNA transcription is affected by biological factors and drugs [6,7,8]. Further drawbacks to the use of 18S or 28S rRNA molecules as standards are their
absence in purified mRNA samples, and their high abundance compared to target mRNA transcripts. The latter makes it difficult to accurately subtract the baseline
value in real-time RT-PCR data analysis.
Statement of the problem
To date, internal control genes are most frequently used to normalize the mRNA fraction. This internal control - often referred to as a housekeeping gene - should not
vary in the tissues or cells under investigation, or in response to experimental treatment. However, many studies make use of these constitutively expressed control
genes without proper validation of their presumed stability of expression. But the literature shows that housekeeping gene expression - although occasionally constant in
a given cell type or experimental condition - can vary considerably (reviewed in [9,10,11,12]). With the increased sensitivity, reproducibility and large dynamic range of
real-time RT-PCR methods, the requirements for a proper internal control gene have become increasingly stringent.
Purpose and what was done
In this study, we carried out an extensive evaluation of 10 commonly used housekeeping genes in 13 different human tissues, and outlined a procedure for calculating a
normalization factor based on multiple control genes for more accurate and reliable normalization of gene-expression data. Furthermore, this normalization factor was
validated in a comparative study with frequently applied microarray scaling factors using publicly available microarray data.
7/2/2019 14
15. While writing the Background, make sure your
citations are:
Well balanced: If experiments have found conflicting results on a
question, have you cited studies with both kinds of results?
Current: Every field is different, but you should aim to cite references
that are not more than 10 years old if possible.
Relevant: This is the most important requirement. The studies you cite
should be strongly related to your research question.
• DO NOT write a literature review in your Background, but
• DO cite reviews where readers can find more information if they want
it.
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16. Once you have provided background material and stated the problem or
question for your study, tell the reader the purpose of your study.
Usually the reason is to fill a gap in the knowledge or to answer a
previously unanswered question.
Eg. if a drug is known to work well in one population, but has never
been tested in a different population, the purpose of a study could be to
test the efficacy and safety of the drug in the second population.
The final thing to include at the end of your Background is a clear and
exact statement of your study aims. You might also explain (very
briefly!) how you conducted the study.
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18. The Introduction
Primary goal:
Build the rationale for your study
Is the topic important?
Will the paper advance knowledge?
Secondary goal:
Sell your paper to reviewers/readers
Is the topic appropriate for the journal?
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19. The Introduction
Move from broad to narrow
The big picture your study
Why your study question is important?
Will the paper advance knowledge in the field?
Objectives and brief description of study
Do not assume that need for your study is obvious-Sell it to your readers
and reviewers
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20. Appearing first = The importance of the topic
Appearing second = Highlights of relevant previous research
Appearing third = Identification of unanswered question(s)
Appearing fourth = Your hypotheses, if any
Appearing last = Approach you used to seek the answer(s)
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21. Outline for an Introduction
Why did you do this study?
1. Define the general area of the problem.
2. Develop the background of the problem. Include previous studies.
3. State the basis for your study (limited data, a new phenomenon, side-
effects, conflicting previous observations, additional investigation, a
new problem)
4. State why the question your study addressed is important.
5. State the specific problem you studied.
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22. Criteria of writing introduction in The lancet Systematic reviews
and meta-analyses
• Give the background to your study, providing references for data
presented and all previous studies mentioned.
• State why now is an appropriate time to do a systematic review/meta-
analysis?
• End with the aim of your study
Source: Systematic reviews and meta-analyses in The Lancet: formatting guidelines
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23. Criteria of writing introduction in BMC
Background
The Background should provide readers with the information needed to
understand your study, and the reasons why you conducted your
experiments.
• The Background should answer the question:
• What question/problem was studied?
Source: BMC
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24. Criteria of writing introduction in NHRC
Provide a context or background for the study (that is, the nature of the
problem and its significance).
State the specific purpose or research objective of, or hypothesis tested
by, the study or observation; the research objective is often more sharply
focused when stated as a question.
Both the main and secondary objectives should be clear, and any pre-
specified subgroup analyses should be described.
Provide only directly pertinent references, and do not include data or
conclusions from the work being reported.
The word limit for introduction is 150.
Source: http://jnhrc.com.np/index.php/jnhrc/information/authors
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25. Take home message
Just by following the instructions closely, you can increase
your chances of getting published.
Write to inform not to impress
7/2/2019
Thank you
25