AI and scientific literature work in sync

When scholars choose a topic to work on their research, they need more sources or materials to review literature and add more value to their findings. According to Canadian science publishing’s article from last year, 2.5 million research papers are published annually while another unidentified source suggests that new researches are published around the world; approximately 1 million each year! Which is equal to one every 30 seconds. With the overload of new papers in each field and more growing every year it is practically impossible for scholars to keep with the information that is put out in each paper. Christian Berger’s team from the University of Gothenburg in Sweden, found a staggering number of papers on the subject; more than 10,000 in the same subject. Fortunately, the team had the support of an AI system, a writing investigation tool called Iris.ai.

Iris.ai is an AI, a tool developed for scholars to make writing research papers easier. It is a Berlin-based company that claims to save 90% of time with 85% precision of data matching, has more than 70 m open access papers. Iris.ai is programmed to learn about the topic provided and perform an elaborate frequency analysis over the text. Then it read the words for which it needs to find results and additional material that could be helpful for the paper. It uses a 500-word description of the researcher’s issue, or the link of their paper and the AI restores a guide to thousands of coordinating reports. As the website suggests, it is a scientific writing assistant.

According to Berger, it was “a quick and nevertheless precise overview of what should be relevant to a certain research question”. Iris.ai is one among many of the new AI-based tools offering targeted results of the knowledge landscape. One such tool is called Semantic Scholar, produced by the Allen Institute for Artificial Intelligence in Seattle, Washington, and Microsoft Academic.

Although every instrument is different from each other and gives different output, they all provide researchers with a different look at the scientific literature than conventional tools such as PubMed and Google Scholar. Semantic Scholar is a browser-based search tool that mimics the engines like Google and it is free. But it is more informative than Google Scholar in terms of specific results required by researchers. Doug Raymond, Semantic Scholar’s general manager, says that one million individuals utilize their service every month. It uses natural language processing or NLP to extract data while building connections to determine if the information is relevant and reputable or not.

Artificial intelligence is saving a lot of time and making it easier and quicker to automate some procedures. In the academic publishing industry, the Al-based innovations are being produced and implemented to help both authors and publishers for peer reviews, searching published content, detecting plagiarism, and identifying data fabrication. AI could be costly, but it can accelerate a researchers’ access to new fields. More and more such AI tools are being developed to cater to various requisites of writing a paper, such as filtering topics for relevance, keyword search, etc.

Experts who need more assistance for their specific concerns might consider free Al­ tool such as Microsoft Academic or Semantic Scholar. While AI is easing so many burdens and saving time for a researcher, let’s not forget that it is still machine intelligence and may require human intervention here and there to make a paper more presentable and precise.

Copyright Myths vs. Facts

The objective of copyright law is to provide a legal framework to assure creators of original work that their artistic talent is fairly rewarded and protected.

The advancement in technology and the Internet has made it much easier for works to be created, published, and copied as well. With increasing digital content, there are many misconceptions regarding the copyright law circulating online. This is an attempt to clarify the myths and facts associated with copyright law.

Copyright covers a wide range of creations that include literary works, artistic works, development software, computer programs, movies, music, etc. There is a myth that one can copy or republish a particular content as long as credit is given to the original owner. But the fact is that the exclusive right to copy content belongs to the owner of the copyright only and no one is entitled to reuse a work in any way unless an explicit permission is obtained from the owner.

It is also assumed that content published online becomes public property as it is in the public domain and it diminishes copyright protection on it. But the fact is that the authors have exclusive right on the published content, so other users should take measures to confirm ownership of the content before publication.

There is a third misconception that any work that doesn’t bear the copyright notice or tag is open to be reused. However, copyright exists in a work from the moment the content is recorded in any fixed and observable form. Non-display of the copyright note does not confer any permission on anyone to reuse that work.

Another area of uncertainty is regarding derivative works. The fact is that such works are still covered by copyright. It is believed that if one modifies or partly uses someone else’s work, it doesn’t breach the copyright law. On the contrary, it is only treated as a ‘fair use’ when a person accesses the work just as a reference.

There is another gray area related to the financial gain from the protected content. It is presumed that a person can use another person’s work if there is no financial gain or profit from it. But in reality, copying someone else’s work is a breach of copyright law, and money is not a consideration in that case.

Many myths persist about fair use, which is an essential right that allows the use of copyrighted material under certain circumstances. Therefore, while creating, sharing, and consuming media on the Internet may be easier than ever before, copyright regulations are still in force and must be respected.

Things to avoid to frame a good research paper title

Drafting a good research paper title needs serious thought. Researchers focus so much on their research findings that they tend to forget the important role played by the title of their paper. Though it seems a simple task, in reality the process of choosing a suitable title demands consistent thinking and attention. This step is a critical one because readers will search online and through databases and bibliographies based on the title. Therefore, it is imperative that you have a title that can drive your targeted audience/readers to your research paper. An interesting research topic combined with an accurate title will definitely draw more attention to your work from peers and the public.

There are many pre-set criteria that help researchers write a perfect research paper title. But it would also be helpful to have a list of what should never go into the title of a journal article. The following list can act as a useful reckoner about what to avoid in your research title in order to increase the impact of your research.

A Handy List of Don’ts

  • The period is generally not used in a title (even a declarative phrase can work without a period)
  • Any type of dashes to separate title elements or hyphens to link words is allowed.
  • Chemical formulae should be noted in their generic or common names. For example H2O, CH4, etc should be avoided.
  • The title should not include roman numerals (e.g., III, IX, etc.).
  • Try not to include semi-colons; however, the colon can be used to make two-part titles.
  • The taxonomic hierarchy of species of plants, animals, fungi, etc. is not needed.
  • Abbreviations confuse readers, so they should be avoided (except for RNA, DNA which is standard now and widely known).
  • Initials and acronyms should not be included as they create confusion. (e.g., “Ca” may get confused with CA, which denotes cancer).
  • It’s good to avoid query marks as they probably decrease the number of citations, but a query mark is useful in economics and philosophy papers or when the findings are undecided).
  • Too many offbeat words can influence the Altmetric Attention Score; using common words is better.
  • Avoid using numerical exponents or units (e.g. km-1 or km/hr).
  • Phrases should be direct and factual (e.g., “with” could be rewritten with the more specific verb “amongst”).
  • Complex drug names should be avoided (use the generic name if allowed to).
  • Do not include obvious or non-specific openings with a conjunction (e.g., “Report on,” “A Study of,” “Results of,” “An Experimental Investigation of,” etc. because they don’t contribute any meaning).
  • Italicize only species names of studied organisms.
  • Avoid using shortened scientific names (write Escherichia coli and not coli).
  • Try to wrap the title within 50 to 100 characters as shorter titles are cited more often.

These steps would help a researcher to form an effective and relevant title for their research paper. A title should be interesting predicts the content of the research paper and also reflect the tone of the writing.

Research Writer’s Block: What is it and how to overcome?

It is very common in the research fraternity to hear discussions going on about the inability to put down their ideas and thoughts on paper. After the completion of their research work when researchers plan to put down their findings in pen and paper they realize that their hands are paralyzed and thoughts do not come to their mind. Most researchers do not know that such a condition is common and is known as writer’s block.

Writer’s Block

Writer’s block is more of a mental block that the writers experience. There are also some psychological researches that suggest that there is no such thing. But the fact that almost all researchers experience this cannot be denied. One of the main causes of writer’s block is anxiety and this problem can be aggravated if the researcher is not familiar with English, as this is the language that is generally used the world over.

Overcoming Writer’s Block

There are some common strategies followed by experienced researchers to overcome this mental block and complete their research writing within time.

Social Writing: It is a very good idea to join a support or writing group to stay motivated till the end. Social writing reduces the root cause of writer’s block – anxiety, which in turn stimulates writing. Writing while sitting in a group, discussing about the progress, sharing writing goals and achievements helps to understand writing better and increase the flow of creativity. Social writing generates realistic goal-setting and dedicated writing time. With social writing, the need for help or instruction may not be required.

Block Some Time of the Day Exclusively for Writing: Reserve some time of the day for writing, so that you write everyday and avoid the writer’s block setting in again. Morning is considered the best part of the day for writing, the mind is fresh and at its creative best. So, try to write in the morning before checking your email or surf the net to avoid any kind of distraction. The key idea is to write daily, even if it is for 15 minutes then gradually increase the time of writing.

Draft: First write down anything that comes to your mind, without worrying about the grammar and correctness of the sentence. First, jot down your ideas and the content that you want to include in your research paper and then refine it to convert it into your final copy.

So, we can say that the writer’s block is only the creation of the mind, which can be avoided by keeping calm, focused and consistent. All other things will fall into place if the brain is tricked to believe that the writing will be over within time and it will be up to the mark, without any data being missed.

Formatting tables, graphs, and other visuals in your research paper

The format in which you present your research data is very important because it helps you communicate your data to your reader and editors in the best possible way. Although there are many formats in which tables, graphs, and other figures can be presented, you need to choose the best format for your category of data, provided it is within the prescribed guidelines of the journal you are targeting for publication. Before reviewing a paper, many journal editors and reviewers first glance at the layout of the manuscript in terms of its text, tables, figures, and illustrations. Therefore, to make your presentation effective while presenting a large amount of information, a suitable distribution between text, tables, and figures comes handy.

How to use

Sometimes using too much text can get tiresome and confusing, making the reader lose interest or miss data. So encapsulating the information into visual representations can help summarize your analysis. Centralizing the important findings will help readers get the outline without reading the whole manuscript. However, excessive use of visuals can hinder the flow of text and make the whole presentation abrupt. To achieve the highest impact, a proportionate combination of text and visuals always pays off.

Understanding the message

The intension of using a chart, graph, or table is for one of four primary reasons. One illustration might be intended to show a relationship, while another wants to show the composition of a dataset. A third visual could be plotting distribution of data, while a fourth could be comparing various data points. Therefore, you need to understand the objective of the visual before choosing the format; one format may justify one goal but might not fit another.

A relationship is the correlation and connection between the variables of the data presented, like the value of a particular share today versus the value over the year.

A composition is the set of all variables present in the manuscript to make a conclusion, like the total sale of your product, sale online, sale in retail, and direct sales.

A distribution is a representation of all the correlated and non-related data to determine the connection and pattern if any, and the interaction between the variables.

A comparison is the process of finding out the similarities and differences between sets of variables.

Best format for you

Graphics are best for putting your point forward while tables work fine for providing a structure to numerical information. Different formats that work best for various situations are:

  • A bar chart or bar graph shows correlation between distinct categories. One axis shows the particular categories being compared, and the other axis depicts a calculated value. Some bar graphs show bars bunched together in groups of more than one, showing the values of multiple measured variables.
  • Pie charts are generally applied to represent the rate and proportionality of information, and the rate of percentage depicted by every category is marked next to the corresponding portion of the full pie.
  • Line graphs can be used for more than one variable to show the change over time simultaneously.
  • Scatter plots and line graphs are alike, as both use horizontal axes and vertical axes to plot data information points. Scatter plots are used to show the degree to which one variable is affected by another variable, or the connection between them.

Understanding the Structure and Purpose of Systematic Reviews

Defining systematic review:

A systematic review is a well-planned literature review that basically answers a focused research problem, with pre-defined inclusion and exclusion criteria.

Steps involved in systematic review:

The first step involved in drafting a systematic review is identification of the REAL research problem. For this you need to search for valid literatures dealing with your subject area and locate the research gaps in those studies. This will assist you in devising an appropriate research question. In general, researchers use the PICO framework to define the question scope. Its anatomic parts are as follows:

P-Problem/Population

I-Intervention

C-Comparison, and

O-Outcome

The second step involves setting the inclusion and exclusion criteria that will further determine which studies are you going to include in the systematic review. Here are few parameters that are taken into consideration while zeroing down on a relevant study:

– Population

– Study design

– Type of intervention

– Grouping

– Outcomes of the study, and so on.

Thirdly, you need to carry out the real work of spotting out those inclusive studies by taking help of databases, such as online libraries, online searches, and so on. Then simply insert this retrieved information into a reference manager, such as EndNote, Cite This For Me, Reference Generator, and so on.

The next approach will be to extract data from these studies by using a tool, software or excel sheet. This will assist the researcher in evaluating the study bias if any. For this, you can use a risk of bias tool, such as Cochrane tool, for assessment of potential study or sample bias.

Finally, the results have to be presented along with the methodology section, which includes the criteria of selection, strategies, and so on. A meta-analysis is done, if necessary. Future recommendations can also be cited in this section, regarding any change in the policy or clinical/non-clinical practice.

In this blog, we have tried to summarize the complete process of writing a systematic review in a uncomplicated manner, and along with this, we have also tried to explain the quality elements included in each step of systematic review.

Does Journal Acceptance Rates Matter?

Generally, journal acceptance rates or rejection rates are journal tools to assess the trend of rejection or acceptance, and also, to monitor any discrepancies occurring in it. These rates are an internal quality control benchmark, whereas the impact factor is an external benchmark.

Factors affecting journal acceptance rates

A lot of factors determine the journal acceptance rate. The two most important factors have been listed below:

  • Quality check: The rates depend on the quality of papers submitted to a journal.
  • Number of papers in the pipeline: It is the number of papers under review and not the papers submitted that is taken into consideration while calculating acceptance rates.

Most of the journals avoid publishing the journal acceptance rate on their website as they are of the view that authors might be put off by a lower rate of acceptance. Those journals that do mention do so mostly in their “About Us” or “Overview” section. Journal editors reject papers for various reasons like low novelty value or the standard of research is not up to the mark. The Editors or peer reviewers provide useful comments to authors as feedback. Most editor review papers only on the basis of scientific content ignoring the language part; but if they feel that the paper stands a chance for publishing after revision, then they also advise authors to seek professional help to improve the language of the paper. Hence, authors should not use acceptance/rejection rates as a basis for selecting a journal. However, a general conclusion can be drawn stating that higher the acceptance rate, higher is the probability of a paper being accepted.

In spite of smaller speciality and open access journals having a higher acceptance rate, most researchers prefer to submit in high impact factor journals as they have higher visibility. It is very evident that journals with high impact factor tend to uphold their quality, and hence, are very particular about the quality of research and also the paper. This leads to higher rejection rate or lower acceptance. Sometimes it also happens that authors submit their papers to high impact factor journals in the hope of getting published, even when they are aware that their paper is not a perfect match to that particular journal, thus decreasing the journal’s acceptance rate.

Journal acceptance rates do not hold much relevance in the era of open access publication. It is upon the researchers as to what matters the most to them- is it the journal acceptance rate or the journal impact factor?

Publication Cycle: An Overview

Every manuscript submitted to a journal has to progress through the complete publication cycle before it finally gets published. The publication cycle takes genesis with the research idea. The researchers take this idea to a new horizon by conducting experiments, taking into account the previous publications that deal with similar topic. The research draft is then submitted to a journal that is followed by the assessment, reviewing, and further production processing before being published. Let’s discuss the different phases in detail so that we can get a bird’s eye view of the entire publication process.

How publication cycle works?

After the manuscript is submitted, it is first screened by the Editor-in-chief; if rejected, the paper is returned to the author, and if accepted, it goes to the next level. Here, the paper is checked for plagiarism, and conformity to the journal guidelines. Once the manuscript clears this technical round it is then sent for review by a panel of reviewers, who are subject experts. Here, the reviewers either reject the paper for lack of novelty or other reasons that might be study specific or they could either accept the paper or suggest revisions before acceptance. The paper with revisions is sent to the Editor-in-chief for approval, before being sent for a second and final round of review. At times, the paper gets rejected even after coming this far. If the paper is accepted it then goes through the in-house publication process, before finally getting published.

Some journals forego this time taking and tedious process and instead publish all manuscripts after checking it for novelty, relevance to the field of study and compliance to the style guide of the journal. This ensures a shorter review time and faster publication.

How long can a publication cycle run?

The publication cycle time of a journal cannot be assessed unless and until it is specified by the journal. Hence, it is difficult to know which journals have a fast publication cycle. Some journals take months before they give their first decision whereas some let their first decision known in a couple of weeks. Generally, the time gap between submission and first decision varies between 2-3 weeks.

What if the publication cycle is slow-moving?

The slow decision process becomes mentally tasking for researchers, as they spent many anxious months and even years before they actually get to know if their manuscript has been published or not. If their manuscript does not get published they have to again go through the same process of preparing their manuscript according to the guidelines of a new journal, submit it there and again wait for its decision. This cycle sometimes goes on for a few years before the manuscript gets published. In this process sometimes it so happens that the relevance of the paper or the research gets outdated by the time it is accepted for publication, thereby making the efforts of the researcher futile.

It is for these reasons that the researchers earnestly want a fast publication cycle, where they do not have to wait for so long to get a decision on their manuscript. Also, the publication houses are trying their best to formulate ways to make the publication process faster so that good and relevant researches do not become irrelevant. However, the authors need to be aware of the millions of predatory journals luring them for publishing within a very short duration. The authors are the best person to judge their options and choose the one that helps their research best.

Bibliometric/Scientometric Indicators

Bibliometrics is a group of mathematical and statistical methods that are used to analyse and measure the quantity and quality of different forms of publications. Basically there are three types of bibliometric indicators:

  • Quantity indicators: These measure the productivity of a researcher.
  • Quality indicators: These measure the performance of a researcher.
  • Structural indicators: These measure the connection between publications, authors, and areas of research.

Bibliometric indicators influence funding decisions, appointments, and promotions of researchers; therefore, it is important for scholars as well as organisations.

Journal-level Bibliometric

Impact Factor

Journal Impact Factor is the most prevalent bibliometric indicator among journals. It is an assessment of how frequently articles published in a particular journal are cited on an average in the two years following their publication. The greater the impact factor, the more prominent the journal. The other well-known and widely accepted bibliometric indicators are:

SCImago Journal Ranking (SJR)

SJR takes into account both the number of citations received and the significance of the journals from where such citations are sourced. SJR computation uses an algorithm similar to Google PageRank.

Source Normalized Impact per Paper (SNIP)

SNIP assesses the impact of contextual citation by measuring citations based on the total number of citations in a particular field of study. SNIP is defined as the ratio of a journal’s citation count per paper and the citation potential in its subject field.

Impact per Publication (IPP)

This mode of measurement calculates the ratio of citations in a year (Y) to scholarly papers published in the three previous years (Y-1, Y-2, Y-3) divided by the number of scholarly papers published in those same years (Y-1, Y-2, Y-3).

Author-level Bibliometric

Bibliometric indicators measuring the impact of individual authors are known as author-level metrics.

H-index

H-index measures both the productivity and impact of the published work of a researcher. It is the most well-known author-level metric at present.
However, h-index has the following shortcomings:
• It does not account for highly cited papers, i.e. the h-index of the author remains the same whether their most highly cited paper has 100 or 10 citations.
• It does not take into consideration the career span of the author. This is because it is only dependent on productivity and impact. Therefore, authors with longer career spans and more publications will always have higher scores.

To overcome these shortcomings of h-index, the following variants were proposed:

G-index

It is an author-level metric for quantifying scientific productivity based on publication record. G-index is found by analysing the distribution of citations received by a specific researcher’s publications.

M-index

It is defined as the h-index divided by the number of years the researcher has been publishing papers.

Usage of machine translation software in academic writing

The number of research articles submitted by non-native English speaking authors is increasing rapidly. However, the language barrier and time constraints are hindering their publication in English journals. With an intention to expand the reach of such innovative researches to other scholars and researchers, automated or machine translation software is a trending tool among academicians.

Akin to online proofreading software, the machine translation system is readily available on the web at little or no fee. Software such as Google Translate, Bing Translator, and Babel Fish are widely used in translating content through the rules-based systems. These systems are based on the translation techniques that analyze word patterns in the text in the documents that have been previously published or translated.

Cons of machine translation

Though useful to some extent, machine translation causes several errors in the document, thus affecting comprehension. Some serious mistakes recorded till date include:

  • Unnecessary fragments of the sentences in the translated material
  • Redundant and lengthy sentences creating confusion
  • Phrases ordered in an illogical manner
  • Word-by-word translation instead of contextual translation

The poor sentence structure along with errors in syntax and terminologies result in lack of clarity in the content and affect readability and comprehension. Eventually, the translated manuscripts or articles get rejected by journal editors because of a lack of clarity and coherence.

Machine translation software vs. Human Translators

Automated translation systems have been used for several years with the aforementioned drawbacks. Hence, the idea of utilizing machine translation software, i.e., Google Translate, Bing Translator, and Babel Fish, etc., is a risky one. Conversely, it is more advisable to use the expertise of academic translators to maintain or even enhance the integrity of the research material. Even if more expensive, manual translation services are worth it because they add credence to your manuscript.