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Two of the most common journal rankings are the Journal Impact Factor (JIF) and SCImago Journal Rank (SJR). They both measure journal impact but in different ways. Knowing how they work can help you choose the right journal to publish in.

If you’re deciding where to publish your research or assessing the significance of journals in your field, understanding these metrics can guide your choices. While both JIF and SJR aim to measure journal impact, they approach it differently. Depending on your needs, you might prefer one over the other or consider both for a well-rounded view.

What Is SJR?

Developed by SCImago using Scopus’s database, SJR ranking evaluates the scientific impact of journals on their citations. Unlike a simple citation count, SJR assigns a higher value to citations from prestigious journals, meaning that not all citations carry the same weight.

Key Features of SJR Ranking:

• Citation Quality Is Important: A journal’s SJR ranking is influenced more by citations from reputable publications.
• Three-Year Citation Window: Citations are taken into account by SJR ranking for a duration of three years.
• Field-Normalised: SJR ranking takes into consideration variations among disciplines, which facilitates the comparison of publications from various fields of study.
• Open Access Inclusion: By incorporating open-access journals, SJR ranking provides a more comprehensive understanding of journal impact.

What Is JIF?

The Journal Impact Factor (JIF) provides one of the most well-known indicators of a journal, run by Clarivate and based on Web of Science database. Unlike SJR ranking, JIF is calculated by a straightforward ratio: the number of citations a journal receives in a given year divided by the number of citable articles published in the previous two years.

Key Features of JIF:

• Two-Year Citation Window – Focuses on recent citations.
• Straightforward Calculation – It is simple to understand because it uses a simple average.
• Exclusive to Web of Science – Only Web of Science-indexed journals are included.
• Heavily Used in Academic Promotion – JIF is often a deciding factor in funding applications and tenure evaluations.

Which Ranking Should You Use?

It depends on what you’re looking for.

• If you need a broader view of journal impact that considers citation prestige, SJR ranking may be more useful.
• If you’re in a field where the Journal Impact Factor is commonly used for assessments (e.g., life sciences, medicine), JIF might be the better choice.
• If you’re working with open-access journals, SJR provides a more inclusive perspective.

In Conclusion

While each ranking system has advantages, none of them is a perfect measurement of journal ranking quality. To have a better view of a journal’s influence, think about examining a variety of classifications rather than depending solely on SJR ranking or JIF ranking. Knowing these distinctions will help you make well-informed choices, whether choosing a journal for publication or assessing a research paper.

Sources:

• Clarivate Analytics Impact Factor
• SCImago Journal Rank (SJR)

An index journal acts as a unique quality check for any academic research, however, it also comes with its unique challenges that even the most experienced researchers get disappointed. Starting with access issues to manoeuvring the virus of predatory journals, here are top 4 common problems researchers face when it comes to an index journal and actionable solutions.

1. Access Restrictions

Yes, we are in 2025 and yet, access is still a problem. Many index journals are locked behind expensive paywalls, making it difficult for researchers to retrieve the articles they need.

The Solution:

• Explore open-access options: Open-access libraries have many useful options that may be free or very inexpensive.
• Use platforms like Zendy: Using such platforms is a cost-effective option that can help researchers access infinite articles with ease.

2. Increased Research Time

Looking for academic articles you need in an index journal can be a boring task with the huge volumes of publications to examine.

The Solution:

• Use advanced search tools: Boolean queries and AI tools like ZAIA on Zendy can help improve results and save time.
• Set up alerts: Tools like Google Scholar can notify you about new publications in your field.
• Be mindful of DOI: A DOI (Digital Object Identifier) can be your best friend for quickly locating specific articles. Understanding how it works can simplify your research workflow.

3. Confusion Around Journal Indexing

Not all indexed journals are reputable, and some low-grade journals claim to be worth considering.

The Solution:

• Verify journal indexing: Use databases like Scopus or Web of Science to check a journal’s legitimacy.
• Avoid being a victim of fake journals: Predatory journals are a common trap for researchers. Read The Trap of Predatory Journals: How to Spot and Avoid Them for tips on how to spot and protect oneself from such journals.
• Seek expert advice: Consult mentors or colleagues to help you sort through confusing index journal lists.

4. Rejection of Manuscripts

Rejection is a common challenge for researchers submitting manuscripts to indexed journals.

The Solution:

• Focus on quality: With a little bit of effort on proofreading and formatting, the manuscript can make it easier to accept the submission. Learn about the importance of expert academic proofreading services for successful research to improve your submission.
• Consider alternative options: Rejections are part of the process. Explore other journals in your field and continue to improve the project.

Conclusion

Publishing in an index journal is important in the academic community, but the process can feel overwhelming at times. Using digital tools, learning how journal systems work, and exploring resources like those on Zendy can make the journey easier and more manageable for researchers.

When researchers start collecting qualitative data, they work with non-number-based information such as interview scripts, images, or audio recordings. Analysing this kind of data might seem tough, however, qualitative data analysis gives us organised ways to understand detailed descriptive info. Let’s look at how researchers address analytics in academic research for texts, images, and audio, while also talking about why research data analysis matters overall.

What is Qualitative Data Analysis?

Qualitative data analysis examines and interprets non-numerical data to understand underlying themes, patterns, or stories. Unlike quantitative analysis, which focuses on numbers and statistical relationships, qualitative analysis emphasises meaning, context, and subjective interpretation. This type of research data analysis is often used in fields like sociology, anthropology, psychology, and education.

Steps in Qualitative Research Data Analysis

1. Data Familiarisation

Before jumping into research data analysis, researchers take time to really get familiar with the data. They might read transcripts, examine images, or listen to audio recordings several times. The goal is to fully understand the content and its context.

For example, a researcher looking at workplace communication might listen to recordings of team meetings to get a sense of the tone, how the conversation flows, and the main topics being discussed.

2. Coding the Data

Coding is a fundamental step in research data analysis. Researchers break the data into smaller parts by assigning labels or codes to segments of text, images, or audio. These codes represent themes, ideas, or categories that emerge from the data.

3. Identifying Themes

After coding the data, researchers start grouping similar codes into bigger themes. These themes are the main ideas or patterns that help answer the research question.

4. Interpreting the Data

Interpretation in research data analysis is about making sense of the themes in light of the research goals. Researchers dig into what the data is showing and how it connects to their questions or hypotheses.

5. Presenting Findings

The final step in research data analysis is putting all the insights into a clear, engaging story. This often involves using quotes from participants, highlighted images, or excerpts from audio transcripts to back up the findings.

Tools for Qualitative Data Analysis

While many researchers still analyze data manually, software tools can make the process a lot easier. Softwares like NVivo, ATLAS.ti, and MAXQDA help with coding, organising, and visualising the data. These tools are especially helpful in research data analysis when working with large datasets.

Applications of Qualitative Data Analysis

1. Text Analysis

Textual data includes interview transcripts, written surveys, and documents. Researchers examine word choice, sentence structure, and overall content to uncover insights.

2. Image Analysis

Analysing images in research data analysis involves looking at visual elements like colour, composition, and symbolism. This is often used in media studies, art history, and cultural research.

3. Audio Analysis

Audio data analysis in research, such as recorded interviews or podcasts, requires careful listening to capture nuances like tone, emphasis, and pauses.

The Importance of Data Analysis in Research

Qualitative data analysis is a vital part of research, it helps to uncover the stories and meanings behind the numbers. It gives context and depth to numerical data. By working with non-numerical data researchers can:

• Understand how people think and behave in different situations
• Explore cultural and social trends to see how they shape communities.
• Build theories based on real-life experiences and observations

Qualitative Research Best Practices  

1. Be Clear About Your Purpose

Start with a straightforward question or goal. Why are you conducting this research? Knowing what you’re looking for helps you stay focused and avoid getting lost in the details when conducting research data analysis.

2. Choose the Right People

Who can give you the best insights? Look for a mix of people with different experiences or perspectives. That makes it more valuable.

3. Build a Comfortable Environment

Imagine yourself as one of the participants. Would you be at ease expressing your opinions in this setting? People are more open in an informal, welcoming environment.

4. Keep an Open Mind

The unexpected may lead to the most insightful discoveries. Be adaptable and curious; go with the flow of the discussion.

5. Pay Attention to the Details

Make thorough notes or, with consent, record the conversation. A person’s tone, pauses, and body language can all give away a lot about their intentions.

6. Treat People with Care

Be mindful of participants’ boundaries, privacy, and time. Make sure they understand how their contributions will be used and that their contributions are valued.

7. Organise Your Findings

Sort your data into themes or patterns once you have it. Look for frequent arguments people give when answering your question.

8. Share What You Learn

Use actual quotes or cases as you write up your findings so that readers may see what others are saying in their own words.

9. Keep Learning

Each project is an opportunity to develop your abilities. To improve even more over time, take note of what went well and what didn’t.

In conclusion

Research data analysis in qualitative studies turns raw data into insights. Whether it’s text, images or audio this process helps researchers explore the personal and cultural aspects of their work, to gain a deeper understanding of the experiences and views behind the data. It combines structure with interpretation to make rich descriptive data meaningful.

The Science Citation Index (SCI) and the Scopus database are two of the most widely used citation indexing systems in academic research. Researchers, institutions, and funding bodies rely on these databases to evaluate research impact, track citations, and facilitate collaborations. But how do they compare? And which one should you use for your research?

Who Founded Science Citation Index?

The Science Citation Index (SCI) was introduced in 1964 by Dr. Eugene Garfield to track the flow of scientific ideas through citations. Started as a print-based resource, it later evolved into a digital database, now integrated into the Web of Science. Over the years, SCI made a huge impact on research careers, helping researchers assess the academic influence of scientific papers based on citation data.

A Brief History of the Scopus Database

In the early 2000s, competing databases featuring citation statistics were introduced, the most notable being Scopus, launched in 2004. The Scopus database, launched by Elsevier, provides a broader multidisciplinary citation index. Unlike the Science Citation Index, which traditionally focused on leading science journals, Scopus includes publications from a wider range of disciplines, including the social sciences, humanities, and technical fields.

What is the Difference Between Science Citation Index and Scopus Database

Feature	Science Citation Index	Scopus
Publisher	Part of Clarivate’s Web of Science platform.	Managed by Elsevier.
Scope and Size	Focuses primarily on high-impact scientific journals.	Covers a broader range of disciplines. (social sciences, humanities, and conference proceedings)
Regional Representation	Has a more selective approach.	Includes journals from diverse regions, including non-English publications.
Tools and Metrics	Focuses on traditional citation counts, traditional citation counts and h-index.	Offers advanced metrics like SCImago Journal Rank (SJR) and SNIP.

Why is Science Citation Index important?

The Science Citation Index (SCI) is a critical database for evaluating scholarly reputation and research impact, particularly in scientific fields. Inclusion in SCI is often seen as a mark of prestige, as it only indexes top-tier, high-impact journals. This gives credibility to both the research published within these journals and the researchers themselves. Also, the Impact Factor derived from SCI data is one of the most respected metrics for assessing the significance of research articles and journals

How Science Citation Index Helps Researchers:

• Tracking Research Impact
• Finding Relevant Literature 
• Facilitating Collaborations
  

Why is Scopus important?

Scopus is a key database for you as a researcher looking to stay on top of your field. It helps you track citation trends, see which studies are getting the most attention, and figure out where to focus your publication efforts. Researchers use it to get a clear picture of how their work fits into the broader conversation and which areas are generating the most interest. Scopus also includes a wider range of high impact journals and additional metrics like SJR and SNIP, which could be valuable for certain types of research, especially interdisciplinary or emerging fields.

How Scopus Database Supports Researchers:

• Broader Coverage
• Additional Metrics
• Regional Inclusion
  

Should You use Scopus or Science Citation Index?

• Use Scopus if you need broader coverage, especially across interdisciplinary fields, or if you’re interested in tracking newer trends and conference papers.
• Use SCI if you prioritise high-quality, curated content with a focus on traditional scholarly disciplines and need detailed citation metrics like the Impact Factor.
  

Conclusion

Both the Science Citation Index and the Scopus are essential citation databases for academic research, each with its strengths and limitations. The best choice depends on your research needs—whether you prioritise broad coverage (Scopus) or deep focus (SCI). As citation databases continue to evolve, the focus will remain on improving accuracy, accessibility, and fairness in academic evaluation.

In a recent blog, we discussed responsible AI in research and why it matters. Now, we’ll discuss some AI ethical issues and what you should not be doing with AI in your research journey. This blog looks at common mistakes people make with AI in research, explains why they happen, and offers practical tips to avoid them.

1. Trusting AI Outputs Without Checking Them

One big AI ethical issue is trusting everything AI tools generate without taking the time to verify it. AI models like ChatGPT can produce convincing answers, but they’re not always accurate. In research, this can lead to spreading incorrect information or drawing the wrong conclusions.

Why It Happens: AI systems learn from existing data, which might include errors or biases. As a result, they can unintentionally repeat those issues.

What You Can Do: Treat AI-generated content as a helpful draft, not the final word. Always double-check the information with reliable sources.

2. Using AI for Tasks That Require Human Judgment

Relying on AI for decisions that need a human touch, like reviewing academic papers, is risky. These tasks often require context and empathy, which AI doesn’t have.

Why It Happens: AI seems efficient, but it doesn’t understand the subtleties of human situations, leading to potential AI ethical issues in judgment and fairness.

What You Can Do: Let AI assist with organizing or summarizing information, but make sure a person is involved in decisions that affect others.

3. Not Giving Credit to AI Tools

Even when AI is used responsibly, failing to acknowledge its role can mislead readers about the originality of your work.

Why It Happens: People might not think of AI as a source that needs to be cited, overlooking important AI ethical issues related to transparency and attribution.

What You Can Do: Treat AI tools like any other resource. Check your institution’s or publisher’s guidelines for how to cite them properly.

4. Over-Reliance on AI for Creative Thinking

AI can handle repetitive tasks, but depending on it too much can stifle human creativity. Research often involves brainstorming new ideas, which AI can’t do as well as people.

Why It Happens: AI makes routine tasks more manageable, so letting it take over more complex ones is tempting.

What You Can Do: Use AI to free up critical thinking and creative problem-solving time. Let it handle the busy work while you focus on the bigger picture to avoid these AI ethical issues.

5. Giving AI Access to Sensitive Data

Allowing AI tools to access personal information without proper permission can pose serious security risks.

Why It Happens: Some AI tools require access to data to function effectively, but their security measures might not be sufficient leading to potential AI ethical issues.

What You Can Do: Limit the data AI tools can access. Use platforms with strong security features and comply with data protection regulations.

Final Thoughts

AI can be a valuable tool for researchers, but it’s not without its challenges. Many of these challenges stem from AI ethical issues that arise when AI is misused or misunderstood. By understanding these common mistakes and taking steps to address them, you can use AI responsibly and effectively. The key is to see AI as an assistant that complements human effort, not a replacement.

‘Insights’, a brand-new feature designed to make your research experience faster, simpler, and more accessible.

Insights gives you short, clear summaries of research papers, pulling out the most important information so you can understand the main points in just a few lines. Instead of reading through pages of dense content, you’ll get a quick overview that helps you decide if the paper is worth exploring further. Here’s how Insights can help:

• Save time by getting to the heart of a paper faster.
• Understand complex topics without feeling stuck.
• Focus on what matters and decide quickly what’s relevant to you.

Why We Created Insights?

We’ve heard from many of you that keeping up with research can feel like a never-ending task. There’s so much to read, and it’s hard to know where to start. That’s where ‘Insights’ comes in, to help you make the most of your time exploring the right research paper you are looking for.

How Does It Work?

Insights uses our AI to scan through a paper and extract key points. It focuses on sections like the introduction, methodology, results, and conclusion, so you can get a clear sense of what the paper is about. You don’t have to worry about missing anything important; it’s all laid out in a simple, easy-to-digest format.

Head over to Zendy, search for what you are looking for, and see how Insights can give you a clearer overview in seconds, Check out Insights now!

Artificial Intelligence (AI) is changing how we live, work, and learn. However, as AI continues to evolve, it is important to ensure it is developed and used responsibly. In this blog, we’ll explore what responsible AI means, why it is essential, and how tools like ZAIA, Zendy’s AI assistant for researchers, implement these principles in the academic sector.

What is Responsible AI?

Responsible AI, also known as ethical AI refers to building and using AI tools guided by key principles: 

• Fairness
• Reliability
• Safety
• Privacy and Security
• Inclusiveness
• Transparency
• Accountability

AI vs Responsible AI: Why Does Responsible AI Matter?

Keep in mind that AI is not a human being.

This means it lacks the ability to comprehend ethical standards or a sense of responsibility in the same way humans do. Therefore, ensuring these concepts are embedded in the development team before creating the tool is more important than building the tool itself.

In 2016, Microsoft launched a Twitter chatbot called “Tay“, a chatbot designed to entertain 18- to 24-year-olds in the US to explore the conversational capabilities of AI. Within just 16 hours, the tool’s responses turned toxic, racist, and offensive due to being fed harmful and inappropriate content by some Twitter users. This led to the immediate shutdown of the project, followed by an official apology from the development team.

In such cases, “Tay” lacked ethical guidelines to help it differentiate harmful content from appropriate content. For this reason, it is crucial to train AI tools on clear principles and ethical frameworks that enable them to produce more responsible outputs.

The development process should also include designing robust monitoring systems to continuously review and update the databases’ training, ensuring they remain free of harmful content. Overall, the more responsible the custodian is, the better the child’s behaviour will be.

The Challenges and The Benefits of Responsible AI

Responsible AI is not a “nice-to-have” feature, it’s a foundational set of principles that every AI-based tool must implement. Here’s why:

• Fairness: By addressing biases, responsible AI ensures every output is relevant and fair for all society’s values. 
• Trust: Transparency in how AI works builds trust among users. 
• Accountability: Developers and organisations adhere to high standards, continuously improving AI tools and holding themselves accountable for their outcomes. This ensures that competition centers on what benefits communities rather than simply what generates more revenue.

Implementing responsible AI comes with its share of challenges:

• Biased Data: AI systems often learn from historical data, which may carry biases. This can lead to skewed outcomes, like underrepresenting certain research areas or groups.
• Awareness Gaps: Not all developers and users understand the ethical implications of AI, making education and training critical.
• Time Constraints: AI tools are sometimes developed rapidly, bypassing essential ethical reviews, which increases the risk of errors.

Responsible AI and ZAIA

ZAIA, Zendy’s AI-powered assistant for researchers, is built with a responsible AI framework in mind. Our AI incorporates the six principles of responsible AI, fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability, to meet the needs of students, researchers, and professionals in academia. Here’s how ZAIA addresses these challenges:

• Fairness: ZAIA ensures balanced and unbiased recommendations, analysing academic resources from diverse disciplines and publishers.
• Reliability and Safety: ZAIA’s trained model is rigorously tested to provide accurate and dependable insights, minimising errors in output.
• Transparency: ZAIA’s functionality is clear and user-friendly, helping researchers understand and trust its outcomes.
• Accountability: Regular updates improve ZAIA’s features, addressing user feedback and adapting to evolving academic needs.

Conclusion

Responsible AI is the foundation for building ethical and fair systems that benefit everyone. ZAIA is Zendy’s commitment to this principle, encouraging users to explore research responsibly and effectively. Whether you’re a student, researcher, or professional, ZAIA provides a reliable and ethical tool to enhance your academic journey.

Discover ZAIA today. Together, let’s build a future where AI serves as a trusted partner in education and beyond.

AI’s real potential lies not just in speeding up processes but also in helping users engage more deeply with academic content. Sabine Louët, CEO of SciencePOD sat down with Kamran Kardan, CEO of Zendy to discuss how technology, particularly AI, is reshaping the way researchers and independent scholars access critical information and how research is published.

Removing Barriers in Academic Research

When asked by Sabine Louët “what drove the creation of Zendy?” Kamran Kardan’s response was clear and purposeful: “Zendy was created to remove the barriers that restrict access to academic research”.

He highlights the significant gap that exists for those outside privileged institutions, who often face prohibitive costs or limitations when trying to access essential research. Zendy, he says, aims to make academic content not only affordable but also widely accessible to researchers, students, and professionals globally.

Accessing scientific literature remains a privilege reserved for those with institutional affiliations, leaving independent researchers or those from less-resourced regions at a disadvantage. As Kardan puts it, “Zendy is committed to levelling the playing field”, it offers a legitimate alternative to illicit means of accessing research.

AI’s Role in Enhancing Research Accessibility

AI has become a buzzword, but Kardan stresses the importance of AI in Zendy’s strategy, describing AI as an enabler rather than the focal point. Zendy, he explains, uses AI to enhance user experience by making vast amounts of data more navigable.

One of the platform’s key AI-driven features is its summarisation tool, which allows users to quickly digest complex academic papers. With this tool, users can identify relevant content faster and focus their research efforts more effectively.

A forthcoming feature called ‘findings’, will use AI to group related articles together, offering a comparative perspective on topics and highlighting differing viewpoints. This tool is designed to empower researchers to explore a topic from multiple angles without having to sift through unrelated material.

Safeguarding Research Integrity in the Age of AI

Another point of discussion between Sabine Louët and Kardan was the issue of integrity while also leveraging AI. Kardan acknowledges that this is critically important and explains that Zendy is built on principles of transparency and respect for intellectual property. Their AI tools do not merely extract data but give due credit to authors and publishers.

In addition, the platform’s revenue-sharing model ensures that content creators benefit from the usage of their work, fostering a more sustainable and fair ecosystem for academic publishing.

Kardan also addresses the issue of AI-generated inaccuracies, commonly referred to as “hallucinations”. He emphasises that Zendy’s AI is structured to avoid these risks. If the AI does not have sufficient data to provide an answer, it refrains from making assumptions, thus maintaining a high standard of accuracy.

AI: Not Just Speed, but Deeper Learning

In Kardan’s view, AI’s real potential lies not just in speeding up processes but also in helping users engage more deeply with academic content. The tools developed by Zendy are designed to simplify complex materials, making them more approachable for users across various disciplines, without compromising on the depth of information.

Louët agrees and notes that these features, particularly AI-driven comparison and summarising tools, align with the needs of modern researchers who require both efficiency and reliability in handling academic content.

Looking Ahead: The Future of AI in Research

What does the future look like? Kardan foresees more AI advancements that will continue to transform research access, making it more affordable, transparent and equitable. The focus is not just on technology for technology’s sake but on providing meaningful solutions that directly address the challenges of the academic community.

“AI’s role in academic publishing is still evolving”, says Kardan, “and Zendy is committed to using AI responsibly to enhance access to knowledge, not to replace human expertise”.

If you’ve ever tried to figure out which journal is the best fit for your research or wondered how journals classification is carried out, you’ve probably come across terms like Quartiles, H-Index, Impact Factor (IF), and Source Normalised Impact per Paper (SNIP). These metrics might sound technical, but they are simply tools to measure how much attention a journal’s research gets. Here’s a straightforward explanation of what they mean and how they work

Quartiles in Journals Classification: Ranking by Performance

The system of dividing journals into four quartiles, Q1, Q2, Q3, and Q4, was created to make it easier to compare their quality and impact within a specific field. This idea became popular through Scopus and Journal Citation Reports (JCR) databases, which rank journals based on metrics like citations. The concept builds on the work of Eugene Garfield, who introduced the Impact Factor, offering a way to see how journals stand up against others. Quartiles break things down further: Q1 represents the top 25% of journals in a category, while Q4 includes those at the lower end. It’s a straightforward way to help researchers determine which journals are most influential in their areas of study.

• Q1: Top 25% of journals in the field (highest-ranked).
• Q2: 25-50% (mid-high-ranked).
• Q3: 50-75% (mid-low-ranked).
• Q4: Bottom 25% (lowest-ranked).

However, not all Q3 or Q4 journals are necessarily a disadvantage. While they may not be as well-known, they are still important in scientific research. Some of the benefits include:

• Affordability: These journals are easier for researchers to access, especially for those on a tight budget.
• Focused Topics: They tend to cover more specific, niche areas of study, making them great for in-depth exploration of certain subjects.
• Great for New Researchers: Q3 and Q4 journals classification can be a good place for new researchers to publish their first paper and gain experience in the publishing world.
• Ideal for Basic Research: They’re a great option for research that focuses on the basics of science

Finally, publishing your article in a Q3 or Q4 journal doesn’t mean it lacks value or won’t make an impact. If your work presents new findings that address a real problem, it can still attract attention, even when published in a lower-ranked journal.

H-Index: A Balance of Quantity and Quality

The H-Index score is an important factor in journal classification. It looks at the number of articles a journal has published and how often those articles are cited. It balances quantity (how many articles a journal publishes) with quality (how many of its articles are referenced).

For example, if a journal has an H-Index of 15, it means it has published 15 articles, each cited at least 15 times. It’s a simple way to measure a journal’s influence without focusing too much on just one super-cited article or a bunch of rarely cited ones.

How H-index works:

Let’s say a journal has published 4 articles, and the number of citations for each article looks like this:

• The 1st article has 10 citations – exceeds 1 citation.
• The 2nd article has 24 citations – exceeds 2 citations.
• The 3rd article has 5 citations – exceeds 3 citations.
• The 4th article falls short of 4 citations.

In this case, the journal has three articles that each have at least three citations. The fourth article doesn’t hit the mark, so the H-index stops at 3.

This metric can help researchers, professionals, and institutions decide if a journal publishes research that gets noticed and cited by the academic community. It’s not the full picture, but it’s a useful starting point for understanding the journal’s influence.

Impact Factor: Citation Average

The Impact Factor (IF) is a number that shows how often a journal’s articles are cited on average over the past two years. It helps you understand how much attention the journal’s research gets from other scholars and it also helps with journals classification.

How it works?

To calculate the IF, look at how many times articles from a journal were cited in the past two years. Then, you divide that by the total number of articles the journal published in those two years. This gives you an average citation count per article. 

Example:

Let’s say we want to figure out the IF for Journal A in 2023:

• In 2021 and 2022, Journal A published 50 articles.
• In 2023, those articles were cited 200 times in total.
• You take the total citations (200) and divide it by the total number of articles (50): 200 ÷ 50 = 4

So, Journal A has an Impact Factor of 4, meaning its articles were cited, on average, four times each. A higher Impact Factor often places journals higher in classification, but keep in mind that it’s not the full story. Some specialised journals may have lower Impact Factors even though they’re highly respected in their niche.

SNIP: Fair Comparisons Across Fields

SNIP (Source Normalised Impact per Paper) is a valuable metric in journals classification because it goes one step further. It measures contextual citation impact and takes into account the fact that different research fields have different citation habits. For instance, medical papers often get cited a lot, while mathematics papers don’t, even if they’re equally important in their fields.

SNIP adjusts the average citations a journal receives based on these differences, making it easier to compare journals across disciplines.

Example:

• Journal A publishes in a low-citation field like social sciences and averages 3 citations per article. Adjusted for its field, its SNIP might be 1.6.
• Journal B publishes in a high-citation field like biomedicine and has an average of 8 citations per article. After adjustment, its SNIP might be 1.2.

SNIP makes sure journals in fields with fewer citations still get the recognition they deserve.

What it tells you:

SNIP is especially useful for journal classification because it levels the playing field between disciplines. A higher SNIP score suggests that a journal’s articles are cited more often than expected for its field. It’s a helpful tool for comparing journals, but it’s just one of many ways to evaluate a journal’s influence or importance.

Conclusion 

Metrics like Quartiles, H-Index, Impact Factor, and SNIP are essential tools for journals classification, helping researchers, librarians, and institutions rank journals and understand their influence. Each metric focuses on a different aspect of a journal’s impact.  

But no single number can tell the whole story. A journal might excel in one metric but be less prominent in another, or it might be vital to a specific audience despite modest scores. These tools are helpful guides, but the best journal for your research depends on your goals.