If you’re doing research, you’ll want to use reliable sources. Peer-reviewed articles are among the best because experts review them before they’re published, ensuring quality and credibility. The benefits of expert peer review in research are significant—it helps maintain high standards, validates findings, and improves the overall reliability of academic work. But how do you find these peer-reviewed journal articles, and how can you tell if a journal is peer-reviewed? Let’s break it down.
What Are Peer-Reviewed Articles?
A peer-reviewed article is one that has been checked by other experts in the field before publication. This process helps make sure the research is solid and trustworthy.
Where Can You Find Peer-Reviewed Journal Articles?
You can find peer-reviewed articles in a few different places:
University Libraries – If you’re a student or faculty member, your university library probably gives you access to research databases.
Academic Databases – Websites like PubMed, JSTOR, ScienceDirect, and Web of Science have collections of scholarly peer-reviewed articles.
There are 4 ways to check if a journal is peer-reviewed or not:
Visit the Journal’s Website – Look for a section about their review process.
Use Library Databases – Many academic databases label peer-reviewed journals.
Check the Editorial Board – A peer-reviewed journal usually lists experts who review submissions.
Look It Up on Ulrichsweb – This directory can tell you if a journal is peer-reviewed.
Are Google Scholar Articles Peer-Reviewed?
Not necessarily. Google Scholar collects all sorts of academic work, including conference papers and preprints, which may not have gone through peer review. To check, see if the article was published in a reputable journal.
What Is a Peer Review Example?
Let’s say a scientist submits a research paper to a journal. The editor sends it to other experts, who review it for accuracy and clarity. And then they might suggest changes or reject the paper if it doesn’t meet the journal’s standards. So if the paper gets approved, it’s published as a peer-reviewed article.
What’s the Difference Between Peer Review and an Original Article?
Peer Review – A process where experts evaluate a research paper before it’s published.
Original Article – A research paper that presents new findings. Some original articles are peer-reviewed, while others aren’t.
How To Select Peer-Reviewed Journal Articles?
When looking for peer-reviewed articles:
Check the Journal – Make sure it’s known for publishing peer-reviewed articles.
Look at the Author’s Background – Are they an expert in the field?
Review the References – Good research builds on other credible studies.
Use Trusted Databases – Databases like Zendy, PubMed, and Scopus focus on peer-reviewed work.
How Do You Know If a Peer-Reviewed Article Is Credible?
Even among scholarly peer-reviewed journals, some are more reliable than others. Here’s what to look for:
Reputation of the Journal – Some journals have stricter standards than others.
Possible Bias – If a study is funded by a company with an interest in the results, for example, a pharmaceutical company funding a study on its own medication might have an interest in positive findings, be cautious.
Strong Research Methods – A reliable, peer-reviewed article clearly explains how the research was conducted and how conclusions were reached.
Retraction History – Some papers are later retracted due to mistakes or misconduct. Check if the article has been retracted.
Final Thoughts
It’s not hard to find peer-reviewed journal articles; it just takes a trusted source and a clear understanding of what you’re looking for. Digital libraries like Zendy give you access to everything you need in one place, including both free and paywalled peer-reviewed articles, with over 40 million articles across disciplines like engineering, medicine, economics, and more.
The impact factor of journals is a crucial academic publishing metric, serving as a measure of a journal’s influence and importance within its field. For you, as a researcher, and institutions alike, understanding this journal classification is essential for making informed decisions about where to publish and which journals to follow.
In the early 1960s, Eugene Garfield with the help of Irving H. Sher created the Journal Impact Factor (JIF) to help select journals for the Science Citation Index (SCI). They developed this metric by re-sorting the researcher citation index into a journal citation index.
Initially, the Impact Factor was used internally by ISI to compile the Science Citation Index. In 1975, ISI began publishing the Journal Citation Reports (JCR), which included the Impact Factor calculations for journals.
How is the Impact Factor of Journals Calculated?
By calculating the average number of citations received by articles published in those journals over a set period of time, typically two years.
For example, the 2022 impact factor of journals is calculated as follows:
Journal Impact Factor (JIF) = A / B
Where:
A = Total number of citations in a given year (e.g., 2023) to articles published in the previous two years (e.g., 2021 and 2022).
B = Total number of citable items (articles, reviews, etc.) published in those same two years (2021 and 2022).
What Does the Impact Factor of Journals Tell a Researcher?
The impact factor of journals provides you with valuable insights into a journal’s influence and importance within its field. Here’s what the impact factor tells you:
1. Journal Quality: A higher impact factor generally indicates a more prestigious and influential journal in its discipline. This can help you identify high-quality publications for your work.
2. Citation Frequency: The impact factor reflects the average number of citations received by articles published in the journal over a specific period. This indicates how frequently the journal’s content is cited by other researchers.
3. Visibility and Reach: Journals with higher impact factors tend to have broader readership and greater visibility in the academic community. Publishing in these journals can increase the exposure of your research.
4. Research Influence: The impact factor of journals can serve as a proxy for the potential influence of research published in a particular journal. It suggests how impactful the average article in that journal might be.
5. Career Advancement: Publishing in high impact factor journals can be crucial for academic and professional advancement, often considered in tenure decisions, grant applications, and professional evaluations.
6. Comparative Tool: Researchers can use the impact factor to compare journals within the same field, helping them make informed decisions about where to submit their work.
However, it’s important to note that the impact factor has limitations. It doesn’t measure the quality of individual articles, and it can be influenced by factors such as the number of review articles a journal publishes. You should consider the impact factor alongside other metrics (e.g., SJR scores), and qualitative assessments when evaluating journals for your research.
What is a good impact factor?
The impact factor (IF) is a metric used to evaluate the influence and quality of academic journals by measuring the frequency with which their articles are cited. Generally, a higher impact factor indicates a more influential journal within its field. However, “good” impact factors vary significantly across different disciplines. For instance, in biochemistry, impact factors are often categorized as follows:
Good: 2–4
Great: 5–8
Awesome: 9–14
Excellent: Above 14
It’s important to note that these ranges are approximate and can vary based on specific research areas. Additionally, while impact factors provide insight into a journal’s citation frequency, they do not necessarily reflect the methodological quality or societal impact of individual articles. Therefore, when assessing research quality, it’s advisable to consider multiple metrics alongside the impact factor.
What are the Highest Impact Factor Journals
Some of the top impact factor journals include:
Medical and Life Sciences – CA-A Cancer Journal for Clinicians (254.7) – The New England Journal of Medicine (91.245) – The Lancet (79.321) – Nature Reviews Molecular Cell Biology (94.444)
Computer Science and Engineering – IEEE Transactions: Systems, Man, and Cybernetics (13.451)
These top-tier journals represent the pinnacle of academic publishing, often featuring groundbreaking research and influential studies.
Academic Journal Impact: Beyond the Numbers
While the impact factor of journals is a valuable journal ranking, it’s important to consider other factors when evaluating academic influence:
Field-specific considerations: Impact factors can vary significantly between different academic disciplines
Citation patterns: Some fields have faster citation cycles than others, affecting impact factor calculations.
Journal scope: Specialised journals may have lower impact factors but still be highly influential in their niche.
Conclusion
Understanding the impact factor of journals is crucial for researchers navigating the academic publishing landscape. While it’s a valuable metric, it should be considered alongside other factors when evaluating journal quality and influence. By staying informed about impact factors and their implications, researchers can make more strategic decisions about where to publish their work and maximise the visibility and impact of their research.
Conducting and writing a literature review has always been the most time-consuming task of any academic research. Weeks of reading countless scientific papers (if not months), summarising key points, and identifying gaps in existing research. Fortunately, AI is making this process a lot easier, faster and more efficient. In this blog, we’ll go through the best AI tool for literature review in 2025.
Why Use AI for Literature Reviews?
Before we dig into the list of the best AI tool for literature review, let’s ask ourselves, why use AI in the first place? The answer is very simple:
Saving Time: AI literature review tools can quickly scan thousands of research papers and extract relevant information in seconds.
Improving Accuracy: AI tools in research can help you identify key themes, citations, and trends, reducing the chances of missing important studies.
Enhancing Organisation: Many AI tools for literature review offer smart categorisation, tagging, and citation management, ensuring a well-structured literature review.
Best 5 AI Tools for Literature Review in 2025
Here are the top AI tools that can help you conduct a literature review:
ZAIA is not just the best AI tool for literature review, it’s also one of the best personal AI research Q&A assistants that will help you effectively explore a large amount of academic research. Keyphrase highlighting, summarisation, PDF analysis, and AI insights make it a great AI tool for the literature review process.
2. Elicit
Elicit uses AI to automate the research process, allowing you to generate structured summaries, find relevant papers, and extract key insights without manual searching.
3. Research Rabbit
This tool is known for its unique visualisation of research connections. It helps users discover related papers and track the evolution of ideas across different studies.
4. Scite
Scite provides citation analysis with AI-powered insights, allowing researchers to evaluate how a study has been cited in different contexts—supportive, contrasting, or neutral.
5. Semantic Scholar
Powered by AI, Semantic Scholar enhances literature discovery by providing smart recommendations, citation tracking, and insights into academic papers.
How to Write a Literature Review Using AI
It can be quite difficult to write a literature review, but AI can help in several ways:
Summarise Key Points: Summarisation by AI condenses long written materials to easily readable insights.
Rewrite and Paraphrase: AI is also useful in manuscript improvements to guarantee clarity and consistency while maintaining professionalism in academia.
Ensure Proper Citations: AI citation tools help with reference management and formatting.
Refine and Edit: Make your literature review more polished and professional by using writing tools to improve readability and flow.
What Is the Difference Between an Annotated Bibliography and a Literature Review?
Annotated Bibliography
Literature Review
Purpose
Summarises and evaluates each source individually
Synthesises and analyses sources collectively
Structure
Organised as a list of citations with annotations
Organised thematically or methodologically
Depth of Analysis
Focuses on each source’s contribution
Identifies patterns, gaps, and trends in research
Use in Research
Often used as a preparatory step for literature reviews
Used as a foundation for research projects or theses
Writing Style
Concise, source-focused
Integrative, argument-driven
Is It Ethical to Use AI for Literature Reviews?
When using AI-powered literature review tools, keep these principles in mind in order to not compromise your research integrity:
Remember that AI is a tool, not a replacement for human expertise
Critically evaluate the information provided by AI tools
Exercise judgment when incorporating AI-generated insights into your research
By following these guidelines and leveraging AI tools effectively, you can conduct a more efficient and insightful literature review while maintaining the integrity of your research process.
Disclaimer: AI-generated content should always be reviewed and verified by researchers to ensure accuracy and ethical compliance in academic work.
Conclusion
AI tools for literature review are making literature reviews easier, faster, and more organised. Whether you’re a student or a researcher, the right tool can help you sort through academic papers, find key insights, and manage citations without getting overwhelmed.
Each tool on this list has something useful to offer. ZAIA is a great choice if you’re looking for the Best AI tool for literature review that highlights key points, summarises research, and helps you navigate academic papers more efficiently. Elicit and Research Rabbit are helpful for finding related studies, while Scite and Semantic Scholar can guide you through citations and academic trends.
AI won’t do all the work for you, but it can take some of the pressure off. If you haven’t tried using AI for your literature review yet, now might be a good time to start.
With millions of scholarly content published every year, in addition to the integration of Artificial Intelligence (AI) into various fields in the past few years, including the educational sector, AI in research has had, and still, a major impact on simplifying research projects, accelerating discoveries, and optimising learning experiences. Giving students and researchers the chance to work efficiently and effectively more than ever. So, how much time do researchers spend on repetitive tasks that AI can simplify?
On the other side of the story, The global market for AI in education was estimated to be worth $2.5 billion in 2022 and is expected to more than double by 2025, according to the most recent data from AIPRM. However, how exactly are researchers using AI, and what are the challenges they face?
This means that Artificial Intelligence (AI) is changing every aspect of modern life, including education and research. It’s reshaping how students learn, how researchers solve problems, and how educators teach. According to our latest survey, 73.6% use AI in research, 51% use it for literature review and 46.3% of students and researchers are using AI in research for writing and editing, showing just how quickly these tools are being adopted.
AI in research helps by sorting through the tremendous amount of scientific information, analysing large datasets of structured or unstructured data, and spotting connections that might take months to find manually. It also takes care of time-consuming tasks like summarising studies and formatting citations, so researchers can focus on bigger questions. With so much information and so little time, AI isn’t just helpful, it’s becoming a necessity.
Zendy surveyed more than 1,500 students and researchers to understand how they use AI tools. The study shows how people incorporate AI into their work, the benefits they find most useful, and the challenges they face. The findings give a clearer picture of AI’s role in academic work and its impact on productivity.
Zendy’s study provides insights into who is using AI in research. Most respondents are young learners, early in their academic journey, which gives us a sense of tools and support they’re looking for.
60.1% of respondents are female, 36% are male while the rest prefer not to disclose.
67.6% are between 18-24 years old, reflecting early-career researchers and students.
45% are undergraduate students, 37.2% are high school students, exploring AI tools for learning and research
Students and researchers are highly engaged in academic literature and are shifting toward AI-driven tools for efficiency. Zendy’s survey reveals how dedicated students and researchers are to expanding their knowledge and staying current in their fields. Regularly engaging with academic literature is a key part of their studies and professional growth, reflecting the effort they invest in learning.
The survey also shows a clear preference for online databases, highlighting a growing reliance on digital tools for easy access to research materials. This shift points to a broader move toward more convenient and centralised platforms, supported by the use of responsible AI and other technologies in academic work. These findings underline the importance of user-friendly, well-resourced tools that meet the changing needs of today’s learners and professionals.
71.5% read research papers daily or several times a week, indicating high engagement
49.3% of respondents spend an average of 4.5 hours each day engaging with research papers
50% prefer online databases for accessing research articles, reflecting the growing digitisation of academic research.
The study highlights how AI in research is transforming scholarly content practices, with more researchers using it in their daily routines. One of the most common uses is for literature reviews, traditionally a time-consuming task that AI is helping to make faster and more manageable. The findings show a willingness to embrace AI and point to key areas where it can have an even greater impact, especially in literature reviews, writing, and editing.
73.6% have used or are exploring AI tools for research.
51% use AI for literature reviews.
46.3% for writing and editing, highlighting key areas for AI development
These findings indicate a widespread acceptance of AI in research and a growing demand for AI-powered tools.
With research becoming increasingly digital, the choice of devices used for academic work is evolving, most users still rely on desktops for research, and more researchers are turning to mobile devices. This shift highlights the need to focus on making mobile access smoother and more user-friendly, all while ensuring that the desktop experience remains just as reliable and effective.
57.9% prefer desktops valuing stability and a larger screen.
34.8% prefer mobile devices for reading research, emphasising convenience and portability.
7.3% prefer tablets.
This trend highlights the need for mobile-friendly AI-powered research platforms while maintaining robust desktop experiences.
Finally, researchers were asked about their perception of AI’s effectiveness in academic work. Over half of the respondents shared they consider AI tools to be highly effective, particularly for simplifying complex tasks. Many highlighted how impactful these tools are in saving hours and making the research journey more efficient, showing just how valuable AI has become in the academic sector.
39.6% find it very effective
33.4% find it effective
21.8% are neutral
3.7% think it’s ineffective
1.5% think it’s very ineffective
The overwhelming majority see AI as a valuable tool, streamlining research and saving time.
AI offers many benefits, but there are still some ethical issues to work through:
Bias and Accuracy – AI in research can reflect biases in the data it’s trained on, which can lead to misleading results.
Ethical Concerns – Researchers need to make sure AI-generated content meets academic integrity standards.
Cost and Access – Some AI tools are expensive, making them harder to access for students and researchers with limited resources.
To address these AI ethical issues, educators, researchers, and technology providers need to work together to ensure AI is used responsibly in academia.
The Future of AI in Research
AI in research is evolving rapidly, and the trends from Zendy’s survey suggest where it’s headed next.
With 73.6% of respondents already using AI in research, its role will only expand. One of the biggest areas of growth is predictive analysis, where AI is expected to help researchers spot patterns in massive datasets—an extension of how AI is already streamlining literature reviews and data organisation today.
Collaboration is another key area. As AI tools become more sophisticated, they will help researchers across different disciplines and countries work together more efficiently, reducing language barriers and improving access to global knowledge.
As AI technology advances, its impact on academic research will deepen, offering both opportunities and challenges. The focus now is on ensuring these tools remain accessible, ethical, and aligned with researchers’ real needs.
Finally, AI is set to transform experimentation and simulations. Innovations in AI-driven modelling, combined with augmented and virtual reality, could make complex experiments more interactive, accurate, and scalable.
Conclusion
The survey offers a closer look at how AI is undeniably shaping the future of research, helping students and researchers work more efficiently. From automating literature reviews to improving writing and editing, it’s clear that AI in research is becoming an indispensable part of academic workflows. However, challenges like affordability and accessibility remain key areas to address, ensuring that AI-powered research tools remain accessible and fair for everyone.
At Zendy, we are committed to developing AI-driven tools that cater to the real needs of students, researchers, and professionals.
Download the full report to learn about the methodology behind our findings, explore deeper insights into AI in research, and discover how it’s shaping the academic world.
Going through mountains of papers, studies, and data is a common task when working on a research project. Finding a logical approach to combine all of this information is the hard part. This process is called synthesis, a skill that can help make your research be clearer, more insightful, and more impactful. In simple terms, synthesis in research means combining ideas from different sources to create a new understanding.
But here’s the thing: synthesis is often confused with summarising or analysing, which can lead to unclear or incomplete results. In this blog, we’ll break down what synthesis really means, how it’s different from other processes, and how you can do it well. Let’s get started.
What is Synthesis in Research?
It’s about connecting ideas, data, and findings from multiple sources to generate a new perspective. It’s not just repeating what others have said—it’s about connecting the dots to see the bigger picture and how sources relate to your main idea
For instance, suppose you are researching the effects of exercise on mental health. One study may indicate that yoga enhances mood, another suggests jogging lowers anxiety and a third that emphasises the advantages of team sports for social well-being. In order to conclude that different kinds of exercise have different positive effects on mental health, synthesis in research involves connecting these studies together.
How is Synthesis in Research Different from Summarising and Analysing?
A lot of researchers mix up synthesis with summarising or analysing. Here’s how they’re different:
Synthesis vs. Summarising
Summarising Summarising means condensing the key points of a source or multiple sources without adding new insights.
Example: If three studies say that exercise improves mental health, a summary would list those findings without connecting them.
Synthesis goes a step further. It combines ideas from multiple sources to create a new understanding.
Example: Connecting findings about running, yoga, and team sports to show how different types of exercise benefit mental health in unique ways.
Synthesis vs. Analysis
Analysis involves breaking down a single source or idea to examine its parts, strengths, or weaknesses. It’s about looking closely at one piece of information.
Example: Analysing a study on exercise and mental health might involve critiquing its methods or interpreting its results in detail.
Synthesis does not just connect ideas but constructs a new argument or framework from them. It’s about seeing how different pieces of information relate to each other.
Example: Synthesising multiple studies on exercise and mental health might reveal patterns, like how different types of exercise affect different aspects of mental health.
Why Is Synthesis in Research Important?
Because it helps you:
See Connections: By combining insights, you can understand a topic more fully.
Find Gaps: It helps you spot what’s missing in the existing research.
Build Stronger Arguments: When you connect ideas from multiple sources, your conclusions become more convincing.
Make Research Useful: In fields like policy or healthcare, synthesis helps turn research into practical solutions.
Without synthesis, research can feel scattered or repetitive. It’s what ties everything together.
How to Do Synthesis in Research: A Step-by-Step Guide
Here’s a simple way to approach synthesis in research:
1. Start with a Clear Question
Before diving into your sources, ask yourself: What am I trying to learn or answer? A clear question will help you stay focused and avoid getting overwhelmed.
2. Gather Your Sources
Look for reliable, relevant studies, articles, and data. Use tools like Zendy to find the latest articles in one place and EndNote to keep everything organised.
3. Look for Patterns and Connections
As you read, take notes on common themes, differences, or trends. For example, do multiple studies point to the same conclusion? Are there conflicting results? A table or chart can help you visualise these patterns.
4. Bring It All Together
Combine the insights you’ve gathered into a cohesive narrative. Explain how the ideas relate to each other and to your research question.
5. Draw Your Conclusions
Summarise what you’ve learned and explain why it matters. Be honest about any limitations and suggest areas for further study.
How can you improve your synthesis skills?
Read your sources multiple times.
Make well-organised notes on each one.
Find relevant ideas and evidence for them.
Rearrange your notes based on concepts.
Put ideas into an outline.
Conclusion
Synthesis in research is more than just combining information—it’s about creating new knowledge and understanding. By following the steps above, you can effectively bring together ideas, spot patterns, and produce work that’s clear, insightful, and useful. Whether you’re a student, academic, or professional, synthesis can help you take your research to the next level.
Struggling to make sense of your research? AI tools can assist with synthesis by identifying key themes and connections across multiple papers, check out our AI tools to simplify the process and get better results. Let’s make your research work for you!
AI tools have made handling complex tasks easier, but not all of them are created equal. While DeepSeek is widely known and accessible, it is a general-purpose AI tool, available to the public and easily understood by many. In contrast, ZAIA, an AI assistant for researchers developed by Zendy, takes a more specialised approach. Designed specifically for researchers and academics, ZAIA easily integrates into academic workflows with customised tools. Let’s take a closer look at how these two AI tools compare.
What is DeepSeek
DeepSeek AI is a Chinese conversational AI chat created by Liang Wenfeng, mostly used as a general-purpose AI tool that can assist with writing, brainstorming, coding, and more. It’s a Swiss Army knife, good for a lot of things but not customised to a specific need. This makes it a great option for users who need an all-in-one AI assistant rather than a highly specialised tool.
What is ZAIA
ZAIA, Zendy’s AI assistant, is built with researchers in mind. You can ask any research-related question, and ZAIA will analyse millions of academic papers to provide credible, reference-backed answers. In addition, It’s integrated into Zendy to give you access to over 40 million academic research papers and offers AI tools that make chatting, summarising, analysing, and extracting insights from research papers much simpler.
ZAIA and DeepSeek Data & Knowledge Sources
One of the key differences between ZAIA and DeepSeek is how they access and process information.
ZAIA is designed by researchers for researchers, providing direct access to a vast collection of 40 million scholarly publications through Zendy’s massive digital library. This means researchers retrieve precise insights for their research, summarise it, and chat with it, all from credible, peer-reviewed sources.
DeepSeek, on the other hand, is trained on a broad dataset covering various topics but lacks integration with academic databases like Zendy, PubMed or even SpringerLink, and cannot verify sources with citations. While it can generate general information, it lacks the ability to pull insights directly from academic literature, with a high possibility of predatory journals in the outputs.
So, what does DeepSeek do better? As we mentioned above, it helps you in answering questions, brainstorming ideas, helping with writing, summarising information, and providing explanations on complex topics in a clear, understandable way.
A Smarter, Lower Energy Consumption Choice
Since AI became available to the public, concerns about energy consumption have begun to rise. For instance, DeepSeek-V3 required 2,788,000 GPU hours for training, equivalent to 836,400 kWh of energy. According to NDTV News, this consumes less energy compared to the current leading U.S. AI model, ChatGPT.
According to RW Digital, the annual energy consumption for ChatGPT is expected to hit 226.8 GWh. To give you an idea of how much energy that is:
It could fully charge 3.13 million electric vehicles, which is nearly 95% of all electric vehicles in the United States.
It could power around 21,602 U.S. homes for a whole year.
It would be enough to run the entire countries of Finland or Belgium for one day.
On the other hand, ZAIA was developed to minimise energy usage even more, consuming as little as several kilowatts (kW) per server in data centres
ZAIA’s focus on academic tasks means it requires fewer resources. This makes it an environmentally friendly choice for researchers who value sustainability.
Who Should Use Which?
If you’re looking for a general-purpose assistant to handle a variety of tasks, DeepSeek is a good option.
But if you’re a student, researcher, or academic professional who works with research papers regularly, ZAIA is a better fit. It’s built to simplify the research process and provide you with the necessary AI tools and access to your academic needs, integrating AI in scientific research for enhanced productivity and quicker discoveries.
Conclusion
DeepSeek is a great all-around tool, but ZAIA’s focus on the academic field makes it the smarter choice for researchers. It’s affordable, consumes less energy and time, and is designed to make your academic journey smoother. That’s why, when it comes to AI tools, personalisation is the key.
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.
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: ADOI (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.
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.
To know the difference between h-index, SNIP, and Impact Factor, read our recent blog about the Journal’s Classification Systems.
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.
