Mar 11, 2025 |
11 Mins Read
Impact Factor of Journals: What You Need To Know
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. What is Impact Factor? The Impact Factor was created by Eugene Garfield, the founder of the Institute for Scientific Information (ISI). Garfield first mentioned the idea of an impact factor in Science magazine in 1955. 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) Multidisciplinary Sciences- Nature (50.5)- Science (47.728) Physical Sciences- Chemical Reviews (60.622)- Nature Materials (43.841)- Nature Nanotechnology (39.213) Environmental Sciences- Energy & Environmental Science (38.532)- Nature Geoscience (16.908) 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. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
Best AI Tool for Literature Review: 5 Top Picks for 2025
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: 1. ZAIA by Zendy 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 BibliographyLiterature ReviewPurposeSummarises and evaluates each source individuallySynthesises and analyses sources collectivelyStructureOrganised as a list of citations with annotationsOrganised thematically or methodologicallyDepth of AnalysisFocuses on each source’s contributionIdentifies patterns, gaps, and trends in researchUse in ResearchOften used as a preparatory step for literature reviewsUsed as a foundation for research projects or thesesWriting StyleConcise, source-focusedIntegrative, 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: Use AI responsibly and ethically to avoid plagiarism or creation of misleading content 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. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
AI for Students and Researchers: 2025 Trends and Statistics
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. Click download to access the full report Download Demographics of AI Users in Research 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 Click download to access the full report Download Research Habits and AI Adoption 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. Click download to access the full report Download How is AI Being Used in 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. Click download to access the full report Download Accessibility and Device Preferences 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. Click download to access the full report Download Impact of AI in Research 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. Click download to access the full report Download Ethical Concerns & AI Limitations 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. Click download to access the full report Download .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
We Are Attending the London Book Fair 2025
We’re pleased to announce our participation in the renowned London Book Fair (LBF) from March 11 to March 13, 2025, at Olympia London. This event marks a significant opportunity for Zendy to connect with industry professionals, showcase its innovative offerings, and contribute to the global publishing sector. About the London Book Fair The London Book Fair is a pivotal event in the publishing industry, attracting over 30,000 attendees from around the world. It provides a dynamic platform for networking, brand awareness, and staying abreast of the latest trends and insights. The fair features over 100 seminars across multiple stages, focusing on topics such as sustainability in publishing, literary translation, diversity and inclusion, emerging trends, responsible AI in research, and pathways to publication. The International Trade Centre (IRC) offers a dedicated space for rights professionals to connect and conduct business during this critical period. Meeting Opportunities If you are attending the London Book Fair and would like to schedule a meeting. Please contact us at hello@zendy.io. Visit the London Book Fair website for more details: https://www.londonbookfair.co.uk/en-gb/about.html .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
What is Synthesis in Research? Synthesis vs. Analysis vs. Summarising
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! .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
ZAIA vs DeepSeek: Which AI Tool is Better For Researchers
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. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
JIF vs. SJR Ranking: What's The Difference?
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) .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
Top 4 Challenges Every Researcher Has With Index Journals – And How To Solve Them
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. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
Qualitative Research Data Analysis: How Researchers Study Text, Images, and Audio
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. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }