Feb 28, 2025 |
11 Mins Read
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; }
Science Citation Index vs. Scopus: Which Database is Right for You?
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 FeatureScience Citation IndexScopusPublisherPart of Clarivate’s Web of Science platform.Managed by Elsevier.Scope and SizeFocuses primarily on high-impact scientific journals.Covers a broader range of disciplines. (social sciences, humanities, and conference proceedings)Regional RepresentationHas a more selective approach.Includes journals from diverse regions, including non-English publications.Tools and MetricsFocuses 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. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }