Speed Up Your Research With “Insights”
Dec 18, 2024 |
3 Mins Read'Insights', a brand-new feature designed to make your research experience faster, simpler, and more accessible.
Insights gives you short, clear summaries of research papers, pulling out the most important information so you can understand the main points in just a few lines. Instead of reading through pages of dense content, you’ll get a quick overview that helps you decide if the paper is worth exploring further. Here’s how Insights can help:
- Save time by getting to the heart of a paper faster.
- Understand complex topics without feeling stuck.
- Focus on what matters and decide quickly what’s relevant to you.
Why We Created Insights?
We’ve heard from many of you that keeping up with research can feel like a never-ending task. There’s so much to read, and it’s hard to know where to start. That’s where 'Insights' comes in, to help you make the most of your time exploring the right research paper you are looking for.
How Does It Work?
Insights uses our AI to scan through a paper and extract key points. It focuses on sections like the introduction, methodology, results, and conclusion, so you can get a clear sense of what the paper is about. You don’t have to worry about missing anything important; it’s all laid out in a simple, easy-to-digest format.
Head over to Zendy, search for what you are looking for, and see how Insights can give you a clearer overview in seconds, Check out Insights now!
Responsible AI In Research And Why It Matters
Artificial Intelligence (AI) is changing how we live, work, and learn. However, as AI continues to evolve, it is important to ensure it is developed and used responsibly. In this blog, we’ll explore what responsible AI means, why it is essential, and how tools like ZAIA, Zendy's AI assistant for researchers, implement these principles in the academic sector. What Is Responsible AI? Responsible AI, also known as ethical AI refers to building and using AI tools guided by key principles: Fairness Reliability Safety Privacy and Security Inclusiveness Transparency Accountability AI vs Responsible AI: Why Does Responsible AI Matter? Keep in mind that AI is not a human being. This means it lacks the ability to comprehend ethical standards or a sense of responsibility in the same way humans do. Therefore, ensuring these concepts are embedded in the development team before creating the tool is more important than building the tool itself. In 2016, Microsoft launched a Twitter chatbot called "Tay", a chatbot designed to entertain 18- to 24-year-olds in the US to explore the conversational capabilities of AI. Within just 16 hours, the tool's responses turned toxic, racist, and offensive due to being fed harmful and inappropriate content by some Twitter users. This led to the immediate shutdown of the project, followed by an official apology from the development team. In such cases, "Tay" lacked ethical guidelines to help it differentiate harmful content from appropriate content. For this reason, it is crucial to train AI tools on clear principles and ethical frameworks that enable them to produce more responsible outputs.The development process should also include designing robust monitoring systems to continuously review and update the databases' training, ensuring they remain free of harmful content. Overall, the more responsible the custodian is, the better the child’s behaviour will be. The Challenges And The Benefits of Responsible AI Responsible AI is not a "nice-to-have" feature, it’s a foundational set of principles that every AI-based tool must implement. Here's why: Fairness: By addressing biases, responsible AI ensures every output is relevant and fair for all society’s values. Trust: Transparency in how AI works builds trust among users. Accountability: Developers and organisations adhere to high standards, continuously improving AI tools and holding themselves accountable for their outcomes. This ensures that competition centers on what benefits communities rather than simply what generates more revenue. Implementing responsible AI comes with its share of challenges: Biased Data: AI systems often learn from historical data, which may carry biases. This can lead to skewed outcomes, like underrepresenting certain research areas or groups. Awareness Gaps: Not all developers and users understand the ethical implications of AI, making education and training critical. Time Constraints: AI tools are sometimes developed rapidly, bypassing essential ethical reviews, which increases the risk of errors. Responsible AI and ZAIA ZAIA, Zendy’s AI-powered assistant for researchers, is built with a responsible AI framework in mind. Our AI incorporates the six principles of responsible AI, fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability, to meet the needs of students, researchers, and professionals in academia. Here’s how ZAIA addresses these challenges: Fairness: ZAIA ensures balanced and unbiased recommendations, analysing academic resources from diverse disciplines and publishers. Reliability and Safety: ZAIA’s trained model is rigorously tested to provide accurate and dependable insights, minimising errors in output. Transparency: ZAIA’s functionality is clear and user-friendly, helping researchers understand and trust its outcomes. Accountability: Regular updates improve ZAIA’s features, addressing user feedback and adapting to evolving academic needs. Conclusion Responsible AI is the foundation for building ethical and fair systems that benefit everyone. ZAIA is Zendy’s commitment to this principle, encouraging users to explore research responsibly and effectively. Whether you’re a student, researcher, or professional, ZAIA provides a reliable and ethical tool to enhance your academic journey. Discover ZAIA today. Together, let’s build a future where AI serves as a trusted partner in education and beyond. .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }
AI is Transforming Academic Research and Publishing – A Conversation with Kamran Kardan, CEO of Zendy
AI's real potential lies not just in speeding up processes but also in helping users engage more deeply with academic content. Sabine Louët, CEO of SciencePOD sat down with Kamran Kardan, CEO of Zendy to discuss how technology, particularly AI, is reshaping the way researchers and independent scholars access critical information and how research is published. Removing Barriers in Academic Research When asked by Sabine Louët “what drove the creation of Zendy?” Kamran Kardan’s response was clear and purposeful: “Zendy was created to remove the barriers that restrict access to academic research”.He highlights the significant gap that exists for those outside privileged institutions, who often face prohibitive costs or limitations when trying to access essential research. Zendy, he says, aims to make academic content not only affordable but also widely accessible to researchers, students, and professionals globally.Accessing scientific literature remains a privilege reserved for those with institutional affiliations, leaving independent researchers or those from less-resourced regions at a disadvantage. As Kardan puts it, “Zendy is committed to levelling the playing field”, it offers a legitimate alternative to illicit means of accessing research. AI’s Role in Enhancing Research Accessibility AI has become a buzzword, but Kardan stresses the importance of AI in Zendy’s strategy, describing AI as an enabler rather than the focal point. Zendy, he explains, uses AI to enhance user experience by making vast amounts of data more navigable. One of the platform’s key AI-driven features is its summarisation tool, which allows users to quickly digest complex academic papers. With this tool, users can identify relevant content faster and focus their research efforts more effectively. A forthcoming feature called ‘findings’, will use AI to group related articles together, offering a comparative perspective on topics and highlighting differing viewpoints. This tool is designed to empower researchers to explore a topic from multiple angles without having to sift through unrelated material. Safeguarding Research Integrity in the Age of AI Another point of discussion between Sabine Louët and Kardan was the issue of integrity while also leveraging AI. Kardan acknowledges that this is critically important and explains that Zendy is built on principles of transparency and respect for intellectual property. Their AI tools do not merely extract data but give due credit to authors and publishers. In addition, the platform’s revenue-sharing model ensures that content creators benefit from the usage of their work, fostering a more sustainable and fair ecosystem for academic publishing. Kardan also addresses the issue of AI-generated inaccuracies, commonly referred to as “hallucinations”. He emphasises that Zendy's AI is structured to avoid these risks. If the AI does not have sufficient data to provide an answer, it refrains from making assumptions, thus maintaining a high standard of accuracy. AI: Not Just Speed, but Deeper Learning In Kardan’s view, AI's real potential lies not just in speeding up processes but also in helping users engage more deeply with academic content. The tools developed by Zendy are designed to simplify complex materials, making them more approachable for users across various disciplines, without compromising on the depth of information. Louët agrees and notes that these features, particularly AI-driven comparison and summarising tools, align with the needs of modern researchers who require both efficiency and reliability in handling academic content. Looking Ahead: The Future of AI in Research What does the future look like? Kardan foresees more AI advancements that will continue to transform research access, making it more affordable, transparent and equitable. The focus is not just on technology for technology’s sake but on providing meaningful solutions that directly address the challenges of the academic community. “AI’s role in academic publishing is still evolving”, says Kardan, “and Zendy is committed to using AI responsibly to enhance access to knowledge, not to replace human expertise”. .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }
Top 4 Journals Classification Systems You Should Know
If you’ve ever tried to figure out which journal is the best fit for your research or wondered how journals classification is carried out, you’ve probably come across terms like Quartiles, H-Index, Impact Factor (IF), and Source Normalised Impact per Paper (SNIP). These metrics might sound technical, but they are simply tools to measure how much attention a journal’s research gets. Here’s a straightforward explanation of what they mean and how they work Quartiles in Journals Classification: Ranking by Performance The system of dividing journals into four quartiles, Q1, Q2, Q3, and Q4, was created to make it easier to compare their quality and impact within a specific field. This idea became popular through Scopus and Journal Citation Reports (JCR) databases, which rank journals based on metrics like citations. The concept builds on the work of Eugene Garfield, who introduced the Impact Factor, offering a way to see how journals stand up against others. Quartiles break things down further: Q1 represents the top 25% of journals in a category, while Q4 includes those at the lower end. It's a straightforward way to help researchers determine which journals are most influential in their areas of study. Q1: Top 25% of journals in the field (highest-ranked). Q2: 25-50% (mid-high-ranked). Q3: 50-75% (mid-low-ranked). Q4: Bottom 25% (lowest-ranked). However, not all Q3 or Q4 journals are necessarily a disadvantage. While they may not be as well-known, they are still important in scientific research. Some of the benefits include: Affordability: These journals are easier for researchers to access, especially for those on a tight budget. Focused Topics: They tend to cover more specific, niche areas of study, making them great for in-depth exploration of certain subjects. Great for New Researchers: Q3 and Q4 journals classification can be a good place for new researchers to publish their first paper and gain experience in the publishing world. Ideal for Basic Research: They’re a great option for research that focuses on the basics of science Finally, publishing your article in a Q3 or Q4 journal doesn’t mean it lacks value or won’t make an impact. If your work presents new findings that address a real problem, it can still attract attention, even when published in a lower-ranked journal. H-Index: A Balance of Quantity and Quality The H-Index score is an important factor in journal classification. It looks at the number of articles a journal has published and how often those articles are cited. It balances quantity (how many articles a journal publishes) with quality (how many of its articles are referenced). For example, if a journal has an H-Index of 15, it means it has published 15 articles, each cited at least 15 times. It’s a simple way to measure a journal’s influence without focusing too much on just one super-cited article or a bunch of rarely cited ones. How H-index works: Let’s say a journal has published 4 articles, and the number of citations for each article looks like this: The 1st article has 10 citations – exceeds 1 citation. The 2nd article has 24 citations – exceeds 2 citations. The 3rd article has 5 citations – exceeds 3 citations. The 4th article falls short of 4 citations. In this case, the journal has three articles that each have at least three citations. The fourth article doesn’t hit the mark, so the H-index stops at 3. This metric can help researchers, professionals, and institutions decide if a journal publishes research that gets noticed and cited by the academic community. It’s not the full picture, but it’s a useful starting point for understanding the journal’s influence. Impact Factor: Citation Average The Impact Factor (IF) is a number that shows how often a journal’s articles are cited on average over the past two years. It helps you understand how much attention the journal’s research gets from other scholars and it also helps with journals classification. How it works? To calculate the IF, look at how many times articles from a journal were cited in the past two years. Then, you divide that by the total number of articles the journal published in those two years. This gives you an average citation count per article. Example: Let’s say we want to figure out the IF for Journal A in 2023: 1. In 2021 and 2022, Journal A published 50 articles. 2. In 2023, those articles were cited 200 times in total. 3. You take the total citations (200) and divide it by the total number of articles (50): 200 ÷ 50 = 4 So, Journal A has an Impact Factor of 4, meaning its articles were cited, on average, four times each. A higher Impact Factor often places journals higher in classification, but keep in mind that it’s not the full story. Some specialised journals may have lower Impact Factors even though they’re highly respected in their niche. SNIP: Fair Comparisons Across Fields SNIP (Source Normalised Impact per Paper) is a valuable metric in journals classification because it goes one step further. It measures contextual citation impact and takes into account the fact that different research fields have different citation habits. For instance, medical papers often get cited a lot, while mathematics papers don’t, even if they’re equally important in their fields. SNIP adjusts the average citations a journal receives based on these differences, making it easier to compare journals across disciplines. Example: Journal A publishes in a low-citation field like social sciences and averages 3 citations per article. Adjusted for its field, its SNIP might be 1.6. Journal B publishes in a high-citation field like biomedicine and has an average of 8 citations per article. After adjustment, its SNIP might be 1.2. SNIP makes sure journals in fields with fewer citations still get the recognition they deserve. What it tells you: SNIP is especially useful for journal classification because it levels the playing field between disciplines. A higher SNIP score suggests that a journal’s articles are cited more often than expected for its field. It’s a helpful tool for comparing journals, but it’s just one of many ways to evaluate a journal’s influence or importance. Conclusion Metrics like Quartiles, H-Index, Impact Factor, and SNIP are essential tools for journals classification, helping researchers, librarians, and institutions rank journals and understand their influence. Each metric focuses on a different aspect of a journal’s impact. But no single number can tell the whole story. A journal might excel in one metric but be less prominent in another, or it might be vital to a specific audience despite modest scores. These tools are helpful guides, but the best journal for your research depends on your goals. .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }