Let's Analyse What Makes a Good H-Index Score


Understanding H-Index
The H-index is a metric that measures an author’s productivity by the number of publications that have published their work and the impact of the work based on the number of citations their research receives. In general, authors with a higher h-index score will have produced more research and therefore published more content which, to their peers, creates their reputation of credibility.
This quantitative metric was brought about in 2005 by Argentinian-American professor of physics Jorge E. Hirsch to analyse publication data.
Finding an author’s H-index
There are multiple platforms on which you may find an author’s H-index score. To name a few, Google Scholar, Scopus, Web of science etc. However, in this blog, we’ll take you through the process of locating an author’s H-index on google scholar as shown below.
- Visit Google Scholar
- Enter the author’s name in the search bar
- If a profile exists for the author, it will appear at the top of the search results, click the author's name, and their profile page will open.
- View their h-index on the right side of the screen.
Calculating H-Index Score
The H-index measures the importance, significance, and impact of research contributions. To calculate an author’s H-index, you’d need to create a list of all publications in which the author has been published and rank them in descending order of the citations his/her work has received. Understanding the H-index of an author is an indication of their credibility, so that brings us to the question:
What is a good H-index score?
J. E. Hirsch (2005) observes that Noble Prize winners in physics have an average H-index score of 30, this highlights that Noble prize winners are selected with a scientific body of research and a history of contributional impact. This proved that successful scientists do need a good h-index score.
Hirsch stated that after 20 years of research; an H-index score of 20 was good, 40 was outstanding and 60 was truly exceptional.
Does the H-index score evaluate an author in all important aspects?
Undoubtedly, it is appealing to have a singular value that measures an author's productivity and impact. Many committees have opted the H-index as their metric of choice as well. Bordons and Costas (2007) stated that the key advantage of the H-index metric is that it measures the scientific output of a researcher with objectivity. This plays a vital role in making decisions about promotions, fund allocation and awarding prizes.
However, there are suggestions that H-index does not take other important variables into account. According to Enago Academy (2022), a higher H-index score does not indicate better quality of research. The article further elaborates that the H-index score does not account for an author’s career stage, research and journal quality and contribution to the scientific community. The score also has potential unintended negative impacts; for example, a younger researcher may not challenge a researcher with a high h-index score and researchers aiming for a higher h-index may only pursue popular fields of science.
Furthermore, BiteSizeBio (2021) states that the H-index score does not take into account the number of authors on a research paper. If a paper has 1 author with about 100 citations, this researcher deserves more recognition than a paper that had 10 authors with similar citations.
The fluctuation of the H-index score
The H-index score does not decrease unless the paper is redacted or deleted. Older papers may continue to gain new citations, and the h-index can potentially increase indefinitely, even after the researcher has stopped actively publishing.
What is the difference between H-index and the journal impact factor?
The Journal Impact Factor metric is used to evaluate the importance of a journal within its respective field or discipline. In simpler terms, it measures the frequency of citations the average article within this journal receives. On the contrary, the H-index metric is used to measure the productivity and quality of an author’s publications. While they are both measures of research quality, they measure different aspects of research and can therefore not be compared.
To conclude, having a good H-index score is impressive. However, every author’s research contrasts with that of another. There are many more aspects to investigate when evaluating a researcher.
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5 Tools Every Librarian Should Know in 2025
The role of librarians has always been about connecting people with knowledge. But in 2025, with so much information floating around online, the challenge isn’t access, it’s sorting through the noise and finding what really matters. This is where AI for libraries is starting to make a difference. Here are five that are worth keeping in your back pocket this year. 1. Zendy Zendy is a one-stop AI-powered research library that blends open access with subscription-based resources. Instead of juggling multiple platforms, librarians can point students and researchers to one place where they’ll find academic articles, reports, and AI tools to help with research discovery and literature review. With its growing use of AI for libraries, Zendy makes it easier to summarise research, highlight key ideas, and support literature reviews without adding to the librarian’s workload. 2. LibGuides Still one of the most practical tools for librarians, LibGuides makes it easy to create tailored resource guides for courses, programs, or specific assignments. Whether you’re curating resources for first-year students or putting together a subject guide for advanced research, it helps librarians stay organised while keeping information accessible to learners. 3. OpenRefine Cleaning up messy data is nobody’s favourite job, but it’s a reality when working with bibliographic records or digital archives. OpenRefine is like a spreadsheet, but with superpowers, it can quickly detect duplicates, fix formatting issues, and make large datasets more manageable. For librarians working in cataloguing or digital collections, it saves hours of tedious work. 4. PressReader Library patrons aren’t just looking for academic content; they often want newspapers, magazines, and general reading material too. PressReader gives libraries a simple way to provide access to thousands of publications from around the world. It’s especially valuable in public libraries or institutions with international communities. 5. OCLC WorldShare Managing collections and sharing resources across institutions is a constant task. OCLC WorldShare helps libraries handle cataloguing, interlibrary loans, and metadata management. It’s not flashy, but it makes collaboration between libraries smoother and ensures that resources don’t sit unused when another community could benefit from them. Final thought The tools above aren’t just about technology, they’re about making everyday library work more practical. Whether it’s curating resources with Zendy, cleaning data with OpenRefine, or sharing collections through WorldShare, these platforms help librarians do what they do best: guide people toward knowledge that matters. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }

Balancing AI Efficiency with Human Expertise in Libraries
AI in libraries is making some tasks quicker and less repetitive. However, even with these advances, there’s something irreplaceable about a librarian’s judgment and care. The real question isn’t whether AI will take over libraries, it’s how both AI and librarians can work side by side. How AI Helps in Libraries According to Clarivate Pulse of the Library 2025 survey, among 2,000 academic library professionals globally, many said they don’t have enough time or budget to learn new tools or skills, a challenge made even harder as global digital content is projected to double every two years. Here’s where AI tools for librarians prove useful: Cataloguing: AI can scan metadata and suggest subject tags in minutes. Search: Smarter search systems help students and researchers find relevant materials without digging through dozens of irrelevant results. Day-to-day tasks: Think overdue notices, compiling basic reading lists, or identifying key sources and trends to support literature reviews. This is where library automation with AI comes in handy. Instead of replacing people, these tools free up time. A librarian who doesn’t have to spend hours sorting through data can focus on supporting students, curating collections, analysing usage statistics to make informed decisions or tracking resource usage against budgets. Where Human Expertise Still Matters AI is fast, but it’s not thoughtful. A student asking, “I’m researching migration patterns in 19th-century Europe, where do I start?” gets much more from a librarian than from a search algorithm. Librarians bring context, empathy, and critical thinking that machines can’t replicate. This is why human-AI collaboration in libraries makes sense. AI takes care of the routine. Humans bring the nuance. Together, they cover ground neither could manage alone. Finding the Balance So how do libraries get this balance right? A few ideas: Think of AI as a helper – not a replacement for staff. Invest in training – librarians need to feel confident using AI tools and knowing when not to rely on them. Keep the focus on people – the goal isn’t efficiency for its own sake, it’s about better service for students, researchers, and communities. Final Thoughts By using AI to handle routine administrative tasks like cataloguing, managing records, or tracking resource usage, librarians free up time to focus on the part of the job that drew them to this profession in the first place: supporting researchers and students, curating meaningful collections, and fostering learning. Combining the efficiency of AI in libraries with the expertise of librarians creates a future where technology supports the human side of education. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }

How AI in Higher Education Is Helping Libraries Support Research
Libraries have always been at the centre of knowledge in higher education. Beyond curating collections, librarians guide researchers and students through complex databases, teach research skills, and help faculty navigate publishing requirements. They also play a key role in managing institutional resources, preserving archives, and ensuring equitable access to information. These days, libraries are facing new challenges: huge amounts of digital content, tighter budgets, and more demand for remote access. In this environment, AI in higher education is starting to make a real difference. How AI Makes Life Easier for Librarians Improving Discovery AI-powered search tools don’t just look for keywords, they can understand the context of a query. That means students and researchers can find related work they might otherwise miss. It’s like having an extra set of eyes to point them toward useful sources. Helping with Curation AI can go through thousands of articles and highlight the ones most relevant to a specific course, project, or research topic. For example, a librarian preparing a reading list for a history class can save hours by letting AI suggest the most relevant papers or reports. Supporting Remote Access Students, researchers and faculty aren’t always on campus. AI can summarise long articles, translate content, or adjust resources for different reading levels. This makes it easier for people to get the information they need, even from home. Working Within Budgets Subscriptions remain a major expense for libraries, and ongoing budget cuts are forcing many academic institutions to make difficult choices about which resources to keep or cancel. For example, recent surveys show that around 73% of UK higher education libraries are making budget cuts this year, sometimes slashing up to 30% of their overall budgets, and collectively spending £51 million less than the previous year. This trend is not limited to the UK, universities in the U.S. and elsewhere are also reducing library funding, which has dropped by nearly 20% per student over recent years. Even top institutions like Princeton have cut library hours and student staffing to save on costs. Subscriptions can be expensive, and libraries often have to make tough choices. AI tools that work across large collections help libraries give students and researchers more access without adding extra subscriptions. Trusted Content Still Matters AI is helpful, but the resources behind it are just as important. Librarians care about trusted, peer-reviewed, and varied sources. Librarians and AI: A Partnership AI isn’t replacing librarians. Instead, it supports the work they already do. Librarians are the ones who guide researchers, check the quality of sources, and teach information skills. By using AI tools, librarians can make research easier for students, researchers and faculty, and they can help their institutions make the most of the resources they have. Final Thoughts AI in higher education is making it easier for libraries to support students and faculty, but librarians are still at the centre of the process. By using AI tools alongside strong content collections, libraries can save time, offer more resources, and help researchers find exactly what they need. With the right AI support, research becomes easier to navigate and more accessible without overcomplicating the process. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
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