Online Research Tools You Need to Know About in 2025
Nov 19, 2024 |
22 Mins ReadIf you’re just starting your research or unsure where to begin, these research tools for researchers can help you get started and make your work easier.
Brainstorming Tools
Defining a clear scientific topic has always been the challenging part of the research process. Even with a deep understanding of the field, researchers cannot stop asking themselves, “Do I provide a real solution?” That’s why brainstorming is such an important step, as it helps you go from Zero to One. Here’s a list of research tools used in brainstorming to help you set up your topics and make sure it’s unique and relevant.
Miro is an online platform for you and your colleagues to gather every idea in one place. It gives you plenty of templates such as mind maps, bubble maps, or even tree maps with sticky notes that will help you pull out your thoughts from your mind right in front of your eyes. This easy and simple online tool used in research will save you hours of thinking inside your head.
What makes MindMeister unique and suitable for research tools is that it has the user interface of what a researcher's mind needs, especially in the phase of choosing a topic. You and your team can use MindMeister on the go with a native mobile application for both Android and iOS. Benefits?
| Tool | Free Plan Features |
| Miro | 3 editable boards, templates, 100+ integrations |
| MindMeister | Up to 3 mind maps, unlimited collaborators and mobile apps. |
Literature Search & Access Tools
Now, after a long day of brainstorming, you finally have your next research topic in hand. It’s time to conduct and structure a literature review to set the stage for your research question.
In the past, researchers would travel to universities and libraries to access academic resources. With the arrival of the internet, they began to search online across many websites. Now, there are millions of research papers available online. Despite the increased accessibility, the need for a one-stop shop for peer-reviewed academic papers has become essential. These research tools have been designed to provide access to millions of articles in one place, while also offering features to help you organise them efficiently.
With over 48 million research papers on a wide range of topics used by over 751,000 researchers worldwide, Zendy is one of the top 10 open access library databases that offer much more than just an online database for academic resources. Its simple, easy-to-use interface and powerful search filters make it one of the most efficient research tools available. This allows you to quickly find the articles you need, organise your sources and build a solid foundation for your research, saving you both time and money during the literature review stage.
With Zendy, you have three options:
- Zendy Open – The free plan gives access to open-access articles.
- Zend Plan gives you unlimited AI research tools, including ZAIA, AI Summarise, Insights, PDF Analysis, and Keyphrase Highlight.
- Zendy Plus – Full access to paywalled articles along with unlimited usage of AI tools.
Google Scholar is one of the most widely used research tools today. It’s a free tool that allows researchers to easily search through millions of academic articles, theses, books, and conference papers across various disciplines. In 2025, it continues to be an essential tool for students, professionals and researchers who need quick access to high-quality, peer-reviewed materials.
While Google Scholar may not have the same in-depth organisational tools as other research tools, its ability to provide access to a vast amount of information with minimal effort makes it a go-to option for many researchers.
Reference and Citation Management Tools
Organising large amounts of data, sharing findings with colleagues, managing complex citations and references, and, of course, keeping up with deadlines, these tasks can be time-consuming and feel overwhelming. However, tools used in research like Zotero and Paperpile can help take care of all these details. These research tools make managing your project a lot easier, allowing you to focus on what really matters without getting weighed down by the small stuff.
Instead of having documents and notes scattered all over the place, tools used in research like Zotero and Paperpile keep everything in one spot, so it’s easy to find what you need, saving time and reducing mistakes.
Zotero is one of the great research tools for organising your project. It automatically grabs citation details from websites and academic databases, and you can attach PDFs, notes and other files to each reference, so everything stays in one place. Zotero allows you to organise your research into collections and tags, making it easier to find what you need later. It also integrates with Word and Google Docs to add citations and create bibliographies while you write. Plus, it syncs across devices, so you can access your research anytime, anywhere. It's a simple way to stay organised and keep everything in one spot.
If you’re looking for research tools that integrate easily with Google’s ecosystem, Paperpile is the best choice. It’s specifically built for Google apps! You can organise, find and collect, download and sync, annotate, share, and cite in Google Docs. With its clean interface and integration with Google, Paperpile is a great choice for anyone looking to simplify research management. It’s straightforward, user-friendly and saves lots of hours.
Data Collection and Survey Tools
Not so long ago, researchers often gathered data based on assumptions and possibilities within a small circle of interest, which wasn’t always accurate. Today, with the help of advanced research tools, you can collect and analyse data more precisely from a broader range of samples online.
I’ve tried Jotform, and in just 1:57 minutes, I created an account and built a form with basic questions (name, age, location, background, etc.) using its drag-and-drop feature. It’s incredibly easy and fast to use. In addition, Jotform offers many features, such as adding collaboration and integration with various project management software. It is a great tool used in research to simplify data collection.
You may have noticed what makes Google's tools on top is their straightforward, fast, free and most of all, their simplicity. This simplicity is what makes Google stand out. Google Forms, for example, allows you to create extensive questionnaires and surveys that are easy for your target audience to use and comprehend. Its integration with other Google tools, like Google Sheets, makes it easy to manage and analyse responses all in one place. For researchers looking for research tools to gather data, Google Forms offers an effortless option for collecting valuable data.
Project Management and Collaboration
The right project management and collaboration research tools can make it much easier to stay on top of everything. Notion and Trello are two popular choices among researchers, and they are reliable options that can help you bring structure to your research management with less effort.
Notion is a one-stop workspace, combining notes, project management, and databases in one place. You can set up a hub for your research, keep notes, track references, and collaborate with others in real time, adapting it to fit the specific needs of your project.
Sometimes, seeing tasks laid out visually makes organising projects a lot easier. Trello is a simple research tool that does just that. It allows you to arrange tasks on boards and lists and view them in either calendar or timeline formats. With Trello, you can sort out to-do lists, create channels for different topics, assign tasks to team members, set deadlines and check your project’s overall progress, all while working together with your colleagues in real time. You will find Trello helpful for managing literature reviews, tracking data collection, and collaborating on research tasks.
Academic Writing and Editing
While these research tools are helpful, they shouldn’t do all the work for you. They’re best used as a way to clean up your writing, not to create it. The main ideas and structure of your research should come from you, with the tools stepping in to help with the fine details. Let’s take a look at some of the best writing tools used in research that can help you improve your academic writing and make your research easier to read and understand.
A very practical writing assistant tool offers more than 400 features, including grammar checks, vocabulary improvement suggestions, plagiarism detection, citation recommendations and more.
What makes Grammarly really useful is how easy it is to use. You can install it as an extension on your browser or use it in its standalone app, and it works across different platforms, from your computer to your phone.
Academic writing isn’t just about getting the facts right, it’s about presenting ideas clearly so they’re easy to understand. Most research tools, like spelling and grammar checkers, can help fix basic mistakes, but they’re often not designed for research-level writing. This is where Paperpal really shines.
Paperpal is built to support the unique needs of researchers and students. It doesn’t just spot typos; it helps you refine your language, follow academic standards, and structure your paper for better readability. For example, when writing a research paper, Paperpal can suggest improvements that make complex ideas easier to follow and help you use the right tone for a professional audience.
With Paperpal, your research writing will meet the standards journals are looking for. This makes it a valuable addition to the tools used in research.
In Conclusion
2025 is proving to be an exciting time for the academic industry. Today, researchers have access to an impressive range of research tools, starting with brainstorming platforms to citation managers, academic writing assistants and project management software that make every stage of research more efficient and effective. The right tools used in research can help you focus on what matters most.
From Boolean to Intelligent Search: A Librarian’s Guide to Smarter Information Retrieval
For decades, librarians have been the trusted guides in the vast world of information. But today, that world has grown into something far more complex. Databases multiply, metadata standards evolve, and users expect instant answers. Traditional search still relies on structured logic, keywords, operators, and carefully crafted queries. AI enhances this by interpreting intent rather than just words. Instead of matching text, AI tools for librarians analyse meaning. A researcher looking for “climate change effects on migration” won’t just get papers containing those words, but research exploring environmental displacement, socioeconomic factors, and regional studies. This shift from keyword to context means librarians can spend less time teaching a researcher how to “speak database” and more time helping them evaluate and use the results effectively. The Evolution of Library Search Traditional search engines focus on keywords and often return long lists of potential matches. With AI, libraries can now benefit from search engines that employ natural language processing (NLP) and machine learning (ML) to understand user queries and map them to the most relevant resources, even when key terms are missing or imprecise. Semantic search, embedding-based retrieval, and vector databases allow AI to find conceptually similar resources and suggest new directions for research. Examples of AI Tools for Librarians AI ToolMain FunctionLibrarian BenefitZendyAI-powered platform offering literature discovery, summarisation, keyphrase highlighting, and PDF analysisSupports researchers with instant insights, simplifies literature reviews, and improves discovery across 40M+ publicationsConsensusAI-powered academic search enginemanaging citation libraries, efficient literature reviewEx Libris PrimoIntegrates AI for discovery and metadata managementImproves record accuracy and user experienceMeilisearchFast, scalable vector search with NLPEnhanced search for large content databases The Ethics of Intelligent Search AI doesn’t just retrieve; it prioritises. AI tools for librarians determine which results appear first, whose research receives visibility, and what remains hidden. This creates ethical questions around transparency and bias. Librarians are uniquely positioned to question those algorithms, advocate for equitable access, and ensure users understand how results are ranked. In an AI-driven world, digital literacy extends beyond knowing how to search—it’s about learning how machines think. In conclusion AI tools for librarians are becoming more accessible. Platforms now integrate summarisation, concept mapping, and citation analysis directly into search. helping librarians and users avoid unreliable content. For libraries, experimenting with these tools can mean faster reference responses, smarter cataloguing, and better support for researchers drowning in information overload. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
Why AI like ChatGPT still quotes retracted papers?
AI models like ChatGPT are trained on massive datasets collected at specific moments in time, which means they lack awareness of papers retracted after their training cutoff. When a scientific paper gets retracted, whether due to errors, fraud, or ethical violations, most AI systems continue referencing it as if nothing happened. This creates a troubling scenario where researchers using AI assistants might unknowingly build their work on discredited foundations. In other words: retracted papers are the academic world's way of saying "we got this wrong, please disregard." Yet the AI tools designed to help us navigate research faster often can't tell the difference between solid science and work that's been officially debunked. ChatGPT and other assistants tested Recent studies examined how popular AI research tools handle retracted papers, and the results were concerning. Researchers tested ChatGPT, Google's Gemini, and similar language models by asking them about known retracted papers. In many cases, they not only failed to flag the retractions but actively praised the withdrawn studies. One investigation found that ChatGPT referenced retracted cancer imaging research without any warning to users, presenting the flawed findings as credible. The problem extends beyond chatbots to AI-powered literature review tools that researchers increasingly rely on for efficiency. Common failure scenarios The risks show up across different domains, each with its own consequences: Medical guidance: Healthcare professionals consulting AI for clinical information might receive recommendations based on studies withdrawn for data fabrication or patient safety concerns Literature reviews: Academic researchers face citation issues when AI assistants suggest retracted papers, damaging credibility and delaying peer review Policy decisions: Institutional leaders making evidence-based choices might rely on AI-summarised research without realising the underlying studies have been retracted A doctor asking about treatment protocols could unknowingly follow advice rooted in discredited research. Meanwhile, detecting retracted citations manually across hundreds of references proves nearly impossible for most researchers. How Often Retractions Slip Into AI Training Data The scale of retracted papers entering AI systems is larger than most people realise. Crossref, the scholarly metadata registry that tracks digital object identifiers (DOIs) for academic publications, reports thousands of retraction notices annually. Yet many AI models were trained on datasets harvested years ago, capturing papers before retraction notices appeared. Here's where timing becomes critical. A paper published in 2020 and included in an AI training dataset that same year might get retracted in 2023. If the model hasn't been retrained with updated data, it remains oblivious to the retraction. Some popular language models go years between major training updates, meaning their knowledge of the research landscape grows increasingly outdated. Lag between retraction and model update Training Large Language Models requires enormous computational resources and time, which explains why most AI companies don't continuously update their systems. Even when retraining occurs, the process of identifying and removing retracted papers from massive datasets presents technical challenges that many organisations haven't prioritised solving. The result is a growing gap between the current state of scientific knowledge and what AI assistants "know." You might think AI systems could simply check retraction databases in real-time before responding, but most don't. Instead, they generate responses based solely on their static training data, unaware that some information has been invalidated. Risks of Citing Retracted Papers in Practice The consequences of AI-recommended retracted papers extend beyond embarrassment. When flawed research influences decisions, the ripple effects can be substantial and long-lasting. Clinical decision errors Healthcare providers increasingly turn to AI tools for quick access to medical literature, especially when facing unfamiliar conditions or emerging treatments. If an AI assistant recommends a retracted study on drug efficacy or surgical techniques, clinicians might implement approaches that have been proven harmful or ineffective. The 2020 hydroxychloroquine controversy illustrated how quickly questionable research spreads. Imagine that dynamic accelerated by AI systems that can't distinguish between valid and retracted papers. Policy and funding implications Government agencies and research institutions often use AI tools to synthesise large bodies of literature when making funding decisions or setting research priorities. Basing these high-stakes choices on retracted work wastes resources and potentially misdirects entire fields of inquiry. A withdrawn climate study or economic analysis could influence policy for years before anyone discovers the AI-assisted review included discredited research. Academic reputation damage For individual researchers, citing retracted papers carries professional consequences. Journals may reject manuscripts, tenure committees question research rigour, and collaborators lose confidence. While honest mistakes happen, the frequency of such errors increases when researchers rely on AI tools that lack retraction awareness, and the responsibility still falls on the researcher, not the AI. Why Language Models Miss Retraction Signals The technical architecture of most AI research assistants makes them inherently vulnerable to the retraction problem. Understanding why helps explain what solutions might actually work. Corpus quality controls lacking AI models learn from their training corpus, the massive collection of text they analyse during development. Most organisations building these models prioritise breadth over curation, scraping academic databases, preprint servers, and publisher websites without rigorous quality checks. The assumption is that more data produces better models, but this approach treats all papers equally regardless of retraction status. Even when training data includes retraction notices, the AI might not recognise them as signals to discount the paper's content. A retraction notice is just another piece of text unless the model has been specifically trained to understand its significance. Sparse or inconsistent metadata Publishers handle retractions differently, creating inconsistencies that confuse automated systems: Some journals add "RETRACTED" to article titles Others publish separate retraction notices A few quietly remove papers entirely This lack of standardisation means AI systems trained to recognise one retraction format might miss others completely. Metadata، the structured information describing each paper, often fails to consistently flag retraction status across databases. A paper retracted in PubMed might still appear without warning in other indexes that AI training pipelines access. Hallucination and overconfidence AI hallucination occurs when models generate plausible-sounding but false information, and it exacerbates the retraction problem. Even if a model has no information about a topic, it might confidently fabricate citations or misremember details from its training data. This overconfidence means AI assistants rarely express uncertainty about the papers they recommend, leaving users with no indication that additional verification is needed. Real-Time Retraction Data Sources Researchers Should Trust While AI tools struggle with retractions, several authoritative databases exist for manual verification. Researchers concerned about citation integrity can cross-reference their sources against these resources. Retraction Watch Database Retraction Watch operates as an independent watchdog, tracking retractions across all academic disciplines and publishers. Their freely accessible database includes detailed explanations of why papers were withdrawn, from honest error to fraud. The organisation's blog also provides context about patterns in retractions and systemic issues in scholarly publishing. Crossref metadata service Crossref maintains the infrastructure that assigns DOIs to scholarly works, and publishers report retractions through this system. While coverage depends on publishers properly flagging retractions, Crossref offers a comprehensive view across multiple disciplines and publication types. Their API allows developers to build tools that automatically check retraction status, a capability that forward-thinking platforms are beginning to implement. PubMed retracted publication tag For medical and life sciences research, PubMed provides reliable retraction flagging with daily updates. The National Library of Medicine maintains this database with rigorous quality control, ensuring retracted papers receive prominent warning labels. However, this coverage is limited to biomedical literature, leaving researchers in other fields without equivalent resources. DatabaseCoverageUpdate SpeedAccessRetraction WatchAll disciplinesReal-timeFreeCrossrefPublisher-reportedVariableFree APIPubMedMedical/life sciencesDailyFree Responsible AI Starts with Licensing When AI systems access research papers, articles, or datasets, authors and publishers have legal and ethical rights that need protection. Ignoring these rights can undermine the sustainability of the research ecosystem and diminish trust between researchers and technology providers. One of the biggest reasons AI tools get it wrong is that they often cite retracted papers as if they’re still valid. When an article is retracted, e.g. due to peer review process not being conducted properly or failing to meet established standards, most AI systems don’t know, it simply remains part of their training data. This is where licensing plays a crucial role. Licensed data ensures that AI systems are connected to the right sources, continuously updated with accurate, publisher-verified information. It’s the foundation for what platforms like Zendy aim to achieve: making sure the content is clean and trustworthy. Licensing ensures that content is used responsibly. Proper agreements between AI companies and copyright holders allow AI systems to access material legally while providing attribution and, when appropriate, compensation. This is especially important when AI tools generate insights or summaries that are distributed at scale, potentially creating value for commercial platforms without benefiting the sources of the content. in conclusion, consent-driven licensing helps build trust. Publishers and authors can choose whether and how their work is incorporated into AI systems, ensuring that content is included only when rights are respected. Advanced AI platforms, such as Zendy, can even track which licensed sources contributed to a particular output, providing accountability and a foundation for equitable revenue sharing. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
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; }
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