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Making Scholarly Research Accessible for Independent Researchers in 2025

Jun 23, 2025 |

21 Mins Read

Many researchers work outside of universities or formal institutions. These independent researchers often rely on public access to scholarly research to study, write, or contribute to their fields.

However, access to scholarly research is not equal. Most academic journals are behind paywalls, which means users must pay to read them unless they are affiliated with an institution that pays for access.

In this blog, we’ll explore the structure of academic publishing and how it affects independent researchers. We’ll break down the current challenges, the systems in place, and recent developments designed to improve research accessibility.

Why Research Accessibility Matters

Research accessibility refers to how easily someone can read, use, and build upon academic studies. For independent researchers, access is often limited because they lack university or library credentials required to unlock paywalled content.

A large portion of scholarly research remains behind subscription paywalls. Many journal articles cost between $30 and $50 each, and full journal subscriptions can reach thousands of dollars per year.

These costs create a divide between researchers affiliated with institutions and those working independently. Independent researchers may be excluded from current findings, which restricts their ability to contribute to academic conversations.

Without equal access, knowledge development becomes uneven. Some communities and individuals are left out, creating a gap in who can participate in scientific and scholarly work.

Understanding Open Access Models

Open access (OA) refers to academic research that anyone can read online without paying. There are different types of open access, and each works in a specific way.

1. Gold Open Access to Scholarly Research

Gold open access means that the final version of a research article is freely available on the publisher's website. The author or their funder usually pays a fee to make the article open.

Researchers can find gold open access content in fully open access journals listed in the Directory of Open Access Journals (DOAJ). These journals allow anyone to read and download the scholarly research directly from the publisher.

Reader benefit: Immediate access to the final, formatted version of articles
Limitation: Authors often pay fees ranging from $500-$3000 to publish

2. Green Open Access

Green open access is when authors share a version of their article in a free online repository. This version may be a preprint (before peer review) or a postprint (after peer review but before journal formatting).

Repositories like arXiv.org specialise in many disciplines, and bioRxiv.org for biology, host these papers. These platforms do not require any affiliation to access the content.

Reader benefit: Free access to research content, often before formal publication
Limitation: The version available might not be the final published version

3. Diamond Open Access

Diamond open access journals make articles freely available to read and do not charge authors any fees to publish. Neither readers nor authors pay. One good example of diamond open access is KnE Publishing, an open access publishing service by Knowledge E, provides high-quality publishing services to support the development and advancement of diamond open access journals, with a particular focus on increasing the visibility and accessibility of scholarly research.

This model is often supported by academic institutions or non-profit organisations. The Free Journal Network lists many of these journals.

Reader benefit: Completely free access with no barriers
Author benefit: No publication fees to share research

Open Access Model	Who Pays	Where to Find	Version Available
Gold	Authors/funders	Publisher websites	Final published version
Green	No one (usually)	Repositories	Preprint or postprint
Diamond	Institutions/grants	Publisher websites	Final published version

Practical Tools For Independent Researchers

Independent researchers need affordable ways to find and use scholarly research. Several tools make this process easier.

AI Summarisers

AI summarisers extract the main points from academic papers. These AI tools help researchers quickly understand if a paper is relevant to their work without reading the entire document.

Zendy's AI summarisation tool identifies key findings, methods, and conclusions from scholarly research papers. This saves time when reviewing large amounts of literature.

Time-saving: Condenses hours of reading into minutes
Comprehension aid: Helps readers understand complex academic language

Literature Discovery Tools

Discovery tools help researchers find academic papers and locate free versions when available.

Google Scholar indexes scholarly research and sometimes links to free versions.

Zendy uses AI to recommend relevant papers based on your interests.

Browser extensions like Unpaywall and Open Access Button automatically find legal, free versions of paywalled articles.

Broader search: Searches across multiple journals and repositories at once
Free alternatives: Identifies open access versions of paywalled content

Scholarly Research Reference Manager Tools

Reference manager tools help organise research papers and create citations. These tools are essential for independent researchers writing their own papers.

Zotero is a free, open-source reference manager that saves papers, creates citations, and integrates with word processors. Mendeley offers similar features with some social networking elements.

Organisation: Keeps research papers in one searchable library
Citation help: Automatically formats citations in different styles

Policy Shifts Empowering Independent Scholars

Recent policy changes are increasing the amount of research that is freely available to everyone. These changes help independent researchers access more content without institutional subscriptions.

Plan S requires that research funded by certain organisations be published with open access. This means more high-quality scholarly research is becoming freely available to read.

Many funding agencies now require researchers to share their findings openly. The National Institutes of Health in the US and UK Research and Innovation have policies requiring funded research to be publicly accessible.

Authors are also finding ways to keep their rights to share their work. Rights retention strategies allow researchers to post copies of their articles in public repositories even when publishing in traditional journals.

The trend toward open science continues to grow. More institutions are adopting policies that make research outputs—including data, software, and educational materials—freely available by default.

Ensuring Accessibility For All Researchers

Accessibility in scholarly research goes beyond open access. It also means making content usable for people with disabilities and those using different devices or internet connections.

Universal Design Principles

Universal design makes scholarly research usable by as many people as possible. This includes clear structure, readable text, and compatibility with assistive tools.

Well-designed articles use proper headings, include descriptions for images, and create documents that work with screen readers. These features help all users navigate and understand the content more easily.

Examples of accessible design in scholarly research:

Structured headings that create a logical outline
Alternative text for images and diagrams
Tables with proper headers and simple layouts
PDF files with proper tagging for screen readers

Assistive Technology Compatibility

Assistive technologies help people with disabilities access digital content. Researchers need to work well with these tools.

Screen readers convert text to speech for people who are blind or have low vision. Text enlargement tools and colour contrast adjusters help people with different visual needs.

When looking for accessible research content:

PDF accessibility: Look for tagged PDFs that work with screen readers
HTML versions: Often more accessible than PDFs for assistive technologies
Plain text options: Simple format that works with most assistive tools

If you need a more accessible version of any scholarly research, you can contact the publisher directly. Many journals now provide alternative formats upon request.

New Innovations in Research Access

The landscape of scholarly access continues to evolve with new models and technologies making research more available to independent scholars.

AI-powered research assistants are changing how people interact with academic literature. These tools can summarise articles, extract key information, and help researchers find connections between papers.

Digital libraries like Zendy are creating alternatives to traditional subscription models. With AI assistants like ZAIA (Zendy's AI assistant for researchers), these platforms not only partner with publishers to offer access to both open and paywalled content at affordable rates for individual researchers, but also enhance the research experience through AI support.

The future of scholarly research access looks increasingly open and innovative. New technologies and business models continue to break down barriers between knowledge and those who seek it.

FAQs about Accessing Scholarly Research

How can independent researchers find free academic articles legally?

Independent researchers can use open access repositories like PubMed Central and preprint servers like arXiv. Public libraries sometimes offer access to academic databases, and contacting authors directly often results in them sharing their papers.

What makes scholarly research accessible to people with disabilities?

Accessible scholarly research uses proper document structure with headings, provides alternative text for images, creates tables that screen readers can navigate, and offers formats compatible with assistive technologies. Articles in HTML format are typically more accessible than PDFs, and properly tagged PDFs are more accessible than untagged ones.

How do researchers evaluate the quality of open access journals?

Researchers can check if an open access journal is listed in the Directory of Open Access Journals (DOAJ), look for clear peer review policies, verify the journal's impact factor, and research the editorial board members. Quality open access journals maintain the same rigorous standards as traditional subscription journals.

What AI tool helps independent researchers conduct a literature review?

ZAIA, Zendy's AI research assistant, helps independent researchers conduct efficient literature reviews by automatically summarising academic papers, extracting key findings, and identifying connections between related studies. Researchers can also use reference managers like Zotero or Mendeley to organise papers and create citations. Literature mapping tools like VOSviewer help visualise research networks and identify influential papers. For comprehensive literature reviews, ZAIA can recommend relevant papers based on your research interests, saving hours of manual searching across multiple databases.

From Curator to Digital Navigator: Evolving Roles for Modern Librarians

With the growing integration of digital technologies in academia, librarians are becoming facilitators of discovery. They play a vital role in helping students and researchers find credible information, use digital tools effectively, and develop essential research skills. At Zendy, we believe this shift represents a new chapter for librarians, one where they act as mentors, digital strategists, and AI collaborators. Zendy’s AI-powered research assistant, ZAIA, is one example of how librarians can enhance their work using technology. Librarians can utilise ZAIA to assist users in clarifying research questions, discovering relevant papers more efficiently, and understanding complex academic concepts in simpler terms. This partnership between human expertise and AI efficiency allows librarians to focus more on supporting critical thinking, rather than manual searching. According to our latest survey, AI in Education for Students and Researchers: 2025 Trends and Statistics, over 70% of students now rely on AI for research. Librarians are adapting to this shift by integrating these technologies into their services, offering guidance on ethical AI use, research accuracy, and digital literacy. However, this evolution also comes with challenges. Librarians must ensure users understand how to evaluate AI-generated content, check for biases, and verify sources. The focus is moving beyond access to information, it’s now about ensuring that information is used responsibly and critically. To support this changing role, here are some tools and practices modern librarians can integrate into their workflows: AI-Enhanced DiscoveryUsing tools like ZAIA to help researchers refine queries and find relevant studies faster. Research Data Management Organising, preserving, and curating datasets for long-term academic use. Ethical AI and Digital Literacy Training Teaching researchers how to verify AI outputs, evaluate bias, and maintain academic integrity. Collaborative Digital Spaces Facilitating research communication through online repositories and discussion platforms. In conclusion, librarians today are more than curators, they are digital navigators shaping how knowledge is accessed, evaluated, and shared. As technology continues to evolve, so will its role in guiding researchers and students through the expanding world of digital information. .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }

Nov 25, 202512 Mins ReadDiscover

Strategic AI Skills Every Librarian Must Develop

In 2026, librarians who understand how AI works will be better equipped to support students and researchers, organise collections, and help patrons find reliable information faster. Developing a few key AI skills can make everyday tasks easier and open up new ways to serve your community. Why AI Skills Matter for Librarians AI tools that recommend books, manage citations, or answer basic questions are becoming more common. Learning how these tools work helps librarians: Offer smarter, faster search results. Improve cataloguing accuracy. Provide better guidance to researchers and students. Remember, AI isn’t replacing professional judgment; it’s supporting it. Core AI Literacy Foundations Before diving into specific tools, it helps to understand some basic ideas behind AI. Machine Learning Basics:Machine learning means teaching a computer to recognise patterns in data. In a library setting, this could mean analysing borrowing habits to suggest new titles or resources. Natural Language Processing (NLP):NLP is what allows a chatbot or search tool to understand and respond to human language. It’s how virtual assistants can answer questions like “What are some journals about public health policy?” Quick Terms to Know: Algorithm: A set of steps an AI follows to make a decision. Training Data: The information used to “teach” an AI system. Neural Network: A type of computer model inspired by how the brain processes information. Bias: When data or systems produce unfair or unbalanced results. Metadata Enrichment With AI Cataloguing is one of the areas where AI makes a noticeable difference. Automated Tagging: AI tools can read through titles and abstracts to suggest keywords or subject headings. Knowledge Graphs: These connect related materials, for example, linking a book on climate change with recent journal articles on the same topic. Bias Checking: Some systems can flag outdated or biased terminology in subject classifications. Generative Prompt Skills Knowing how to “talk” to AI tools is a skill in itself. The clearer your request, the better the result. Try experimenting with prompts like these: Research Prompt: “List three recent studies on community reading programs and summarise their findings.” Teaching Prompt: “Write a short activity plan for a workshop on evaluating online information sources.” Summary Prompt: “Give me a brief overview of this article’s key arguments and methods.” Adjusting tone or adding detail can change the outcome. It’s about learning how to guide the tool rather than letting it guess. Ethical Data Practices AI tools can be useful, but they also raise questions about privacy and fairness. Librarians have always cared deeply about protecting patron information, and that remains true with AI. Keep personal data anonymous wherever possible. Review AI outputs for signs of bias or misinformation. Encourage clear policies around how data is stored and used. Ethical AI is part of a librarian’s duty to maintain trust and fairness. Automating Everyday Tasks AI can take over some of the small, routine jobs that fill up a librarian’s day. Circulation: Systems can send overdue reminders automatically or manage renewals. Chatbots: Basic questions like “What are the library hours?” can be handled instantly. Collection Management: AI can spot patterns in borrowing data to suggest which books to keep, reorder, or retire. Building Your Learning Path Getting comfortable with AI doesn’t have to mean earning a new degree. Start small: Take short online courses or micro-certifications in AI literacy. Join librarian groups or online forums where people share practical tips. Block out one hour a week to try out a new tool or attend a webinar. A little consistent learning goes a long way. Making AI Affordable Many smaller libraries worry about cost, but not every tool is expensive. Free Tools: Some open-access AI platforms, like Zendy, offer affordable access to research databases and AI-powered features. Shared Purchases: Partnering with other libraries to share licenses can cut costs. Cloud Services: Pay-as-you-go plans mean you only pay for what you actually use. There’s usually a way to experiment with AI without stretching the budget. Showing Impact Once AI tools are in use, it’s important to show their value. Track things like: Time saved on cataloguing or circulation tasks. Patron feedback on new services. How often are AI tools used compared to manual systems? Numbers matter, but so do stories. Sharing examples, like a student who found research faster thanks to a new search feature, can make your case even stronger. And remember, the future of librarianship is about using AI tools in libraries thoughtfully to keep libraries relevant, reliable, and welcoming spaces for everyone. .wp-block-image img { max-width: 75% !important; margin-left: auto !important; margin-right: auto !important; }

Nov 25, 202511 Mins ReadDiscover

Key Considerations for Training Library Teams on New Research Technologies

The integration of Generative AI into academic life appears to be a significant moment for university libraries. As trusted guides in the information ecosystem, librarians are positioned to help researchers explore this new terrain, but this transition requires developing a fresh set of skills. Training your library team on AI-powered research tools could move beyond technical instruction to focus on critical thinking, ethical understanding, and human judgment. Here is a proposed framework for a training program, organised by the new competencies your team might need to explore. Foundational: Understanding Access and Use This initial module establishes a baseline understanding of the technology itself. Accessing the Platform: Teach the technical steps for using the institution's approved AI tools, including authentication, subscription models, and any specific interfaces (e.g., vendor-integrated AI features in academic databases, institutional LLMs, etc.). Core Mechanics: Explain what a Generative AI platform (like a Large Language Model) is and, crucially, what it is not. Cover foundational concepts like: Training Data: Familiarise staff with how to access the institution’s chosen AI tools, noting any specific authentication requirements or limitations tied to vendor-integrated AI features in academic databases. Prompting Basics: Introduce basic prompt engineering, the art of crafting effective, clear queries to get useful outputs. Hallucinations: Directly address the concept of "hallucinations," or factually incorrect/fabricated outputs and citations, and emphasise the need for human verification. Conceptual: Critical Evaluation and Information Management This module focuses on the librarian's core competency: evaluating information in a new context. Locating and Organising: Train staff on how to use AI tools for practical, time-saving tasks, such as: Generating keywords for better traditional database searches. Summarising long articles to quickly grasp the core argument. Identifying common themes across a set of resources. Evaluating Information: This is perhaps the most critical skill. Teach a new layer of critical information literacy: Source Verification: Always cross-check AI-generated citations, summaries, and facts against reliable, academic sources (library databases, peer-reviewed journals). Bias Identification: Examine AI outputs for subtle biases, especially those related to algorithmic bias in the training data, and discuss how to mitigate this when consulting with researchers. Using and Repurposing: Demonstrate how AI-generated material should be treated—as a raw output that must be heavily edited, critiqued, and cited, not as a final product. Social: Communicating with AI as an Interlocutor The quality of AI output is often dependent on the user’s conversational ability. This module suggests treating the AI platform as a possible partner in a dialogue. Advanced Prompt Engineering: Move beyond basic queries to teach techniques for generating nuanced, high-quality results: Assigning the AI a role (such as a 'sceptical editor' or 'historical analyst') to potentially shape a more nuanced response. Practising iterative conversation, where librarians refine an output by providing feedback and further instructions, treating the interaction as an ongoing intellectual exchange. Shared Understanding: Practise using the platform to help users frame their research questions more effectively. Librarians can guide researchers in using the AI to clarify a vague topic or map out a conceptual framework, turning the tool into a catalyst for deeper thought rather than a final answer generator. Socio-Emotional Awareness: Recognising Impact and Building Confidence This module addresses the human factor, building resilience and confidence Recognising the Impact of Emotions: Acknowledge the possibility of emotional responses, such as uncertainty about shifting professional roles or discomfort with rapid technological change, and facilitate a safe space for dialogue. Knowing Strengths and Weaknesses: Reinforce the unique, human-centric value of the librarian: critical thinking, contextualising information, ethical judgment, and deep disciplinary knowledge, skills that AI cannot replicate. The AI could be seen as a means to automate lower-level tasks, allowing librarians to focus on high-value consultation. Developing Confidence: Implement hands-on, low-stakes practice sessions using real-world research scenarios. Confidence grows from successful interaction, not just theoretical knowledge. Encourage experimentation and a "fail-forward" mentality. Ethical: Acting Ethically as a Digital Citizen Ethical use is the cornerstone of responsible AI adoption in academia. Librarians must be the primary educators on responsible usage. Transparency and Disclosure: Discuss the importance of transparency when utilizing AI. Review institutional and journal guidelines that may require students and faculty to disclose how and when AI was used in their work, and offer guidance on how to properly cite these tools. Data Privacy and Security: Review the potential risks associated with uploading unpublished, proprietary, or personally identifiable information (PII) to public AI services. Establish and enforce clear library policies on what data should never be shared with external tools. Copyright and Intellectual Property (IP): Discuss the murky legal landscape of AI-generated content and IP. Emphasise that AI models are often trained on copyrighted material and that users are responsible for ensuring their outputs do not infringe on existing copyrights. Advocate for using library-licensed, trusted-source AI tools whenever possible. Combating Misinformation: Position the librarian as the essential arbiter against the spread of AI-generated misinformation. Training should include spotting common AI red flags, teaching users how to think sceptically, and promoting the library’s curated, authoritative resources as the gold standard. .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }

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