Starting a PhD degree is daunting. You’re probably beginning your first full-time job in academia and you have to write numerous papers in a research field you’re not fully familiar with. Day-to-day you’ll spend a lot of time discovering new knowledge, writing literature reviews and publishing papers, which will ultimately form the foundation of your PhD degree. But as a new researcher, it’s often hard to know where to start your research discovery.
At Iris.ai, we’ve spoken with our users about their literature review process. They gave us tips and tricks on how to improve the research discovery, which have helped them find more relevant papers, save time and organize research in visual maps.
Traditional keyword-based literature search is constrained to what you already know as a researcher, and finding the most relevant papers becomes an exercise in pairing the perfect match of words. That isn’t to say that keyword search is outdated, but that it must be coupled with additional powerful research discovery tools. Only then can you be sure you’ve found the most relevant papers.
4 steps to boost research discovery
So here’s a step-by-step guide on how to research like the pros (aka Irisians aka our users).
1. Find a research paper
Head over to our friends at PubMed, and look up an existing research paper or systematic review which covers the topic you want to discover. For example, on the image below I’ve searched for a systematic review for autonomous vehicles.
2. Copy the link
When you have found a paper, copy the URL or DOI of the article.
3. Search in Iris.ai
Go to your account at Iris.ai (available for free), and paste the URL or DOI into Explore. Iris.ai will now look for related articles.
To get a bit techy: Iris.ai builds a fingerprint of your chosen paper based on the most meaning-bearing words in the abstract and contextual synonyms and hypernyms. Then, Iris.ai matches the fingerprint against more than 200 million papers.
(We’re connected to several Open Access databases, including arXiv.org, PubMed and CORE)
4. Explore the visual research map
After a few seconds, you’ll have a map of research papers divided into topics. In the below example, Iris.ai has identified 186 related papers, ready to be discovered.
Click on a topic to see underlying papers, and start reading papers that are relevant to your PhD degree.
Reading a lot of research papers is daunting (and sometimes boring), we know. That’s why we built Iris.ai — to help researchers get an overview of the seemingly unsurpassable mountain of published papers, and seamlessly identify the relevant ones. Good luck exploring!
The development of innovative technology has reshaped the way we consume, access, process, and distribute information. Academic and research libraries are adopting new technology, searching to improve their services and competitive advantage. Artificial intelligence has been a major force driving this change, and in this article we are going to answer: how is AI shaping the world of libraries and researchers?
What is AI? And what are the different categories of artificial intelligence?
Artificial intelligence is a wide-ranging branch of computer science that attempts to build smart machines with human intelligence aspects. It’s so far one of the most complex and impressive human inventions but the field remains mostly unexplored and with huge growth potential. AI is divided into 3 categories:
Artificial narrow intelligence (ANI)
Referred to as weak AI with a narrow range of abilities, it is the only type of AI we have available for now. Artificial narrow intelligence is used in facial recognition, speech recognition/voice assistants, and driving cars.
Artificial general intelligence (AGI)
Referred to as strong AI with the ability to mimic human intelligence or behaviors to solve any problem. For now, strong or deep AI is not yet available but currently researchers are working on improving machines’ ability to see, understand, and learn as humans do.
Artificial superintelligence (ASI)
It is the hypothetical AI that surpasses human intelligence and abilities. It has always been a source of inspiration for science fiction in which robots take over the world. Having powerful and self-aware super-intelligent machines may be an exciting idea, but their impact on humanity remains uncertain. For now, there are still many years before artificial superintelligence will be achievable.
How AI will change the job of librarians
AI has been implemented in more and more libraries, and here are some ways in which AI will have a significant impact:
1. Content indexing
Up until today, indexing has been a tedious and manual task. It is done partly by publishers and partly by authors. Indexing provides an overview of the context in which the book, journal, or paper was originally thought up. However, indexing says very little about, for example, other fields the information could potentially be useful for, and human-made labeling and indexing is hampering interdisciplinary discovery. It also limits the literature’s ability to stay relevant over time because the indexing was done in a specific category in a specific context, and over time that context of what we know about the world will change.
AI tools for indexing will improve consistency and quality. It can identify concepts and assign them corresponding keywords. Index automation will also help the reader discover new literature and navigate through different disciplines, which is not applicable through manual indexing. This type of AI tools will surpass human capabilities in indexing by providing more specific and accurate material for the readers and as a result, help university librarians improve their job.
2. Document matching
AI machines are better at processing documents fast and accurately than humans. Thanks to automatic proper indexing, AI tools are now identifying similarities and differences between documents or patents. Matching documents with similar ones or connecting sections that are describing the same topics, solutions, or phenomena is now possible. When a document can be indexed based on its actual content, it means that you can compare the content of thousands of documents that are contextually relevant to the search topic. It can be limited to only sections of a document, such as certain book chapters or research paper sections. Then you compare the content in these sections to find exactly what you’re looking for in the literature rather than doing a five keyword summary in the indexing. It is an essential operation that helps researchers and libraries to get to their knowledge easier and faster.
3. Death of citation
The citation system can be perceived as a popularity contest, but it doesn’t do much more than providing a very biased overview of a researcher network. When doing research landscape mapping and literature reviews, it is clear that using the citation system for snowballing is not an ideal method for covering everything. AI algorithms, which are based on the actual content of papers, will create far better mapping systems of the actual research, and be of major help to librarians and researchers alike (as opposed to the network of researchers presented in the citation system).
4. Content summarization
Automatic content summarization is about condensing documents to a shorter version, independently from human interference, while preserving the key elements and the meaning of the original text. Instead of summarising the whole article or book, AI tools are able to summarize just a section of a book or five documents into three sentences. AI tools for content summarizations are already available online and gaining popularity as well as machine learning algorithms that are continuously improving this task.
There are two types of automatic summarization: extraction and abstraction
Extracted summarization:
Extractive summarization depends on extracting sentences from the original text based on a scoring function. It selects the most important sections of the input based on the statistical survey and rearranges them together to produce a new condensed version of the document.
Abstracted summarization:
Abstractive summarization used advanced natural language techniques to produce a new summarized version of the document that is different from the original one. It aims at preserving the most important sentences while rephrasing them and incorporating critical information, like a human-written summary.
Most of the summarizations today use the extractive approach as it is easier and requires less linguistic analysis.
5. Quality of service
AI has penetrated the world of librarians and researchers in the form of chatbots that can answer directional or simple questions, alert when a new book is published, and direct a customer to specific library resources. The automation of conversations between a user and a machine will enable librarians to embed their focus on more difficult questions and save time answering repetitive ones. This will also enable libraries to extend the opening hours of both in-person and online services.
6. The Impact Factor of the Future
The impact factor is a measure of the relative importance and quality of the individual publication, journal, or researcher to literature. In the future, the algorithm will be capable of breaking down scientific research into arguments and validating them against other pieces of research. Or it could build for each document a truth tree of arguments and evidence, verify each branch, and then find the overall validity score. Having validated or rephrased research is more important than the number of its readers, as it is the solid and validated research that deserves a broader readership.
7. Better Operational Efficiency
Libraries can identify and magnify operational efficiency by improving service effectiveness and reducing operational costs with process automation, optimized research data management, and digital asset management (DAM). Implementation of machine learning in the library’s processes and digital resources can optimize collection analysis, visualization, and preservation, and reduce expenses associated with the provision of services. The adoption of advanced library service platforms can help in the development of operational efficiency.
The road ahead for libraries
Artificial intelligence is changing the information landscape while disrupting librarians’ traditional jobs. They are required to embrace AI not as a user but as an active leader to better serve the new upcoming generations. However, some reservations hinder the integration of AI in the world of libraries. The fear of being replaced by AI robots is totally understandable but we cannot neglect that advanced technologies will open up new horizons for librarians. It will help them maintain new innovative positions and roles, solve current challenges, and prevent them from becoming old fashioned. The focus on traditional tasks should be shifted to a new direction that embraces the advanced technologies and assists the upcoming generation with their evolving needs.
“There are many research tools that I recommend to my students, but Iris.ai is maybe the best one.”
Name: Josmel Pacheco Mendoza
Position: Researcher and Veterinarian
University: Universidad San Ignacio de Loyola
Region: Lima, Peru
My name is Josmel and I am currently working with multiple universities, besides being a partner of the editorial team of the Journal of Veterinary Research of Peru (RIVEP).
I found Iris.ai when I needed a tool to help me find the right documents for my COVID-19 research in a short time frame.
In one of the projects where I used Iris.ai, I worked with Mexican universities on two COVID-19 papers. There is a huge number of papers and patents published and available online, which makes it difficult and time consuming for my students and me to find the exact information that is related to my research.
How did Iris.ai help your research?
Iris.ai helped me to find the right collection of papers. One of the most powerful features is how you can add one paper into Iris.ai, and in return the software gives you a map of relevant papers. For me that’s like magic!
The Iris.ai tools also organize my work by clustering and filtering the papers. It is important for my work to have a map that organizes documents by their concepts and content.
Finally, the Iris.ai tools saved me and my students a lot of time, which is an important factor while doing research in medical fields.
It is exciting to use an academic research tool that relies on artificial intelligence to better serve students and researchers. There are many research tools that I recommend to my students, but Iris.ai is maybe the best one.
To get started with Iris.ai, sign up for a free account.
“Thanks to Iris.ai I found useful resources to build the thematic background of my paper. I ended up with around 20 papers that were immensely important and relevant.”
Name: Diego Raza Position: Lecturer – Research methodology
University: Universidad Andina Simon Bolivar Region: Quito, Ecuador
My name is Diego Raza and I am a lecturer at Universidad Andina Simon Bolivar in Quito, Ecuador. I teach subjects related to research methodology and help my students with their main thesis. When I was recently doing research for my literature review, I needed a tool to help me find research papers. I found that Iris.ai has higher quality sources in comparison to other tools on the market, and that it has access to papers that are of better academic quality.
How did Iris.ai help your research?
I was using Iris.ai for my own research in self-efficacy. I began my research by doing a broad exploration of papers in the Explore tool, then I imported the research map into the Focus tool to narrow down the results into a reading list. The tool helped me get an overview of the topic. Thanks to Iris.ai I found useful resources to build the thematic background of my paper. I ended up with around 20 papers that were immensely important and relevant.
Finally, I want to mention that I use Iris.ai to help my students identify literature that they may have overseen when preparing their main theses.
The biggest value with using Iris.ai, is how much time it’s saving. It’s doing a part of my work for me.
To get started with Iris.ai, sign up for a free account.
“Iris.ai organizes all my research in maps, which is divided nicely into different categories in the Explore tool. It also validates my own work for the literature review, as I can see whether the map matches my own research.”
Name: Amel Attatfa
Position: PhD in Cyber Diplomacy
University: Abertay University
Region: Dundee, Scotland
My name is Amel Attatfa. I am currently pursuing a PhD in Cyber Diplomacy, more specifically an analysis of diplomatic actions in international relations in cyberspace.
As I’m in the data collection phase, I needed a tool where I could organize all my research on a map, and that’s when I found Iris.ai.
The process of using Iris.ai is straight forward, as the website teaches the user how to use it. When I started, the website prompted me to enter my research question and explain the academic definitions. Then I got my research map, which I was able to adjust by using filters. I got used to the Iris.ai tools pretty quickly.
How did Iris.ai help your research?
Iris.ai organizes all my research in maps, which is divided nicely into different categories in the Explore tool. It also validates my own work for the literature review, as I can see whether the map matches my own research.
The website is also like a repository of information, on which I found papers that were possibly relevant to my research and potential participants for interviews, dealing with cyber matters.
To get started with Iris.ai, sign up for a free account.
— Export your bookmarks, bulk actions, repository overview and more…
We’re very excited to say we launched the latest version of our academic tools this week, version 6.1! As always, we’ve improved the backend and done some bug fixes to ensure you have a smooth experience using Iris.ai — but we have of course some juicy features for you as well.
I’ve listed the new features below. Do you want to try them out? Register and sign in to get started with a free account. We hope you enjoy the new update! ????
Now you can finally export all the papers you have bookmarked. Simply, click the brain icon in the top right corner, go to your reading list and select the papers you’d like to export. You can choose between CSV and BIBTEX.
Bulk actions
We’ve added checkboxes in front of every entry, allowing you to bulk actions. For example, exporting multiple papers.
Free or premium?
If you were ever in doubt, now you can see whether you’re subscribing to a premium or free version of Iris.ai — whether you’re an individual subscriber or a university member.
“But where are all these papers coming from?”
One question our users often ask us is, “but where are all these papers coming from?” Now we’ve given you a neat way to quickly check the repositories in which the papers were found. (Also, hot tip: if you find repositories in your list that you don’t want to see papers from, use the repository filter to exclude them!)
Undo/redo actions in a session
When you’re editing your map in hierarchy, you’re now able to undo and redo all the actions you’ve done in that session.
Hierarchy Editor: Duplication of concepts
Using the hierarchy editor, there are sometimes subconcepts that are highly relevant under multiple top concepts. Now we’ve given you the ability to duplicate the concepts so you can place them in several parts of the fingerprint — more customization and control over the fingerprinting process.
If you have any features on your wish list that haven’t been created yet, let us know at support@iris.ai!