Boosting Research Productivity with Lesser-Known AI Tools: Scite and Julius

Introduction

The explosion of artificial intelligence tools in recent years means many powerful applications are emerging beyond the well-known names. While mainstream AI like ChatGPT, Llama or Perplexity grab headlines, a lot of equally compelling AI-powered tools often fly under the radar. Tech experts have highlighted little-known AI tools that can accomplish in minutes tasks that would otherwise take hours of tedious work. These underrated tools span a wide range of functionalities, and they hold immense potential for students and researchers looking to work smarter.

Academic researchers, in particular, can benefit from these niche AI solutions to boost productivity and deepen analysis. This guide introduces two such lesser-known AI tools – Scite and Julius – and illustrates how they can streamline scholarly work. Scite is an AI-powered platform for intelligent citation analysis, ideal for speeding up literature reviews. Julius is an AI data analyst that enables complex data exploration through natural language. By incorporating tools like Scite and Julius into their workflow, UCLA students and researchers can save time, enhance the quality of their research, and uncover insights that might be missed through manual methods.

Scite: Smart Citations for Literature Reviews

Conducting a thorough literature review or critical analysis is often a time-consuming process. Researchers must engage deeply with prior studies: reading countless references, tracking how papers cite one another, and mapping out debates in the field. Collecting and reviewing all these sources can take weeks. Scite is designed to alleviate this burden by using AI to instantly show what other papers say about a given publication. In other words, Scite helps you spend less time hunting through reference lists and more time understanding the context of a work. By presenting how an article is discussed in other papers right away, Scite can both speed up your research and boost the quality of your writing. It facilitates key parts of the literature review process – from critically engaging with publications to identifying gaps in the research that your work can address.

At the heart of Scite is its innovative Smart Citations feature. Unlike traditional citation indexes that only count how many times a paper is cited, Scite analyzes the context of those citations. Each citation to a paper is categorized as supporting, contrasting, or mentioning.

A supporting citation means the citing paper provides evidence that agrees with or reinforces the original paper’s findings. A contrasting citation indicates the citing work disagrees with or offers conflicting evidence against the original paper’s claims. A mentioning citation is a neutral reference where the paper is cited without clear approval or disapproval (for example, citing as background or related work).

Scite’s natural language processing examines the textual citation statements in the full text of articles to determine these categories. As a result, when you look up a paper on Scite, you don’t just see that it has (say) 50 citations; you see how many of those are supporting, how many are contrasting, and you can read the snippets of text showing how each citation was made. This qualitative insight into citations helps researchers quickly gauge a study’s reception in the community – is it widely confirmed by others, or is it controversial? – at a glance.

Using Scite for literature review is straightforward. After creating a free account and logging in, you can search for a topic or a specific paper by title, DOI, or other identifier. Scite will display a list of relevant articles along with citation metrics. By clicking on a result, you open the Scite report for that article. The Scite report shows the paper’s references and, more importantly, all the citation statements from other papers that have cited it. Each citation statement is labeled as supporting, contrasting, or mentioning, so you can immediately see examples of how other researchers cited that work. For instance, if you’re reviewing a seminal article in your field, Scite can quickly show you which later studies backed up that article’s results and which questioned them. This capability is invaluable for writing the related work or background sections of a paper, as you can identify which sources strengthen your argument and which present alternate viewpoints. In essence, Scite serves as an intelligent lens over the scholarly landscape, helping you discover whether consensus or debate surrounds a particular finding. It’s like having a map of the scientific conversation: you can follow which papers agree on a point and where disagreements or gaps exist.

Scite also offers powerful features to refine your literature search and organize findings. You can apply filters to narrow down citation results – for example, showing only supporting citations if you want evidence that bolsters a certain claim, or only contrasting ones if you’re investigating challenges to a theory. This filter by citation type helps in quickly gathering a list of papers that take a particular stance. Additionally, Scite allows users to sort and filter by other criteria (such as publication year, journal, or even the section of the paper where the citation appears) to focus on the most relevant evidence. When you find a set of relevant articles, you can export the citation data for further analysis or reference management. Scite lets you export search results to a CSV file, or save articles to a personal dashboard for later review. This means you can compile all the supporting studies for your hypothesis into a spreadsheet, or build a custom collection of sources within Scite’s interface. Such features not only streamline the workflow of managing references but also make it easier to share insights with colleagues or include the data in your own writing. For example, if you were preparing a literature review on a topic, you could use Scite to gather all contrasting viewpoints, export those citation contexts, and then summarize them in a table for your paper. In sum, Scite acts as an AI research assistant that aggregates scholarly opinion: it quickly surfaces how each source is regarded by others, helping students and researchers at UCLA and beyond to perform more efficient and informed literature reviews.

Julius: An AI Data Analyst for Natural Language Data Exploration

While Scite tackles the challenge of literature review, Julius addresses another common hurdle in academia: data analysis. Students and researchers often have datasets to analyze but may lack advanced programming skills or the time to write complex code. Julius is an AI assistant designed for statistical analysis and data science tasks, allowing you to interact with your data through simple conversational prompts. In essence, Julius functions as a powerful AI data analyst that you can chat with. You ask questions about your dataset in plain English, and Julius does the heavy lifting—running calculations, producing charts, and even building predictive models—to give you the answers you need. This opens up advanced analytics to everyone, from seasoned data scientists looking to speed up their workflow to students who are beginners in data analysis. By making data exploration as easy as having a conversation, Julius makes complex analysis accessible to a broader audience, regardless of programming expertise.

Using Julius is refreshingly straightforward. To get started, you would go to the Julius platform (a web application) and begin a new conversation with the AI. Uploading your dataset is one of the first steps – Julius accepts common file formats like CSV or Excel spreadsheets, and you can upload these data files directly into the chat interface. Once your data is loaded, you interact with Julius through natural language prompts as if you were talking to a colleague. For example, you might type: “Show me a summary of the data,” or “What are the top trends in this dataset?”Julius will then analyze the data and respond with the results. You can ask follow-up questions or request specific analyses, such as calculating statistics (“What’s the average value for column X by group Y?”), generating a visualization (“Graph the trend of variable Z over time”), or even running a more complex procedure (“Perform a linear regression on these two variables”).

Under the hood, Julius leverages large language models and can execute the necessary Python code or statistical operations to fulfill your request. The key benefit is that you don’t have to write any code yourself – your natural language request is enough for Julius to figure out what analysis to run.

Once you issue a command or question, Julius will interpret your query and output the results right in the conversation. If you asked for a statistical summary, it might return a table of summary statistics. If you requested a chart, Julius will generate the chart and display it for you. The platform is capable of creating a variety of visualizations like bar graphs, line charts, scatter plots, or even maps, depending on the data, to help illustrate the insights. For instance, if your dataset is a collection of survey responses, you could ask, “Visualize the distribution of ages of respondents,” and Julius would produce a histogram or bar chart of the age distribution. If you’re working with time-series data, a prompt like “Plot the trend of sales over the last 12 months” would yield a line chart. These visual outputs are not only immediately helpful for understanding patterns in the data but can also be downloaded and used in your research reports or presentations. In addition to basic analyses, Julius can handle advanced data science tasks. It can execute predictive modeling (for example, training a regression model or a classifier on your data) and even assess feature importance in machine learning models. The results of such analyses – say, the coefficients of a regression or the important predictors identified by a random forest – are delivered in a human-readable format. Julius might say, for example, “The linear regression suggests variable A has a significant positive effect on outcome B,” potentially accompanied by the relevant statistics or charts. By handling the computational complexity, Julius lets researchers focus on interpreting the results rather than on coding.

To illustrate the power of Julius, consider a scenario where you have a dataset but aren’t sure where to start your analysis. Instead of manually writing R or Python scripts, you can simply ask Julius exploratory questions. You could begin with, “What does this dataset contain?” Julius might respond with a brief overview of columns and data types. Then you might ask, “Are there any notable correlations?” Julius could compute correlation coefficients between variables and highlight any strong relationships. If you see something interesting, you can drill deeper: “Create a scatter plot of Variable X vs Variable Y.” Julius will generate the plot on the fly, letting you visually inspect the relationship.

Throughout this process, you can iteratively refine your questions – much like having an interactive dialogue with a knowledgeable data analyst. Julius also supports natural language processing on text data, which means if your dataset includes text (say, survey comments or social media posts), you could request sentiment analysis or keyword extraction and Julius will perform those NLP tasks for you. At any point, if you need to share your findings, Julius allows you to download the outputs (such as charts or modified datasets) and even provides assurances that your data is handled securely (it does not retain your data after your session ends, according to its documentation). This combination of flexibility, power, and ease of use makes Julius an exceptional tool for academic data analysis – freeing you to concentrate on asking the right questions rather than on the mechanics of coding.

Conclusion

In an era where time is precious and information is plentiful, tools like Scite and Julius offer a strategic advantage for academic work. They exemplify how lesser-known AI tools can dramatically improve research productivity and insight. By quickly revealing citation contexts, Scite enables students and scholars to conduct literature reviews with greater depth and speed than traditional methods. By conversing with your data in plain language, Julius empowers you to perform complex analyses and visualizations without specialized programming knowledge. Both tools lower the barrier to advanced research techniques: Scite does the reading for you, and Julius does the number-crunching – all while you remain in control of the critical thinking and decision-making. As UCLA students and researchers continue to push the frontiers of knowledge, integrating AI assistants like Scite and Julius into their workflows can help ensure that their time is spent on analysis and creativity rather than on rote tasks. Embracing these underrated AI tools could mean faster discoveries, stronger evidence, and ultimately a more efficient path from question to insight in your academic journey. Using Scite and Julius is not about replacing the researcher, but about augmenting your capabilities – allowing you to focus on what truly matters: asking insightful questions and making impactful contributions to your field.