mac app research paper

Your personal research assistant

Zotero is a free, easy-to-use tool to help you collect, organize, annotate, cite, and share research.

Available for Mac, Windows, Linux, and iOS

Just need to create a quick bibliography? Try ZoteroBib .

Meet Zotero.

Collect with a click..

Zotero automatically senses research as you browse the web. Need an article from JSTOR or a preprint from arXiv.org? A news story from the New York Times or a book from a library? Zotero has you covered, everywhere.

Organize your way.

Zotero helps you organize your research any way you want. You can sort items into collections and tag them with keywords. Or create saved searches that automatically fill with relevant materials as you work.

Cite in style.

Zotero instantly creates references and bibliographies for any text editor, and directly inside Word, LibreOffice, and Google Docs. With support for over 10,000 citation styles, you can format your work to match any style guide or publication.

Stay in sync.

Zotero can optionally synchronize your data across devices, keeping your files, notes, and bibliographic records seamlessly up to date. If you decide to sync, you can also always access your research from any web browser.

Collaborate freely.

Zotero lets you co-write a paper with a colleague, distribute course materials to students, or build a collaborative bibliography. You can share a Zotero library with as many people you like, at no cost.

Zotero is open source and developed by an independent, nonprofit organization that has no financial interest in your private information. With Zotero, you always stay in control of your own data.

Still not sure which program to use for your research? See why we think you should choose Zotero .

Ready to try Zotero?

• Solution home
• Papers 3 (Legacy Application)

Introduction to Papers 3 for Mac

Papers revolutionizes how you discover, organize, read, share and cite. The first thing you’ll notice when starting Papers 3 is the new stunning user interface. One of the most important changes in Papers 3 is however almost invisible: how easy it is to sync your library between your computers (Macs or PCs) and your iPad and iPhone. We’ve taken a hard look at the whole application and built cloud based syncing as a core feature to all of Papers for Mac, Windows and iOS. Papers now uses Dropbox instead of a local wireless network for syncing, making it easier to get access to your library both at home and in institutional networks (other cloud based syncing services, as well as local wireless network syncing in addition to Dropbox to follow).

With Papers 3, we have also introduced Papers Online . Papers Online offers a convenient reading list and shared collections. You can create a collection to share with your team and collaborators. You can access the reading list and shared collections also from a web browser when you don't have access to Papers for Mac.

Papers 3 for Mac features:

A fresh, new user interface : the new Papers experience is simpler and more organised.

Navigation modes to keep your work and thoughts organized .

Relevant article suggestions based on the content you are reading.

Automatic PDF download when you import articles.

Better than ever meta-data discovery and matching (including batch matching ).

Stay connected: discover tweets and posts about the articles you are reading.

Download, view and annotate supplemental files directly within Papers 3.

Import formatted references via "File > New Paper > New from Reference".

Auto-suggested matches when adding/editing papers.

Improved BibTeX support

New Reader mode with annotation support , including free hand annotations.

Spotlight search support - search your Papers library using Spotlight in Finder .

Cloud syncing with Dropbox in addition to cleaner Wi-Fi syncing .

Shared collections and a reading list with Papers Online . Papers Online can also be deployed on demand, privately, to your researchers only - either hosted or internal. Contact us for more information.

Script Papers with AppleScript support.

You can also view Papers 3 introductory video from here: Introducing Papers 3 for Mac

Related Articles

• How to find your original PDFs in Papers 3 using the virtual file system on Papers for Mac
• Papers 3 for Mac System Requirements
• Existing Papers 3 users: accessing Papers 3 program files for additional device installs
• End of Life for Papers 3

Documents that say it all. Beautifully.

Pages is a powerful word processor that lets you create stunning documents, and comes included with most Apple devices. And with real-time collaboration, your team can work together from anywhere, whether they’re on Mac, iPad, iPhone, or a PC.

See what’s new in Pages

Creative writing. Or designing.

Pages puts all the right tools in all the right places, so it’s easy to choose a look, customize fonts, personalize text styles, and add beautiful graphics. And everyone collaborating on a document has access to the same powerful features.

Start with something beautiful.

Choose from over 90 beautiful Apple‑designed templates, and customize your reports, letters, and other documents any way you like. And if you’ve ever dreamed of writing a novel, or just a fairy tale, it’s easy to create interactive digital books right inside Pages.

Make progress you can see.

Track changes, add highlights, and have threaded conversations with your team. Your edits are saved automatically.

Stunning results. Effortlessly.

Liven up your text by filling it with color gradients or images. And take the entire page to the next level by adding photos, galleries, audio clips, video, math equations, charts, or more than 700 customizable shapes.

Work seamlessly from any device.

Jumping from your Mac to your iPad to your iPhone is no problem with Pages. And with Screen View on iPhone, your content will be optimized to fit the screen — making reading and editing on the go a breeze.

What’s new in Pages.

Learn about everything you can do in Pages

Get the updates. Then get in touch.

Stay up to date when people join, edit, or comment in collaborative documents and easily get in touch with your team using Messages and FaceTime.

Get more done on iPad.

Quickly insert objects, find settings, and get to your favorite tools with the customizable toolbar. And Stage Manager makes it easy to multitask across multiple documents and apps at the same time. 1

Jump-start your designs with new templates.

Create invitations for your next big event with colorful photo card templates, or reward your students with a new coding certificate.

Batch mailing made easy.

With mail merge, you can create a letter, card, or envelope in Pages, and then insert a personalized greeting or address from the Contacts app or a Numbers spreadsheet.

An optimal view for your documents on iPhone.

Screen View makes it even easier to read and edit your docs on iPhone. Turn it on and text, images, and tables are optimized to fit your screen. Turn it off to see the full layout.

Quickly translate text.

Instantly view a translation of any selected text — you can even replace it with a tap if you like. 2 Perfect for foreign language classes, businesses, and more when you need a quick in-document translation.

Turn handwriting into text. Magically.

With Scribble for iPadOS and Apple Pencil, your handwritten words will automatically be converted to typed text. Take notes, write a book, or annotate a paper quickly and easily.

Write reports easier.

With report templates, there’s no staring at a blank page. Jump-start an essay, research paper, or school report by choosing one of the beautifully designed templates.

Skim through in style.

On your iPhone and iPad, you can read through your document, zoom in and out, and interact with it — without accidentally changing anything.

Play videos right in your documents.

Play YouTube and Vimeo videos right in Pages, without the need to open a web browser. Simply add a link, and play your web video inside your document or book.

Any document. Any device. Anytime.

You don’t work in one place on just one device. The same goes for Pages. Work seamlessly across all your Apple devices. The documents you create using a Mac or iPad will look the same on an iPhone or web browser — and vice versa. And with Screen View, it’ll be easier than ever to work on your iPhone.

You can also work on documents stored on iCloud or Box using a PC.

Start using Pages at iCloud.com

Collaborate with anyone. Anywhere.

Work together in the same document, from across town or across the world. You can see your team’s edits as they make them — and they can watch as you make yours, too. You can even get notifications when people join, edit, or comment. Just click or tap the Share button and invite people to join.

Use Apple Pencil when inspiration strikes.

Use Apple Pencil on your iPad to sketch, illustrate, and create documents. Draw and fine-tune your idea, then press play to watch each stroke animate onto the page. And with Smart Annotation, your edits stay with the marked-up text, making it easy for you and your team to incorporate changes.

Plays well with Office.

Teaming up with someone who uses Microsoft Word? Pages makes it simple. You can save Pages documents as Word files. Or import and edit Word documents right in Pages.

Learn more about Microsoft Word compatibility

See everything that’s new in Pages

• What's new in Pages for iPhone and iPad
• What’s new in Pages for Mac
• Pages for iCloud release notes

Additional Resources

• Get Pages support
• Learn about Microsoft Office compatibility
• Learn about collaboration for Pages, Numbers, and Keynote
• Learn more about writing and publishing books with Pages

Pages User Guides

• Pages User Guide for Mac
• Pages User Guide for iPad
• Pages User Guide for iPhone
• Pages User Guide for Web

Build spreadsheets that are bottom-line brilliant.

Design stunning, memorable presentations.

5 Mac Word Processors To Help You Write That College Paper

Finding sources to cite is easy. Planning a paper is easy. Sitting down and writing the thing? Much harder — do yourself a favour and get the right tool for the job.

Finding sources to cite is easy . Planning a paper is easy. Sitting down and writing the thing? Much harder, and though there's no shortage of word processors, not all are well-suited to academic writing.

As someone currently working on my dissertation, I know this problem all too well. So I found five popular Mac applications commonly used for academic writing and  reviewed each in order  to see which excelled the most when it comes to writing college papers and dissertations.

Here's what I found.

Ulysses  ($45)

At just short of $45, Ulysses is one of the more expensive applications in this rundown. I reviewed version 2.0, which runs exclusively on 64-bit Macs running Yosemite. There's also an iPad version  ($19.99), which Bakari reviewed recently .

Ulysses is, like Desk and iA Writer, a markdown-oriented text editor. Markdown allows you to format text using a special syntax, rather than pressing a button in an application. The advantage of this is that it doesn't break your workflow, and text written in MarkDown can be copied between applications without losing formatting.

Another advantage of Markdown is that it's incredibly easy to learn, not just because we published a guide to it last year. Ulysses is different from other markdown editors in a number of ways that distinguish it from the pack.

Firstly, it allows you to separate texts into individual sections, each within their own writing space. This is handy if your university project is effectively an anthology of texts, as most dissertations are.

Secondly, Ulysses allows you to change the theme from a bright one, to a more subdued night-mode version which looks great when working in the dark. It also comes with a command palette that feels oddly reminiscent of Sublime Text 2 , which allows you to navigate your document without endlessly scrolling,  just like Vim .

Ulysses also makes it easy to set goals, which is handy when you're unmotivated and trudging through the tedium of a literature review. Unfortunately it doesn't natively support any major reference managers, such as EndNote and Zotero , and it doesn't allow you to embed images or graphics.

Despite these limitations, it's a perfectly adequate markdown editor, and one that lends itself favorably to academic applications.

iA Writer Pro ($20)

I'm a fan of iA Writer. We  reviewed the non-pro version of it back in 2013 and it immediately became my writing application of choice. Why?

The app is markdown-based, so you can add formatting as you write without getting distracted or having your writing pane filled with superfluous toolbars and ribbons. It also allows you to focus on the writing, as it puts the text in the center of your screen and a simple, readable typeface contrasts with the austere, white background.

That's the cheaper, non-pro version. I've since moved on to the professional version, and I'm convinced it too is an excellent choice for markdown aficionados tasked with academic writing.

iA Writer Pro comes all the same features of the cheaper version that allow you to focus on the writing, but brings with it a 'night mode' theme, which is great for late night work.

It also allows you to drill-down on your text and identify parts of your writing you can remove and refactor, such as adverbs, verbs, and prepositions. Given academic writing strongly emphasizes conciseness and precision, this is really helpful.

But iA Writer Pro is lacking some features that are helpful when it comes to academic writing. It doesn't support third-party plugins, which makes it hard to import your citations in from Zotero, or any other reference manager. It also only lets you to work one document at a time, unlike Ulysses's multi-sheet approach to document editing.

Despite those drawbacks, it's only $20  and makes it easy to be focused and productive, and is therefore worth a consider.

Scrivener 2 ($45)

Scrivener is an inexpensive application with an excruciatingly steep learning curve. It's commonly used by people working in the creative industries, and has found a niche as a tool for writing screenplays and scripts. But despite this pedigree, it is also worth considering for your next academic paper.

Scrivener, like Ulysses, lets you break your document into manageable chunks, and tackle them one at a time. Editing is done through a graphical interface, with formatting added through the application, rather than using Markdown syntax.

But perhaps the killer feature of Scrivener is its 'cork board'. This allows you to manage, collect, and collate resources you might want to use in your paper, such as images, notes and references.

Scrivener supports a handful of popular third-party bibliography applications, which means you don't have to adjust your system of managing citations and references. It also allows you to create snapshots - or versions - of your text, and revert back to them when you want to return to an earlier form of your work. This is similar to how Git works , which is a version control system used by programmers.

However, Scrivener lacks the sleek, distraction-free aesthetics of iA Writer and Ulysses, which makes it less than ideal for long writing sprints where your focus might wander. It's also rather expensive, and takes a few hours (and a lot of reading) to fully get to grips with.

Microsoft Word 2016 Preview Edition (Free)

It's hard not to talk about word processors, and not mention Microsoft Word. It's the incumbent, and has been for a couple of decades now. Go to any university, and you'll find Microsoft Word is the de-facto word processor. This due to that fact that it's well understood, supported by Microsoft, and works well with other the packages in the Microsoft Office family.

Microsoft recently released the preview version of Word 2016 , and is currently available as a free download before being publicly released.

This latest version represents the biggest change to Microsoft Word on OS X for almost 5 years. It comes with a sleek new aesthetic that makes it feel like the modern, premium word processor it is. For once, you're going to want to write with Word.

But as a tool for writing Academic papers, how does it stand up? Well, it's not a distraction-free editor like iA Writer is, but that's fine. It makes up for that by being well-rounded and complete, boasting all the features any university student or academic could possibly need.

One of the most compelling features for any student is its built-in citation manager, which offers many of the features of Zotero, and can produce references in APA, MLM and Chicago style.

Unlike iA Writer Pro and Ulysses, Word allows you to insert and embed figures and graphics, and create charts that underscore the points you make.

This makes it one of the more compelling packages for academic writing. The only problem is that when it exits the beta phase, it will ultimately cost a good chunk of change. This free version will eventually cease to work, so you'll have to purchase Word as part of the Office 2016 release if you want to keep the functionality you've gotten used to. In the Apple Store, Office 2011 costs $139.95, so expect Office 2016 to cost something approaching that.

It's also worth noting that beta applications can ship with bugs that might end up destroying all your hard work. With that in mind, it's a good idea to make regular backups if you decide to use it.

Pages (Free/$19.99)

Pages is part of iWork , Apple's flagship productivity suite. Apple made it available free of charge to anyone who purchased Mac on or after October 1, 2013. Everyone else can purchase it for $19.99 on the Mac App Store, which is pretty good for a fully-fledged word processor.

As a tool for getting words on a page, it's solid. It comes with a number of templates for academic writing. However, these overwhelmingly are geared towards a style of academic writing that's more common in the American university system, than in the British and Antipodean ones. That said, it's easy enough to tweak a template, and formatting text in Pages is simple enough for this not to be too much of a barrier.

Pages also supports academic citations through EndNote , a perfectly competent though expensive reference manager, with a license costing around $250. The closest free alternative, Zotero, hasn't released a plugin for iWork and given the niche status of Apple's iWork when it comes to productivity software, I doubt they ever will.

Pages can also produce incredible graphics and charts with a button's press. This makes it ideal for those writing papers with a somewhat data-driven emphasis.

For those on a tight budget, it remains the best option, and poses a serious challenge to the likes of Scrivener and Ulysses.

No Surprises Here

It should come as absolutely no surprise that the two packages I'm ultimately going to recommend are ones made by Microsoft and Apple; both giants in what they do. Pages and Word are just too complete and functional to not recommend, and offer the most value for money (at least while Word is free).

As a close second, I'd also recommend iA Writer Pro, which despite lacking a number of killer features like EndNote integration and bibliography management, offers the best writing experience of any application listed in my opinion.

What do you use to write your academic papers? Leave me a comment below and we'll chat.

Image Credits: student with laptop Via Shutterstock

The PDF Reader for Research

Use it for FREE on your iPhone, iPad or Mac today.

Coming very soon to your iPhone, iPad and Mac.

Unlock advanced features by becoming a Pro subscriber. A free trial is available in the app.

Export Anywhere

Annotate Faster

Lookup Citations

Files Integration

Your Smart Research Tool

Turns your pdf annotations into powerful notes..

It's your notes, you decide where they go.

You can also export as Markdown , TextBundle , HTML , WebArchive and PDF files.

Highlights is built with you who annotate a lot in mind and the tools are where you want them. No additional taps through menus.

If you want to go even faster you can set the default annotation color for each tool and even double tap the Apple Pencil to switch tool.

Automatically find DOIs for scientific articles and download the correct metadata.

Markup a citation and tap the link icon to look it up. If you have already read the citation Highlights will link it to the PDF on your device.

Customize Appearance

Highlights supports dark mode and custom accent colors on all your devices..

- John Voorhees,

US App Store

- Joe White,

Average rating of 4.6 in the App Store.

"best pdf annotation tool out there".

- Ramon Rempel, Mac App Store Canada

"Incredible. The best PDF reader for compulsive note takers I’ve ever used"

- matty98384, App Store UK

"I've literally been waiting for something this simple to use and effective for adding notes to academic PDFs for ages."

- Smidge17, Mac App Store UK

Document Browser

Browse your files using the iOS document browser or the Finder on your Mac. Store your files on device, in iCloud Drive, Dropbox or other sync service.

Continue working on the same file on another device using Handoff. Highlights stores your annotations as standard annotations and does not lock your PDF files in.

Multilingual OCR

Extract text from PDFs without searchable text using built-in OCR. Highlights supports a range of languages and does all processing on device.

Highlights can also recognize tables and with Smart Copy you can convert them on the fly and paste the data right into a spreadsheet.

Smart Copy gives you the desired output for any annotation when you copy it. No additional steps required, just paste it where you want it.

Images as PNG

Tables as CSV

Citations as BibTeX

And Much More..

See the full feature list, try highlights for free.

This Mac app is essential if you read a lot of PDFs

If you read a lot of PDFs for research, you need Highlights.

Mac / iPhone / iPad - Free (In-app purchases) 

Want more apps? Check out our hand-picked lists:

- Best iPhone apps - Best iPad apps - Best macOS apps - Best Apple Watch apps

Back in 2020 I wrote a book that involved huge amounts of research. It was during the first lockdown so this research didn't look like trips to a library or in-person interviews. Instead, it was all conducted via the internet and many, many copies of surveys, studies and research papers. 

The majority of these documents were PDFs and I found them really difficult to markup and organize. I only wish I had discovered Highlights back then, an app that works on your iPhone and iPad, but I highly recommend using it on your Mac for the best reading experience. 

The Highlights app does a lot of things, but one of the simplest and yet most useful features is that you can use it to highlight and markup any text and images in a PDF that are then formatted into notes on the fly. It can also extract notes from any existing PDFs as soon as you open them up. Research tasks are about to get a whole lot more straightforward. 

Make PDF research quicker and easier

When you open Highlights, you'll see a toolbar — this is the main hub of the app. It contains thumbnails to documents, annotation and markup tools and notes, all in one place. This enables you to really quickly and easily move between different documents and all of the notes you've been making across them.

It's very easy to annotate a PDF, just drag your pointer or finger over the bit you want to markup and a pop-up appears that allows you to copy, underline, highlight and lookup text or add it to a note. Highlights also works with images and is optimized for scientific research, so that includes graphs, charts and any other sorts of diagrams too, all of which can be added to a note along with text. 

Once you've created lots of notes from your PDFs you can export them anywhere as PDF files themselves. If you get the Highlights Pro version you can export notes in editable formats too, like Markdown and HTML. The Pro app costs $3.99/£3.99 monthly and $29.99/£29.99 a year and comes with more extra features, like custom default colors for different annotation tools and integration with reference managers. 

If you're doing a lot of research and writing it up for a paper, a study, a book or anything else, I'd also recommend checking out some of the best comprehensive writing apps. My favorite, and the one I used to write my book is Scrivener . 

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iMore's daily App of the Day post helps you find great apps you've never heard of on your iPhone, iPad, Mac, and Apple Watch, curated each day by our expert team!

Becca Caddy is a contributor to iMore, as well as a freelance journalist and author. She’s been writing about consumer tech and popular science for more than a decade, covering all kinds of topics, including why robots have eyes and whether we’ll experience the overview effect one day. She’s particularly interested in VR/AR, wearables, digital health, space tech and chatting to experts and academics about the future. She’s contributed to TechRadar, T3, Wired, New Scientist, The Guardian, Inverse and many more. Her first book, Screen Time, came out in January 2021 with Bonnier Books. She loves science-fiction, brutalist architecture, and spending too much time floating through space in virtual reality. Last time she checked, she still holds a Guinness World Record alongside iMore Editor in Chief Gerald Lynch for playing the largest game of Tetris ever made, too.

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Apps for Research & Writing: Recommended Apps

• EBSCO eBooks
• Overdrive eBooks
• Internet Archive
• Research Articles
• Note-Taking
• Group Work/Collaboration
• Mind-Mapping
• Help This link opens in a new window

Using This Guide

This guide will cover apps that I find useful for research, ebook reading,  and organizing references and notes.

iPhone/iPad Apps

• Evernote Create text, photo and audio notes ● Auto-synchronize your notes to your Mac, PC, and Web ● Magically makes text within snapshots searchable ● All notes include geo-location information for mapping and search
• Dropbox Dropbox is a free service that lets you bring your photos, docs, and videos anywhere and share them easily.
• QR Reader Barcode and QR code scanning app.

Android Apps

• Xodo PDF Reader & Editor Xodo is an all-in-one PDF reader and PDF editor. With Xodo, you can read, annotate, sign, and share PDFs and fill in PDF forms, open .docx/.pptx as PDFs, plus sync with Google Drive, Dropbox and OneDrive.
• QR Droid Code Scanner Barcode and QR code scanning app
• Next: eBooks >>
• Last Updated: Jan 18, 2023 12:47 PM
• URL: https://library.mcla.edu/apps

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Take advantage of all the reference management and citation writing tools that Papers has to offer and enjoy seamless syncing across all of your devices.

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Papers synced across all of your devices.

Seamlessly cite from your library as you write.

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Top 11 Apps for Researchers in 2023

Published on 01 May 2022

The evolution of new technologies has caused a digital transformation in almost every industry and field of interest, including academia. Technology has changed the way that academics conduct research, document findings, and collaborate with peers. 

Academics can now rely on new avenues of collaboration that didn’t even exist when they launched their careers. Networks like SSRN and Mendeley provide opportunities for researchers to share their work for increased collaboration, and abstract management tools streamline the peer review process required by legitimate academic conferences and journals. 

As this digital transformation accelerates, researchers can now access a vast array of apps aimed at simplifying their workflows and facilitating information sharing. While these apps have the potential to improve the way scientists conduct and share their research, the selection can be overwhelming. 

Based on our experience and extensive research, here are the 11 best apps available for researchers in 2023.

1. Fourwaves 

Fourwaves is a conference management software for researchers. Their free web application allows you to create a complete event website, manage abstract submissions, peer reviews, host virtual poster sessions , manage registrations and more. 

It’s the easiest way to organize scientific events as the tool was crafted with researchers in mind every step of the way. 

Fourwaves can be used not only for in-person events but also for hybrid and virtual conferences . They offer a complete virtual venue to access live streams, chat or call other participants and attend virtual poster sessions.

You can go as far as mass email your attendees, automatically generate your event schedule or even print out your name tags; everything you need for your event is in one place.

Most interesting features:

• Ready-to-go event website ; all you have to do is enter your event’s content and you’re ready to publish. 
• Abstract management & Peer review tool ; you can easily collect submissions, review them according to your criterias, email authors and publish your material and the full conference schedule online.
• Registration and payment management ; attendees can easily register to your event and pay online on your Fourwaves event website. 

2. R Discovery: Academic Research

R Discovery is a free app that empowers researchers to save time wading through a sea of academic research papers by finding the articles that are most relevant to your work and delivering them to you each day. It curates over 96 million research articles which includes over 24 million open access articles. 

The app is mobile-only, available for download on the Google Play App Store and the Apple App store for mobile use on your Android device, iPhone or iPad. The app scans papers from all major disciplines in the arts and sciences. 

• As soon as you sign up and submit your areas of interest, R Discovery will serve you the top three related articles in a news feed each day.
• R Discovery uses AI to learn your reading interests over time and populate your news feed with content increasingly tailored to your specific interests.
• The app provides export functions for easy integration with reference managers to organize your citations.

3. LabArchives  

LabArchives is a web-based application that acts as a digital lab notebook, helping researchers keep their work and notes organized to improve productivity in their labs. Users can access LabArchives to make notes, store images and data, and use the search feature for simple access to all of their material. 

There are also Android and iOS versions of this app available in the Apple App Store and Google Play App Store that allow users to access their digital notebooks from their Android devices, iPhones and iPads and have instant access to all of their data, from anywhere. While there are Premium and Enterprise versions of the platform for more advanced use and collaboration, individuals and small teams can access a free version that still includes unlimited notebooks and 1GB of storage. 

Most interesting features: 

• Makes it easy to store and share data between your team members, with user-friendly search functions. You can even share DNA sequence files in over 30 formats! 
• Access information from your desktop or your phone, thanks to the free iOS app for your iPhone or iPad. There is also an Android app available in the Google Play store, but based on reviews it appears that functionality is limited. 
• Data security that lets you determine file access and sharing limitations, so you know exactly who is viewing your files and when.

Typeset is a web-based application that was created to help researchers write, collaborate, format and submit research papers for publication. Typeset allows you to upload your work to their platform, and use their AI to reformat your research and submissions to meet the publication requirements of various journal and conference organizers. 

Typeset works seamlessly with reference management software like Mendeley, Zotero, Paperpile and more. It allows users to choose from over 45,000 verified journal formats and export your work to Word, LaTex and PDF formats. 

Typeset does not offer mobile apps for Apple or Android devices. There are a variety of subscription levels available with pricing ranging from free to $20 per month. 

• Editing features that increase the chances of being published.
• Integrations that enable you to submit research for publication directly from the app.
• Plagiarism and grammar checker for increased quality and peace of mind.

5. BenchSci

The BenchSci platform was built to use advanced biomedical AI to help source the materials that scientific researchers need to move forward with their work. 

Once the app user enters their protein target into the BenchSci platform, the app will sift through thousands of reliable information sources like websites and scientific publications, delivering options that will help determine the antibody or reagent needed. BenchSci is a web-based application that is not available for Android or iOS. It is used by more than 48,000 individual scientists and over 4,000 institutions. BenchSci boasts that their tools can accelerate projects through their AI-powered reagent and antibody selection process, cutting the selection time from 12 weeks to 30 seconds. By empowering researchers to find the antibodies and reagents they need easier and faster, BenchSci reduces the number of materials they need to purchase and experiment with, therefore reducing costs. 

• AI-Assisted Reagent Selection, which uses AI and automation to reduce the errors and inefficiencies in the reagent and model system selection for scientists. 
• AI-Assisted Antibody Selection, which follows the same principle as the reagent selection but focuses on antibodies. This feature is free for you to use if you are a student or researcher at an academic, government, or nonprofit institution. 
• Things change quickly, so the platform is constantly updated to add new antibody and reagent products to ensure that users can access everything available.

6. eLabJournal

There are many Electronic Lab Notebooks (ELNs) available on the market, but the eLabJournal takes the concept of ELNs to the next step. eLabJournal was designed to increase productivity and efficiency in your research lab and simplify the process of organizing and locating data, collaborating with peers, and exporting files into a variety of formats. 

This is a web-based application with mobile versions available on the Google Play and Apple App Stores. Academics can purchase a subscription to the eLabJournal for $15.55 per month, while Industry users are charged $41.95 per month. 

• This ELN uses a simple, intuitive interface that was specifically designed to meet the needs of those in the life science research and development field. 
• Facilitates the ability to link data with functionality to upload images (via the Android and iPhone apps) and a wide range of file types. 
• Seamlessly integrates with eLab’s other products through their SDK and APIs, providing extensive customization opportunities to meet the specific needs of your lab.

7. Connected Papers

Connected Papers is a web-based application that provides a uniquely visual representation of the published research available in a certain field. This helps researchers and scientists browse the information available related to their field of study and ensure that nothing is being missed as they prepare their work for submission. 

The app works when a scientist enters their research topic into the search bar. Within seconds, Connected Papers reviews tens of thousands of papers related to that topic, and creates a visual map showcasing all of the work available for the scientist to review and consider in their research. Connected Papers is currently not available on the Apple App Store or Google Play App Store. It is completely free to use. 

• The visual maps create an easy-to-follow pathway that showcases how closely related particular sources are to the work you’re conducting.
• The app creates clusters that groups papers based on their level of similarities, and pushes less relevant papers away.
• Connected works scans the citations used by various sources and classified papers to be closely related based on how many citations overlap. 

8. Papership

The Papership app allows you to store, annotate, manage and share research papers from anywhere. Available on your Mac, iPhone, and iPad, Papership syncs with popular web-based platforms Zotero and Mendeley to allow app users to access their curated research libraries stored in their Zotero and Mendeley accounts conveniently and remotely. 

• You can choose a free version of the app which can integrate with annotation apps like Evernote, or purchase the annotation function of Papership for $9.99 per month.
• Documents annotated through Papership can be shared via email, SMS, iMessage, Facebook and Twitter. 
• Papership provides quantitative measurements of the significance of a publication to alert the reader as to the legitimacy of the research. It measures both peer-reviewed and non-peer reviewed sources. 

9. GanttPRO

Ganttpro is a web-based project management application that helps research teams plan and organize projects through the use of collaborative Gantt charts. By providing the ability to create interactive Gantt charts online, GanttPRO makes it possible to plan and control many projects at the same time. It empowers researchers to organize and schedule tasks, set deadlines, identify dependencies and manage resources, all while making this information readily available to all collaborators. GanttPRO is available in a mobile version that can be downloaded for your Android and Apple mobile devices. The company offers a free trial and once that is complete different app packages are available that range from $7.99 to $19.99 per month. 

• Drag and drop capabilities to make it simple to organize and reorganize as inputs, outputs and priorities change
• Allows for the creation of multiple workspaces to separate personal tasks from overall team projects
• Collaborative functions make it easy to track the progress of each team member and step in to help whenever needed. 

Trello is an app that can be used by academics, researchers, marketers, computer scientists and basically any other student, professor or business person interested in seamlessly collaborating and managing projects on-the-go. Trello is organized in boards, lists and cards that are customizable and expandable as the project and team grows. Trello easily integrates with other popular apps like Dropbox, Slack, Chrome, Teams and more. It is available for Android and Apple mobile devices on the App Store and Google Play App Store. 

• Timelines that allow all team members to stay on track and be held accountable to deadlines
• Table views that connect work across a variety of related Trello boards
• A handy Dashboard that highlights usage and engagement stats for all of your boards.

11. Researcher

The Researcher app was built to make it easier for researchers to find academic articles relevant to their work. By aggregating over 19,000 sources that include peer-reviewed academic journals, blogs, podcasts and recordings from live events, Researcher helps scientists stay up-to-date on emerging trends and information related to any given field of study or interest. The creators of Researcher claim that their app is “like social media, but better.” The Researcher app is free to use and is available for download on the Apple App Store, the Google Play App Store and the AppInChina App Store. 

• Filter options that allow you to sift through tens of thousands of sources in seconds
• Notification options to ensure that any time a new source is published that relates to your stated interests, you’ll find out about it right away.
• Bookmarks that make it easy for you to come back to an interesting piece when the time is right, without having to search.

Conclusion 

The apps listed above can help you be more efficient, collaborate better with your colleagues, and get more organized. We hope one or more of them considerably help you with your research. Let us know if we missed any! 

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Manifold Diffusion Fields

When can transformers reason with abstract symbols, label-efficient sleep staging using transformers pre-trained with position prediction, catlip: clip-level visual recognition accuracy with 2.7× faster pre-training on web-scale image-text data, openelm: an efficient language model family with open training and inference framework, talaria: interactively optimizing machine learning models for efficient inference, think while you write hypothesis verification promotes faithful knowledge-to-text generation, the slingshot effect: a late-stage optimization anomaly in adam-family of optimization methods, hindsight priors for reward learning from human preferences, fedhyper: a universal and robust learning rate scheduler for federated learning with hypergradient descen, frequency-aware masked autoencoders for multimodal pretraining on biosignals, hierarchical and dynamic prompt compression for efficient zero-shot api usage, overcoming the pitfalls of vision-language model finetuning for ood 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algorithms for precise measurement of differentially-private, personalized recommendations, veclip: improving clip training via visual-enriched captions, axnav: replaying accessibility tests from natural language, merge vision foundation models via multi-task distillation, moonwalk: advancing gait-based user recognition on wearable devices with metric learning, vision-based hand gesture customization from a single demonstration, humanizing word error rate for asr transcript readability and accessibility, human following in mobile platforms with person re-identification, privacy-preserving quantile treatment effect estimation for randomized controlled trials, synthdst: synthetic data is all you need for few-shot dialog state tracking, what can clip learn from task-specific experts, multichannel voice trigger detection based on transform-average-concatenate, keyframer: empowering animation design using large language models, resource-constrained stereo singing voice cancellation, efficient convbn blocks for transfer learning and beyond, the entity-deduction arena: a playground for probing the conversational reasoning and planning capabilities of llms, differentially private heavy hitter detection using federated analytics, scalable pre-training of large autoregressive image models, acoustic model fusion for end-to-end speech recognition, co-ml: collaborative machine learning model building for developing dataset design practices, flexible keyword spotting based on homogeneous audio-text embedding, investigating salient representations and label variance modeling in dimensional speech emotion analysis, large-scale training of foundation models for wearable biosignals, user-level differentially private stochastic convex optimization: efficient algorithms with optimal rates, bin prediction for better conformal prediction, fastsr-nerf: improving nerf efficiency on consumer devices with a simple super-resolution pipeline, hybrid model learning for cardiovascular 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in handling controversial issues, towards real-world streaming speech translation for code-switched speech, livepose: online 3d reconstruction from monocular video with dynamic camera poses, never-ending learning of user interfaces, slower respiration rate is associated with higher self-reported well-being after wellness training, when does optimizing a proper loss yield calibration, single-stage diffusion nerf: a unified approach to 3d generation and reconstruction, fastvit: a fast hybrid vision transformer using structural reparameterization, hyperdiffusion: generating implicit neural fields with weight-space diffusion, neilf++: inter-reflectable light fields for geometry and material estimation, gender bias in llms, reinforce data, multiply impact: improved model accuracy and robustness with dataset reinforcement, self-supervised object goal navigation with in-situ finetuning, all about sample-size calculations for a/b testing: novel extensions and practical guide, intelligent assistant language understanding on-device, rapid and scalable bayesian ab testing, consistent collaborative filtering via tensor decomposition, dataset and network introspection toolkit (dnikit), finerecon: depth-aware feed-forward network for detailed 3d reconstruction, improving the quality of neural tts using long-form content and multi-speaker multi-style modeling, voice trigger system for siri, conformalization of sparse generalized linear models, duet: 2d structured and equivariant representations, pdp: parameter-free differentiable pruning is all you need, population expansion for training language models with private federated learning, resolving the mixing time of the langevin algorithm to its stationary distribution for log-concave sampling, the role of entropy and reconstruction for multi-view self-supervised learning, upscale: unconstrained channel pruning, learning iconic scenes with differential privacy, nerfdiff: single-image view synthesis with nerf-guided distillation from 3d-aware diffusion, boot: data-free distillation of denoising diffusion models with bootstrapping, referring to screen texts with voice assistants, towards multimodal multitask scene understanding models for indoor mobile agents, 5ider: unified query rewriting for steering, intent carryover, disfluencies, entity carryover and repair, monge, bregman and occam: interpretable optimal transport in high-dimensions with feature-sparse maps, private online prediction from experts: separations and faster rates, spatial librispeech: an augmented dataset for spatial audio learning, stabilizing transformer training by preventing attention entropy collapse, the monge gap: a regularizer to learn all transport maps, cross-lingual knowledge transfer and iterative pseudo-labeling for low-resource speech recognition with transducers, symphony: composing interactive interfaces for machine learning, a unifying theory of distance from calibration, approximate nearest neighbour phrase mining for contextual speech recognition, latent phrase matching for dysarthric speech, matching latent encoding for audio-text based keyword spotting, near-optimal algorithms for private online optimization in the realizable regime, roomdreamer: text-driven 3d indoor scene synthesis with coherent geometry and texture, semi-supervised and long-tailed object detection with cascadematch, less is more: a unified architecture for device-directed speech detection with multiple invocation types, collaborative machine learning model building with families using co-ml, efficient multimodal neural networks for trigger-less voice assistants, fast class-agnostic salient object segmentation, application-agnostic language modeling for on-device asr, actionable data insights for machine learning, growing and serving large open-domain knowledge graphs, robustness in multimodal learning under train-test modality mismatch, learning language-specific layers for multilingual machine translation, modeling spoken information queries for virtual assistants: open problems, challenges and opportunities, learning to detect novel and fine-grained acoustic sequences using pretrained audio representations, pointconvformer: revenge of the point-based convolution, state spaces aren’t enough: machine translation needs attention, improved speech recognition for people who stutter, autofocusformer: image segmentation off the grid, joint speech transcription and translation: pseudo-labeling with out-of-distribution data, from robustness to privacy and back, generalization on the unseen, logic reasoning and degree curriculum, self-supervised temporal analysis of spatiotemporal data, considerations for distribution shift robustness in health, f-dm: a multi-stage diffusion model via progressive signal transformation, high-throughput vector similarity search in knowledge graphs, naturalistic head motion generation from speech, on the role of lip articulation in visual speech perception, facelit: neural 3d relightable faces, angler: helping machine translation practitioners prioritize model improvements, feedback effect in user interaction with intelligent assistants: delayed engagement, adaption and drop-out, text is all you need: personalizing asr models using controllable speech synthesis, pointersect: neural rendering with cloud-ray intersection, continuous pseudo-labeling from the start, mobileone: an improved one millisecond mobile backbone, neural transducer training: reduced memory consumption with sample-wise computation, tract: denoising diffusion models with transitive closure time-distillation, variable attention masking for configurable transformer transducer speech recognition, from user perceptions to technical improvement: enabling people who stutter to better use speech recognition, i see what you hear: a vision-inspired method to localize words, more speaking or more speakers, pre-trained model representations and their robustness against noise for speech emotion analysis, improvements to embedding-matching acoustic-to-word asr using multiple-hypothesis pronunciation-based embeddings, mast: masked augmentation subspace training for generalizable self-supervised priors, paedid: patch autoencoder-based deep image decomposition for unsupervised anomaly detection, rgi: robust gan-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection, robust hybrid learning with expert augmentation, self supervision does not help natural language supervision at scale, audio-to-intent using acoustic-textual subword representations from end-to-end asr, fastfill: efficient compatible model update, loss minimization through the lens of outcome indistinguishability, mobilebrick: building lego for 3d reconstruction on mobile devices, diffusion probabilistic fields, heimdal: highly efficient method for detection and localization of wake-words, designing data: proactive data collection and iteration for machine learning, improving human 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learning methods under transfer, mean estimation with user-level privacy under data heterogeneity, learning to break the loop: analyzing and mitigating repetitions for neural text generation, learning to reason with neural networks: generalization, unseen data and boolean measures, subspace recovery from heterogeneous data with non-isotropic noise, a large-scale observational study of the causal effects of a behavioral health nudge, improving generalization with physical equations, maeeg: masked auto-encoder for eeg representation learning, mbw: multi-view bootstrapping in the wild, statistical deconvolution for inference of infection time series, ape: aligning pretrained encoders to quickly learn aligned multimodal representations, emphasis control for parallel neural tts, the slingshot mechanism: an empirical study of adaptive optimizers and the grokking phenomenon, a treatise on fst lattice based mmi training, non-autoregressive neural machine translation: a call for clarity, 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cross-lingual question answering, optimal algorithms for mean estimation under local differential privacy, position prediction as an effective pre-training strategy, private frequency estimation via projective geometry, self-conditioning pre-trained language models, style equalization: unsupervised learning of controllable generative sequence models, critical regularizations for neural surface reconstruction in the wild, a multi-task neural architecture for on-device scene analysis, deploying transformers on the apple neural engine, neural face video compression using multiple views, efficient multi-view stereo via attention-driven 2d convolutions, robust joint shape and pose optimization for few-view object reconstruction, forward compatible training for large-scale embedding retrieval systems, bilingual end-to-end asr with byte-level subwords, end-to-end speech translation for code switched speech, streaming on-device detection of device directed speech from voice and touch-based invocation, training a tokenizer for free with private federated learning, utilizing imperfect synthetic data to improve speech recognition, data incubation - synthesizing missing data for handwriting recognition, a platform for continuous construction and serving of knowledge at scale, neo: generalizing confusion matrix visualization to hierarchical and multi-output labels, low-resource adaptation of open-domain generative chatbots, differentiable k-means clustering layer for neural network compression, enabling hand gesture customization on wrist-worn devices, learning compressed embeddings for on-device inference, neural fisher kernel: low-rank approximation and knowledge distillation, towards complete icon labeling in mobile applications, understanding screen relationships from screenshots of smartphone applications, mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer, synthetic defect generation for display front-of-screen quality inspection: a survey, differential secrecy for distributed data and applications to robust differentially secure vector summation, information gain propagation: a new way to graph active learning with soft labels, can open domain question answering models answer visual knowledge questions, non-verbal sound detection for disordered speech, hierarchical prosody modeling and control in non-autoregressive parallel neural tts, element level differential privacy: the right granularity of privacy, learning spatiotemporal occupancy grid maps for lifelong navigation in dynamic scenes, collaborative filtering via tensor decomposition, modeling the impact of user mobility on covid-19 infection rates over time, fast and explicit neural view synthesis, federated evaluation and tuning for on-device personalization: system design & applications, lyric document embeddings for music tagging, acoustic neighbor embeddings, model stability with continuous data updates, reconstructing training data from diverse ml models by ensemble inversion, fair sa: sensitivity analysis for fairness in face recognition, learning invariant representations with missing data, interpretable adaptive optimization, robust robotic control from pixels using contrastive recurrent state-space models, challenges of adversarial image augmentations, self-supervised semi-supervised learning for data labeling and quality evaluation, batchquant: quantized-for-all architecture search with robust quantizer, high fidelity 3d reconstructions with limited physical views, arkitscenes - a diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data, do self-supervised and supervised methods learn similar visual representations, enforcing fairness in private federated learning via the modified method of differential multipliers, individual privacy accounting via a renyi filter, probabilistic attention for interactive segmentation, rim: reliable influence-based active learning on graphs, stochastic contrastive learning, it’s complicated: characterizing the time-varying relationship between cell phone mobility and covid-19 spread in the us, plan-then-generate: controlled data-to-text, randomized controlled trials without data retention, interdependent variables: remotely designing tactile graphics for an accessible workflow, breiman's two cultures: you don't have to choose sides, cross-domain data integration for entity disambiguation in biomedical text, evaluating the fairness of fine-tuning strategies in self-supervised learning, learning compressible subspaces for adaptive network compression at inference time, mmiu: dataset for visual intent understanding in multimodal assistants, on-device neural speech synthesis, on-device panoptic segmentation for camera using transformers, entity-based knowledge conflicts in question answering, finding experts in transformer models, deeppro: deep partial point cloud registration of objects, using pause information for more accurate entity recognition, conditional generation of synthetic geospatial images from pixel-level and feature-level inputs, multi-task learning with cross attention for keyword spotting, screen parsing: towards reverse engineering of ui models from screenshots, self-supervised learning of lidar segmentation for autonomous indoor navigation, a survey on privacy from statistical, information and estimation-theoretic views, improving neural network subspaces, combining speakers of multiple languages to improve quality of neural voices, audiovisual speech synthesis using tacotron2, user-initiated repetition-based recovery in multi-utterance dialogue systems, dexter: deep encoding of external knowledge for named entity recognition in virtual assistants, managing ml pipelines: feature stores and the coming wave of embedding ecosystems, smooth sequential optimization with delayed feedback, learning to generate radiance fields of indoor scenes, estimating respiratory rate from breath audio obtained through wearable microphones, online automatic speech recognition with listen, attend and spell model, subject-aware contrastive learning for biosignals, hypersim: a photorealistic synthetic dataset for holistic indoor scene understanding, model-based metrics: sample-efficient estimates of predictive model subpopulation performance, retrievalfuse: neural 3d scene reconstruction with a database, joint learning of portrait intrinsic decomposition and relighting, non-parametric differentially private confidence intervals for the median, recognizing people in photos through private on-device machine learning, unconstrained scene generation with locally conditioned radiance fields, trinity: a no-code ai platform for complex spatial datasets, a simple and nearly optimal analysis of privacy amplification by shuffling, when is memorization of irrelevant training data necessary for high-accuracy learning, implicit acceleration and feature learning in infinitely wide neural networks with bottlenecks, a discriminative entity aware language model for virtual assistants, analysis and tuning of a voice assistant system for dysfluent speech, implicit greedy rank learning in autoencoders via overparameterized linear networks, learning neural network subspaces, lossless compression of efficient private local randomizers, private adaptive gradient methods for convex optimization, private stochastic convex optimization: optimal rates in ℓ1 geometry, streaming transformer for hardware efficient voice trigger detection and false trigger mitigation, tensor programs iib: architectural universality of neural tangent kernel training dynamics, uncertainty weighted actor-critic for offline reinforcement learning, spatio-temporal context for action detection, bootleg: self-supervision for named entity disambiguation, instance-level task parameters: a robust multi-task weighting framework, morphgan: controllable one-shot face synthesis, evaluating entity disambiguation and the role of popularity in retrieval-based nlp, hdr environment map estimation for real-time augmented reality, extracurricular learning: knowledge transfer beyond empirical distribution, learning to optimize black-box evaluation metrics, on the role of visual cues in audiovisual speech enhancement, an attention free transformer, cread: combined resolution of ellipses and anaphora in dialogues, dynamic curriculum learning via data parameters for noise robust keyword spotting, error-driven pruning of language models for virtual assistants, knowledge transfer for efficient on-device false trigger mitigation, multimodal punctuation prediction with contextual dropout, noise-robust named entity understanding for virtual assistants, on the transferability of minimal prediction preserving inputs in question answering, open-domain question answering goes conversational via question rewriting, optimize what matters: training dnn-hmm keyword spotting model using end metric, progressive voice trigger detection: accuracy vs latency, sapaugment: learning a sample adaptive policy for data augmentation, making mobile applications accessible with machine learning, video frame interpolation via structure-motion based iterative feature fusion, when can accessibility help an exploration of accessibility feature recommendation on mobile devices, streaming models for joint speech recognition and translation, neural feature selection for learning to rank, generating natural questions from images for multimodal assistants, a comparison of question rewriting methods for conversational passage retrieval, screen recognition: creating accessibility metadata for mobile applications from pixels, set distribution networks: a generative model for sets of images, sep-28k: a dataset for stuttering event detection from podcasts with people who stutter, question rewriting for end to end conversational question answering, leveraging query resolution and reading comprehension for conversational passage retrieval, learning soft 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tracking, efficient inference for neural machine translation, how effective is task-agnostic data augmentation for pre-trained transformers, generating synthetic images by combining pixel-level and feature-level geospatial conditional inputs, making smartphone augmented reality apps accessible, mage: fluid moves between code and graphical work in computational notebooks, rescribe: authoring and automatically editing audio descriptions, class lm and word mapping for contextual biasing in end-to-end asr, complementary language model and parallel bi-lrnn for false trigger mitigation, controllable neural text-to-speech synthesis using intuitive prosodic features, hybrid transformer and ctc networks for hardware efficient voice triggering, improving on-device speaker verification using federated learning with privacy, stacked 1d convolutional networks for end-to-end small footprint voice trigger detection, downbeat tracking with tempo-invariant convolutional neural networks, modality 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generalized reinforcement meta learning for few-shot optimization, learning to rank intents in voice assistants, detecting emotion primitives from speech and their use in discerning categorical emotions, lattice-based improvements for voice triggering using graph neural networks, automatic class discovery and one-shot interactions for acoustic activity recognition, tempura: query analysis with structural templates, understanding and visualizing data iteration in machine learning, multi-task learning for voice trigger detection, speech translation and the end-to-end promise: taking stock of where we are, embedded large-scale handwritten chinese character recognition, generating multilingual voices using speaker space translation based on bilingual speaker data, leveraging gans to improve continuous path keyboard input models, least squares binary quantization of neural networks, unsupervised style and content separation by minimizing mutual information for speech synthesis, sndcnn: self-normalizing deep cnns with scaled exponential linear units for speech recognition, on modeling asr word confidence, capsules with inverted dot-product attention routing, improving language identification for multilingual speakers, multi-task learning for speaker verification and voice trigger detection, stochastic weight averaging in parallel: large-batch training that generalizes well, adversarial fisher vectors for unsupervised representation learning, app usage predicts cognitive ability in older adults, filter distillation for network compression, multiple futures prediction, an exploration of data augmentation and sampling techniques for domain-agnostic question answering, data parameters: a new family of parameters for learning a differentiable curriculum, nonlinear conjugate gradients for scaling synchronous distributed dnn training, modeling patterns of smartphone usage and their relationship to cognitive health, worst cases policy gradients, empirical evaluation of active learning techniques for neural mt, skip-clip: self-supervised spatiotemporal representation learning by future clip order ranking, single training dimension selection for word embedding with pca, overton: a data system for monitoring and improving machine-learned products, leveraging user engagement signals for entity labeling in a virtual assistant, reverse transfer learning: can word embeddings trained for different nlp tasks improve neural language models, variational saccading: efficient inference for large resolution images, jointly learning to align and translate with transformer models, connecting and comparing language model interpolation techniques, active learning for domain classification in a commercial spoken personal assistant, coarse-to-fine optimization for speech enhancement, developing measures of cognitive impairment in the real world from consumer-grade multimodal sensor streams, raise to speak: an accurate, low-power detector for activating voice assistants on smartwatches, language identification from very short strings, learning conditional error model for simulated time-series data, bridging the domain gap for neural models, improving knowledge base construction from robust infobox extraction, protection against reconstruction and its applications in private federated learning, data platform for machine learning, speaker-independent speech-driven visual speech synthesis using domain-adapted acoustic models, addressing the loss-metric mismatch with adaptive loss alignment, lower bounds for locally private estimation via communication complexity, exploring retraining-free speech recognition for intra-sentential code-switching, parametric cepstral mean normalization for robust speech recognition, voice trigger detection from lvcsr hypothesis lattices using bidirectional lattice recurrent neural networks, neural network-based modeling of phonetic durations, mirroring to build trust in digital assistants, foundationdb record layer: a multi-tenant structured datastore, leveraging acoustic cues and paralinguistic embeddings to detect expression from voice, bandwidth embeddings for mixed-bandwidth speech recognition, sliced wasserstein discrepancy for unsupervised domain adaptation, towards learning multi-agent negotiations via self-play, optimizing siri on homepod in far‑field settings, can global semantic context improve neural language models, a new benchmark and progress toward improved weakly supervised learning, finding local destinations with siri’s regionally specific language models for speech recognition, personalized hey siri, structured control nets for deep reinforcement learning, learning with privacy at scale, an on-device deep neural network for face detection, hey siri: an on-device dnn-powered voice trigger for apple’s personal assistant, real-time recognition of handwritten chinese characters spanning a large inventory of 30,000 characters, deep learning for siri’s voice: on-device deep mixture density networks for hybrid unit selection synthesis, inverse text normalization as a labeling problem, improving neural network acoustic models by cross-bandwidth and cross-lingual initialization, learning from simulated and unsupervised images through adversarial training, improving the realism of synthetic images.

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10 Best AI Tools for Academic Research in 2024 (Free and Paid)

Ayush Chaturvedi

20 min read

Research can be a time-consuming endeavour. Sifting through mountains of literature, analyzing data, and crafting clear arguments can feel overwhelming. 

However, you can streamline much of this research process with Artificial Intelligence (AI) tools, some of which are the best for research.

These AI-powered assistants can search vast databases in seconds, pinpoint relevant studies, and customize data to your specific research question. 

They can also recommend key research articles and highlight emerging trends within your field, saving you time.

Additionally, with the help of the best AI tools for research, you can improve your writing and streamline your workflow with real-time grammar and punctuation checks, stylistic suggestions, and clear explanations of complex concepts.

But how do you choose?

Don't worry; we've got you covered. 

We have created a list of all the best AI tools for research on the internet, filtering based on various factors and handpicked the top 10. 

These research AI tools not only assist you in research but also integrate with your workflow and reduce your overall workload. 

So let's get started.

Best AI Tools for Research at a Glance

What are research ai tools, benefits of using ai tools for research, factors to consider when choosing the best ai tools for research, top 10 best ai tools for research, key features of elephas , elephas pricing , elepahs reviews, chatgpt key features , chatgpt pricing , chatgpt reviews , typeset.io features:, typeset.io pricing , typeset.io reviews , quillbot key features , quillbot pricing , quillbot review , wordvice.ai features:, wordvice.ai pricing , wordvice.ai reviews , consensus ai key features , consensus ai pricing , consensus ai reviews , scite.ai features , scite.ai pricing , scite.ai reviews , scholarly key features, scholarcy pricing , scholarcy reviews , proofhub key features , proofhub pricing , proofhub reviews , research rabbit key features , research rabbit pricing , research rabbit reviews , limitations of ai tools for research, case study: how a professor used elephas in his lesson research process.

• Conclusion 

1. Which AI is better for research?

2. is chatgpt good for research, 3. how can ai be used for research, 4. what is the best ai for phd.

Elephas: Summarize research, rewrite content in different styles, and organize summaries in a central "Super Brain" for easy access.

ChatGPT: Summarize news articles and answer research questions

Typeset.io: Streamline academic writing with templates and citation management. 

Quillbot: Rephrase text and summarize complex materials for research. 

Wordvice.ai : Ensure clarity, grammar, and originality in your academic writing.

Consensus AI: Search vast databases and filter research papers for quality.

Scite.ai: Get real citations and measure the credibility of research claims.

Scholarcy: Summarize complex articles and build a searchable research library.

ProofHub: Manage research projects with tasks, collaboration tools, and scheduling.

ResearchRabbit: Build a research library and get recommendations for new papers. 

Research AI tools are game-changers for students, academics, and researchers, streamlining the entire research process. 

With the help of the best AI tools for research as your personal research assistant, they help you find relevant articles, analyze information, and even improve your writing!

Imagine being able to find hundreds of relevant research papers in minutes,  or getting a clear summary of a complex article with the click of a button. That's the magic of AI research assistants.

Some specialize in specific areas, like grammar and plagiarism checking, while others focus on broader tasks like literature review and research question development.  

No matter your research needs, there's an AI tool out there to help you save time, improve your work, and produce higher-quality research. 

Let's look closer at the features that a research AI tool offers 

These AI-powered tools offer a variety of features such as:

• Effortless searching: Quickly find high-quality research papers by entering your topic.
• Smarter literature reviews: Get suggestions for key studies, authors, and research trends.
• Enhanced writing: Improve your writing with grammar checks, stylistic suggestions, and help with complex concepts.
• Citation management: Easily manage and format your citations to avoid plagiarism.
• Research organization: Build your research library and organize articles for easy access.

These are just a few examples of how AI research tools can save you time and effort, allowing you to focus on the analysis and critical thinking that truly matters. 

Some tools even go beyond and offer a complete suite of AI features that cut down more than half of the research time.

Research can be a time-consuming endeavour. Sifting through mountains of literature, analyzing data, and crafting clear arguments can feel overwhelming. However, you can streamline much of this research process with Artificial Intelligence (AI) tools like Research AI tools. 

Here are some benefits you can gain with Research AI tools:

Effortless Information Retrieval: AI tools can search vast databases in seconds, pinpointing relevant studies and data tailored to your specific research question.

Smarter Literature Reviews: No more wading through mountains of papers. AI can recommend key research articles, and influential authors, and highlight emerging trends within your field, saving you time and ensuring a comprehensive review.

Idea Generation: If you struggle to spark new research ideas, then AI can help you. It can brainstorm fresh research questions, and hypotheses, and even suggest innovative experiment designs to propel your research forward.

Writing Assistant & Editor:  You can improve your writing and streamline your workflow with AI's editing prowess. Get real-time grammar and punctuation checks , stylistic suggestions, and clear explanations of complex concepts, all designed to elevate the quality of your research writing.

Enhanced Efficiency: AI automates tedious tasks like citation management and formatting, freeing you to focus on the analysis and interpretation of your research findings.

Personalized Research Assistant: AI tools can adapt to your research interests, suggesting relevant articles, recommending new avenues for exploration, and even summarizing complex research papers for a clearer understanding.

There are different AI tools present on the internet for different needs. So with the vast array of AI-powered research assistants available, selecting the most suitable tool can be problematic. 

Here are some key factors to consider, when you choosing the best AI Tools for Research:

Your Research Needs: Identify your specific needs. Are you searching for literature, summarizing complex papers, or improving your writing? Different tools excel in various areas.

Features Offered: Align the tool's features with your needs. Do you require real-time citation suggestions or plagiarism checkers?

Data Accuracy and Credibility: Ensure the tool retrieves information from reliable sources. Scite.ai stands out for highlighting the credibility of research claims.

Ease of Use: Consider the platform's user-friendliness. Look for intuitive interfaces and clear instructions.

Cost: AI tools often have varying pricing structures. Some offer free trials or basic plans, while others require subscriptions. Determine your budget and choose a tool that aligns with it.

Integration Capabilities: Does the tool integrate with your existing workflow? Look for options that seamlessly connect with your preferred reference managers or writing platforms.

Most importantly, remember that AI research assistants are only there to increase your productivity in the research process, not to replace it .

1. Elephas  

Elephas is an innovative AI tool designed to supercharge your research and writing efficiency. It utilizes advanced technology to break down complex research papers, YouTube videos, and other content, extracting the key points and saving you valuable time.

Additionally, Elephas goes beyond summarizing – it can seamlessly integrate with your workflow and rewrite content in various tones, making it a versatile companion for all your writing needs. 

Elephas doesn't just summarize research papers; it extracts key points and integrates seamlessly with your workflow. Whether you're a student, researcher, or content creator, Elephas helps you achieve more in less time.

Effortless Sum marization: Extract key points from research papers and YouTube videos with ease.

Centralized Hub: Keep all your research summaries organized in one place with Elephas Super Brain .

Seamless Content Creation: Create professional emails, engaging social media posts, and documents in just a few clicks.

Multiple Rewrite Modes: Choose from a variety of writing styles to make your content more engaging.

Super-Command Bar: Increase your productivity with features like article summarization and data extraction.

Elephas is also one of the best AI Tools for Summarizing Research Papers in the market right now. And it bundles up with a powerful iOS app as well.

It works locally and it's 100% privacy friendly!

If you own a Mac, you should definitely try it out.

ChatGPT , the tool behind the existence of many AI tools, is undeniably one of the best AI tools for research. With the right prompts, you can easily summarize any news articles , long notes, etc., in seconds. You can also ask ChatGPT research-related questions to gain a better understanding of research papers. Furthermore, you can improve your writing and avoid any grammar and punctuation mistakes. With the help of ChatGPT, the number of things you can do is endless.

Effortless Information Retrieval: Find the studies and data you need in a flash.

Smarter Literature Reviews: Get suggestions for key papers, authors, and research trends.

Idea Generation on Demand: Spark new research questions, hypotheses, and experiment designs.

Writing Assistant: Improve your writing with grammar checks, stylistic suggestions, and simplified explanations of complex concepts.

• Premium Plan Starts at $20/month 

Some users have reported false money deductions and low-quality service provided in the premium subscription.

3. Typeset.io

Typeset.io streamlines the entire academic writing process, saving you time and frustration.  This user-friendly platform offers a variety of features to help you write, collaborate, and publish top-notch research. From predefined templates to AI-powered writing assistance, Typeset.io empowers researchers of all levels to achieve their scholarly goals.

Effortless Formatting: Predefined templates ensure your paper meets journal requirements.

Citation Breeze: Manage citations and references effortlessly, with automatic generation.

Seamless Collaboration: Work together on research papers in real time.

Smart Journal Selection: Find the perfect fit for your research with a built-in journal database.

Premium Plan Starts at $7.78/month

Users have reported that the tool doesn't notify at the end of the free trial and sneakily charges for the premium plan. Additionally, once the plan is purchased, the money is non-refundable. Some have claimed that even after cancelling the subscription, the customer service did not cancel it and still charged their cards.

4. Quillbot 

Quillbot is your AI research companion, offering several time-saving features to streamline your workflow. It is designed to assist researchers of all levels. This tool utilizes advanced learning algorithms to enhance your writing and comprehension skills. With Quillbot, you can confidently paraphrase text, summarize complex materials, and ensure clear, plagiarism-free writing. Additionally, you can perform citations with high accuracy. Quillbot streamlines your workflow and strengthens your writing.

Paraphrasing & Summarizing: Quillbot rewrites sentences and condenses lengthy passages, saving you time and effort.

Language Enhancement & Learning: Improve your writing with advanced suggestions and explanations, perfect for non-native speakers.

Research Brainstorming: Generate fresh ideas from just a few keywords, overcoming writer's block.

Academic Accuracy & Citation Help: Ensure your writing matches specific citation styles and uses precise academic language.

• Premium Plan starts at $4.17/month 

Users have reported that the tool is working slowly when used in Microsoft Word, and it often uses complex words while paraphrasing. Some have also reported that the rephrased content on Quillbot is detected as AI-generated content on various AI detection tools.

5. Wordvice.ai

Wordvice AI is one of the best AI tools for research, it is your one-stop shop for powerful writing assistance. This AI-powered tool uses cutting-edge technology to streamline your research workflow, saving you time and effort. From basic grammar and clarity checks to advanced plagiarism detection, Wordvice AI helps you to write with confidence and produce polished, original academic content.

All-in-one editing: Grammar, style, clarity, and fluency checks with real-time feedback.

Vocabulary booster: Get suggestions for synonyms and alternative phrasing to diversify your writing.

Academic writing companion: Ensures proper citation format, maintains a scholarly tone, and adheres to research conventions.

Originality assured: Scans millions of sources to prevent plagiarism in your work.

Premium Plan starts at $9.95/month 

Users have reported that certain sentence patterns generated by AI are already found on existing web pages, which has led to an increase in plagiarism within content.

6. Consensus AI

Consensus AI is an innovative platform that uses artificial intelligence to simplify your search process. In just minutes, Consensus AI can search through vast databases and deliver hundreds of relevant, high-quality research papers directly to you. Also, Consensus AI filters results by date, study type, and journal quality, ensuring you find high-quality, credible sources to strengthen your research.

AI-powered Search Engine: Enter your research question and let Consensus AI scour vast databases to find relevant papers.

Time-Saving Efficiency: Gather hundreds of papers in minutes, freeing you up to focus on analysis and writing.

Comprehensive Results: Access a diverse range of studies, including randomized trials, reviews, and observational studies.

High-Quality Papers: Filter results by journal quality to ensure the credibility of your sources.

• Premium Plan Starts at $8.99/month 

Users have reported that when we try to share the live demo over Zoom, the tool becomes slow and hangs. They think it is a hassle to jump between the browser and Zoom. They suggest introducing some integration features in the tool as a good solution.

7. Scite.ai 

Scite.ai is one of the best for reliable research assistance powered by Artificial Intelligence.  Scite.ai tackles a common problem with AI research tools – unreliable citations.  Unlike others, Scite.ai provides you with real citations to published papers,  so you can be confident in the information you use. Even better, Scite.ai can analyze the research and tell you how many studies support or challenge a specific claim. 

Create Dashboards: Organize your research findings in a user-friendly format.

Journal and Institution Metrics: Gain insights into the reputation of academic sources.

Interactive Visualizations: You can see research trends and connections come through visualizations of the tool. 

Measure Claim Credibility: Scite.ai analyzes the strength of a claim by showing you how many studies support or refute it.

Premium Plan starts at $20/month 

Users have noticed that sometimes the tool produces inaccurate citations, which can be problematic for researchers who rely on its accuracy. Additionally, some users believe that the tool's pricing is significantly higher compared to its competitors.

8. Scholarcy

Scholarcy is an AI-powered tool that acts like a personal research assistant, summarizing complex articles, reports, and even book chapters for you.  Scholarcy quickly helps you understand the key points of any document and assess its relevance to your work, saving you precious time and effort. Whether you're a researcher, student, or just curious about the latest advancements, Scholarcy helps you quickly grasp key findings and identify relevant sources

Key Points at a Glance: Scholarcy extracts crucial information and organizes it into clear categories, making it easy to grasp the main ideas.

Seamless Integration: Scholarcy offers handy Chrome and Edge browser extensions, allowing you to summarize research directly from your web browser.

Visual Aids: Scholarcy can extract figures, tables, and images from articles, providing a more comprehensive understanding of the research.

Organized Knowledge: Build your searchable database of summarized research, making it easy to revisit key information later.

• Premium Plan Starts at $4.99/month 

Some users are not satisfied with the complete summaries produced by Scholarcy, as some of the sentences are not actual sentences and need to be corrected. Additionally, some sentences do not make any sense. Other users have claimed that the quality of the tool has significantly dropped in recent months and it feels glitchy while using it.

9. ProofHub

ProofHub is one of the best AI tools for research to streamline research projects. It's an all-in-one project management tool designed specifically to make research teams more efficient and effective. ProofHub centralizes everything your team needs in a single platform, allowing seamless collaboration and communication.  Save valuable time and avoid confusion by ditching the scattered emails, documents, and endless meetings.

Effortless Task & Project Management: Organize your research projects with ease using powerful tools like Kanban boards and Gantt charts.

Centralized Hub for Collaboration: Keep your team on the same page with a central platform for file sharing, discussions, and real-time feedback.

Streamlined Time Tracking & Scheduling: Never miss a deadline again! ProofHub's time tracking and scheduling features help you stay on top of your research project's progress.

Automated Workflows: Save even more time by automating repetitive tasks and creating custom workflows perfectly suited to your research needs.

• Premium Plan Starts at $45/month 

Users have expressed dissatisfaction with the user interface and email notifications of the tool, stating that they are not up to par. In addition, some have reported that certain features in Proofhub are not as impressive as those of its competitors.

10. Research Rabbit

ResearchRabbit is another best AI tools for research, it helps you navigate through the vast world of scientific literature. Nicknamed the "Spotify for Papers," this innovative tool lets you explore research like never before. Build collections of articles you find interesting, and ResearchRabbit will cleverly suggest new papers that align with your specific interests. No more endless searches – ResearchRabbit becomes your personalized research assistant, saving you time and frustration.

Build your research library: Collect and organize articles you find interesting, all in one place.

Smart recommendations: Never miss a groundbreaking study! ResearchRabbit suggests new papers based on your interests, saving you valuable time.

Visualize connections: See how different research areas, authors, and ideas are linked together.

Collaboration made easy: Share your research collections with colleagues to work together more effectively.

Free Forever 

We couldn't find any public reviews for the Research Rabbit. Therefore, we advise users to proceed with caution.

Many best AI tools for research suit different types of people, and these research AI tools have streamlined tasks and uncovered connections. However, they still have many limitations compared to manual research processes. Here's a closer look.

1. Accuracy and Bias: AI tools rely on the data they're trained on. If the data is biased or inaccurate, the results can be misleading. It's crucial to critically evaluate AI outputs and not rely solely on them.

2. Depth vs. Breadth: AI tools can efficiently scan vast amounts of literature, but they may miss nuances or subtleties within research papers. In-depth analysis and critical thinking remain essential for a comprehensive understanding.

3. Overreliance on Automation: AI shouldn't replace the core research process. Researchers should use AI to streamline tasks, not eliminate critical steps like evaluating source credibility and understanding research context.

4. Black Box Problem:  Sometimes, AI won't explain its reasoning behind results. This lack of transparency can make it difficult to assess the trustworthiness of findings or suggestions.

5. Limited Scope: AI tools might not cover all relevant sources, especially niche or emerging research areas. Supplement your search with traditional methods like library databases and expert consultations.

In our community, we have found Elephas being used by some professors at a university, and they have shared their experiences on how they used it in their lesson research process. Here is how they did it:

1. Summarization: The professor utilized Elephas' ability to generate concise summaries of different textbooks and research papers. This allowed him to quickly grasp the core arguments and findings of numerous studies, saving him hours of dedicated reading time.

2. Video Research: Then the professor had to gather more knowledge to create a lesson plan, so he searched for some of the best lengthy video lectures. Packed with historical insights, these videos were no longer a trouble because Elephas efficiently summarized key points from them, enabling our professor to include this valuable information in his lessons without spending hours glued to the screen.

3. Building Knowledge Base: Finally, the professor used Elephas Super Brain to create a centralized hub for all his research summaries. This eliminated the need to sift through countless folders and documents, allowing him to access critical information instantly. Additionally, he utilized the Super Brain to better understand the lesson plan through the Super Brain chat feature of Elephas.

Let's see what Elephas was able to do for our professor who is striving to teach his students in-depth subject knowledge:

1. Increased Efficiency: The professor has seen a significant reduction in research time, freeing up valuable hours for lesson planning and development.

2. Deeper Lesson Understanding: With more time at his disposal, our professor was able to delve into the research he found most compelling, leading to a deeper understanding of historical topics.

3. Engaging Lectures: By using key insights from research summaries provided by Elephas, the professor's lectures became more informative and engaging for his students, helping in their understanding of the topic faster than before.

The professor's experience explains how Elephas can revolutionize the research process for academics. By saving time and streamlining workflows, Elephas helps researchers get deeper into their respective fields and create truly impactful learning experiences and also cut their research process to more than half.

Conclusion  

In summary, AI research assistants are transforming how researchers approach their work. These tools can summarize complex information, find relevant studies, and even suggest new research ideas. Top choices include Elephas (which summarizes research papers and YouTube videos), ChatGPT (which summarizes articles and answers questions), and Typeset.io (which streamlines academic writing).

However, make sure to pick the best AI tool for research based on your requirements. Also, remember that while AI offers significant time savings and improved efficiency, it shouldn't replace critical thinking and human expertise in research because AI has several limitations that can degrade your research quality.

Elephas is the best AI tool for research, offering key features for researchers such as summarizing research papers, articles, and YouTube videos. Additionally, you can upload data to a "super brain" for retrieval and chat with uploaded PDFs for deeper understanding. This makes Elephas a strong AI tool for research tasks

Yes, ChatGPT can be a helpful tool for initial research exploration. It can brainstorm ideas, summarize complex topics, and even find relevant sources. However, for in-depth research, specialized academic databases and citation tools are better suited. These resources provide more reliable and accurate information, often with features like peer-reviewed content and advanced search options.

AI is revolutionizing research by summarizing complex information and assisting with content creation. AI tools can analyze research papers, articles, and even videos to extract key findings, saving researchers time and effort. AI can also rewrite content in different tones, making it a valuable asset for researchers who need to communicate their findings to various audiences.

Elephas is an AI tool designed to boost research and writing efficiency for PhD students and researchers. It summarizes complex research papers, YouTube videos, and other content, saving you time. Elephas also integrates with your workflow and rewrites content in various tones, making it a versatile PhD buddy.

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Apple Hired Dozens of AI Experts From Google for a Secretive Zurich Research Lab

Apple has poached dozens of artificial intelligence experts from Google and created a "secretive European laboratory" in Zurich to house a new team of staff tasked with building new AI models and products, according to a paywalled Financial Times report.

Apple's main AI team works out of California and Seattle, but the company has recently expanded offices dedicated to AI work in Zurich, Switzerland. Apple's acquisition of local AI startups FaceShift (VR) and Fashwell (image recognition) is believed to have influenced its decision to build a secretive research lab known as "Vision Lab" in the city.

According to the report, employees based in the lab have been involved in Apple's research into the underlying technology that powers OpenAI's ChatGPT chatbot and similar products based on large language models (LLMs). The focus has been on designing more advanced AI models that incorporate text and visual inputs to produce responses to queries.

The report suggests that Apple's recent work on LLMs is a natural outgrowth of the company's work on Siri over the last decade:

The company has long been aware of the potential of "neural networks" — a form of AI inspired by the way neurons interact in the human brain and a technology that underpins breakthrough products such as ChatGPT. Chuck Wooters, an expert in conversational AI and LLMs who joined Apple in December 2013 and worked on Siri for almost two years, said: "During the time that I was there, one of the pushes that was happening in the Siri group was to move to a neural architecture for speech recognition. Even back then, before large language models took off, they were huge advocates of neural networks."

Currently, Apple's leading AI group includes notable ex-Google personnel such as Giannandrea, former head of Google Brain, which is now part of DeepMind. Samy Bengio, now senior director of AI and ML research at Apple, was also previously a leading AI scientist at Google. The same goes for Ruoming Pang, who directs Apple's "Foundation Models" team focusing on large language models. Pang previously headed AI speech recognition research at Google.

In 2016, Apple acquired Perceptual Machines, a company that worked on generative AI-powered image, detection, founded by Ruslan Salakhutdinov from Carnegie Mellon University. Salakhutdinov is said to be a key figure in the history of neural networks, and studied at the University of Toronto under the "godfather" of the technology, Geoffrey Hinton, who left Google last year citing concerns about the dangers of generative AI.

Salakhutdinov told FT that one reason for Apple's slow AI rollout was the tendency of language models to provide incorrect or problematic answers: "I think they are just being a little bit more cautious because they can't release something they can't fully control," he said.

iOS 18 is rumored to include new generative AI features for Siri, Spotlight, Shortcuts, Apple Music, Messages, Health, Keynote, Numbers, Pages, and other apps. These features are expected to be powered by Apple's on-device LLM , although Apple is also said to have discussed partnerships with Google, OpenAI, and Baidu.

• Should Apple Kill Siri and Start Over?

A first look at the AI features that Apple has planned should come in just over a month, with ‌iOS 18‌ set to debut at the Worldwide Developers Conference that kicks off on June 10.

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Top Rated Comments

Seems like Apple is late to the party...

Apple going full steam for another big ticket project that they will dump just before launch.

So Apple believes it can secretly catch up to the competitors.

Hmmm, interesting. I‘m from Zurich and a former Apple employee. If anyone knows more details of a whereabout or so, let me know, would be interesting to sneak a location and send some pictures?

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“This is a super-clean writing space with a lot of configurability that stays out of sight when you don’t need it.” App Store Review, 2020 # STORY Read it here → papereditor.app/story # HIGHLIGHTS · Gorgeous writing space · Silky-smooth typing · Deep personalization (Pro) · Markdown or Plain Text · Syncs with iCloud · Previews in Marked 2 · Publishes drafts to Medium, WordPress, or Ghost · Exports to PDF, HTML, RTF, DOCX, image, or clipboard · Works seamlessly across your Mac, iPad, and iPhone # PRO FEATURES Basic adjustments like text size are free in Paper. More advanced personalization can be purchased with Pro Features. You will not be buying anything blindly though. The trial period for Pro Features is untimed so you can take your time to decide if they are a worthy investment. While trialing Pro Features you might occasionally see a popup. Simply press Reset in the popup to get rid of it completely. Pro Features is either a subscription or a one-time purchase. Both the subscription and the one-time purchase unlock Pro Features on all your Macs that share the same Apple ID. This subscription (or one-time purchase) applies only to Macs. iPhones and iPads require a separate subscription (or one-time purchase). The one-time purchase is hidden by default. To enable it press the "Show one-time purchase" button in the Pro Features popup that appears in the View menu. If you want to upgrade from a Monthly subscription to a Yearly one (or to a Lifetime license) then click the Paper menu, hold the Option key of your keyboard, and under Purchases select the respective upgrade. # BUSINESSES, SCHOOLS & ORGANIZATIONS Businesses, schools, and organizations can use one of the following methods to purchase the Pro version of the app: · Apple Business Manager · Apple School Manager · Volume Purchasing The Pro version of the app can be accessed from this link → papereditor.app/pro # MARKDOWN FORMATTING SYNTAX # Heading 1 ## Heading 2 ### Heading 3 #### Heading 4 ##### Heading 5 ###### Heading 6 **Bold** __Bold__ *Italic* _Italic_ ~Underline~ ~~Strikethrough~~ ==Highlight== ::Highlight:: `Inline Code` ``` Code Block ``` [Link Text](https://link.url.com)  - Bulleted List - Bulleted List - Nested Bulleted List - Nested Bulleted List * Bulleted List * Bulleted List * Nested Bulleted List * Nested Bulleted List 1. Numbered List 2. Numbered List 3. Numbered List 1. Nested Numbered List 2. Nested Numbered List > Blockquote >> Nested Blockquote >>> 2x Nested Blockquote Text that needs a footnote[^Footnote description] ++ Inline comment ++ /* Inline comment */ %% Comment block \_ ← Markdown Escape ° ← HTML Escape # FREQUENTLY ASKED QUESTIONS ## Can I draw in the app? No. Paper is purely a plain-text editor. ## Can I add pictures? No. Paper is purely a plain-text editor. ## Can I password-protect my notes? No. ## Paper changes the font when I switch between alphabetic and non-alphabetic input sources. How to prevent it? · Change the input source to a non-alphabetic one (Japanese, Chinese, Korean, etc). · Click the "View" menu. · Hold the "Option" key on your keyboard. · Under the "Paragraph" submenu toggle "Keep for Alphabetic Input Sources". ## Need more help? You can reach me from the in-app chat. · Go to the "Help" menu. · Select "Ask the Developer" from the menu. # PAPER Paper is the perfect writing space for: · Drafts · Books · Poetry · Essays · Novels · Scripts · Stories · Letters · Articles · Blogging · Note-taking · Screenplays · Screenwriting · Aesthetic notes · Creative writing · Blank paper inspiration · Distraction-free writing · Overcoming writer’s blocks # ALTERNATIVES Paper is a less cluttered, more customizable alternative to: · iA Writer · Ulysses · Byword · Typora · Notes · Bear · Q10 · Taio · Effie · Blurt · Noto · Celtx · Craft · Frost · Novlr · Plottr # TERMS OF USE https://www.apple.com/legal/internet-services/itunes/dev/stdeula/

Dear User, You can now pick custom colors for text and background under Contrast. Pleasant update, Your Paper PS → If you like Paper, please rate the app, or even leave a review — they help a lot! Thank you for your support!

Ratings and Reviews

136 Ratings

Great App, But Why Reinvent System Keyboard Shortcute

I thouroughly love the app and it's clean design! It brings simplicty to note taking on the computer that I have always wanted. However, as I am starting to use it more often, I am becoming frustrated that the app chooses to use it's own keyboard shortcuts for some things instead of using what the system provides. For example, I would love to be able to indent in a list just by hitting the tab key and then outdent with shift-tab like on most other apps on the mac.

Developer Response ,

Thank you for taking the time to review Paper! 😄 Taking your feedback into account, we have reverted Tab and Shift-Tab shortcuts to their defaults. Now they indent and outdent list items respectively.

This little app will not disappoint.

Others have said it, and I agree: this is a brilliantly concieved app. Simple and elegant - a pleasure to see and to use. I use it for notes, letters, memos and email. Now that lists are supported (thank you, Mihhail!) I use Paper for outlining, too. Paper won’t waste your screen space or your time with frivolous things, it just lets you get on with your writing. Paper is a serious tool: but there is a marvelous element of whimsy mixed in: you willl see when you try it! I mean, how often does a “productivity” app make you smile? Mihhail has a vision of what he wants his app to be, but, I can tell you that if you have ideas or suggestions for him, then by all means contact him. He always responds and will listen, and help. It has been fun to watch this app grow with each of Mihhail’s “pleasant updates…” Cheers!

Thank you for taking the time to review Paper! 😄

A Dream Come True for an ADD Writer

I like the simplicity. I have pretty creative mind paired with severe A.D.D. and am very easily distracted by other elements within the usual writing applicatioins like Pages & Word (Don't get me wrong, Pages is my program of choice when it comes to creating my final documents with its impressive design elements and photo enhancement abilities), but Paper is where my actual writing takes place. It is simple to use, and still beautiful. So many "wrtiting focused" apps are dull beyond recognition to the point where it feels like you are working on an old DOS command line system. Paper takes that simplicty and pairs it with modern elegance. I absolutely love that.

App Privacy

The developer, Mihhail Lapushkin , indicated that the app’s privacy practices may include handling of data as described below. For more information, see the developer’s privacy policy .

Data Not Linked to You

The following data may be collected but it is not linked to your identity:

• Diagnostics

Privacy practices may vary, for example, based on the features you use or your age. Learn More

Information

• Pro Features $29.99
• Pro Features $149.99
• Pro Features $99.99
• Developer Website
• App Support
• Privacy Policy

Family Sharing

Some in‑app purchases, including subscriptions, may be shareable with your family group when family sharing is enabled., more by this developer.

Paper – Writing App & Notes

Microsoft Research Blog

Microsoft at asplos 2024: advancing hardware and software for high-scale, secure, and efficient modern applications.

Published April 29, 2024

By Rodrigo Fonseca , Sr Principal Research Manager Madan Musuvathi , Partner Research Manager

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Modern computer systems and applications, with unprecedented scale, complexity, and security needs, require careful co-design and co-evolution of hardware and software. The ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) (opens in new tab) , is the main forum where researchers bridge the gap between architecture, programming languages, and operating systems to advance the state of the art.

ASPLOS 2024 is taking place in San Diego between April 27 and May 1, and Microsoft researchers and collaborators have a strong presence, with members of our team taking on key roles in organizing the event. This includes participation in the program and external review committees and leadership as the program co-chair.

We are pleased to share that eight papers from Microsoft researchers and their collaborators have been accepted to the conference, spanning a broad spectrum of topics. In the field of AI and deep learning, subjects include power and frequency management for GPUs and LLMs, the use of Process-in-Memory for deep learning, and instrumentation frameworks. Regarding infrastructure, topics include memory safety with CHERI, I/O prefetching in modern storage, and smart oversubscription of burstable virtual machines. This post highlights some of this work.

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Paper highlights

Characterizing power management opportunities for llms in the cloud.

The rising popularity of LLMs and generative AI has led to an unprecedented demand for GPUs. However, the availability of power is a key limiting factor in expanding a GPU fleet. This paper characterizes the power usage in LLM clusters, examines the power consumption patterns across multiple LLMs, and identifies the differences between inference and training power consumption patterns. This investigation reveals that the average and peak power consumption in inference clusters is not very high, and that there is substantial headroom for power oversubscription. Consequently, the authors propose POLCA: a framework for power oversubscription that is robust, reliable, and readily deployable for GPU clusters. It can deploy 30% more servers in the same GPU clusters for inference tasks, with minimal performance degradation.

PIM-DL: Expanding the Applicability of Commodity DRAM-PIMs for Deep Learning via Algorithm-System Co-Optimization

PIM-DL is the first deep learning framework specifically designed for off-the-shelf processing-in-memory (PIM) systems, capable of offloading most computations in neural networks. Its goal is to surmount the computational limitations of PIM hardware by replacing traditional compute-heavy matrix multiplication operations with Lookup Tables (LUTs). PIM-DL first enables neural networks to operate efficiently on PIM architectures, significantly reducing the need for complex arithmetic operations. PIM-DL demonstrates significant speed improvements, achieving up to ~37x faster performance than traditional GEMM-based systems and showing competitive speedups against CPUs and GPUs.

Cornucopia Reloaded: Load Barriers for CHERI Heap Temporal Safety

Memory safety bugs have persistently plagued software for over 50 years and underpin some 70% of common vulnerabilities and exposures (CVEs) every year. The CHERI capability architecture (opens in new tab) is an emerging technology (opens in new tab) (especially through Arm’s Morello (opens in new tab) and Microsoft’s CHERIoT (opens in new tab) platforms) for spatial memory safety and software compartmentalization. In this paper, the authors demonstrate the viability of object-granularity heap temporal safety built atop CHERI with considerably lower overheads than prior work.

AUDIBLE: A Convolution-Based Resource Allocator for Oversubscribing Burstable Virtual Machines

Burstable virtual machines (BVMs) are a type of virtual machine in the cloud that allows temporary increases in resource allocation. This paper shows how to oversubscribe BVMs. It first studies the characteristics of BVMs on Microsoft Azure and explains why traditional approaches based on using a fixed oversubscription ratio or based on the Central Limit Theorem do not work well for BVMs: they lead to either low utilization or high server capacity violation rates. Based on the lessons learned from the workload study, the authors developed a new approach, called AUDIBLE, using a nonparametric statistical model. This makes the approach lightweight and workload independent. This study shows that AUDIBLE achieves high system utilization while enforcing stringent requirements on server capacity violations.

Complete list of accepted publications by Microsoft researchers

Amanda: Unified Instrumentation Framework for Deep Neural Networks Yue Guan, Yuxian Qiu, and Jingwen Leng; Fan Yang , Microsoft Research; Shuo Yu, Shanghai Jiao Tong University; Yunxin Liu, Tsinghua University; Yu Feng and Yuhao Zhu, University of Rochester; Lidong Zhou , Microsoft Research; Yun Liang, Peking University; Chen Zhang, Chao Li, and Minyi Guo, Shanghai Jiao Tong University

AUDIBLE: A Convolution-Based Resource Allocator for Oversubscribing Burstable Virtual Machines Seyedali Jokar Jandaghi and Kaveh Mahdaviani, University of Toronto; Amirhossein Mirhosseini, University of Michigan; Sameh Elnikety , Microsoft Research; Cristiana Amza and Bianca Schroeder, University of Toronto, Cristiana Amza and Bianca Schroeder, University of Toronto

Characterizing Power Management Opportunities for LLMs in the Cloud (opens in new tab) Pratyush Patel, Microsoft Azure and University of Washington; Esha Choukse (opens in new tab) , Chaojie Zhang (opens in new tab) , and Íñigo Goiri (opens in new tab) , Azure Research; Brijesh Warrier (opens in new tab) , Nithish Mahalingam,  Ricardo Bianchini (opens in new tab) , Microsoft AzureResearch

Cornucopia Reloaded: Load Barriers for CHERI Heap Temporal Safety Nathaniel Wesley Filardo , University of Cambridge and Microsoft Research; Brett F. Gutstein, Jonathan Woodruff, Jessica Clarke, and Peter Rugg, University of Cambridge; Brooks Davis, SRI International; Mark Johnston, University of Cambridge; Robert Norton , Microsoft Research; David Chisnall, SCI Semiconductor; Simon W. Moore, University of Cambridge; Peter G. Neumann, SRI International; Robert N. M. Watson, University of Cambridge

CrossPrefetch: Accelerating I/O Prefetching for Modern Storage Shaleen Garg and Jian Zhang, Rutgers University; Rekha Pitchumani, Samsung; Manish Parashar, University of Utah; Bing Xie , Microsoft; Sudarsun Kannan, Rutgers University

Kimbap: A Node-Property Map System for Distributed Graph Analytics Hochan Lee, University of Texas at Austin; Roshan Dathathri, Microsoft Research; Keshav Pingali, University of Texas at Austin

PIM-DL: Expanding the Applicability of Commodity DRAM-PIMs for Deep Learning via Algorithm-System Co-Optimization Cong Li and Zhe Zhou, Peking University; Yang Wang , Microsoft Research; Fan Yang, Nankai University; Ting Cao and Mao Yang , Microsoft Research; Yun Liang and Guangyu Sun, Peking University

Predict; Don’t React for Enabling Efficient Fine-Grain DVFS in GPUs Srikant Bharadwaj , Microsoft Research; Shomit Das, Qualcomm; Kaushik Mazumdar and Bradford M. Beckmann, AMD; Stephen Kosonocky, Uhnder

Conference organizers from Microsoft

Program co-chair, madan musuvathi, submission chairs.

Jubi Taneja Olli Saarikivi

Program Committee

Abhinav Jangda (opens in new tab) Aditya Kanade (opens in new tab) Ashish Panwar (opens in new tab) Jacob Nelson (opens in new tab) Jay Lorch (opens in new tab) Jilong Xue (opens in new tab) Paolo Costa (opens in new tab) Rodrigo Fonseca (opens in new tab) Shan Lu (opens in new tab) Suman Nath (opens in new tab) Tim Harris (opens in new tab)

External Review Committee

Career opportunities.

Microsoft welcomes talented individuals across various roles at Microsoft Research, Azure Research, and other departments. We are always pushing the boundaries of computer systems to improve the scale, efficiency, and security of all our offerings. You can review our open research-related positions here .

Related publications

Predict; don’t react for enabling efficient fine-grain dvfs in gpus, amanda: unified instrumentation framework for deep neural networks, crossprefetch: accelerating i/o prefetching for modern storage, kimbap: a node-property map system for distributed graph analytics, meet the authors.

Rodrigo Fonseca

Sr Principal Research Manager

Partner Research Manager

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