Disclosure first: I'm the developer. I built WebSum for the part of research where you have twenty tabs open and need to decide which ones deserve a proper read.

What it does: click the icon on a web article and a summary appears in Chrome's side panel, beside the page: a TL;DR, key takeaways, why it matters and caveats.

Why it might suit research:

• Every point is checkable. Each one has a ¶ link that scrolls the page to the paragraph it came from and highlights it, so you verify the claim instead of trusting the summary.
• It flags caveats, such as a claim with no evidence behind it, or an author who sells a related product.
• It reads the charts and diagrams, including numbers the text leaves out.
• Your notes keep the sources. Copy a summary into a document and each ¶ stays a link that opens the article at that paragraph.

What it doesn't do: it reads web pages only, so no PDFs, which rules out most journal papers. There's no saved history, no follow-up questions, and it's Chrome only. On a paywalled article it summarises what your browser can see and tells you when that's only a preview. Treat it as a way to sort and check sources, not as a replacement for reading the ones that matter.

Cost: free for 2 summaries a day without an account, or 5 with Google sign-in. Pro is US$10 a month for 20 a day on a stronger model.

Chrome Web Store: WebSum on the Chrome Web Store

42-second demo: https://www.youtube.com/watch?v=J_Yf4dPCDFQ

Two things I'd like to hear from people who research for a living or a degree:

• Where would this fit in how you work, if anywhere: first pass over sources, or checking a claim you half remember?
• What would it need before you'd rely on it: PDFs, saved summaries, export to a reference or notes app?

Hello! I want to hear from you, what are the current pain points in data analytics and visualisation in the research community? For example,

- How easy is it to analyse and visualise chromatography & mass spec data, sequencing data, X-ray diffraction data, etc.

- Do you require paid subscriptions of tools which help you analyse such data?

- Do you use platforms like Benchling to track your samples, inventory, experiments, etc?

I'm in the process of democratising data analytics and visualisation of scientific and research data. Your feedback would be invaluable to me.

I am a post-grad cognitive neuropsych student and I made an an early-stage app for collecting and organizing research papers, with the option to link them to regions on a 3D brain, all while having access to a community forum. I made this app as a student and for the community. I now need your help to find out whether this would be useful to other students and researchers, and what needs to improve before I take the project further.

I am giving access to anyone who wants to use my webapp through this link: https://mapped-brain.pages.dev/

You can organize papers into personal projects and connect them to brain areas. The idea is that users could also share those projects with the community, helping others explore papers by topic or brain region. It links a classic research paper manager with much more. There’s also a community forum which aims to show a heatmap of all contributions as well as host discussions and interactions for students, researchers, and anyone else interested.

The app currently includes a bilateral brain model, along with some other structures around the brain: vascularity, dura and other layers. However, this is still in its experimental phase.

Keep in mind the app is in very early access but I would welcome any and all critique and feedback.

Over the past week, I added something I've been wanting to build for a while: claim comparison.

I actually tried building this much earlier. I experimented with cosine similarity, vector dot product scores, and other approaches, but they only tell you how similar two pieces of text are. That's not really what I needed.

What I needed was a classifier that could actually determine the relationship between two claims.

I built a basic classifier myself, but it wasn't reliable enough for something I wanted people to actually use. So I put the idea aside and waited.

Then JEV was released last week, and it turned out to be almost exactly what I was looking for.

Now you can compare claims against each other and inspect the supporting evidence directly.

This is probably the most interesting feature I've added to the project so far, and I'm really curious to see what people think of it.

This is still being tested for edge cases, so there are definitely things I want to improve. But it's free and open source, and anyone can try it out right now.

If you're interested in how it works, want to try it, contribute, or have ideas for where this could go, feel free to check it out.

GitHub: https://github.com/Sreehari05055/thesys-core

Hello everyone,

Disclaimer : This project is heavily vibe coded and even if I had minimal code interventions, it's mostly written by Claude.

I'm posting here to present a free app (no freemium, not even donations), Cadenza, a method-agnostic coding/qualitative analysis app.

The project started because I was unhappy with existing CAQDAS tools and wanted a board-like view to sort codes/items. Also, I'm interested in non-coding based methods in qualitative data analysis.

I'm at a point where I need potential users feedback, in addition to my own research projects. So here's the link if you'd like to give it a spin : https://github.com/seelebrn/cadenza/

I'd be very happy to know what you liked, disliked, how it felt using it and what you would like to see added.

Thanks in advance!

Version v0.5.0 is about adding more trust to your shared libraries.

Host a DB, your own relay, give specific users permission to access this DB, keep track of your changes and theirs in a CRDT log based on Automerge.

I am going to be testing it out myself soon with my research group to get feedback on UX, usefulness, etc... Curious if anyone wants to try it out and could give feedback?

https://github.com/linxiv-dev/linXiv

https://github.com/linxiv-dev/linXiv/releases/latest

Hi r/research_apps,

Disclosure: I’m the founder and developer of EvidenceFlow.

I built it because systematic-review work is often split across too many tools and files: references are screened in one place, data extraction is kept in spreadsheets, analysis happens elsewhere, and PRISMA reporting is reconstructed at the end.

EvidenceFlow is a web-based workspace intended to bring the core workflow together:

• PICO/question framing

• Citation import and duplicate handling

• Title/abstract and full-text screening

• Multi-reviewer decisions and conflict resolution

• Structured data extraction

• Effect-size and heterogeneity calculations

• Meta-analysis workflow

• PRISMA 2020 flow-diagram reporting

• Exportable research records

The core workflow is available without requiring a card. AI-assisted features are optional and are meant to support, not replace, researcher judgment.

I’m currently improving the product and would value practical feedback from people who have conducted systematic reviews, scoping reviews, meta-analyses, or evidence syntheses. In particular:

1. Which step in your review workflow creates the most manual work?

2. What do you currently use for screening, extraction, and meta-analysis?

3. What would make you trust or distrust a new review-management tool?

4. Which export formats or collaboration features are essential?

5. Would a versioned workflow for search strategies, peer review, and PRISMA-S-style documentation be useful to your team?

You can try or inspect it here: https://evidenceflow.in

I’m happy to answer questions about the workflow, data handling, method limitations, and why certain features were designed the way they were.

I will also use feedback from this thread to prioritize improvements.

Thanks.

Basically the thought came from how people in the workplace or even academia tend to submit documents or templates with AI place holders or language, giving it a very unprofessional look. There are “AI detectors” but I decided to experiment with an AI powered document reviewer that annotates your document with feedback. The reason is that AI is here to stay and many use it for their papers… but how it comes off can make a huge impact.

What it looks for is:

- AI writing signs
- Redundancy/repetition
- Vague language
- Potential contradictions
- unsupported claims
- writing issues

I currently have a very simple MVP and was hoping to get your thoughts and feedback.

App link: https://ugao2xnsrngkmdvzfkahg6.streamlit.app/

Would really appreciate feedback in this form here:

https://forms.gle/gkFG1hxQfS9HpKv19

Thanks!

Hi everyone,

When conducting literature reviews or digging through collections of papers, standard desktop search tools either only find the filename or dump you at the top of a 30-page document. On the other hand, feeding sensitive preprints or unpublished drafts into cloud AI tools creates privacy and copyright concerns, not to mention hallucinated page citations.

I built Silica-core, a free, open-source (MIT) retrieval tool designed specifically to locate exact evidence in local folders of PDFs and Markdown notes.

What it does:

• Exact passage & page location: Indexes PDFs with selectable text layers directly. A search hit returns the exact page (e.g. p. 7), the section, the text excerpt, and an IDF coverage score showing which query terms matched.
• 100% Local & Privacy-preserving: No cloud uploads, no external API keys, and no heavy background embedding models. The index builds in seconds using deterministic BM25.
• Handles edge cases: Explicitly reports which files couldn't be parsed and which search terms were absent from the entire collection, so you know when a concept genuinely isn't there.
• Flexible interface: Run it directly from your terminal (silica search "methodology name") or connect it via MCP (Model Context Protocol) to your preferred local or desktop assistant if you want an AI to read the retrieved passage.

It's completely free and open source. GitHub: https://github.com/kiycoh/silica-core

I’d love to get feedback from anyone managing large literature folders: what is currently the biggest bottleneck in your paper reading and reference retrieval workflow?

Hi everyone! I've been developing a medical vocabulary game called NMTS. It's a web app that quizzes you on synonyms/entry terms for topics from the MeSH database. I'm aiming for this to be a handy practice tool for med students, researchers, and anyone who uses PubMed a lot.

I'm not selling anything or running ads; I'm just looking for around 30 people who are familiar with MeSH to break-test the synonym logic and tell me whether it's actually useful/fun.

You can give the beta app a go at: https://nmts-beta.web.app/

If you need to know more about it, I've set up a simple Discord server to explain the beta test in detail here: https://discord.gg/rQ3JD7Kdr

Thanks for reading, and looking forward to hearing from you!

Listen Step, the app guides you one step at a time, listening for voice. Stay focused and build a repeatable habit of disciplined execution.

WHY IT WORKS

• Easily upload a protocal from OCR, and edit.

• Voice-driven flow keeps your attention on the task.

• Pre-run checklists catch missing materials before you start.

• Voice recording is available for reporting any issues during the experiment. • The final report shall include the experiment date, step duration, and voice remarks.

• Post-run self-rating and annotations turn every run into feedback.

HANDS-FREE VOICE CONTROL

• Say "next", "check", "back" or "repeat" to move through steps — no touching the screen

• Offline on-device speech recognition: works without a network, audio never leaves your phone

• Every step read aloud, with automatic timers and long-timer alarms

I've used tools like Perplexity, ChatGPT, Claude and others for research, and they've been incredibly useful for finding papers and getting through large amounts of information.

The one thing I personally wanted was a simple way to see exactly which parts of the paper were used to answer my question.

When you're working with a 100+ page PDF, even having a page number can still mean a lot of scrolling and searching.

So I ended up building something for myself.

You ask a question and the relevant paragraphs in the PDF are highlighted directly on the document. You can see the context behind the answer and quickly check whether it actually answers what you're looking for.

I originally built this because I wanted something for this workflow without having to pay for another subscription. What started as a personal project has now become completely open source.

The underlying idea is pretty simple. And yes, if you're thinking "isn't this just RAG?" then yes, you're absolutely right. It's RAG with the visual highlighting that I wanted.

I think the same idea could be useful for more than research papers too. Legal contracts, financial reports, technical documentation, or anywhere you need answers alongside the actual source.

If anyone wants to have a look, contribute, or just give some feedback, here's the repo:

GitHub: https://github.com/Sreehari05055/thesys-core.git

This will probably be my last post about the project. Thanks to everyone who checked it out and gave feedback along the way.

Research is crucial for IMGs and medical students, however opportunities are scarce. I've been in your shoes before, and I realized that opportunities increase tremendously when I mention that I can actually do some sort of analysis.

A lot of you here know the A to Z of research and methodologies but cannot actually perform the analysis, either because it requires daunting coding skills on R or python, or that the softwares in use are very expensive (over 200USD).

That's why I built MetaStudio.

Through years and 30+ research projects, and many many instances of polishing, fine tuning and improving my R code, I reached to an excellent version of the code that is essentially bulletproof, and allows me to do the analysis in 2 minutes (I am absolutely not joking by any means).

Now I know that most of you here don't know much coding and how to run a code, therefore, I converted the code into an interactive interface/platform (called GUI for those who know).

It is done and ready, and I am releasing a form to pre-order it at a discounted 80% price (total is 10USD). Yes, for 10USD you can change your entire research trajectory forever.

This is for a short period of time, after that it will go back to its original price of 50USD.

5 lucky people here will get the chance to get it for free as long as they're willing to actively test and provide genuine feedback.

Please note that while the GUI platform looks like an app, yet it is not an app in the technical sense. When you buy through gum road, you will get a zip folder, which when unzipped you will get a local version of that GUI which runs and executes R code through a very very easy to use interface. This basically and merely adapts the settings of the R code according to your will. And once you finish the analysis, you will get the same exact output as if you ran an R code. there is no need to cite anything than R, and you can tell people that the analysis was executed on R.

For utmost transparency, the code can be also seen within the app, can you imagine?

Here is the form: https://forms.gle/muyJb6M4g3FxfxX88

What the title says. Most searches are really bad, and you end up with many garbage papers that you have to scan through. No matter if you just want to find papers for a specific topic, run a systematic review, or even a meta-analysis, we really need a tool to scan through the research results, find those closely related, and throw the garbage out.

If you know relevant apps please suggest them.

I've worked in cell culture labs as a student and as faculty. A while back I watched someone ask a lab meeting whether they had a certain cell type. One person said yes, one said no, and the rest had no idea. That's normal. Usually the record is a spreadsheet nobody has updated, or a logbook sitting next to the dewar.

So I built a completely free app that tracks cryovials in a liquid nitrogen dewar: which box and slot a vial is in, who took the last one, and what each vial was frozen down from.

It runs on an Android tablet and on Windows. Everything stays on the device. There's no account and no server, and does not require internet access other than for the initial download, so nothing you type into it can leave. You can import your existing spreadsheet.

It isn't a LIMS. No ELN, no scheduling, no instrument integration.

Demo with fake data, nothing to install: https://cryoindex.app/demo

It's free and there are no ads. I used AI coding assistants while building and reviewing it, alongside automated tests and a lot of hands-on testing.

Google won't let me publish on Play until 12 people join a closed test and stay in for 14 days. I can get that number from other subreddits where people install each other's apps and never really test them, and if that's the only route available I may take it. However, this produces a published app that nobody who works in a lab has ever actually used and does not really tell me if the app is useful. What would be worth something is twelve people who open a dewar every week, install it, and tell me where it's wrong. If you have an Android device and are willing, that would help a lot.

I've been frustrated by how much AI-assisted research still feels like this:

Question -> answer -> follow-up -> follow-up -> 30 messages later -> "wait, what was that other thing I wanted to investigate?"

Research isn't really linear.

You start with one question, discover 4 interesting directions, then each direction creates 3 more questions.

So I built Parallely around a different workflow:

Ask once → explore multiple research directions simultaneously.

https://parallely.xyz

For example, if I'm researching "How do ZK rollups work?", instead of getting one long answer, Parallely creates separate branches for things like:

• How the technology works
• Architecture
• Applications
• Trade-offs / criticisms

I can then drill into any branch without losing the others.

The part I've found surprisingly useful is what happens after the research.

Any card can be turned into a ~90-second, two-host podcast. One voice plays the curious learner and the other explains the topic, with the transcript available alongside the audio.

So a research session can become:

Question -> explore -> branch -> compare -> synthesize -> listen

You can even generate separate podcasts for different branches of the same research canvas.

I'm experimenting with this as a way to make research less like "asking an AI questions" and more like building a map of what you understand.

I'd genuinely like feedback from people who do serious research:

Where does your current research workflow break down?

Is it:

1. Finding useful sources?
2. Keeping track of different research directions?
3. Connecting ideas across papers?
4. Synthesizing everything at the end?
5. Actually consuming the research after you've collected it?

And would turning parts of your research into short conversational podcasts actually be useful, or is that just a gimmick?

It's free to try. I'm mainly looking for researchers who can tell me where this workflow is genuinely useful and where it isn't.

Tool: PRECISE / PRECISE LITE

What it helps with:

PRECISE helps turn a research question into a structured, source-based paper or report.

It guides the work through four stages:

Define the topic, purpose, audience, scope, and required output.

Turn the request into a precise research plan.

Find and verify sources, collect supporting evidence, and identify gaps.

Write the requested paper or report and audit its factual claims.

You can upload your own files, use web search, or begin with your files and then add web research.

PRECISE LITE - free demonstration version:

Works in ChatGPT

Includes the same four stages.

Limited to one research loop and up to three sources.

Suitable for experiencing the methodology or completing a small research task.

Full PRECISE - complete version:

Works inside ChatGPT or Claude.

Supports repeated research loops and additional sources.

Designed for larger and more complex papers and reports.

Builds a detailed evidence trail and identifies remaining gaps.

Produces complete, tailored deliverables.

Audits the final factual claims against the collected evidence.

Why it is useful:

Writing a strong paper requires the research question, structure, evidence, claims, citations, and level of certainty to remain aligned. PRECISE organizes this complete process and makes the connection between the sources and the final writing inspectable.

Alternative:

Using ChatGPT or Claude directly, without a structured research and verification workflow.

I created PRECISE and PRECISE LITE.

Try PRECISE LITE for demonstration:

https://chatgpt.com/g/g-6a5e26093f488191a1fba0261cbcbe39-precise-lite

I’m an academic researcher and built Papertrek after getting increasingly frustrated with keeping manuscripts in spreadsheets and trying to remember which paper was waiting for whom, which revision deadline was approaching, and which “in prep” project had quietly been sitting there for a year.

https://papertrek.app

Papertrek currently lets you:

- track manuscripts from draft through submission, revision and publication

- keep revision and other manuscript deadlines in one place

- import publications through ORCID

-share manuscript entries with co authors or supervisors

-get an overview of everything currently in your publication pipeline

I’m the developer. It is currently an open beta and free to use.

The main thing I’m looking for at this point is feedback from researchers who actually manage several papers at once: whether the workflow makes sense, what is missing, and which parts are unnecessary.

Live: https://deekshithvodela.github.io/FormsOffline/
GitHub: https://github.com/deekshithvodela/formsoffline

I built Forms Offline, a free, open-source, offline-first alternative to Google Forms for research data collection.

The idea came from seeing researchers avoid online form services because they don't want confidential participant data processed by third-party cloud platforms. Some instead continue using paper questionnaires and manually transferring responses to Excel.

Forms Offline keeps the entire workflow on the device. You can create form templates, collect responses, attach photos/files/signatures, manage datasets, maintain an edit audit trail, and export to Excel or ZIP.

It also supports collaborative offline collection: share a form template with your team, collect data independently, then consolidate the exported datasets and detect duplicates.

Full details here ✨: https://deekshithvodela.substack.com/p/im-building-formsoffline

I'm curious: Would you use something like this for your research? What workflow would you use it for?

Feedback is very welcome.

I'm in the final year of my PhD and often struggle to stay on top of new literature in my field (medicine). I did not want to subscribe to several separate notification systems for new literature (via pubmed, scholar etc), so I built a small tool for myself that pulls and saves papers from multiple sources and sends a notification whenever a new paper matching a topic or author I follow gets published. I found it especially useful when writing my systematic review and the intro/discussion of my thesis, since I could stay on top of the latest literature without manually checking every database. Sharing it here in case it's useful to anyone else, not trying to sell anything, it's free to use: paperbeats.com

The most exciting message I got this year came from a physics professor I'd met only two weeks earlier.

--

A year ago I built an AI news agency with no humans running it - StoryChase. Thousands of messages a day, dozens of languages, and the hard part was never getting AI to write a sentence. It was turning raw, messy information into something AI could reason over. Not summarize. Reason over.

Once that worked, I looked for what else the method could solve. Markets, for one - still working on that. What I did not see coming was finding myself one evening in a lab, across from two physicists, with a product that fits scientific problems remarkably well.

Prof. Yonatan Dubi heads a theoretical quantum chemistry and physics lab in the Chemistry Department at Ben-Gurion University. Prof. Amos Sharoni heads an experimental solid-state lab in the Physics Department at Bar-Ilan University. Decades of person-years between them and their teams. Tens of thousands of files - measurements, papers, lab notes, presentations. Point an AI at those folders, ask it something real, and it reads a few and quietly invents the rest from its general knowledge.

Which is exactly the problem. A lab is where scientists generate knowledge, long before it might someday become general. It is the last place you want gaps filled in from general knowledge.

The models are genuinely good now. Claude Opus does great work. In a lab, it just has nothing to work with.

They asked me how to get AI into the research itself, not just the writing. So I took what StoryChase taught me about structuring data for AI - read everything, turn it into meaningful records, find the connections between them - so the AI stops guessing and starts reasoning over what the lab knows.

Then the moment of truth. Amos asked Claude something only his lab could answer: graphs and quantitative summaries of experiments in one of their areas, how those results might explain a physical phenomenon, and a structure for a paper.

Claude and Binaly worked for twenty minutes and produced an analysis spanning 2007 to 2026 - almost twenty years of experiments.

Amos read it and sent me this:

"Finished reading the report. Very impressive, and surprising. If I'd had this a month ago it would have significantly strengthened a paper we submitted, turning a qualitative analysis into a quantitative one. Binaly and Claude ran statistical analyses on my old data and found a phenomenon we'd missed, one worth trying to understand. Binaly doesn't just organize the knowledge. It becomes a research partner that comes up with ideas."

I danced around the room. We cracked it!

That's the part of building things I like most: what you learn in one place applies somewhere you weren't looking.

--

If you run a lab or work in one - physics, chemistry, materials science, economics, anywhere the real asset is years of your own measurements and papers - let's connect.

Tell me about your research. Let's see what Binaly can add to it.

Binaly is free for personal use, and free to try for lab use.

Link: https://binaly.ai

Second-year ML PhD and I’m starting to think “I should read a few more papers first” is just procrastination with citations.

Every new research direction goes the same way.

I start with 5 papers. Then it becomes 15. Then 30+. Zotero is full, my Notion page is getting ridiculous, I’ve asked ChatGPT to summarize a few things, used Connected Papers to chase related work, and tried Mira by Deep Principle to compare some of the papers.

And somehow I still haven’t run an experiment.

That’s the part that gets me. You can spend days doing “research” and feel productive the entire time, while still being unable to answer a very basic question:

What exactly am I testing?

Recently I stopped trying to understand an entire subfield before touching code. I just looked for recurring baselines, common metrics, disagreements between papers, and one or two things I could actually falsify.

That got me to a baseline and a couple experiments way faster than another 20 PDFs probably would have.

Now I’m wondering if the real skill isn’t reading more papers, but knowing when you have enough information to start being wrong.

How do you decide when to stop reading and just run the experiment?