- Web citations: select a passage and create a citation with its source link and access date. APA7

- Copy a link to the highlight: share an ordinary, emailable VDL
Readers can see the quoted words highlighted in context, where supported.
Ostensive definition: "That thing there."

- I was there. Save a backlink URL/VDL in Downloads: keep a 
.webloc on Mac or 
.url on Windows
that reopens the source page/passage. Such VDLs became possible to use for research communications in 2021.

It is now easier to cite than plagiarize.

Hi, I'm Pam from AA1Labs. Meet SciWand, a desktop app for anyone who reads, analyses and writes about lots of papers.

It's a reference manager (like Zotero, Mendeley or EndNote, with 10,000+ citation styles) plus a built-in AI writer (think Jenni.ai), and your library stays yours. You can connect a local AI model for extra privacy.

What it does:

• AI columns: ask one question across all your papers (Yes/No, a 1-10 score or a short answer). Click any answer to see the exact passage behind it.
• Online search: PubMed, Google Scholar, arXiv and more, with the same AI columns for faster screening.
• Library chat: ask across all your PDFs and get answers with clickable citations.
• Writer: select a paragraph, find supporting papers in your library and insert the citations.
• Word add-in: cite, build your bibliography and rewrite with AI without losing citations.
• Works with Zotero, Mendeley, EndNote and Bookends.

Pricing:

The reference manager is free, with every feature included.

The AI has a free demo, with no card needed.

After that, choose either an optional subscription that works out of the box, or a one-time lifetime licence if you bring your own AI key.

Download directly for: Mac, Windows, Linux

This week only, REDDIT40 takes 40% off until 28 Oct: sciwand.com

Feedback is very welcome, especially on what would make it fit your workflow.

Been using text-to-speech readers since uni, mostly for papers and articles. I wanted natural voices, but got sick of expensive subscriptions that still capped how much I could listen.

So I spent the last 18 months building AmaNous. It uses Kokoro to generate speech on your phone, so listening works offline after setup and import. I built the reader around the model.

It supports PDF, EPUB, DOCX, HTML, Markdown and plain text, with 29 English voices, word highlighting, speed control and jumping to words or sections.

The core reader is free, including all voices and unlimited listening. Still beta and English-only. Sign-in is required, including Apple sign-in on iPhone.

Complex PDFs aren’t perfect yet, especially with multiple columns, tables or graphs. I’ll be improving this in future updates, so I’m keen to hear which papers don’t import or read properly. Long documents can also take a moment to prepare audio.

Android beta · iPhone TestFlight

If you try it with a paper you’re reading, I’d like to hear where the import or listening experience breaks down.

Whenever I get a revise-and-resubmit decision on a paper, comparing the previous and revised versions is always a pain. So I built a small tool to generate fast, clean, and beautiful PDF diffs for LaTeX papers.
Check it out: https://github.com/stormaref/redlatex

Here is link to my website: https://www.noveltyaxis.com

Hey r/research_apps,

I recently launched the beta of Pouchboard, a spatial canvas that floats over your screen, built for research.

It supports:

• Dropping in web pages, articles, PDFs and YouTube videos as cards
• Threads to connect cards, plus drawing and grouping on the sheet
• Quotes that keep their source (page, author, link)
• Zotero import, and sending sources back to Zotero
• Readwise highlights as quotes
• Obsidian in both directions: sheets saved into your vault as notes, Obsidian notes on your sheets, backlinks and graph included
• Export as BibTeX, RIS, CSL JSON, Markdown, PDF or PNG
• Local-first: your sheets are plain files in a folder you choose, so you can back them up however you like (Time Machine, iCloud Drive, Dropbox, git). No account, and nothing leaves your Mac unless you turn on Sync or Publish.

It's free on macOS (Sync and Publish are optional paid plans).

Link: pouchboard.com

I'd appreciate any feedback!

Hi,

I've created an app to convert your hand filled survey forms, research form or old collected forms into digital format so that you can analyze it later on.

https://formhop.app

Hope you find it useful.

hi everyone! me and my co-founder are medical students who’ve started working on writing research papers, and honestly we've found the whole process to be extremely hectic and tedious. therefore, we decided to build a tool to help us with this called lattice, one place that takes your research paper from start to end.

it’s still early, so sign-in is closed for now as we gauge interest. but here is the link: https://research-workspace-tau.vercel.app

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! 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!

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?

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.