Well, I definitely agree with your sentiment in a normative sense that scientific papers should be free and readily accessible to all -- in part because a lot of it is funded through tax dollars!
But given the current state of affairs, we're looking at making that information accessible to people without having to pay exorbitant fees to access individual research. We also offer steep discounts for students or anyone in academia.
With that in mind I would push back a little that we're just a scientific paper search engine -- our system does a lot of work in extracting and classifying those citation statements, which makes it more powerful than traditional scientific search engines.
And besides just using our search, a huge time-saving value of our service is the report pages which helps you quickly build a qualitative understanding of how something was cited.
Even if all scientific papers were freely accessible, our report pages allow you to see the direct, relevant snippets from citing papers without having to manually read each and every single one. I think that is quite valuable!
I know I've gone on a little tangent from the original discussion about scihub, and having free and open access to papers, but I did just want to throw that in because I think it's an important distinction. And as much as we all want that free and open world to exist, I think it's also interesting to think about how we can open up that information for people in the interim.
You can more or less do this with scite's Citation Statement Search[1,2], or by setting email notifications when we detect new citation statements to one or more papers (grouped by a topic you're interested in like a disease or drug, an author, or more)[3].
Have you come across scite (https://scite.ai) yet? We're also innovating in this space by extracting citation statements from full-text articles and classifying their intent.
So let's say paper A cites paper B. If you look at paper B, we show you:
- how many times it was cited
- the direct paragraphs from paper A where it was cited
- the sections from paper A where paper B was referenced
- ... and a lot more
You can also now search these citation statements directly to find evidence-based information pretty quickly.
You can also visualize citation networks similar to ConnectedPapers, set notifications for new citations on groups of papers you're interested in, and much more.
Hi Jerry -- thank you for sharing your story, and for your note about donating to those organizations; they are all doing incredibly important work.
I know it isn't fun being advertised to, but I really believe that what we're building at scite.ai will be directly useful for you in staying on top of the research in that field.
I wasn't sure how best to reach you, but if you drop me a note at the email in my profile, I would be happy to give you access to our platform (and run through how you can use it to stay up to date).
Yep, I agree. I would like to point out though that one of our aims with this is to buffer against that bad research you mention.
Along with our extraction of citation statements, we have a deep learning model that classifies each statement as "supporting", "contrasting", or "mentioning" of the claims made in the original paper.
Indeed there is a lot of nuance here, and we've tried to address this in a number of ways (including a manual flag process for review where someone believes it is incorrectly labeled), and we're constantly improving in this area. In general we would expect that research that is not sound will receive more contrasting citations rather than supporting citations because their findings won't be as well supported by future research.
I should say explicitly that a "contrasting" citation is not a bad thing per se, especially given the nature of scientific discovery. But a study that was published through p-hacking whose findings are not reproducible should be easier to discern through subsequent contrasting citations. In today's world, all we might know is that it was cited a bunch, with the nuance buried underneath.
At the moment we use DOIs to link papers / citations, and get our information on DOIs from crossref (https://www.crossref.org/).
So every DOI there is also in our system (and any citation coming from a source without a DOI currently won't be picked up).
From crossref we also have metadata on which publications a given paper references, so we can always show information on how many publications cite a given paper ("traditional citations").
As for our database of Smart Citations (the extracted in-text citation statements) -- the answer is that it depends on our ability to access full-text articles to actually do the citation statement extraction. We're constantly growing in this area through new indexing agreements with publishers and so on.
I want to clarify one point in your comment because I know a lot of people read HN comments before clicking through: we do NOT offer AI summaries!
scite is fundamentally built on the idea that not all citations are equal. We offer disambiguation into citations by extracting the in-text contexts when citations happen between two papers. With that, you can qualitatively see how a paper has been cited by others by seeing the citation contexts from each citing paper.
As someone who used to be in research before at the National Cancer Institute, I certainly agree that scientists who are advanced in their field should know the literature inside out.
But the utility of scite is I think quite a bit more than that:
- If you're an expert scientist, you can configure alerts to know when new citations happen to papers that you care about (e.g. ones you published, or that are important in your field, etc). Except unlike Google Scholar alerts and such, you can now immediately see the citation contexts (as opposed to opening up each paper, and trying to locate the reference(s) and the in-text citations).
- If you're a student or an experienced researcher trying to ramp up in a new field (which is increasingly important given how interdisciplinary science is these days), this is a useful tool in your arsenal to digest and discover new papers.
- That last comment also applies if you are in the industry! e.g. someone in pharma doing drug discovery or evaluation and you're working on different drugs every <X> months.