Web Apps for Researchers

Hacker News

Labii (http://www.labii.com) provides web apps for researchers. To date, 3 millions of apps have been developed to simplify our personal life. However, in academia, there are extremely limited number of apps for researchers, with the existing ones often outdated and difficult to use. Our platform brings advanced information technology into the process of academic research, by building research-orientated apps to integrate research resources, simplify research process and promote scientific collaboration. Currently, we provide these apps: Academic profiles - Online academic CV (http://profiles.labii.com). You can document your academic experiences and activities in Academic Profile, and stay tuned with the newest updates from fellow researchers by connecting with others in your field of interest. Research groups - Build laboratory websites (http://groups.labii.com). If you have a Lab, or are collaborating with other scientists, you can start a Research Group on Labii, where you can build lab websites, post job openings and organize meetings etc. Reference manager - IMDb for research papers. (http://references.labii.com) The Labii Reference Manager, adopting the idea of “IMDb for research papers”, provides a platform for researchers to find, share, rate and comment on papers. Electronic laboratory notebook. (Coming soon) Laboratory Notes is an electronic notebook for lab notes. It offers a solution to increase efficiency in the process of repetitive experiments and data recording.

Visit product View on Hacker NewsJun 10, 2015

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best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, notes · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, soon · Missing: plus, intuitive, reviews

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, apps · Missing: mobile apps, ios, entrepreneurs

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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