Scientific conferences and meetings have an important role in research, but they also suffer from a number of disadvantages: in particular, they can have a massive carbon footprint, they are time-consuming, and the high costs involved in attending can exclude many potential participants. The COVID-19 pandemic has led to the cancellation of many conferences, forcing the scientific community to explore online alternatives. Here, we report on our experiences of organizing an online neuroscience conference, neuromatch, that attracted some 3000 participants and featured two days of talks, debates, panel discussions, and one-on-one meetings facilitated by a matching algorithm. By offering most of the benefits of traditional conferences, several clear advantages, and with fewer of the downsides, we feel that online conferences have the potential to replace many legacy conferences.

As academic careers become more competitive, junior scientists need to understand the value that mentorship brings to their success in academia. Previous research has found that, unsurprisingly, successful mentors tend to train successful students. But what characteristics of this relationship predict success, and how? We analyzed an open-access database of 18,856 researchers who have undergone both graduate and postdoctoral training, compiled across several fields of biomedical science with an emphasis on neuroscience. Our results show that postdoctoral mentors were more instrumental to trainees’ success compared to graduate mentors. Trainees’ success in academia was also predicted by the degree of intellectual synthesis between their graduate and postdoctoral mentors. Researchers were more likely to succeed if they trained under mentors with disparate expertise and integrated that expertise into their own work. This pattern has held up over at least 40 years, despite fluctuations in the number of students and availability of independent research positions.

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.

The number of biomedical publications is increasing exponentially every year. If we had the ability to access, manipulate, and link this information, we could extract knowledge that is perhaps hidden within the figures, text, and citations. In particular, the repositories made available by the PubMed and MEDLINE databases enable these kinds of applications at an unprecedented level. Examples of applications that can be built from this dataset range from predicting novel drug-drug interactions, classifying biomedical text data, searching specific oncological profiles, disambiguating author names, or automatically learning a biomedical ontology. Here, we describe Pubmed Parser (pubmed_parser), a software to mine Pubmed and MEDLINE efficiently. Pubmed Parser is built on top of Python and can therefore be integrated into a myriad of tools for machine learning such as scikit-learn and deep learning such as tensorflow and pytorch.

Generalized linear models (GLMs) are well-established tools for regression and classification and are widely applied across the sciences, economics, business, and finance. Owing to their convex loss, they are easy and efficient to fit. Moreover, they are relatively easy to interpret because of their well-defined noise distributions and point-wise nonlinearities. Mathematically, a GLM is estimated as follows: min β0,β