Science Concierge: A fast content-based recommendation system for scientific publications

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.Comment: 12 pages, 5 figure

Intellectual synthesis in mentorship determines success in academic careers

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\u27 success compared to graduate mentors. Trainees\u27 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

Pyglmnet : Python implementation of elastic-net regularized generalized linear models

Graceful handling of small Hessian term in coordinate descent solver that led to exploding update term Ensure full compatibility of GLM class with scikit-lear

Neuromatch Academy: Teaching Computational Neuroscience with global accessibility

Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility.Comment: 10 pages, 3 figures. Equal contribution by the executive committee members of Neuromatch Academy: Tara van Viegen, Athena Akrami, Kate Bonnen, Eric DeWitt, Alexandre Hyafil, Helena Ledmyr, Grace W. Lindsay, Patrick Mineault, John D. Murray, Xaq Pitkow, Aina Puce, Madineh Sedigh-Sarvestani, Carsen Stringer. and equal contribution by the board of directors of Neuromatch Academy: Gunnar Blohm, Konrad Kording, Paul Schrater, Brad Wyble, Sean Escola, Megan A. K. Peter

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

Using Machine Learning And Natural Language Processing To Improve Scientific Processes

Scientific information has been growing exponentially over the past decades. Ar- guably, traditional processes of doing science cannot keep up with this growth. This expansion has a scaling impact on scientific activities such as funding, the review process, conferences, and exploring the literature. To improve on the traditional sci- entific processes, useful tools and understanding of these processes are crucial. This dissertation advances the scientific processes by incorporating knowledge and tools from machine learning (ML) and natural language processing (NLP). We discuss the applications in three applications of scientific endeavors including (1) improving on traditional conferences with data driven approaches, (2) extracting scientific claims for scientific literature, and (3) understanding the funding process using content of applications. To complement our findings, we provided open-source softwares, tools, and real-world implementation for other researchers. In sum, this thesis serves as both a conceptual point of view and a proof-of-concept implementation of how we can improve science through the use of ML and NLP

Using Machine Learning and Natural Language Processing to Improve Scientific Processes

Scientific information has been growing exponentially over the past decades. Arguably, traditional processes of doing science cannot keep up with this growth. This expansion has a scaling impact on scientific activities such as funding, the review process, conferences, and exploring the literature. To improve on the traditional scientific processes, useful tools and understanding of these processes are crucial. This dissertation advances the scientific processes by incorporating knowledge and tools from machine learning (ML) and natural language processing (NLP). We discuss the applications in three applications of scientific endeavors including (1) improving on traditional conferences with data driven approaches, (2) extracting scientific claims for scientific literature, and (3) understanding the funding process using content of applications. To complement our findings, we provided open-source softwares, tools, and real-world implementation for other researchers. In sum, this thesis serves as both a conceptual point of view and a proof-of-concept implementation of how we can improve science through the use of ML and NLP