This study identified factors related to mentorship that influence the academic success of postdoctoral trainees in biomedical research. Limited data in other life science leaves uncertain correlations and remains to be investigated.
The objective was to uncover how patterns in the network of mentors and protégés shape their academic success.
Metrics used but not limited to: academic proliferation (the number of progeny trained by a
mentor, sometimes termed academic fecundity), publication and citation rates, funding levels, attrition rate, and scientific proficiency.
During the data collection process, the authors encountered almost 900 inactive OA journals that were still accessible at the time of our study but at high risk for vanishing in the near future
This study found that 176 open-access journals that have vanished from the web. Journals that were affiliated with academic institutions or scholarly societies, located in North America, or that published social sciences and humanities research, represent a larger share of vanished journals compared to other types
We can use the internet to build tools that expand our ability to solve the most challenging intellectual problems. Tools which actively amplify our collective intelligence in much the same way as for millennia we've used physical tools to amplify our strength.
Formally recognizing contributions to a Polymath project is difficult; research papers from such a project are usually written under a pseudonym. This is a particular issue for early career mathematicians considering devoting substantial research time to a Polymath project.
A huge collaborative open science model is proposed. Many authors collaborating in a paper leads to a substantial reduction for the Article Processing Charges (APCs) in the Open Access Journals. This can significantly stimulate research within a healthier citizen and open science culture.
Ongoing technological developments have made it easier than ever before for scientists to share their data, materials, and analysis code. Sharing data and analysis code makes it easier for other researchers to reuse or check published research. However, these benefits will emerge only if researchers can reproduce the analyses reported in published articles and if data are annotated well enough so that it is clear what all variable and value labels mean. Because most researchers are not trained in computational reproducibility, it is important to evaluate current practices to identify those that can be improved. We examined data and code sharing for Registered Reports published in the psychological literature from 2014 to 2018 and attempted to independently computationally reproduce the main results in each article. Of the 62 articles that met our inclusion criteria, 41 had data available, and 37 had analysis scripts available. Both data and code for 36 of the articles were shared. We could run the scripts for 31 analyses, and we reproduced the main results for 21 articles. Although the percentage of articles for which both data and code were shared (36 out of 62, or 58%) and the percentage of articles for which main results could be computationally reproduced (21 out of 36, or 58%) were relatively high compared with the percentages found in other studies, there is clear room for improvement. We provide practical recommendations based on our observations and cite examples of good research practices in the studies whose main results we reproduced.