One of the major hurdles I face as the head of a computational biology laboratory is convincing my research team—particularly those pursuing exclusively mathematical and computational modeling—that they need to keep a laboratory notebook. There seems to be a misconception in the computational biology community that a lab notebook is only useful for recording experimental protocols and their results. A lab notebook is much more than that. It is an organizational tool and memory aid, which serves as the primary record of scientific research and activity for all scientists. It also serves as a legal record of ownership of the ideas and results obtained by a scientist. Here, I present the best practices (summarized as ten rules) for keeping a lab notebook in computational biology, for scientists pursuing exclusively “dry” research.
Article-level metric (ALM) data need to be secure and reliable if they are to be trusted and used by all. This Perspective explores the different approaches taken by two organizations to establish ALM data integrity.
Bioinformatics is a fast-growing interdisciplinary field in which the demand for quality education exceeds the supply, especially in developing regions and countries. A massive open online course (MOOC) is a new model for education that delivers videotaped lectures and other course materials over the Internet for all interested persons around the globe to learn for free. Here we present our MOOC “Bioinformatics: Introduction and Methods,” which is the second bioinformatics MOOC in the world and one of the first batch of seven MOOCs from China. In the first two runs of this bilingual MOOC, more than 30,000 students with diverse backgrounds registered from 110 countries and regions. In this manuscript, we present the content design of the MOOC, the demographic profiles and learning patterns of the students, the requirement for English support, and feedback from on-campus students. We offer a few suggestions to other scientists who may be interested in creating a MOOC. We also remember the S* course, a successful open online bioinformatics course that ran from 2001 to 2007, long before the current wave of MOOCs. We believe that MOOC education has great potential to enhance global bioinformatics education.
Scientific visualization is classically defined as the process of graphically displaying scientific data. However, this process is far from direct or automatic. There are so many different ways to represent the same data: scatter plots, linear plots, bar plots, and pie charts, to name just a few. Furthermore, the same data, using the same type of plot, may be perceived very differently depending on who is looking at the figure. A more accurate definition for scientific visualization would be a graphical interface between people and data. In this short article, we do not pretend to explain everything about this interface; rather, see ,  for introductory work. Instead we aim to provide a basic set of rules to improve figure design and to explain some of the common pitfalls
The funding climate is “the worst in 20 years,” says Helena Nader, president of the Brazilian Society for the Advancement of Science (SBPC). Herculano-Houzel is even more pessimistic: “Brazilian science is bankrupt.”
Peer review is an institution of enormous importance for the careers of scientists and the content of published science. The decisions of gatekeepers—editors and peer reviewers—legitimize scientific findings, distribute professional rewards, and influence future research. However, appropriate data to gauge the quality of gatekeeper decision-making in science has rarely been made publicly available. Our research tracks the popularity of rejected and accepted manuscripts at three elite medical journals. We found that editors and reviewers generally made good decisions regarding which manuscripts to promote and reject. However, many highly cited articles were surprisingly rejected. Our research suggests that evaluative strategies that increase the mean quality of published science may also increase the risk of rejecting unconventional or outstanding work.
Collaboration among researchers is an essential component of the modern scientific enterprise, playing a particularly important role in multidisciplinary research. However, we continue to wrestle with allocating credit to the coauthors of publications with multiple authors, because the relative contribution of each author is difficult to determine. At the same time, the scientific community runs an informal field-dependent credit allocation process that assigns credit in a collective fashion to each work. Here we develop a credit allocation algorithm that captures the coauthors’ contribution to a publication as perceived by the scientific community, reproducing the informal collective credit allocation of science. We validate the method by identifying the authors of Nobel-winning papers that are credited for the discovery, independent of their positions in the author list. The method can also compare the relative impact of researchers working in the same field, even if they did not publish together. The ability to accurately measure the relative credit of researchers could affect many aspects of credit allocation in science, potentially impacting hiring, funding, and promotion decisions.
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