Open source lessons for synthetic biology – O’Reilly Radar

“So, that’s software. How does open source work in biology? Examples lie on a spectrum ranging from “garage” to “academic lab.”

 

Biohackers, for one, in many ways resemble the original “two nerds in a garage” origins of the computer movement. Biohackers use open source protocols and designs for equipment, such as PCR to set up personal laboratories that would normally be beyond the scope of casual tinkerers. This is assisted by recent attempts to standardize genetic elements, as seen, for example, in the BioBrick movement (which curates various DNA sequences designed to easily clone together into a biological circuit) or the OpenPlant collaborative initiative (which promotes an open source approach to plant synthetic biology). Supported by a surprising number of open, collaborative labs around the world, these groups aim to bring about the same sort of changes as were seen with the start of the PC era.

 

At the other end, we have institutions such as CambiaLabs and the BiOS Initiative, which aim to support open source IP initiatives for biological systems via collaborative licensing agreements. A good example of their work would be the Transbacter project, an attempt to perform an end-run around the multitude of Agrobacteria-mediated plant engineering techniques patents by identifying other vectors — which were then released to the community.

 

Both of these are attempts to democratize biological research and development, and tie into a general increase in popular interest over biotechnology — as can be seen by the success of the crowdfunded “Glowing Plants” synthetic biology project….”

Synthetic biology: Cultural divide : Nature News & Comment

“[Andrew] Hessel represents an increasingly impatient and outspoken faction of synthetic biology that believes that the patent-heavy intellectual-property model of biotechnology is hopelessly broken. His plan relies instead on freely available software and biological parts that could be combined in innovative ways to create individualized cancer treatments — without the need for massive upfront investments or a thicket of protective patents. He calls himself a “catalyst for open-source synthetic biology”.

This openness is one vision of synthetic biology’s future. Another is more akin to what happens at big pharmaceutical companies such as Pfizer, Merck and Roche, where revenues from blockbuster drugs fund massive research initiatives behind locked doors. For such businesses, the pursuit of new drugs and other medical advances depends heavily on protecting discoveries through patents and restrictive licensing agreements….”

Reproducible and reusable research: Are journal data sharing policies meeting the mark? [PeerJ Preprints]

Abstract:  Background. There is wide agreement in the biomedical research community that research data sharing is a primary ingredient for ensuring that science is more transparent and reproducible. Publishers could play an important role in facilitating and enforcing data sharing; however, many journals have not yet implemented data sharing policies and the requirements vary widely across journals. This study set out to analyze the pervasiveness and quality of data sharing policies in the biomedical literature. Methods. The online author’s instructions and editorial policies for 318 biomedical journals were manually reviewed to analyze the journal’s data sharing requirements and characteristics. The data sharing policies were ranked using a rubric to determine if data sharing was required, recommended, required only for omics data, or not addressed at all. The data sharing method and licensing recommendations were examined, as well any mention of reproducibility or similar concepts. The data was analyzed for patterns relating to publishing volume, Journal Impact Factor, and the publishing model (open access or subscription) of each journal. Results. 11.9% of journals analyzed explicitly stated that data sharing was required as a condition of publication. 9.1% of journals required data sharing, but did not state that it would affect publication decisions. 23.3% of journals had a statement encouraging authors to share their data but did not require it. There was no mention of data sharing in 31.8% of journals. Impact factors were significantly higher for journals with the strongest data sharing policies compared to all other data sharing mark categories. Open access journals were not more likely to require data sharing than subscription journals. Discussion. Our study confirmed earlier investigations which observed that only a minority of biomedical journals require data sharing, and a significant association between higher Impact Factors and journals with a data sharing requirement. Moreover, while 65.7% of the journals in our study that required data sharing addressed the concept of reproducibility, as with earlier investigations, we found that most data sharing policies did not provide specific guidance on the practices that ensure data is maximally available and reusable.

Let’s speed up science by embracing open access publishing

“Despite this success story, most scientific research today is not published openly — meaning freely, to everyone, without delay from the time of publication. Instead, it lives behind time embargoes and paywalls, costing as much as $35 per article to access. Even when scientific information is free to read, it is subject to copyright restrictions that prevent it from being recast quickly in new ways.”

NOT-OD-17-015: NIH Request for Information (RFI) on Strategies for NIH Data Management, Sharing, and Citation

“This Request for Information (RFI) seeks public comments on data management and sharing strategies and priorities in order to consider: (1) how digital scientific data generated from NIH-funded research should be managed, and to the fullest extent possible, made publicly available; and, (2) how to set standards for citing shared data and software….”

Preprints: biomedical science publication in the era of twitter and facebook – the Node

“Earlier this week, I took part in a workshop on preprints – organised by Alfonso Martinez-Arias and held in Cambridge, UK. Inspired by the ASAPbio movement in the States, Alfonso felt it would be useful to bring discussion of the potential value of preprints more to the forefront in the UK. Happily, he was able to get John Inglis, co-founder of bioRxiv (the primary preprint server for the life sciences), to speak at this event, and also invited several other speakers  – including myself – to talk about their experiences with preprint servers.”

Penetration of Nigerian predatory biomedical open access journals 2007–2012: a bibliometric study – NWAGWU – 2015 – Learned Publishing – Wiley Online Library

Abstract:  This paper presents the bibliometric characteristics of 32 biomedical open access journals published by Academic Journals and International Research Journals – the two Nigerian publishers in Jeffery Beall’s list of 23 predatory open access publishers in 2012. Data about the journals and the authors of their articles were collected from the websites of the publishers, Google Scholar and Web of Science. As at December 2012, the journals had together produced a total of 5,601 papers written by 5,599 authors, and received 12,596 citations. Authors from Asia accounted for 56.79% of the publications; those from Africa wrote 28.35% while Europe contributed 7.78%. Authors from Africa accounted for 18.25% of the citations these journals received, and this is about one-third the number of citations by authors in Asia (54.62%). At country level, India ranks first in the top 10 citer countries, while Nigeria, the host country of the journals, ranked eighth. More in-depth studies are required to develop further information about the journals such as how much scientific information the journals contain, as well as the science literacy of the authors and the editorial.

MQ researcher says open access keeps medical research honest – Macquarie Library Open Access week

“More broadly, I advocate for publishing research in ways that make it more accessible to the public regardless of whether it is via gold, green, as a pre-print, in an institutional repository, or even shared through social media or peer-to-peer networks. But there are other aspects of “open” in research that impact on research integrity, and this journal is likely to deal with them as it grows.”

PLOS ONE: Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science

Abstract: “One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.”

PLOS ONE: Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science

Abstract: “One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.”