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Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources

Posted on by Hans Dillaerts

Authors : Andra Waagmeester,  Martina Kutmon, Anders Riutta, Ryan Miller,  Egon L. Willighagen, Chris T.  Evelo , Alexander R. Pico

The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data.

The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org.

Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries.

In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web.

WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development.

We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web.

URL : Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources

DOI : http://dx.doi.org/10.1371/journal.pcbi.1004989

EN

Open Access, Innovation, and Research Infrastructure

Posted on by Hans Dillaerts

Authors : Benedikt Fecher, Gert G. Wagner

In this article we argue that the current endeavors to achieve open access in scientific
literature require a discussion about innovation in scholarly publishing and research infrastructure.

Drawing on path dependence theory and addressing different open access (OA) models and recent political endeavors, we argue that academia is once again running the risk of outsourcing the organization of its content.

URL : Open Access, Innovation, and Research Infrastructure

Alternative location : http://www.mdpi.com/2304-6775/4/2/17

EN

A Vision for Open Cyber-Scholarly Infrastructures

Posted on by Hans Dillaerts

Author : Costantino Thanos

The characteristics of modern science, i.e., data-intensive, multidisciplinary, open, and heavily dependent on Internet technologies, entail the creation of a linked scholarly record that is online and open.

Instrumental in making this vision happen is the development of the next generation of Open Cyber-Scholarly Infrastructures (OCIs), i.e., enablers of an open, evolvable, and extensible scholarly ecosystem.

The paper delineates the evolving scenario of the modern scholarly record and describes the functionality of future OCIs as well as the radical changes in scholarly practices including new reading, learning, and information-seeking practices enabled by OCIs.

URL : A Vision for Open Cyber-Scholarly Infrastructures

Alternative location : http://www.mdpi.com/2304-6775/4/2/13

EN

ARL Libraries and Research: Correlates of Grant Funding

Posted on by Hans Dillaerts

Author : Ryan P. Womack

While providing the resources and tools that make advanced research possible is a primary mission of academic libraries at large research universities, many other elements also contribute to the success of the research enterprise, such as institutional funding, staffing, labs, and equipment.

This study focuses on U.S. members of the ARL, the Association for Research Libraries. Research success is measured by the total grant funding received by the University, creating an ordered set of categories.

Combining data from the NSF National Center for Science and Engineering Statistics, ARL Statistics, and IPEDS, the primary explanatory factors for research success are examined.

Using linear regression, logistic regression, and the cumulative logit model, the best-fitting models generated by ARL data, NSF data, and the combined data set for both nominal and per capita funding are compared. These models produce the most relevant explanatory variables for research funding, which do not include library-related variables in most cases.

URL : http://arxiv.org/abs/1601.05104

EN

What does the UK public want from academic science communication?

Posted on by Hans Dillaerts

The overall aim of public academic science communication is to engage a non-scientist with a particular field of science and/or research topic, often driven by the expertise of the academic.

An e-survey was designed to provide insight into respondent’s current and future engagement with science communication activities. Respondents provided a wide range of ideas and concerns as to the ‘common practice’ of academic science communication, and whilst they support some of these popular approaches (such as open-door events and science festivals), there are alternatives that may enable wider engagement.

Suggestions of internet-based approaches and digital media were strongly encouraged, and although respondents found merits in methods such as science festivals, limitations such as geography, time and topic of interest were a barrier to engagement for some.

Academics and scientists need to think carefully about how they plan their science communication activities and carry out evaluations, including considering the point of view of the public, as although defaulting to hands-on open door events at their university may seem like the expected standard, it may not be the best way to reach the intended audience.

URL : What does the UK public want from academic science communication?

Alternative location : http://f1000research.com/articles/5-1261/v1

EN

Twittering About Research: A Case Study of the World’s First Twitter Poster Competition

Posted on by Hans Dillaerts

Authors : Edward P. Randviir, Samuel M. Illingworth, Matthew J. Baker, Matthew Cude, Craig E. Banks

The Royal Society of Chemistry held, to our knowledge, the world’s first Twitter conference at 9am on February 5 th, 2015. The conference was a Twitter-only conference, allowing researchers to upload academic posters as tweets, replacing a physical meeting.

This paper reports the details of the event and discusses the outcomes, such as the potential for the use of social media to enhance scientific communication at conferences. In particular, the present work argues that social media outlets such as Twitter broaden audiences, speed up communication, and force clearer and more concise descriptions of a researcher’s work.

The benefits of poster presentations are also discussed in terms of potential knowledge exchange and networking.

This paper serves as a proof-of-concept approach for improving both the public opinion of the poster, and the enhancement of the poster through an innovative online format that some may feel more comfortable with, compared to face-to-face communication.

URL : Twittering About Research: A Case Study of the World’s First Twitter Poster Competition

Alternative location : http://f1000research.com/articles/4-798/v3

EN

The Natural Selection of Bad Science

Posted on by Hans Dillaerts

Authors : Paul E. Smaldino, Richard McElreath

Poor research design and data analysis encourage false-positive findings. Such poor methods persist despite perennial calls for improvement, suggesting that they result from something more than just misunderstanding.

The persistence of poor methods results partly from incentives that favor them, leading to the natural selection of bad science. This dynamic requires no conscious strategizing—no deliberate cheating nor loafing—by scientists, only that publication is a principle factor for career advancement.

Some normative methods of analysis have almost certainly been selected to further publication instead of discovery. In order to improve the culture of science, a shift must be made away from correcting misunderstandings and towards rewarding understanding. We support this argument with empirical evidence and computational modeling.

We first present a 60-year meta-analysis of statistical power in the behavioral sciences and show that power has not improved despite repeated demonstrations of the necessity of increasing power.

To demonstrate the logical consequences of structural incentives, we then present a dynamic model of scientific communities in which competing laboratories investigate novel or previously published hypotheses using culturally transmitted research methods.

As in the real world, successful labs produce more « progeny », such that their methods are more often copied and their students are more likely to start labs of their own.

Selection for high output leads to poorer methods and increasingly high false discovery rates. We additionally show that replication slows but does not stop the process of methodological deterioration. Improving the quality of research requires change at the institutional level.

URL : The Natural Selection of Bad Science

DOI : http://dx.doi.org/10.1098/rsos.160384