Social media are fundamentally changing core practices in various industries. Although surveys indicate that social media are impacting social scientists, we know little about how education scholars, specifically, use social media for their work or professional learning. This article explores how educational scholars incorporated the social media, Twitter, as a conference backchannel. Using qualitative interview data collected from members of the American Educational Research Association (AERA) and considering previous analysis of AERA conference tweet data, we describe participants' purposes and practices and their perceptions of how using this social media impacts participation in the conference community. We discuss implications for those concerned with research dissemination, faculty professional development, and academic identity.

As new technologies shape and are shaped by human practices, educators and researchers must consider the impact that participating in social media—to access, reflect upon, question, evaluate and disseminate scholarship—is having on their professional development and practices. This paper investigates how members of the educational research community use social media to advance professional learning and scholarship dissemination in online–offline networks. Specifically, we examine whether and how participating in the microblogging service, Twitter, as a conference backchannel, facilitated professional learning and participation in the annual meetings of American educational researchers in 2012 and 2016, respectively, and the nature of that participation. Insights from this paper will benefit educators of varying disciplines and experience levels interested in the changing nature of social media in education, scholarship, and professional learning ecologies.

Motivation One central goal of systems biology is to infer biochemical regulations from large-scale OMICS data. Many aspects of cellular physiology and organismal phenotypes can be understood as results of metabolic interaction network dynamics. Previously, we have proposed a convenient mathematical method which addresses this problem using metabolomics data for the inverse calculation of biochemical Jacobian matrices revealing regulatory checkpoints of biochemical regulations. The proposed algorithms for this inference are limited by two issues: they rely on structural network information that needs to be assembled manually, and they are numerically unstable due to ill-conditioned regression problems for large-scale metabolic networks. Results To address these problems we developed a novel regression-loss based inverse Jacobian algorithm, combining metabolomics COVariance and genome-scale metabolic RECONstruction, which allows for a fully automated, algorithmic implementation of the COVRECON workflow. It consists of two parts: a, Sim-Network and b, Inverse differential Jacobian evaluation. Sim-Network automatically generates an organism-specific enzyme and reaction dataset from Bigg and KEGG databases, which is then used to reconstruct the Jacobian’s structure for a specific metabolomics dataset. Instead of directly solving a regression problem as in the previous workflow, the new inverse differential Jacobian is based on a substantially more robust approach and rates the biochemical interactions according to their relevance from large-scale metabolomics data. The approach is illustrated by in silico stochastic analysis with differently-sized metabolic networks from the BioModels database and applied to a real-world example. The characteristics of the COVRECON implementation are that i) it automatically reconstructs a data-driven superpathway model; ii) more general network structures can be investigated and iii) the new inverse algorithm improves stability, decreases computation time, and extends to large-scale models. Availability The code is available in the website https://bitbucket.org/mosys-univie/covrecon. Supplementary information Supplementary data are available at Bioinformatics online.

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Considering variations is essential for the development of robust products, but the applicability of existing robust design approaches in early stages is challenging due to the lack of product information and high levels of abstraction. To overcome this, a combined model is presented, which enables a holistic robustness evaluation in a linked approach. This approach uses the contact and channel approach to identify the relations between embodiment and functions as well as the robustness evaluation based on tolerance graphs. The combined model is implemented with the Systems Modeling Language (SysML) and applied to a coining machine use case. An initial assessment of the model combination and a proposal for a methodically supported workflow for the holistic robustness evaluation is given.