This commentary provides a brief history of the U.S. funding initiatives associated with promoting multiscale modeling of the physiome since 2003. An effort led in the United States is the Interagency Modeling and Analysis Group (IMAG) Multiscale Modeling Consortium (MSM). Though IMAG and the MSM have generated much interest in developing MSM models of the physiome, challenges associated with model and data sharing in biomedical, biological and behavioral systems still exist. Since 2013, the IEEE EMBS Technical Committee on Computational Biology and the Physiome (CBaP TC) has supported discussions on promoting model reproducibility through publication. This Special Issue on Model Sharing and Reproducibility is a realization of the CBaP TC discussions. Though open questions remain on how we can further facilitate model reproducibility, accessibility and reuse by the worldwide community for different biomedical domain applications, this special issue provides a unique demonstration of both the challenges and opportunities for publishing reproducible computational models.
The International Working Group on Antibody Validation (IWGAV), an independent group of international scientists with diverse research interests in the field of protein biology, today announced the publication of initial strategies developed to address a critical unmet need for antibody specificity, functionality and reproducibility in the online issue of Nature Methods. The IWGAV is the first initiative of its size and scope to establish strategic recommendations for antibody validation for both antibody producers and users. Thermo Fisher Scientific, the world leader in serving science, provided financial support to the IWGAV in 2015 to spearhead the development of industry standards and help combat the common challenges associated with antibody specificity and reproducibility.
The American Academy of Microbiology convened a colloquium to discuss problems in the biological sciences, with emphasis on identifying mechanisms to improve the quality of research. Participants from various disciplines made six recommendations: (i) design rigorous and comprehensive evaluation criteria to recognize and reward high-quality scientific research; (ii) require universal training in good scientific practices, appropriate statistical usage, and responsible research practices for scientists at all levels, with training content regularly updated and presented by qualified scientists; (iii) establish open data at the timing of publication as the standard operating procedure throughout the scientific enterprise; (iv) encourage scientific journals to publish negative data that meet methodologic standards of quality; (v) agree upon common criteria among scientific journals for retraction of published papers, to provide consistency and transparency; and (vi) strengthen research integrity oversight and training. These recommendations constitute an actionable framework that, in combination, could improve the quality of biological research.
Please join us for a free afternoon of clinical research transparency and reproducibility discussion and learning co-hosted by New York University, Center for Open Science, and AllTrials USA (part of Sense About Science USA).
Molecular Biology of the Cell (MBoC) has developed a checklist for authors to help them ensure that their work can be reproduced by others. In so doing, the journal is mboc logofollowing the recommendations in the 2015 whitepaper by the ASCB Reproducibility Task Force. The checklist was developed by a committee of MBoC Editorial Board members chaired by Editor Jean Schwarzbauer and including Associate Editors Rick Fehon, Carole Parent, Greg Matera, Alex Mogilner, and Fred Chang with input from Editor-in-Chief David Drubin and other members of the board.
Animal models are needed to better understand the relationship between diffusion MRI (dMRI) and the underlying tissue microstructure. One promising model for validation studies is the common squirrel monkey, Saimiri sciureus. This study aims to determine (1) the reproducibility of in vivo diffusion measures both within and between subjects; (2) the agreement between in vivo and ex vivo data acquired from the same specimen and (3) normal diffusion values and their variation across brain regions.