Community-driven governance of FAIRness assessment: an open issue, an open discussion.

Although FAIR Research Data Principles are targeted at and implemented by different communities, research disciplines, and research stakeholders (data stewards, curators, etc.), there is no conclusive way to determine the level of FAIRness intended or required to make research artefacts (including, but not limited to, research data) Findable, Accessible, Interoperable, and Reusable. The FAIR Principles cover all types of digital objects, metadata, and infrastructures. However, they focus their narrative on data features that support their reusability. FAIR defines principles, not standards, and therefore they do not propose a mechanism to achieve the behaviours they describe in an attempt to be technology/implementation neutral. Various FAIR assessment metrics and tools have been designed to measure FAIRness. Unfortunately, the same digital objects assessed by different tools often exhibit widely different outcomes because of these independent interpretations of FAIR. This results in confusion among the publishers, the funders, and the users of digital research objects. Moreover, in the absence of a standard and transparent definition of what constitutes FAIR behaviours, there is a temptation to define existing approaches as being FAIR-compliant rather than having FAIR define the expected behaviours. This whitepaper identifies three high-level stakeholder categories -FAIR decision and policymakers, FAIR custodians, and FAIR practitioners - and provides examples outlining specific stakeholders' (hypothetical but anticipated) needs. It also examines possible models for governance based on the existing peer efforts, standardisation bodies, and other ways to acknowledge specifications and potential benefits. This whitepaper can serve as a starting point to foster an open discussion around FAIRness governance and the mechanism(s) that could be used to implement it, to be trusted, broadly representative, appropriately scoped, and sustainable. We invite engagement in this conversation in an open Google Group fair-assessment-governance@googlegroups.com.

Image analysis of time-lapse movies--a precision control guided approach to correct motion artefacts.

In long-term time-lapse studies of cell migration, it is often important to distinguish active movement of individual cells from global tissue motion caused, for instance, by morphogenetic changes, or due to artefacts. We have developed a method to define and correct global movements. This is realized by the sequential morphing of image sequences to the initial image based on the position of immobile reference objects. Technically, the approach is implemented in ImageJ, using the plugin UnwarpJ. We describe an efficient way to select parameter settings such as to optimize image correction. To this end, we implemented a strict statistical control that allows to quantify image registration quality. We document this approach using a time-lapse sequence of migrating interneurons in slice cultures of the developing cerebellum.

Systems Biology Approaches to the Study of Biological Networks Underlying Alzheimer's Disease: Role of miRNAs.

MicroRNAs (miRNAs) are emerging as significant regulators of mRNA complexity in the human central nervous system (CNS) thereby controlling distinct gene expression profiles in a spatio-temporal manner during development, neuronal plasticity, aging and (age-related) neurodegeneration, including Alzheimer's disease (AD). Increasing effort is expended towards dissecting and deciphering the molecular and genetic mechanisms of neurobiological and pathological functions of these brain-enriched miRNAs. Along these lines, recent data pinpoint distinct miRNAs and miRNA networks being linked to APP splicing, processing and Aß pathology (Lukiw et al., Front Genet 3:327, 2013), and furthermore, to the regulation of tau and its cellular subnetworks (Lau et al., EMBO Mol Med 5:1613, 2013), altogether underlying the onset and propagation of Alzheimer's disease. MicroRNA profiling studies in Alzheimer's disease suffer from poor consensus which is an acknowledged concern in the field, and constitutes one of the current technical challenges. Hence, a strong demand for experimental and computational systems biology approaches arises, to incorporate and integrate distinct levels of information and scientific knowledge into a complex system of miRNA networks in the context of the transcriptome, proteome and metabolome in a given cellular environment. Here, we will discuss the state-of-the-art technologies and computational approaches on hand that may lead to a deeper understanding of the complex biological networks underlying the pathogenesis of Alzheimer's disease.