FAIRly big: A framework for computationally reproducible processing of large-scale data.

Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the framework's performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).

A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation.

Here we present an update of the studyforrest (http://studyforrest.org) dataset that complements the previously released functional magnetic resonance imaging (fMRI) data for natural language processing with a new two-hour 3 Tesla fMRI acquisition while 15 of the original participants were shown an audio-visual version of the stimulus motion picture. We demonstrate with two validation analyses that these new data support modeling specific properties of the complex natural stimulus, as well as a substantial within-subject BOLD response congruency in brain areas related to the processing of auditory inputs, speech, and narrative when compared to the existing fMRI data for audio-only stimulation. In addition, we provide participants' eye gaze location as recorded simultaneously with fMRI, and an additional sample of 15 control participants whose eye gaze trajectories for the entire movie were recorded in a lab setting-to enable studies on attentional processes and comparative investigations on the potential impact of the stimulation setting on these processes.

Macrophage migration inhibitory factor: a central regulator of wound healing.

Age-associated differences in estrogen levels critically modify the cutaneous wound healing response. Using a microarray-based approach, we profiled changes in gene expression within the wounds of mice that were wild type or null for the pro-inflammatory cytokine macrophage migration inhibitory factor (MIF) in the presence or absence of estrogen. This experimental design identified more than 600 differentially expressed genes and established MIF as a key player in the wound healing process, regulating many novel repair/inflammation-associated gene targets. Moreover, MIF affected virtually all of the effects of reduced estrogen on wound repair. In humans, serum and wound levels of MIF increased with age and were strongly down-regulated by estrogen in vivo. Estrogen-regulated MIF transcription in vitro via a nuclear factor kappaB-dependent mechanism. These findings have wide-ranging implications for the many pathophysiological states in which MIF plays an important regulatory role and suggest a potential therapeutic role for MIF in modulating clinical conditions associated with age-related decline in estrogen levels.