Federating heterogeneous datasets to enhance data sharing and experiment reproducibility

Abstract

Recent studies have demonstrated the difficulties to replicate scientific findings and/or experiments published in past.1 The effects seen in the replicated experiments were smaller than previously reported. Some of the explanations for these findings include the complexity of the experimental design and the pressure on researches to report positive findings. The International Committee of Medical Journal Editors (ICMJE) suggests that every study considered for publication must submit a plan to share the de-identified patient data no later than 6 months after publication. There is a growing demand to enhance the management of clinical data, facilitate data sharing across institutions and also to keep track of the data from previous experiments. The ultimate goal is to assure the reproducibility of experiments in the future. This paper describes Shiny-tooth, a web based application created to improve clinical data acquisition during the clinical trial; data federation of such data as well as morphological data derived from medical images; Currently, this application is being used to store clinical data from an osteoarthritis (OA) study. This work is submitted to the SPIE Biomedical Applications in Molecular, Structural, and Functional Imaging conference.

Incentivising research data sharing: a scoping review

“… Prado & Baranauskas (2016) looks at the effects of data sharing software using actor network theory suggesting that this provides a shared point of contact for numerous actors in the system and has potential to improve data sharing through better collaboration. Prieto et al . (2017) reports on ‘Shiny Tooth’ software designed to capture clinical trial data.…”

“…Prieto et al (2017) reports on 'Shiny Tooth' software designed to capture clinical trial data.Inconclusive, mixed picture, some fields do not have policies in place.…”

A web-based system for neural network based classification in temporomandibular joint osteoarthritis

“…An average 3D condylar shape was generated for the TMJ OA groups and control group using the ShapeVariationAnalyzer (SVA) extension for the 3D Slicer open-source software (Dumast, 2017, Fedorov et al, 2012). In order to compute the group average and group variability, we established correspondence between each of the 1002 vertices in the condylar surface models across all subjects (Prieto et al, 2017). This homology is further optimized via group-wise correspondence (Lyu et al, 2013).…”

“…The collection is the level below the project and is defined by a name and a list of de-identified/encoded patients. The lowest level is each patient document that can contain imaging, clinical and biological data for each project (Prieto et al, 2017).…”