MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses.

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses.

FaceBase 3: analytical tools and FAIR resources for craniofacial and dental research.

The FaceBase Consortium was established by the National Institute of Dental and Craniofacial Research in 2009 as a 'big data' resource for the craniofacial research community. Over the past decade, researchers have deposited hundreds of annotated and curated datasets on both normal and disordered craniofacial development in FaceBase, all freely available to the research community on the FaceBase Hub website. The Hub has developed numerous visualization and analysis tools designed to promote integration of multidisciplinary data while remaining dedicated to the FAIR principles of data management (findability, accessibility, interoperability and reusability) and providing a faceted search infrastructure for locating desired data efficiently.

Toward FAIR Knowledge Turns in Bioinformatics.

Sharing of bioinformatics data within research communities holds the promise of facilitating more rapid discovery, yet the volume of data is growing at a pace exponentially greater than what traditional biocuration can support. We present here an approach that we have used to empower data producing researchers to curate high quality shared data that is ready for reuse and re-analysis.

Experiences with Deriva: An Asset Management Platform for Accelerating eScience.

The pace of discovery in eScience is increasingly dependent on a scientist's ability to acquire, curate, integrate, analyze, and share large and diverse collections of data. It is all too common for investigators to spend inordinate amounts of time developing ad hoc procedures to manage their data. In previous work, we presented Deriva, a Scientific Asset Management System, designed to accelerate data driven discovery.