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There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the "last mile" implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.

Data analysis requires new approaches in many domains for evaluating tools and techniques, particularly when the data sets grow large and more complex. Evaluation-as-service (EaaS) was coined as a term to represent evaluation approaches based on APIs, virtual machines or source code submission, different from the common paradigm of evaluating techniques on a distributed test collection, tasks and submitted results files. Such new approaches become necessary when data sets become extremely large, contain confidential information or might change quickly over time. The workshop on cloud-based evaluation (CBE) took place in Boston, MA, USA on November 5, 2015 and explored several approaches for data analysis evaluation and frameworks in this field. The objective was to include several stakeholders from academic partners, companies to funding agencies to cover various interests and viewpoints in the discussion of evaluation infrastructures. The workshop focused on the biomedical domain but the results are easily applicable to many domains of information analysis and retrieval.

The Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC - www.nitrc.org) suite of services include a resources registry, image repository and a cloud computational environment to meet the needs of the neuroimaging researcher. NITRC provides image-sharing functionality through both the NITRC Resource Registry (NITRC-R), where bulk data files can be released through the file release system (FRS), and the NITRC Image Repository (NITRC-IR), a XNAT-based image data management system. Currently hosting 14 projects, 6845 subjects, and 8285 MRI imaging sessions, NITRC-IR provides a large array of structural, diffusion and resting state MRI data. Designed to be flexible about management of data access policy, NITRC provides a simple, free, NIH-funded service to support resource sharing in general, and image sharing in particular.

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the ‘last mile’ implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.

Information management is critical as the landscape of neuroscience related shared resources (data, software, computation, etc.) expands. Since 2006, the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) has provided a comprehensive support infrastructure for resources in the neuroimaging domain1. Funded by the NIH Blueprint for Neuroscience Research as well as four NIH institutes 2 3, NITRC’s mission is to facilitate finding and comparing neuroimaging resources for neuroimaging analyses. Over the years the scope of these resources has expanded to support scientific domains from MR to PET, SPECT, CT, MEG/EEG, optical imaging, genetic imaging, clinical neuroinformatics and computational neuroscience. A broad set of initiatives have been developed to support these research areas. Early in the course of the development of NITRC, it was realized that a ‘clearinghouse’ of resources was just one component in a desired infrastructure for a complete integration of neuroimaging resources. Specifically, while the original NITRC website—referred to as NITRC-Resources Repository (NITRC-R) facilitated the finding of software and data, there still existed the need for expanding the capacity for data hosting. Furthermore, once a user finds software and data, the current model of downloading each resource to one’s own local computer quickly becomes rate limiting as the magnitude of the shared datasets gets larger and the processing software gets more complex, specific, and CPU intensive. Datasets such as the ‘1000 Functional Connectomes’4, ‘Pediatric Imaging Neurocognition and Genetics (PING)’5 and the ‘Autism Brain Data Exchange (ABIDE)’6, for example, contain thousands of subjects each with structural and resting-state fMRI data for which standard processing can take days per subject. Such examples quickly outstrip the local analysis capability of many investigators and thus motivate the development of additional solutions. To address these challenges, we embarked upon the creation of the NITRC Image Registry (NITRC-IR) to facilitate a data sharing solution that was closely integrated with the resource description, support, promotion, and management functions provided by NITRC-R for its hosted projects. We also embarked upon the development of the NITRC Computational Environment (NITRC-CE), a cloud-based, dynamic, high-performance, and easy-to-use computational plaform that could be tailored to the computational needs of the NITRC community..

The Neuroimaging Informatics Tools and Resources Clearinghouse, NITRC is a newly established Web site for organizing knowledge about the resources publicly available functional MRI and related structural imaging analysis. Based on GForge, it provides a common environment for downloading, discussion, education, rating, and documentation for a growing number of resources. Its design and current status is presented.

We report a new Web-based resource (LAMHDI) integrating information about animal models for human disease research. This NCRR supported resource provides a simple interface to existing animal model information for the biomedical research community. It provides more efficient access to animal model information, better categorizes and organizes existing data, facilitates interactions between researchers and resource providers, and promotes broader and more applicable use of animal models for human disease research. Approach Phase 1: A first-generation prototype is available at www.LAMHDI.org. This portal provides searchable access to existing mouse and zebrafish databases through an integrated search engine, as well as linked access to information about a variety of other species including nonhuman primates and rats. The portal also allows researchers to contribute data on animal models, thereby extending the impact of existing information through direct posting by the research community. Phase 2: The second generation will add extensive new information for additional animal model species and will increase the depth and connectivity of the data through development of semantic datastores. A sophisticated software architecture will support intelligent relational searches across diverse datasets to highlight associations between diseases, animal models, and biologic pathways.