Big data from small data: data-sharing in the 'long tail' of neuroscience

Abstract

The launch of the US BRAIN and European Human Brain Projects coincides with growing international efforts toward transparency and increased access to publicly funded research in the neurosciences. The need for data-sharing standards and neuroinformatics infrastructure is more pressing than ever. However, ‘big science’ efforts are not the only drivers of data-sharing needs, as neuroscientists across the full spectrum of research grapple with the overwhelming volume of data being generated daily and a scientific environment that is increasingly focused on collaboration. In this commentary, we consider the issue of sharing of the richly diverse and heterogeneous small data sets produced by individual neuroscientists, so-called long-tail data. We consider the utility of these data, the diversity of repositories and options available for sharing such data, and emerging best practices. We provide use cases in which aggregating and mining diverse long-tail data convert numerous small data sources into big data for improved knowledge about neuroscience-related disorders.

The Neotoma Paleoecology Database, a multiproxy, international, community-curated data resource

“…Neotoma is part of the emerging field of paleoecoinformatics (Brewer et al, 2012), which itself contributes to larger efforts in bioinformatics and geoinformatics to overcome bottlenecks associated with data access, mobilize dark data, and maximize the power of scientific data collected by networks of researchers (Heidorn, 2008;Howe et al, 2008;Lynch, 2008;Hampton et al, 2013;Ferguson et al, 2014). Related efforts include the Paleobiology Database (https://paleobiodb.org/#/), the International Tree Ring Databank (https://www.ncdc.noaa.…”

Ethical Challenges of Simulation-Driven Big Neuroscience

“…One example concerns the re-use of so-called "long-tail" data, small data sets whose reuse is often stymied by a lack of community data-sharing standards (Ferguson et al 2014 In summary, models, simulations and large-scale ICT infrastructures acquire new functions within neuroscience as instruments to integrate systemic biological knowledge, gained on all levels of neuronal organization. Indeed, it is hard to imagine how neuroscience would be able to address the most difficult questions of its field -e.g., understanding how changes on all levels of neuronal organization affect behavior or how to address neurodegenerative diseases -without an integrative perspective that focuses not only on the processes in each level or organization, but also on the interplay of levels -an insight that in social neuroscience (and other fields) has led to the notion of "multi-level analysis" (Cacioppo & Decety 2011).…”

An Ecosystem for Linked Humanities Data

“…We argue that current efforts to increase the availability and accessibility of this data do not suffice. They do not cater for the "long tail of research data" [8], the large volumes of small datasets produced by individual researchers; and existing Linked Data tooling is too technology-oriented to be suitable for humanities researchers at large. This paper presents QBer and the underlying CLARIAH Structured Data Hub (CSDH), 8 whose aim is to address the limitations of current data-publishing practice in the digital humanities, and socio-economic history in particular.…”