Document Type

Article

Publication Date

4-1-2018

Publication Title

PLoS Computational Biology

ISSN

1553-7358

Volume

14

Issue

4

First Page

1006038

Last Page

1006038

DOI

http://dx.doi.org/10.1371/journal.pcbi.1006038

PubMed ID

29649206

Abstract

Over the past decades, science has experienced rapid growth in the volume of data available for research—from a relative paucity of data in many areas to what has been recently described as a data deluge [‎1]. Data volumes have increased exponentially across all fields of science and human endeavour, including data from sky, earth, and ocean observatories; social media such as Facebook and Twitter; wearable health-monitoring devices; gene sequences and protein structures; and climate simulations [‎2]. This brings opportunities to enable more research, especially cross-disciplinary research that could not be done before. However, it also introduces challenges in managing, describing, and making data findable, accessible, interoperable, and reusable by researchers [‎3].

When this vast amount and variety of data is made available, finding relevant data to meet a research need is increasingly a challenge. In the past, when data were relatively sparse, researchers discovered existing data by searching literature, attending conferences, and asking colleagues. In today’s data-rich environment, with accompanying advances in computational and networking technologies, researchers increasingly conduct web searches to find research data. The success of such searches varies greatly and depends to a large degree on the expertise of the person looking for data, the tools used, and, partially, on luck. This article offers the following 11 quick tips that researchers can follow to more effectively and precisely discover data that meet their specific needs.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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