DATA ORIGINAL ARTICLE Published online: 9 July 2013 # Springer Science+Business Media New York 2013 Abstract The multi-scan resting state fMRI (rs-fMRI) dataset was recently released; thus the test-retest (TRT) reliability of rs-fMRI measures can be assessed. However, because this dataset was acquired only from a single group under a single condition, we cannot directly evaluate whether the rs-fMRI measures can generate reproducible between-condition or between-group results. Because the modulation of resting state activity has gained increasing attention, it is important to know whether one rs-fMRI metric can reliably detect the alteration of the resting activity. Here, we shared a public Eyes-Open (EO)/Eyes-Closed (EC) dataset for evaluating the split-half reproducibility of the rs-fMRI measures in detecting changes of the resting state activity between EO and EC. As examples, we assessed the split-half reproduc- ibility of three widely applied rs-fMRI metrics: ampli- tude of low frequency fluctuation, regional homogeneity, and seed-based correlation analysis. Our results demon- strated that reproducible patterns of EO-EC differences can be detected by all three measures, suggesting the feasibility of the EO/EC dataset for performing repro- ducibility assessment for other rs-fMRI measures. Keywords Resting state fMRI measures . Reproducibility . Data sharing . Eyes open . Eyes closed Introduction Low frequency fluctuations (LFFs) of functional magnetic resonance imaging (fMRI) signal during the resting state are found to be temporally correlated across functionally related brain regions (Biswal et al. 1995; Lowe et al. 1998; Cordes et al. 2000; Greicius et al. 2003; Fox et al. 2006; Di Martino et al. 2008); these findings imply the organization of the resting human brain. Apart from functional connectivity, other attributes of resting state brain activity have been explored by various computational techniques (see Cole et al., 2010; Margulies et al. 2010; Zhang and Raichle 2010 for reviews). In recent years, there has been interest in the test-retest (TRT) reliability of these resting state fMRI (rs-fMRI) metrics. Sev- eral rs-fMRI metrics, such as seed-based function connectivity (Biswal et al. 1995; Lowe et al. 1998; Cordes et al. 2000), amplitude of LFF (ALFF, Zang et al. 2007), independent component analysis (ICA, Beckmann et al. 2005), were found to have moderate to high TRT reliability (Shehzad et al. 2009; Zuo et al. 2010a, b). Although good TRT reliability indicates that the rs-fMRI metrics are stable across repeated scans, the multi-scan dataset (Shehzad et al. 2009) used for such reliability assess- ment was acquired from a single group (healthy subjects) under a single condition. Thus we cannot obtain sufficient information about whether these rs-fMRI metrics can gener- ate reproducible results when examining the modulation of resting state activity by diseases (Salvador et al. 2007; Zang et al. 2007; Lai et al. 2010; Wang et al. 2010) or by the subjects’ experiences (Albert et al. 2009; Barnes et al. 2009; Wang et al. 2009; Tambini et al. 2010). In this paper, we shared an Eyes-Open (EO)/Eyes-Closed (EC) rs-fMRI dataset, in which both the continuous EO and EC rs-fMRI Electronic supplementary material The online version of this article (doi:10.1007/s12021-013-9187-0) contains supplementary material, which is available to authorized users. D. Liu (*) : J. Wang : Y. Zang Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 310015, China e-mail: charlesliu116@gmail.com Z. Dong : J. Wang : Y. Zang National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China X. Zuo Laboratory for Functional Connectome and Development, Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China Neuroinform (2013) 11:469–476 DOI 10.1007/s12021-013-9187-0 Eyes-Open/Eyes-Closed Dataset Sharing for Reproducibility Evaluation of Resting State fMRI Data Analysis Methods Dongqiang Liu & Zhangye Dong & Xinian Zuo & Jue Wang & Yufeng Zang