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research-article2017 AUT0010.1177/1362361317692950AutismEditorial Editorial Toward a more collaborative research culture: Extending translational science from research to community and back again Rigorous research addressing complicated questions tradi- tionally requires controlled and complex scientific infra- structure with a strong emphasis on internal validity. This research often is conducted uni-directionally (from researcher to communities) and yields results years after the study starts (Bohland et al., 2009; Fiks et al., 2015; Landsverk et al., 2011). In many ways, this method is anti- thetical to rapid discoveries that are valid and meaningful for families and other community stakeholders (Pellicano et al., 2014; Stadnick et al., 2013). Autism research, like other areas of science, has typi- cally followed this traditional, unidirectional research pipe- line from basic science to intervention development, then to efficacy research, and finally to attempts at dissemina- tion and implementation (see the black shapes in Figure 1 adapted from Landsverk et al., 2011). This often slow pro- cess has led community stakeholders to criticize scientists for conducting fragmented research that is disconnected from the community needs. This criticism has led to recom- mendations for a more comprehensive research plan that integrates stakeholders at all phases of inquiry to ensure goals from basic science to implementation stages are uni- fied and meaningful to the community. Specifically, there have been calls for bi-directional knowledge exchange that involves active collaboration and partnership between researchers and community stakeholders at both basic and applied levels (Addis, 2002; Beutler et al., 1995; Wells et al., 2004). One promising method of ensuring the rele- vance of research to all stakeholders, expediting translation to community settings, and increasing innovation at all lev- els of science is through the use of participatory or collabo- rative models between researchers and stakeholders (Brookman-Frazee et al., 2012a). Treatment researchers in autism spectrum disorder (ASD) have begun to use community-based participatory research strategies to facilitate effective use of evidence- based interventions in community service settings (Brookman-Frazee et al., 2015; Drahota et al., 2016). Examples of this type of research can be seen in early inter- vention (Stahmer et al., 2016), schools (Locke et al., 2014; Mandell, 2016; Stahmer et al., 2012), and community men- tal and behavioral health settings (Brookman-Frazee et al., 2012a; Drahota et al., 2012). These projects have relied on bi-directional collaboration between applied researchers Autism 2017, Vol. 21(3) 259­–261 © The Author(s) 2017 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav https://doi.org/10.1177/1362361317692950 DOI: journals.sagepub.com/home/aut Figure 1. Traditional research pipeline with recommended stakeholder collaboration. and community stakeholders to adapt intervention and training methods to increase effective implementation and sustainment of evidence-based strategies in the community. By utilizing methods that facilitate the collaboration between community and academic partners (Drahota et al., 2016), these projects have been highly productive in terms of building community capacity for effective services and improving clinical outcomes. However, these methods have, thus far, been limited primarily to intervention trans- lation and have not been used further up the research pipe- line in basic research. Some first steps are being made in animal model research, where basic researchers have started to collabo- rate with clinical experts in autism to develop mouse behavioral assays relevant to human behavior (Crawley, 2007). Similarly, basic science results are being interpreted for clinical relevance (Kim et al., 2016). This work has been conducted with applied researchers translating rele- vant clinical information for basic scientists. Some early collaborative efforts have also led to promising methods of measuring child-level brain-related outcomes that may offer innovative ways to detect intervention effects that may not be detected by standard behavioral measurements