PurposeTo create an inventory of image processing pipelines of arterial spin labeling (ASL) and list their main features, and to evaluate the capability, flexibility, and ease of use of publicly... Show morePurposeTo create an inventory of image processing pipelines of arterial spin labeling (ASL) and list their main features, and to evaluate the capability, flexibility, and ease of use of publicly available pipelines to guide novice ASL users in selecting their optimal pipeline.MethodsDevelopers self-assessed their pipelines using a questionnaire developed by the Task Force 1.1 of the ISMRM Open Science Initiative for Perfusion Imaging. Additionally, each publicly available pipeline was evaluated by two independent testers with basic ASL experience using a scoring system created for this purpose.ResultsThe developers of 21 pipelines filled the questionnaire. Most pipelines are free for noncommercial use (n = 18) and work with the standard NIfTI (Neuroimaging Informatics Technology Initiative) data format (n = 15). All pipelines can process standard 3D single postlabeling delay pseudo-continuous ASL images and primarily differ in their support of advanced sequences and features. The publicly available pipelines (n = 9) were included in the independent testing, all of them being free for noncommercial use. The pipelines, in general, provided a trade-off between ease of use and flexibility for configuring advanced processing options.ConclusionAlthough most ASL pipelines can process the common ASL data types, only some (namely, ASLPrep, ASLtbx, BASIL/Quantiphyse, ExploreASL, and MRICloud) are well-documented, publicly available, support multiple ASL types, have a user-friendly interface, and can provide a useful starting point for ASL processing. The choice of an optimal pipeline should be driven by specific data to be processed and user experience, and can be guided by the information provided in this ASL inventory. Show less
In this article, we develop a method that uses altmetric data to analyse researchers’ interactions, as a way of mapping the contexts of potential societal impact. In the face of an increasing... Show moreIn this article, we develop a method that uses altmetric data to analyse researchers’ interactions, as a way of mapping the contexts of potential societal impact. In the face of an increasing policy demand for quantitative methodologies to assess societal impact, social media data (altmetrics) have been presented as a potential method to capture broader forms of impact. However, current altmetric indicators were extrapolated from traditional citation approaches and are seen as problematic for assessing societal impact. In contrast, established qualitative methodologies for societal impact assessment are based on interaction approaches. These argue that assessment should focus on mapping the contexts in which engagement among researchers and stakeholders takes place, as a means to understand the pathways to societal impact. Following these approaches, we propose to shift the use of altmetric data towards network analysis of researchers and stakeholders. We carry out two case studies, analysing researchers’ networks with Twitter data. The comparison illustrates the potential of Twitter networks to capture disparate degrees of policy engagement. We propose that this mapping method can be used as an input within broader methodologies in case studies of societal impact assessment. Show less
