Two main theories of visual word recognition have been developed regarding the way orthographic units in printed words map onto phonological units in spoken words. One theory suggests that a string of single letters or letter clusters corresponds to a string of phonemes (Coltheart, 1978; Venezky, 1970), while the other suggests that a string of single letters or letter clusters corresponds to coarser phonological units, for example, onsets and rimes (Treiman & Chafetz, 1987). These theoretical assumptions were critical for the development of coding schemes in prominent computational models of word recognition and reading aloud. In a reading-aloud study, we tested whether the human reading system represents the orthographic/phonological onset of printed words and nonwords as single units or as separate letters/phonemes. Our results, which favored a letter and not an onset-coding scheme, were successfully simulated by the dual-route cascaded (DRC) model (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001). A separate experiment was carried out to further adjudicate between 2 versions of the DRC model.
The second decade of the twenty-first century witnessed a new challenge in the handling of microscopy data. Big data, data deluge, large data, data compliance, data analytics, data integrity, data interoperability, data retention and data lifecycle are terms that have introduced themselves to the electron microscopy sciences. This is largely attributed to the booming development of new microscopy hardware tools. As a result, large digital image files with an average size of one terabyte within one single acquisition session is not uncommon nowadays, especially in the field of cryogenic electron microscopy. This brings along numerous challenges in data transfer, compute and management. In this review, we will discuss in detail the current state of international knowledge on big data in contemporary electron microscopy and how big data can be transferred, computed and managed efficiently and sustainably. Workflows, solutions, approaches and suggestions will be provided, with the example of the latest experiences in Australia. Finally, important principles such as data integrity, data lifetime and the FAIR and CARE principles will be considered.
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