14 research outputs found

    Regionally aggregated, stitched and de‐drifted CMIP‐climate data, processed with netCDF‐SCM v2.0.0

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    The world's most complex climate models are currently running a range of experiments as part of the Sixth Coupled Model Intercomparison Project (CMIP6). Added to the output from the Fifth Coupled Model Intercomparison Project (CMIP5), the total data volume will be in the order of 20PB. Here, we present a dataset of annual, monthly, global, hemispheric and land/ocean means derived from a selection of experiments of key interest to climate data analysts and reduced complexity climate modellers. The derived dataset is a key part of validating, calibrating and developing reduced complexity climate models against the behaviour of more physically complete models. In addition to its use for reduced complexity climate modellers, we aim to make our data accessible to other research communities. We facilitate this in a number of ways. Firstly, given the focus on annual, monthly, global, hemispheric and land/ocean mean quantities, our dataset is orders of magnitude smaller than the source data and hence does not require specialized ‘big data’ expertise. Secondly, again because of its smaller size, we are able to offer our dataset in a text-based format, greatly reducing the computational expertise required to work with CMIP output. Thirdly, we enable data provenance and integrity control by tracking all source metadata and providing tools which check whether a dataset has been retracted, that is identified as erroneous. The resulting dataset is updated as new CMIP6 results become available and we provide a stable access point to allow automated downloads. Along with our accompanying website (cmip6.science.unimelb.edu.au), we believe this dataset provides a unique community resource, as well as allowing non-specialists to access CMIP data in a new, user-friendly way

    A beginning exploration of text generation abilities in university students with a history of reading difficulties

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    There is a fundamental lack of understanding of how university students with a history of reading difficulties perform on various demanding literacy tasks. We compared the text generation skills, measured with timed summary writing and proofreading tasks, of university students with a history of reading difficulties to those of students with no such history. We further examined whether between-group differences in text generation skills remained after controlling for transcription skills (spelling and handwriting fluency), word reading, and reading comprehension. Forty-six university students with a history of reading difficulties were matched on age, gender, and non-verbal intelligence to 46 students without this history. We found that the students with a history of reading difficulties performed poorer on both measures of text generation than students without this history. When differences in transcription skills, word reading, and reading comprehension were controlled, we found that only differences in timed summary writing remained significant. These results suggest that students with a history of reading difficulties experience challenges with specific aspects of text generation that are beyond what one would expect from their difficulties with transcription and word reading. We suggest that, if not addressed, text generation deficits are likely to create obstacles for academic success
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