![]() ![]() In an ideal world, when a paper is published, researchers should also be able (and encouraged) to publish the data on which the paper is based, as well as the script showing exactly how those data were analysed. This is reassuring for me, but it doesn’t mean that somebody else, looking at my data with fresh eyes and a different perspective, would not come to an entirely different set of conclusions. Most of the past week has been spent convincing myself that it doesn’t really matter how I analyse my data because the results come out the same regardless. It allows us to extract the information presented visually in the published article, but no further. Years of work and terabytes of data may be compressed into just two or three pages.ĭataThief only takes us so far. There’s an imperative to present the data in a neat, sanitized package, with all the rough edges and anomalies smoothed out to tell a coherent story that will convince reviewers and editors that it’s worthy of publication in a reputable journal. ![]() DATATHIEF TOOL HOW TOThe data reported in a journal article are really just a snapshot of the actual data recorded, filtered through the authors’ preconceptions about what questions are interesting to ask and how to go about doing that. Hardly evidence for fundamentally different neural mechanisms in the two disorders.Īt the risk of sounding like a broken record, this once again highlights the importance of looking at individual variation within diagnostic groups such as autism and SLI, rather than (or as well as) looking at group averages.īut it also emphasizes a more general point (and this I have to stress is no criticism of the authors of this particular paper). Likewise, the overlap between the distributions of the autism and SLI groups is almost complete. On average, kids with SLI have lower than ‘normal’ fractional anisotropy, but looking at the spread of scores, you’d be hard pressed to conclude that this was a characteristic of SLI. On the y-axis is fractional anisotropy (FA) - the imaging measure used to assess the integrity of the left superior longitudinal fasciculus. On the x-axis is performance on a language test. The figure below is a scatterplot with each coloured shape representing a single child. Taken at face value, this is a pretty major blow to the idea that autism and SLI have anything more than a superficial resemblance. However, the same was not true of kids with autism, even though they had poorer language skills than those with SLI. reported that integrity of the superior longitudinal fasciculus was compromised in kids with specific language impairment (SLI) - that is, kids who have language difficulties for no obvious reason. In the study, Judith Verhoeven and colleagues used diffusion tensor MRI to examine the superior longitudinal fasciculus, a bundle of nerve fibres that is assumed (although see this paper) to connect two brain regions involved in language production and comprehension - Broca’s area (left front-ish) with Wernicke’s area (left and back a bit). The other week, for example, I came across an intriguing neuroimaging study reported on the SFARI website. Sometimes, this provides insights that really aren’t obvious from the original paper. Recently, I’ve discovered DataThief - an application that allows you to scan in a graph from a paper and extract the data points. (Figure courtesy Du et al.Having spent much of the past week struggling to make sense of my data, it’s good to come home, pour a glass of wine, put on some Sharon Jones, and, er… play with somebody else’s data! See the examples folder for more information. On this input (NB, you might need to zoom in to see the individual pixels): datathief ( filename, xlim = xlim, ylim = ylim ) ![]() It will warn you if too many or too few pixels are detected.įor example, running this code: import datathief as dt filename = 'du_fig1a_annotated.png' xlim = ylim = data = dt. This function will then return the x and y coordinates of each data point. Then one pixel for each data point you wish to extract (default color: pure green). Do the same for the y-axis (default color: pure red). To use this tool, first annotate the plot by adding a single pixel at the start and end of the x-axis in a specified color that does not exist anywhere else in the image (default color: pure blue). If you want to extract a lot of data, or extract data from a continuous line, you are better off using the original Java DataThief package, or one of the many online tools that do exactly this. However, it might be annoying for a large amount of data. This makes it more transparent how the data are being read and makes the results more reproducible. Unlike the Java DataThief package and similar online tools, here the user manually annotates the figure with the data points of their choosing. Inspired by the Java package of the same name. Small utility for retrieving data from figures. ![]()
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