869 research outputs found
Data-Driven Reporting - an On-Going (R)Evolution? A Longitudinal Analysis of Projects Nominated for the Data Journalism Awards 2013-2015
The emergence of data-driven journalism (DDJ) can be understood as journalism’s response to the datafication of society. We retrace the development of this emerging reporting style by looking at what may be considered the gold‐standard in data‐driven reporting: projects that were nominated for the Data Journalism Awards (DJA), a prize issued annually by the Global Editors Network. Using a content analysis of the nominees from 2013 to 2015 (n=179) we examine if and how, among other aspects, data sources and types, visualisation strategies, interactive features, topics, and types of nominated media outlets have changed over the years. Results suggest, for instance, that the set of structural elements data‐driven pieces are built upon remains rather stable, that data journalism is increasingly personnel intensive and progressively spreading around the globe, and that journalists, while still concentrating on data from official institutions, are increasingly looking to unofficial datasources for their stories
Інтеграція та диференціація: їх роль у вдосконаленні сучасної системи екологічного управління за законодавством України
Iagttagelser paa en Reise i Udlandet i Decbr. 1859 og Januar 1860.
Iagttagelser paa en Reise i Udlandet i Decbr. 1859 og Januar 1860
Journalism and participatory practices – Blurring or reinforcement of boundaries between journalism and audiences?
Loopbaansmotivering, houdings en politieke denkpatrone by lede van die Suid-Afrikaanse Kaapse Korps
Thesis (MA)--Stellenbosch University, 1980.No abstract available
Quantification of Arm Swing during Walking in Healthy Adults and Parkinson's Disease Patients: Wearable Sensor-Based Algorithm Development and Validation
Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson's disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from -0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson's disease
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