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Träfflista för sökning "WFRF:(Menzel Uwe) srt2:(2020-2022)"

Sökning: WFRF:(Menzel Uwe) > (2020-2022)

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  • Heinze, Stanley, et al. (författare)
  • A unified platform to manage, share, and archive morphological and functional data in insect neuroscience
  • 2021
  • Ingår i: eLife. - 2050-084X. ; 10
  • Tidskriftsartikel (refereegranskat)abstract
    • Insect neuroscience generates vast amounts of highly diverse data, of which only a small fraction are findable, accessible and reusable. To promote an open data culture, we have therefore developed the InsectBrainDatabase (IBdb), a free online platform for insect neuroanatomical and functional data. The IBdb facilitates biological insight by enabling effective cross-species comparisons, by linking neural structure with function, and by serving as general information hub for insect neuroscience. The IBdb allows users to not only effectively locate and visualize data, but to make them widely available for easy, automated reuse via an application programming interface. A unique private mode of the database expands the IBdb functionality beyond public data deposition, additionally providing the means for managing, visualizing, and sharing of unpublished data. This dual function creates an incentive for data contribution early in data management workflows and eliminates the additional effort normally associated with publicly depositing research data.
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3.
  • van Zoest, Vera, et al. (författare)
  • Spatio-temporal predictions of COVID-19 test positivity in Uppsala County, Sweden : a comparative approach
  • 2022
  • Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 12:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Previous spatio-temporal COVID-19 prediction models have focused on the prediction of subsequent number of cases, and have shown varying accuracy and lack of high geographical resolution. We aimed to predict trends in COVID-19 test positivity, an important marker for planning local testing capacity and accessibility. We included a full year of information (June 29, 2020-July 4, 2021) with both direct and indirect indicators of transmission, e.g. mobility data, number of calls to the national healthcare advice line and vaccination coverage from Uppsala County, Sweden, as potential predictors. We developed four models for a 1-week-window, based on gradient boosting (GB), random forest (RF), autoregressive integrated moving average (ARIMA) and integrated nested laplace approximations (INLA). Three of the models (GB, RF and INLA) outperformed the naïve baseline model after data from a full pandemic wave became available and demonstrated moderate accuracy. An ensemble model of these three models slightly improved the average root mean square error to 0.039 compared to 0.040 for GB, RF and INLA, 0.055 for ARIMA and 0.046 for the naïve model. Our findings indicate that the collection of a wide variety of data can contribute to spatio-temporal predictions of COVID-19 test positivity.
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