| 1. |
- Bock, O., et al.
(författare)
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Use of GNSS Tropospheric Products for Climate Monitoring (Working Group 3)
- 2020
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Ingår i: Advanced GNSS Tropospheric Products for Monitoring Severe Weather Events and Climate. - Cham : Springer International Publishing. - 9783030139001 ; , s. 267-402
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- There has been growing interest in recent years in the use of homogeneously reprocessed ground-based GNSS, VLBI, and DORIS measurements for climate applications. Existing datasets are reviewed and the sensitivity of tropospheric estimates to the processing details is discussed. The uncertainty in the derived IWV estimates and linear trends is around 1 kg m^2 RMS and ± 0.3 kg m^2 per decade, respectively. Standardized methods for ZTD outlier detection and IWV conversion are proposed. The homogeneity of final time series is limited however by changes in the stations equipment and environment. Various homogenization algorithms have been evaluated based on a synthetic benchmark dataset. The uncertainty of trends estimated from the homogenized times series is estimated to ±0.5 kg m^2 per decade. Reprocessed GNSS IWV data are analysed along with satellites data, reanalyses and global and regional climate model simulations. A selection of global and regional reprocessed GNSS datasets and ERA-interim reanalysis are made available through the GOP-TropDB tropospheric database and online service. A new tropo SINEX format, providing new features and simplifications, was developed and it is going to be adopted by all the IAG services.
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| 2. |
- Hege, Inga, et al.
(författare)
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Developing a European longitudinal and interprofessional curriculum for clinical reasoning
- 2023
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Ingår i: Diagnosis. - : Walter de Gruyter GmbH. - 2194-8011 .- 2194-802X.
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Tidskriftsartikel (refereegranskat)abstract
- Clinical reasoning is a complex and crucial ability health professions students need to acquire during their education. Despite its importance, explicit clinical reasoning teaching is not yet implemented in most health professions educational programs. Therefore, we carried out an international and interprofessional project to plan and develop a clinical reasoning curriculum with a train-the-trainer course to support educators in teaching this curriculum to students. We developed a framework and curricular blueprint. Then we created 25 student and 7 train-the-trainer learning units and we piloted 11 of these learning units at our institutions. Learners and faculty reported high satisfaction and they also provided helpful suggestions for improvements. One of the main challenges we faced was the heterogeneous understanding of clinical reasoning within and across professions. However, we learned from each other while discussing these different views and perspectives on clinical reasoning and were able to come to a shared understanding as the basis for developing the curriculum. Our curriculum fills an important gap in the availability of explicit clinical reasoning educational materials both for students and faculty and is unique with having specialists from different countries, schools, and professions. Faculty time and time for teaching clinical reasoning in existing curricula remain important barriers for implementation of clinical reasoning teaching.
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| 3. |
- Vomberg, Arnd, et al.
(författare)
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The cold-start problem in nascent AI strategy : Kickstarting data network effects
- 2023
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Ingår i: Journal of Business Research. - : Elsevier. - 0148-2963 .- 1873-7978. ; 168, s. 114236-114236
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Tidskriftsartikel (refereegranskat)abstract
- While many artificial intelligence (AI) strategies are successful, countless others fail. Why do some strategies succeed while others fail? We adopt a network effects (NEs) perspective to conceptualize AI strategies, highlighting the AI context’s specifics. We argue that nascent AI strategies’ success depends on data NEs: companies establishing a functional “running system” to capitalize on these effects. However, this presents a challenge known as the cold-start problem (CSP), which involves initiating and accelerating a virtuous cycle: more data benefits the AI system, enhancing performance, which then attracts more data. In this paper, we examine the CSP in nascent AI strategy, exploring how it can be understood in terms of its technological and business dimensions and ultimately be overcome to kick-start a virtuous cycle of data NEs. By drawing insights from existing literature and practitioner interviews, we present a research agenda to encourage further investigation into overcoming the CSP.
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