Job Mobilities and Family Lives in Europe - Second Wave: Panel Data Set & Oversampling

Der Methodenbericht beschreibt die Erhebung der zweiten Welle der Studie "Job Mobilities and Family Lives in Europe - Modern Mobile Living and its Relation to Quality of Life" sowie die Struktur der resultierenden Paneldaten. Eine erste Welle wurde im Jahr 2007 in sechs europäischen Ländern durchgeführt: Deutschland, Frankreich, Spanien, Polen, Belgien und der Schweiz. Insgesamt wurden 7.220 zufällig ausgewählte Personen befragt. Die Studie fokussierte auf drei zentrale Aspekte: Erstens, die Verbreitung und Vielfalt berufsbedingter räumlicher Mobilität in Europa, zweitens, die Ursachen und Entstehungszusammenhänge, sowie, drittens, die Konsequenzen von berufsbedingter räumlicher Mobilität für subjektives Wohlbefinden, Familie, Beruf und soziale Beziehungen. Zwischen 2010 und 2012 wurde eine Wiederholungsbefragung unter 1.735 Personen in vier Ländern durchgeführt: Deutschland, Frankreich, Spanien und der Schweiz (Wiederbefragungsquote: 34,5 %). Das Paneldesign eröffnet neue Möglichkeiten durch Längsschnittanalysen und damit tiefere Einblicke in die genannten Forschungsfragen. Diese Möglichkeiten werden zudem ergänzt durch eine retrospektive Erfassung umfangreicher Biographien zu Mobilität, Berufen, Familie und Partnerschaften. Daneben wurden im Rahmen der zweiten Welle neue Befragungsinhalte zu Themen wie soziale Integration, freiwilliges bürgerschaftliches Engagement und soziale Mobilität aufgenommen. Die Datendokumentation bietet eine Beschreibung der erhobenen Formen von Mobilität, der Inhalte des Erhebungsinstruments, der Stichprobengewinnung, der Feldphase, der Panelselektivität sowie der Datengewichtung

Ultrasound in augmented reality: a mixed-methods evaluation of head-mounted displays in image-guided interventions

Purpose: Augmented reality (AR) and head-mounted displays (HMD) in medical practice are current research topics. A commonly proposed use case of AR-HMDs is to display data in image-guided interventions. Although technical feasibility has been thoroughly shown, effects of AR-HMDs on interventions are not yet well researched, hampering clinical applicability. Therefore, the goal of this study is to better understand the benefits and limitations of this technology in ultrasound-guided interventions. Methods: We used an AR-HMD system (based on the first-generation Microsoft Hololens) which overlays live ultrasound images spatially correctly at the location of the ultrasound transducer. We chose ultrasound-guided needle placements as a representative task for image-guided interventions. To examine the effects of the AR-HMD, we used mixed methods and conducted two studies in a lab setting: (1) In a randomized crossover study, we asked participants to place needles into a training model and evaluated task duration and accuracy with the AR-HMD as compared to the standard procedure without visual overlay and (2) in a qualitative study, we analyzed the user experience with AR-HMD using think-aloud protocols during ultrasound examinations and semi-structured interviews after the task. Results: Participants (n = 20) placed needles more accurately (mean error of 7.4 mm vs. 4.9 mm, p = 0.022) but not significantly faster (mean task duration of 74.4 s vs. 66.4 s, p = 0.211) with the AR-HMD. All participants in the qualitative study (n = 6) reported limitations of and unfamiliarity with the AR-HMD, yet all but one also clearly noted benefits and/or that they would like to test the technology in practice. Conclusion: We present additional, though still preliminary, evidence that AR-HMDs provide benefits in image-guided procedures. Our data also contribute insights into potential causes underlying the benefits, such as improved spatial perception. Still, more comprehensive studies are needed to ascertain benefits for clinical applications and to clarify mechanisms underlying these benefits

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Using co-occurrence models for placename disambiguation

This paper describes the generation of a model capturing information on how placenames co-occur together. The advantages of the co-occurrence model over traditional gazetteers are discussed and the problem of placename disambiguation is presented as a case study. We begin by outlining the problem of ambiguous placenames. We demonstrate how analysis of Wikipedia can be used in the generation of a co-occurrence model. The accuracy of our model is compared to a handcrafted ground truth; then we evaluate alternative methods of applying this model to the disambiguation of placenames in free text (using the GeoCLEF evaluation forum). We conclude by showing how the inclusion of placenames in both the text and geographic parts of a query provides the maximum mean average precision and outline the benefits of a co-occurrence model as a data source for the wider field of geographic information retrieval (GIR)

Geographic and textual data fusion in Forostar

In this paper we provide some analysis of data fusion techniques employed at GeoCLEF 2008 to merge textual and geographic relevance. These methods are compared to our own experiments, where using our GIR system, Forostar, we show that an aggressive filter-based data fusion method can outperform a more sophisticated penalisation method

Geographic co-occurrence as a tool for GIR

In this paper we describe the development of a geographic co-occurrence model and how it can be applied to geographic information retrieval. The model consists of mining cooccurrences of placenames from Wikipedia, and then mapping these placenames to locations in the Getty Thesaurus of Geographical Names. We begin by quantifying the accuracy of our model and compute theoretical bounds for the accuracy achievable when applied to placename disambiguation in free text. We conclude with a discussion of the improvement such a model could provide for placename disambiguation and geographic relevance ranking over traditional methods

Forostar: A System for GIR

We detail our methods for generating and applying co-occurrence models for the purpose of placename disambiguation. We explain in detail our use of co-occurrence models for placename disambiguation using a model generated from Wikipedia. The presented system is split into two stages: a batch text & geographic indexer and a real time query engine. Four alternative query constructions and six methods of generating a geographic index are compared. The paper concludes with a full description of future work and ways in which the system could be optimised

GIR experiments with Forostar

In this paper we present Forostar, our GIR system. Forostar augments a traditional IR VSM approach with geographic information. We detail our placename disambiguation and geographic relevance ranking methods, as well as how textual and geographic relevance assessments are combined. The paper concludes with an analysis of our results including significance testing where we show our baseline method, in fact, to be best. Finally we identify weaknesses in our approach and ways in which the system could be optimised and improved