Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention towards the computational interpretation of visual data. When analyzing large numbers of images, both traditional content analysis as well as cultural analytics have proven valuable. However, these techniques do not take into account the circulation and contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by networked publics, bound through networked practices, these visuals should be analyzed on the level of their networked contextualization. Although machine vision is increasingly adept at recognizing faces and features, its performance in grasping the meaning of social media images is limited. However, combining automated analyses of images - broken down by their compositional elements - with repurposing platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This paper explores the capacities of platform data - hashtag modularity and retweet counts - to complement the automated assessment of social media images; doing justice to both the visual elements of an image and the contextual elements encoded by networked publics that co-create meaning.
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Background: Due to multimorbidity and geriatric problems, older people often require both psychosocial and medical care. Collaboration between medical and social professionals is a prerequisite to deliver high-quality care for community-living older people. Effective, safe, and person-centered care relies on skilled interprofessional collaboration and practice. Little is known about interprofessional education to increase interprofessional collaboration in practice (IPCP) in the context of community care for older people. This study examines the feasibility of the implementation of an IPCP program in three community districts and determines its potential to increase interprofessional collaboration between primary healthcare professionals caring for older people. Method: A feasibility study was conducted to determine the acceptability and feasibility of data collection and analysis regarding interprofessional collaboration in network development. A questionnaire was used to measure the learning experience and the acquisition of knowledge and skills regarding the program. Network development was assessed by distributing a social network survey among professionals attending the program as well as professionals not attending the program at baseline and 5.5 months after. Network development was determined by calculating the number, reciprocity, value, and diversity of contacts between professionals using social network analysis. Results: The IPCP program was found to be instructive and the knowledge and skills gained were applicable in practice. Social network analysis was feasible to conduct and revealed a spill-over effect regarding network development. Program participants, as well as non-program participants, had larger, more reciprocal, and more diverse interprofessional networks than they did before the program. Conclusions: This study showed the feasibility of implementing an IPCP program in terms of acceptability, feasibility of data collection, and social network analysis to measure network development, and indicated potential to increase interprofessional collaboration between primary healthcare professionals. Both program participants and non-program participants developed a larger, more collaborative, and diverse interprofessional network.
In this paper we present visual methodologies attuned to the networked nature of digital images. First, we describe approaches to image research in which images are not separated from their network, but rather studied 'en groupe'. Here, we contrast approaches that treat images as data, and those that regard images as content. Second, we focus on the production of images for digital research, presenting three of their functions: a) the creation of diagrams that facilitate collaboration in interdisciplinary research teams; b) the use of visualizations for cross-platform image analysis; and c) designing images for public participation. Most importantly, such visualizations are not used to form the esthetic culmination of analytical work, but are rather functional tools for digital research that serve parts of the entire research process, from its formulation and operationalization to the engagement of a broader public.
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National forestry Commission (SBB) and National Park De Biesbosch. Subcontractor through NRITNational parks with large flows of visitors have to manage these flows carefully. Methods of data collection and analysis can be of help to support decision making. The case of the Biesbosch National Park is used to find innovative ways to figure flows of yachts, being the most important component of water traffic, and to create a model that allows the estimation of changes in yachting patterns resulting from policy measures. Recent policies oriented at building additional waterways, nature development areas and recreational concentrations in the park to manage the demands of recreation and nature conservation offer a good opportunity to apply this model. With a geographical information system (GIS), data obtained from aerial photographs and satellite images can be analyzed. The method of space syntax is used to determine and visualize characteristics of the network of leisure routes in the park and to evaluate impacts resulting from expected changes in the network that accompany the restructuring of waterways.
Industry 4.0 omvat de toenemende digitalisatie binnen bedrijven, resulterend in een inter-connectiviteit tussen mensen, objecten en systemen in real time. Dit resulteert in fundamentele veranderingen in de manier waarop mensen werken, beslissingen nemen en hun activiteiten managen. Deze nieuwe technologieën, zoals robotoplossingen beïnvloeden ook de manier waarop kennis wordt verworven, overgedragen en gebruikt en vragen om nieuwe managementpraktijken om het leren, de kennisdeling en zodoende het continu verbeteren te faciliteren (Lepore, et al., 2022). Dit onderzoek bouwt voort op bevindingen uit eerdere onderzoeken (RAAK Integraal Robotiseren). Waar eerder is gekeken naar succesfactoren voor het implementeren van de robot oplossing, wordt nu gekeken naar het continue verbeteren van de robotoplossing, met de focus op de impact van interne sociale relaties. De Social Network Analysis (SNA) zou kunnen helpen om de ontwikkeling en dynamiek van kennisdelingsrelaties tijdens robotiseringstrajecten in kaart te brengen en interventies te plannen, voor het verbeteren van dergelijke relaties. De uitkomst van dit onderzoek geeft het MKB een meetinstrument, waarmee een nulmeting kan worden gecreëerd. De nulmeting geeft inzicht hoe de inrichting van de interne kennisdelingsrelaties zijn opgebouwd. Met de interpretatie van de resultaten kan bepaald worden hoe effectief deze relaties zijn. Doelstelling van dit onderzoek is het ontwikkelen van een SNA meetinstrument waarmee inzicht gecreëerd wordt in het ontstaan van- en dynamiek binnen kennisdelingsrelaties. Met deze kennis kunnen Mkb’ers interventies uitvoeren om kritische kennis gerelateerd aan de robotoplossing bij de juiste personen te borgen.
