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Publications (10 of 12) Show all publications
Bae, J., Falkman, G., Helldin, T. & Riveiro, M. (2019). Visual Data Analysis. In: A. Said, & V. Torra (Ed.), Data science in Practice: (pp. 133-155). Springer
Open this publication in new window or tab >>Visual Data Analysis
2019 (English)In: Data science in Practice / [ed] A. Said, & V. Torra, Springer, 2019, p. 133-155Chapter in book (Refereed)
Abstract [en]

Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:hj:diva-48365 (URN)10.1007/978-3-319-97556-6_8 (DOI)000464719500009 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-24 Created: 2020-05-13Bibliographically approved
Bae, J., Ventocilla, E., Riveiro, M., Helldin, T. & Falkman, G. (2017). Evaluating Multi-Attributes on Cause and Effect Relationship Visualization. In: Alexandru Telea, Jose Braz, Lars Linsen (Ed.), Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017): Volumne 3: IVAPP. Paper presented at 8th International Conference on Information Visualization Theory and Applications (IVAPP), part of the 12th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), February 27-March 1, 2017, in Porto, Portugal (pp. 64-74). SciTePress
Open this publication in new window or tab >>Evaluating Multi-Attributes on Cause and Effect Relationship Visualization
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2017 (English)In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017): Volumne 3: IVAPP / [ed] Alexandru Telea, Jose Braz, Lars Linsen, SciTePress , 2017, p. 64-74Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents findings about visual representations of cause and effect relationship's direction, strength, and uncertainty based on an online user study. While previous researches focus on accuracy and few attributes, our empirical user study examines accuracy and the subjective ratings on three different attributes of a cause and effect relationship edge. The cause and effect direction was depicted by arrows and tapered lines; causal strength by hue, width, and a numeric value; and certainty by granularity, brightness, fuzziness, and a numeric value. Our findings point out that both arrows and tapered cues work well to represent causal direction. Depictions with width showed higher conjunct accuracy and were more preferred than that with hue. Depictions with brightness and fuzziness showed higher accuracy and were marked more understandable than granularity. In general, depictions with hue and granularity performed less accurately and were not preferred compared to the ones with numbers or with width and brightness.

Place, publisher, year, edition, pages
SciTePress, 2017
Keywords
Cause and effect, uncertainty, evaluation, graph visualization
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:hj:diva-43243 (URN)10.5220/0006102300640074 (DOI)000444939500005 ()2-s2.0-85040593124 (Scopus ID)0;0;miljJAIL (Local ID)978-989-758-228-8 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
8th International Conference on Information Visualization Theory and Applications (IVAPP), part of the 12th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), February 27-March 1, 2017, in Porto, Portugal
Funder
Knowledge Foundation
Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Bae, J., Helldin, T. & Riveiro, M. (2017). Identifying Root Cause and Derived Effects in Causal Relationships. In: Sakae Yamamoto (Ed.), Human Interface and the Management of Information: Information, Knowledge and Interaction Design: 19th International Conference, HCI International 2017, Vancouver, BC, Canada, July 9–14, 2017, Proceedings, Part I. Paper presented at Thematic track on Human Interface and the Management of Information, held as part of the 19th International Conference on Human–Computer Interaction, HCI International 2017, Vancouver, Canada, 9 July 2017 through 14 July 2017 (pp. 22-34). Springer
Open this publication in new window or tab >>Identifying Root Cause and Derived Effects in Causal Relationships
2017 (English)In: Human Interface and the Management of Information: Information, Knowledge and Interaction Design: 19th International Conference, HCI International 2017, Vancouver, BC, Canada, July 9–14, 2017, Proceedings, Part I / [ed] Sakae Yamamoto, Springer , 2017, p. 22-34Conference paper, Published paper (Refereed)
Abstract [en]

This paper focuses on identifying factors that influence the process of finding a root cause and a derived effect in causal node-link graphs with associated strength and significance depictions. We discuss in detail the factors that seem to be involved in identifying a global cause and effect based on the analysis of the results of an online user study with 44 participants, who used both sequential and non-sequential graph layouts. In summary, the results show that participants show geodesic-path tendencies when selecting causes and derived effects, and that context matters, i.e., participant’s own beliefs, experiences and knowledge might influence graph interpretation.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10273
Keywords
cause and effect, strenght and significance, graph visualization, user study
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:hj:diva-43245 (URN)10.1007/978-3-319-58521-5_2 (DOI)2-s2.0-85025150109 (Scopus ID)0;0;miljJAIL (Local ID)978-3-319-58520-8 (ISBN)978-3-319-58521-5 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
Thematic track on Human Interface and the Management of Information, held as part of the 19th International Conference on Human–Computer Interaction, HCI International 2017, Vancouver, Canada, 9 July 2017 through 14 July 2017
Projects
BIDAF
Funder
Knowledge Foundation
Available from: 2017-10-02 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Bae, J., Helldin, T. & Riveiro, M. (2017). Understanding Indirect Causal Relationships in Node-Link Graphs. Paper presented at 19th Eurographics/IEEE VGTC Conference on Visualization (EuroVis), JUN 12-16, 2017, Barcelona, SPAIN. Computer graphics forum (Print), 36(3), 411-421
Open this publication in new window or tab >>Understanding Indirect Causal Relationships in Node-Link Graphs
2017 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 36, no 3, p. 411-421Article in journal (Refereed) Published
Abstract [en]

To find correlations and cause and effect relationships in multivariate data sets is central in many data analysis problems. A common way of representing causal relations among variables is to use node-link diagrams, where nodes depict variables and edges show relationships between them. When performing a causal analysis, analysts may be biased by the position of collected evidences, especially when they are at the top of a list. This is of crucial importance since finding a root cause or a derived effect, and searching for causal chains of inferences are essential analytic tasks when investigating causal relationships. In this paper, we examine whether sequential ordering influences understanding of indirect causal relationships and whether it improves readability of multi-attribute causal diagrams. Moreover, we see how people reason to identify a root cause or a derived effect. The results of our design study show that sequential ordering does not play a crucial role when analyzing causal relationships, but many connections from/to a variable and higher strength/certainty values may influence the process of finding a root cause and a derived effect.

National Category
Human Computer Interaction Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:hj:diva-43246 (URN)10.1111/cgf.13198 (DOI)000404881200038 ()2-s2.0-85022207775 (Scopus ID)0;0;miljJAIL (Local ID)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
19th Eurographics/IEEE VGTC Conference on Visualization (EuroVis), JUN 12-16, 2017, Barcelona, SPAIN
Available from: 2017-08-10 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Helldin, T., Riveiro, M., Pashami, S., Falkman, G., Byttner, S. & Nowaczyk, S. (2016). Supporting analytical reasoning: A study from the automotive industry. In: Sakae Yamamoto (Ed.), Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016 Toronto, Canada, July 17-22, 2016. Proceedings, Part II. Paper presented at 18th International Conference on Human Interface and the Management of Information (HCI International 2016), Toronto, Canada, July 17-22, 2016. (pp. 20-31). Springer
Open this publication in new window or tab >>Supporting analytical reasoning: A study from the automotive industry
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2016 (English)In: Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016 Toronto, Canada, July 17-22, 2016. Proceedings, Part II / [ed] Sakae Yamamoto, Springer, 2016, p. 20-31Conference paper, Published paper (Refereed)
Abstract [en]

In the era of big data, it is imperative to assist the human analyst in the endeavor to find solutions to ill-defined problems, i.e. to “detect the expected and discover the unexpected” (Yi et al., 2008). To their aid, a plethora of analysis support systems is available to the analysts. However, these support systems often lack visual and interactive features, leaving the analysts with no opportunity to guide, influence and even understand the automatic reasoning performed and the data used. Yet, to be able to appropriately support the analysts in their sense-making process, we must look at this process more closely. In this paper, we present the results from interviews performed together with data analysts from the automotive industry where we have investigated how they handle the data, analyze it and make decisions based on the data, outlining directions for the development of analytical support systems within the area.

Place, publisher, year, edition, pages
Springer, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9735
Keywords
analytical reasoning, sense-making, visual analytics, truck data analysis, big data
National Category
Human Computer Interaction
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:hj:diva-43254 (URN)10.1007/978-3-319-40397-7_3 (DOI)000389467600003 ()2-s2.0-84978877445 (Scopus ID)0;0;miljJAIL (Local ID)978-3-319-40396-0 (ISBN)978-3-319-40397-7 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
18th International Conference on Human Interface and the Management of Information (HCI International 2016), Toronto, Canada, July 17-22, 2016.
Projects
BIDAF - A Big Data Analytics Framework for a Smart Society
Funder
Knowledge Foundation, BIDAF 2014/32
Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Riveiro, M., Helldin, T., Falkman, G. & Lebram, M. (2014). Effects of visualizing uncertainty on decision-making in a target identification scenario. Computers & graphics, 41(1), 84-98
Open this publication in new window or tab >>Effects of visualizing uncertainty on decision-making in a target identification scenario
2014 (English)In: Computers & graphics, ISSN 0097-8493, E-ISSN 1873-7684, Vol. 41, no 1, p. 84-98Article in journal (Refereed) Published
Abstract [en]

This paper presents an empirical study that addresses the effects the visualization of uncertainty has on decision-making. We focus our investigations on an area where uncertainty plays an important role and the decision time is limited. For that, we selected an air defense scenario, where expert operators have a few minutes to make a well-informed decision based on uncertain sensor data regarding the identity of an object and where the consequences of a late or wrong decision are severe. An approach for uncertainty visualization is proposed and tested using a prototype that supports the interactive analysis of multivariate spatio-temporal sensor data. The uncertainty visualization embeds the accuracy of the sensor data values using the thickness of the lines in the graphical representation of the sensor values. Semi-transparent filled circles represent the uncertain position, while a track quality value between 0 and 1 accounts for the quality of the estimated track for each target. Twenty-two experienced air traffic operators were divided into two groups (with and without uncertainty visualization) and carried out identification and prioritization tasks using the prototype. The results show that the group aided by visualizations of uncertainty needed significantly fewer attempts to make a final identification, and a significant difference between the groups when considering the identities and priorities assigned was observed (participants with uncertainty visualization selected higher priority values and more hostile and suspect identities). These results may show that experts put themselves in the ``worst-case scenario" in the presence of uncertainty when safety is an issue. Additionally, the presentation of uncertainty neither increased the participants' expressed workload, nor the time needed to make a classification. However, the inclusion of the uncertainty information did not have a significant effect on the performance (true positives, false negatives and false positives) or the participants' expressed confidence in their decisions.

Place, publisher, year, edition, pages
Elsevier, 2014
Keywords
Uncertainty visualization, Decision-making, Confidence, Performance, Workload, Target identification
National Category
Human Computer Interaction
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL); Interaction Lab (ILAB)
Identifiers
urn:nbn:se:hj:diva-43265 (URN)10.1016/j.cag.2014.02.006 (DOI)000336349700008 ()2-s2.0-84897553740 (Scopus ID)0;0;miljJAIL (Local ID)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Available from: 2014-05-02 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Riveiro, M., Helldin, T. & Falkman, G. (2014). Influence of Meta-Information on Decision-Making: Lessons Learned from Four Case Studies. In: Proceedings of the 4th International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA 2014): . Paper presented at 2014 IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), March 3-6, San Antonio, USA. IEEE Communications Society
Open this publication in new window or tab >>Influence of Meta-Information on Decision-Making: Lessons Learned from Four Case Studies
2014 (English)In: Proceedings of the 4th International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA 2014), IEEE Communications Society , 2014Conference paper, Published paper (Refereed)
Abstract [en]

This paper discusses the results of four empirical evaluations that assess the effects that visualizing system metainformation have on decision-making, particularly on confidence, trust, workload, time and performance. These four case studies correspond to the analysis of (1) the effects that visualizing uncertainty associated with sensor values (position, speed, altitude, etc. and track quality) have on decision-making on a ground to air defense scenario; (2) the effects that the visualization of the car’s certainty on its own capability of driving autonomously have on drivers’ trust and performance; (3) the influence that the visualization of various qualifiers associated with the proposals given by the support system has on air traffic operators carrying out identification tasks and (4) the effects that the presentation of different abstraction levels of information have on classification tasks carried out by fighter pilots. We summarize the results of these four case studies and discuss lessons learned for the design of future computerized support systems regarding the visualization of meta-information.

Place, publisher, year, edition, pages
IEEE Communications Society, 2014
Keywords
system meta-information, uncertainty, decisionmaking, trust, situation awareness, decision support
National Category
Computer Sciences Human Computer Interaction
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:hj:diva-43267 (URN)10.1109/CogSIMA.2014.6816534 (DOI)000341577900003 ()2-s2.0-84902076083 (Scopus ID)0;0;miljJAIL (Local ID)978-1-4799-3564-2 (ISBN)978-1-4799-3565-9 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
2014 IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), March 3-6, San Antonio, USA
Note

Presentation: http://www.cogsima2014.org/Program.html

Available from: 2014-05-02 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Helldin, T., Ohlander, U., Falkman, G. & Riveiro, M. (2014). Transparency of Automated Combat Classification. In: Don Harris (Ed.), Engineering Psychology and Cognitive Ergonomics: 11th International Conference, EPCE 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014. Proceedings. Paper presented at 11th International Conference on Engineering Psychology and Cognitive Ergonomics, EPCE 2014, Held as Part of 16th International Conference on Human-Computer Interaction, HCI International 2014, Heraklion, Crete, Greece, 22 June 2014 through 27 June 2014 (pp. 22-33). Springer
Open this publication in new window or tab >>Transparency of Automated Combat Classification
2014 (English)In: Engineering Psychology and Cognitive Ergonomics: 11th International Conference, EPCE 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014. Proceedings / [ed] Don Harris, Springer , 2014, p. 22-33Conference paper, Published paper (Refereed)
Abstract [en]

We present an empirical study where the effects of three levels of system transparency of an automated target classification aid on fighter pilots’ performance and initial trust in the system were evaluated. The levels of transparency consisted of (1) only presenting text–based information regarding the specific object (without any automated support), (2) accompanying the text-based information with an automatically generated object class suggestion and (3) adding the incorporated sensor values with associated (uncertain) historic values in graphical form. The results show that the pilots needed more time to make a classification decision when being provided with display condition 2 and 3 than display condition 1. However, the number of correct classifications and the operators’ trust ratings were the highest when using display condition 3. No difference in the pilots’ decision confidence was found, yet slightly higher workload was reported when using display condition 3. The questionnaire results report on the pilots’ general opinion that an automatic classification aid would help them make better and more confident decisions faster, having trained with the system for a longer period.

Place, publisher, year, edition, pages
Springer, 2014
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8532
Keywords
Classification support, automation transparency, uncertainty visualization, fighter pilots
National Category
Computer Sciences
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:hj:diva-43271 (URN)10.1007/978-3-319-07515-0_3 (DOI)000342845800003 ()2-s2.0-84903643576 (Scopus ID)0;0;miljJAIL (Local ID)978-3-319-07514-3 (ISBN)978-3-319-07515-0 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
11th International Conference on Engineering Psychology and Cognitive Ergonomics, EPCE 2014, Held as Part of 16th International Conference on Human-Computer Interaction, HCI International 2014, Heraklion, Crete, Greece, 22 June 2014 through 27 June 2014
Funder
Vinnova
Available from: 2014-11-04 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Helldin, T., Falkman, G., Riveiro, M. & Davidsson, S. (2013). Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving. In: Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI’13): . Paper presented at 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI’13), 28-30 October, 2013, Eindhoven, The Netherlands (pp. 210-217). New York: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving
2013 (English)In: Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI’13), New York: Association for Computing Machinery (ACM) , 2013, p. 210-217Conference paper, Published paper (Refereed)
Abstract [en]

To investigate the impact of visualizing car uncertainty on drivers' trust during an automated driving scenario, a simulator study was conducted. A between-group design experiment with 59 Swedish drivers was carried out where a continuous representation of the uncertainty of the car's ability to autonomously drive during snow conditions was displayed to one of the groups, whereas omitted for the control group. The results show that, on average, the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead. Thus, drivers provided with the uncertainty information could, to a higher degree, perform tasks other than driving without compromising with driving safety. The analysis of trust shows that the participants who were provided with the uncertainty information trusted the automated system less than those who did not receive such information, which indicates a more proper trust calibration than in the control group.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2013
Keywords
Uncertainty visualization, trust, automation, driving, acceptance
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
urn:nbn:se:hj:diva-43272 (URN)10.1145/2516540.2516554 (DOI)2-s2.0-84888184123 (Scopus ID)0;0;miljJAIL (Local ID)978-1-4503-2478-6 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI’13), 28-30 October, 2013, Eindhoven, The Netherlands
Available from: 2014-01-07 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Riveiro, M., Helldin, T., Lebram, M. & Falkman, G. (2013). Towards future threat evaluation systems: user study, proposal and precepts for design. In: Proceedings of the 16th International Conference on Information Fusion, FUSION 2013: . Paper presented at 16th International Conference on Information Fusion (FUSION), 2013, 9-12 July 2013, Istanbul, Turkey (pp. 1863-1870). IEEE Press
Open this publication in new window or tab >>Towards future threat evaluation systems: user study, proposal and precepts for design
2013 (English)In: Proceedings of the 16th International Conference on Information Fusion, FUSION 2013, IEEE Press , 2013, p. 1863-1870Conference paper, Published paper (Refereed)
Abstract [en]

In the defense domain, to estimate if a targetis threatening and to which degree is a complex task, thatis typically carried out by human operators due to the highrisks and uncertainties associated. To their aid, different supportsystems have been implemented to analyze the data and providerecommendations for actions. Since the ultimate responsibilitylies in human operators, it is of utmost importance that theytrust and know how to use these systems, as well as have anunderstanding of their inner workings, strengths and limitations.This paper presents, first, a formative user study to char-acterize how air traffic operators carry out threat evaluationrelated tasks. Grounded in these findings and in guidelinesfound in the literature, we present a transparent and highlyinteractive prototype that aims at reducing operator’s cognitiveload and support threat assessment activities. The literaturereview provided on design guidelines, the outcomes of the userstudy, the design of the prototype as well as the results of aninitial evaluation can provide guidance for both researchers andprospective developers of future threat evaluation systems.

Place, publisher, year, edition, pages
IEEE Press, 2013
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
urn:nbn:se:hj:diva-43274 (URN)2-s2.0-84890850366 (Scopus ID)0;0;miljJAIL (Local ID)978-605-86311-1-3 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
16th International Conference on Information Fusion (FUSION), 2013, 9-12 July 2013, Istanbul, Turkey
Available from: 2014-01-07 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6245-5850

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