Christine Kunzmann, Andreas P. Schmidt, Carmen Wolf
Pattern-based approaches are becoming increasingly popular to capture design experiences for a wider audience. This rises to particular importance in participatory processes, such as user-driven design approaches. However, the creation process of such patterns is challenging, especially when it comes to motivational, affective and other soft factors. In this paper, we view the pattern development as a knowledge maturing process, i.e., a process of collective knowledge development. We describe the pattern development process, identify barriers in this process, and explain how various social learning approaches, such as peer coaching, social learning programmes (i.e., online courses with a collaborative focus), and reflective instruments in agile processes contribute to the key issue of decontextualizing and recontextualizing experiences in a continuous way.
{"title":"Facilitating maturing of socio-technical patterns through social learning approaches","authors":"Christine Kunzmann, Andreas P. Schmidt, Carmen Wolf","doi":"10.1145/2809563.2809575","DOIUrl":"https://doi.org/10.1145/2809563.2809575","url":null,"abstract":"Pattern-based approaches are becoming increasingly popular to capture design experiences for a wider audience. This rises to particular importance in participatory processes, such as user-driven design approaches. However, the creation process of such patterns is challenging, especially when it comes to motivational, affective and other soft factors. In this paper, we view the pattern development as a knowledge maturing process, i.e., a process of collective knowledge development. We describe the pattern development process, identify barriers in this process, and explain how various social learning approaches, such as peer coaching, social learning programmes (i.e., online courses with a collaborative focus), and reflective instruments in agile processes contribute to the key issue of decontextualizing and recontextualizing experiences in a continuous way.","PeriodicalId":20526,"journal":{"name":"Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73258749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While Citizen Science projects involve people in passive or active project tasks, Citizen Inquiry offers the opportunity for deeper involvement through initiating and facilitating science investigations. This study aims to explore the creation and evolution of Weather-it, a Citizen Inquiry online community hosted by the nQuire-it platform. Weather-it enables people to create and maintain their own weather missions (investigations), to which other people can contribute. The evolution of Weather-it community is explored through social network graphs of Weather-it members and their interactions. Information regarding other aspects of the community such as the type of members, their recruitment and motivations, and the identity and sustainability of the community, is collected through a survey comprising open and closed-ended questions. The results indicate differences in these community engagement aspects between Citizen Science and Citizen Inquiry projects, providing insight into the behaviour of people in projects that require more active involvement throughout the scientific investigations.
{"title":"Weather-it: evolution of an online community for citizen inquiry","authors":"M. Aristeidou, E. Scanlon, M. Sharples","doi":"10.1145/2809563.2809567","DOIUrl":"https://doi.org/10.1145/2809563.2809567","url":null,"abstract":"While Citizen Science projects involve people in passive or active project tasks, Citizen Inquiry offers the opportunity for deeper involvement through initiating and facilitating science investigations. This study aims to explore the creation and evolution of Weather-it, a Citizen Inquiry online community hosted by the nQuire-it platform. Weather-it enables people to create and maintain their own weather missions (investigations), to which other people can contribute. The evolution of Weather-it community is explored through social network graphs of Weather-it members and their interactions. Information regarding other aspects of the community such as the type of members, their recruitment and motivations, and the identity and sustainability of the community, is collected through a survey comprising open and closed-ended questions. The results indicate differences in these community engagement aspects between Citizen Science and Citizen Inquiry projects, providing insight into the behaviour of people in projects that require more active involvement throughout the scientific investigations.","PeriodicalId":20526,"journal":{"name":"Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88319838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of social media amongst children, adolescents and families is nowadays a common practise in our everyday lives. Social networking sites allow social interaction between people through various channels, such as Twitter, Facebook, YouTube and blogs. Even if this interaction is generally healthy, these sites bring several risks, such as cyberbullying, depression and exposure of inappropriate content. In this paper we tackle the problem of cyberbullying via a novel approach that analyses online posts in trending world events. These generally cause a lot of interest and controversy among online Web users. Twitter is the social network of choice, where a large dataset of tweets is collected. The two current world events selected are the Ebola virus outbreak in Africa and the shooting of Michael Brown in Ferguson, Missouri. Collected tweets are carefully analysed to identify the most popular hashtags and named entities used within cyberbullying tweets. This analysis provides a basis towards several useful applications, such as a cyberbullying online post detector for certain current trending world events. This will help reduce the number of cyberbullying cases in social networking sites. Results obtained from this evaluation can be applied to other cyberbullying scenarios.
{"title":"Analysis of cyberbullying tweets in trending world events","authors":"Keith Cortis, S. Handschuh","doi":"10.1145/2809563.2809605","DOIUrl":"https://doi.org/10.1145/2809563.2809605","url":null,"abstract":"The use of social media amongst children, adolescents and families is nowadays a common practise in our everyday lives. Social networking sites allow social interaction between people through various channels, such as Twitter, Facebook, YouTube and blogs. Even if this interaction is generally healthy, these sites bring several risks, such as cyberbullying, depression and exposure of inappropriate content. In this paper we tackle the problem of cyberbullying via a novel approach that analyses online posts in trending world events. These generally cause a lot of interest and controversy among online Web users. Twitter is the social network of choice, where a large dataset of tweets is collected. The two current world events selected are the Ebola virus outbreak in Africa and the shooting of Michael Brown in Ferguson, Missouri. Collected tweets are carefully analysed to identify the most popular hashtags and named entities used within cyberbullying tweets. This analysis provides a basis towards several useful applications, such as a cyberbullying online post detector for certain current trending world events. This will help reduce the number of cyberbullying cases in social networking sites. Results obtained from this evaluation can be applied to other cyberbullying scenarios.","PeriodicalId":20526,"journal":{"name":"Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business","volume":"165 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86207074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. One possibility to describe these entities within a knowledge base is via entity-annotated documents (document-centric knowledge base). It has been shown that entity disambiguation with search-based algorithms that use document-centric knowledge bases perform well on the biomedical domain. In this context, the question remains how the quantity of annotated entities within documents and the document count used for entity classification influence disambiguation results. Another open question is whether disambiguation results hold true on more general knowledge data sets (e.g. Wikipedia). In our work we implement a search-based, document-centric disambiguation system and explicitly evaluate the mentioned issues on the biomedical data set CALBC and general knowledge data set Wikipedia, respectively. We show that the number of documents used for classification and the amount of annotations within these documents must be well-matched to attain the best result. Additionally, we reveal that disambiguation accuracy is poor on Wikipedia. We show that disambiguation results significantly improve when using shorter but more documents (e.g. Wikipedia paragraphs). Our results indicate that search-based, document-centric disambiguation systems must be carefully adapted with reference to the underlying domain and availability of user data.
{"title":"Search-based entity disambiguation with document-centric knowledge bases","authors":"Stefan Zwicklbauer, C. Seifert, M. Granitzer","doi":"10.1145/2809563.2809618","DOIUrl":"https://doi.org/10.1145/2809563.2809618","url":null,"abstract":"Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. One possibility to describe these entities within a knowledge base is via entity-annotated documents (document-centric knowledge base). It has been shown that entity disambiguation with search-based algorithms that use document-centric knowledge bases perform well on the biomedical domain. In this context, the question remains how the quantity of annotated entities within documents and the document count used for entity classification influence disambiguation results. Another open question is whether disambiguation results hold true on more general knowledge data sets (e.g. Wikipedia). In our work we implement a search-based, document-centric disambiguation system and explicitly evaluate the mentioned issues on the biomedical data set CALBC and general knowledge data set Wikipedia, respectively. We show that the number of documents used for classification and the amount of annotations within these documents must be well-matched to attain the best result. Additionally, we reveal that disambiguation accuracy is poor on Wikipedia. We show that disambiguation results significantly improve when using shorter but more documents (e.g. Wikipedia paragraphs). Our results indicate that search-based, document-centric disambiguation systems must be carefully adapted with reference to the underlying domain and availability of user data.","PeriodicalId":20526,"journal":{"name":"Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86605122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The software known as creativity support systems (CSS) have become a critical catalyst of the knowledge creation process. Nonaka and Konno integrated CSS into wider knowledge management processes with the concept of ba. Ba are shared spaces in which relationships between knowledge and individuals can develop. This study finds that minor changes to the appearance of icons users interact with in CSS impact user knowledge creation dialogue. Under laboratory conditions, this study uses a 2×2 factorial experiment to investigate the impact of icon and typeface appearance on the idea generation processes of 37 pairs of active managers within a synchronous CSS. Participants used icons which differed on their appearance of finishedness to enter ideas into a shared working space. Some icons looked like rough first drafts, others looked perfectly complete and refined. Participants initial ideas were equally unripe but the icons created different levels of perceived finishedness (PF). Participant ideas were also displayed on-screen using a typeface which was either easy or difficult to cognitively process. Icon PF and typeface processing difficulty level had a crossover interaction effect. A low PF icon and an easy-to-process typeface resulted in more original ideas. Conversely, a high PF icon and a difficult-to-process typeface resulted in less original ideas. These findings and their implications are discussed in terms of enabling spaces, or ba, semantic memory, and difficulty of information processing.
{"title":"The power of provisionality: an experimental evaluation of idea appearance in online knowledge creation","authors":"L. McGrath","doi":"10.1145/2809563.2809572","DOIUrl":"https://doi.org/10.1145/2809563.2809572","url":null,"abstract":"The software known as creativity support systems (CSS) have become a critical catalyst of the knowledge creation process. Nonaka and Konno integrated CSS into wider knowledge management processes with the concept of ba. Ba are shared spaces in which relationships between knowledge and individuals can develop. This study finds that minor changes to the appearance of icons users interact with in CSS impact user knowledge creation dialogue. Under laboratory conditions, this study uses a 2×2 factorial experiment to investigate the impact of icon and typeface appearance on the idea generation processes of 37 pairs of active managers within a synchronous CSS. Participants used icons which differed on their appearance of finishedness to enter ideas into a shared working space. Some icons looked like rough first drafts, others looked perfectly complete and refined. Participants initial ideas were equally unripe but the icons created different levels of perceived finishedness (PF). Participant ideas were also displayed on-screen using a typeface which was either easy or difficult to cognitively process. Icon PF and typeface processing difficulty level had a crossover interaction effect. A low PF icon and an easy-to-process typeface resulted in more original ideas. Conversely, a high PF icon and a difficult-to-process typeface resulted in less original ideas. These findings and their implications are discussed in terms of enabling spaces, or ba, semantic memory, and difficulty of information processing.","PeriodicalId":20526,"journal":{"name":"Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90242149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we report preliminary results of a small-scale case study about the data citation quantity and quality in research output of the National Educational Panel Study (NEPS), a longitudinal study analyzing educational processes in Germany across the lifespan. In order to collect research output based on NEPS data, we searched for and examined publications of a randomly selected sample of 72 NEPS data users. Altogether, we found 18 publications to be relevant for citation analysis. Compared to previous studies, the citation behavior in our sample can be assessed as better. However, publications often lack the inclusion of central data citation elements, such as a persistent identifier. The quality of data citations seems to vary across different types of research output. In a follow-up study, we plan to do a comprehensive sampling and analysis of NEPS related research output in order to verify our findings, and also to include further panel studies to compare citation behavior across different studies.
{"title":"Data citation quantity and quality in research output of a large-scale educational panel study","authors":"Nadine Mahrholz, Anke Reinhold, Marc Rittberger","doi":"10.1145/2809563.2809617","DOIUrl":"https://doi.org/10.1145/2809563.2809617","url":null,"abstract":"In this paper, we report preliminary results of a small-scale case study about the data citation quantity and quality in research output of the National Educational Panel Study (NEPS), a longitudinal study analyzing educational processes in Germany across the lifespan. In order to collect research output based on NEPS data, we searched for and examined publications of a randomly selected sample of 72 NEPS data users. Altogether, we found 18 publications to be relevant for citation analysis. Compared to previous studies, the citation behavior in our sample can be assessed as better. However, publications often lack the inclusion of central data citation elements, such as a persistent identifier. The quality of data citations seems to vary across different types of research output. In a follow-up study, we plan to do a comprehensive sampling and analysis of NEPS related research output in order to verify our findings, and also to include further panel studies to compare citation behavior across different studies.","PeriodicalId":20526,"journal":{"name":"Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80607515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Schlegel, Emanuel Berndl, M. Granitzer, H. Kosch, T. Kurz
Whereas the former Web mostly consisted of information represented in textual documents, nowadays the Web includes a huge number of multimedia documents like videos, photos, and audio. This enormous increase in volume in the private, and above all in the industry sector, makes it more and more difficult to find relevant information. Besides the pure management of multimedia documents, finding hidden semantics and interconnections of heterogeneous cross-media content is a crucial task and stays mostly untouched. To overcome this tendency we see the need for a generic cross-media analysis platform, ranging from extracting relevant features from media objects over representing and publishing extraction results to integrated querying of aggregated findings. In this paper we propose the underlying foundation for a common and contextual multimedia platform in terms of an unified model for publishing multimedia analysis results. The proposed model is based on existing ontologies, adapted and extended to the cross-media environment. Besides the introduction of the already mentioned platform and model, this paper also briefly introduces specific use-case applications as well as possibilities to query the persisted data.
{"title":"A platform for contextual multimedia data: towards a unified metadata model and querying","authors":"K. Schlegel, Emanuel Berndl, M. Granitzer, H. Kosch, T. Kurz","doi":"10.1145/2809563.2809586","DOIUrl":"https://doi.org/10.1145/2809563.2809586","url":null,"abstract":"Whereas the former Web mostly consisted of information represented in textual documents, nowadays the Web includes a huge number of multimedia documents like videos, photos, and audio. This enormous increase in volume in the private, and above all in the industry sector, makes it more and more difficult to find relevant information. Besides the pure management of multimedia documents, finding hidden semantics and interconnections of heterogeneous cross-media content is a crucial task and stays mostly untouched. To overcome this tendency we see the need for a generic cross-media analysis platform, ranging from extracting relevant features from media objects over representing and publishing extraction results to integrated querying of aggregated findings. In this paper we propose the underlying foundation for a common and contextual multimedia platform in terms of an unified model for publishing multimedia analysis results. The proposed model is based on existing ontologies, adapted and extended to the cross-media environment. Besides the introduction of the already mentioned platform and model, this paper also briefly introduces specific use-case applications as well as possibilities to query the persisted data.","PeriodicalId":20526,"journal":{"name":"Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business","volume":"266 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76596509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leonid Glanz, Sebastian Schmidt, S. Wollny, Ben Hermann
The National Vulnerability Database (NVD) is a rich source of information for system administrators, software engineers, IT security consultants, and researchers in software security. Relevant information is provided in machine readable form and hence can be used for automated software security management. However, we discovered that information on affected software versions and fix information is not always available in structured form. We therefore propose to enrich the NVD database with this information and use a rule-based approach to extract this information from the informal vulnerability description. Such information is useful in software development to exchange or avoid vulnerable components as well as in security research for directed cause analysis.
{"title":"A vulnerability's lifetime: enhancing version information in CVE databases","authors":"Leonid Glanz, Sebastian Schmidt, S. Wollny, Ben Hermann","doi":"10.1145/2809563.2809612","DOIUrl":"https://doi.org/10.1145/2809563.2809612","url":null,"abstract":"The National Vulnerability Database (NVD) is a rich source of information for system administrators, software engineers, IT security consultants, and researchers in software security. Relevant information is provided in machine readable form and hence can be used for automated software security management. However, we discovered that information on affected software versions and fix information is not always available in structured form. We therefore propose to enrich the NVD database with this information and use a rule-based approach to extract this information from the informal vulnerability description. Such information is useful in software development to exchange or avoid vulnerable components as well as in security research for directed cause analysis.","PeriodicalId":20526,"journal":{"name":"Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73982977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advances for visual concept detection based on deep convolutional neural networks have only been successful because of the availability of huge training datasets provided by benchmarking initiatives such as ImageNet. Assembly of reliably annotated training data still is a largely manual effort and can only be approached efficiently as crowd-working tasks. On the other hand, user generated photos and annotations are available at almost no costs in social photo communities such as Flickr. Leveraging the information available in these communities may help to extend existing datasets as well as to create new ones for completely different classification scenarios. However, user generated annotations of photos are known to be incomplete, subjective and do not necessarily relate to the depicted content. In this paper, we therefore present an approach to reliably identify photos relevant for a given visual concept category. We have downloaded additional metadata for 1 million Flickr images and have trained a language model based on user generated annotations. Relevance estimation is based on accordance of an image's annotation data with our language model and on subsequent visual re-ranking. Experimental results demonstrate the potential of the proposed method -- comparison with a baseline approach based on single tag matching shows significant improvements.
{"title":"Learning from the uncertain: leveraging social communities to generate reliable training data for visual concept detection tasks","authors":"C. Hentschel, Harald Sack","doi":"10.1145/2809563.2809587","DOIUrl":"https://doi.org/10.1145/2809563.2809587","url":null,"abstract":"Recent advances for visual concept detection based on deep convolutional neural networks have only been successful because of the availability of huge training datasets provided by benchmarking initiatives such as ImageNet. Assembly of reliably annotated training data still is a largely manual effort and can only be approached efficiently as crowd-working tasks. On the other hand, user generated photos and annotations are available at almost no costs in social photo communities such as Flickr. Leveraging the information available in these communities may help to extend existing datasets as well as to create new ones for completely different classification scenarios. However, user generated annotations of photos are known to be incomplete, subjective and do not necessarily relate to the depicted content. In this paper, we therefore present an approach to reliably identify photos relevant for a given visual concept category. We have downloaded additional metadata for 1 million Flickr images and have trained a language model based on user generated annotations. Relevance estimation is based on accordance of an image's annotation data with our language model and on subsequent visual re-ranking. Experimental results demonstrate the potential of the proposed method -- comparison with a baseline approach based on single tag matching shows significant improvements.","PeriodicalId":20526,"journal":{"name":"Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business","volume":"236 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79703511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nancy Pontika, Petr Knoth, M. Cancellieri, Samuel Pearce
The term "Open Science" is recently widely used, but it is still unclear to many research stakeholders - funders, policy makers, researchers, administrators, librarians and repository managers - how Open Science can be achieved. FOSTER (Facilitate Open Science Training for European Research) is a European Commission funded project, which is developing an e-learning portal to support the training of a wide range of stakeholders in Open Science and related areas. In 2014 the FOSTER project co-funded 28 training activities in Open Science, which include more than 110 events, while in 2015 the project has supported 24 community training events in 18 countries. In this paper, we describe the FOSTER approach in structuring the Open Science domain for educational purposes, present the functionality of the FOSTER training portal and discuss its use and potential for training the key stakeholders using self-learning and blended-learning methods.
{"title":"Fostering open science to research using a taxonomy and an eLearning portal","authors":"Nancy Pontika, Petr Knoth, M. Cancellieri, Samuel Pearce","doi":"10.1145/2809563.2809571","DOIUrl":"https://doi.org/10.1145/2809563.2809571","url":null,"abstract":"The term \"Open Science\" is recently widely used, but it is still unclear to many research stakeholders - funders, policy makers, researchers, administrators, librarians and repository managers - how Open Science can be achieved. FOSTER (Facilitate Open Science Training for European Research) is a European Commission funded project, which is developing an e-learning portal to support the training of a wide range of stakeholders in Open Science and related areas. In 2014 the FOSTER project co-funded 28 training activities in Open Science, which include more than 110 events, while in 2015 the project has supported 24 community training events in 18 countries. In this paper, we describe the FOSTER approach in structuring the Open Science domain for educational purposes, present the functionality of the FOSTER training portal and discuss its use and potential for training the key stakeholders using self-learning and blended-learning methods.","PeriodicalId":20526,"journal":{"name":"Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85129358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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