1,721,006 research outputs found
Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs
Full text linkAnalysing research trends and predicting their impact on academia and industry is crucial to gain a deeper understanding of the advances in a research field and to inform critical decisions about research funding and technology adoption. In the last years, we saw the emergence of several publicly-available and large-scale Scientific Knowledge Graphs fostering the development of many data-driven approaches for performing quantitative analyses of research trends. This chapter presents an innovative framework for detecting, analysing, and forecasting research topics based on a large-scale knowledge graph characterising research articles according to the research topics from the Computer Science Ontology. We discuss the advantages of a solution based on a formal representation of topics and describe how it was applied to produce bibliometric studies and innovative tools for analysing and predicting research dynamics
OpenAIRE Graph: status & enrichments via the SciLake project
Full text linkSlides describing SciLake activities on extending the OpenAIRE Graph. The presentation was made during the OSFair 2023
BIP! Scholar: Going Beyond Researcher Profiles
No full text<p>Slides of a training event that aimed to introduce the audience to the main functionalities of <a href="https://bip.imsi.athenarc.gr/scholar">BIP! Scholar</a>, a service that offers researchers the opportunity to set up academic profiles that summarize their research careers. BIP! Scholar is designed to help researchers in emphasizing their important works and putting them into context. Multiple types of works are supported while, for each work, the researchers can report contribution roles and relevant narratives. Finally, the services can help the exploration of different perspectives of a researcher's career, offering tailored views of each profile based on particular topics, roles, work types, and so on.</p>
BIP! Services: Demonstrating the Potential of Open Scholarly Data in Research Assessment
No full text<p>Presentation in the Workshop on Open Citations & Open Scholarly Metadata 2023 (October 27th, Bologna, Italy).</p>
Transit-based Task Assignment in Spatial Crowdsourcing
Full text linkWorker movement information can help the spatial crowdsourcing platform to identify the right time to assign a task to a worker for successful completion of the task. However, the majority of the current assignment strategies do not consider worker movement information. This paper aims to utilize the worker movement information via transits in an online task assignment setting. The idea is to harness the waiting periods at different transit stops in a worker transit route (WTR) for performing the tasks. Given the limited availability of workers’ waiting periods at transit stops, task deadlines and workers’ preference of performing tasks with higher rewards, we define the Transit-based Task Assignment (TTA) problem. The objective of the TTA problem is to maximize the average worker rewards for motivating workers, considering the fixed worker transit models. We solve the TTA problem by considering three variants, step-by-step, from offline to batch-based online versions. The first variant is the offline version of the TTA, which can be reduced to a maximum bipartite matching problem, and be leveraged for the second variant. The second variant is the batch-based online version of the TTA, for which, we propose dividing each batch into an offline version of the TTA problem, along with additional credibility constraints to ensure a certain level of worker response quality. The third variant is the extension of the batch-based online version of the TTA (Flexible-TTA) that relaxes the strict nature of the WTR model and assumes that a task with higher reward than a worker-defined threshold value will convince the worker to stay longer at the transit stop. Through our extensive evaluation, we observe that the algorithm solving the Flexible-TTA problem outperforms the algorithms proposed to solve other variants of the TTA problems, by 55% in terms of the number of assigned tasks, and by at least 35% in terms of average reward for the worker. With respect to the baseline (online task assignment) algorithm, the algorithm solving the Flexible-TTA problem results in three times higher reward and at least three times faster runtime
Scientific and Statistical Database Management 32th International Conference, SSDBM 2020 Vienna, Austria, July 7 - 9, 2020, Proceedings
No full textBIP! Scholar Indicators Calculator
No full text<p>This software package contains implementations required for the calculation of the researcher-level indicators (mostly related to citation-based impact) that are being used by the <a href="https://bip.imsi.athenarc.gr/scholar">BIP! Scholar service</a>. </p>
Advanced search and data management services in Life Sciences
No full textThe need for data management and processing approaches in life sciences is becoming more intense due to the continuous technological advances in the machines that produce data from biological samples. In today's era, these machines produce vast amount of data that need to be processed. Most of these data are represented as sequences and their processing consists, mainly, of applying sequence alignment algorithms on them. State-of-the-art sequence alignment algorithms fail to perform efficiently for such big data, thus, the introduction of novel approaches is apparent. To make the condition worse, novel findings sometimes raise novel processing needs that cannot be fulfilled by adapting already existent approaches. Again, new methods are required. Finally, new rapidly evolving fields in life sciences, like that of miRNA research, lack centralised information resources. The knowledge in such fields is scattered in a multitude of scientific publications slowing down the work of researchers. Thesis studies all the aforementioned issuesΗ ανάγκη για προσεγγίσεις διαχείρισης και επεξεργασίας δεδομένων στις βιοεπιστήμες γίνεται όλο και πιο έντονη εξαιτίας των συνεχών τεχνολογικών εξελίξεων στις μηχανές που παράγουν δεδομένα από βιολογικά δείγματα. Στη σημερινή εποχή, αυτές οι μηχανές παράγουν τεράστιο όγκο δεδομένων τα οποία πρέπει στη συνέχεια να επεξεργαστούν. Τα περισσότερα από αυτά τα δεδομένα αναπαρίστανται ως ακολουθίες και η επεξεργασία τους αποτελείται κυρίως από την εφαρμογή αλγορίθμων στοίχισης ακολουθιών πάνω τους. Οι αλγόριθμοι στοίχισης ακολουθιών αιχμής αποτυγχάνουν να συμπεριφερθούν αποδοτικά για τόσο μεγάλα δεδομένα, έτσι η εισαγωγή καινούριων προσεγγίσεων είναι επιβεβλημένη. Η κατάσταση γίνεται ακόμα χειρότερη καθώς συχνά νέα ευρήματα στις βιοεπιστήμες δημιουργούν νέες ανάγκες για επεξεργασία δεδομένων οι οποίες δεν μπορούν να καλυφθούν προσαρμόζοντας τις υπάρχουσες τεχνικές. Και πάλι νέες μέθοδοι απαιτούνται. Τέλος, σε νέους, ταχύτατα εξελισσόμενους τομείς των βιοεπιστημών, όπως αυτόν της έρευνας των μορίων miRNA, υπάρχει έλλειψη κεντρικών πληροφοριακών πόρων. Η γνώση σε αυτά τα πεδιά είναι διασκορπισμένη σε ένα πλήθος από επιστημονικές δημοσιεύσεις κάτι το οποίο επιβραδύνει την εργασία των ερευνητών. Η διατριβή ασχολείται με όλα τα προηγούμενα θέματα
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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