Relevant bibliographies by topics / Concept recognition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Concept recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Concept recognition"

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Jurist, Elliot L. "Hegel’s Concept of Recognition." Owl of Minerva 19, no. 1 (1987): 5–22. http://dx.doi.org/10.5840/owl198719132.

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Kozaczynski, W., J. Ning, and A. Engberts. "Program concept recognition and transformation." IEEE Transactions on Software Engineering 18, no. 12 (1992): 1065–75. http://dx.doi.org/10.1109/32.184761.

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Marshall, S. A. "Speciation and the Recognition Concept." Annals of the Entomological Society of America 88, no. 4 (July 1, 1995): 597–98. http://dx.doi.org/10.1093/aesa/88.4.597.

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Mendelson, Tamra C., and Kerry L. Shaw. "The (mis)concept of species recognition." Trends in Ecology & Evolution 27, no. 8 (August 2012): 421–27. http://dx.doi.org/10.1016/j.tree.2012.04.001.

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Stuart, Robin J. "Kin recognition as a functional concept." Animal Behaviour 41, no. 6 (June 1991): 1093–94. http://dx.doi.org/10.1016/s0003-3472(05)80650-5.

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Dyer, A. "International Recognition of the Trust Concept." Trusts & Trustees 2, no. 3 (February 1, 1996): 5–11. http://dx.doi.org/10.1093/tandt/2.3.5.

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Dyer, A. "International Recognition of the Trust Concept." Trusts & Trustees 3, no. 7 (June 1, 1997): 23. http://dx.doi.org/10.1093/tandt/3.7.23.

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Bergadano, F., and A. Giordana. "Concept recognition: An approximate reasoning framework." International Journal of Intelligent Systems 4, no. 1 (March 1989): 23–44. http://dx.doi.org/10.1002/int.4550040103.

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Chen, Zhi, Yijie Bei, and Cynthia Rudin. "Concept whitening for interpretable image recognition." Nature Machine Intelligence 2, no. 12 (December 2020): 772–82. http://dx.doi.org/10.1038/s42256-020-00265-z.

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Kozaczynski, Wojtek, and Jim Q. Ning. "Automated program understanding by concept recognition." Automated Software Engineering 1, no. 1 (March 1994): 61–78. http://dx.doi.org/10.1007/bf00871692.

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Dissertations / Theses on the topic "Concept recognition"

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Liu, Ningning. "Contributions to generic and affective visual concept recognition." Thesis, Ecully, Ecole centrale de Lyon, 2013. http://www.theses.fr/2013ECDL0038.

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Cette thèse de doctorat est consacrée à la reconnaissance de concepts visuels (VCR pour "Visual Concept Recognition"). En raison des nombreuses difficultés qui la caractérisent, cette tâche est toujours considérée comme l’une des plus difficiles en vision par ordinateur et reconnaissance de formes. Dans ce contexte, nous avons proposé plusieurs contributions, particulièrement dans le cadre d’une approche de reconnaissance multimodale combinant efficacement les informations visuelles et textuelles. Tout d’abord, nous avons étudié différents types de descripteurs visuels de bas-niveau sémantique pour la tâche de VCR incluant des descripteurs de couleur, de texture et de forme. Plus précisément, nous pensons que chaque concept nécessite différents descripteurs pour le caractériser efficacement pour permettre sa reconnaissance automatique. Ainsi, nous avons évalué l’efficacité de diverses représentations visuelles, non seulement globales comme la couleur, la texture et la forme, mais également locales telles que SIFT, Color SIFT, HOG, DAISY, LBP et Color LBP. Afin de faciliter le franchissement du fossé sémantique entre les descripteurs bas-niveau et les concepts de haut niveau sémantique, et particulièrement ceux relatifs aux émotions, nous avons proposé des descripteurs visuels de niveau intermédiaire basés sur l’harmonie visuelle et le dynamisme exprimés dans les images. De plus, nous avons utilisé une décomposition spatiale pyramidale des images pour capturer l’information locale et spatiale lors de la construction des descripteurs d’harmonie et de dynamisme. Par ailleurs, nous avons également proposé une nouvelle représentation reposant sur les histogrammes de couleur HSV en utilisant un modèle d’attention visuelle pour identifier les régions d’intérêt dans les images. Ensuite, nous avons proposé un nouveau descripteur textuel dédié au problème de VCR. En effet, la plupart des photos publiées sur des sites de partage en ligne (Flickr, Facebook, ...) sont accompagnées d’une description textuelle sous la forme de mots-clés ou de légende. Ces descriptions constituent une riche source d’information sur la sémantique contenue dans les images et il semble donc particulièrement intéressant de les considérer dans un système de VCR. Ainsi, nous avons élaboré des descripteurs HTC ("Histograms of Textual Concepts") pour capturer les liens sémantiques entre les concepts. L’idée générale derrière HTC est de représenter un document textuel comme un histogramme de concepts textuels selon un dictionnaire (ou vocabulaire), pour lequel chaque valeur associée à un concept est l’accumulation de la contribution de chaque mot du texte pour ce concept, en fonction d’une mesure de distance sémantique. Plusieurs variantes de HTC ont été proposées qui se sont révélées être très efficaces pour la tâche de VCR. Inspirés par la démarche de l’analyse cepstrale de la parole, nous avons également développé Cepstral HTC pour capturer à la fois l’information de fréquence d’occurrence des mots (comme TF-IDF) et les liens sémantiques entre concepts fournis par HTC à partir des mots-clés associés aux images. Enfin, nous avons élaboré une méthode de fusion (SWLF pour "Selective Weighted Later Fusion") afin de combiner efficacement différentes sources d’information pour le problème de VCR. Cette approche de fusion est conçue pour sélectionner les meilleurs descripteurs et pondérer leur contribution pour chaque concept à reconnaître. SWLF s’est révélé être particulièrement efficace pour fusion des modalités visuelles et textuelles, par rapport à des schémas de fusion standards. [...]
This Ph.D thesis is dedicated to visual concept recognition (VCR). Due to many realistic difficulties, it is still considered to be one of the most challenging problems in computer vision and pattern recognition. In this context, we have proposed some innovative contributions for the task of VCR, particularly in building multimodal approaches that efficiently combine visual and textual information. Firstly, we have proposed semantic features for VCR and have investigated the efficiency of different types of low-level visual features for VCR including color, texture and shape. Specifically, we believe that different concepts require different features to efficiently characterize them for the recognition. Therefore, we have investigated in the context of VCR various visual representations, not only global features including color, shape and texture, but also the state-of-the-art local visual descriptors such as SIFT, Color SIFT, HOG, DAISY, LBP, Color LBP. To help bridging the semantic gap between low-level visual features and high level semantic concepts, and particularly those related to emotions and feelings, we have proposed mid-level visual features based on the visual harmony and dynamism semantics using Itten’s color theory and psychological interpretations. Moreover, we have employed a spatial pyramid strategy to capture the spatial information when building our mid-level features harmony and dynamism. We have also proposed a new representation of color HSV histograms by employing a visual attention model to identify the regions of interest in images. Secondly, we have proposed a novel textual feature designed for VCR. Indeed, most of online-shared photos provide textual descriptions in the form of tags or legends. In fact, these textual descriptions are a rich source of semantic information on visual data that is interesting to consider for the purpose of VCR or multimedia information retrieval. We propose the Histograms of Textual Concepts (HTC) to capture the semantic relatedness of concepts. The general idea behind HTC is to represent a text document as a histogram of textual concepts towards a vocabulary or dictionary, whereas its value is the accumulation of the contribution of each word within the text document toward the underlying concept according to a predefined semantic similarity measure. Several variants of HTC have been proposed that revealed to be very efficient for VCR. Inspired by the Cepstral speech analysis process, we have also developed Cepstral HTC to capture both term frequency-based information (like TF-IDF) and the relatedness of semantic concepts in the sparse image tags, which overcomes the HTC’s shortcoming of ignoring term frequency-based information. Thirdly, we have proposed a fusion scheme to combine different sources of Later Fusion, (SWLF) is designed to select the best features and to weight their scores for each concept to be recognized. SWLF proves particularly efficient for fusing visual and textual modalities in comparison with some other standard fusion schemes. While a late fusion at score level is reputed as a simple and effective way to fuse features of different nature for machine-learning problems, the proposed SWLF builds on two simple insights. First, the score delivered by a feature type should be weighted by its intrinsic quality for the classification problem at hand. Second, in a multi-label scenario where several visual concepts may be assigned to an image, different visual concepts may require different features which best recognize them. In addition to SWLF, we also propose a novel combination approach based on Dempster-Shafer’s evidence theory, whose interesting properties allow fusing different ambiguous sources of information for visual affective recognition. [...]
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Huang, Yu. "Hand gesture recognition methods based on concept learning." Thesis, University of Wales Trinity Saint David, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.667760.

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Li, Yi. "Object and concept recognition for content-based image retrieval /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/7006.

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Osborne, John D., Matthew B. Neu, Maria I. Danila, Thamar Solorio, and Steven J. Bethard. "CUILESS2016: a clinical corpus applying compositional normalization of text mentions." BIOMED CENTRAL LTD, 2018. http://hdl.handle.net/10150/626563.

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Background: Traditionally text mention normalization corpora have normalized concepts to single ontology identifiers ("pre-coordinated concepts"). Less frequently, normalization corpora have used concepts with multiple identifiers ("post-coordinated concepts") but the additional identifiers have been restricted to a defined set of relationships to the core concept. This approach limits the ability of the normalization process to express semantic meaning. We generated a freely available corpus using post-coordinated concepts without a defined set of relationships that we term "compositional concepts" to evaluate their use in clinical text. Methods: We annotated 5397 disorder mentions from the ShARe corpus to SNOMED CT that were previously normalized as "CUI-less" in the "SemEval-2015 Task 14" shared task because they lacked a pre-coordinated mapping. Unlike the previous normalization method, we do not restrict concept mappings to a particular set of the Unified Medical Language System (UMLS) semantic types and allow normalization to occur to multiple UMLS Concept Unique Identifiers (CUIs). We computed annotator agreement and assessed semantic coverage with this method. Results: We generated the largest clinical text normalization corpus to date with mappings to multiple identifiers and made it freely available. All but 8 of the 5397 disorder mentions were normalized using this methodology. Annotator agreement ranged from 52.4% using the strictest metric (exact matching) to 78.2% using a hierarchical agreement that measures the overlap of shared ancestral nodes. Conclusion: Our results provide evidence that compositional concepts can increase semantic coverage in clinical text. To our knowledge we provide the first freely available corpus of compositional concept annotation in clinical text.
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Savkov, Aleksandar Dimitrov. "Deciphering clinical text : concept recognition in primary care text notes." Thesis, University of Sussex, 2017. http://sro.sussex.ac.uk/id/eprint/68232/.

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Electronic patient records, containing data about the health and care of a patient, are a valuable source of information for longitudinal clinical studies. The General Practice Research Database (GPRD) has collected patient records from UK primary care practices since the late 1980s. These records contain both structured data (in the form of codes and numeric values) and free text notes. While the structured data have been used extensively in clinical studies, there are significant practical obstacles in extracting information from the free text notes. The main obstacles are data access restrictions, due to the presence of sensitive information, and the specific language of medical practitioners, which renders standard language processing tools ineffective. The aim of this research is to investigate approaches for computer analysis of free text notes. The research involved designing a primary care text corpus (the Harvey Corpus) annotated with syntactic chunks and clinically-relevant semantic entities, developing a statistical chunking model, and devising a novel method for applying machine learning for entity recognition based on chunk annotation. The tools produced would facilitate reliable information extraction from primary care patient records, needed for the development of clinically-related research. The three medical concept types targeted in this thesis could contribute to epidemiological studies by enhancing the detection of co-morbidities, and better analysing the descriptions of patient experiences and treatments. The main contributions of the research reported in this thesis are: guidelines for chunk and concept annotation of clinical text, an approach to maximising agreement between human annotators, the Harvey Corpus, a method for using a standard part-of-speech tagging model in clinical text chunking, and a novel approach to recognising clinically relevant medical concepts.
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Laerhoven, Kristof van. "Embedded perception : concept recognition by learning and combining sensory data." Thesis, Lancaster University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443519.

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Wei, Xiaoyong. "Concept-based video search by semantic and context reasoning /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-cs-b23750509f.pdf.

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Thesis (Ph.D.)--City University of Hong Kong, 2009.
"Submitted to Department of Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 122-133)
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Binder, Alexander Verfasser], and Klaus-Robert [Akademischer Betreuer] [Müller. "Bag of Machine Learning Concepts for Visual Concept Recognition in Images / Alexander Binder. Betreuer: Klaus-Robert Müller." Berlin : Universitätsbibliothek der Technischen Universität Berlin, 2013. http://d-nb.info/1033640409/34.

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Wright, Christopher Paul. "Software architectures for visual concept refinement in digital mapping." Thesis, University of Hull, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318373.

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Lam, Yuk-chau Emily. "The development of the 'word unit' concept by Cantonese-speaking children." Click to view the E-thesis via HKUTO, 1997. http://sunzi.lib.hku.hk/hkuto/record/B36209478.

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Thesis (B.Sc)--University of Hong Kong, 1997.
"A dissertation submitted in partial fulfilment of the requirements for the Bachelor of Science (Speech and Hearing Sciences), The University of Hong Kong, April 30, 1997." Also available in print.
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Books on the topic "Concept recognition"

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F, McEvey Shane, ed. Evolution and the recognition concept of species: Collected writings. Baltimore: Johns Hopkins University Press, 1993.

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service), SpringerLink (Online, ed. Soft Computing Approach to Pattern Classification and Object Recognition: A Unified Concept. New York, NY: Springer New York, 2012.

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Vat︠s︡ov, Dimitŭr. Svoboda i priznavane: Interaktivnite izvori na identichnostta. Sofii︠a︡: Nov bŭlgarski universitet, 2006.

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Honneth, Axel. Das Ich im Wir: Studien zur Anerkennungstheorie. Berlin: Suhrkamp, 2010.

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Cavadini, Marco. Concept and model of a multiprocessor system for high resolution image correlation. Konstanz: Hartung-Gorre, 1999.

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Chicana/o subjectivity and the politics of identity: Between recognition and revolution. New York: Palgrave Macmillan, 2011.

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Recognizing other subjects: Feminist pastoral theology and the challenge of identity. Eugene, Oregon: Pickwick Publications, 2015.

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1968-, Garland Christina, ed. Life review in health and social care: A practitioner's guide. Hove [U.K.]: Brunner-Routledge, 2001.

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service), SpringerLink (Online, ed. Bisociative Knowledge Discovery: An Introduction to Concept, Algorithms, Tools, and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Joaquim P. Marques de Sá. Pattern Recognition: Concepts, Methods and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001.

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Book chapters on the topic "Concept recognition"

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Yan, Wei, and Bob Zhang. "Robust Constrained Concept Factorization." In Computational Intelligence for Pattern Recognition, 207–25. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89629-8_7.

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Bertet, Karell, and Jean-Marc Ogier. "Graphic Recognition: The Concept Lattice Approach." In Graphics Recognition. Recent Advances and Perspectives, 265–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25977-0_25.

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Berchtold, Martin, Matthias Budde, Hedda R. Schmidtke, and Michael Beigl. "An Extensible Modular Recognition Concept That Makes Activity Recognition Practical." In KI 2010: Advances in Artificial Intelligence, 400–409. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16111-7_46.

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Pinho, Armando J., Sara P. Garcia, Paulo J. S. G. Ferreira, Vera Afreixo, Carlos A. C. Bastos, António J. R. Neves, and João M. O. S. Rodrigues. "Exploring Homology Using the Concept of Three-State Entropy Vector." In Pattern Recognition in Bioinformatics, 161–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16001-1_14.

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Nowak, Stefanie, Allan Hanbury, and Thomas Deselaers. "Object and Concept Recognition for Image Retrieval." In ImageCLEF, 199–219. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15181-1_11.

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Meena, Seema, and Bipul Kumar. "Number Plate Recognition: Concept and Its Applications." In Algorithms for Intelligent Systems, 667–72. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6707-0_65.

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Bichindaritz, Isabelle, and Sarada Akkineni. "Concept Mining for Indexing Medical Literature." In Machine Learning and Data Mining in Pattern Recognition, 682–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11510888_68.

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Bouthinon, Dominique, Henry Soldano, and Véronique Ventos. "Concept Learning from (Very) Ambiguous Examples." In Machine Learning and Data Mining in Pattern Recognition, 465–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03070-3_35.

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AL-Qawasmeh, Najla, and Ching Y. Suen. "Gender Detection from Handwritten Documents Using Concept of Transfer-Learning." In Pattern Recognition and Artificial Intelligence, 3–13. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_1.

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Rusiñol, Marçal, Karell Bertet, Jean-Marc Ogier, and Josep Lladós. "Symbol Recognition Using a Concept Lattice of Graphical Patterns." In Graphics Recognition. Achievements, Challenges, and Evolution, 187–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13728-0_17.

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Conference papers on the topic "Concept recognition"

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Liu, Jingen, Qian Yu, Omar Javed, Saad Ali, Amir Tamrakar, Ajay Divakaran, Hui Cheng, and Harpreet Sawhney. "Video event recognition using concept attributes." In 2013 IEEE Workshop on Applications of Computer Vision (WACV). IEEE, 2013. http://dx.doi.org/10.1109/wacv.2013.6475038.

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Ma, Xiang, Tong Zhao, Ruoshi Wen, Zhaojun Wu, and Qiang Wang. "Motion recognition based on concept learning." In 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2017. http://dx.doi.org/10.1109/i2mtc.2017.7969730.

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Xu, Baohan, Yingbin Zheng, Hao Ye, Caili Wu, Heng Wang, and Gufei Sun. "Video Emotion Recognition with Concept Selection." In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019. http://dx.doi.org/10.1109/icme.2019.00077.

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Teptiuk, Alisa. "BETTER WORLD WITH FACIAL RECOGNITION TECHNOLOGY." In THEORETICAL AND EMPIRICAL SCIENTIFIC RESEARCH: CONCEPT AND TRENDS, chair Olha Denisova. European Scientific Platform, 2021. http://dx.doi.org/10.36074/logos-28.05.2021.v1.59.

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"A Multi-Resolution Learning Approach to Tracking Concept Drift and Recurrent Concepts." In 5th International Workshop on Pattern Recognition in Information Systems (PRIS-2004). SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0002568900520062.

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Ma, Xiang, Lin Yuan, Ruoshi Wen, and Qiang Wang. "Sign language recognition based on concept learning." In 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2020. http://dx.doi.org/10.1109/i2mtc43012.2020.9128734.

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Nero, Matthew, Chuanhe Shan, Li-C. Wang, and Nik Sumikawa. "Concept Recognition in Production Yield Data Analytics." In 2018 IEEE International Test Conference (ITC). IEEE, 2018. http://dx.doi.org/10.1109/test.2018.8624714.

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Hataoka, Amano, and Ichikawa. "Large vocabulary speech recognition using concept networks." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118319.

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Ruprecht, Blake, Derek T. Anderson, Fred E. Petry, James M. Keller, Chris Michael, Andrew Buck, Grant J. Scott, and Curt H. Davis. "Concept learning based on human interaction and explainable AI." In Pattern Recognition and Tracking XXXII, edited by Mohammad S. Alam. SPIE, 2021. http://dx.doi.org/10.1117/12.2587950.

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Ji, Zhong, and Yuting Su. "Video shot classification with concept detection." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by S. J. Maybank, Mingyue Ding, F. Wahl, and Yaoting Zhu. SPIE, 2007. http://dx.doi.org/10.1117/12.749160.

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Reports on the topic "Concept recognition"

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Ross, Timothy D., Lori A. Westerkamp, David A. Gadd, and Robert B. Kotz. Feature and Extractor Evaluation Concepts for Automatic Target Recognition (ATR). Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada388215.

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Perrin, Jean-Patrick. Why We Care: An overview of the distribution of unpaid care work in Ma’an, southern Jordan. Oxfam, June 2021. http://dx.doi.org/10.21201/2021.7741.

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The concept of unpaid care work is not widely known in Ma’an or other parts of Jordan. As a result, the benefits of unpaid care to individuals’ lives, as well as its negative impact on women who bear a disproportionate share of it at the household level, are overlooked by both local communities and policy makers. As such, women remain largely excluded from playing an active role in the economy, and receive limited or no recognition for the significant role that they play within the household. In 2020, Oxfam commissioned a study on unpaid care work in Jordan’s southern region of Ma’an. The purpose of the study was to better understand what care work women and men do, how it is distributed, and how people think about it. The study found that women perform the vast majority of care work activities, and that gender norms compound an unequal redistribution of unpaid care. This paper presents the study results and makes recommendations on how the Government of Jordan, donors and NGOs can encourage the redistribution of unpaid care work and improve women’s access to livelihood opportunities.
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African Open Science Platform Part 1: Landscape Study. Academy of Science of South Africa (ASSAf), 2019. http://dx.doi.org/10.17159/assaf.2019/0047.

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This report maps the African landscape of Open Science – with a focus on Open Data as a sub-set of Open Science. Data to inform the landscape study were collected through a variety of methods, including surveys, desk research, engagement with a community of practice, networking with stakeholders, participation in conferences, case study presentations, and workshops hosted. Although the majority of African countries (35 of 54) demonstrates commitment to science through its investment in research and development (R&D), academies of science, ministries of science and technology, policies, recognition of research, and participation in the Science Granting Councils Initiative (SGCI), the following countries demonstrate the highest commitment and political willingness to invest in science: Botswana, Ethiopia, Kenya, Senegal, South Africa, Tanzania, and Uganda. In addition to existing policies in Science, Technology and Innovation (STI), the following countries have made progress towards Open Data policies: Botswana, Kenya, Madagascar, Mauritius, South Africa and Uganda. Only two African countries (Kenya and South Africa) at this stage contribute 0.8% of its GDP (Gross Domestic Product) to R&D (Research and Development), which is the closest to the AU’s (African Union’s) suggested 1%. Countries such as Lesotho and Madagascar ranked as 0%, while the R&D expenditure for 24 African countries is unknown. In addition to this, science globally has become fully dependent on stable ICT (Information and Communication Technologies) infrastructure, which includes connectivity/bandwidth, high performance computing facilities and data services. This is especially applicable since countries globally are finding themselves in the midst of the 4th Industrial Revolution (4IR), which is not only “about” data, but which “is” data. According to an article1 by Alan Marcus (2015) (Senior Director, Head of Information Technology and Telecommunications Industries, World Economic Forum), “At its core, data represents a post-industrial opportunity. Its uses have unprecedented complexity, velocity and global reach. As digital communications become ubiquitous, data will rule in a world where nearly everyone and everything is connected in real time. That will require a highly reliable, secure and available infrastructure at its core, and innovation at the edge.” Every industry is affected as part of this revolution – also science. An important component of the digital transformation is “trust” – people must be able to trust that governments and all other industries (including the science sector), adequately handle and protect their data. This requires accountability on a global level, and digital industries must embrace the change and go for a higher standard of protection. “This will reassure consumers and citizens, benefitting the whole digital economy”, says Marcus. A stable and secure information and communication technologies (ICT) infrastructure – currently provided by the National Research and Education Networks (NRENs) – is key to advance collaboration in science. The AfricaConnect2 project (AfricaConnect (2012–2014) and AfricaConnect2 (2016–2018)) through establishing connectivity between National Research and Education Networks (NRENs), is planning to roll out AfricaConnect3 by the end of 2019. The concern however is that selected African governments (with the exception of a few countries such as South Africa, Mozambique, Ethiopia and others) have low awareness of the impact the Internet has today on all societal levels, how much ICT (and the 4th Industrial Revolution) have affected research, and the added value an NREN can bring to higher education and research in addressing the respective needs, which is far more complex than simply providing connectivity. Apart from more commitment and investment in R&D, African governments – to become and remain part of the 4th Industrial Revolution – have no option other than to acknowledge and commit to the role NRENs play in advancing science towards addressing the SDG (Sustainable Development Goals). For successful collaboration and direction, it is fundamental that policies within one country are aligned with one another. Alignment on continental level is crucial for the future Pan-African African Open Science Platform to be successful. Both the HIPSSA ((Harmonization of ICT Policies in Sub-Saharan Africa)3 project and WATRA (the West Africa Telecommunications Regulators Assembly)4, have made progress towards the regulation of the telecom sector, and in particular of bottlenecks which curb the development of competition among ISPs. A study under HIPSSA identified potential bottlenecks in access at an affordable price to the international capacity of submarine cables and suggested means and tools used by regulators to remedy them. Work on the recommended measures and making them operational continues in collaboration with WATRA. In addition to sufficient bandwidth and connectivity, high-performance computing facilities and services in support of data sharing are also required. The South African National Integrated Cyberinfrastructure System5 (NICIS) has made great progress in planning and setting up a cyberinfrastructure ecosystem in support of collaborative science and data sharing. The regional Southern African Development Community6 (SADC) Cyber-infrastructure Framework provides a valuable roadmap towards high-speed Internet, developing human capacity and skills in ICT technologies, high- performance computing and more. The following countries have been identified as having high-performance computing facilities, some as a result of the Square Kilometre Array7 (SKA) partnership: Botswana, Ghana, Kenya, Madagascar, Mozambique, Mauritius, Namibia, South Africa, Tunisia, and Zambia. More and more NRENs – especially the Level 6 NRENs 8 (Algeria, Egypt, Kenya, South Africa, and recently Zambia) – are exploring offering additional services; also in support of data sharing and transfer. The following NRENs already allow for running data-intensive applications and sharing of high-end computing assets, bio-modelling and computation on high-performance/ supercomputers: KENET (Kenya), TENET (South Africa), RENU (Uganda), ZAMREN (Zambia), EUN (Egypt) and ARN (Algeria). Fifteen higher education training institutions from eight African countries (Botswana, Benin, Kenya, Nigeria, Rwanda, South Africa, Sudan, and Tanzania) have been identified as offering formal courses on data science. In addition to formal degrees, a number of international short courses have been developed and free international online courses are also available as an option to build capacity and integrate as part of curricula. The small number of higher education or research intensive institutions offering data science is however insufficient, and there is a desperate need for more training in data science. The CODATA-RDA Schools of Research Data Science aim at addressing the continental need for foundational data skills across all disciplines, along with training conducted by The Carpentries 9 programme (specifically Data Carpentry 10 ). Thus far, CODATA-RDA schools in collaboration with AOSP, integrating content from Data Carpentry, were presented in Rwanda (in 2018), and during17-29 June 2019, in Ethiopia. Awareness regarding Open Science (including Open Data) is evident through the 12 Open Science-related Open Access/Open Data/Open Science declarations and agreements endorsed or signed by African governments; 200 Open Access journals from Africa registered on the Directory of Open Access Journals (DOAJ); 174 Open Access institutional research repositories registered on openDOAR (Directory of Open Access Repositories); 33 Open Access/Open Science policies registered on ROARMAP (Registry of Open Access Repository Mandates and Policies); 24 data repositories registered with the Registry of Data Repositories (re3data.org) (although the pilot project identified 66 research data repositories); and one data repository assigned the CoreTrustSeal. Although this is a start, far more needs to be done to align African data curation and research practices with global standards. Funding to conduct research remains a challenge. African researchers mostly fund their own research, and there are little incentives for them to make their research and accompanying data sets openly accessible. Funding and peer recognition, along with an enabling research environment conducive for research, are regarded as major incentives. The landscape report concludes with a number of concerns towards sharing research data openly, as well as challenges in terms of Open Data policy, ICT infrastructure supportive of data sharing, capacity building, lack of skills, and the need for incentives. Although great progress has been made in terms of Open Science and Open Data practices, more awareness needs to be created and further advocacy efforts are required for buy-in from African governments. A federated African Open Science Platform (AOSP) will not only encourage more collaboration among researchers in addressing the SDGs, but it will also benefit the many stakeholders identified as part of the pilot phase. The time is now, for governments in Africa, to acknowledge the important role of science in general, but specifically Open Science and Open Data, through developing and aligning the relevant policies, investing in an ICT infrastructure conducive for data sharing through committing funding to making NRENs financially sustainable, incentivising open research practices by scientists, and creating opportunities for more scientists and stakeholders across all disciplines to be trained in data management.
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