Relevant bibliographies by topics / Dependence modelling

Contents

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

Academic literature on the topic 'Dependence modelling'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Dependence modelling"

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Kallenberg, Wilbert C. M. "Modelling dependence." Insurance: Mathematics and Economics 42, no. 1 (February 2008): 127–46. http://dx.doi.org/10.1016/j.insmatheco.2007.01.008.

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Aminuddin Jafry, Nurul Hanis, Ruzanna Ab Razak, and Noriszura Ismail. "Dependence Modelling using GARCH, EGARCH, and Copula Models:." Asia Proceedings of Social Sciences 2, no. 2 (December 3, 2018): 55–59. http://dx.doi.org/10.31580/apss.v2i2.317.

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Copula become a popular tool to measure the dependency between financial data due to its ability to capture the non-normal distributions. Hence, this paper will inspect the impact of input models towards the parameter estimation of marginal and copula models for KLCI and FBMHS returns series by considering the ARMA-GARCH model and the ARMA-EGARCH model. This study also investigates the dependency of Islamic-conventional pair for Malaysia indices by using static copula and time-varying copula approach. The closing prices of Malaysia indices represented by KLCI (conventional) index and FBMHS (Islamic) index for the period of 21 May 2007 until 28 September 2018 are used as a sample data. The results show that KLCI-FBMHS pair is strongly correlated, different input models (ARMA-GARCH and ARMA-EGARCH) have identical dependence structure but slightly different value of parameter estimated, and the time-varying Gaussian copula is chosen as the best dependence model. Finding suggest that the diversification between Islamic-conventional pair is worthwhile during stable period.
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Patton, Andrew J. "MODELLING ASYMMETRIC EXCHANGE RATE DEPENDENCE*." International Economic Review 47, no. 2 (May 2006): 527–56. http://dx.doi.org/10.1111/j.1468-2354.2006.00387.x.

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Wadsworth, J. L., J. A. Tawn, A. C. Davison, and D. M. Elton. "Modelling across extremal dependence classes." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79, no. 1 (February 17, 2016): 149–75. http://dx.doi.org/10.1111/rssb.12157.

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Wadsworth, J. L., and J. A. Tawn. "Dependence modelling for spatial extremes." Biometrika 99, no. 2 (March 13, 2012): 253–72. http://dx.doi.org/10.1093/biomet/asr080.

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El Hannoun, Wafaa, Salah-Eddine El Adlouni, and Abdelhak Zoglat. "Vine-Copula-Based Quantile Regression for Cascade Reservoirs Management." Water 13, no. 7 (March 31, 2021): 964. http://dx.doi.org/10.3390/w13070964.

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This paper features an application of Regular Vine (R-vine) copulas, a recently developed statistical tool to assess composite risk. Copula-based dependence modelling is a popular tool in conditional risk assessment, but is usually applied to pairs of variables. By contrast, Vine copulas provide greater flexibility and permit the modelling of complex dependency patterns using a wide variety of bivariate copulas which may be arranged and analysed in a tree structure to explore multiple dependencies. This study emphasises the use of R-vine copulas in an analysis of the co-dependencies of five reservoirs in the cascade of the Saint-John River basin in Eastern Canada. The developed R-vine copulas lead to the joint and conditional return periods of maximum volumes, for hydrologic design and cascade reservoir management in the basin. The main attraction of this approach to risk modelling is the flexibility in the choice of distributions used to model heavy-tailed marginals and co-dependencies.
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Gitman, I. M., H. Askes, and L. J. Sluys. "Multiscale Modelling of Softening Materials." Key Engineering Materials 348-349 (September 2007): 1–4. http://dx.doi.org/10.4028/www.scientific.net/kem.348-349.1.

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A hierarchical multiscale procedure for softening materials is proposed in this paper. A standard multiscale model has been analysed with respect to macro-level mesh dependence and meso-level cell size dependence. In order to eliminate spurious macro-level mesh dependence and meso-level cell size dependence a coupled-volume approach has been proposed. A discussion on the various interacting length scales in the model is included.
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Lakner, G., J. Lakner, G. Racz, and M. Kłos. "Concentration dependence modelling of reverse osmosis." DESALINATION AND WATER TREATMENT 192 (2020): 437–43. http://dx.doi.org/10.5004/dwt.2020.25906.

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MONNEY, PAUL-ANDRE, and MOSES CHAN. "MODELLING DEPENDENCE IN DEMPSTER-SHAFER THEORY." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 01 (February 2007): 93–114. http://dx.doi.org/10.1142/s021848850700439x.

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Belief functions can only be combined by Dempster's rule when they are based on independent items of evidence. This paper proposes a method for handling the case where there is some probabilistic dependence among the items of evidence. The method relies on compact representations of joint probability distributions on the assumption variables associated with the belief functions. These distributions are then used to compute degrees of support of hypotheses of interest. It is shown that the theory of hints is the appropriate general framework for this method.
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Ledford, Anthony W., and Jonathan A. Tawn. "Modelling Dependence within Joint Tail Regions." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 59, no. 2 (1997): 475–99. http://dx.doi.org/10.1111/1467-9868.00080.

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Dissertations / Theses on the topic "Dependence modelling"

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Taku, Marie Manyi. "Modelling Dependence of Insurance Risks." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-9064.

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Modelling one-dimensional data can be performed by different wellknown ways. Modelling two-dimensional data is a more open question. There is no unique way to describe dependency of two dimensional data. In this thesis dependency is modelled by copulas. Insurance data from two different regions (Göinge and Kronoberg) in Southern Sweden is investigated. It is found that a suitable model is that marginal data are Normal Inverse Gaussian distributed and copula is a better dependence measure than the usual linear correlation together with Gaussian marginals.
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Lecei, Ivan [Verfasser]. "Modelling extremal dependence / Ivan Lecei." Ulm : Universität Ulm, 2018. http://d-nb.info/1173249745/34.

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Johnson, Jill Suzanne. ""Modelling Dependence in Extreme Environmental Events"." Thesis, University of Newcastle upon Tyne, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525050.

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Yu, Lanhua. "Risk management : modelling dependence between asset returns." Thesis, Imperial College London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.420966.

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Navarrete, Miguel A. Ancona. "Dependence modelling and spatial prediction for extreme values." Thesis, Lancaster University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369658.

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Kereszturi, Monika. "Assessing and modelling extremal dependence in spatial extremes." Thesis, Lancaster University, 2017. http://eprints.lancs.ac.uk/86369/.

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Offshore structures, such as oil platforms and vessels, must be built such that they can withstand extreme environmental conditions (e.g., high waves and strong winds) that may occur during their lifetime. This means that it is essential to quantify probabilities of the occurrence of such extreme events. However, a difficulty arises in that there are very limited data available at these levels. The statistical field of extreme value theory provides asymptotically motivated models for extreme events, hence allowing extrapolation to very rare events. In addition to the risk to a single site, we are also interested in the joint risk of multiple offshore platforms being affected by the same extreme event. In order to understand joint extremal behaviour for two or more locations, the spatial dependence between the different locations must be considered. Extremal dependence between two locations can be of two types: asymptotic independence (AI) when the extremes at the two sites are unlikely to occur together, and asymptotic dependence (AD) when it is possible for both sites to be affected simultaneously. For finite samples it is often difficult to determine which type of dependence the data are more consistent with. In a large ocean basin it is reasonable to expect both of these features to be present, with some close by locations AD, with the dependence decreasing with distance, and some far apart locations AI. In this thesis we develop new diagnostic tools for distinguishing between AD and AI and illustrate these on North Sea wave height data. We also investigate how extremal dependence changes with direction and find evidence for spatial anisotropy in our data set. The most widely used spatial models assume asymptotic dependence or perfect independence between sites, which is often unrealistic in practice. Models that attempt to capture both AD and AI exist, but they are difficult to implement in practice due to their complexity and they are restricted in the forms of AD and AI they can model. In this thesis we introduce a family of bivariate distributions that exhibits all the required features of short, medium and long range extremal dependence required for pairwise dependence modelling in spatial applications.
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Menguturk, Levent Ali. "Information-based jumps, asymmetry and dependence in financial modelling." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/10953.

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In mathematical finance, economies are often presented with the specification of a probability space equipped with a filtration that encodes information flow. The information-based framework of Brody, Hughston and Macrina (BHM) emphasises the role of market information in deriving asset price dynamics, instead of assuming price behaviour from the start. We extend the BHM framework by (i) modelling the nature of access to information through information blockages and activations of new information sources, and (ii) introducing a new class of multivariate Markov processes that we call Generalised Liouville Processes (GLPs) which can model the flow of information about vectors of assets. The analysis of access to information allows us to derive price dynamics with jumps. It additionally enables us to develop an information-switching framework, and price derivatives under regime-switching economies. We also indicate some geometrical aspects of appearances of new information sources. We represent information jumps on the unit sphere in the Hilbert space of square-integrable functions, and on hyperbolic spaces. We use differential geometry, information theory and what we call n-order piecewise enlargements of filtrations to dynamically quantify the impact of sudden changes in the sources of information. This helps us to model the stochastic evolution of what may be viewed as information asymmetry. In related work, we construct GLPs on finite time horizons by splitting so-called Levy random bridges into non-overlapping subprocesses. The terminal values of GLPs have generalised multivariate Liouville distributions, and GLPs can model a wide spectrum of information-driven dependence structures between assets. The law of an n-dimensional GLP under an equivalent measure is that of an n-vector of independent Levy processes. We focus on a special type of GLPs that we call Archimedean Survival Processes (ASPs). The terminal value of an ASP has an [Symbol appears here. To view, please open pdf attachment] 1-norm symmetric distribution, and hence, an Archimedean survival copula.
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Carreira, Inês Duarte. "Modelling dependence between frequency and severity of insurance claims." Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/14631.

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Mestrado em Actuarial Science
A estimação da perda individual é uma importante tarefa para calcular os preços das apólices de seguro. A abordagem padrão assume independência entre a frequência e a severidade dos sinistros, o que pode não ser uma suposição realística. Neste texto, a dependência entre números e montantes de sinistros é explorada, num contexto de Modelos Lineares Generalizados. Um modelo de severidade condicional e um modelo de Cópula são apresentados como alternativas para modelar esta dependência e posteriormente aplicados a um conjunto de dados fornecido por uma seguradora portuguesa. No final, a comparação com o cenário de independência é realizada.
The estimation of the individual loss is an important task to price insurance policies. The standard approach assumes independence between claim frequency and severity, which may not be a realistic assumption. In this text, the dependence between claim counts and claim sizes is explored, in a Generalized Linear Model framework. A Conditional severity model and a Copula model are presented as alternatives to model this dependence and later applied to a data set provided by a Portuguese insurance company. At the end, the comparison with the independence scenario is carried out.
info:eu-repo/semantics/publishedVersion
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Werner, Christoph. "Structured expert judgement for dependence in probabilistic modelling of uncertainty : advances along the dependence elicitation process." Thesis, University of Strathclyde, 2018. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=30519.

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In decision and risk analysis problems, modelling uncertainty probabilistically provides key insights and information for decision makers. A common challenge is that uncertainties are typically not isolated but interlinked which introduces complex (and often unexpected) effects on the model output. Therefore, dependence needs to be taken into account and modelled appropriately if simplifying assumptions, such as independence, are not sensible. Similar to the case of univariate uncertainty, relevant historical data to quantify a (dependence) model are often lacking or too costly to obtain. This may be true even when data on a model's univariate quantities, such as marginal probabilities, are available. Then, specifying dependence between the uncertain variables through expert judgement is the only sensible option. A structured and formal process to the elicitation is essential for ensuring methodological robustness. This thesis consists of three published works and two papers which are to be published (one under review and one working paper). Two of these works provide comprehensive overviews from different perspectives about the research on dependence elicitation processes. Based on these reviews, novel risk assessment and expert judgement methods are proposed - (1) allowing experts to structure and share their knowledge and beliefs about dependence relationships prior to a quantitative assessment and (2) ensuring experts' (detailed) quantitative assessments are feasible while their elicitation is intuitive. The original research presented in this thesis is applied in case-studies with experts in real risk modelling contexts for the UK Higher Education sector, terrorism risk and future risk of antibacterial multi-drug resistance.
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Xia, Xinghua. "Essays on dependence modelling with vine copulas and its applications." Thesis, University of Leicester, 2018. http://hdl.handle.net/2381/42235.

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This thesis contains three essays on dependence modelling with high dimension vine copulas and its applications in credit portfolio risk, asset allocation and international financial contagion. In the first essay, we demonstrate the superiority of vine copulas over multivariate Gaussian copula when modelling the dependence structure of a credit portfolio risk factors. We introduce the vine copulas to modelling the dependence structure of multi risk factors log returns in the combined framework of both threshold model and mixture model credit risk modelling. The second essay studies asset allocation decisions in the presence of regime switching on asset allocation with alternative investments. We find evidence that two regimes, characterized as bear and bull states, are required to capture the joint distribution of stock, bond and alternative investments returns. Optimal asset allocation varies considerably across these states and changes over time. Therefore, in order to capture observed asymmetric dependence and tail dependence in financial asset returns, we introduce high dimensional vine copula and construct a multivariate vine copula regime-switching model, which account for asymmetric dependence and tail dependence in high dimensional data. The third essay explores the cross-market dependence between six popular equity indices (S&P 500, NASDAQ 100, FTSE 100, DAX 30, Euro Stoxx 50 and Nikkei 225), and their corresponding volatility indices (VIX, VXN, VFTSE, VDAX, VSTOXX and VXJ). In particular, we propose a novel dynamic method that combine the Generalised Autoregressive Score (GAS) Method with high dimension R-vine copula approach which is able to capture the time-varying tail dependence coefficient (TDC) of index returns.
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Books on the topic "Dependence modelling"

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1942-, Cooke Roger, ed. Uncertainty analysis with high dimensional dependence modelling. Chichester, England: Wiley, 2006.

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Kurowicka, Dorota, and Roger Cooke. Uncertainty Analysis with High Dimensional Dependence Modelling. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470863072.

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Breeze, Jonathan. Temperature and Frequency Dependence of Complex Permittivity in Metal Oxide Dielectrics: Theory, Modelling and Measurement. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44547-2.

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Shaw, Michelle. Temperature dependence of conformationally-averaged molecular surface areas and volumes for modelling log pow values. Sudbury, Ont: Laurentian University, 1998.

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Müller, Gereon, Artemis Alexiadou, and Tibor Kiss. Local Modelling of Non-local Dependencies in Syntax. Berlin: De Gruyter, 2013.

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Kähkönen, Kalle. Modelling activity dependencies for building construction project scheduling. Espoo: Technical Research Centre of Finland, 1993.

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Alexiadou, Artemis, Tibor Kiss, and Gereon Müller, eds. Local Modelling of Non-Local Dependencies in Syntax. Berlin, Boston: DE GRUYTER, 2012. http://dx.doi.org/10.1515/9783110294774.

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Habib, Ammari, Capdeboscq Yves 1971-, and Kang Hyeonbae, eds. Multi-scale and high-contrast PDE: From modelling, to mathematical analysis, to inversion : Conference on Multi-scale and High-contrast PDE:from Modelling, to Mathematical Analysis, to Inversion, June 28-July 1, 2011, University of Oxford, United Kingdom. Providence, R.I: American Mathematical Society, 2010.

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Awrejcewicz, Jan, Anton V. Krysko, Maxim V. Zhigalov, and Vadim A. Krysko. Mathematical Modelling and Numerical Analysis of Size-Dependent Structural Members in Temperature Fields. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-55993-9.

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England) International DSM Conference (12th 2010 Cambridge. Managing complexity by modelling dependencies: Proceedings of the 12th International DSM Conference, Cambridge, UK, 22-23 July 2010. Munich, Germany: Hanser, 2010.

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Book chapters on the topic "Dependence modelling"

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Shevchenko, Pavel V. "Modelling Dependence." In Modelling Operational Risk Using Bayesian Inference, 235–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15923-7_7.

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Kushner, Harold J. "State Dependence." In Stochastic Modelling and Applied Probability, 305–39. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0005-2_8.

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Benedini, Marcello, and George Tsakiris. "Temperature Dependence." In Water Quality Modelling for Rivers and Streams, 87–89. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5509-3_8.

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Ellner, Stephen P., Dylan Z. Childs, and Mark Rees. "Density Dependence." In Lecture Notes on Mathematical Modelling in the Life Sciences, 111–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28893-2_5.

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Boland, Philip J. "Modelling Dependence in Coherent Systems." In Reliability and Decision Making, 287–99. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4459-7_16.

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Roy, Sisir. "Quantum Ontology and Context Dependence." In Decision Making and Modelling in Cognitive Science, 141–52. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-3622-1_8.

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D'Inverno, Mark, and Michael Luck. "A formal view of social dependence networks." In Distributed Artificial Intelligence Architecture and Modelling, 115–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61314-5_25.

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Kempson, Ruth. "Modelling Context-Dependence: Ellipsis in Conversational Dialogue." In Modeling and Using Context, 1–4. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24279-3_1.

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Koshelev, V. V., and N. N. Kolpakidi. "An Investigation of the Dependence of Structural Parameters of the Middle Atmosphere on the Intensity of Photochemical and Dynamical Processes." In Ionospheric Modelling, 381–401. Basel: Birkhäuser Basel, 1988. http://dx.doi.org/10.1007/978-3-0348-6532-6_9.

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Munková, Daša, Michal Munk, and L’udmila Adamová. "Modelling of Language Processing Dependence on Morphological Features." In ICT Innovations 2013, 77–86. Heidelberg: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01466-1_7.

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Conference papers on the topic "Dependence modelling"

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Eickhoff, Carsten, Arjen P. de Vries, and Thomas Hofmann. "Modelling Term Dependence with Copulas." In SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2766462.2767831.

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Kulakowski, Krzysztof, Krzysztof Malarz, and Malgorzata J. Krawczyk. "Heavy Context Dependence — Decisions Of Underground Soldiers." In 29th Conference on Modelling and Simulation. ECMS, 2015. http://dx.doi.org/10.7148/2015-0125.

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Petermann, Arne, and Alexander Simon. "Path Dependence In Hierarchical Organizations: The Influence Of Environmental Dynamics." In 30th Conference on Modelling and Simulation. ECMS, 2016. http://dx.doi.org/10.7148/2016-0040.

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Petermann, Arne, and Alexander Simon. "Path Dependence In Hierarchical Organizations: The Influence Of Environmental Dynamics." In 30th Conference on Modelling and Simulation. ECMS, 2016. http://dx.doi.org/10.7148/2016-0041.

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Petermann, Arne, and Alexander Simon. "The Influence Of Changing Environment For Path Dependence In Hierarchical Organizations." In 28th Conference on Modelling and Simulation. ECMS, 2014. http://dx.doi.org/10.7148/2014-0202.

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"Evaluating extremal dependence in stock markets using Extreme Value Theory." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.d6.singh2.

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Petermann, Arne, Stefan Klaußner, and Natalie Senf. "Organizational Path Dependence: The Prevalence Of Positive Feedback Economics In Hierarchical Organizations." In 26th Conference on Modelling and Simulation. ECMS, 2012. http://dx.doi.org/10.7148/2012-0721-0730.

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Clegg, Richard G., Raul Landa, and Miguel Rio. "Criticisms of Modelling Packet Traffic Using Long-Range Dependence." In 2009 Proceedings of 18th International Conference on Computer Communications and Networks - ICCCN 2009. IEEE, 2009. http://dx.doi.org/10.1109/icccn.2009.5235309.

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Kwon, Han-Seung, Artem Lenskiy, and Kseniya Koneva. "Trading Foreign Currencies Based on Long Range Dependence." In 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM). IEEE, 2012. http://dx.doi.org/10.1109/cimsim.2012.73.

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Fuerstenau, Daniel. "Agent-Based Simulation Analysis Of Path Dependence In Corporate IS Networks For Strategic IT Management." In 27th Conference on Modelling and Simulation. ECMS, 2013. http://dx.doi.org/10.7148/2013-0340.

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Reports on the topic "Dependence modelling"

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Haslett, John, and Adrian E. Raftery. Reply to the Discussion of Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Resource. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada201678.

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Phillips, Michael, James Glass, and Victor Zue. Modelling Context Dependency in Acoustic-Phonetic and Lexical Representations. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada460564.

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Rai, Suresh. Multiprocessing Systems: Reliability Modelling and Analysis Using Multimode Components and Dependent Failures. Fort Belvoir, VA: Defense Technical Information Center, October 1992. http://dx.doi.org/10.21236/ada264230.

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Ren, W. Time-Dependent Deformation Modelling for a Chopped-Glass Fiber Composite for Automotive Durability Design Criteria. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/788361.

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Sandermann, Heinrich, Duncan Jr., and Thomas M. Lipid-Dependent Membrane Enzymes. Kinetic Modelling of the Activation of Protein Kinase C by Phosphatidylserine. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada302987.

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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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