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Data and progress in peace and conflict research

Author

Listed:
  • Kristian Skrede Gleditsch

    (University of Essex & Peace Research Institute Oslo (PRIO))

  • Nils W Metternich

    (University College London)

  • Andrea Ruggeri

    (University of Amsterdam)

Abstract

We highlight how efforts to collect systematic data on conflict have helped foster progress in peace and conflict research. The Journal of Peace Research has played a key role in these developments, and has become a leading outlet for the new wave of disaggregated conflict data. We survey progress in the development of conflict data and how this interacts with theory development and progress in research, drawing specifically on examples from the move towards a greater focus on disaggregation and agency in conflict research. We focus on disaggregation in three specific dimensions, namely the resolution of conflict data, agency in conflict data, and the specific strategies used in conflict, and we also discuss new efforts to study conflict processes beyond the use of violence. We look ahead to new challenges in conflict research and how data developments and the emergence of ‘big data’ push us to think harder about types of conflict, agency, and the ‘right’ level of aggregation for querying data and evaluating specific theories.

Suggested Citation

  • Kristian Skrede Gleditsch & Nils W Metternich & Andrea Ruggeri, 2014. "Data and progress in peace and conflict research," Journal of Peace Research, Peace Research Institute Oslo, vol. 51(2), pages 301-314, March.
    • Handle: RePEc:sae:joupea:v:51:y:2014:i:2:p:301-314
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    Cited by:

    1. Duursma, Allard, 2017. "Data synthesis paper, July 2017," LSE Research Online Documents on Economics 100163, London School of Economics and Political Science, LSE Library.
    2. Duursma, Allard & Twagiramungu, Noel & Gebrehiwot Berhe, Mulugeta & De Waal, Alex, 2019. "Introducing the transnational conflict in Africa dataset," LSE Research Online Documents on Economics 101658, London School of Economics and Political Science, LSE Library.
    3. Buscema, Massimo & Ferilli, Guido & Sacco, Pier Luigi, 2017. "What kind of ‘world order’? An artificial neural networks approach to intensive data mining," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 46-56.
    4. Mat'uv{s} Maciak & Ostap Okhrin & Michal Pev{s}ta, 2019. "Infinitely Stochastic Micro Forecasting," Papers 1908.10636, arXiv.org, revised Sep 2019.

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