大數據和先進計算能力的結合促進了用於跨科學領域科學發現的日益複雜的軟體，從而導致複雜的計算研究。與此同時，計算研究的挑戰正在增加，但不僅限於計算可重複性。軟體堆棧的複雜性和科學軟體的脆弱性等挑戰使得審閱者難以在他們自己的計算環境上複製和驗證研究，以及社群中的其他科學家重用和擴展代碼。我們研究了從概念形成到研究發表的計算研究如何通過轉向計算韌性(計算可再現性、計算 可複制性、操作穩健性、代碼可重用性和代碼可擴展性)來提高其應對破壞性挑戰的固有能力。然後，我們提出了支持原則(開放性、透明性、可驗證性、可分發性和代碼可讀性)及其相關技術，以介紹本身社群很重要的價值觀，並描述了我們希望建立 良好的計算研究。我們還通過回顧《管理科學》上發表的 264 項研究中的 36 項，發現這些研究中確實存在計算韌性問題，這些研究的科學軟體主要是用 R 或 Python 編寫的。最後，我們為科學家們提出了計算韌性的路線圖和設計，幫助他們評估研究工件的狀態和方向，以改進和組織它們在一個乾淨的結構中。

The nexus of big data and advanced computing power has facilitated increasingly complex software used for scientific discovery across scientific fields, resulting in complex computational research. At the same time, the challenges of computational research are increasing but are not only limited to computational reproducibility. Challenges such as the complexity of software stack and the fragility of scientific software make it difficult for reviewers to reproduce and verify research on their own computational environments, and for other scientists in the community to reuse and extend code.
We examine how computational research, from concept formation to research publication, can improve its inherent ability to respond to disruptive challenges, through moving towards computational resilience (computational reproducibility, computational replicability, operational robustness, code reusability, and code extensibility). Then, we propose supporting principles (openness, transparency, verifiability, distributability, and code readability) with their relevant techniques to introduce the community values that are important in themselves, and they describe how we want to get there to build good computational research. We also find that computational resilience issues do exist in the studies by reviewing 36 of the 264 studies published in Management Science, whose software was primarily written in R or Python. Finally, we present a roadmap and design toward computational resilience for scientists, assisting them in evaluating the status and direction of research artifacts to improve and organizing them in a clean package structure.

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