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This replication package contains the code and data used to obtain the results presented in the paper “How Digital Intelligence Transformation Creates Firm Value: A Real Options Perspective” by Chengxing Xie, Liang Wu, Zhijie Tong and Yu Chen. Specifically, this reproduction package contains two folders (Code and Data). The Data folder includes all data used in the paper; executing the commands in the Code folder will reproduce the figures and tables reported in the paper.

List of articles after abstract selection and List of articles after title selection

本文以2020—2024年中国沪深A股上市公司为研究样本，探究数字化创新与企业环境绩效之间的关系。企业数字化转型数据是基于上市公司年度报告，通过Python文本挖掘方法提取数字化相关关键词词频，来源于CNRDS数据库和CSMAR数据库的上市公司年报文本；企业环境绩效数据主要来源于上市公司年度报告、社会责任报告和可持续发展报告中的环境信息披露，同时结合华政数据库中的ESG评级数据；绿色专利数据来源于国家知识产权局专利数据库，依据世界知识产权组织发布的“国际专利分类绿色清单”识别绿色专利类型，数据获取自CNRDS绿色专利研究数据库；财务与公司治理数据来源于CSMAR数据库和Wind数据库，包括企业总资产、独立董事占比等信息。为确保数据的有效性与研究的严谨性，对初始样本进行筛选，考虑到极端值的影响，剔除了五年内绿色专利申请数少于20且大于1000的企业数据；剔除核心变量数据缺失的样本。考虑到企业的持续创新能力，本文选取2020年至2024年间至少有三年申请绿色专利的企业数据。

All data underlying this study are publicly available from USDA NASS (Cost of Pollination, Honey Bee Colonies, Honey, California Almond Acreage), USDA APHIS / us-beedata.org, BLS (CPI-U, PPI Farm Inputs), and Project Apis m. annual loss surveys. The replication package — raw data, cleaned panels, regression code, and figure scripts — will be deposited in Mendeley Data with DOI to follow.

Appendix A: This folder contains the equations of the model used for calculating the hydrogen storage cost. The equations are provided in detail to facilitate understanding and reproducibility of the cost assessment methodology. Appendix B: This folder includes the MATLAB Software code developed for the calculation of hydrogen storage cost. The code implements the model equations described in Appendix A and allows users to perform cost analyses under various scenarios.

Raman spectra and SEM EDS data for "Moissanite and carbonaceous material record organic carbon recycling and reduced conditions in supra-subduction zones."

With the help of IBM SPSS Statistics version 27 software, we have completed the data analysis work. The distribution of the data obtained from the questionnaire after the experiment was examined, and the basic assumptions required to meet the parameter test were verified. For the questionnaire data using the Likert scale, we use the one-way ANOVA method to deal with it. In order to compare the differences between different experimental conditions, the Bonferroni post-exact test was further applied. The average score of the respondents on all 189 questions (each experimental condition corresponds to 63 questions) was calculated, and then the one-way ANOVA was used to test whether the difference between the three conditional groups.

This dataset contains 1,545 leaf images collected from 7 different crops, including both healthy and diseased leaves. The images were captured in different environmental conditions to make the dataset more diverse and useful for real-world applications. The dataset contains images of Bitter gourd (303)- Bunchy top of bitter gourd (12), Downy mildew (118), Leaf curl(mosaic virus) (132), Healthy leaf (41); Brinjal (298)- Cercospora leaf spot (110), Early blight (81), Leaf curl (58), Healthy leaf (49); Ladies finger (252)- Cercospora leaf spot (41), , Leaf curl(68), mosaic virus (46), Healthy leaf (44); Mung bean (135)- caterpillars (Phaseolus vulgaris) (37), Mung bean Yellow Mosaic Virus (MYMV) (12), Leaf curl (50), Healthy leaf) (36); Sesame (222)- Bacterial leaf spot (Pseudomonas sesami) (36), caterpillars(Phaseolus vulgaris)(65), Sesame yellow mosaic virus (SYMV)(27), Leaf curl(38), Healthy leaf(56); Snake gourd(153)- (Anthracnose (Colletotrichum spp.) (31), Leaf Curl(54) Yellow Mosaic Virus (35), Healthy leaf (33)and Yard long bean-(182) (Cercospora leaf spot (55), leaf curl virus (48), yellow mosaic virus (48), Healthy leaf (31).All images were resized to 256×256 pixels for faster and more efficient processing. After preprocessing, the dataset was augmented and expanded to a total of 12,360 images while maintaining class balance. This dataset can be used for image classification, transfer learning, and deep learning models such as CNNs and transformers in plant disease detection and precision agriculture research.

raw data

This dataset contains the primary empirical findings from a comprehensive cybersecurity audit conducted across 30 domestic financial institutions in Ethiopia, including Tier-1 commercial banks, mid-tier lenders, and microfinance institutions (MFIs). The research was designed to evaluate the "Techno-Functional" alignment of the sector following the implementation of the National Digital Payments Strategy (NDPS) 2030. ​Key components of the dataset include: ​Maturity Matrices: Quantitative assessment of 153 technical and administrative controls across five core domains: (1) Cyber Risk Management, (2) Threat Intelligence, (3) Technical Controls, (4) Vendor Risk, and (5) Incident Management. ​The Resilience Divide Data: Stratified mean scores identifying the maturity gap between systemically important banks (Group A) and evolving lenders (Group C), supported by a One-Way ANOVA (F=159.23, p < 0.001). ​Recovery Performance Metrics: Empirical analysis of the "Golden Hour" containment window, utilizing data from the 2024 fraud surge to map unrecoverable losses against mean-time-to-recovery (MTTR). ​Structural Correlation: Data demonstrating the high-degree correlation (r = 0.88) between specialized staffing levels and institutional technical maturity. ​This data provides the foundational evidence for the proposed Sovereign Shield (Regulator-as-a-Service) architecture, offering a blueprint for national-level cybersecurity resilience in emerging digital economies.