Data underlying the thesis: Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data

This thesis-mpc-dataset-public-readme.txt file was generated on 2020-10-20 by Masud Petronia<br><br>GENERAL INFORMATION<br>1. Title of Dataset: Data underlying the thesis: Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data<br>2. Author Information A. Principal Investigator Contact Information Name: Masud Petronia Institution: TU Delft, Faculty of Technology, Policy and Management Address: Mekelweg 5, 2628 CD Delft, Netherlands Email: [e-mail] ORCID: https://orcid.org/0000-0003-2798-046X<br>3: Description of dataset: This dataset contains perceptual data of firms' willingness to contribute protected data through multi party computation (MPC). Petronia (2020, ch. 6) draws several conclusions from this dataset and provides recommendations for future research Petronia (2020, ch. 7.4).<br>4. Date of data collection: July-August 2020<br>5. Geographic location of data collection: Netherlands<br>6. Information about funding sources that supported the collection of the data: Horizon 2020 Research and Innovation Programme, Grant Agreement no 825225 – Safe Data Enabled Economic Development (SAFE-DEED), from the H2020-ICT-2018-2<br><br>SHARING/ACCESS INFORMATION<br>1. Licenses/restrictions placed on the data: CC 0<br>2. Links to publications that cite or use the data: Petronia, M. N. (2020). Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data (Master's thesis). Retrieved from http://resolver.tudelft.nl/uuid:b0de4a4b-f5a3-44b8-baa4-a6416cebe26f<br>3. Was data derived from another source? No<br>4. Citation for this dataset: Petronia, M. N. (2020). Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data (Master's thesis). Retrieved from https://data.4tu.nl/. doi:10.4121/13102430<br><br>DATA &amp; FILE OVERVIEW<br>1. File List: thesis-mpc-dataset-public.xlsxthesis-mpc-dataset-public-readme.txt (this document)<br>2. Relationship between files: Dataset metadata and instructions<br>3. Additional related data collected that was not included in the current data package: Occupation and role of respondents (traceable to unique reference), removed for privacy reasons.<br>4. Are there multiple versions of the dataset? No<br><br>METHODOLOGICAL INFORMATION<br>1. Description of methods used for collection/generation of data: A pre- and post test experimental design. For more information; see Petronia (2020, ch. 5)<br>2. Methods for processing the data: Full instructions are provided by Petronia (2020, ch. 6)<br>3. Instrument- or software-specific information needed to interpret the data: Microsoft Excel can be used to convert the dataset to other formats.<br>4. Environmental/experimental conditions: This dataset comprises three datasets collected through three channels. These channels are Prolific (incentive), LinkedIn/Twitter (voluntarily), and respondents in a lab setting (voluntarily). For more information; see Petronia (2020, ch. 6.1)<br>5. Describe any quality-assurance procedures performed on the data: A thorough examination of consistency and reliability is performed. For more information; see Petronia (2020, ch. 6).<br>6. People involved with sample collection, processing, analysis and/or submission: See Petronia (2020, ch. 6)<br><br>DATA-SPECIFIC INFORMATION<br>1. Number of variables: see worksheet experiment_matrix of thesis-mpc-dataset-public.xlsx<br>2. Number of cases/rows: see worksheet experiment_matrix of thesis-mpc-dataset-public.xlsx<br>3. Variable List: see worksheet labels of thesis-mpc-dataset-public.xlsx<br>4. Missing data codes: see worksheet comments of thesis-mpc-dataset-public.xlsx<br>5. Specialized formats or other abbreviations used: Multiparty computation (MPC) and Trusted Third Party (TTP).<br><br>INSTRUCTIONS<br>1. Petronia (2020, ch. 6) describes associated tests and respective syntax.

thesis-mpc-dataset-public-readme.txt文件由Masud Petronia于2020年10月20日生成。

一、基本概况
1. 数据集标题：支撑学位论文的数据集：多方计算（Multiparty Computation, MPC）：多方计算对企业自愿贡献受保护数据意愿的影响
2. 作者信息
A. 主要研究者联系方式：姓名：Masud Petronia；所属机构：代尔夫特理工大学（TU Delft）技术、政策与管理学院；通讯地址：荷兰代尔夫特市Mekelweg 5号，邮编2628 CD；电子邮箱：[e-mail]；开放研究者与贡献者身份识别码（ORCID）：https://orcid.org/0000-0003-2798-046X
3. 数据集描述：本数据集包含企业通过多方计算（Multiparty Computation, MPC）自愿贡献受保护数据意愿的感知数据。Petronia（2020年，第6章）基于本数据集得出多项结论，并为后续研究提供了建议（详见Petronia 2020年，第7.4章）
4. 数据采集时间：2020年7月至8月
5. 数据采集地理范围：荷兰
6. 数据采集资助信息：欧盟地平线2020研究与创新计划，资助协议编号825225——安全数据赋能经济发展（Safe Data Enabled Economic Development, SAFE-DEED），项目来源为H2020-ICT-2018-2

二、共享与获取信息
1. 数据许可与使用限制：CC0协议
2. 引用或使用本数据集的出版物链接：Petronia, M. N. (2020). 多方计算：多方计算对企业自愿贡献受保护数据意愿的影响（硕士学位论文）. 检索自：http://resolver.tudelft.nl/uuid:b0de4a4b-f5a3-44b8-baa4-a6416cebe26f
3. 本数据集是否源自其他数据源？否
4. 本数据集引用格式：Petronia, M. N. (2020). 多方计算：多方计算对企业自愿贡献受保护数据意愿的影响（硕士学位论文）. 检索自：https://data.4tu.nl/. doi:10.4121/13102430

三、数据与文件概览
1. 文件列表：thesis-mpc-dataset-public.xlsx、thesis-mpc-dataset-public-readme.txt（即本文档）
2. 文件关联关系：数据集元数据与操作说明
3. 本次数据包未包含的额外采集相关数据：受访者的职业与岗位信息（可追溯至唯一标识），因隐私原因已移除
4. 本数据集是否存在多个版本？否

四、方法学信息
1. 数据采集/生成方法描述：采用前后测实验设计。更多信息详见Petronia（2020年，第5章）
2. 数据处理方法：完整处理说明详见Petronia（2020年，第6章）
3. 解读数据所需的工具或软件信息：可使用Microsoft Excel将本数据集转换为其他格式
4. 环境/实验条件：本数据集包含通过三种渠道采集的三份子数据集，分别为：Prolific平台（带激励机制）、LinkedIn/Twitter平台（自愿参与），以及实验室环境下的受访者（自愿参与）。更多信息详见Petronia（2020年，第6.1章）
5. 质量保障流程说明：已对数据的一致性与可靠性进行全面核查。更多信息详见Petronia（2020年，第6章）
6. 参与样本采集、处理、分析及/或提交的人员：详见Petronia（2020年，第6章）

五、数据专项信息
1. 变量数量：详见`thesis-mpc-dataset-public.xlsx`的`experiment_matrix`工作表
2. 样本量/行数：详见`thesis-mpc-dataset-public.xlsx`的`experiment_matrix`工作表
3. 变量列表：详见`thesis-mpc-dataset-public.xlsx`的`labels`工作表
4. 缺失值编码：详见`thesis-mpc-dataset-public.xlsx`的`comments`工作表
5. 专用格式与缩写说明：多方计算（Multiparty Computation, MPC）与可信第三方（Trusted Third Party, TTP）

六、操作说明
1. Petronia（2020年，第6章）描述了相关测试及其对应的语法代码。