## 1 Introduction
Retrieved from: [[https://www.weforum.org/reports/gender-gap-2020-report-100-years-pay](https://www.weforum.org/reports/gender-gap-2020-report-100-years-pay)]([https://www.weforum.org/reports/gender-gap-2020-report-100-years-pay](https://www.weforum.org/reports/gender-gap-2020-report-100-years-pay)) -equality, accessed on 1 June 2022, World Economic Forum. The Future of Gender Parity: A Labour Market Shift. Retrieved from: [[https://reports.weforum.org/global-gender-gap-report-2020/the-future-of-gender-parity/?doing_wp_cron=1612906910.182411909103393554](https://reports.weforum.org/global-gender-gap-report-2020/the-future-of-gender-parity/?doing_wp_cron=1612906910.182411909103393554)]([https://reports.weforum.org/global-gender-gap-report-2020/the-future-of-gender-parity/?doing_wp_cron=1612906910.182411909103393554](https://reports.weforum.org/global-gender-gap-report-2020/the-future-of-gender-parity/?doing_wp_cron=1612906910.182411909103393554)) 6875, accessed on 1 June 2022, as backbone tools to promote data-driven solutions opens the risk of overlooking women in the loop. Women are underrepresented in observation and data collection tasks. Their workforce in AI and DS is small, and they are underrepresented in algorithm design and testing teams. Thus, their characteristics are not represented because of how data are engineered. Their needs are not visible for designing units, even if more than the 50% of the planet's population are women (The National Centre for Women & Information Technology (NCWIT [[https://ncwit.org](https://ncwit.org)]([https://ncwit.org](https://ncwit.org)), accessed on 1 June 2022.) argues about the importance and interest for organizations to search for gender and race equality. Besides the economic welfare, teams respecting gender equality seem to be more creative and effective in performing tasks and sharing knowledge). When science is combined with economy, politics and social impact, it is possible to classify disciplines and reason about the capital they produce and who produces that capital. To activate the economy, every asset that is potentially productive must participate in the active STEM market. Women are a massive part of the assets that remain excluded from the equation, weakening the economy. Countries with dynamic economies like Germany lack human force in these disciplines and import brains to fulfil the requirements of their economy. According to the economist [PERSON] in his TV show \"La guerre des ages\", two strategies are possible for these economies: either hiring an immigrant brain force or integrating women into the production market. Surprisingly, Germany is developing a very aggressive strategy for attracting immigrant experts (!) (Is the old German saying that associates women to the three K's (i.e., Kirche, Kuche, Kinder), still valid? The German gender issues are out of the scope of this paper. Thus, we leave this topic open and we invite the interested reader willing to seek for more information to visit [10,11]). Why are women excluded from the equation? Are they recognised as a potential labour force, or are they hidden Cinderellas invisible to statistics? Are gender bias and discrimination playing a role in women exclusion in STEM? We believe that it is critical to study the phenomenon of data and women's absence in STEM from many different perspectives. Since women come in many different sizes, colours, and formats, the study must consider several communities from the global north and south instead of general studies. This paper studies missing data under a qualitative approach [12] that combines grounded theory and use case strategies for answering the following research questions:  
* Do missing data concerning women's STEM research participation prevent a comprehensive view of their contribution across history?