Women in Data Science
Featuring Rosemary Shrestha, Data coordinator (CIMMYT)
Data coordinator (CIMMYT)
Data coordinator from International Maize and Wheat Improvement Center (CIMMYT)
In my role, I contribute to:
- Establishing data standards, documentation, data-curation processes, timelines for data entry into institutional databases/repositories, in agreement with the Institutional Research Data Management Policy;
- Participating in the design, development, and testing of versatile institutional databases/repositories, interfaces and output tools required for managing various types of research data such as genealogy, genotypic, phenotypic data;
- Managing and documenting the dissemination of data to co-operators within the institution and with partner organizations;
- Introducing new tools to scientist, research assistants, technicians, and project collaborators, assist them with training, collect and report back user requirements to developers/software engineers.
How did you come to pursue a career in data science?
I was always fond of biology, loved biology practical classes the most while doing my undergraduate and graduate because there was an opportunity to learn and understand biology plus the practical classes required drawing diagrams. I loved drawing diagrams because we can highlight the important features of a specimen and show the result of a long period of observation at different depths of focus and at different magnifications through drawings. I became more interested at molecular level later and obtained my Master and Ph.D. in molecular plant pathology. Having a little bit knowledge of biology, plant pathology, molecular biology, and the experiences of working in fields and labs, it made me easier to start my career in data management which requires to have knowledge in such fields and makes easier interacting with multidisciplinary teams and understanding their work.
What are the things you love about your role as a data scientist?
I always find great pleasure while interacting with multidisciplinary teams like scientists, software developers, data curators/managers, address issues related to databases/tools required for managing research data and satisfaction to solve problems in collaboration with the teams. Also, it gives me an opportunity to learn something new every day when I work with them.
What has been the most exciting project you have worked on?
At the beginning of my career at CIMMYT, I was heavily involved in developing ontologies for crops and had an opportunities to collaborate with colleagues from various CG centers and other internationally recognized institutions. It was very difficult for us to come up with the rules for standardizing traits and variables that are mainly measured/observed in nurseries and trials. In addition, it was hard to disseminate the importance of ontologies and convince people to use/implement ontologies in databases/repositories/tools. Implementation of ontologies/CV terms/metadata has become one of the essential parts while designing databases/tools that allows us to integrate data across systems. Being able to contribute a little bit to Crop Ontology team and seeing ontologies being used in recent databases/repositories/tools satisfies me.
Do you feel this is an exciting time to be a woman working in the data sector?
Yes, it is always exciting for being able to work as a woman scientist in this field because there are less woman working in this technical field. Good thing is that number of woman scientists in this area is increasing day by day.
Why do you think there is a lack of women in Tech/Data sectors?
I personally think, and it actually applies to both men and women, that in technical fields, like data science, we have to provide more services and assist scientists rather than doing research in fields and labs – unless they are involved in data analysis. Mainly for data curators, data manager, there is less opportunity of writing scientific articles, publish them in high impact journals, and get rewarded for the work they do because their roles could be recognized as support services at times. Job titles, positions, or career paths are not always important but the contribution of their work should be recognized so that more people are encouraged to join in this technical field.
As a woman, did you face any obstacles/challenges in your career pathway to become a data scientist?
So far I have not faced any.
Why should more women get into data science?
This is one of the technical fields where women still remain statistically a minority but it does not mean that we have to force women to get into this field. It entirely depends on their personal choices and skills required for working in this field. One thing that I can see very positive about getting into data science for women, is that this job does not require spending time in fields, meaning there is less travel more time for family. Additionally, its demand is increasing rapidly, the role of data scientists/curators/manager are acknowledged, including them in their research projects and becoming a part of integrated teams which is really encouraging for newcomers.
What advice would you give to women wanting to get into data science?
It is no surprise today that Data Scientist (or related roles such as Data Manager, Statistician, Data Analyst, etc.) is one of the most sought-after career paths. It requires good skills in communications, interaction, and collaboration, as well as the ability to effectively share your knowledge with multidisciplinary teams. Do not underestimate yourself, or hesitate to ask any kind of question – no body is perfect.