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   https://doi.org/10.1162/99608f92.4f3ac3da  

   Israel-Fishelson, Rotem; Moon, Peter; Tabak, Rachel; Weintrop, David (April 2024, Harvard data science review)
2. Protecting Sensitive Data Early in the Research Data Lifecycle

   https://doi.org/10.29012/jpc.846  

   Karcher, Sebastian; Secen, Sefa; Weber, Nic (December 2023, Journal of Privacy and Confidentiality)

   How do researchers in fieldwork-intensive disciplines protect sensitive data in the field, how do they assess their own practices, and how do they arrive at them? This article reports the results of a qualitative study with 36 semi-structured interviews with qualitative and multi-method researchers in political science and humanitarian aid/migration studies. We find that researchers frequently feel ill-prepared to handle the management of sensitive data in the field and find that formal institutions provide little support. Instead, they use a patchwork of sources to devise strategies for protecting their informants and their data. We argue that this carries substantial risks for the security of the data as well as their potential for later sharing and re-use. We conclude with some suggestions for effectively supporting data management in fieldwork-intensive research without unduly adding to the burden on researchers conducting it. 

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3. Prospecting (in) the data sciences

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   Slota, Stephen_C; Hoffman, Andrew_S; Ribes, David; Bowker, Geoffrey_C (February 2020, Big Data & Society)

   Data science is characterized by engaging heterogeneous data to tackle real world questions and problems. But data science has no data of its own and must seek it within real world domains. We call this search for data “prospecting” and argue that the dynamics of prospecting are pervasive in, even characteristic of, data science. Prospecting aims to render the data, knowledge, expertise, and practices of worldly domains available and tractable to data science method and epistemology. Prospecting precedes data synthesis, analysis, or visualization, and is constituted by the upstream work of discovering disordered or inaccessible data resources, thereafter to be ordered and rendered available for computation. Through this work, data science positions itself in the middle of all things—capable of engaging this, that, or any domain—and thus prospecting is a key driver of data science’s ongoing formation as a universal(izing) science. 

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4. Data Security and Privacy for Outsourced Data in the Cloud

   Sahin, Cetin; El Abbadi, Amr (April 2018, 34th IEEE International Conference on Data Engineering, ICDE 2018)

   Although outsourcing data to cloud storage has become popular, the increasing concerns about data security and privacy in the cloud limits broader cloud adoption. Ensuring data security and privacy, therefore, is crucial for better and broader adoption of the cloud. This tutorial provides a comprehensive analysis of the state-of-the-art in the context of data security and privacy for outsourced data. We aim to cover common security and privacy threats for outsourced data, and relevant novel schemes and techniques with their design choices regarding security, privacy, functionality, and performance. Our explicit focus is on recent schemes from both the database and the cryptography and security communities that enable query processing over encrypted data and access oblivious cloud storage systems. 

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5. What Data is in K-12 Data Science? An Analytic Approach to Understanding the Data Used in K-12 Data Science Courses

   Israel-Fishelson, R. (October 2023, Proceedings of the International Society of the Learning Sciences Annual Conference 2023)