@article{Sauermann,
  author    = {Sauermann, Stefan},
  title     = {Preservation of Individuals' Privacy in Shared COVID-19 Related Data},
  series = {COVID-19 Data sharing in epidemiology, version 0.054.},
  journal   = {COVID-19 Data sharing in epidemiology, version 0.054.},
  editor    = {RDA-COVID19-Epidemiology WG., Research Data Alliance},
  pages     = {13},
  abstract  = {This paper provides insight into how restricted data can be incorporated in an open-be-default-by-design digital infrastructure for scientific data. We focus, in particular, on the ethical component of FAIRER (Findable, Accessible, Interoperable, Ethical, and Reproducible) data, and the pseudo-anonymization and anonymization of COVID-19 datasets to protect personally identifiable information (PII). First we consider the need for the customisation of the existing privacy preservation techniques in the context of rapid production, integration, sharing and analysis of COVID-19 data. Second, the methods for the pseudo-anonymization of direct identification variables are discussed. We also discuss different pseudo-IDs of the same person for multi-domain and multi-organization. Essentially, pseudo-anonymization and its encrypted domain specific IDs are used to successfully match data later, if required and permitted, as well as to restore the true ID (and authenticity) in individual cases of a patient's clarification.Third, we discuss application of statistical disclosure control (SDC) techniques to COVID-19 disease data. To assess and limit the risk of re-identification of individual persons in COVID-19 datasets (that are often enriched with other covariates like age, gender, nationality, etc.) to acceptable levels, the risk of successful re-identification by a combination of attribute values must be assessed and controlled. This is done using statistical disclosure control for anonymization of data. Lastly, we discuss the limitations of the proposed techniques and provide general guidelines on using disclosure risks to decide on appropriate modes for data sharing to preserve the privacy of the individuals in the datasets.},
  subject      = {COVID19},
  language  = {en}
}
@article{Sauermann,
  author    = {Sauermann, Stefan},
  title     = {COVID-19 Questionnaires, Surveys, and Item-Banks: Overview of Clinical- and Population-Based Instruments},
  series = {SSRN},
  journal   = {SSRN},
  abstract  = {New COVID-19 related instruments are being rapidly developed around the world to collect patient- and population-based information. Heterogeneity between instruments limits comparability of results. The present study provides an overview of instruments and resources. We scoped the content domain on a selection of instruments using the Maelstrom taxonomy. Content of the instruments varied widely, from proximal measures (e.g., clinical symptoms, comorbidities, etc.) to distal measures such as political attitudes. We recommend that researchers reuse existing instruments to the greatest extent possible, and that they make results openly available in machine-readable format to facilitate reuse and maximize comparability of results across studies and countries.},
  subject      = {COVID19},
  language  = {en}
}
@misc{Sauermann,
  author    = {Sauermann, Stefan},
  title     = {FSR Online Debate: Facilitating interoperability of energy services in Europe - (what) can we learn from existing experience?"},
  subject      = {Interoperability},
  language  = {en}
}
@misc{Sauermann,
  author    = {Sauermann, Stefan},
  title     = {Software -eine Aufgabe f{\"u}r den TSB?Orientierung im Dschungel der Beauftragten!},
  subject      = {eHealth},
  language  = {de}
}
@article{Sauermann,
  author    = {Sauermann, Stefan},
  title     = {RDA COVID-19 Working Group. Recommendations and Guidelines on data sharing},
  series = {Research Data Alliance, 2020.},
  journal   = {Research Data Alliance, 2020.},
  abstract  = {The Research Data Alliance (RDA) COVID-19 Working Group members bring various, global expertise to develop a body of work that comprises how data from multiple disciplines inform response to a pandemic combined with guidelines and recommendations on data sharing under the present COVID-19 cicumstances. This extends to research software sharing, in recognition of the key role in software in analysing data. The work has been divided into four research areas (namely, clinical, omics, epidemiology, social sciences) with four cross cutting themes (namely, community participation, indigenous data, legal and ethical considerations, research software), as a way to focus the conversations, and provide an initial set of guidelines in a tight timeframe. The detailed guidelines are aimed to help stakeholders follow best practices to maximise the efficiency of their work, and to act as a blueprint for future emergencies. The recommendations in the document are aimed at helping policymakers and funders to maximise timely, quality data sharing and appropriate responses in such health emergencies. This work was executed in an intense period over just over 6 weeks, with five iterations, all of which were opened for public community comment. Draft releases and comments are avaialable here (https://doi.org/10.15497/rda00046). This activity has been conducted under the RDA guiding principles of Openness, Consensus, Balance, Harmonization, Community-driven, and Non-profit and technology-neutral.},
  subject      = {COVID19 Data Sharing},
  language  = {en}
}