Open data in the COVID-19 pandemic
A collection presenting a series of rapidly evolving resources that aggregate and bring cohesion to the massive volume of data being generated in the COVID-19 crisis
Ten lessons for data sharing with a data commons
A data commons is a cloud-based data platform with a governance structure that allows a community to manage, analyze and share its data. Data commons provide a research community with the ability to manage and analyze large datasets using the elastic scalability provided by cloud computing and to share data securely and compliantly, and, in this way, accelerate the pace of research. Over the past decade, a number of data commons have been developed and we discuss some of the lessons learned from this effort.
An agenda for addressing bias in conflict data
With increased availability of disaggregated conflict event data for analysis, there are new and old concerns about bias. All data have biases, which we define as an inclination, prejudice, or directionality to information. In conflict data, there are often perceptions of damaging bias, and skepticism can emanate from several areas, including confidence in whether data collection procedures create systematic omissions, inflations, or misrepresentations. As curators and analysts of large, popular data projects, we are uniquely aware of biases that are present when collecting and using event data. We contend that it is necessary to advance an open and honest discussion about the responsibilities of all stakeholders in the data ecosystem – collectors, researchers, and those interpreting and applying findings – to thoughtfully and transparently reflect on those biases; use data in good faith; and acknowledge limitations. We therefore posit an agenda for data responsibility considering its collection and critical interpretation.
Data from the MOSAiC Arctic Ocean drift experiment
The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) is a multinational interdisciplinary endeavor of a large earth system sciences community.
From biomedical cloud platforms to microservices: next steps in FAIR data and analysis
The biomedical research community is investing heavily in biomedical cloud platforms. Cloud computing holds great promise for addressing challenges with big data and ensuring reproducibility in biology. However, despite their advantages, cloud platforms in and of themselves do not automatically support FAIRness. The global push to develop biomedical cloud platforms has led to new challenges, including platform lock-in, difficulty integrating across platforms, and duplicated effort for both users and developers. Here, we argue that these difficulties are systemic and emerge from incentives that encourage development effort on self-sufficient platforms and data repositories instead of interoperable microservices. We argue that many of these issues would be alleviated by prioritizing microservices and access to modular data in smaller chunks or summarized form. We propose that emphasizing modularity and interoperability would lead to a more powerful Unix-like ecosystem of web services for biomedical analysis and data retrieval. We challenge funders, developers, and researchers to support a vision to improve interoperability through microservices as the next generation of cloud-based bioinformatics.
Capturing the COVID-19 Crisis through Public Health and Social Measures Data Science
In response to COVID-19, governments worldwide are implementing public health and social measures (PHSM) that substantially impact many areas beyond public health. The new field of PHSM data science collects, structures, and disseminates data on PHSM; here, we report the main achievements, challenges, and focus areas of this novel field of research.