This is a peer-reviewed article in Digital Studies/Le champ numérique, a journal published by the Open Library of Humanities.

Data management and sharing are growing concerns for scientists and funding organizations throughout the world. Funding organizations are implementing requirements for data management plans, while scientists are establishing new infrastructures for data sharing. One of the difficulties is sharing data among a diverse set of research disciplines. Astrobiology is a unique community of researchers, containing over 110 different disciplines. The current study reports the results of a survey of data management practices among scientists involved in the astrobiology community and the NASA Astrobiology Institute (NAI) in particular. The survey was administered over a 2-month period in the first half of 2013. Fifteen percent of the NAI community responded (n = 114), and additional (n = 80) responses were collected from members of an astrobiology Listserv. The results of the survey show that the astrobiology community shares many of the same concerns for data sharing as other groups. The benefits of data sharing are acknowledged by many respondents, but barriers to data sharing remain, including lack of acknowledgement, citation, time, and institutional rewards. Overcoming technical, institutional, and social barriers to data sharing will be a challenge into the future.

The “information ecosystem” metaphor is widely used in academic libraries and has become nearly ubiquitous when speaking of the information systems that support scholarly communication and varied forms of data sharing and publication. The trending use of this language arises from non-academic applications — for example in big data (the Hadoop ecosystem) or software development (the node.js ecosystem) — and there remains little critical examination of the use of this metaphor. Indeed, the definition of ecosystem as the set of relations between living organisms and their surrounding non-living environment is apparently not directly a part of the metaphor. This paper first describes the emergence of ecological thinking and how it was influenced by early information science and then explores how different “ecologies” are used within the academy, including in the emergent field of information ecology. A short critique of the metaphor is then posed and the paper concludes that the information ecosystem metaphor is useful, yet at the same time there are dangerous elements that render aspects of human societies and natural ecosystems invisible.

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Data quality (DQ) is a major concern in citizen science (CS) programs and is often raised as an issue among critics of the CS approach. We examined CS programs and reviewed the kinds of data they produce to inform CS communities of strategies of DQ control. From our review of the literature and our experiences with CS, we identified seven primary types of data contributions. Citizens can carry instrument packages, invent or modify algorithms, sort and classify physical objects, sort and classify digital objects, collect physical objects, collect digital objects, and report observations. We found that data types were not constrained by subject domains, a CS program may use multiple types, and DQ requirements and evaluation strategies vary according to the data types. These types are useful for identifying structural similarities among programs across subject domains. We conclude that blanket criticism of the CS data quality is no longer appropriate. In addition to the details of specific programs and variability among individuals, discussions can fruitfully focus on the data types in a program and the specific methods being used for DQ control as dictated or appropriate for the type. Programs can reduce doubts about their DQ by becoming more explicit in communicating their data management practices.