Open scientific data
Open scientific data or open research data is a type of open data focused on publishing observations and results of scientific activities available for anyone to analyze and reuse. A major purpose of the drive for open data is to allow the verification of scientific claims, by allowing others to look at the reproducibility of results, and to allow data from many sources to be integrated to give new knowledge.
The modern concept of scientific data emerged in the second half of the 20th century, with the development of large knowledge infrastructure to compute scientific information and observation. The sharing and distribution of data has been early identified as an important stake but was impeded by the technical limitations of the infrastructure and the lack of common standards for data communication. The World Wide Web was immediately conceived as a universal protocol for the sharing of scientific data, especially coming from high-energy physics.
Definition
Scientific data
The concept of open scientific data has developed in parallel with the concept of scientific data.Scientific data was not formally defined until the late 20th century. Before the generalization of computational analysis, data has been mostly an informal terms, frequently used interchangeably with knowledge or information. Institutional and epistemological discourses favored alternative concepts and outlooks on scientific activities: "Even histories of science and epistemology comments, mention data only in passing. Other foundational works on the making of meaning in science discuss facts, representations, inscriptions, and publications, with little attention to data per se."
The first influential policy definition of scientific data appeared as late as 1999, when the National Academies of Science described data as "facts, letters, numbers or symbols that describe an object, condition, situation or other factors". Terminologies have continued to evolve: in 2011, the National Academies updated the definition to include a large variety of dataified objects such as "spectrographic, genomic sequencing, and electron microscopy data; observational data, such as remote sensing, geospatial, and socioeconomic data; and other forms of data either generated or compiled, by humans or machines" as well as "digital representation of literature"
While the forms and shapes of data remain expansive and unsettled, standard definitions and policies have recently tended to restrict scientific data to computational or digital data. The open data pilot of Horizon 2020 has been voluntarily restricted to digital research: "'Digital research data' is information in digital form, collected to be examined and used as a basis for reasoning, discussion or calculation; this includes statistics, results of experiments, measurements, observations resulting from fieldwork, survey results, interview recordings and images"
Overall, the status scientific data remains a flexible point of discussion among individual researchers, communities and policy-makers: "In broader terms, whatever 'data' is of interest to researchers should be treated as 'research data'" Important policy reports, like the 2012 collective synthesis of the National Academies of science on data citation, have intentionally adopted a relative and nominalist definition of data: "we will devote little time to definitional issues, except to acknowledge that data often exist in the eyes of the beholder." For Christine Borgman, the main issue is not to define scientific data but to contextualize the point where data became a focal point of discussion within a discipline, an institution or a national research program. In the 2010s, the expansion of available data sources and the sophistication of data analysis method has expanded the range of disciplines primarily affected by data management issues to "computational social science, digital humanities, social media data, citizen science research projects, and political science."
Open scientific data
Opening and sharing have both been major topic of discussion in regard to scientific data management, but also a motivation to make data emerge as a relevant issue within an institution, a discipline or a policy framework.For Paul Edwards, whether or not to share the data, to what extent it should be shared and to whom have been major causes of data friction, that revealed the otherwise hidden infrastructures of science: "Edwards' metaphor of data friction describes what happens at the interfaces between data 'surfaces': the points where data move between people, substrates, organizations, or machines Every movement of data across an interface comes at some cost in time, energy, and human attention. Every interface between groups and organizations, as well as between machines, represents a point of resistance where data can be garbled, misinterpreted, or lost. In social systems, data friction consumes energy and produces turbulence and heat – that is, conflicts, disagreements, and inexact, unruly processes." The opening of scientific data is both a data friction in itself and a way to collectively manage data frictions by weakening complex issues of data ownership. Scientific or epistemic cultures have been acknowledged as primary factors in the adoption of open data policies: "data sharing practices would be expected to be community-bound and largely determined by epistemic culture."
In the 2010s, new concepts have been introduced by scientist and policy-makers to more accurately define what open scientific data. Since its introduction in 2016, FAIR data has become a major focus of open research policies. The acronym describe an ideal-type of Findable, Accessible, Interoperable, and Reusable data. Open scientific data has been categorized as a commons or a public good, which is primarily maintained, enriched and preserved by collective rather than individual action: "What makes collective action useful in understanding scientific data sharing is its focus on how the appropriation of individual gains is determined by adjusting the costs and benefits that accrue with contributions to a common resource"
History
Development of knowledge infrastructures (1945-1960)
The emergence of scientific data is associated with a semantic shift in the way core scientific concepts like data, information and knowledge are commonly understood. Following the development of computing technologies, data and information are increasingly described as "things": "Like computation, data always have a material aspect. Data are things. They are not just numbers but also numerals, with dimensionality, weight, and texture".After the Second World War large scientific projects have increasingly relied on knowledge infrastructure to collect, process and analyze important amount of data. Punch-cards system were first used experimentally on climate data in the 1920s and were applied on a large scale in the following decade: "In one of the first Depression-era government make-work projects, Civil Works Administration workers punched some 2 million ship log observations for the period 1880–1933." By 1960, the meteorological data collections of the US National Weather Records Center has expanded to 400 millions cards and had a global reach. The physically of scientific data was by then fully apparent and threatened the stability of entire buildings: "By 1966 the cards occupied so much space that the Center began to fill its main entrance hall with card storage cabinets. Officials became seriously concerned that the building might collapse under their weight".
By the end of the 1960s, knowledge infrastructure have been embedded in a various set of disciplines and communities. The first initiative to create a database of electronic bibliography of open access data was the Educational Resources Information Center in 1966. In the same year, MEDLINE was created – a free access online database managed by the National Library of Medicine and the National Institute of Health with bibliographical citations from journals in the biomedical area, which later would be called PubMed, currently with over 14 million complete articles. Knowledge infrastructures were also set up in space engineering, library search or the social sciences: "The 1960s and 1970s saw the establishment of over a dozen services and professional associations to coordinate quantitative data collection".
Opening and sharing data: early attempts (1960-1990)
Early discourses and policy frameworks on open scientific data emerged immediately in the wake of the creation of the first large knowledge infrastructure. The World Data Center system, aimed to make observation data more readily available in preparation for the International Geophysical Year of 1957–1958. The International Council of Scientific Unions established several World Data Centers to minimize the risk of data loss and to maximize data accessibility, further recommending in 1955 that data be made available in machine-readable form. In 1966, the International Council for Science created CODATA, an initiative to "promote cooperation in data management and use".These early forms of open scientific data did not develop much further. There were too many data frictions and technical resistance to the integration of external data to implement a durable ecosystem of data sharing. Data infrastructures were mostly invisible to researchers, as most of the research was done by professional librarians. Not only were the search operating systems complicated to use, but the search has to be performed very efficiently given the prohibitive cost of long-distance telecommunication. While their conceptors have originally anticipated direct uses by researcher, that could not really emerge due to technical and economic impediment:
Christine Borgman does not recall any significant policy debates over the meaning, the production and the circulation of scientific data save for a few specific fields after 1966. The insulated scientific infrastructures could hardly be connected before the advent of the web. Projects, and communities relied on their own unconnected networks at a national or institutional level: "the Internet was nearly invisible in Europe because people there were pursuing a separate set of network protocols". Communication between scientific infrastructures was not only challenging across space, but also across time. Whenever a communication protocol was no longer maintained, the data and knowledge it disseminated was likely to disappear as well: "the relationship between historical research and computing has been durably affected by aborted projects, data loss and unrecoverable formats".