---
title: "Open scientific data"
chunk: 1/11
source: "https://en.wikipedia.org/wiki/Open_scientific_data"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:42.862927+00:00"
instance: "kb-cron"
---

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 (in particular facts or numbers), 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 (e.g., what are data?), 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 ("what are data") but to contextualize the point where data became a focal point of discussion within a discipline, an institution or a national research program ("when are data"). 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) ===