Project leader, Professor Yang Wei, proposed a twin sub-spaces model based on openness and inclusiveness, as shown in the figure below. This is to quantify the development status and process of open science, so as to put the open science framework in place.

Figure 1. Twin sub-spaces portraying the dimensions of open and inclusive science. 

(a) Topological dimensions and quadrants;		(b) Dynamic dimensions and drivers of knowledge chains.

Wei Yang. Open and inclusive science: A Chinese perspective [J]. Culture of Science,2022.

The first sub-space addresses topological features in two dimensions: the openness and the inclusiveness of scientific knowledge. Those dimensions are in contrast to the closeness and the exclusiveness of scientific knowledge, and they emphasise the common knowledge of humanity in discoveries, inventions and communications. If presented in a plane, as shown in Figure 1(a), the abscissa would measure the openness of the research data or the willingness to share the data, while the ordinate would measure the willingness to share research incentives. The value of ‘openness’ ranges from negative (closed) to positive (open), passing through various categories: confidential data, data under patent protection, data transferable upon intellectual property purchase, limited sharing, a need for round-robin verification, open to the public, active encouragement for citations, and promotion to the public. The value of ‘inclusiveness’ ranges from negative (exclusive) to positive (inclusive), passing through various categories: strictly private exploration, free and private exploration, team exploration, organised research, collaborative research, research dedicated to regional interests, research dedicated to the national interest, and global science for major challenges.

The second quadrant represents collaborative research from which data is not shared with people outside the collaboration. The term ‘organized research’ describes this quadrant. Inclusiveness may involve an institution, an enterprise, a region or a nation. An example of national inclusiveness would be strategic research (such as tasks in national laboratories) with possible far-reaching effects on the development of a nation. Research institutes confront strategic challenges via collaboration, and their objectives are competitive with rival institutes. The data obtained is commonly regarded as a corporate or national secret and is often heavily protected.

The third quadrant covers non-collaborative research, the data from which is not shared with competitors. The term ‘private research’ describes endeavours in this quadrant. Studies are conducted for private interests and are generally driven by potential profits or personal well-being. This quadrant does not form part of open and inclusive science.

The fourth quadrant consists of individual research, the data from which is shared with the public. The term ‘blue-sky research’ is appropriate for this quadrant, in which the research is curiosity-driven and deals with challenges faced by humanity.

Project leader, academician Yang Wei, proposed Open Science Readiness Index (OSRI) to quantify the dynamic status and process of open science. The index is composed of three additive-sum but independently measurable dimensions, which represent the readiness of open access, open data and open policy, respectively. The readiness of open access is characterized by the volume and fullness of academic output under the open access model and form a vector in the two dimensions of openness and inclusiveness with the volume and share of international cooperation that characterizes participation. The readiness of open data is represented by the amount of data that can be shared and accessible, which also forms a vector in the two-dimensional space of openness and inclusiveness. The readiness of the open policy is constructed on the basis of the demand hierarchy theory, and the readiness is described coarsely by 5 layers and 20 sub-items.

The total value of OSRI is conjectured to be composed of three related but separately measured dimensions: the readiness for open access R_OA, open data R_OD and open policy R_OP. Each composition is characterized by proxies attainable from publicly available data. The additive composition of OSRI can be read as:

The coefficients  represent weights on open access, open data and open policy, respectively. They are normalized by:

As a simple demonstration, we will take , 0.2 throughout the subsequent calculations. The relatively higher weights for open access and open data are attributed to the facts that hard efforts are involved to raise the readiness for open access or open data, and the quantifications of OA and OD are built upon relatively solid proxies.