Data policy should not only simplify the path towards “effective research data stewardship and infrastructure development” (Parsons, 2013), but also help maximize the data benefits through data sharing (Federer, et al, 2018). Table 1 provides an overview of the current state-level rules governing open research data in China.

According to Table 1, generally, the “Law of the People’s Republic of China on Science and Technology Progress” (2008) established the fundamental rules for research data stewardship, stating that, “The Science and Technology Administrative Department of the State Council shall, in conjunction with the relevant competent departments of the State Council, establish information systems for scientific and technological resources, such as S&T research bases, scientific instruments and scholarly literature, S&T data and natural resources…and should release the distribution and usages of all the research sources as well”.1 That law also addresses the disclosure of governmental information since it is one of the largest contributors of open research data in China.

Other rules also affect research data stewardship by providing provisions on cybersecurity and intellectual property, as well as those governing particular elements, such as research outcomes. In particular, the “Data Security Law of the People’s Republic of China (Draft Version)”2 has been released during June 2020 for public comment. Like the practice of General Data Protection Regulation (GDPR) in Europe, this law aims to ensure the flow of data for the protection of data rights within a safe environment, nationwide. Key measures include clarity and implementation of data security protection obligations for different stakeholders, promotion of various data flow in sound manners, and guidance for institutional measures to ensure the safety of governmental data sharing.

By contrast, “Measures for Managing Scientific Data”,3 brings research data management and sharing to a new phase in which general rules covering comprehensive data aspects have been set up for implementation, including enhanced entire lifecycle data management, strengthened capability for data assets protection, sustained open data ecology supported by continuing funding for specific data programs, clear data property rights, and responsibility for long-term data stewardship. In effect, many institutions are revising their implementation guidelines and plans accordingly. For example, CAS plans to initiate several steps to complete this implementation, including the development of CAS level data policies following national laws, construction of strengthened CAS data centers and mature data infrastructure, exploration of innovative open data models, and improvements in fair incentive mechanisms for multiple stakeholders, etc. (AOCAS, 2018). By the end of 2019, fifteen provincial-level administrative regions, including Anhui, Chongqing, Gansu, Guangxi Autonomous Region, Hainan, Heilongjiang, Hubei, Inner Mongolia Autonomous Region, Jiangsu, Jilin, Shaanxi, Shandong, Sichuan, Tianjin, Xinjiang, and Yunnan, have published regional rules to govern scientific data in line with the national-level law. And more steps, in accordance with the “Measures for Managing Scientific Data” (2018), also are being planned for completion.

In addition to these national laws, administrative institutions have produced more guidelines for sound data stewardship and these call for open data and sharing of other research resources generated from public funded research programs.

Government information, serving as one of the major sources for data generation and sharing, is governed by the “Regulations of the People’s Republic of China on Disclosure of Government Information”.4 Also, the document, “Interim Measures for the Management and Sharing of Government Information resources”,5 provides guidance for electronic records, which helps to facilitate the governance of scientific data from an administrative perspective.

The Ministry of Science and Technology in China (MOST) provides another research data governance example. Early in 2001, the “Interim Provisions on the Administration of National S&T Plans”6 states the responsibilities of the MOST to establish databases, archiving systems, and rules for the preservation, usage, and sharing of data and related information. Following up with rules for program management, the MOST also points out that “All national science and technology plans should establish compatible databases to share information and data resources”.7 In particular, as a key component of research outcomes, scientific data is highlighted for deposit into archives and research facilities that provide guidance to ensure their integrity, completeness, and accuracy (MOST, 2003).8 In effect, the MOST becomes the motivator for initializing and implementing first state-level guidance on “Measures for Managing Scientific Data” (2018). In line with these efforts, the Chinese Academy of Sciences (2019),9 The Chinese Academy of Agricultural Sciences (2019)10 and others also published their institutional open data rules based on a decade of data work experience, in which, mechanisms, responsibilities, workflow, and plan for data center development are underlined accordingly.

Data has not been emphasized enough in every field to be regulated by specific data rules, but there are some data-intensive research fields taking the lead in developing data policies. For example, searching the China Legal Knowledge Database (CLKD)11 retrieved 254 excerpts of disciplinary policies that were entitled with “data”. Shown in Table 2 are several selected disciplinary research data policy examples covering geoscience, medical and health sciences, meteorology, ocean science, seismology and space science.

According to the 13th Five-year science plan,12 there are five types of major projects, and among those, three major research projects have policies governing data and information. The other two are talents and enterprise innovation-related grants, in which we suppose more flexibility is left due to the complexity of such work as well as priorities for Intellectual Property protection.

Shown in Table 3, data responsibilities, data infrastructures (i.e. data platforms and databases), data curation and sharing, are stressed within these three program provisions. Moreover, rules for sharing of national major scientific research infrastructures and large-scale scientific research instruments also strengthen the capability of data production and broaden the scope of data sharing by reducing investment costs and increasing the efficiency of facility usage (Table 3).

Furthermore, especially after the release of the national-level rules, “Measures for Managing Scientific Data” (2018), the “data submission agreement” is compulsory for MOST grant programs (Fan, 2018). This requirement can be traced back to early 2016 (MOST, 2016) and the main purpose is to reach legal agreements between the funding agency (MOST), individual researchers, and their affiliated institutions to guarantee complete and on-time data capture and submission to the MOST platforms during the research and data sharing that commences after an embargo period. Such conditions are considered mandatory requirements during proposal review and award processes.

Data repositories, such as data centers and archives, often provide users with integrated data platforms that offer data curation capabilities to enable efficient data publication. Data services can include in-depth quality control as well as data sharing. Some data repositories also provide linkage between datasets, data papers, and publications (Karcher, Kirilova and Weber, 2016). Many data repositories specialize in providing services to specific disciplines, such as the geoscience data repository, GSCloud (www.gscloud.cn), omics data center, GSA (bigd.big.ac.cn/gsa/), and institutional repositories (i.e., Peking University Open Research Data Platform, opendata.pku.edu.cn), as well as facilities serving the general public (i.e. ScienceDB, www.sciencedb.cn).

In addition to data published as supplements to scholarly journals, data journals, which jointly publish data papers with datasets, have become popular. China Scientific Data (www.csdata.org), which was established in 2015 and began releasing data in 2016, and Global Change Research Data Publishing & Repository (www.geodoi.ac.cn), which established online services in 2014 and began publishing in 2017, take the lead in promoting FAIR data by publishing data papers and datasets. Until June 2020, China Scientific Data has17 shared over 224 data papers and datasets, from across sundry disciplines, with around 300,000 page views yearly. Among the represented disciplines, geoscience and biology data sharing rank highly. In addition, Global Change Research Data Publishing & Repository (GCRDP) has published 267 GB of datasets online with over 245,000 data files downloaded cumulatively.18 Distribution of disciplinary data articles in the two journals is listed in Figure 2. Furthermore, GigaScience also publishes datasets with joint efforts from GigaDB (Sneddon, Li and Edmunds, 2012). Also, Big Earth Data includes geoscience big data publishing as part of their scope (Guo, 2017).

In addition, the implementation of norms for data also guarantees and facilitates the reuse of data. According to the CNKI standard database,19 there are over 700 data-related works of national-level and disciplinary standards, which contribute to the quality of data throughout the data life cycle. Such data norms include quality control for metadata and data (i.e. “Information Technology – Big Data – Terminology GB/T 35295-2017”), methods for data security (i.e. “Information Security Technology – Personal Information Security Specification GB/T 35273-2017”), data processing, exchange & communication (i.e. “Technical Specification for Environmental Thematic Spatial Data Processing HJ 927-2017”, “Specification for Drafting Basic Dataset of Ecology and Environment Information HJ 966-2018”), data sharing (i.e. “Information Technology Big Data Governmental Data Sharing Part 1: General Provisions GB/T 38664.1-2020”), data metrics and evaluation (Zhang and Yuan, 2017; Ou and Zhou, 2016), as well as information systems (Briney, Goben and Zilinski, 2017; Bao, Fan and Li, 2015) and information technology (i.e. “Information Technology – Big Data – Technical Reference Model GB/T 35589-2017”) for data.

Citizen science techniques offer another way of collecting open data, leveraging multiple contributions to capture and analyze data. This approach is becoming popular for researchers who study daily life and have access to data collection communities, such as Birdnet (www.birdnet.cn) and the Chinese Field Herbarium (CFH, www.cfh.ac.cn). Records in the “Database of Cetacean Stranding Records around Hainan Island” are also partly contributed by volunteers (Zhang, Li, Lin, et al, 2016). However, data exchanges are not enough, as some citizen scientists go even further. For example, some contribute to the exploration of new species or participate in a non-governmental organization (NGO) for more citizen science data collection and analysis opportunities (Science China, 2014).

As is illustrated in the discussion of institutional data policies, being one of the major funding agencies for research, the MOST20 takes the lead in pushing open data across domains. Since its initial efforts in 2001, the MOST began supporting the NSTI program with the initial establishment of 13 scientific data centers covering agriculture, forestry, seismicity, meteorology, marine science, Earth systems, population and health, biology, chemistry, materials, and energy, as well as others.21 In recent years, the funding mechanism has evolved from pre-funding awards to subsidies for further development of selected data portals to foster data sustainability (MOF, 2013). Design, establishment, and evaluation of national scientific data centers is another major contribution enhancing the generic service capability of national data infrastructures facing open data and open science currently (MOST and MOF, 2019).

As one of the most important and largest research institutions in China, CAS takes the lead in promoting the production of research data, contributing substantially to the efforts of the science community in different disciplines (i.e. Chen, 2018; Zhang, 2018). Among all of the CAS programs, the Scientific Database Program (SDP) has been focusing on research data generation, curation, and sharing as its primary goals since 2006 (CESDCAS, 2009).

Before 2000, data was disseminated offline, near-line, and, to some extent, online. The SDP program mainly focused on expanding the scale of research data and curation to improve data management and data sharing capabilities (Zhang and Li, 1997). Subsequently, during the next five years, data sharing capabilities were emphasized as online data services were developed further (Gui, 2007). Then, data-sharing efforts expanded considerably through 2010, as the volume of research data increased and the data grid was employed to integrate data from different sources to foster new scientific discoveries (CIOCAS, 2011; Hu, et al, 2012). From 2011 to 2015, the adoption of the data cloud has provided an opportunity for attaining flexible, but more robust data infrastructures, and also for supporting value-added data analysis (Liu, et al, 2016). By the end of 2015, multidisciplinary scientific data volume had reached 655 TB with over 96,290,000 unique visits and 456 TB downloads in total (Li, et al, 2016). The following “Big Data Engineering” program continues the trend of open data based on consolidating engineering construction (CAS, 2017). And the “Measures of Managing Scientific Data” further clarify the general duty to provide open, public-funded data by default. These actions leave no doubt that open data shall prevail across domains and regions in accordance with the open science paradigm. The evolving history of SDP is depicted in Figure 3.

As one of the vital funding agencies in China, the NSFC supports the sharing of research outcomes, including data. In 2014, NSFC jointly announced, with CAS, open-access rules for all their programs, providing direction to release articles as open access, with an embargo of no more than 12 months after publishing (OpenAIRE, 2014). Moreover, during the last 30 years, data-intensive research has gained notable support from the NSFC through approximately 6,000 different programs. With up to 3.58 billion yuan in total, the annual tendency for data-related grants is depicted in Figure 4.22 The NSFC also supports data stewardship activities as a necessary part of other types of research. Incidentally, the NSFC has been merged into the MOST in 2018 (Cyranoski, 2018; NSFC, 2018), but will continue to be one of the major funding agencies, especially for fundamental research.

CAST23 is the largest society of science and technology (S&T) professionals that operates as a non-governmental organization in China. The focus of CAST includes various topics, like database sharing and data exchanges. In the typical project, titled “Discipline development in CAST member societies”, data have been recognized as the main source of research outcomes, and sharing of databases for scientific research, provided by over 200 national-level academic societies, has been highlighted in particular (CAST, n.d.).

Moreover, other driving forces include international organizations in China, such as CODATA China, and the World Data System (WDS). These international organizations and their members promote regional data exchanges through comprehensive rules, showcases, trainings, and workshops, as well as through other innovative ways to facilitate lifecycle data sharing and communication. Furthermore, local administrative departments, research associations and their sub-branches also serve as stakeholders, contributing to the driving forces within the data ecosystem. While the importance of people and the research community is implied in the discussion of such organizational stakeholders, it is vital to emphasize that data producers, data stewards, and data users are the stakeholders whose efforts and needs are of paramount importance and necessary for the development and evolution of an ecosystem for curating and sharing open data.