(1) Property of respondents

Table 3 shows the research fields of the respondents. Social sciences (17.4%), earth sciences (12.5%), and humanities (10.3%) were well represented among respondents, while mathematics and astronomy were not (both 0.2%). Other responses included ‘library and information science’, nursing, nutrition, and so on. Responses from library staff and related persons and private companies were also recorded. Fifty-eight (14.2%) respondents selected, “I am not currently engaged in any research activities.”

(2) Experience in obtaining published data and publishing data by themselves

In this question, we asked for experience in obtaining published data and publishing data by themselves from the nine sources. The respondent’s choices are “Obtain,” “Publish,” and “None” and set as follows: “Obtain” and “Publish” are multiple selections, and “None” cannot be selected when “Obtain” or “Publish” is selected. Table 4 shows the aggregate results.

Highly selected answers as regards “where to obtain” are institutional repositories/data archives (62.3%), government repositories/data archives (48.4%), and personal/research lab websites and blogs (47.9%). Highly selected answers about “where to publish” are institutional repositories/data archives (25.7%), personal/research lab websites and blogs (23.5%), and academic SNS services (11.5%). Compared to the experience of obtaining data, the proportion of respondents with experience in publishing data is lower.

Table 5 presents the results of obtaining public data and having experience in releasing data. Respondents who selected “Yes” for one or more of the items in Table 4 are tabulated as having “Yes” experience in obtaining and publishing. Consequently, 84.1% of the respondents had experience in obtaining data, and 46.5% of the respondents had experience in publishing data. One respondent did not respond at all.

(3) Awareness of existing licenses

We asked for the awareness of three licenses, which are well known in Japan to identify the extent to which existing licenses are recognized. To eliminate answers based on fuzzy memories, we also set a link to the license or a page explaining the license in this question form. Figure 1 shows the aggregate results.

The highest recognition is for CC license, but less than half (46.9%) of the respondents are aware of it. ODC (19.3%) and Government Standard Terms of Use (15.9%) follow, and both are less than two in 10. The survey respondents would be expected to have some level of interest in licensing their research data, but awareness of existing licenses was not high.

(4) Usage of existing licenses

To ascertain the use of the licenses listed in the previous question (3), we asked for respondents who are aware of each license about their experience in using each one. Figure 2 shows the aggregate results.

Fifty-nine respondents (30.7%) had used the CC license, which was the highest proportion, as was the case with (3). Only four (5.1%) and six (9.2%) respondents had experience using ODC and Government Standard Terms of Use.

(5) Desired condition of use when respondents publish their research data

We asked respondents to select their desired conditions of use from a list to quantify the extent of the requests they would make. The list was assembled from the results of the interview survey and the CC license elements. Figure 3 depicts the aggregate results, with the following order: the sum of “Yes” and “It depends on cases” is the highest.

The highest percentage of “Yes” and “It depends on cases” is for “Credit on the results” (93.4%). The “Yes” percentage was higher than the credit indication (80.0%) for the “Prohibition of use when improper use of data.” The total of “It depends on cases” was 90.5%. The items for which the total of “Yes” and “It depends on cases” exceeded 80% were “Request to use the latest version” (84.1%), “Impose the same conditions when publishing results” (83.1%), and “Noncommercial” (83.1%).

On the contrary, 43.5% of the respondents selected “No” when they asked for “Nothing (freely available).” In other words, just over 40% of respondents wanted to set some kinds of conditions to release their data. In addition, 40.3% of the respondents answered “No” to the question of “Fee for use,” indicating that a certain number of respondents do not want to be compensated for data publishing. Note that there is an error of 0.1% between the Figure 3 and the main text due to rounding off numbers after the decimal point.

(6) License compliance and willingness to publish data

We asked whether they would be willing to publish their own data if the conditions listed in (5) were complied with. This question was asked to all respondents, including those whose data had already been exposed. Figure 4 depicts the aggregate results.

Consequently, 64.1% of the respondents said that they were “Agree,” and 24.4% said they were “Somewhat agree” (total: 88.5%), exceeding “Somewhat disagree” (4.6%) and “Disagree” (2.4%).

(7) An appropriate method of displaying the use of published data

The respondents were asked about the display method they thought was appropriate for using published data by a third party. We allowed the respondents to select multiple choices. Table 6 presents the number and percentage of respondents who chose each option. Note that three respondents, who did not select any of the options, were excluded from the tabulation.

The highest selection rate was “Cite the source of the data in a paper (include it in the bibliography)” (90.4%). Most of the respondents judged that it would be appropriate to cite the data and the paper if the data were used. 55.2% of the respondents selected “Include source of the data information in the main text” as their next choice. None of the respondents selected, “It is not necessary to describe the data in a paper.”

(8) Requests and concerns about data reuse

The respondents were asked, “Do you have any requests or concerns if the data you’ve published will be used for commercial activities, patents, press, literature, art, etc.?” in an additional comment space. This question aims to identify any other requests or concerns not raised in the literature or interview survey. As a result, 197 respondents responded. The major concerns were as follows: citation or indication of authorship (99 respondents), concern about misuse or inappropriate use (35 respondents), and concern about commercial use (14 respondents).

(9) Desired approach to data use and publishing

The respondents were asked in a multiple-choice format about their preferred approach of data use and publishing. The choices were made with reference to the interview survey results. Table 7 presents the number and rate of respondents who chose each option. Note that five respondents who did not select any of the options were excluded.

The highest rate was “Establishment of standard data licenses (conditions of use)” (77.2%), followed by “Establishment of guidelines for data licenses” (70.5%). Moreover, “establishment of a data licensing consultation, support, and management department (organization)” (41.6%) was selected higher than “establishment of a data licensing management organization (external organization)” (23.5%) as a contact point of data licensing issues.

(10) Free comments

A total of 84 respondents described the situation in the free comments. Regarding data publishing in general, various issues were pointed out, including inadequate systems, infrastructure, and technical difficulties and concerns about data publishing.

1) Discipline agreement and international treaties

In some cases, research data publishing is restricted by the discipline agreement of the field or research community, such as cases in which the research data publishing causes harm to the research subject or cases in which the subsequent research activities themselves are severely affected. Although protection policies are established as international treaties in many cases, let us keep in mind that these policies are not always clearly defined, known, nor applied.

2) Personal Information

In some cases, research data publishing is restricted to protect personal information. Its cases also include a restriction for disclosure, transfer, and anonymization or so by relevant local Japanese laws, cross-border rules (e.g., GDPR), and specific globalized guidelines (e.g., medical information).

3) Diplomatic/national security

If the data is related to national security or international relations (please see examples above), research data publishing is restricted. These data are strictly operated in the global context, including the conditions of use.

4) Agreements, contracts, and intellectual property rights

In some cases, data disclosure is restricted by contracts. For example, when a company and a researcher collaborate on a research project because it is not a direct matter of concern, the conditions of use and the embargo period for publishing are not uniformly determined in many cases. Then many agreements, contracts, and intellectual property rights are concluded in a local context.

5) Data Policy

In various cases, the data policy is defined by the data holder’s organization (department) or research funding agency. There are many possible reasons, for example, a research funder has a policy on limiting data publishing for the research to be funded; when a patent has been applied for; or the commercialization of research results is expected. These cases are restricted as an individual strategic decision and similar to previous data disclosure through contracts. The difference is that it is a management decision based on an “open-closed strategy”, which is a strategy to handle data by separating what should be released (open) and what should be protected (closed) based on the characteristics of the data.

As a result, external constraints consist of five categories, and the specific constraint requirements are determined by localizing in each subject matter. However, some external constraints have an ambiguity that arises from a global perspective, such as international treaties or GDPR. When standardized conditions of use are to be designed, the requirements of each external constraints must be localized for a category.

1) Waiver

This declaration waives copyright and all other related rights; hence, it can be evaluated to be definitely intended for the public. The cost to the data user is minimized because it is consistent with the legal requirements. The author’s name need not be displayed; therefore, responsibility for misunderstanding (social risk) is less likely to occur. However, data providers are not credited, and no incentive is given to publish. Moreover, changing the conditions of use in a manner that makes them more stringent is extremely difficult, even when there is a desire to prevent unwanted use due to changes in circumstances, such as increased property values. Although this condition of use is ideal from the viewpoint of use or redistribution, note that the number of data actually published may be limited.

2) Credit on the results (CC license term: Attribution)

This condition of use requires displaying the creator’s name and data URL information. The cost to data users can be assessed as a negligible level because it remains a “minimal constraint” found in the openness debate. Therefore, no problems are encountered when evaluating this condition of use aiming for the public.

The creator’s name is displayed; thus, a certain incentive can be given to the data provider. In addition, the data citation expectation is particularly high (from Q7) as a method of presentation considered to be appropriate when published data are reused by a third party. Although a certain amount of recognition is given for data inclusion in the text and acknowledgments, the direction of the data is that they should be treated as independent artifacts rather than as complements to a specific article.

On the contrary, as data-specific concerns, it may be too costly and impractical for the data user to deal with a lot of different data sources as the source data for data-intensive science (e.g., machine learning). Describing all credits in the presence of multiple data sets takes time and effort. One of the commenters stated that this should be resolved as a problem with citation and notation methods. Although it is out of the scope of this paper, much discussion on this topic has taken place within the Data Citation Synthesis Group in FORCE11 (Data Citation Synthesis Group, 2014) and elsewhere.

3) Impose the same conditions when publishing results (CC license term: Share Alike)

This condition of use requires the same condition of use under redistribution and combination of multiple pieces of data. Unlike “Attribution,” it may prevent them from combining the data with other sets that have an incompatible license. Although it remains the “minimum constraint” found in the openness debate for redistribution, from the viewpoint of data utilization, it should be used with caution.

4) Noncommercial

This condition of use requires the “non-commercial” use of data. The habit of prohibiting commercial use is deeply rooted in the academic community, and it seems unavoidable given the significance of academia’s freedom from the society. On the contrary, although we may consider it to be out of the philosophy of open data, the criteria for judgment fluctuate depending on people because “commercial use” is not clearly defined. Also, the limitation is on redistribution and data processing (e.g., selling visualizations derived from the data). his result implies that the data of academia may be more public by adopting this condition of use, but it should be used with a more clarifying scope of commercial use. The ambiguity of commercial use was also pointed out in discussions on copyrighted materials (Creative Commons, 2009). A more careful survey by each field will be necessary for the future.

5) NoDerivs (No Derivatives)

This condition of use prohibits data publishing after any modification. Although opening to the public is not restricted, based on these conditions of use, the cost to the data user is high because data use requires permission. The data will generally be published for new knowledge through processing or combination. From the viewpoint of data utilization, it should be used in a limited manner.

From the viewpoint of the data holders, the most frequent concerns are data alteration, falsification, fabrication, and misuse.

Furthermore, the case in which the prohibition of modification is effective is presumed to largely depend on the type of data and the manner of use (e.g., image data that are practically a work of art). Another survey for each type of data should be conducted, and more specific conditions of use for each must be set.

6) Improper use of data

This condition of use prohibits “Improper use” of data in the data processing phase. The data can be reused by both public or specific situations under this condition of use. However, the inappropriate use of data in the legal context would be covered by the Unfair Competition Prevention Act after its publication. Therefore, it is just a clear statement to users that legal and customary inappropriate treatment is prohibited. The definition of inappropriate use will probably depend on the conventions of the field. However, unclear conditions of use lead to contraction of usage. Therefore, the “improper” use in terms and conditions of use must be enumerated and specified.

7) Reporting

This condition of use requires post-reuse reporting, suggesting that the objective is to know detailed usage practices rather than mechanical access statistics. Although the data can be reused by both public and specific situations, the condition of use is stricter because of the “proactive” obligation. On the other hand, effectiveness may be realized if the data user is identified by linking to the relevant data. However, traceability cannot be guaranteed in the case of published data. In reality, it may be only at the level of a “request.”

8) Secondary use prohibited

This condition of use prohibits the secondary use of data. A mix of concerns about misuse/responsibility for quality and a desire to accurately understand the users have been observed. This license clearly prohibits data redistribution, translation, or adaption and is intended for one-on-one use of its original form. Although the data are not restricted, they cannot be re-distributed at all and have to be excluded from the definition of data publishing.

9) Request to use the latest version

This condition of use is used to limit the use of data to the latest one. Data in the past cannot be reused; thus, a large amount of data will be replaced when the latest data are published, resulting in marked costs of data usage. In addition, it is impossible to know in advance when the condition will be violated, and the manner to notify the version update is very limited.

10) Fee for use

This condition of use requires some fee for data use. The requirement of a user fee before data use is considered to be out of the scope of a condition of use that assumes that the data will be open to the public. The survey results also suggest that approximately half of the respondents are still uncomfortable with the act of monetizing data. However, given the sustainability of the data repository, monetization may be a major challenge in the future. The fee could be obtained in various ways, including shareware on a request basis, charging through a freemium model, download speed limits, and whether or not ads are displayed. In cases where the data holder itself requires a user fee, under what conditions the fee will be incurred must be clarified.

Step 1: Appraisal and selection of data to publish

In this step, the data holder identifies various data used in the research, which can be curated and made available to the public. There are various types of data publishing motivation; mandated by publishers, funders, or institutional policies, and by researchers’ requests. Although the scope of “research data” differs depending on the field of expertise, this guideline defines “research data” as data that can be managed by digital means and released as research results and do not include physical objects such as samples, specimens, and recording media (paper, disks, etc.). In addition, although research articles and software can be treated as research data, the guideline does not change or override the established methods for publishing in each content area (e.g., CC licenses for paper publishing and GPL or other software licenses for software publishing). If a researcher has received research funding, she/he should follow the rule of the treatment of research data defined by the funding agency. The guideline does not apply to such data; hence, their rules should be applied.

Step 2: Confirmation for legal restrictions/regulations/remarks

In this step, the data holder considers whether or not the data identified in Step 1 falls under the following categories of external constraints shown in Section 5:

• – Disciplinary customs, including international treaty
• – Personal information
• – Diplomatic/national security
• – Agreements, contracts, and intellectual property rights
• – Data policy

The abovementioned factors correspond to the constraints set out in 5.1, which can be confirmed with some examples.

Step 3: Release constraint

In this step, the data holder identifies and sets the conditions or time period required before the constraints found in Step 2 can be lifted by category. The terms or periods set out here will be written into the conditions as special conditions.

Even in cases where legal/customary restrictions are imposed, restrictions may be lifted with appropriate data processing (e.g., anonymization) or data release restrictions for a certain period time. At this time, there is no legal provision for the termination of the protection period for data, as there is for copyrighted works. For example, even if the term of the collaboration agreement has expired, the data are apparently not open to the public after any length of time, unless the term is clearly defined. To prevent these unnecessary restrictions, the guideline provides explicit steps for lifting the restrictions and encourage users to keep them to a minimum. If the research data publishing cannot be decided at the time of review, the “in case research data cannot be published” option recommends creating metadata and data storage to enable a later decision.

Step 4: Select a data repository

In this step, the data holder selects the repository, where he/she wants to publish the data. Well-known repositories/archives are likely to be the first candidate. However, the confirmation of external restrictions in Step 2 is the step built on the premise of Japanese law or regulations. Therefore, repositories constrained by other foreign rules may not necessarily cover all points for consideration. There are some famous registry sites such as re3data.org and FAIRsharing, however, only a limited number of registrations are available in Japan. In light of this background situation, we provide the list of recommended domestic data repositories in cooperation with the Japan Data Repository Network subcommittee under the RDUF. We also prepared a list of legal measures that can be applied under the Japanese law and clearly indicated them to eliminate them and respond to concerns about inappropriate use, which may accompany the lifting of restrictions.

Step 5: Choose appropriate conditions of use

In this step, the data holder selects the conditions of use that fulfils the requirements of data users and completes the standard conditions of use (covenants) that set out conditions consistent with those protected by copyright law. The data requirements are more diverse than those for copyrighted works, and the situation has not yet been systematically organized. Furthermore, there are high demands for standardization and simple explanations. As discussed, a concern has been raised: the simple recommendation of open licenses avoiding the copyright problem will not lead to the promotion of use.

We already analyzed the questionnaire survey results in Section 5 to identify the “Preferable” conditions of use. However, as a practical consideration, we added “Impose the same conditions when publishing results,” “Noncommercial,” and “Noderivs” to the preferable requirements in the guideline because some contents are difficult to distinguish from copyrighted works when giving conditions for data usage. To the previous CC license discussions and to ensure interoperability, we provided “Impose the same conditions when publishing results,” “Noncommercial,” and “Noderivs” with the explanation of its validity only under limited conditions. We take care to minimize the effort involved in setting the conditions by explaining how to describe specific conditions and usage information (agreements). These standard conditions of use (covenants) are designed to function as a part of the data usage policy of each data repository.