<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" "http://jats.nlm.nih.gov/publishing/1.2/JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="data-paper" dtd-version="1.2" xml:lang="en">
    <front>
        <journal-meta>
            <journal-id journal-id-type="pmc">F1000Research</journal-id>
            <journal-title-group>
                <journal-title>F1000Research</journal-title>
            </journal-title-group>
            <issn pub-type="epub">2046-1402</issn>
            <publisher>
                <publisher-name>F1000 Research Limited</publisher-name>
                <publisher-loc>London, UK</publisher-loc>
            </publisher>
        </journal-meta>
        <article-meta>
            <article-id pub-id-type="doi">10.12688/f1000research.147798.1</article-id>
            <article-categories>
                <subj-group subj-group-type="heading">
                    <subject>Data Note</subject>
                </subj-group>
                <subj-group>
                    <subject>Articles</subject>
                </subj-group>
            </article-categories>
            <title-group>
                <article-title>A data set of Japanese corporates&#x2019; green transformation technologies patents</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 1; peer review: 1 approved with reservations]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Jiang</surname>
                        <given-names>Jiaming</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Funding Acquisition</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Resources</role>
                    <role content-type="http://credit.niso.org/">Software</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <uri content-type="orcid">https://orcid.org/0000-0003-2727-6149</uri>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Zhao</surname>
                        <given-names>Yu</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Feng</surname>
                        <given-names>Junshi</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <xref ref-type="aff" rid="a3">3</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>School of Information and Data Sciences, Nagasaki University, Nagasaki, Japan</aff>
                <aff id="a2">
                    <label>2</label>School of Management, Department of Management, Tokyo University of Science, Tokyo, Japan</aff>
                <aff id="a3">
                    <label>3</label>School of International Economics and Trade, Jilin University of Finance and Economics, Jilin, China</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:jiaming@nagasaki-u.ac.jp">jiaming@nagasaki-u.ac.jp</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>18</day>
                <month>4</month>
                <year>2024</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2024</year>
            </pub-date>
            <volume>13</volume>
            <elocation-id>288</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>9</day>
                    <month>4</month>
                    <year>2024</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2024 Jiang J et al.</copyright-statement>
                <copyright-year>2024</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <self-uri content-type="pdf" xlink:href="https://f1000research.com/articles/13-288/pdf"/>
            <abstract>
                <title>Abstract*</title>
                <p>In 2020, the Government of Japan declared 2050 &#x201c;carbon neutral&#x201d; and launched a long-term strategy to create a &#x201c;virtuous cycle of economy and environment.&#x201d; Japanese corporations possess many technologies that contribute to decarbonization, which is important for expanding investments in green transformation technology inventory (GXTI) development. Patent data are the most reliable measure of business performance for applied research and development activities when investigating knowledge domains or technological evolution. Our paper describes a Japanese patent dataset of Japanese corporations&#x2019; green transformation (GX) patent applications on the Japan Platform for Patent Information, using a search method of bombinating International Patent Classification (IPC) codes and keywords. The dataset contains 37,476 GX patent applications from 298 corporations during the period 1999&#x2013;2022.</p>
            </abstract>
            <kwd-group kwd-group-type="author">
                <kwd>Green transformation technology</kwd>
                <kwd>innovation</kwd>
                <kwd>Japanese corporates</kwd>
                <kwd>environment</kwd>
                <kwd>patents</kwd>
            </kwd-group>
            <funding-group>
                <funding-statement>The author(s) declared that no grants were involved in supporting this work.</funding-statement>
            </funding-group>
        </article-meta>
    </front>
    <body>
        <sec id="sec1" sec-type="intro">
            <title>Introduction</title>
            <p>Currently, we are expected to shift economic, social, and industrial structures, which have depended on fossil fuels since the Industrial Revolution, into structures driven by clean energy through green transformation (GX). With the Paris Agreement of 2015, the world has been moving toward carbon neutrality. As pointed out by 
                <xref ref-type="bibr" rid="ref7">Sueyoshi and Goto (2019)</xref>, Japan has historically faced various environmental problems, along with industrialization. Therefore, Japan has also made a &#x201c;2050 Carbon Neutrality Declaration&#x201d; in 2020. The declaration indicated that &#x201c;the goal is to reduce overall greenhouse gas emissions to zero by 2050, that is, to achieve a carbon-neutral, decarbonized society in 2050&#x201d;. The trend toward a decarbonized society has become irreversible in Japan. Most recently, based on the &#x201c;GX Promotion Law&#x201d; enacted in May of the previous year, the GX Promotion Strategy was approved by the Cabinet of Ministers, and all the moves toward GX are taking shape. The Green Transformation Technologies Inventory (GXTI), released by the JPO shows how various GX technologies can be categorized, and how patent documents related to each category of GX technology can be searched. GXTI helps enterprises and others to objectively explain their GX efforts. GXTI categorizes GX technologies and searches for patent documents pertinent to the five respective GX technology categories: energy supply; energy saving; electrification; demand-supply flexibility; batteries; energy storage; 
                <inline-formula>
                    <mml:math display="inline">
                        <mml:msub>
                            <mml:mi>CO</mml:mi>
                            <mml:mn>2</mml:mn>
                        </mml:msub>
                    </mml:math>
                </inline-formula> reduction in the non-energy sector; and capture, storage, utilization, and removal of greenhouse gas.</p>
            <p>On the other hand, patents are the most common method in the automotive industry to protect intellectual property (
                <xref ref-type="bibr" rid="ref1">Borgstedt et al., 2017</xref>), and patent analysis has been used to evaluate the competitiveness of firms, develop technology plans, prioritize research and development (R&amp;D) investment, and monitor technological change in firms (
                <xref ref-type="bibr" rid="ref8">Yoon and Park, 2004</xref>). Japanese respondents also reported patents as being as effective as other mechanisms for protecting product innovation. Furthermore, patents are the most important channel of information flow in Japan (
                <xref ref-type="bibr" rid="ref2">Cohen et al., 2002</xref>). Although 
                <xref ref-type="bibr" rid="ref4">Jiang et al. (2022)</xref> have searched for &#x201c;green&#x201d; patents in the vehicle powertrains field, and 
                <xref ref-type="bibr" rid="ref5">Jiang et al. (2023)</xref> collected cited information about them, there have not been reports of research on GX patents, because of a delay in the development of a database, which is a void that we are trying to fill. We generated this dataset for GX patent applications of Japanese corporations and published GX patents in Mendeley Data, which is available to everyone. The full dataset may be used to conduct further searches, such as natural language analysis and machine learning, on patent description documents as well as statistical data analysis for empirical economics. This dataset may also be merged with many other databases such as the EPO&#x2019;s PATSTAT database and the Corporate Social Responsibility Database of Toyo Keizai Inc. We expect our dataset to be meaningful for forecasting the development of new technologies and encouraging environmental innovation.</p>
        </sec>
        <sec id="sec2" sec-type="methods">
            <title>Methods</title>
            <p>We generated this dataset by searching for GX patent applications of Japanese corporations on the Japan Platform for Patent Information 
                <ext-link ext-link-type="uri" xlink:href="https://www.j-platpat.inpit.go.jp/p0100">(J-PlatPat</ext-link>). The search formulas are published at 
                <ext-link ext-link-type="uri" xlink:href="https://www.jpo.go.jp/e/resources/statistics/gxti.html">https://www.jpo.go.jp/e/resources/statistics/gxti.html</ext-link> and use a combined search strategy of International Patent Classification (IPC) codes and keywords. Applications of similar methods that have been previously reported include those of 
                <xref ref-type="bibr" rid="ref4">Jiang et al. (2022)</xref>, the authors proposed a method of combining IPC and keywords to define &#x201c;green&#x201d; patents in vehicle powertrain systems, based on data of patent applications to the Japan Patent Office as recorded in EPO&#x2019;s PATSTAT.
                <sup>
                    <xref ref-type="fn" rid="fn1">1</xref>
                </sup> 
                <xref ref-type="bibr" rid="ref1">Borgstedt et al. (2017)</xref> proposed a search strategy using IPC-classes and keywords is introduced that reveals all relevant patents and clearly differentiated four vehicle powertrain technologies. And 
                <xref ref-type="bibr" rid="ref6">Mirzadeh Phirouzabadi et al. (2020)</xref> introduced a database using a combined search strategy of keywords and IPC codes.</p>
            <p>From the present search, we obtained 37,476 patent applications by 298 corporations during the period 1999&#x2013;2022. When analyzing patents, it is necessary to consider the social situation of each country, including language background (
                <xref ref-type="bibr" rid="ref4">Jiang et al., 2022</xref>). Thus, since the names of Japanese corporates are written in both kanji and katakana, when we searched for one corporate&#x2019;s patents, we searched in both kanji and katakana.
                <sup>
                    <xref ref-type="fn" rid="fn2">2</xref>
                </sup> The name list of corporations and their industry sectors was supplied by Toyo Keizai Inc., a leading business publisher in Japan, with the most well-known comprehensive database of Japanese companies. Most companies are listed in the first section of the Tokyo Stock Exchange Market (
                <xref ref-type="bibr" rid="ref7">Sueyoshi and Goto, 2019</xref>). 
                <xref ref-type="fig" rid="f1">Figure 1</xref> aggregates the sampled patents by the industry sector.</p>
            <fig fig-type="figure" id="f1" orientation="portrait" position="float">
                <label>Figure 1. </label>
                <caption>
                    <title>Aggregation of patents by industry sector.</title>
                </caption>
                <graphic id="gr1" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/162033/9f5b115c-31c7-48c8-b4b6-680b16319f57_figure1.gif"/>
            </fig>
            <p>From this figure, we can see that most GX patents were from the transportation equipment sector, there are 20,385 patents, account for 54%, and second-most common sector of GX patents was the electrical appliances sector, there are 6,290 patents, account for 17%. The number of GX patents from the metal products, Glass &amp; Stone products, non-ferrous metals, and chemistry sectors was also over 1000. There are comparatively few GX patents from the retail, banking, securities, and commodity futures sectors.</p>
            <p>
                <xref ref-type="fig" rid="f2">Figure 2</xref> shows the yearly number of GX patent applications for the period 1999&#x2013;2022. As shown, the number increased every year until peaking in 2012, after which it remained relatively stable for several years and then decreased from 2019 because of a delay in opening in J-PlatPat.</p>
            <fig fig-type="figure" id="f2" orientation="portrait" position="float">
                <label>Figure 2. </label>
                <caption>
                    <title>Number of GX patent applications in 1999-2022.</title>
                </caption>
                <graphic id="gr2" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/162033/9f5b115c-31c7-48c8-b4b6-680b16319f57_figure2.gif"/>
            </fig>
            <p>
                <xref ref-type="table" rid="T1">Table 1</xref> lists the top 10 companies in terms of the number of patents owned. As shown, in our sample, Toyota Motor Corporation owns the most GX patents (11,514), followed by Mitsubishi Electric Corporation (4,443), Honda Motor Co., Ltd. (4,011), and Nissan Motor Corporation (3,256), who also had numerous GX patents.</p>
            <table-wrap id="T1" orientation="portrait" position="float">
                <label>Table 1. </label>
                <caption>
                    <title>GX patents: Top 10 Companies.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Industry Sector</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Company</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Number of patents</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Transportation equipment</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Toyota Motor Corporation</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">11,514</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Electrical appliances</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Mitsubishi Electric Corporation</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">4,443</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Transportation equipment</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Honda Motor Co., Ltd.</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">4,011</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Transportation equipment</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Nissan Motor Corporation</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">3,256</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Electrical appliances</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Konica Minolta, Inc.</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">1,020</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Transportation equipment</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Mitsubishi Motors Corporation</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">757</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Other products</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Toppan Inc.</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">674</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Non-Ferrous metals</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Sumitomo Electric Industries, Ltd.</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">660</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Glass &amp; Stone products</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Niterra Co., Ltd.</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">638</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Metal products</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Noritsu Corporation</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">624</td>
                        </tr>
                    </tbody>
                </table>
            </table-wrap>
            <sec id="sec3">
                <title>Dataset validation</title>
                <p>The limitation of this dataset is that it focuses almost exclusively on large publicly traded corporations, as little is known about small and medium-size enterprises (SMEs). Further research on SMEs is needed.</p>
            </sec>
            <sec id="sec4">
                <title>Ethical considerations</title>
                <p>Not applicable.</p>
            </sec>
        </sec>
    </body>
    <back>
        <sec id="sec7" sec-type="data-availability">
            <title>Data availability</title>
            <p>Figshare: Japanese GX patents with English titles. 
                <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.25434724.v3">https://doi.org/10.6084/m9.figshare.25434724.v3</ext-link> (
                <xref ref-type="bibr" rid="ref3">Jiang, 2024</xref>).</p>
            <p>This project contains the following underlying data:
                <list list-type="bullet">
                    <list-item>
                        <label>&#x2022;</label>
                        <p>Japanese GX patents with English titles</p>
                    </list-item>
                </list>
            </p>
            <p>Data are available under the terms of the 
                <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International license</ext-link> (CC-BY 4.0).</p>
        </sec>
        <ack>
            <title>Acknowledgements</title>
            <p>Not applicable.</p>
        </ack>
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                    <year>2019</year>;<volume>12</volume>(<issue>9</issue>):<fpage>1785</fpage>.
                    <pub-id pub-id-type="doi">10.3390/en12091785</pub-id>
                </mixed-citation>
            </ref>
            <ref id="ref8">
                <mixed-citation publication-type="journal">
                    <person-group person-group-type="author">

                        <name name-style="western">
                            <surname>Yoon</surname>
                            <given-names>B</given-names>
                        </name>

                        <name name-style="western">
                            <surname>Park</surname>
                            <given-names>Y</given-names>
                        </name>
</person-group>:
                    <article-title>A text-mining-based patent network: Analytical tool for high-technology trend.</article-title>
                    <source>

                        <italic toggle="yes">J. High Technol. Manag.</italic>
</source>
                    <year>2004</year>;<volume>15</volume>:<fpage>37</fpage>&#x2013;<lpage>50</lpage>.
                    <pub-id pub-id-type="doi">10.1016/j.hitech.2003.09.003</pub-id>
                </mixed-citation>
            </ref>
        </ref-list>
        <fn-group content-type="footnotes">
            <fn id="fn1">
                <label>

                    <sup>1</sup>
                </label>
                <p>See 
                    <ext-link ext-link-type="uri" xlink:href="https://www.epo.org/en/searching-for-patents/business/patstat">https://www.epo.org/en/searching-for-patents/business/patstat</ext-link>
                </p>
            </fn>
            <fn id="fn2">
                <label>

                    <sup>2</sup>
                </label>
                <p>For example, the ホンダ can also be written in 本田; the トヨタ sometimes is written in 豊田.</p>
            </fn>
        </fn-group>
    </back>
    <sub-article article-type="reviewer-report" id="report296596">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.162033.r296596</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Pingjian</surname>
                        <given-names>Yang</given-names>
                    </name>
                    <xref ref-type="aff" rid="r296596a1">1</xref>
                    <role>Referee</role>
                </contrib>
                <contrib contrib-type="author">
                    <name>
                        <surname>Chang</surname>
                        <given-names>Hongwang</given-names>
                    </name>
                    <xref ref-type="aff" rid="r296596a1">1</xref>
                    <role>Co-referee</role>
                </contrib>
                <aff id="r296596a1">
                    <label>1</label>Chinese Research Academy of Environmental Sciences, Beijing, Beijing, China</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>12</day>
                <month>7</month>
                <year>2024</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2024 Pingjian Y and Chang H</copyright-statement>
                <copyright-year>2024</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport296596" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.147798.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>As mentioned in the article, green transition (GX) technologies are crucial to the achievement of carbon neutrality goals. Enterprises, especially large ones, can not only lead to the improvement of green technology level in their industries, but also achieve the green transformation of the whole society. Although the authors have good ideas, the amount of work done by the authors is inadequate, both in terms of literature review, methodology presentation, and sample selection.</p>
            <p> </p>
            <p> 1.The end of the first paragraph of the introduction seems to be 8 categories of green transformation technologies, please check it. &#x00a0;</p>
            <p> </p>
            <p> 2.The authors are requested to add a section which should describe which IPC codes and which keywords the authors used in their search.</p>
            <p> </p>
            <p> 3. Sample size issues. The authors need to explain the following issues or add appropriate content to the article.</p>
            <p> i) Why were only 298 large enterprises selected?</p>
            <p> ii) What are the conditions on which the 298 large enterprises were selected?</p>
            <p> iii) Most of the top ten companies are "transport equipment" companies. This leads to the suspicion that the reason for this result is related to the sample selection. Therefore, the authors need to add more details about the sample selection.</p>
            <p> iv) In addition, what are the limitations in sample selection?</p>
            <p> </p>
            <p> 4. Data description issues.</p>
            <p> i) Too few descriptive statistics for the data.</p>
            <p> ii) It is recommended to add the description of data such as annual mean, annual standard deviation, and maximum and minimum values of each industry in order to have a more intuitive insight into the development trend of each industry.</p>
            <p> iii) Figure 1 and Figure 2 are not suitable.</p>
            <p> </p>
            <p> We suggest the authors to dig further into the data content. For example: 1. the level of spatial distribution of the number of patents; 2. whether there is a spillover effect of technological innovation; 3. the level of green transformation in each industry, etc.</p>
            <p>Are sufficient details of methods and materials provided to allow replication by others?</p>
            <p>Partly</p>
            <p>Is the rationale for creating the dataset(s) clearly described?</p>
            <p>Partly</p>
            <p>Are the datasets clearly presented in a useable and accessible format?</p>
            <p>Partly</p>
            <p>Are the protocols appropriate and is the work technically sound?</p>
            <p>Partly</p>
            <p>Reviewer Expertise:</p>
            <p>environmental policy</p>
            <p>We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.</p>
        </body>
    </sub-article>
</article>
