Kernel Metadata Declaration — on demand

ISO 26324 asks a DOI Registration Agency to produce a Kernel Metadata Declaration for every DOI it issues. Give any DOI: its public record becomes a record in the Smart Scholars DOI Metadata Format 1.0, checked against every rule, and declared as the Kernel XML.

✓ Meets every rule of the format

The record below is complete and every controlled value is a DOI Attribute Value Set 2.3 spelling. The Declaration on the right follows DOIMetadataKernel.xsd element by element.

What the DOI identifies

Language to language Translation using GRU method

10.46243/jst.2020.v5.i3.pp192-194 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2020-05-29

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 5 · no. 3 · pp. 192–194

Principal agents

Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 3 links, 7 references. Source: Crossref (member 25296), registered 2020-05-29, last deposited 2026-09-17.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

DOI Name DOI name	10.46243/jst.2020.v5.i3.pp192-194
Referent Type referentType	Creation
Referent Sub-Type referentSubType	JournalArticle — an article in a journal
Referent Name(s) referentName(s)	Language to language Translation using GRU method (PrincipalTitle, en)
Basic Metadata basicMetadata	publisher: Longman Publishers published: 2020-05-29 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 5 · no. 3 · pp. 192–194 language: en form: Digital · Visual · Language
Referent Identifier(s) alternateIdentifier(s)	none besides the DOI
Registration Authority registrationAuthorityCode	Crossref — issued by Crossref (member 25296); held here as a copy
Created Date issueDate	2020-05-29
relatedIdentifiers	none needed — the descriptive metadata is in this record

The record

{
    "format": "smartscholars-doi-metadata/1.0",
    "doi": "10.46243/jst.2020.v5.i3.pp192-194",
    "referent": "Creation",
    "type": "JournalArticle",
    "structural_type": "Digital",
    "modes": [
        "Visual"
    ],
    "characters": [
        "Language"
    ],
    "titles": [
        {
            "value": "Language to language Translation using GRU method",
            "type": "PrincipalTitle",
            "lang": "en"
        }
    ],
    "identifiers": [
        {
            "type": "DOI",
            "value": "10.46243/jst.2020.v5.i3.pp192-194"
        }
    ],
    "agents": [
        {
            "role": "publisher",
            "name": {
                "org": "Longman Publishers"
            }
        }
    ],
    "dates": {
        "published": "2020-05-29",
        "date_type": "PublicationDate",
        "online": "2020-05-29"
    },
    "language": "en",
    "container": {
        "type": "Journal",
        "titles": [
            {
                "value": "Journal of Science & Technology",
                "type": "PrincipalTitle"
            }
        ],
        "identifiers": [
            {
                "type": "ISSN",
                "value": "2456-5660",
                "medium": "electronic"
            }
        ],
        "volume": "5",
        "issue": "3",
        "pages": {
            "first": "192",
            "last": "194"
        }
    },
    "links": [
        {
            "url": "https://www.jst.org.in/index.php/pub/article/view/452",
            "return_type": "text/html",
            "primary": true
        },
        {
            "url": "https://www.jst.org.in/index.php/pub/article/download/452/399",
            "purpose": "text-mining",
            "return_type": "application/pdf"
        },
        {
            "url": "https://www.jst.org.in/index.php/pub/article/download/452/3152",
            "purpose": "text-mining",
            "return_type": "application/xml"
        }
    ],
    "abstract": {
        "value": "Current state of the art translation systems for speech to speech rely heavily on a text representation for the interpretation. By transcoding speech to text we lose important information about the characteristics of the voice like the emotion, pitch and accent. The thesis examine the likelihood of using an GRU neural network model to translate speech to speech without the requirement of a text representation that's by translating using the raw audio data directly so as to persevere the characteristics of the voice that otherwise stray within the text transcoding a part of the interpretation process. As a part of the research we create an information set of phrases suitable for speech to speech translation tasks. The thesis leads to a signal of concept system which requires scaling the underlying deep neural network so as to figure better.",
        "lang": "en"
    },
    "license": {
        "url": "https://creativecommons.org/licenses/by/4.0",
        "start": "2020-05-29",
        "applies_to": "vor"
    },
    "references": [
        {
            "key": "ref1",
            "unstructured": "Skype translator. https://www.skype.com/en/features/skype-translator/. [Ac-cessed: 2016-05-16]"
        },
        {
            "key": "ref2",
            "unstructured": "Skype voip software. https://www.skype.com/. [Accessed: 2016-05-16]"
        },
        {
            "key": "ref3",
            "unstructured": "Martín Abadi, Ashish Agarwal, and Paul Barham et al. Tensorflow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org"
        },
        {
            "key": "ref4",
            "unstructured": "Chi-Ho Li, Minghui Li, Dongdong Zhang, Mu Li, Ming Zhou, and Yi Guan. A probabilistic approach to syntax-based reordering for statistical machine translation. In Annual Meeting-association for Computational Linguistics, vol-ume 45, page 720, 2007"
        },
        {
            "key": "ref5",
            "unstructured": "Microsoft. Skype translator presentation. https://www.youtube.com/watch? v=rek3jjbYRLo. [Accessed: 2016-04-30]"
        },
        {
            "key": "ref6",
            "unstructured": "Microsoft. How technology can bridge language gaps. http://research. microsoft.com/en-us/research/stories/speech-tospeech.aspx, 2015. [Accessed: 2016-05-14]"
        },
        {
            "key": "ref7",
            "doi": "10.1109/icassp.2014.6854318",
            "unstructured": "Y. Qian, Y. Fan, W. Hu, and F. K. Soong. On the training aspects of deep neural network (dnn) for parametric tts synthesis. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3829–3833, May 2014. 194 | Page"
        }
    ],
    "record": {
        "registrant": "Longman Publishers",
        "registered": "2020-05-29",
        "updated": "2026-09-17",
        "issue_number": 1,
        "source": "crossref-api",
        "source_agency": "Crossref (member 25296)"
    }
}

The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

<?xml version="1.0" encoding="UTF-8"?>
<!-- DRAFT declaration: Smart Scholars is not yet a DOI Registration Agency. registrationAgencyDoiName is a marker (10.0/ is no RA's prefix) and is filled in on accreditation. -->
<kernelMetadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.doi.org/2010/DOISchema https://www.doi.org/doi_schemas/DOIMetadataKernel.xsd" xmlns="http://www.doi.org/2010/DOISchema">
  <referentDoiName>10.46243/jst.2020.v5.i3.pp192-194</referentDoiName>
  <primaryReferentType>Creation</primaryReferentType>
  <registrationAgencyDoiName>10.0/smart-scholars-draft</registrationAgencyDoiName>
  <issueDate>2026-10-04</issueDate>
  <issueNumber>1</issueNumber>
  <referentCreation>
    <name primaryLanguage="en">
      <value>Language to language Translation using GRU method</value>
      <type>PrincipalTitle</type>
    </name>
    <identifier>
      <nonUriValue>10.46243/jst.2020.v5.i3.pp192-194</nonUriValue>
      <uri returnType="text/html" doesContentNegotiation="true">https://doi.org/10.46243/jst.2020.v5.i3.pp192-194</uri>
      <type>DOI</type>
    </identifier>
    <identifier>
      <uri returnType="text/html">https://www.jst.org.in/index.php/pub/article/view/452</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri>https://www.jst.org.in/index.php/pub/article/download/452/399</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri returnType="application/xml">https://www.jst.org.in/index.php/pub/article/download/452/3152</uri>
      <type>URI</type>
    </identifier>
    <structuralType>Digital</structuralType>
    <mode>Visual</mode>
    <character>Language</character>
    <type>JournalArticle</type>
    <principalAgent>
      <name>
        <value>Longman Publishers</value>
        <type>PrincipalName</type>
      </name>
      <role>Publisher</role>
    </principalAgent>
    <linkedCreation>
      <name>
        <value>Journal of Science &amp; Technology</value>
        <type>PrincipalTitle</type>
      </name>
      <identifier>
        <nonUriValue>2456-5660</nonUriValue>
        <type>ISSN</type>
      </identifier>
      <referentCreationRole>Part</referentCreationRole>
      <referentCreationSequenceIdentifier>
        <value>5</value>
        <type>VolumeNumber</type>
      </referentCreationSequenceIdentifier>
      <referentCreationSequenceIdentifier>
        <value>3</value>
        <type>IssueNumber</type>
      </referentCreationSequenceIdentifier>
      <referentCreationSequenceIdentifier>
        <value>192-194</value>
        <type>PageNumber</type>
      </referentCreationSequenceIdentifier>
    </linkedCreation>
    <language>en</language>
    <languageOfReferentContent>
      <language>en</language>
      <languageOfReferentContentType>Original</languageOfReferentContentType>
    </languageOfReferentContent>
    <creationDate>
      <date>2020-05-29</date>
      <creationDateType>PublicationDate</creationDateType>
    </creationDate>
  </referentCreation>
</kernelMetadata>

Kernel Metadata Declaration — on demand

✓ Meets every rule of the format

What the DOI identifies

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

The record

The Kernel Metadata Declaration

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