Add "IsDerivedFrom" to DOI metadata

[bemoody]

If a published project is derived from other published projects on the site, include that information in the DOI metadata.

Formally, "IsDerivedFrom indicates B [the related resource] is a source upon which A [the resource being registered] is based. IsDerivedFrom should be used for a resource that is a derivative of an original resource."

https://schema.datacite.org/meta/kernel-4.4/doc/DataCite-MetadataKernel_v4.4.pdf (page 63)

This changes the metadata for newly created DOIs. We should update the metadata for existing projects too; I don't know if we have a process for doing that.

[tompollard]

We should update the metadata for existing projects too; I don't know if we have a process for doing that.

Currently just a manual process (clicking the "update metadata" buttons on the project management page in the admin console):

[tompollard]

Looks good to me, thanks!

[tompollard]

I tested a manual push to DataCite for the following project:
https://physionet.org/content/cxr-lt-iccv-workshop-cvamd/1.1.0/

Before:

<?xml version="1.0" encoding="UTF-8"?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4/metadata.xsd">
  <identifier identifierType="DOI">10.13026/C4TR-KR83</identifier>
  <creators>
    <creator>
      <creatorName>Holste, Gregory</creatorName>
      <givenName>Gregory</givenName>
      <familyName>Holste</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="https://orcid.org/">https://orcid.org/0000-0002-5657-3081</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Wang, Song</creatorName>
      <givenName>Song</givenName>
      <familyName>Wang</familyName>
    </creator>
    <creator>
      <creatorName>Jaiswal, Ajay</creatorName>
      <givenName>Ajay</givenName>
      <familyName>Jaiswal</familyName>
    </creator>
    <creator>
      <creatorName>Yang, Yuzhe</creatorName>
      <givenName>Yuzhe</givenName>
      <familyName>Yang</familyName>
    </creator>
    <creator>
      <creatorName>Lin, Mingquan</creatorName>
      <givenName>Mingquan</givenName>
      <familyName>Lin</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="https://orcid.org/">https://orcid.org/0000-0003-0862-6588</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Peng, Yifan</creatorName>
      <givenName>Yifan</givenName>
      <familyName>Peng</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="https://orcid.org/">https://orcid.org/0000-0001-9309-8331</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Wang, Atlas</creatorName>
      <givenName>Atlas</givenName>
      <familyName>Wang</familyName>
    </creator>
  </creators>
  <titles>
    <title>CXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays</title>
  </titles>
  <publisher>PhysioNet</publisher>
  <publicationYear>2023</publicationYear>
  <resourceType resourceTypeGeneral="Dataset"/>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.13026/5mbq-9k80</relatedIdentifier>
  </relatedIdentifiers>
  <sizes/>
  <formats/>
  <version>1.1.0</version>
  <descriptions>
    <description descriptionType="Abstract">Many real-world problems, including diagnostic medical imaging exams, are
"long-tailed" - there are a few common findings followed by more relatively
rare conditions. In chest radiography, diagnosis is both a **long-tailed** and
**multi-label** problem, as patients often present with multiple disease
findings simultaneously. This is distinct from most large-scale image
classification benchmarks, where each image only belongs to one label and the
distribution of labels is relatively balanced. While researchers have begun to
study the problem of long-tailed learning in medical image recognition, few
have studied its interplay with label co-occurrence. This competition will
provide a challenging large-scale multi-label long-tailed learning task on
chest X-rays (CXRs), encouraging community engagement with this emerging
interdisciplinary topic. This project contains labels for the CXR-LT 2023
competition dataset, containing 377,110 CXRs from 26 classes, and a related
subset used in the MICCAI 2023 paper,  "How Does Pruning Impact Multi-Label
Long-Tailed Learning?" containing 257,018 frontal CXRs from 19 classes.

</description>
  </descriptions>
</resource>

After, with successful addition of the IsDerivedFrom fields:

<?xml version="1.0" encoding="UTF-8"?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4/metadata.xsd">
  <identifier identifierType="DOI">10.13026/C4TR-KR83</identifier>
  <creators>
    <creator>
      <creatorName>Holste, Gregory</creatorName>
      <givenName>Gregory</givenName>
      <familyName>Holste</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="https://orcid.org/">https://orcid.org/0000-0002-5657-3081</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Wang, Song</creatorName>
      <givenName>Song</givenName>
      <familyName>Wang</familyName>
    </creator>
    <creator>
      <creatorName>Jaiswal, Ajay</creatorName>
      <givenName>Ajay</givenName>
      <familyName>Jaiswal</familyName>
    </creator>
    <creator>
      <creatorName>Yang, Yuzhe</creatorName>
      <givenName>Yuzhe</givenName>
      <familyName>Yang</familyName>
    </creator>
    <creator>
      <creatorName>Lin, Mingquan</creatorName>
      <givenName>Mingquan</givenName>
      <familyName>Lin</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="https://orcid.org/">https://orcid.org/0000-0003-0862-6588</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Peng, Yifan</creatorName>
      <givenName>Yifan</givenName>
      <familyName>Peng</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="https://orcid.org/">https://orcid.org/0000-0001-9309-8331</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Wang, Atlas</creatorName>
      <givenName>Atlas</givenName>
      <familyName>Wang</familyName>
    </creator>
  </creators>
  <titles>
    <title>CXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays</title>
  </titles>
  <publisher>PhysioNet</publisher>
  <publicationYear>2023</publicationYear>
  <resourceType resourceTypeGeneral="Dataset"/>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.13026/5mbq-9k80</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsDerivedFrom">10.13026/8360-t248</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsDerivedFrom">10.13026/C2JT1Q</relatedIdentifier>
  </relatedIdentifiers>
  <sizes/>
  <formats/>
  <version>1.1.0</version>
  <descriptions>
    <description descriptionType="Abstract">Many real-world problems, including diagnostic medical imaging exams, are
"long-tailed" - there are a few common findings followed by more relatively
rare conditions. In chest radiography, diagnosis is both a **long-tailed** and
**multi-label** problem, as patients often present with multiple disease
findings simultaneously. This is distinct from most large-scale image
classification benchmarks, where each image only belongs to one label and the
distribution of labels is relatively balanced. While researchers have begun to
study the problem of long-tailed learning in medical image recognition, few
have studied its interplay with label co-occurrence. This competition will
provide a challenging large-scale multi-label long-tailed learning task on
chest X-rays (CXRs), encouraging community engagement with this emerging
interdisciplinary topic. This project contains labels for the CXR-LT 2023
competition dataset, containing 377,110 CXRs from 26 classes, and a related
subset used in the MICCAI 2023 paper,  "How Does Pruning Impact Multi-Label
Long-Tailed Learning?" containing 257,018 frontal CXRs from 19 classes.

</description>
  </descriptions>
</resource>