promoting open science data open science data promotion...
TRANSCRIPT
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DataCite Services: Promoting Open Science Dataデータサイトサービス: オープンサイエンスデータの推進 By Kristian GarzaOpen Science Data Promotion Workshop, Kyoto 2018
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Before... Now..
データを価値あるものにする
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Agenda発表内容
1. DataCite Introduction データサイトの紹介
2. Services and a Framework for Crosslinking Data,
Authors and Publications データ・著者・論文の相互
リンクのためのサービス及びフレームワーク
3. Services to Globally Identify Data Authors
データ作成者をグローバルに特定するサービス
4. Guidelines for Research Data Metrics 研究データメ
トリックのためのガイドライン
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DataCite a quick snapshot
● Not-for-profit Global initiative since 2009
● > 60 Members worldwide (Large institutions)
● > 1300 Datacenters
● >13,000,000 DOIs registered
● > 6,000,000 resolutions per month
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DataCite’s growth
Proportional Area Chart for DOI registrations by 2012 superposed on total of number of DOI Registrations by 2018
Proportional Area Chart for datacenters with DataCite by 2012 superposed on total of number of datacenters with DataCite by 2018
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Persistent Identifier Systems 永続的 ID システム
Persistent Uniform Resource Locator (PURL) 永続的 URL
Life Science Identifier (LSID)
ライフサイエンス ID
Digital Object Identifier (DOI)
デジタルオブジェクトID
Handles ハンドラ
Archival Resource Key (ARK) アーカイブ資源キー
…
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The benefits can be enjoyed worldwide
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Agenda発表内容 1. DataCite Introduction
2. Services and a Framework for Crosslinking
Data, Authors and Publications データ・著者
・論文の相互リンクのためのサービス及びフ
レームワーク
3. Services to Globally Identify Data Authors
4. Guidelines for Research Data Metrics
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データ・著者・論文を相互にリンクするためのサービス及びフレームワークServices and a Framework for Crosslinking Data, Authors and Publications
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PIDs Cross-linking PIDの相互リンク
Cross-linking refers to the explicit relationship between two or more PIDs. We
worked on identifying the best practices to link different types of identifiers and
data types in the context the services that provide the PID, whether this be
connecting data to publications, to researchers, to organizations, to funders, or
some combination thereof.
https://project-thor.eu/the-thor-mission/
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Benefits of Cross-linking Data and Publications
● Increased visibility, discovery and retrieval of both literature and data
● Facilitating reuse, reproducibility and transparency of research
● Enabling better attribution of credit for published data, providing an
additional incentive for researchers to share their data.
https://doi.org/10.1101/097196
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Using Related-Identifier field in the Metadata メタデータの関係するIDフィールドを利用
「派生源」確認ハンドラ
「新しいバージョンのDOIか?」確認ハンドラ
「Crossref DOI で引用されているか?」確認ハンドラ
「arXiv により文書化されているか?」確認ハンドラ
「URLで補足されているか?」確認ハンドラ
https://schema.datacite.org
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Discovery of Links between Data and Scholarly Articles
https://eventdata.datacite.org
Dataset Publication
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Discovery of Links between Data and Scholarly Articles
https://eventdata.datacite.org
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Improving support for Data Citation in Data Repositories
https://doi.org/10.1101/097196
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Providing services for crosslinking and embedding metadata in landing pages
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Using Content-Negotiation
$(document).ready(function() { var identifier = $("meta[name='DC.identifier']").attr("content"); if (identifier === undefined) { return; } var doi = new URL(identifier); var url = $('#site-title').attr('data-conneg'); url += '/application/vnd.schemaorg.ld+json/' + doi.pathname;
$.ajax({ url: url, dataType: 'text', // don't convert JSON to Javascript object success: function(data) { $('<script>') .attr('type', 'application/ld+json') .text(data) .appendTo('head'); }, error: function (error) { console.log(error.responseJSON); } });});
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DEMO デモhttps://doi.pangaea.de/10.1594/PANGAEA.760907
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How to embed metadata in web pages
DataCite exposes webpages with crawl-able structured data using schema.org markup.
Using the dataset class from schema.org
Mapping from DataCite Schema (v4.0, 2016) ( which has only 6 mandatory fields )
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Facilitating schema conversion and formatting.
http://www.github.com/datacite/bolognese
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Uptake
Repositories with metadata embedded in Schema.org. Some examples:
● Dryad● Dataverse● Zenodo● And others…● Full list:
https://github.com/datacite/freya/blob/master/schema_org.csv
28
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Cross-linking and the Scholix WG
A framework for exchanging information about the links between scholarly literature and data.
http://www.scholix.org/
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Scholix Information Model
SourceObject
TargetObject
Link
Relationship
….
Providers
● Information about the two objects● Information about the nature of the link and the link package itself http://www.scholix.org/
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Scholix Link Schema
● This schema represents the broad community consensus after two in-person workshops and lots of discussion
● The schema has intentionally very few required elements● Focus is on the link, only required properties for source and target object
are PID and type (either literature or dataset)● Optional metadata for objects where those are not available via PID● Small controlled vocabulary for relation type: isReferencedBy,
isSupplementTo, isRelatedTo
http://www.scholix.org/
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Scholix Link Package
http://www.scholix.org/
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Scholix Schema 3.0
http://www.scholix.org/
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データ作成者をグローバルに特定するためのサービスServices to Globally Identify Data Authors
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Claiming Dataset to your ORCID record from DataCite Search
www.search.datacite.org https://search.datacite.org
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Support datasets claiming in multiple workflows.
https://project-thor.eu/
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Support datasets claiming in multiple workflows.
https://project-thor.eu/
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研究データメトリックのためのガイドラインGuidelines for Research Data Metrics
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There has been previous efforts on counting citations
but ● Does not include usage● How can we do metrics? ?
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Therefore…
The implementation of a code of practice for data level metricsデータレベルメトリックのための実践規約の実装
https://makedatacount.org
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43
Dev
elop
New
Dat
a Le
vel M
etric
s H
ub
10111001101001101001010011100110100110
Data Citations
Repository Usage Metrics
Future Metrics
Leverage Existing Initiatives
Develop New Recommendation
Ser
ver L
og
Pro
cess
ing
Drive Adoption - Engage Communities
Display Data Metrics
https://makedatacount.org
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Protocol for exchanging Data Metrics
SUSHI (Standardized Usage Statistics
Harvesting Initiative) protocol
(http://www.niso.org/workrooms/sushi/ )
has greatly facilitated the handling of large
volumes of usage data, and its
implementation by vendors allows the
automated retrieval of the COUNTER usage
reports into local systems, making this
process much less time consuming for the
librarian or library consortium administrator.
http://www.niso.org/workrooms/sushi/
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MDC Milestones 一里塚
2017 2018
June 2017MDC Project Started
February 2018CoP Release
March 2018Hub prototype
May 2018MVP
https://github.com/datacite/sashimi
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Get Involved 参加方法
Repositories – contact us if you would like to be an early adopter
Publishers – support data citations & ensure your citation data and metadata are open
Institutions- contact us if you would like to support in your institutional repositories and help us spread the word
Researchers- we are looking for beta testers for our front end development of data level metrics
Funders- if you are interested in supporting this initiative, please get in touch.
Follow us on Twitter @MakeDataCount
Join our mailing list at
https://makedatacount.org/contact/
https://makedatacount.org
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ご静聴ありがとうご
ざいました
Visit:www.datacite.org