Category: Uncategorized

I would like to propose a session on the International Standard Name Identifier (ISO 27729). The ISNI identifies persons and corporate bodies.
The session will provide an update on how the ISNI system works and how it relates to its base file the Virtual International Authority File which currently links identities from over 50 national library authority files worldwide. The ISNI extends the scope of VIAF by linking to identities expressed in other domains including rights management organisations, publishers, archives and data agencies.
ISNI is built from authoritative data assertions about people which can be connected at scale by running matching algorithms. Unlike VIAF the ISNI system supports the ongoing creation of new identifiers and curation of the links that form the integrity of the identifier itself. As the database and its active contributors grow in scale the expectations on the reliability of the ISNI grow with it. Its potential for the secure identification of rights holders in automated transactions requires robust maintenance and curation of the links on which it is built and the links that it generates as it is diffused widely as linked open data., but it will grow by
I am interested in sharing the experiences of working with ISNI to manage personal and corporate identities and engage in a wider discussion on the challenges of managing linked data entities such as personal identifiers across multiple domains. The experiences of ISNI cover linking diverse data sets, identifying and fixing false links (cluster errors), crowd sourcing, merging duplicate identifiers and splitting metadata from mixed identities, quality sampling, handling pseudonyms and hierarchical relationships.

This winter, the Digital Humanities Winter Institute will be offering a course devoted to “Publishing and Using Linked Open Data” led by Richard Urban, Assistant Professor, Florida State University College of Communication and Information.

The publication of structured knowledge representations and open data on the Web opens new possibilities for collaboration among humanities researchers and cultural heritage organizations. This course will introduce participants to the core principles of Linked Open Data (LOD), techniques for building and understanding LOD models, how to locate LOD sources for research, tools for manipulating, visualizing, and integrating available data, and best practice methodologies for publicizing and sharing datasets. Interested members of the LODLAM community can follow the development of the course and other humanities-related linked data activities by following the #lod4h hashtag on Twitter.

The Digital Humanities Winter Institute at the Maryland Institute for Technology in the Humanities (MITH) is an extension of the highly-successful Digital Humanities Summer Institute (DHSI) at the University of Victoria. DHWI provides an opportunity for scholars to learn new skills relevant to digital scholarship and mingle with like-minded colleagues through coursework, social events, and lectures during an intensive, week-long event. Taking place during intersession, just prior to start of the spring semester at many institutions, DHWI especially welcomes participants not just from the academic community but also from cultural heritage institutions, government, libraries, and the broader public.

Space is still available!—register now for “Publishing and Using Linked Open Data”

I learned about a workshop discussing ideas around persistent identifiers held in the Netherlands last month as a result of seeing an email from Andrew Treloar @atreloar (Australian National Data Service – ANDS).  This workshop organised by the Knowledge Exchange was a seminar to pay:

“attention to the usage of PIDs for publications, and increasingly for data, and for combinations of text, media and data. Also the relation with Author Identifiers was discussed. Standardisation and specifications for transparency between systems was addressed.  In break out sessions participants discussed the benefits and challenges in operating multiple persistent identifier systems and the relation of persistent identifiers to Linked Data.”

This grabbed my attention because of some of the discussions both semantic and technical at #lodlam back in May and some of the architectural conundrums facing linked open data enthusiasts.

“more than 40 experts involved in various Persistent Object Identifier (POID) communities met for a Knowledge Exchange seminar to discuss the challenges and opportunities involved in interoperability between multiple PID-systems.  Three major systems – Handle, URN:NBN and DOI – presented their current state of affairs and examples of their systems in practice….”

The presentations from this seminar are online and provide some food for thought for the techies thinking around how to set up IDs in linked open data systems.

So I figure this community if it isn’t already aware of this discussion might like to be.  I know this is a conundrum that many of those involved with undertaking ANDS funded projects are trying to get their heads around what identifier systems to use and there has been a heap of documentation made available on the ANDS website in an effort to support this.  There is information to guide those into the area of system identifiers; there are several pages designed to inform the newby, familiar, and the expert on persistent identifiers, and there is a focused page on DOI (Digital Object Identifiers).

If you’re interested to know more about the Party Infrastructure soon to be launched in Australia through the National Library of Australia, keep your eye on the NLA Party Infrastructure project wiki.

I hope some of this information comes in handy!

Ingrid @1n9r1d

One of the outcomes of last week’s LOD-LAM Summit was a draft document proposing a new way to assess the openness/usefulness of linked data for the LAM community. This is a work in progress, but is already provoking interesting debate on our options as we try to create a shared strategy. Here’s what the document looks like today, and we welcome your comments, questions and feedback as we work towards version 1.0.

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DRAFT

A 4 star classification-scheme for linked open cultural metadata

Publishing openly licensed data on the Web and contributing to the Linked Open Data ecosystem can have a number of benefits for libraries, archives and museums.

1. Driving users to your online content (e.g., by improved search engine optimization);
2. Enabling new scholarship that can only be done with open data;
3. Allowing the creation of new services for discovery;
4. Stimulating collaboration in the library, archives and museums world and beyond.

In order to achieve these benefits libraries, museums and archives are faced with decisions about releasing their metadata under various open terms. To be open and useful as linked data requires deliberate design choices and systems must be built from the beginning with openness and utility in mind. To be useful for third parties, all metadata made available online must be published under a clear rights statement.

This 4-star classification system arranges those rights statements (e.g. licenses or waivers) that comply with the relevant conditions (2-11) of the open knowledge definition (version 1.1) by order of openness and usefulness: the more stars the more open and easier the metadata is to used in a linked data context. Libraries, archives and museums wanting to contribute to the Linked Open Data ecosystem should strive to make their metadata available under the most open instrument that they are comfortable with that maximizes the data’s usefulness to the community..

Note: This system assumes that libraries, archives and museums have the required rights over the metadata to make it available under the waivers and licenses listed below. If the metadata you want to make available includes external data (for example vocabularies) you may be constrained by contract or copyright to release the data under one of the licenses below.

★★★★ Public Domain (CC0 / ODC PDDL / Public Domain Mark)

as a user:

• metadata can be used by anyone for any purpose
• permission to use the metadata is not contingent on anything
• metadata can be combined with any other metadata set (including closed metadata sets)

as a provider:

• you are waiving all rights over your metadata so it can be most easily reused
• you can specify whether and how you would like acknowledgement (attribution or citation, and by what mechanism) from users of your metadata, but it will not be legally binding

This option is considered best since it requires the least action by the user to reuse the data, and to link or integrate the data with other data. It supports the creation of new services by both non-commercial and commercial parties (e.g. search engines), encourages innovation, and maximizes the value of the library, archive or museum’s investment in creating the metadata.

★★★ Attribution License (CC-BY / ODC-BY) when the licensor considers linkbacks to meet the attribution requirement

as a user:

• metadata can be used by anyone for any purpose
• permission to use the metadata is contingent on providing attribution by linkback to the data source
• metadata can be combined with any other metadata set, including closed metadata sets, as long as the attribution link is retained

as a provider:

• you get attribution whenever your data is used

This option meets the definition of openness, but constrains the user of the data by requiring them to provide attribution (in the legal sense, which is not the same as citation in the scholarly sense). Here, attribution is satisfied by a simple, standard Web mechanism from the new data product or service. By using standard practice such as a linkback, attribution is satisfied without requiring the user to discover which attribution method is required and how to implement it for each dataset reused. Note that there are other methods of satisfying a legal attribution requirement (see below) but here we propose a specific mechanism that would minimize the effort needed to use the data if the LAM community collectively agrees to it. Also note that even this simple (ideally shared) attribution method could prevent some applications of linked data if linkbacks are required by many datasets from many sources.

★★ Attribution License (CC-BY / ODC-BY) with another form of attribution

as a user:

• metadata can be used by anyone for any purpose
• permission to use the metadata is contingent on providing attribution in a way specified by the provider
• metadata can be combined with any other metadata set (including closed metadata sets)

as a data provider:

• you get attribution whenever your data is used by the method you specify

This option meets the definition of openness in the same way as the linkback attribution open,  but requires the user to provide attribution is some way other than a linkback, as specified by the data provider. The provider could specify an equally simple mechanism (e.g. by retention of another field, such as ‘creator’ from the original metadata record) or by a more complex mechanism  (e.g. a scholarly citation in a Web page connected to the new data product or service). The disadvantage of this option is that the user must discover what mechanism is wanted by the particular data provider and how to comply with it, potentially needing a different mechanism for each dataset reused. For large-scale open data integration (e.g. mashups) this option is difficult to implement.

★ Attribution Share-Alike License (CC-BY-SA/ODC-ODbL)

as a user:

• metadata can be used by anyone for any purpose

• permission to use the metadata is contingent on providing attribution in a way specified by the provider
• metadata can only be combined with data that allows re-distributions under the terms of this license

as a provider:

• you get attribution whenever your data is used
• you only allow use of your data by entities that also make make their data available for open reuse under exactly the same license

This option meets the definition of openness but potentially limits reuse of data since if more than one dataset is reused and if each dataset has an associated Share-Alike license. Under an Share-Alike license, the only way to legally combine two datasets is if they share exactly the same SA license, since most SA licenses require that reused data be redistributed under exactly same license. If the source datasets had different Share-Alike licenses originally (e.g. CC-BY-SA and ODC-ODbl) then there is no way for the user to comply with the requirements of both source data licenses so this option only allows users to link or integrate data distributed under one particular SA license (or one SA license and any of the other license or waiver options above). In the LAM domain, where significant value is created by combining datasets, the Share-Alike license requirement severely reduces the utility of a dataset.

Related Material

• Open Knowledge Definition
• Principles on Open Bibliographic Data
• Discovery Open Metadata Principles

• Linked Open Data star scheme by example