Thinking About Years Past for Research: Relations for Research (R4R)

A Proposed Conceptual Model for Publishing Research Articles, Data, and Code

The year 2013 will come to an end soon. However, new progresses in scientific sharing and publishing are just beginning. The global demands on public access to research data have been endorsed by many government policies. The movement toward Open Science has also been welcomed by several key scientific publishing actors.

From Open Science To Science Open

To name just a few, Nature Publishing Group has just announced the launch of an online data journal, Scientific Data, for the open access to detailed data descriptions. The data journal, Earth System Science Data (ESSD), adopts a new form of the reviewing policy, which allows scientists and general public to review and comment articles. Later, these interactive comments plus author’s responses and revisions are published and archived openly in fully citable and paginated forms. Web services like figshare, f1000research, or Research Compendia provide scientists new tools and alternative platforms to curate their research outputs.

High-level requirements of science reproducibility result in the coming of a new science publishing paradigm. This paradigm requires the packaging of articles, data and code, and encourages their joint publications. The initial task has been taken by some bio-medical science practices, and until recently, the Executable Papers of ScienceDirect in computer science implemented this vision online.

Thus rethinking the dilemma we face today is both for new possibilities and problems carried by “big data” and “open data”. While we embrace the coming of big data and open research in a data-driven context, we have to tackle the data deluge problems caused by data generation, data sharing, and data publishing. Problems like data heterogeneity, interoperability, accessibility, citability, reproducibility as well as legal issues remain major challenges to the research communities. Despite huge varieties existing in different domains, the difficulty falls into two main categories: technical issues and policy instruments. What we need are an intelligent openness strategy as outlined in Geoffrey Boulton’s proposal that we present the scientific argument (the data and concept) together, as well as an integrated infrastructure for this new research paradigm.

The Reasoning

• Why do we need to explore semantics and semantic relations when we publish research data?

The emerging collaboration on scientific publishing, between Scientific Data Repository (SDR) community, Library Metadata community, as well as Linked Open Data (LOD) and Semantic Web communities, suggests that the semantic discourse has an important implication on research publishing. Among these, library communities have played a significant role in managing research data due to their expertise in managing metadata and data curation.

Examples of semantic enhancement to scientific publishing.

Yet, current state of metadata standards in the scientific context is not sufficient for data integration and reuse. More efforts need to be taken on scientific publishing and citation. Also, it is difficult to agree on a single metadata schema or standard in the open Web context. Challenges still remain in the technical complexity of mapping to achieve metadata interoperability. In addition, linking has been a major feature of how scientific datasets can be managed. Among which, the problem of lacking dataset identity is the major obstacle to citation and metadata developments. In particular, metadata schemas for scientific data modelling can sometimes be too general or too specific in describing relations of multiple domains.

Accordingly, we argue that the LOD approach provides a possible solution to the above problems. The LOD approach provides unique URIs for object identity;

1. Users are free to set different URIs to refer to an object (e.g. using links like owl:sameAs).
2. For the accessibility, identity links such as the URI links (in subject and object) help machines to find more data; the property links (in predicate) provide meaning and context for data to be linked.
3. In general, RDF (Resource Framework Description) links help to decrease the interoperability problem by pointing data to the vocabulary they use, and to the definitions of related terms in other vocabularies.

Thus the LOD approach assists scientific datasets to be accessible, to be related to other data sources, and to be linked between different datasets semantically. (see more details from Bizer, Cyganiak & Heath, 2007; Seneviratne, Kagal & Berners-Lee, 2009)

• What kinds of policy mechanisms support the open research?

Data reusing and remixing are part of the charm of open science. Yet, the data quality and usability are not easy to understand as long as provenance and licence information are not clear enough both for human and for machines.

While scientific data repositories need provenance metadata as the data preservation policy, the computational traceability has been required for the purpose of quality control and data reuse. Additionally, endless pages of copy right statements or license agreements are as complex as they can be. Thus people may have been violating others’ rights without awareness of it. In such cases, if provenance and license information are machine-readable and further packaged with scientific datasets when they are travelling, policy-aware tools like Semantic Clipboard can thus help to detect license violations when exposing Creative Commons (CC) license metadata as RDF or RDFa.

(source: Semantic Clipboard http://dig.csail.mit.edu/2009/Clipboard/)

Accordingly, a portable and packaged metadata policy is the key to the open research. For a data collection to be open, they shall be freely downloaded, adapted, mixed with others, and re-hosted for other services. Being available and accessible on the Web by itself is not sufficient. A data collection must be easily ported to other computer systems, either on or on the Web, for it to be called open. The portable principle is also applied in modelling metadata for scientific data.

In other words, a portable and packaged research component should contain articles, data, code, as well as associated provenance and licence metadata as a completed knowledge package for research results reusing and remixing.

The Relations for Research (R4R) Conceptual Model

Identity functions for scientific publications require the dataset to be constructed as a semantically and logically concrete object. Thus we define two core classes: Research Related Object (RRObject) and Research Related Policy (RRPolicy). Three objects, Article, Data, and Code, are classified as subclasses of RRObject. Two classes, Provenance and License are subclass to RRPolicy. For object properties, we identify seven relations in between RRObject objects and RRPolicy objects. Here we only present a summary table below for the R4R conceptual model.

Table: A Summary of R4R.

Class	Property	Domain	Range
RRObject	locateAt	RRObject	rdfs:Resource (URL/URI/DOI/ISBN…)
Article	hasTime	RRObject	time:TemporalEntity
Data	isPartOf	RRObject	RRObject; void:Dataset
Code	isCitedBy / cite	RRObject	RRObject;cito:CitationAct; void:linkset
RRPolicy	isPackagedWith	RRObject	Article; Data; Code; RRPolicy
Provenance	hasProvenance	RRPolicy	prov:Entity
License	hasLicense	RRPolicy	dcterms:LicenseDocument

Our rationale for the design of the two core classes has been flexible in the definition of scientific publishing. RRObject is not necessary only for the publication purpose.

RRObject can use the property of “isPackagedWith” to combine all related objects whether been published or not.

Instead of using direct statements about sharing research and publishing rights, we can use the License, subclass of the RRPolicy, to refer to well-known licenses. For instance, licenses include. the Creative Commons (CC) licenses for creative works; Open Data Commons Open Database License (ODbL) for databases and datasets, or your own Open Data Certificates; as well as the GNU General Public License (GNU GPL or GPL) for software source code. These licenses and certificates can be bundled and packaged with RRObject through the property of “isPackagedWith”.

The ability to identify the relationship between articles, data and code is essential to a full understanding of the R4R design. To help explain the conceptual model, seven correlations are discussed.

1. Article-Article: This is the most conventional relation that has been used in scientific publishing and citation through the bibliography. We use “isCitedBy” to provide a general relation between article and article, and refer to CiTO ontology for further semantics of various relation types.
2. Article-Data: Data or datasets collected, created, and derived for a research itself “isPackagedWith” the Article. Article can also “isPackagedWith” Data. Furthermore, according to CiTO, Article can cite Data, and Data can cite Article.
3. Article-Code: The relation of Article and Code share the same logic with Article and Data for “isPackagedWith” and “isCitedBy”.
4. Data-Data: Data can be “isPartOf” other Data based on the granularity and scalability of the dataset. However, the relation of “isPartOf” is transitive and reflexive. Data can also “cite” Data.
5. Data-Code: Code can be “isPartOf” Data; Data can be “isPartOf” Code. However, according to CiTO, Article can cite Data, and Data can cite Article. Although “isPartOf” and “isCitedBy” share some similar semantics, we use “isPartOf” for being capable of representing transitive relation.
6. Code-Code: The relation of Code and Code share the same logic with Data and Data for “isPartOf” and “isCitedBy”.
7. Article-Data-Code: The relations between the three kinds of RRObject can raise some interesting questions. For example, when CodeA “isPartOf” DataB, and CodeA “isCitedBy” ArticleC, what relation between DataB and ArticleC can we say about? Is there a concise term we can use to express such a relationship?

The relations described above for Article, Data and Code are such some of the initial steps prepared for some clarifications of research components. Note that, the proposed R4R conceptual model here is not yet a formal ontology, as we have not finalized the detailed vocabulary to be used. In the meantime, we also need to elaborate the model with some use cases.

R4R Conceptual Model

In addition, data citation standards and practices are still in progress. Most models and tools of provenance for web databases and scientific workflow are still in the experimental level. Definition and relation of R4R may need to be refined in the near future. However, we here describe R4R in the form of a conceptual model so as to enter into discussion with research communities. We expect to develop and extend R4R later into a formal ontology.

Related Works and Discussion

Scientific Publication Packages (SPP) is similar to our view in packaging textual publications, raw data, derived products, algorithms, and software altogether. The major differences are two: (1) SPP has taken those research components in one concept, namely Data, based on the extension of the ABC model, a model for the library, museum and archival domains. (2) SPP does not consider license packaging in the SPP components (but in the descriptive metadata for the SPP). 

In contrast, the notion of Research Objects (ROs) as first class citizens for sharing and publishing is similar to our R4R design. However, both ROs and SPP are workflow and life-cycle centric. While SPP emphasises data preservation and publishing, ROs focuses more on aggregation. Both ROs and R4R notice the necessity of packaging and licensing issues, but only R4R includes licensing in the core model. ROs packages the workflow with data, results and provenance; while R4R packages articles, data, code, provenance, license, and their semantic links. One specific difference distinguished R4R from the other two is that R4R stresses the importance of relations between research components for research publications and citations.

In addition, existing vocabularies and ontology which are similar to R4R are the MESUR ontology and the Semantic Publishing and Referencing Ontologies (SPAR). MESUR and SPAR share the same purpose with R4R in that they describe the scholarly publishing tasks, but MESUR concerns more on scalability and the bibliometric usage, while SPAR provides core vocabularies and semantics for publishing and referencing in eight ontology modules.

A further consideration is that it would be interesting to explore more on relationships between provenance and event, since provenance information provides relations of research components changed over time (e.g. editing history). In the editorial note of the Preservation Metadata: Implementation Strategies (PREMIS) Ontology, it describes that “Digital provenance often requires that relationships between objects and events are documented”.

SPP, ROs and MESUR all apply event concepts for describing life-cycle of objects. Three out of eight ontology modules in SPAR have event concepts (for example, by taking citation as an event, or taking care of event occurring in the publishing process and workflow). As R4R is still in its preliminary stage of design, we may consider to include event concepts in the future (note that our event based concept is more toward the "relation" property which focuses more on spatio-temporal relationships and cause-effect relationships explained in here). As for an overall view of mapping between different provenance vocabularies, this can be seen from the task of W3C Provenance Incubator Group.

Conclusion

As discussed at the beginning, research challenges on technical complexity and policy instrument for an Open Research are still not well understood nor agreed on in many aspects. We hope this discussion and reasoning on why a LOD approach can serve as a semantic solution to various problems. And R4R can provide a conceptual framework to relating major components and relations to scientific publication and citation. To conclude, we summarise them in the following:

1) A research publication has to be packaged with both RRobject (article, data, and code) and RRPolicy (provenance and license) in a portable and policy-aware manner.

2) A research model that highlights relations between various research components has been proposed. We propose a Linked Open Data approach into scientific publishing process.

3) We argue that as long as we take the challenge of technical complexity and policy instrument together, as long as research publications stay open, the use of shared and linked semantics will help scientific research continue to grow in a new way.

So at the coming of the New Year 2014, we are optimistic about Open Science, as well as the semantic relation between research articles, data and code. We see a lot of efforts on shared semantics which hold machine understandable promises that data can be represented and reasoned semi-auto or automatically. We see a strong potential in the scientific research, the same scientific referents can be reused, reanalysed and remixed effectively from distributed agents (human and machine) of various domains. In other words, finding new use of known scientific facts, linking new relationship of established research, or generating new science are under the way.

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• Citation Information: Andrea Wei-Ching Huang and Tyng–Ruey Chuang (2013) Relations for Research (R4R): A Proposed Conceptual Model for Publishing Research Articles, Data, and Code, URL: http://andrea-index.blogspot.com/2013/12/R4R.html
• PDF download: [[Huang_Chuang-2013-R4R-Relations for Research.pdf]]
• An XML file of this article (auto-converted by pdfx, using the DoCO, the Document Components Ontology for annotation).
• The new version of the R4R.

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References: (DOIs are auto generated by pdfx )

• 1. Altman M, Arnaud E, Borgman C, Callaghan S, Brase J, Carpenter T, Chavan V, Cohen D, Hahnel M, & Helly J. Out of Cite, Out of Mind: The Current State of Practice, Policy and Technology for Data Citation. Data Science Journal [Internet]. 2013;12:1–75. [possible DOI]
• 2. Bechhofer, S., Buchan, I., De Roure, D., Missier, P., Ainsworth, J., Bhagat, J., ... & Goble, C. (2011). Why linked data is not enough for scientists. Future Generation Computer Systems.  [DOI]
• 3. Bizer, C., Cyganiak, R., & Heath, T. (2007). How to publish linked data on the web. Retrieved October, 20, 2013  [possible DOI]  [alternative DOI]
• 4. Chuang, T.R. (2013) Packaging and Distributing Data Collections for the Web, Open Data on the Web, 23 - 24 April 2013, London, Retrieved from http://www.w3.org/2013/04/odw/odw13_submission_44.pdf  [possible DOI] [alternative DOI]
• 5. Cox, A. M., & Pinfield, S. (2013). Research data management and libraries: Current activities and future priorities. Journal of Librarianship and Information Science.  [DOI]
• 6. Haslhofer, B., & Klas, W. (2010). A survey of techniques for achieving metadata interoperability. ACM Computing Surveys (CSUR), 42(2), 7  [DOI]
• 7. Hunter, J. (2008). Scientific Publication Packages–A selective approach to the communication and archival of scientific output. International Journal of Digital Curation, 1(1), 33-52  [DOI]
• 8. Marcial, L. H., & Hemminger, B. M. (2010). Scientific data repositories on the Web: An initial survey. Journal of the American Society for Information Science and Technology, 61(10), 2029-2048  [DOI]
• 9. Qin, J., Ball, A., & Greenberg, J. (2012). Functional and Architectural Requirements for Metadata: Supporting Discovery and Management of Scientific Data. In Twelfth International Conference on Dublin Core and Metadata Applications. University of Bath.  [possible DOI]  [alternative DOI]
• 10. Rodriguez, M. A., Bollen, J., & Van de Sompel, H. (2007, June). A practical ontology for the large-scale modeling of scholarly artifacts and their usage. In Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries (pp. 278-287). ACM.  [DOI]
• 11. Peng, Roger D. "Reproducible research in computational science." Science (New York, Ny) 334.6060 (2011): 1226-1227  [DOI]
• 12. Seneviratne, O., Kagal, L., & Berners-Lee, T. (2009). Policy-Aware content reuse on the web. In The Semantic Web-ISWC 2009 (pp. 553-568). Springer Berlin Heidelberg. [DOI]
• 13. Shotton, D., Portwin, K., Klyne, G., & Miles, A. (2009). Adventures in semantic publishing: exemplar semantic enhancements of a research article. PLoS computational biology, 5(4), e1000361.  [DOI]
• 14. Wynholds, L. (2011). Linking to scientific data: Identity problems of unruly and poorly bounded digital objects. International Journal of Digital Curation, 6(1), 214-225 [DOI]
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Activating Linked Open Data in Libraries, Archives and Museums - II

A Report on LODLAM 2013 --- Part I, Part II

In contrast to some personal feelings on LODLAM 2013 described in Part I, a rather formal oral presentation to my institution has been presented and shared here. This report (Part II) is written as a supplement to the presentation and takes into some discussion notes with my colleagues for a more comprehensive report.  

An overview of the LODLAM 2013 agenda reflects current status of the LAM progress in LOD trends. The two day program was consist of 43 sessions in total and conveyed 7 emerging issues. Together with the observation which indicates the most frequent semantic web activities within the library domain are data integration, semantic search and semantic annotation, thus, I selected three cases to share with my colleagues, namely LODLAM Patterns (for vocabulary), KRAMA(for data integration and semantic search), and PUNDIT (for semantic annotation).

These cases represent my major interests in vocabulary patterns and tool progress in LODLAM 2013, and my presentation was structured in 7P (Preface, People, Place, Program, Presentation and Proposition). Preface, People, Place and Program are general information about the LODLAM and this event; Presentation and Proposition concern about vocabulary patterns and tool progress through identifying problems and providing suggestions that I have learned from the LODLAM 2013.      

Vocabulary Confusion

“We feel we’ve got too many choices. All those vocabularies and metadata schemas confuse us about what to do next. We don’t think our archives should remain in the pre-LOD age. But how are we supposed to make the right decision on which vocabulary to use for LOD-lizing our data?”  Major doubts I have heard at Montreal and Taipei reflect the fact that we are not alone in having experienced such confusions. The transformation of a record-based and document-centric metadata structure into a data-centric and link-based web format remains the major challenge both for Library and Web communities.

For the majority of us, the real reason for concern is not the big intellectual constructs of precise models or elaborate vocabularies. Rather, we worry about the library data is short of contextual information; about the weak structure of the Web and the lack of consideration about semantic content during indexing; and about the type of resources and institutional considerations for criteria choices. 

These worries brought my consideration in how to share my LODLAM experience with my colleagues. That is, instead of being trapped in some weakness of current problems, focusing more on the strength of LOD may open more positive possibilities. In other words, by activating common languages and international standards, looking for sound tool practices as well as learning from others are the basic approaches to making the LAM stakeholoders move forward to the LOD vision further and faster. 

A Common Pattern Helps.

No one disagrees with that the international standards or common data models are the keys to data sharing and interoperation. Although I did not participate in specific sessions about this issue at Montreal, I first introduced the Europeana Data Model (EDM), which is the core of the Europeana projects (with more than 130 data partners), and which is related to KARMA and PUNDIT to my colleagues as a must-know model. The EDM provides links between Europeana objects, and between an object and its contextual information, while at the same time assists data partners making their metadata visible on the Europeana portal.

Underlying everything the EDM provides is exemplar of semantic data modelling that: (1) the enrichment for datasets with contextual information such as agent, place, or events; (2) the mechanism (i.e. ORE proxy) that allows different descriptions (views) of a same object to co-exit, and later for the use of provenance. The core data structure is based on Open Archive Object Reuse and Exchange Model (OAI-ORE), which supports the context of different aggregations and referencing resources. However, the later may become the major shortcoming of this model.

Of course, it would be a mistake to presume the process of a new model goes smoothly, or a single model could support all practices efficiently. Critics of the EDM also include (1) the vocabulary is not sufficient or too distinct; (2) problems of imprecise mappings from original metadata (because of the difficulty of managing varieties of metadata vocabularies). As vocabulary and ontology mapping/alignment are key issues in Semantic Web, LAM for LOD certainly needs to face this same challenge without missing a bit.

Nevertheless, alignment/mapping requires deep understanding of each vocabulary. Therefore, a common pattern recognized by domain or cross-domain experts may help. For instance, Ontology Design Patterns (ODP), Linked Data Patterns, or Multilingual Linked Data Patterns (MLOD) are such cases which have been practiced well in Semantic Web and Linked Open Data communities. And that’s what LODLAM Patterns to the cultural heritage resources here for.

LODLAM Patterns is a method that can assist to learn a variety of regularities among categories of linked objects. Personally I find the template is helpful. The seven key issues: Problem, Context, Forces, Solution, Related Patterns, Examples, and References offer the prospect of capturing ideas among different vocabulary designs (particularly helpful from my experience for reconsidering several event ontologies). In other words, cultural heritage resources are the ones we all have in abundance. It may not be in our power to build new vocabularies or models for the new emerging LOD, but it definitely always be an option to give our attention to a pattern that serves LODLAM.

Practice first, Decision later.

When we talk about vocabulary confusion we often reach for making decisions between original vocabularies we have used for years and between new emerging ones for LOD. The good news is that we do not need to “upgrade” our whole data at once. Learning from the experience of Europeana, mostly are portions of the data with their own preference, which includes individually preferred data model and metadata schema. For instance, data partners of the Europeana support partial datasets through EDM on the Europeana portal. But the individual institution like Bibliothèque nationale de France (data.bnf.fr) uses FRBR, FRAD and FRSAD models and record identifiers such as ARK identifiers and URIs for their own LOD project. Also quite differently, the British National Bibliography (BNB) Linked Open Data is in favour of Bibliographic Ontology, Bio ontology, British Library Terms, Dublin Core, and Event Ontology, etc. (e.g. data models for book and serials)

The problem with the conventional process of “decision-plan-practice” approach for the situation of LAM toward LOD is that it rarely works. The quantity, quality and complexity are the major characteristics of LAM data. A big decision making with a complete detail planning for LOD-lizing the LAM data may result in fails. The main reason is that most of us haven’t had any experience of what the task is rely like in LOD clouds. Vocabularies, data models, tools for LOD are still in the process of “evolution”. The experiential learning process both for LAM and LOD community individually and collaboratively is required.

Taking a small step of practical actions step by step is suggested in our discussion session in IIS at my presentation:

The building behind is IIS, Taipei Taiwan.
An interesting thing is that I found this when I was at Montreal.

1. identifying the priority portion of datasets for possible sharing  (data that we believe that are common to the LODLAM and that can be shared most meaningfully);
2. practicing data model conversion tools, e.g. OpenRefine or KARMA will offer a numerous choices of vocabulary adjustment functions for data integration and help curators to convert local thesaurus to external vocabularies; 
3. trying out semantic annotation of web contents. The tool like PUNDIT, which provides annotation approaches for textual comments, semantic tagging, name entities recognition and the use of taxonomies and ontologies, as well as supports RDF statement made by general users,  is pretty promising;
4. publishing the trial data to LOD to have the experience of meaningful linkages between datasets of different domains, between datasets of the past, present, and unknown future for surprising applications.   
5. participating more in LODLAM activities to have face-to-face and personal communication and interactions.    

Then we will be in a position to make better decisions on how to and what to LOD-lizing the LAM data, with confidence and success.

Activating Linked Open Data in Libraries, Archives and Museums - I

A Report on LODLAM 2013 --- Part I, Part II

I was sitting in a hotel restaurant near the BAnQ, the National Library and Archives of Quebec, Montreal, Canada, having my third coffee in the early morning on June 19, 2013. The 3^rd coffee is unusual for me; normally I would have double espresso instead. This is part for waking me up from a terrible jet lag (a long distance flight from Taipei); and part for my nerve about the LODLAM Summit 2013 I was going to participate in a few minutes.

The nerve feeling was mixed up with some fear and anticipation. The fear was simply because this conference was totally different from my past experiences. It uses the open space technology that has no pre-set program. More than 100 participants from 16 countries created the agenda during the conference (see sessions on day 1 and day 2), even though a few session proposals have been posted before this event. The anticipation was that I could sit here simply because my boss cannot make it in the last minutes. Therefore, I got this great opportunity to join this event; without much time to make proper preparation in advance though. And the choices I made on the site about which sessions I attend would decide what I have learned, participated and contributed to this community.  

@trudatted /One word from everyone to sum up
(pic.twitter.com/hnkTMShdrC)

At the moment of writing, I would say I wish my choices of sessions could be more sound and balancing on topics during those two days. This is the dilemma of decision making for an unconference-type meeting. All results depend on unexpected topics raised by the participants at that moment, as well as quick choices on which sessions/topics to participate in. While the part of this “inconvenient” design for preparation has challenged my capability both mentally and physically (serious time zone problems in my case, and feel pity that I could not make it to many exciting social events as well), the free-style of communication-oriented design and the outcome from participants’ feedback have been proved effectively and successfully. I could not forget what people said in the closing of this event: “more works to do” after the LODLAM 2013 Summit.                

What's more is that my understanding about the Web has been like a big library, and the linked open data resources such as DBpedia and Freebase have played important roles as the Web Library Sources for years. On the other hand, traditional libraries have long preserved and curated massive and quality datasets. The hidden treasure of the library data not only provides opportunities but also offerings a backbone of trust for the Semantic Web. The collaboration of the web and library communities definitely will bring enormous potentials for human knowledge. This event was full of librarians and archivists who have been trained for tackling cross-disciplinary and inter-disciplinary resources. The way they make library data into linked (open) data represents a new step for me: that is, a vision of semantic interoperability is committed by classical metadata players, and a vision that rich meaningful links within research resources are going to be shared, connected, and reused enormously.

This is exciting and stimulating especially for us. We have been working on LOD and semantic web for a few years. Not long ago, we just completed the task of hosting a conference specifically targeting on open data and open information for the open science. Although not in the specific domain of library data, we have come across several research and case studies, such as Cultural Heritage and Digital Library, and the Open Knowledge Environment (OKE). This background makes me feel exciting to have the opportunity to participate in this event which focuses not only on the general types of data curation in gallery, library, archive, and museum (i.e. GLAM or LAM), but also technical interoperability for linked data, as well as legal interoperability for open data.

More interesting is that from the history of LODLAM, we understand that the special character of the LODLAM community is action oriented with passion and responsibility. During two days of the Summit, the passion and the devotion of this community have been beautifully revealed and mixed with the sunshine of Montreal’s spring. It’s really nice to meet Joan Cobb who has long-term relationship with our digital archive programme, and even more positively is to hear that Getty vocabularies are going to be published as LOD.

Furthermore, having some lovely chatting with Silvia Southwick convinces me more about the LODLAM future that various and abundant resources hidden in libraries and archives will be soon reused and reconnected through LOD. Just imagine what kind of reusing and remixing LOD datasets about the culture in Las Vegas will present to us? Digital collections like Showgirls in Las Vegas and in Taiwan certainly will be connected in a surprising way to our eyes through linked open data, I believe.

Even more luckily blessing is to meet a sweet and warm friend, Debra Riley-Huff, from the first opening session. My nerve and tension to this event have been shared and comforted by Debra’s friendship. Debra has also brought me the attention to OpenRefine that the session I did not participate in. Her interview by David Weinberger about the necessity of Library and Web standards working together has been shared here. As a reflecting story of this event, now I would say to Debra that my travelling problem without full energy at Montreal maybe is not a bad thing at all. Some more interrupting events after Montreal make me have more time on wrapping up this LODLAM mission.

More time demands more works to be done. Without these delays, I would not spend more time and more survey on clarifying vocabularies, testing the PUNDIT, and collaborative mapping this event, simply because several other notes have been published, and simply because I have not completed my formal report on the LODLAM 2013. (to be continued in Part II)       

Activating Linked Open Data in Libraries, Archives and Museums - I by Andrea Wei-Ching Huang is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.