1. Moving forward our shared data agenda: a view from the publishing industry ICSTI, March 2012

2. Data and the Scientific Article Researchers perceive data sets as “important, but hard to access” Publishing Research Consortium, 2010 Researchers, N = 3824 Important, but hard to access

3. Overview: Data & the Scientific Article Current approaches Thoughts for the future

4. Supplementary Material Authors can upload Supplementary Material with their paper Pro’s Coupling of data and article Peer review Citation mechanism Preservation (byte-wise) Con’s Limited data type support Compatibility (format support) Limited capacity Data not centrally stored

5. Connecting with Data Repositories, 1 Link to CCDC database (indicates that information for this article is available) Screenshot of journal article on ScienceDirect (http://dx.doi.org/10.1016/j.jfluchem.2009.07.015)  Article Linking example: CCDC

6. Connecting with Data Repositories, 2... clicking on the CCDC logo takes the reader to a page at the CCDC repository with data related to the article Screenshot of information page at CCDC (Cambridge Crystallographic Data Centre)  Article Linking example: CCDC

7. Connecting with Data Repositories, 3 Tagged Genbank entry (genetic sequence) Screenshot of journal article on ScienceDirect (http://dx.doi.org/10.1016/j.biortech.2010.03.063 )  Entity Linking example: Genbank Accession Number

8. Connecting with Data Repositories, 4... clicking on the linked Genbank accession code takes the reader to an information page on the NCBI data repository about that specific genetic sequence Screenshot of information page at NCBI (National Center for Biotechnology Information)  Entity Linking example: Genbank Accession Number

9. Connecting with Data Repositories, 5 DatabaseSubjectType of Linking CCDCCrystallographyArticle-level PANGAEAEarth SciencesArticle-level* EMBL Molecular InteractionsChemistryEntity, tagging Molecular INTeraction DBChemistryEntity, tagging GenbankNucleotidesEntity, tagging UniProtProteinsEntity, tagging Protein Data BankProteinsEntity, tagging ClinicalTrialsMedicineEntity, tagging TAIR (Arabidopsis)Model organismEntity, tagging Mendelian Inheritance in MenGenetics, inheritanceEntity, tagging *: with Application

10. The Article of the Future

11. Discovery and Use via SciVerse Applications  Use information from SciVerse and the web  Support for rich user interfaces  Integrated directly into the online article  Simple to build using Content and Framework APIs  Open standards (Apache Shindig, Open Social) Features & Benefits

12. Discovery and Use via SciVerse Applications Libraries can become focal point for applications Researchers can save time and improve their information discovery process “Apps interacting with results are very important to help save time…” Specific information can be targeted by applications to facilitate content mining and speed up the search time, utilising more time for analysis. “what faculty is really after is something that ties this altogether, so its all in one place…” Applications assist researchers to extract all information – content, data, figures etc. to a single analysis source which can be on a local database at the customer’s institute.

13. Applications example: NCBI Genome Viewer  Scans the article and builds list of sequences based on NCBI accession numbers tagged in the article  View/analyze sequence data from genes in the article using NCBI Sequence Viewer  See specific information about each strand; zoom in/out; export data Screenshots of journal article on ScienceDirect (http://dx.doi.org/10.1016/j.ygeno.2007.07.010)

14. Applications example: PANGAEA  Document identifier sent to PANGAEA data repository for earth sciences  PANGAEA returns map plotted with locations where cited data was collected  Push-pins open with details of dataset and direct link to data on PANGAEA.de Screenshots of journal article on ScienceDirect (http://dx.doi.org/10.1016/S0377-8398(01)00044-5)

15. Elsevier Enables Content Mining CONTENT Customers may: Run extensive searches and use locally loaded content for text mining purposes for their own research. Perform extensive mining operations on subscribed content.  Structuring input text  Deriving patterns within the structured text  Evaluation and interpretation of the output. Perform extensive mining operations on subscribed content.  Structuring input text  Deriving patterns within the structured text  Evaluation and interpretation of the output. Extract semantic entities from Elsevier content for the purpose of recognition and classification of the relations between them Integrate results on a server used for the customer’s own mining system for access and use by its researchers through the customer’s internal secure network. Enabling developers who wish to design and implement applications to analyse our content, or test applications as part of their research within Elsevier content

16. Our Content Mining Solution Suite CONTENT DELIVERY SEARCH & WORKFLOW SOLUTIONS ANALYSIS

17. Current initiative overview ◦ Supplementary Material ◦ Linking to Data Repositories ◦ Presentation via Article of the Future ◦ Discovery and Use via SciVerse Applications ◦ Empower scientists to mine content and use locally *************************** ◦ Data store (600 terrabytes as present) ◦ Executable papers ◦ Workflow tools ◦ Etc.

18. Conclusions: some thoughts for the future RESEARCHERS FUNDERS PUBLISHERS INSTITUTIONS Need for aligned strategies and policies, sustainable business models, and concerted collaboration