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DRAFTBuilding Global Science

Collaboratories

VIVO 2012 Conference Workshop

August 22, 1:00 pm – 4:30 pm

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Workshop Faculty Anil Srivastava, President, Open Health Systems Laboratory

(OHSL) co-located at Johns Hopkins University Montgomery County Campus, Shady Grove, MD, USA

Paul Courtney, Project Manager, Dana-Farber Cancer Institute, Boston, MA, USA

Ajai Kumar/Hemant Darbari/Swati Mehta/Vivek Koul, Center for Development of Advances Computing C-DAC, Pune, India

Rubayi Srivastava, Project Manager, Open Health Systems Laboratory (OHSL), CA, USA

Juliusz Pukacki, Poznan Supercomputing and Networking Center (PSNC), Poznan, Poland

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DRAFT Agenda 1:05 Anil - introductions 1:15 Anil – Background and Overview 1:50 Paul – Bootstrapping the global collaboratory

(Methods) 2:30 – 2:45 Break 2:45 CDAC – Techniques and experiences in extracting

data & transforming it for VIVO (Results) 3:30 Julius – Role of VIVO, Semantic Web and Linked

Open Data in advancing global science collaboration & enabling collaboratories (Discussion)

4:15 Anil – Future work/discussant 4:30 Workshop Ends

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Faculty “assignments” Anil

Provide context, history, mission & vision of OHSL, What programs & projects concern OHSL and VIVO fits into the portfolio

Paul Provide vision of developing the Global Cancer Collaboratory, where it is going How this effort is connected with other informatics initiatives; historical context of caBIG, the NCI-

NCRI informatics collaborations How is this different from simply putting up a VIVO instance at the OHSL campus in Shady Grove,

MD? Incubating and nurturing connectivity across international boundaries requires a different “business model” than putting up an institutional VIVO site. Aggregating information (early web model) and the role of imperfect data (and its relationship to Tim Berners-Lee’s Linked Data model)

Rubayi Challenges of providing project management support for an international program spanning 12+ time

zones, needing to provide support for logistical and knowledge management for multiple platforms and differing levels of technological infrastructures.

CDAC Technical challenges of obtaining the same information required from multiple sites Examples of what was easily available at some sites, what was difficult, how were the challenges

addressed Julius

Semantic Web and Linked Data

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Anil

Background Current Indo-US collaboration projects underway Indo-US Cancer Research Grid

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Research Networking Systems (RNS)

“…support individual researchers’ efforts to form and maintain optimal collaborative relationships for conducting productive research within a specific context.”1

Criteria: Involve shared 2-way interests Ongoing, sporadic interaction Creation of joint work products

1Schleyer T, Butler BS, Song M and Spallek, H. 2012. Conceptualizing and advancing research networking systems. ACM Trans. Comput.-Hum. Interact. 19, 1, Article 2 (March 2012), 26 pages.

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Research Networking Systems (RNS)1

Within institutions VIVO Harvard Catalyst Stanford CAP

Across institutions Distributed Interoperable Research Experts Collaboration Tool

(DIRECT) as a federated search tool that leverages the “within instution tools”

Research Gate Epernicus Academia.edu BioMed Experts (Elsevier) Elsevier SciVal® Experts Nature Network

1Schleyer T, Butler BS, Song M and Spallek, H. 2012. Conceptualizing and advancing research networking systems. ACM Trans. Comput.-Hum. Interact. 19, 1, Article 2 (March 2012), 26 pages.

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Research Networking System Models

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Global Cancer Collaboratory (GCC) as RNS

1. Support individual researchers’ efforts to form and maintain optimal collaborative relationships –

GCC will use VIVO as a tool to capture and store researcher information aggregated from cancer centers in India & United States.

2. For conducting productive research – GCC will be a repository for papers written, presentations & workshops produced.

3. Within a specific context – GCC focuses on support of international collaborations in cancer research.

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Global Cancer Collaboratory (GCC) as RNS

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Information Aggregation in India

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GCC Framework Socio-technical approach

Bootstrap by starting as information aggregator VIVO

Using a combination of manual and automated methods – to pull in information from Indian cancer centers as well as from US cancer centers as matter of necessity.

Imperfect data, missing data are expected OHSL, in partnership with CDAC, has established a VIVO environment

[http://cdac-ohsl-vivo.cdac.in/vivo] as a core piece of a Research Network System to serve both countries with a view to foster the creation of team science consortia.

Discovered/developed tools to ease process of information extraction from existing web sites

Confluence wiki for document and mind sharing Other logistical efforts (Rubayi later)

Awareness of cultural, organizational & working style differences is critical

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Model: Early Internet Portals

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GCC Goals

Demonstrate efficacy of VIVO to provide an efficient means of discovering potential international collaboration partners.

Develop criteria & roadmap for researcher information to encourage institutional websites to be semantically compliant using shared ontologies.

Establish metrics to assess the effectiveness of our methods

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GCC Activities Tasks:

Standardization of data and terminology across Cancer Centers Explore sources of data for researchers; lowest hanging fruit

model Explore sources of publication data with IndMED and medIND

repositories included. To date:

Sent SugarCRM profiles to CDAC for ingestion into VIVO Semi-automatically & manually extracted data from cancer

sites in India and US Addressed legal concerns by our partners in India about

web-scraping information from cancer center websites and repackaging for this project

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Explicit steps

Unstructured & inconsistent data

Use CSS tags to provide structure; consistent provision of data

Use RDFa tagging using VIVO ontology namespace

Implement VIVO, Catalyst or CAP across institution

Perl, Python, DEiXTo

Manual extraction

Perl, Python, DEiXTo

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How to link RNS’ together?

National network using Direct2Experts

What about international networks?

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GCC Future Work

To be done: Add in publications: PubMED search of with cancer as

MESH Major Topic and [PL] India over last decade results in 4844 articles

Investigate use of IndMED and medIND databases of publications in India

Establish metrics to assess the effectiveness of our methods to Increase awareness of the potential for international

collaboration Increase awareness of the role of institutions to expose

researcher data that will benefit funding & research opportunities

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Rubayi

Logistical challenges Communication Collaboration tools

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CDAC Technical Challenges & Lessons Learned

Data extraction and conditioning Ontology for each cancer center

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DFCI

Full Name, Specialization, Department, Interests

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Fred Hutch

Full Name, Designation/Appointment, Division, Interests, Phone, email, Fax

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HCGOncology Cancer Center

Only one profile can be accessed at a time

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Doctor Profile in HCGOncology Cancer Center

Fig: Doctor’s Profile in HCGOcology 24

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Doctor Profile in HCGOncology Cancer Center

Fig: Doctor’s Profile in HCGOcology 25

<table ><tr>

<td><span class="txtblue">Name:</span></td><td ><span class="txtcont"> Dr Sanjay Mishra </span></td> </tr><tr>

<td><span class="txtblue">Qualification:</span></td><td><span class="txtcont">M.D. (RT) </span></td> </tr> <tr>

<td><span class="txtblue">Specialisation:</span></td><td><span class="txtcont"> Radiation Oncology </span></td> </tr><tr>

<td><span class="txtblue">Location:</span></td><td><span class="txtcont"> Hubli </span></td> </tr>

</table>

Data Structure: class=“txtblue” is the label; class=“txtcont” is the content

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Doctor Profile in HCGOncology Cancer Center

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Name: Dr. N.K.Vinod Qualification: AD, PDCCA Specialization: Anesthesiologist Location: Bangalore

Name: Dr.Prabha Seshachar Qualification: MBBS, DA Specialization: Anesthesiologist Location: Bangalore

Name: Dr. H.C.Rajesh Qualification: MD Specialization: Anesthesiologist Years of Experience: 16 yrs

Name: Dr. Gaurav Dwivedi Qualification: MBBS, MD Specialization: Anesthesiologist Location: Delhi

Name: Dr. Kshirod Kumar Acharya Qualification: MBBS, MS Specialization: Anesthesiologist Location: Cuttack

Name: Dr. Ganesh Nayak Qualification: MS Specialization: Cardio Thoracic Surgery Location: Bangalore

Name: Dr. B C Bommaiah Qualification: MD Specialization: Cardiologist Location: Bangalore

Name: Dr. Kshitish Ch. Mishra Qualification: MBBS, MD Specialization: Clinical Oncology Location: Cuttack

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<table > <tr> <td>< table> <tr>

<td > <img src="phpThumb.php?src=uploads/doctors_images/4f840e5181aa2.png&amp ”/> </td>

<td><table >

<tr> <td ><span class="txtblue">Name:</span></td> <td ><span class="txtcont"> Dr Sanjay Mishra

</span></td> </tr>

<tr> <td><span class="txtblue">Qualification:</span></td><td><span class="txtcont">M.D. (RT) </span></td>

</tr> <tr>

<td><span class="txtblue">Specialisation:</span></td>

<td><span class="txtcont"> Radiation Oncology </span></td> </tr>

<tr> <td><span class="txtblue">Location:</span></td> <td><span class="txtcont"> Hubli </span></td>

</tr> ……..

</table>

Structure of Data for Profile in HCGOncology Cancer Center

• Data of HCG Oncology site is present

in the form of embedded tables.

• Every Profile is present in a separate page, so the structure of data and pages is difficult to retrieve using DEiXTo.

• CDAC has developed an extraction tool to get the data from this site.

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Researcher Profile In Dana Farber Cancer Institute

Fig: Researcher’s Profile in Dana Farber28

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Researcher Profile In Dana Farber Cancer Institute

Fig: Researcher’s Profile in Dana Farber29

<div class="abcGroup"> <h2>A</h2> <ul> <li class="fLeft title"> <a href="/directory/profile.asp?pgt=Gregory+A%2E+Abel%2C+MD%2C+MPH">Gregory A. Abel, MD, MPH</a> <em>Medical Oncologist</em>, Hematologic Oncology </li> <li class="oHide"> <strong>Clinical Interest</strong>Leukemia, Myelodysplastic syndromes, Myeloproliferative disorders </li> <li class="clear"> </li> </ul>

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<div class="abcGroup"> <ul> <li class="fLeft title"> <a href="/directory/profile.asp?pgt=Gregory+A%2E+Abel%2C+MD%2C+MPH ">

Gregory A. Abel, MD, MPH</a> <em>Medical Oncologist</em>, Hematologic Oncology </li> <li class="oHide"> <strong>Clinical Interest</strong> Leukemia, Myelodysplastic syndromes, Myeloproliferative disorders </li> <li class="clear">&nbsp;</li></ul>....</div>

Structure of Data for Profile in Dana Farber Cancer Center

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<div class="abcGroup"> <h2>A</h2> <ul> <li class="fLeft title"> <a href="/directory/profile.asp? pgt=Gregory+A%2E+Abel%2C+MD%2C+MPH&">

Gregory A. Abel, MD, MPH</a><em>Medical Oncologist</em>, Hematologic Oncology

</li> <li class="oHide"> <strong>Clinical Interest</strong> Leukemia, Myelodysplastic syndromes, Myeloproliferative disorders </li> <li class="clear"> </li> </ul> <ul> <li class="fLeft title"> <a href="/directory/profile.asp?dbase=main&amp;setsize=16&amp;last_name=A&amp;pgt=Janet+L%2E+Abrahm%2C+MD&amp;grouptype_typeid_data=2&amp;gs=r&amp;nxtfmt=r&amp;display=Y&amp;pict_id=0000312">Janet L. Abrahm, MD</a><em>Palliative Medicine Physician</em>, Palliative Care (Adult) </li> <li class="oHide"> <strong>Clinical Interests</strong> Palliative medicine, Symptom management, End-of-life care </li> <li class="clear"> </li> </ul>

Structure of Data for Profile in Dana Farber Cancer Center

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• In Dana Farber Cancer Institute Profile data is present in structured

form which DEiXTO is able to extract .

• Since Data is organized in Structured manner, we can extract data

using “DEiXTo” Tool.

Observation on Structure Profile Data Present in Dana Farber Cancer Center

DEiXTo (or ΔEiXTo) is a powerful web data extraction tool that is based on the

W3C Document Object Model (DOM). It allows users to create highly accurate

“extraction rules” (wrappers) that describe what pieces of data to scrape from

a website. 32

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<tr>

<td ><font size="1“ ><b><font size="2">Kailash S. Sharma </font>&nbsp;</b>M.D., D.A. (Anesthesiology)</font></td>

<td> <img src="../../images/anaesthesia/drsharma.jpg“ ></td>

</tr>

<tr>

<td> <font size="1“ face="Verdana, Arial” ><b>Designation:</b> &nbsp;</font></td>

<td><font size="1“ >Director Academics TMC </font></td>

</tr>

<tr>

<td><font size="1“ face="Verdana, Arial”><b>Area of Work:</b>&nbsp;</font></td>

<td><font size="1“>Anaesthesia</font></td>

</tr>

<tr>

<td valign="top"><font size="1“ face="Verdana, Arial”><b>Special Interests:</b></font></td>

<td><font size="1”>Difficult Airway<br> Monitoring<br> Cancer Pain</font></td>

</tr>

<tr>

<td valign="top"><font size="1” face="Verdana, Arial”><b>Email :</b></font> </td>

<td valign="top"><font size="1“ ><a href="mailto:rashmikailashsharma@yahoo.co.in">rashmikailashsharma@yahoo.co.in</a>

<br> Phone No.</font> (+9122) 24177044

</td>

</tr>

Structure of Profile Data Present in TATA Memorial Hospital

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Profile page data structure is not uniform. Insufficient data with profiles Format in which profiles are present are not

uniformly structured. Data extracted manually

Observation on Structure Profile Data Present in TATA Memorial Hospital

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References

1. http://www.dana-farber.org/

2. http://www.hcgoncology.com/

3. http://tmc.gov.in

4. http://en.wikipedia.org/wiki/Web_scraping

5. http://deixto.com/

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Indo-US Cancer Collaboratory: A VIVO Pilot

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Data Extraction from Website

Fig: Dana Farber Profiles 37

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Data Extraction from DFCI and represented in CSV format

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Ontology Creation

Fig: Create New Ontology 38

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Ontology Creation Success

Fig: Creation of New Ontology is Success39

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Class Creation

Fig: Create New Class inside a Ontology40

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Create New Link to Super Class

Fig: Add a Super class link to this class 41

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Selection of SuperclassTu

esda

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pril

18, 2

023

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Create New Link to Super class

Fig: Select a class as Super class from dropdown list 43

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Create New Link to Super Class

Fig: Identify the super class link at cursor position44

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Create Data Property

Fig: Create a Data property inside class45

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Data Property Created

Fig: Data Property Created successfully46

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Create Object Property

Fig: Create a New Object Property47

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Model Creation

Fig: Creation of Models to get the URI’s

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After Model Creation

Fig: Models Created Successfully

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Convert CSV to RDF

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Convert CSV to RDF

Fig: Convert CSV File to RDF51

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<http://192.168.81.96/vivo/individual/n1834679> a <http://192.168.81.96/vivo/cdac:DanaFarberProfiles> ; <http://192.168.81.96/vivo/cdac:danafarberprofiles_Department> "Hematologic Oncology" ; <http://192.168.81.96/vivo/cdac:danafarberprofiles_FirstName> "Edwin" ; <http://192.168.81.96/vivo/cdac:danafarberprofiles_FullName> "Edwin P. Alyea III ,MD" ; <http://192.168.81.96/vivo/cdac:danafarberprofiles_Interest> "Stem cell/ bone marrow transplant, Leukemia" ; <http://192.168.81.96/vivo/cdac:danafarberprofiles_LastName> "Alyea" ; <http://192.168.81.96/vivo/cdac:danafarberprofiles_MiddleName> "P." ; <http://192.168.81.96/vivo/cdac:danafarberprofiles_Organization> "Dana Farber" ; <http://192.168.81.96/vivo/cdac:danafarberprofiles_Qualification> "III,MD" ; <http://192.168.81.96/vivo/cdac:danafarberprofiles_Specialization> "Medical Oncologist" .

Subject

Predicate

Object

Predicate

Object

Ingested Data URI's

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SPARQL Query

> .

Fig: SPARQL Query to Construct data53

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}

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Execute SPARQL Query

Fig: Execution of Constructed SPARQL query54

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Execute SPARQL Query

Fig: SPARQL Query executed successfully55

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Upload RDF

Fig: Upload RDF file

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Fig: RDF Successfully Uploaded

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View Uploaded Profiles

Fig: Upload Profiles57

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Juliusz: Poznan Supercomputing and Networking Center

Semantic web and interoperability

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Future work

Incorporate medIND and IndMED biomedical journal databases as well as PubMED into VIVO.