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Introduction

This work is the result of the showcase 45 Collaboration Spheres Mockup. Right now is being updated constantly due to it is actually onward.

The collaboration spheres intents to provide mechanishm to improve, share and reuse of ROs and Users Experience based on the explotation of semantic descriptions, relations and similarities between them in order to support advanced search mechanisms, such as metadata-based searches. The visualization of those similarities have a very strong social analysis aspect and are based on collaborative filtering and personalization (user roles). The visualization methaphor proposed aims to be simple and very interpretatability oriented and provides an easy way of adapting to different cases. These different interpretation cases are also provided in this wiki page. 

The source of data to be used for this purposes are obtained via myExperiment. Mainly friends and tags by users are the two type of data used.

Terminology

The next terms are defined to be uses later:

  • Research Object: are semantically rich aggregations of resources [1] that bring together data, methods and people in scientific investigations. As described in [2] their goal is to create a class of artifacts that can encapsulate our digital knowledge and provide a mechanism for sharing and discovering assets of reusable research and scientific knowledge.
  • Users: are each one of the actors that interact with the portal and can do it for different purposes: reusing, reviewing, creating, etc.

Type of relations

The different types of relations that are studied are:

  • RO-RO: represents the relation between two different research objects. This relation allows to identify ROs which share common information or funcionalities. The relation is at RO level and therefore takes into account all the aggregated data that it contains. It can provide information about ROs knwoledge domains, or ROs common funcionalities groups.
  • U-U: represents the relation between two different users. This relations is mostly social due to the fact that relates user roles/preferences based on user to user similarities. It can provide information about user communities which share commons interests or knowledge.
  • RO-U: represents the relation between research objects and users. This relations is due to the historical use of  ROs by a user or to the matching between user rol and ROs description. It can provide information about ROs which are probably useful for an specific user.

Visual metaphor

The main metaphor to be used is based on spheres. This metaphor will be used mainly in two different ways:

  • Epicentric circles: the similarity is represented using the distance from the center of the sphere to the place where the object is shown. This visualization allows to tackle different similarity of items to the reference by displaying them at larger distances (bigger radio r).
  • Separated circles: different type of information or ranges can be displayed by representing different separated circles.

The main metaphor interpretation regarding the first point is the three circles of collaborative search [3]. The three circles are:

  • Inner circle: it represents the nucleous of collaboration. It's mainly focused on sharing common goals (at any level) e.g. to discover a new protein (probably by using protein discovery workflow). This is a task oriented collaboration.
  • Intermediate circle:  it represents shared interests, locations, or roles. It is related with the content infromation of users and ROs (tags/annotations). This is a social interests oriented collaboration and it is based on the fact that people are many times more reliable than search engines ("I trust my friends more than I trust strangers.")
  • Outter circle: it represents the implicit information, unintentional collaboration, mainly regarding other items (users / ROs) which are strangers. Some recommendation algorithms or systems can be used to obtain these user-driven personalization information.

The main metaphor interpretation regarding the second point is to represent at different circles different type of information (e.g. bionformatic people domain, astrnomers domain, friends, family, etc.)

Similarity measurements

In the following picture we summarize the different types of user information that we consider to provide different types of similarity measures:

User Profile Similarity

User profile similarity takes into consideration the information contained in the profile of a user, such as institution, location, gender, etc. The proposed similarity function is a simplification of the one defined in (Akora et al., 2011), as initially we do not contemplate subfields and multivalued properties. This similarity is itself based in the information retrieval concept of Occurrence Frequency (Jones, 1988). Given a user u and a user x, we define the Profile Similarity as follows:
where P is the set of properties of the user's profile, bp is a parameter associated with each property that weights its importance, and Similartiyp(u,x) is defined as follows:

where N is the total number of values in the property p dataset, and frecuency the frequency of the value of the property is such dataset. 

User Activities Similarity

This similarity measure is based on the actions that the user performs in the environment that are related with resources. The activities that we contemplate that the user can tag a resource, upload a resource to make it publicly available, to provide a rating for the resource and to label it as user's favourite. For comparing users using this type of information we propose using collaborative filtering techniques. Collaborative filtering techniques (e.g. (Goldberg et al. 1992), (Resnick et al. 1994), (Shardanand & Maes,1995), (Hill et al. 1995), etc.) predict user's affinity for resources on the basis of the ratings that other users have made to these resources in the past. Therefore, the first steps in such systems consist on finding people with similar tastes to the user and this is the similarity measure that we will provide. User tastes are defined by means of its past ratings; and by means of these ratings extrapolate the user future ratings.  The rating sources that we consider right now are:

  • Ratings made by users in the myExperiment environment.
  • Files favourited by users in the myExperiment environment (i.e. if a user favourites a resource we assign it the highest rating).
  • Files uploaded by users to the myExperiment environment (i.e. if a user uploads  a resources we assign it with the highest rating, as ratings represent the subjective usefulness of a resource).

User Interactions Similarity

The user interactions similarity measures similarity in the way that user interacts with other users; both from a social perspective (i.e. interactions with other users that have been previously labelled as friends by the user); and authorship network (i.e. other user's that have co-authored scientific content in the past).

Our approach firstly consist in the creation of each network graph, we use:

  • For the social network we start by using the friendship and group belonging relationships that are defined in myExperiment.
  • For the authorship network we create the graph by harvesting different digital repositories, scientific content databases, e-print services, etc.

Once we define the graph, we use the similarity measure defined in (Akora et al., 2011). We define the similarity between and user u and user x as follows:

Where:

  • FG(u) is defined as the friends graph of the user u, that is, the part of the whole social/authorship graph that contains the users directly connected with the user as nodes, and their relationship as edges.  FG(u).E represents precisely the set of edges of such subgraph.

 

  • MFG is defined as the mutual friends graph that user u and user x share. It represent the part of the social/authorship graph that contains those users that are directly connected to both user u and user x. MFG(u).E is the shorthand for the set of edges of such graph. Formally:
  •  
    MFG(u,x).N is the set of nodes of the mutual friends graph defined as follows (we note as G to the graph that represents the social/authorship network). We represent examples of these subgraphs for the user u with regard to users x and y in the next figure:
    Informally speaking, this measure of similarity compares the number of common friends of the users being compared with those friends that each of them posses.  The more similar these sets are, the more similar these users are.

Users-ROs Matrix Evaluation (esteban)

The next matrix introduces the description of the interpretation for each one of the pairs relation/information identified by the columns and rows:

 

RO-RO

USER-USER

RO-USER

Collaborative Filtering

(1) Based on other ROs similarity preferences

(2) Based on other users similarity preferences

(3) Based on historical ROs used by users

Content-based

(4) Based on identical ROs features/tags

(5) Based on identical users preferences/tags

(6)  Based on identifical RO-user tags

(1) It identifies that a RO1 is close to other RO1 if they share common relations with any other RO (EG this relation is not that clear to me at this time, it seems to be more a clustering approach non-discrete)

(2) It identifies that a user1 is close to other user2 if they share common relations with any other user (EG this relation is not that clear to me at this time, it seems to be more a clustering approach non-discrete)

(3) It identifies that a user1 is close to a RO1 if any other user2 share common ROs likes with user1 and user2 likes RO1

(4) It identifies that a RO1 is close to RO2 if the have many identical tags (e.g. bioinformatics) (This is a discrete approach)

(5) It identifies that a user1 is close to user2 if they have many identifical preferences/tags (e.g. bioinformatics) (This is a discrete approach)

(6)It identifies that a user1 is close to RO1 if they have many identifical preferences/tags (e.g. bioinformatics) (This is a discrete approach)

Use cases interpretation

The different interpretations of the different measurements between the ROs and users are:

  • User similar interests?
  • ROs similar funcionalities?
  • ...

----------

Highly recommended reading: On Being a Scientist 

General vision

Researches talk to their colleagues and supervisors in laboratories, in hallways, offices, on the phone and via email. They trade data and speculations over computer networks on specific shared folders and emails. They give presentations in seminars and conferences, and they may spend some time in stays in other laboratories. Posters, abstracts, lectures at professional gatherings, and proceedings volumes are being used more often to present preliminary results before full review. They write up their results in a collaborative process mainly through emails and in general they do not make use of any versioning and sophisticated tool. They send the publications to specific journals, which in turn send the papers to be scrutinized by reviewers (other scientists). After a paper is published or a finding is presented, it is judged by other scientists in the context of they already know from other sources.  

U-U Similar Work Domain and Knowledge

Scientists are informed of developments and advances on their domain of work mainly by mail contact with colleagues, attendance to conferences and mailing lists. Sometimes, an internal group discussion may also reveal useful information about similar works. Very often scientific interests are different from work or knowledge domain, in this case attendance to conferences or subscription to mailing lists may trigger interests on a new related research field, and new contacts for potential collaborations. Technical people do also make use of website forums to ask for specific help, e.g. how to use specific tools, develop scripts, etc.

U-U Geographical Location/Country/Language

Proximity may be a reason to foster collaboration among scientists. Working in the same building, city, or research institution may be incentives for a fruitful cooperation, as well as other cultural or extra-scientific factors like sharing the same language, interests, etc. While english is the main language to communicate in science, not sharing the same language still may be a handicap to many people.

U-U Similar Responsibilities and Hierarchy

Hierarchy may be another potential factor to improve or impede collaborations. Big bosses, postDocs and phD students they often do not share the same interests or practice the same working methodology. Their goals may be as different as earning a degree, renewing a grant, achieving tenure, or maintaining a reputation. While seniors tend to communicate by telephone as well as mails, juniors may also use other tools as new social networking platforms and mailing lists. We can also see this separation in conferences, where people with different responsibilities communicate in different groups.

U-RO Discovery and Publishing

The main channel to discover and expose publications are via Journal subscription, consultation and publication on electronic journals. These journals provide the possibility to filter the research with criteria based on different themes (e.g. arXiv.org, ADS) Discovery of scripts is possible via technical website forums (e.g. Astrobetter, ASCL ) or mailing lists. ADS uses its citation and co-readership network to generate recommendations to individual readers, both on an article-by-article basis and in the aggregate, through its myADS notification service. Reviewing may be another source of discovery. Related to publishing, in some fields the practice of salami publishing (deliberately dividing research results into the "least publishable units" to increase the count of one's publications) is seen as more questionable than in other fields. The Virtual Observatory provides an e-infrastructure to discover data, where the scientists act as consumers and big facilities archives as data and services providers.  

U-RO Proprietary Data

Many scientists are generous in discussing their preliminary theories or results with colleagues, and some even provide copies of raw data to others prior to public disclosure to facilitate related work. But scientists are not expected to make their data and thinking available to others at all times. During the initial stages of research, a scientist deserves a period of privacy in which data are not subject to disclosure. This privacy allows individuals to advance their work to the point at which they have confidence both in its accuracy and its meaning. After publication, scientists expect that data and other research materials will be shared with qualified colleagues upon request.

RO-RO Similarities and Differences

In multi-Wf ROs composed of several Wfs, when comparing several similar ROs, the workflows that are unusual might be very relevant to a user ---they are a distinguishing feature of the RO. On the contrary, Wfs that occur together in different ROs represent a pattern that can be of interest to the users. This use case may be expanded to comparison of several Wfs having unusual or common/patterns as scripts, web services or modules.

RO-RO Linked RO Elements

Astronomy was one of the first disciplines to take advantage of the early developments of the web. As early as 1993 it became possible to perform a literature search in ADS in conjunction with an object query in SIMBAD, as the result of a collaboration between the two projects. Article records in ADS were linked to object records in SIMBAD, and vice-versa, allowing users to seamlessly move across the two databases and explore the relationships between their data holdings. In 1997 links were created between ADS records and observational data available from the main NASA archives as well as ESO. Thus, authors could easily access observations which had been studied in a paper, and conversely, access all publications which referenced a particular dataset. An agreement between the data centers codifying a system to uniquely identify bibliographic records allowed to create links between them and other resources. The introduction of such identifiers (bibcodes) took place ten years before publishers agreed on a system to uniquely identify articles via the DOI (Digital Object Identifier) system. Beginning in the late 1990s, libraries began playing an important role in maintaining links between bibliographies and data products. Several institutions today use ADS as a search tool to keep lists of bibliographies related to their missions and share some of this metadata back with ADS. This allows the possibility of searching the literature with a filter limiting results to the contributions of a particular institution. Some journals are forcing authors to send tabular data and the data behind plots to CDS Vizier Catalogs Service in order to make a provide a proper connection between data and papers when discovering through ADS Digital Library. ADS is making a big effort (See ADSLabs) on curation and linking of

  • Authors
  • Publications
  • Journals
  • Tabular Data contained
  • SIMBAD Objects
  • ACS reference of used software 
  • Proposals for observing time
  • Used facilities, surveys or missions

Simple Mockup 

It was agreed to accept the following description for the different circles in order to get a better "non-formal" understanding:

  • Inner circle: it includes the item who is being represented
  • Intermediate circle: it includes the items explicitly defined (p.e. in the case of being a user represented the explicitly defined are the friends of him)
  • Outer circle: it includes the items which are not explicitly defined but that are obtained by a recommender system or predicted by using explicit data.

This description is a straightforward transformation from the above described metaphor avoiding the abstraction and making things more concrete.

It has been also discussed at iSOCO and agreed at this showcase level  that the visual metaphor can also be complemented with an exploratory property for allowing the exploration from users towards ROs. This will allow to select one of the items shown and display its characteristics using a table.

USER centric approach:

  • Inner: itself
  • Intermediate: based on its FOAF and the created ROs by that author
  • Outter: recommendation of users based on activities o content based (tags), and recommendation of ROs bases on CF

RO centric approach:

  • Inner: itself
  • Intermediate: based on ROs created by the same author (same pack ROs?)  (favourited by users?)
  • Outter: recommendation of users and ROS based on content  (tags) (similar ROs and similar USERS tags measurement)

Format example

This in an example in JSON for representing the three circles and the items included in each of them. We can make a distinction between ROs and User type of items.

Some useful queries (examples)

This shows some examples of queries which are related with information from myExperiment

Web Services

There are being developed two different webservices in order to provide the data that has to be shown in the visualization of the collaboration spheres.

Both services are extracting data from myExperiment. This source (and the set queries for retrieving the knowledge), should be substituted by the information that is stored in the wf4ever portal about RO's, resources and users.

Web service 1: Filling the related friends/wfs

This service should be called when initializing the collaboration spheres visualization for a specific user. This service gathers different data about the collaborators and workflows and provides related information at different levels. For each of the resources (collaborator or workflow) we are providing information of their relation:

  • This source has been recently accesed (we will need to store this information)
  • This source has a direct relation with the user (a direct collaborator or an owned RO).
  • This source has an indirect relation with the user (a collaborator of a collaborator or a RO property of a collaborator)
  • This source has different kind of relation ("surprise me". Currently we are using the top 10 more visited workflows of myExperiment)

The generated json has the fllowing format:

The call to the service will be: $HOST/Collab/rest/getCollab?userIni=userID where userID is the uri of the user that is using the collaboration spheres.

The service can be called from: http://ia-wf4ever.isoco.com/Collab/rest/getCollab?userIni=[userID]

And and example of a real call to the service could be: http://ia-wf4ever.isoco.com/Collab/rest/getCollab?userIni=http://www.myexperiment.org/users/18

Web service 2: Filling the spheres

The second webservice follows the json format that has been described in the mockup (with different sections for different spheres: inner, intermediate, outter).

This service will be called each time that a collaborator or RO is added to the inner sphere. The intermediate sphere will provide a set of collaborators or ROs recommended for the group that form the inner sphere. The outter sphere will provide a general recommendation for the user.

The call to the service will be: *$HOST/Collab/rest/getSpheres?userID=inner *where "inner" is the an array of users or ROs that are included in the inner sphere.

The service can be called from: http://ia-wf4ever.isoco.com/Collab/rest/getSpheres?inner=[inner]

And and example of a real call to the service could be: http://ia-wf4ever.isoco.com/Collab/rest/getSpheres?inner=http://www.myexperiment.org/users/18&inner=http://www.myexperiment.org/users/5&inner=http://www.myexperiment.org/users/16&inner=http://www.myexperiment.org/users/14936

Notes

The next topics appeared during the sprint planning meeting

User Roles
[12:15:01] Esteban García Cuesta: Use of measuremnts for specific interpretations
[12:18:58] Esteban García Cuesta: use of myexperiment users
[12:33:58] Esteban García Cuesta: Hamming
[12:34:08] Esteban García Cuesta: http://es.wikipedia.org/wiki/Distancia_de_Hamming

[1] S.Bechhofer et.al. Why linked data is not enough for scientists. Future Generation Computer Systems, 2011.
[2] Improving Future Research Communication and e-Scholarship. FORCE11 Manifesto. http://force11.org/white_paper

Other Links

[3] http://irsg.bcs.org/informer/2012/01/three-circles-of-collaborative-search/

References

(Akora et al., 2011) Akcora C., Carminati B., Ferrari E., (2011) Network and Profile-based Measures for User Similarites on Social Networks. In Proc. of the 12th IEEE International Conference on Information Reuse and Integration (IRI 2011), August 2011.

(Goldberg et al., 1992) Goldberg, D. Nichols, D., Oki, B. M., and Terry, D. Using collaborative filtering to weave an information tapestry. Commun. ACM 35, 12 (Dec.1992), 61---70.

(Jones, 1998) Jones K. J., (1998) A statistical interpretation of term specificity and its application in retrieval. In Document retrieval systems, Peter Willett (Ed.). Taylor Graham Series In Foundations Of Information Science, Vol. 3. Taylor Graham Publishing, London, UK, UK 132-142.

(Hill et al., 1995) Hill, W., Stead, L., Rosenstein, M. and Furnas, G.: 1995, 'Recommending and evaluating choices in a virtual community of use'. In: CHI '95: Conference Proceedings on Human Factors in Computing Systems, Denver, CO,pp. 194-201.

(Resnick et al., 1994) Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. and Riedl, J.: 1994, 'GroupLens: An Open Architecture for Collaborative Filtering of Netnews'. In: Proceedings of the Conference on Computer Supported Cooperative Work, Chapel Hill, NC, pp. 175-186.

(Shardanand  and Maes, 1995) Shardanand, U. and Maes, P.: 1995, 'Social Information Filtering: Algorithms for Automating "Word of Mouth"'.  In: CHI '95: Conference Proceedings on Human Factors in Computing Systems, Denver, CO, pp. 210-217.

Real similarity outputs and examples

The different similarity criteria can be used to provided recomendation but also can be used to the visualization of collaboration spheres. Here we show some examples of outputs values for the one of similarities introduced above, the User Activities Similarity.

Also a depper explanation and also the API to call to this services can be found at : Recommender+System

  • Brandon

    User

    Similarity Value

    Katy Wolstencroft

    1.0

    Alan Williams

    1.0

    Asif Karedia

    0.5

    Andriuz

    1.0

    Yzdxtyatbj

    1.0

    Sureerat

    1.0

  • Pipeline

    User

    Similarity Value

    Sathish9

    1.0

  • AGeduldig

    User

    Similarity Value

    Jhermes

    1.0

  • Matt Lee

    User

    Similarity Value

    David De Roure

    1.0

    Y. Liu

    1.0

    J. Seitz

    1.0

  • Delistyle777

    User

    Similarity Value

    David Withers

    0.5

    Katy Wolstencroft

    1.0

    Paul Fisher

    1.0

    George

    1.0

    Wei Tan

    1.0

    Xiaoliang

    1.0

    Kawther

    1.0

    Taverna

    1.0

    ??

    1.0

    ???

    1.0

    AbuJarour

    1.0

    Ali Rezaee

    1.0

    Narenuday

    1.0

  • Priyanka

    User

    Similarity Value

    Alan Williams

    1.0

    Danius Michaelides

    1.0

  • Angela Luijf

    User

    Similarity Value

    GentleYang

    1.0

  • Lanianne

    User

    Similarity Value

    Marco Roos

    1.0

    Paul Fisher

    1.0

    Chris

    0.3333333333333333

    Xiaoliang

    1.0

    Nolanfyh

    1.0

    Kawther

    1.0

    Liz Masterman

    1.0

    AbuJarour

    1.0

  • J. Seitz

    User

    Similarity Value

    Matt Lee

    1.0

    Franck Tanoh

    1.0

  • Bbrunet

    User

    Similarity Value

    Duncan Hull

    1.0

    Antoon Goderis

    0.5

    John Brookes

    0.5

  • Nolanfyh

    User

    Similarity Value

    David Withers

    1.0

    Katy Wolstencroft

    1.0

    Marco Roos

    1.0

    Paul Fisher

    1.0

    Jan Aerts

    1.0

    Lanianne

    1.0

    Chris

    0.3333333333333333

    Jerzyo

    0.3333333333333333

    Xiaoliang

    1.0

    Francois Belleau

    1.0

    Kawther

    1.0

    Ahmetz

    1.0

    Liz Masterman

    1.0

    AbuJarour

    1.0

  • Sven

    User

    Similarity Value

    Pieter Neerincx

    1.0

  • Zuofeng

    User

    Similarity Value

    David Withers

    1.0

    Katy Wolstencroft

    0.5

  • Esca

    User

    Similarity Value

    Don Cruickshank

    0.3333333333333333

    Ronny van Aerle

    0.5

  • Hamish McWilliam

    User

    Similarity Value

    Giovanni Dall'Olio

    0.5

    Paul Dobson

    1.0

    Niall Haslam

    1.0

    Barker adam

    1.0

    hong nguyen

    0.5

    bibliogum

    1.0

    Cjmei7

    0.5

    http://elchia.myopenid.com/

    1.0

  • Lipeter1

    User

    Similarity Value

    Anika Joecker

    1.0

    Maulik

    0.5

  • Jan Aerts

    User

    Similarity Value

    Paul Fisher

    1.0

    Nolanfyh

    1.0

  • Sureerat

    User

    Similarity Value

    Alan Williams

    1.0

    Asif Karedia

    0.5

    Brandon

    1.0

    Andriuz

    1.0

  • Samuel Lampa

    User

    Similarity Value

    Egon Willighagen

    1.0

  • Matej Mihel?i?

    User

    Similarity Value

    Stian Soiland-Reyes

    0.5

  • *http://zakmck.myopenid.com/*|| User || Similarity Value ||

    Paul Fisher

    1.0

    Ahmetz

    1.0

    Paolo sonego

    1.0

  • E3matheus

    User

    Similarity Value

    Alan Williams

    1.0

    Franck Tanoh

    1.0

    Maria Paula Balcazar-Vargas

    1.0

  • Capemaster

    User

    Similarity Value

    David Withers

    0.5

    Duncan Hull

    1.0

    Kieren Lythgow

    1.0

    Sathish9

    1.0

  • Yoshinobu Kano

    User

    Similarity Value

    Yangchen

    1.0

  • Jiten Bhagat

    User

    Similarity Value

    Sergejs Aleksejevs

    1.0

    Jose Enrique Ruiz

    1.0

  • Ian Laycock

    User

    Similarity Value

    Paul Fisher

    1.0

    Antoon Goderis

    1.0

    Franck Tanoh

    1.0

    Jim Craig

    1.0

    David Lapointe

    1.0

    Peter Li

    1.0

    Rexissimus

    0.5

    George

    1.0

    Bela

    1.0

    Michael Gerlich

    1.0

    Rich Edwards

    1.0

    Steffen Möller

    0.5

    Jelena (Obradovic) Dreskai

    1.0

    Andrew David King

    1.0

    AbuJarour

    1.0

  • Ahmetz

    User

    Similarity Value

    Paul Fisher

    1.0

    Nolanfyh

    1.0

    http://zakmck.myopenid.com/

    1.0

    Paolo sonego

    1.0

  • Jmartinbrooke

    User

    Similarity Value

    Anja Le Blanc

    1.0

  • Chris

    User

    Similarity Value

    Marco Roos

    0.3333333333333333

    Paul Fisher

    0.3333333333333333

    Lanianne

    0.3333333333333333

    Xiaoliang

    0.3333333333333333

    Nolanfyh

    0.3333333333333333

    Kawther

    0.3333333333333333

    Liz Masterman

    0.3333333333333333

    AbuJarour

    0.3333333333333333

  • Jean-Claude Bradley

    User

    Similarity Value

    Peter Li

    1.0

  • Yuwei Lin

    User

    Similarity Value

    Sirisha Gollapudi

    1.0

    Scotty

    0.4

    Jerzyo

    1.0

  • M.B.Monteiro

    User

    Similarity Value

    George

    0.3333333333333333

    AbuJarour

    1.0

  • Jorgejesus

    User

    Similarity Value

    Antarch

    1.0

    Chrisser

    1.0

  • Aniraj

    User

    Similarity Value

    David De Roure

    1.0

    Don Cruickshank

    1.0

  • Pieter Neerincx

    User

    Similarity Value

    Sven

    1.0

  • Xdchen716

    User

    Similarity Value

    Saeedeh

    1.0

    Soltan

    1.0

  • Yzdxtyatbj

    User

    Similarity Value

    Katy Wolstencroft

    1.0

    Antoon Goderis

    1.0

    Brandon

    1.0

  • Stian Soiland-Reyes

    User

    Similarity Value

    Jun Zhao

    1.0

    Paul Fisher

    0.8696938456699069

    Mustafa

    1.0

    Matej Mihel?i?

    0.5

  • Egon Willighagen

    User

    Similarity Value

    David De Roure

    0.5

    Peter Li

    0.5

    http://sneumann.pip.verisignlabs.com/

    1.0

    Jerzyo

    0.3333333333333333

    Architpuri

    0.2

    Samuel Lampa

    1.0

  • Wilbo

    User

    Similarity Value

    Steve Crouch

    1.0

  • Adambel

    User

    Similarity Value

    Marco Roos

    0.5

  • Petra Kralj Novak

    User

    Similarity Value

    Andriuz

    1.0

    Mustafa

    1.0

  • Yangchen

    User

    Similarity Value

    Yoshinobu Kano

    1.0

  • Stuart Owen

    User

    Similarity Value

    Glatard

    1.0

    Soton sbs

    0.3333333333333333

  • Prateek

    User

    Similarity Value

    Alan Williams

    0.3333333333333333

  • Mark Borkum

    User

    Similarity Value

    Rexissimus

    0.2

  • Mustafa

    User

    Similarity Value

    Stian Soiland-Reyes

    1.0

    Jun Zhao

    1.0

    Simon Jupp

    1.0

    Alan Williams

    1.0

    Paul Fisher

    0.5

    James Eales

    1.0

    Wassinki

    1.0

    Alex Nenadic

    1.0

    Andriuz

    1.0

    trybik

    1.0

    Petra Kralj Novak

    1.0

    Philip Y.

    1.0

  • Thomas

    User

    Similarity Value

    Panos

    0.5

  • Hubert

    User

    Similarity Value

    Sanna aizad

    1.0

  • Francois Belleau

    User

    Similarity Value

    David De Roure

    1.0

    Jerzyo

    0.7379854876009858

    Nolanfyh

    1.0

    Hyunjungyi6

    0.2

  • Anja Le Blanc

    User

    Similarity Value

    Jmartinbrooke

    1.0

    David PS

    1.0

    Jose Enrique Ruiz

    1.0

  • Rory

    User

    Similarity Value

    Chrisser

    1.0

  • Steffen Möller

    User

    Similarity Value

    Katy Wolstencroft

    1.0

    Ian Laycock

    0.5

  • Antoon Goderis

    User

    Similarity Value

    Duncan Hull

    0.5

    Marco Roos

    1.0

    Bbrunet

    0.5

    Yzdxtyatbj

    1.0

    Andrew David King

    1.0

    Ian Laycock

    1.0

    John Brookes

    1.0

  • Rich Edwards

    User

    Similarity Value

    Bela

    1.0

    Ian Laycock

    1.0

  • Wei Tan

    User

    Similarity Value

    David Withers

    0.5

    Katy Wolstencroft

    1.0

    Paul Fisher

    1.0

    Ravi

    1.0

    George

    1.0

    Xiaoliang

    1.0

    Kawther

    1.0

    Taverna

    1.0

    ??

    1.0

    ???

    1.0

    AbuJarour

    1.0

    Ali Rezaee

    1.0

    Narenuday

    1.0

    Delistyle777

    1.0

  • David PS

    User

    Similarity Value

    Anja Le Blanc

    1.0

  • Kawther

    User

    Similarity Value

    David Withers

    0.5

    Katy Wolstencroft

    1.0

    Marco Roos

    1.0

    Paul Fisher

    1.0

    Lanianne

    1.0

    George

    1.0

    Chris

    0.3333333333333333

    Wei Tan

    1.0

    Xiaoliang

    1.0

    Nolanfyh

    1.0

    Taverna

    1.0

    ??

    1.0

    Liz Masterman

    1.0

    ???

    1.0

    AbuJarour

    1.0

    Ali Rezaee

    1.0

    Narenuday

    1.0

    Delistyle777

    1.0

  • Kayleigh Ayn Bohémier

    User

    Similarity Value

    Andrea Wiggins

    1.0

  • Gstalloch

    User

    Similarity Value

    Andrea Wiggins

    1.0

  • Andrew David King

    User

    Similarity Value

    Alan Williams

    1.0

    Paul Fisher

    1.0

    Antoon Goderis

    1.0

    Franck Tanoh

    1.0

    Jim Craig

    1.0

    David Lapointe

    1.0

    Peter Li

    1.0

    Rexissimus

    0.5

    Ian Laycock

    1.0

    AbuJarour

    1.0

  • John Brookes

    User

    Similarity Value

    Duncan Hull

    0.5

    Antoon Goderis

    1.0

    Bbrunet

    0.5

  • Yosr Bouhlal

    User

    Similarity Value

    Sharmarajesh

    0.3333333333333333

  • Soltan

    User

    Similarity Value

    Saeedeh

    1.0

    Xdchen716

    1.0

  • Y. Liu

    User

    Similarity Value

    Matt Lee

    1.0

    David De Roure

    1.0

  • David Lapointe

    User

    Similarity Value

    Paul Fisher

    1.0

    Jim Craig

    1.0

    Peter Li

    1.0

    Rexissimus

    0.5

    Andrew David King

    1.0

    Ian Laycock

    1.0

    AbuJarour

    1.0

  • Socal sam

    User

    Similarity Value

    Marco Roos

    1.0

  • Paul Dobson

    User

    Similarity Value

    Paul Fisher

    1.0

    Hamish McWilliam

    1.0

  • Taverna

    User

    Similarity Value

    David Withers

    0.5

    Katy Wolstencroft

    1.0

    Paul Fisher

    1.0

    George

    1.0

    Wei Tan

    1.0

    Xiaoliang

    1.0

    Kawther

    1.0

    ??

    1.0

    ???

    1.0

    AbuJarour

    1.0

    Ali Rezaee

    1.0

    Narenuday

    1.0

    Delistyle777

    1.0

  • Don Cruickshank

    User

    Similarity Value

    David De Roure

    1.0

    Franck Tanoh

    0.3333333333333333

    Andrea Wiggins

    1.0

    Esca

    0.3333333333333333

    Ronny van Aerle

    0.5

    Aniraj

    1.0

  • Alex Nenadic

    User

    Similarity Value

    Mustafa

    1.0

  • Yergens

    User

    Similarity Value

    Alan Williams

    1.0

    Franck Tanoh

    1.0

  • Sanna aizad

    User

    Similarity Value

    Hubert

    1.0

  • Barker adam

    User

    Similarity Value

    Hamish McWilliam

    1.0

    http://elchia.myopenid.com/

    1.0

  • trybik

    User

    Similarity Value

    Mustafa

    1.0

  • Katy Wolstencroft

    User

    Similarity Value

    David Withers

    1.0

    Paul Fisher

    1.0

    George

    1.0

    Zuofeng

    0.5

    Wassinki

    1.0

    Wei Tan

    1.0

    Steffen Möller

    1.0

    Brandon

    1.0

    Xiaoliang

    1.0

    Nolanfyh

    1.0

    Yzdxtyatbj

    1.0

    Kawther

    1.0

    Taverna

    1.0

    ??

    1.0

    ???

    1.0

    AbuJarour

    1.0

    Ali Rezaee

    1.0

    Narenuday

    1.0

    Delistyle777

    1.0

  • Asif Karedia

    User

    Similarity Value

    Alan Williams

    0.5

    Brandon

    0.5

    Andriuz

    0.5

    Sureerat

    0.5

  • hong nguyen

    User

    Similarity Value

    Hamish McWilliam

    0.5

  • *http://kwang.myopenid.com/*|| User || Similarity Value ||

    Marco Roos

    1.0

    Paul Fisher

    0.5

  • Ali Rezaee

    User

    Similarity Value

    David Withers

    1.0

    Katy Wolstencroft

    1.0

    Paul Fisher

    1.0

    George

    1.0

    Wei Tan

    1.0

    Xiaoliang

    1.0

    Kawther

    1.0

    Taverna

    1.0

    ??

    1.0

    ???

    1.0

    AbuJarour

    1.0

    Narenuday

    1.0

    Delistyle777

    1.0

  • Saeedeh

    User

    Similarity Value

    Xdchen716

    1.0

    Soltan

    1.0

  • AbuJarour

    User

    Similarity Value

    David Withers

    1.0

    Katy Wolstencroft

    1.0

    Marco Roos

    1.0

    Paul Fisher

    1.0

    Jim Craig

    1.0

    David Lapointe

    1.0

    Peter Li

    1.0

    Rexissimus

    0.5

    Lanianne

    1.0

    Ravi

    1.0

    George

    0.6666666666666666

    Andrea Wiggins

    1.0

    M.B.Monteiro

    1.0

    Chris

    0.3333333333333333

    Wei Tan

    1.0

    Xiaoliang

    1.0

    Nolanfyh

    1.0

    Andriuz

    1.0

    Andrew David King

    1.0

    Ian Laycock

    1.0

    Kawther

    1.0

    Taverna

    1.0

    ??

    1.0

    Liz Masterman

    1.0

    ???

    1.0

    Ali Rezaee

    1.0

    Trisha Adamus

    1.0

    Narenuday

    1.0

    Delistyle777

    1.0

  • Jerzyo

    User

    Similarity Value

    Yuwei Lin

    1.0

    Egon Willighagen

    0.3333333333333333

    Scotty

    0.2

    Francois Belleau

    0.7379854876009858

    Nolanfyh

    0.3333333333333333

  • Jeffersontan

    User

    Similarity Value

    Blair Bethwaite

    1.0

  • tpacurtis

    User

    Similarity Value

    Andrea Wiggins

    1.0

  • *http://gnuband.org/*|| User || Similarity Value ||

    Andrea Wiggins

    1.0

  • Paul Fisher

    User

    Similarity Value

    David Withers

    1.0

    Stian Soiland-Reyes

    0.8696938456699069

    Jun Zhao

    0.5

    Katy Wolstencroft

    1.0

    Marco Roos

    1.0

    Paul Dobson

    1.0

    Franck Tanoh

    1.0

    Jim Craig

    1.0

    David Lapointe

    1.0

    Jan Aerts

    1.0

    Peter Li

    1.0

    Rexissimus

    0.5

    Lanianne

    1.0

    George

    1.0

    http://kwang.myopenid.com/

    0.5

    Chris

    0.3333333333333333

    Wei Tan

    1.0

    Xiaoliang

    1.0

    Nolanfyh

    1.0

    http://zakmck.myopenid.com/

    1.0

    Andrew David King

    1.0

    Ian Laycock

    1.0

    Mustafa

    0.5

    Kawther

    1.0

    4dot

    0.3333333333333333

    Taverna

    1.0

    Ahmetz

    1.0

    Philip Y.

    1.0

    ??

    1.0

    Liz Masterman

    1.0

    Paolo sonego

    1.0

    ???

    1.0

    AbuJarour

    1.0

    Ali Rezaee

    1.0

    Narenuday

    1.0

    Delistyle777

    1.0

  • Jelena (Obradovic) Dreskai

    User

    Similarity Value

    Ian Laycock

    1.0

  • GentleYang

    User

    Similarity Value

    Angela Luijf

    1.0

  • Chrisser

    User

    Similarity Value

    George

    1.0

    Andrea Wiggins

    1.0

    Rory

    1.0

    Nick Malleson

    1.0

    Antarch

    1.0

    Jorgejesus

    1.0

  • Peter Li

    User

    Similarity Value

    Paul Fisher

    1.0

    Jim Craig

    1.0

    David Lapointe

    1.0

    Rexissimus

    0.5

    Egon Willighagen

    0.5

    Jean-Claude Bradley

    1.0

    Andrew David King

    1.0

    Ian Laycock

    1.0

    Jzohren

    1.0

    AbuJarour

    1.0

    Trisha Adamus

    1.0

  • Kbbe

    User

    Similarity Value

    Wassinki

    1.0

  • Baj

    User

    Similarity Value

    Sathish9

    1.0

  • *http://mayol.myopenid.com/*|| User || Similarity Value ||

    Alan Williams

    1.0

  • Hyunjungyi6

    User

    Similarity Value

    Francois Belleau

    0.2

  • Alan Williams

    User

    Similarity Value

    Duncan Hull

    1.0

    Jun Zhao

    1.0

    Franck Tanoh

    1.0

    Asif Karedia

    0.5

    Brandon

    1.0

    Priyanka

    1.0

    Andrew Su

    1.0

    Andriuz

    1.0

    Yergens

    1.0

    Sureerat

    1.0

    Prateek

    0.3333333333333333

    Andrew David King

    1.0

    Mustafa

    1.0

    http://mayol.myopenid.com/

    1.0

    Sukhdeep Singh

    0.5

    http://elchia.myopenid.com/

    1.0

    Trisha Adamus

    1.0

    E3matheus

    1.0

    Maria Paula Balcazar-Vargas

    1.0

  • ??

    User

    Similarity Value

    David Withers

    0.5

    Katy Wolstencroft

    1.0

    Paul Fisher

    1.0

    George

    1.0

    Wei Tan

    1.0

    Xiaoliang

    1.0

    Kawther

    1.0

    Taverna

    1.0

    ???

    1.0

    AbuJarour

    1.0

    Ali Rezaee

    1.0

    Narenuday

    1.0

    Delistyle777

    1.0

  • Liz Masterman

    User

    Similarity Value

    Marco Roos

    1.0

    Paul Fisher

    1.0

    Lanianne

    1.0

    Chris

    0.3333333333333333

    Xiaoliang

    1.0

    Nolanfyh

    1.0

    Kawther

    1.0

    AbuJarour

    1.0

  • Andriuz

    User

    Similarity Value

    Simon Jupp

    1.0

    Alan Williams

    1.0

    Andrea Wiggins

    1.0

    Asif Karedia

    0.5

    Brandon

    1.0

    Sureerat

    1.0

    Mustafa

    1.0

    Petra Kralj Novak

    1.0

    AbuJarour

    1.0

  • *http://elchia.myopenid.com/*|| User || Similarity Value ||

    Alan Williams

    1.0

    Barker adam

    1.0

    Hamish McWilliam

    1.0

  • George

    User

    Similarity Value

    David Withers

    0.5

    Katy Wolstencroft

    1.0

    Paul Fisher

    1.0

    Franck Tanoh

    1.0

    M.B.Monteiro

    0.3333333333333333

    Wei Tan

    1.0

    Xiaoliang

    1.0

    Ian Laycock

    1.0

    Kawther

    1.0

    Taverna

    1.0

    ??

    1.0

    ???

    1.0

    AbuJarour

    0.6666666666666666

    Ali Rezaee

    1.0

    Chrisser

    1.0

    Narenuday

    1.0

    Delistyle777

    1.0

  • Maulik

    User

    Similarity Value

    Anika Joecker

    0.5

    Lipeter1

    0.5

  • EdwardKawas

    User

    Similarity Value

    Mark Wilkinson

    1.0

    Tigre

    1.0

  • Jzohren

    User

    Similarity Value

    Peter Li

    1.0

  • Ferenc HORVATH

    User

    Similarity Value

    James Eales

    1.0

  • 4dot

    User

    Similarity Value

    Paul Fisher

    0.3333333333333333

  • Sathish9

    User

    Similarity Value

    David Withers

    0.5

    Duncan Hull

    1.0

    Capemaster

    1.0

    Pipeline

    1.0

    Baj

    1.0

  • Sergejs Aleksejevs

    User

    Similarity Value

    Jiten Bhagat

    1.0

    Wassinki

    1.0

  • Scotty

    User

    Similarity Value

    Yuwei Lin

    0.4

    Jerzyo

    0.2

  • Anika Joecker

    User

    Similarity Value

    Lipeter1

    1.0

    Maulik

    0.5

  • Sirisha Gollapudi

    User

    Similarity Value

    Yuwei Lin

    1.0

  • Panos

    User

    Similarity Value

    Thomas

    0.5

  • Carol Lushbough

    User

    Similarity Value

    Giovanni Dall'Olio

    0.5

  • Michael Gerlich

    User

    Similarity Value

    Ian Laycock

    1.0

  • Jose Enrique Ruiz

    User

    Similarity Value

    Jiten Bhagat

    1.0

    Anja Le Blanc

    1.0

    Susana

    1.0

  • Blair Bethwaite

    User

    Similarity Value

    Jeffersontan

    1.0

  • Marco Roos

    User

    Similarity Value

    Paul Fisher

    1.0

    Antoon Goderis

    1.0

    Lanianne

    1.0

    http://kwang.myopenid.com/

    1.0

    Chris

    0.3333333333333333

    Adambel

    0.5

    Xiaoliang

    1.0

    Nolanfyh

    1.0

    Socal sam

    1.0

    Kawther

    1.0

    Liz Masterman

    1.0

    AbuJarour

    1.0

  • Robertm

    User

    Similarity Value

    Markmuetz

    0.5

  • Mark Wilkinson

    User

    Similarity Value

    David Withers

    1.0

    Tigre

    1.0

    EdwardKawas

    1.0

  • Jim Craig

    User

    Similarity Value

    Paul Fisher

    1.0

    David Lapointe

    1.0

    Peter Li

    1.0

    Rexissimus

    0.5

    Andrew David King

    1.0

    Ian Laycock

    1.0

    AbuJarour

    1.0

  • Sharmarajesh

    User

    Similarity Value

    Yosr Bouhlal

    0.3333333333333333

  • David Withers

    User

    Similarity Value

    Duncan Hull

    0.5

    Katy Wolstencroft

    1.0

    Paul Fisher

    1.0

    Franck Tanoh

    0.25

    George

    0.5

    Mark Wilkinson

    1.0

    Zuofeng

    1.0

    Capemaster

    0.5

    Wei Tan

    0.5

    Xiaoliang

    0.5

    Nolanfyh

    1.0

    Kawther

    0.5

    Taverna

    0.5

    Sathish9

    0.5

    ??

    0.5

    ???

    0.5

    AbuJarour

    1.0

    Ali Rezaee

    1.0

    Narenuday

    0.5

    Delistyle777

    0.5

  • ???

    User

    Similarity Value

    David Withers

    0.5

    Katy Wolstencroft

    1.0

    Paul Fisher

    1.0

    George

    1.0

    Wei Tan

    1.0

    Xiaoliang

    1.0

    Kawther

    1.0

    Taverna

    1.0

    ??

    1.0

    AbuJarour

    1.0

    Ali Rezaee

    1.0

    Narenuday

    1.0

    Delistyle777

    1.0

  • Philip Y.

    User

    Similarity Value

    Simon Jupp

    1.0

    Paul Fisher

    1.0

    James Eales

    1.0

    Mustafa

    1.0

  • Tigre

    User

    Similarity Value

    Mark Wilkinson

    1.0

    EdwardKawas

    1.0

  • Jun Zhao

    User

    Similarity Value

    Stian Soiland-Reyes

    1.0

    Duncan Hull

    1.0

    Alan Williams

    1.0

    Paul Fisher

    0.5

    Mustafa

    1.0

  • Andrea Wiggins

    User

    Similarity Value

    Don Cruickshank

    1.0

    http://gnuband.org/

    1.0

    Andriuz

    1.0

    AbuJarour

    1.0

    tpacurtis

    1.0

    Gstalloch

    1.0

    Chrisser

    1.0

    Trisha Adamus

    1.0

    Kayleigh Ayn Bohémier

    1.0

  • Andrew Su

    User

    Similarity Value

    Alan Williams

    1.0

  • Susana

    User

    Similarity Value

    Jose Enrique Ruiz

    1.0

  • Bela

    User

    Similarity Value

    Rich Edwards

    1.0

    Ian Laycock

    1.0

  • Paolo sonego

    User

    Similarity Value

    Paul Fisher

    1.0

    http://zakmck.myopenid.com/

    1.0

    Ahmetz

    1.0

  • Soton sbs

    User

    Similarity Value

    Stuart Owen

    0.3333333333333333

  • David De Roure

    User

    Similarity Value

    Matt Lee

    1.0

    Don Cruickshank

    1.0

    Y. Liu

    1.0

    Egon Willighagen

    0.5

    Francois Belleau

    1.0

    Aniraj

    1.0

  • Wassinki

    User

    Similarity Value

    Katy Wolstencroft

    1.0

    Giovanni Dall'Olio

    1.0

    Sergejs Aleksejevs

    1.0

    Mustafa

    1.0

    Kbbe

    1.0

  • Danius Michaelides

    User

    Similarity Value

    Priyanka

    1.0

  • J. Dai Scientist

    User

    Similarity Value

    Hong Chang Bum

    1.0

  • Jhermes

    User

    Similarity Value

    AGeduldig

    1.0

  • Antarch

    User

    Similarity Value

    Jorgejesus

    1.0

    Chrisser

    1.0

  • Architpuri

    User

    Similarity Value

    Egon Willighagen

    0.2

  • Simon Jupp

    User

    Similarity Value

    James Eales

    1.0

    Andriuz

    1.0

    Mustafa

    1.0

    Philip Y.

    1.0

  • bibliogum

    User

    Similarity Value

    Hamish McWilliam

    1.0

  • Sukhdeep Singh

    User

    Similarity Value

    Alan Williams

    0.5

  • Glatard

    User

    Similarity Value

    Stuart Owen

    1.0

  • *http://sneumann.pip.verisignlabs.com/*|| User || Similarity Value ||

    Egon Willighagen

    1.0

  • Jean-Claude Bradley

    User

    Similarity Value

    Duncan Hull

    1.0

  • Markmuetz

    User

    Similarity Value

    Robertm

    0.5

  • Narenuday

    User

    Similarity Value

    David Withers

    0.5

    Katy Wolstencroft

    1.0

    Paul Fisher

    1.0

    George

    1.0

    Wei Tan

    1.0

    Xiaoliang

    1.0

    Kawther

    1.0

    Taverna

    1.0

    ??

    1.0

    ???

    1.0

    AbuJarour

    1.0

    Ali Rezaee

    1.0

    Delistyle777

    1.0

  • Niall Haslam

    User

    Similarity Value

    Giovanni Dall'Olio

    0.5

    Hamish McWilliam

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